University of Cincinnati

UNIVERSITY OF CINCINNATI

DATE: July, 29, 2003

I, Ivan Maximov , hereby submit this as part of the requirements for the degree of: Doctor of Philosophy in:

Geography It is entitled:

Integrated Assessment of Climate and Land-use Change Effects on Hydrology and Water Quality of the Upper and Lower Great Miami River.

Approved by: Dr. Susanna Tong Dr. Robert Frohn Dr. Nicholas Dunning Dr. James Goodrich

INTEGRATED ASSESSMENT OF CLIMATE AND LAND USE CHANGE EFFECTS ON HYDROLOGY AND WATER QUALITY OF THE UPPER AND LOWER GREAT MIAMI RIVER

A dissertation submitted to the

Division of research and Advanced Studies Of the University of Cincinnati

In partial fulfillment of the Requirements for the degree of

DOCTORATE OF PHILOSOPHY (Ph.D.)

In the Department of Geography Of the College of Arts and Sciences

2003 by Ivan A. Maximov

B.S., Moscow State University, 1995 M.S., Moscow State University, 1997

Committee Chair: Dr. Susanna Tong

ABSTRACT

In the next 50-100 years it is likely to expect changes in climate and patterns of land-use activities in the Great Miami River basin in combination rather than individually. The ultimate goal of this research work was to apply the integrative approach in examining the potential impacts of hypothetically constructed climate and land-use changes on hydrological system and water quality of the Great Miami River. The research results show that dry climate scenarios paired with future land-use change scenario would reduce the annual flow of the Great Miami River and slightly increase the annual concentrations of phosphorus, total ammonia nitrogen and sum of nitrites and nitrates. The simulation results from combined wet climate scenario and land- use change scenario show a significantly larger increase in annual flow as well as greater presence of nutrients in the water. The study shows very high enrichments of phosphorus in the waters regardless whether it is a dry or wet scenario. In average, total orthophosphates annual concentrations in the Great Miami River showed a 40-50% increase compared to current conditions (from 0.45 mg/l to 0.65 mg/l), which is 2-3 times higher than the EPA suggested Water Quality Criteria for freshwaters. The results from hydrologic simulations indicate that if future climate changes to wet (+20% increase in precipitation), the volume of annual flow will increase in average by 70% compared to current conditions. This will result in a larger number and higher magnitudes of stormflows, which would cause more frequent devastating floods, elevating the risks of economic damages to the populated areas adjacent to the streams. The present study investigated the potential effects of Best Management Practices applications on water quantity and quality of the Stillwater River. Simulations of a number of BMPs showed their effectiveness in reducing pollutant concentrations in the stream, and, hence, improving the water quality. Finally, this dissertation has demonstrated that application of the GIS-based U.S EPA BASINS multipurpose environmental analysis system and HSPF model in concert is a very comprehensive water quality and quantity analysis tool. It can be useful in the implementations of long-term water resources management and development programs.

Key words: climate change, land-use change, hydrologic regime, water quality, BASINS, HSPF.

At first, I would like to thank the members of my dissertation committee: Dr. Tong, Dr. Frohn,

Dr. Dunning and Dr. Goodrich. Especially, I want to express my sincere gratitude to Dr. Susanna

Tong, my dissertation advisor, for her invaluable guidance and encouragement throughout the whole process. She is an excellent graduate advisor and I really learned a great deal from her knowledgeable suggestions and her incredible experience. This study would not have been possible without invaluable assistance of Dr. Robert Frohn. He has been a mentor and an advisor to me. I am deeply indebted to him and I would like to specially thank him for providing me this opportunity to finish this research work. His suggestions and support are greatly appreciated.

I also would like to acknowledge other faculty members of the Geography Department: Dr.

Nicholas Dunning, Dr. Wendy Eisner, Dr. Ken Hinkel, Dr. Lin Liu, Dr. Byron Miller, Dr.Wolf

Roder, Dr. Roger Selya, Dr. Robert South, Dr. Howard Stafford for the immensely rewarding graduate school experience. I am particularly thankful to Dr. Ken Hinkel for sharing his scientific experience and teaching me many things. I am also grateful to Bev Mueller for her kind help during my first year in Cincinnati. I would like to thank all the students of our beloved Geography

Department for providing an excellent academic atmosphere.

And finally, I would like to thank my parents, my brother and my sister for helping me though this. They always supported and encouraged me for all these times. TABLE OF CONTENTS

LIST OF FIGURES ……………………………………………………………………. 3 LIST OF TABLES……………………………………………………………………... 6 INTRODUCTION………………………………………………………………………. 8 CHAPTER I: LITERATURE REVIEW……………………………………………… 16 1. 1 HYDROLOGICAL ASSESSMENT…………………………………………………... 18 1.1.1 Water quantity assessment…………………………………………………………... 18 1.1.1.1 Purposes of water quantity assessment……………………………………………….. 18 1.1.1.2 Hydrological Modeling……………………………………………………………… 19 1.1.1.3 Advantages and limitations of simulation models……………………………………... 24 1.1.2 Water quality assessment……………………………………………………………. ..26 1.1.2.1 Purposes of Water Quality Assessment………………………………………………. 27 1.1.2.2 Types of water pollution…………………………………………………………….. 27 1.1.2.3 Water Quality modeling……………………………………………………………... 28 1.1.2.4 Types of water quality models……………………………………………………….. 29 1.1.2.5 Reliability and limitations of water quality models……………………………………. 33 1.2. LAND USE CHANGE AND HYDROLOGICAL ASSESSMENT…………………….. 33 1.3. CLIMATE CHANGE AND HYDROLOGICAL ASSESSMENT……………………... 36 1.3.1 Brief discussion of climate change…………………………………………………….. 36 1.3.2 Climate change and water resources…………………………………………………… 40 1.4. GIS AND REMOTE SENSING IN WATER QUALITY ANALYSIS………………… 41 1.5 BIOLOGICAL INTEGRITY CONCEPT IN IMPACT ANALYSIS………………….. 46 1.6 COMBINED CLIMATE AND LAND-USE CHANGE EFFECTS IN HYDROLOGICAL ASSESSMENT……………………………………………….. 51 CHAPTER II: METHODOLOGY…………………………………………………….. 55 Overview 2.1 STUDY AREA AND ENVIRONMETAL SETTINGS…………………………………. 55 2.1.1 Climate ……………………………………………………………………………… 59 2.1.2 Hydrology and hydrography…………………………………………………………… 64 2.1.3 Reservoirs and flood control…………………………………………………………… 65 2.1.4 Wetlands……………………………………………………………………………… 67 2.1.5 Geology and physiography…………………………………………………………….. 67 2.1.6 Groundwater………………………………………………………………………….. 72 2.1.7 Soils………………………………………………………………………………….. 73 2.1.8 Land Use……………………………………………………………………………... 74 2.1.9 Demographics, Urban development and Industry……………………………………….. 81 2.1.10 Water use and water quality………………………………………………………….. 85 2.1.11 Stream sediments……………………………………………………………………. 92 2.2 MODEL SELECTION AND DEVELOPMENT………………………………………. 92 2.2.1 Selection Criteria………………………………………………………………………92 2.2.2 Model selection and compatibility……………………………………………………....93 2.2.3 US EPA BASINS 3.0 and HSPF………………………………………………………. 94 2.2.4 Brief overview of HSPF structure for Non-point source modeling………………………..99 2.2.5 Modeling approach……………………………………………………………………107 2.2.6 Database Summary……………………………………………………………………109 2.3 DEVELOPMENT CLIMATE CHANGE SCENARIOS………………………………..111 2.4.1 Brief overview………………………………………………………………………..111 2.4.2 Climate scenarios……………………………………………………………………...112 2.4 DEVELOPMENT LAND USE CHANGE SCENARIOS……………………………….113 2.4.1 Brief overview………………………………………………………………………...113

1 2.4.2 Land use scenarios…………………………………………………………………….114 2.5 APPROACHES FOR SCENARIO ANALYSIS………………………………………...126 CHAPTER III: MODEL CALIBRATION AND VALIDATION…………………… 127 Overview 3.1 HYDROLOGY CALIBRATION/VALIDATION………………………………………127 3.1.1 Brief analysis of flow regime………………………………………………………….. 127 3.1.2 Summary of Hydrologic Simulation…………………………………………………….137 3.1.3 Brief overview: Calibration and Validation…………………………………………….. 137 3.1.4 Hydrologic model calibration and validation…………………………………………….139 3.2 WATER QUALITY CALIBRATION/VALIDATION………………………………….157 3.2.1 Analysis of the current water quality conditions and summary of water quality simulation…………………………………………………………………………………...157 3.2.2 Water quality model calibration and validation…………………………………………. 163 3.2.2.1 Water temperature and Dissolved oxygen simulation………………………………………164 3.2.2.2 Nutrient simulations…………………………………………………………………………165 3.2.3 Water quality calibration………………………………………………………………………167 CHAPTER IV: CLIMATE SCENARIOS AND SIMULATION RESULTS…………211 4.1 REVIEW OF CLIMATE SCENARIOS…………………………………………………211 4.2 FLOW REGIME UNDER CLIMATE CHANGE……………………………………….214 4.2 WATER QUALITY AND CLIMATE CHANGE………………………………………..237 CHAPTER V: LAND USE SCENARIOS AND SIMULATION RESULTS………… 260 Overview 5.1 IMPACTS OF LAND USE DEVELOPMENT……………………………………….….260 5.2 LAND USE SCENARIO DEVELOPMENT FOR GMR BASIN……………………….. 265 5.3 HYDROLOGY UNDER FUTURE LAND USE SCENARIO……………………………273 5.4 WATER QUALITY UNDER FUTURE LAND USE SCENARIO………………………278 Summary CHAPTER VI: COMBINED CLIMATE AND LAND USE CHANGE SCENARIOS…………………………………………………………………………….. 288 6.1 INTEGRATED ASSESSMENT OF COMBINED CLIMATE AND LAND USE CHANGES.…………………………………………………………………………………………288 6.2 FLOW REGIME UNDER COMBINED FUTURE CLIMATE AND LAND USE SCENARIOS………………………………………………………………………………………..290 6.3 WATER QUALITY UNDER COMBINED CLIMATE AND LAND USE SCENARIOS………………………………………………………………………………………..310 CHAPTER VII: APPLICATION OF BMPs and WATER QUALITY………………. 349 7.1 BMP: GENERAL PRINCIPLES………………………………………………………...349 7.1.1 Overview of watershed protection planning……………………………………………….350 7.1.2 Methodological aspects and BMP planning principles for the Great Miami River…………...352 7.2 BMPs FOR THE STILLWATER RIVER ……………………………………………….356 CHAPTER VIII: DISSCUSSION OF THE RESULTS……………………………….. 359 8.1 HYDROLOGY AND TESTED SCENARIOS……………………………………………360 8.2 WATER QUALITY UNDER TESTED SCENARIOS…………………………...……….367 8.2.1 Brief overview of water quality parameters and their importance to aquatic ecosystem………368 8.2.2 Water quality modeling results……………………………………………………………372 8.3 BMP EFFECTS ON HYDROLOGY AND WATER QUALITY OF STILLWATER RIVER……………………………………………………………………………………….375

CONCLUSION………………………………………………………………………… .. 377 APPENDICES………………………………………………………………………….. 381 REFERENCES………………………………………………………………………….. 430

LIST OF FIGURES

Chapter I: Figure 1.1 Taxonomy of hydrological models Figure 1.2 Conceptual representation of watershed model Figure 1.3 A conceptual structure of GIS/Remote Sensing relations to Hydrology Figure 1.4 Concept of ecological integrity Figure 1.5 New concept of ecological integrity Figure 1.6 Five major factors influencing water resource integrity in streams

Chapter II: Figure 2.1 Location of the region of study Figure 2.2 The Great Miami River Basin (GMR) with Counties Figure 2.3 The GMR basin and major urban areas Figure 2.4 Annual precipitation variation at Dayton Airport Weather Station for the period 1951- 2000 Figure 2.5 Seasonal precipitation variations, Dayton, OH Figure 2.6 Dams within the Great Miami River basin Figure 2.7 Geologic map of Ohio (State of Ohio, Division of Geologic Survey) Figure 2.8 Glacial map of Ohio (State of Ohio, Division of Geologic Survey) Figure 2.9 Physiographic regions of Ohio (State of Ohio, Division of Geologic Survey) Figure 2.10 DEM map, GMR basin Figure 2.11 Soils of the study region Figure 2.12 Population, number of farms and acres of land in farms dynamic in the study area, mapped for the Counties located within the GMR basin Figure 2.13 Upper and Lower GMR percentage change in number of households Figure 2.14 BASINS 3.0 Systems overview (courtesy of Tetra Tech Inc.) Figure 2.15 Schematic representation of basic stages in integrated analysis with BASINS 3.0 and HSPF Figure 2.16 Conceptual representation of Hydrological cycle Figure 2.17 Conceptual structure of HSPF (U.S EPA source) Figure 2.18 Basic modules of HSPF Figure 2.19 Structure of HSPF PERLND Module (Bicknell, et al, 2000) Figure 2.20 Structure of HSPF IMPLND Module (Bicknell et al., 2000) Figure 2.21 Conceptual scheme of RCHRES module Figure 2.22 Matrix of HSPF sections required versus pollutants and processes for PERLND and IMPLND Figure 2.23 Matrix of HSPF sections required versus pollutants and processes for RCHRES section Figure 2.24 Climate change scenarios for the HSPF model simulations Figure 2.25 LUCAS modules Figure 2.26 Major urban areas in Ohio (courtesy of the Ohio Department of Development) Figures 2.27-2.34 Changes in land cover for some urban counties within the GMR basin for the period of 1982-1997 (Source: National Resource Inventory)

Chapter III: Figure 3.1 Precipitation and discharge, GMR at Dayton, OH and annual flow dynamics at Hamilton, OH Figure 3.2 Flow and precipitation changes, GMR at Dayton, OH

3 Figure 3.3 Flow and precipitation changes, GMR at Dayton, OH Figure 3.4 Flow duration curve, GMR at Dayton, OH Figures 3.5-3.8 Graphs showing flow variation, GMR at Dayton, OH Figure 3.9 Fluctuations of flow, Stillwater river at Pleasant Hill, OH Figure 3.10 Fluctuations of flow, Stillwater river at Englewood, OH Figure 3.11 Fluctuations of flow, Mad river at Urbana, OH Figure 3.12 Upper GMR and sub-basins. Figure 3.13 Lower GMR and sub-basins. Figure 3.14-3.15 Sub-basins and their boundaries used in calibration and validation analysis Figure 3.16-3.27 Results from hydrology calibration Figures 3.28 – 3.35 Annual and monthly variations of water quality constituents of primary interest in this study observed at water quality stations at Dayton and at New Baltimore, OH Figure 3.36-3.40 Water Quality Validation Plots for Upper GMR Basins (1985-1991), showing annual means and standard deviations for simulated and observed values. Figures 3.41-3.57 Upper Great Miami River at Dayton, OH. Water Quality Calibration/Validation and Correlation Coefficients between Simulated and Observed values. Figures 3.58-3.75Lower Great Miami River at Hamilton, OH. Water Quality Calibration and Validation and Correlation Coefficients between Simulated and Observed values.

Chapter IV: Figures 4.2.1 – 4.2.3 Flow of Mad River near Dayton, OH, Stillwater River at Englewood, OH and Great Miami River at Taylorsville, OH under Hot and Dry Scenario (HD) Figures 4.2.4-4.2.6 Observed and Simulated Flows of Upper Great Miami River at Dayton, OH Figures 4.2.7 – 4.2.9 Observed and Simulated Flows of Lower Great Miami River at Hamilton, OH Figures 4.2.10 – 4.2.12 Flow of Mad River near Dayton, OH, Stillwater River at Englewood, OH and Great Miami River at Taylorsville, OH under Hot and Wet Scenario (HW) Figures 4.2.13 – 4.2.15 Observed and Simulated Flows of Upper Great Miami River at Dayton, OH Figures 4.2.16-4.2.18 Observed and Simulated Flows of Lower Great Miami River at Hamilton, OH Figures 4.2.19-4.2.21 Flow of Mad River near Dayton, OH, Stillwater River at Englewood, OH and Great Miami River at Taylorsville, OH under Warm and Dry Scenario (WD) Figures 4.2.22-4.2.24 Observed and Simulated Flows of Upper Great Miami River at Dayton, OH Figures 4.2.25-4.2.27 Observed and Simulated Flows of Lower Great Miami River at Hamilton, OH Figures 4.2.28-4.2.30 Flow of Mad River near Dayton, OH, Stillwater River at Englewood, OH and Great Miami River at Taylorsville, OH under Warm and Wet Scenario (WW) Figures 4.2.31-4.2.33 Observed and Simulated Flows of Upper Great Miami River at Dayton, OH Figures 4.2.34-4.2.36 Observed and Simulated Flows of Lower Great Miami River at Hamilton, OH Figures 4.3.1-4.3.10 Results from water quality modeling under HD scenario Figures 4.3.11-4.3.20 Results from water quality modeling under HW scenario Figures 4.3.21-4.3.30 Results from water quality modeling under WD scenario Figures 4.3.31-4.3.40 Results from water quality modeling under WW scenario

Chapter V: Figure 5.1 Impacts of urbanization Figure 5.2 Current (Base Case) Land Use for the Upper GMR Basin Figure 5.3 Hypothetically created Future Land Use scenario for Upper GMR Basin Figure 5.4 Current (Base Case) Land Use for the Lower GMR Basin

4 Figure 5.5 Hypothetically created Future Land Use scenario for Lower GMR Basin. Figures 5.6-5.9 Results of Hydrology simulation under Current and Future Land Use Scenarios, Upper GMR at Dayton, OH Figures 5.10-5.13 Results of Hydrology simulation under Current and Future Land Use Scenarios, Upper GMR at Dayton, OH Figures 5.14- 5.18 Results of Water quality under Current and Future Land Use Scenarios, Upper GMR at Dayton, OH Figures 5.18- 5.23 Results of Water quality under Current and Future Land Use Scenarios, Upper GMR at Dayton, OH

Chapter VI: Figure 6.1 The integrative structure and interrelationships of changes in the climate system with the natural and human systems Figures 6.2-6.5 Results of Hydrologic simulation under combined Hot and Dry and Future Hypothetical Land Use Scenarios, Upper GMR at Dayton, OH Figures 6.6-6.9 Results of Hydrologic simulation under combined Hot and Dry and Future Hypothetical Land Use Scenarios, Lower GMR at Hamilton, OH Figures 6.10-6.17 Results of Hydrologic simulation under combined Hot and Wet and Future Hypothetical Land Use Scenarios, Upper GMR at Dayton, OH and Lower GMR at Hamilton, OH Figures 6.18-6.25 Results of Hydrologic simulation under combined: Warm and Dry and Future Hypothetical Land Use Scenarios, Upper GMR at Dayton, OH and Lower GMR at Hamilton, OH Figures 6.26-6.33 Results of Hydrologic simulation under combined Warm and Wet and Future Hypothetical Land Use Scenarios, Upper GMR at Dayton, OH and Lower GMR at Hamilton, OH Figures 6.34-6.43 Results of Water Quality simulations under combined Hot and Dry and Future Hypothetical Land Use Scenarios, Upper GMR at Dayton, OH and Lower GMR at Hamilton, OH Figures 6.44-6.53 Results of Water Quality simulations under combined Hot and Wet and Future Hypothetical Land Use Scenarios, Upper GMR at Dayton, OH and Lower GMR at Hamilton, OH Figures 6.54-6.63 Results of Water Quality simulations under combined Warm and Dry and Future Hypothetical Land Use Scenarios, Upper GMR at Dayton, OH and Lower GMR at Hamilton, OH Figures 6.64-6.73 Results of Water Quality simulations under combined Warm and Wet and Future Hypothetical Land Use Scenarios, Upper GMR at Dayton, OH and Lower GMR at Hamilton, OH

Chapter VII: Figure 7.1 Location of the Stillwater River basin

Chapter VIII: Figure 8.1 Conceptual scheme of Nitrogen Cycle

5 LIST OF TABLES

Chapter II: Table 2.1 Summary statistics on long-term annual changes in precipitation at Dayton, OH Table 2.2 Reservoirs and flood control structures within the GMR basin Table 2.3 Land use by category, GMR basin, USGS LULC GIRAS dataset, 1980s Table 2.4 Land use by category, GMR basin, USGS MRLC dataset, 1992 Table 2.5 Population of the GMR basin with its major urban areas Table 2.6 HSPF Non-point Source Simulation summary Table 2.7 Basins/HSPF Meteorological Input Data

Chapter III: Table 3.1 Years by magnitudes of flow, GMR at Dayton, OH Table 3.2 Seasonal changes of flow, GMR, Dayton, OH Table 3.3 Calibration/Validation targets for HSPF. The figures shown are the % difference between simulated and observed values. (Donigian, 2000) Table 3.4 Results from Hydrology calibration Table 3.5 Water quality calibration results (Upper GMR) Table 3.6 Water quality calibration results (Lower GMR) Table 3.7-3.11 Water quality calibration results (monthly values)

Chapter IV: Table 4.2.1 Hydrologic modeling results under Hot and Dry climate scenario Table 4.2.2 Hydrologic modeling results under Hot and Wet climate scenario Table 4.2.3 Hydrologic modeling results under Warm and Dry climate scenario Table 4.2.4 Hydrologic modeling results under Warm and Wet climate scenario Table 4.2.5 Summary results from the modeling result of flow regime under climate change scenarios Table 4.3.1 Water quality results from simulations under Climate change scenarios (Hot group) Table 4.3.2 Water quality results from simulations under Climate change scenarios (Wet group)

Chapter V: Table 5.1 Estimated Mean Runoff concentrations for land use Table 5.2 Sources of urban Runoff Pollutants Table 5.3 Sub-basins and reaches, which were modified in terms of urban area coverage with affiliated urban regions, centers and types of urbanization (Upper Greta Miami River) Table 5.4 Lower GMR Basin (sub-basins and reaches, which were modified in terms of urban area coverage with affiliated urban regions, centers and types of urbanization). Table 5.5 Hydrologic modeling results under future land-use scenario Table 5.6 Water quality modeling results under future land-use scenario

Chapter VI: Table 6.1 Hydrologic modeling results from simulating the combined effects of climate and future land-use changes on flow regime Table 6.2 Water quality modeling results from simulating combined effects of climate and future land-use changes Table 6.3 Summary of results from the hydrologic modeling under combined Climate and Land Use scenarios Table 6.4 Summary results from water quality modeling for Great Miami River under combined climate and land-use scenarios

6 Chapter VII: Table 7.1 Typical pollutant removal rates (%) for a certain type of BMP for Stormwater management Table 7.2 Summary results from simulating BMPs in the Stillwater river watershed

7 INTRODUCTION

Throughout the human history, natural waters have been used extensively. Water resources play an important role in sociological and economic development of many countries all over the world. There are two fundamental types of water usage in human activities: (1) uptake type- it is characterized by uptake of natural waters from water bodies and ground-water aquifers for industrial, agricultural and municipal services and its return back to sources at different places, in small amounts and, frequently, in different qualities. Industrial activities, power plants, agriculture, municipal services are typical agents of this type of water use; (2) non-uptake type- it is characterized by using the water in its source and the exploitation of water bodies as an element of landscape and water energy. Examples of such activities include hydroenergetics, water transportation, fishing industry, recreation and water tourism/sports.

In the United States and many other countries, the quality of waters is determined by their suitability for use by cities, industries, and individuals. Under U.S legislation, for example, each body of surface water must be put in a quality class. The U.S Environmental Protection Agency

(EPA) publishes criteria that specify the conditions deemed consistent with specified uses. State authorities then establish broad water use classifications (water supplies, swimming, indigenous aquatic life, etc) and apply the federal criteria in determining standards that must be met within each class. With the enactment of the Clean Water Act in 1972, the nation rejected past practices that had resulted in widespread pollution of rivers, lakes, and coastal waters and made a new commitment to restore and maintain the chemical, physical, and biological integrity of the nation's waters.

Despite impressive progress, many of the nation's rivers, lakes, and coastal waters do not meet water quality goals. Many waters that are now clean, face the threat of degradation from diverse pollution sources. Close to 40 percent of the waters surveyed are found to be in the polluted state for basic uses like fishing or swimming. The success in cleaning up pollution from point sources

8 (e.g., factories and sewage treatment plants) has not yet been matched by controls over polluted runoff from diffused sources such as farms, urban areas, forestry, ranching, and mining operations. In addition, water pollution poses a continuing threat to public health.

In recent years, regulation of pollution in surface waters has required accounting for non-point sources (besides point source) of pollution in watersheds. Many monitoring stations have been set up, usually on small watersheds containing a single land-use type. The purpose of these monitoring programs is to develop the information, which will allow the estimation of pollutant loadings generated in large, multi-land-use watersheds. Example of such a program is the USGS

NAWQA (National Water Quality Assessment Program) that has been designed to collect and analyze data and information in more than 50 major river basins and aquifers across the Nation.

The goal is to develop long-term consistent and comparable information on streams, ground water, and aquatic ecosystems to support sound management and policy decisions (NAWQA web site).

One of the most pressing issues of global change research is the interaction of land-cover changes with those caused by global climate (Veldkamp et al, 1996). No projection of the future state of the aquatic ecosystem can be made without taking into account the past, present, and future human land-use patterns (IPCC, 1998). Land-use is known to have significant impacts on water quantity and quality. A number of studies have been done to estimate the impacts of land- use changes on water quality (Gardi, C, 2001, Ha, S, Bae, M, 2001, Brun, S, Band, L., 1999,

Klein, 1978). Recent reports acknowledge that a principal water quality problem in the United

States is non-point source (NPS) pollution (Browne, 1990, U.S EPA, 1993).

Non-point source pollution is defined to be the runoff transport of constituents from diffuse sources on the land to streams (Browne 1990, Huber 1993). Some of the constituents composing non-point source pollution include oxygen-demanding substances, suspended solids, nutrients and toxic matter. Oxygen-demanding substances are organic and inorganic residues that consume the dissolved oxygen (DO) of the water. The impact of low dissolved oxygen concentrations in the

9 water (anaerobic condition) is reflected in an unbalanced ecosystem, fish mortality, odors and other aesthetic nuisances. Nutrients such as nitrogen and phosphorus create nuisance algae and aquatic weed conditions in water bodies and accelerate the eutrophication of lakes and impoundments. Toxic materials affect public health in as yet unknown ways, but there are real concerns over potential cancer, tumors and birth defects connected to these substances. Toxic materials can be ingested directly in drinking water as well as indirectly through the food chain, if provisions are not taken to ensure adequate water quality. Metals, industrial and agricultural chemicals, hydrocarbons, and radioactive materials are the main sources of toxic substances.

Non-point source pollution originates in urban, agriculture, and mining areas.

Since many biological phenomena and human activities are water dependent, the watershed is a natural unit of study when assessing ecological stress. Since surface water drains to one outlet in a watershed or sub-watershed, the land activities upstream affect the water quality and quantity at that point. The spatial pattern of measured water quality parameters helps to interpret the relationship between land use and water quality especially when investigating diffuse, non-point source pollution. In order to quantify the condition of and changes in aquatic and land resources, a set of indicators and indices, which characterize those resources, must be developed.

One type of land use index is percentage of land use type (e.g., agricultural, residential/urban, forested) by area, or percentage change in area from one land-use type to another between time periods. The intensity of the activity is also a vital land-use variable and can be characterized by population density, transportation routes density, proportion of impervious surfaces, livestock density, number of farms etc. and can be used to represent the particular dynamics of the area.

These can then be related to the condition or change in water quality, which is represented by water quality indicators.

Generally, water quality indicators are chosen based on the type of land use evident in a watershed, and may include both stream water and riverbed sediment chemistry. Nitrate-N, fecal coliforms, orthophosphate, chloride, ammonia-N and total dissolved carbon all occur naturally in

10 an aquatic environment. Concentrations above natural background levels are often indicative of human influence. Dissolved oxygen, conductivity, pH and temperature provide supplementary information on the water quality and living conditions for the aquatic biota.

Changing climate is expected to affect both evaporation and precipitation in most areas of the

United States. In those areas where evaporation increases more than precipitation, soil will become drier, lake levels will drop, and rivers will carry less water. Lower river flows and lower lake levels could impair navigation, hydroelectric power generation, and water quality and reduce the supplies of water available for agricultural, residential, and industrial uses. Some areas may experience both increased flooding during winter and spring, as well as lower supplies during summer. Generally, rainfall will be more concentrated in large storms, therefore, increasing river flooding. Decreased river flows and higher temperatures could harm the water quality of rivers, bays, and lakes. In areas where river flows decrease, pollution concentrations will rise because there will be less water to dilute the pollutants. Increased frequency of severe rainstorms could increase the amount of chemicals that run off from farms, lawns, and streets into rivers, lakes, and bays. According to NAST (National Assessment Synthesis Team), during the 21st century, GCM models project that temperature will increase throughout the Midwest, and at the greater rate than has been observed in the 20th century. The average minimum temperature is likely to increase as much as 0.5 to 1oC and precipitation is likely to continue its upward trend at a slightly accelerated range (10 to 30% increases are projected across much of the region). Despite the increases in precipitation, increases in temperature and other meteorological factors are likely to lead to a substantial increase in evaporation, causing a soil moisture deficit, reduction in river levels, and more drought-like conditions in much of the region. In addition, increases in the proportion of precipitation coming from heavy and extreme precipitation are very likely.

All in all, the major water quality issues being addressed by water suppliers and water resource managers include the following: (1) degradation of surface water and ground-water quality by urban and agricultural sources of nutrients and pesticides; (2) determination of the

11 relative contributions of point and non-point sources to contaminant loads in streams and rivers;

(3) the effect of rapid urbanization on water quality, stream habitat and aquatic biota; (4) The effect of climatic changes on water quality and quantity; (5) assessment of the role of surface water/shallow ground water interactions on ground-water quality (Debrewer, L.M et al, 2000).

Management of the complex interacting factors of the quality of river ecosystems presents enormous scientific and management challenges.

Early and present studies on climate and land use effects on water quality and quantity of surface water often are very confined and limited, focusing primarily on characterizing the impacts and assessing their significance to ecological condition of a watershed. There is a growing interest in studies focused on the combined effects of climate and land use changes on water quantity and quality.

Therefore, firstly, there is a need to develop integrated and complex methods for interpreting and better understanding the mechanisms of combined climate and land-use changes on the hydrology and ecology of aquatic ecosystems. Secondly, based on improved understanding of the principles and mechanisms of these interactions, it is important to be able to predict the effects of climate and land-use changes on water quality and quantity, in terms of the spatial extent, dynamics, transformations and the actual magnitudes.

MAIN FOCUS AREAS OF THIS STUDY

The first, and perhaps the most important, step in a modeling project is to clearly define the modeling objectives (McKeon and Segna, 1987). Development of the appropriate modeling approach and the selection of modeling tools must be closely linked to the decision-making needs and management objectives. When performing this step, it is important to consider the impacts of climate and land-use changes on hydrology and water quality in concert. Such integrative approach includes detailed analysis of individual effects of future climate and land use changes on the behavior of aquatic ecosystem. After individual effects are investigated, the combined

12 effects could be simulated and characterized. The watershed modeling tools should address the following: (a) Describe present hydrological and water quality conditions in the study area; (b)

Define the contributions of flow, sediments, and water quality parameters from various sources within the watersheds of study; (c) Assess the response in terms of hydrology and water quality to future changes and propose management alternatives to minimize potential impacts.

This integrated approach, presented in this study, will improve our understanding of the interrelationships between land-use-climate-hydrology-water quality. Ultimately, it could become a useful practical tool in water resource planning which would help us to analyze, evaluate and predict the possible impacts of climate and land-use changes on water resources.

MAIN RESEARCH OBJECTIVES

- To characterize qualitatively and quantitatively the current conditions in the Great Miami River basin in terms of land use, climatic conditions, water quality and quantity.

- To develop the hydrological and water quality model of the Great Miami River.

- To estimate the individual effects of land-use change and climate change on water quantity and quality of the Great Miami River using hypothetical scenarios.

- To assess the combined impacts of climate and land-use changes on water quantity and quality of the Great Miami River using hypothetical scenarios.

- To test a variety of Best Management Practices (BMPs) and evaluate their potential effects on water quality and quantity.

13 MAIN RESEARCH TASKS

- Develop, calibrate and validate the hydrologic and water quality model for the Great Miami

River Basin using U.S EPA GIS-based BASINS 3.0 and HSPF model.

- Using properly calibrated and validated model to simulate changes in water quality and quantity of the Great Miami River under hypothetically created climate scenarios.

- To simulate impacts of future land-use (increase in pervious and impervious urban area in forms of high/low density residential, industry, commercial and transportation) on water quality and quantity of the Great Miami River.

- After examining the results of individual effects of future land-use and climate changes on hydrology and water quality, to simulate the hydrology and water quality regimes under the combined land-use and climate change scenarios and analyze the output results.

- Simulate Best Management Practices (BMPs) and examine their effects on water quality and quantity.

SIGNIFICANCE ANT POTENTIAL CONTRIBUTION

- This research will help us to better understand the complex interplay of climate and land-use changes and their effects on the stream ecosystem.

- This research could contribute to current knowledge of the effects of land-use and climate changes on water resources and could help and demonstrate the effectiveness of the integrated approach as presented in this study in predicting the long-term impacts of future land-use use changes on water quality and quantity in context of future climate change.

- The present study could estimate the potential effects of BMPs on water quantity and quality of the Great Miami River minimizing the impacts of future urbanization in the watershed.

14 This study is an extension of earlier pilot studies: Chen, W., 2000, “Modeling nitrogen and phosphorus non-point source pollution in the most southern HUC of Little Miami River”; Tong,

S., and Chen, W., 2002, “Modeling the relationships between land use and surface water quality” and Liu, A., 2002, “Employing land-use schemes as a mitigation strategy for the water quality impacts of global climate change”.

15 CHAPTER I: LITERATURE REVIEW

The complexity of today’s environmental problems suggests, even more than before, the need to understand the nature of natural systems and how our actions affect them. Our ability to manage water resources is influenced, to a large extent, by our ability to understand the hydrologic systems that exist in the regions we are.

Water is essential to human life and to the health of the environment. As a valuable natural resource, it comprises marine, estuarine, freshwater (river and lakes) and groundwater environments, across coastal and inland areas. Water has two dimensions that are closely linked - quantity and quality. Water quality is commonly defined by its physical, chemical, biological and aesthetic (appearance and smell) characteristics. A healthy environment is one in which the water quality supports a rich and varied community of organisms and protects public health. Water quality in a body of water influences the way in which communities use the water for activities such as drinking, swimming or commercial purposes. More specifically, the water may be used by the community for: drinking water, recreation (swimming, boating), irrigating crops and watering stock, industrial processes, navigation and shipping, production of edible fish, shellfish and crustaceans, protection of aquatic ecosystems and wildlife habitats. Water quantity is a characteristic describing a region’s water resources allocation, the hydrological processes and availability.

Managing of water resources means resolving issues concerning the quality and quantity of surface water and groundwater. Amongst those issues are impacts from land uses, contaminant discharges, the overall health of our freshwater aquatic ecosystems and the natural character of the water bodies.

The history of water resources development in the United States can be traced to the early days of the country’s development. Water was an important element in transportation, agriculture and industry. For many years, in fact well into the 20th century, the focus of most developmental

16 efforts was on single-purpose water projects. Eventually, the philosophy of multiple-use facilities emerged, but even then the solution of water resource problems was usually carried out on an individual project level, not on the basis of comprehensive plan. Since 80s, a fresh approach to the solution of water problems has started. The water problems faced by the United States and other industrialized nations are both physical (that is the availability of water), and institutional

(that is the nature of laws, organizations and customs that are in force) (Viesmann and Welty,

1985).

A number of federal agencies in the US have responsibility for one or more aspects of water resource development and management. On the water quality side, the Environmental Protection

Agency (EPA) is the principal actor. On the water supply side, the Army Corps of Engineers

(CE), the Soil Conservation Service (SCS) and some others.

The use of water to satisfy competing users is affected by, and often constrained by legal, social, political, organizational, environmental and socioeconomic factors. As the nation’s waters have been more and more dedicated to specific uses, the potential for escalation in conflicts among environmentalists, irrigators, well users, energy firms, cities and industries has grown.

Although, there is a general technical understanding of the inseparability of water quality and water quantity, little, if any of this knowledge is centered into operational planning and management programs. This is true not only at the federal level but also at state and local levels of government. The EPA is concerned with water quality planning and management, whereas other federal water agencies direct most of their attention to issues of water allocation and control. Counterparts at state and local levels function in a similar fashion and the result is that the nation appears to be getting less than its money’s worth out of the large investments in water- related facilities (Viesmann and Welty, 1985). Rapid progress toward a “total water management” philosophy cannot be attained unless all aspects of water-use are considered simultaneously.

Although the quantity of water available is fixed, local depletions and excesses occur and the quality of the water resource is continually subjected to change. Therefore, there are three basic

17 components in water problem: institutional (those topic dealing with laws, regulations, and/or organizations), water quality (those subjects in which constituents of or substances in the water are of primary concern), and water quantity (for those issues in which water allocation is the main focus). The combination of these three elements determines water resource management.

Implementing water quality and quantity tasks within water resource management policy allocates hydrological assessment of a particular geographical area.

1.1 HYDROLOGICAL ASSSESSMENT

1.1.1 Water quantity assessment

1.1.1.1 Purposes of water quantity assessment

Providing water in sufficient quantity for various human uses has long been an objective of water resource developers. Speaking from the perspective of water engineering and water resources management, there are several initial goals in water quantity assessment. According to

Viesmann and Welty, 1985 and Viesmann et. al. 1989, the majority of them are:

• Water allocation among competing uses (a basic problem for water resource managers is

how to allocate water for environmental enhancement, food and energy production,

recreation, municipal and industrial water supply, and other purposes). This includes

development of water storage and convenience facilities, reduction of the quantities of

water used, and better management. Water engineers usually provide the fundamental

framework for this section of water quantity assessment studies.

• Floodplain management (Efforts to mitigate flooding problems, organization of long-term

flood plain development to avoid flood damages)

• Instream flows (flows may be reserved for sustaining fish and wildlife populations,

outdoor recreation, navigation, hydroelectric power generation, waste assimilation and so

forth.) In particular, the determination of the quantities of water needed to protect the

18 aquatic, biologic, and aesthetic values of a stream and preserve existing fisheries is very

difficult.

• Water for energy resources development (industrial needs, processing, cooling, waste

treatment, hydroelectric power development).

• Water supply and wastewater disposal

• Urban water systems (this problem is related to sewer lines constructions within

municipal water supply system).

• Water for agriculture (Water demands for agriculture and irrigation).

• Navigation (Availability of water resources for navigation).

Assessing water quantity is a task needed to be resolved mainly in order to quantitatively characterize hydrological processes within a region in order to improve the understanding of the processes and mechanisms involved in hydrologic cycle (e.g. land use change impacts on water quantity and quality, climate change impact on water flows).

1.1.1.2 Hydrological Modeling

Types of models

One of the earliest classifications separates the simulation hydrological models into physical and mathematical categories. Physical models include analog technologies and principles applied to small-scale models. In contrast, mathematical models rely on mathematical statements to represent the system.

A second classification is achieved by considering physical, analog, and some digital models as continuous because the processes occur and are modeled continuously. Many simulation models rely on the necessity and advantages of slicing space and time into finite increments, and thus qualify as discrete models.

Processes that involve changes over time and time-varying interactions can be simulated by dynamic models. In contrast, models that examine time-independent processes are frequently called static (Viesmann et. al. 1989 and McCuen and Snyder, 1986).

19 Simulation models are also classified as descriptive and conceptual. The former have had the greatest application and are of particular interest to practicing hydrologists because they are designed to account for observed phenomena through empiricism and the use of basic fundamentals such as continuity or momentum conservation assumptions. Conceptual models on the other hand, rely heavily on theories to interpret phenomena rather than to represent the physical process. Examples of the conceptual models include models based on probability theory.

Models that ignore spatial variations in parameters throughout an entire system are classified as lumped parameter models. An example is the use of a unit hydrograph for predicting time distributions of surface runoff for different storms over a homogeneous drainage area. The lumped parameter is the n-hour unit hydrograph used for convolution with rain to give the storm hydrograph. The time from end of rain to end of runoff is also a lumped parameter as it is held constant for all storms. Distributed parameter models account for behavior variations from point to point throughout the system. For example, most modern groundwater simulation models are distributes in that they allow variations in storage and transmissivity parameters over a grid superimposed over the plane of an aquifer. Commonly, hydrological models can be divided into 3 typical groups: (1) Physical-mathematical models (are characterized by good imitational capabilities, using remote sensing methods, differential equations 1 and 2nd orders and a good feedback). This type of models requires good experimental data records and knowledge of physical dynamics of the system written in sets of equations. For example, such models are used in modeling of flood runoff formation tasks; (2) Conceptual models are built on general knowledge of nature of physical processes. Models consist of empirical equations and parameters that are determined through optimization process; (3) Dynamic-stochastic models are used in hydrologic and engineering calculations. In the base of stochastic approach, modeled hydrologic data is used instead of real data (Browne, 1990).

Chow, Maidment and Mays, (1988) argue that hydrological models can be classified according to their conceptualizations and assumptions of three key parameters – randomness,

20 space and time. Depending on how randomness, space and time are conceptualized, we can

outline eight different types of models in general (Figure 1.1)

Figure 1.1 Taxonomy of hydrological models (Source: Chow, Maidment and Mays (1988)

System f (randomness, space, time) Input Output

Deterministic Stochastic

Lumped Distributed Space-dependent Space-correlated

Steady Unsteady Steady Unsteady Time- Time- Time- Time- flow flow flow flow indepen correlated independent correlated dent

McCuen and Snyder, 1986 indicate that the stochastic approach uses simplest concepts of

watershed processes, although not necessary the simplest mathematics in formalization of those

concepts. In the stochastic approach watershed outputs are thought of as a time series of random

events. Stochastic elements are not without physical basis. Certainly, some properties of the time

series, such as mean values and variabilities, must derive magnitudes from the watershed in

which the stochastic generating processes are at work. The essence of stochastic processes is the

nonpredictability of exact magnitudes of each element of the series.

At the opposite boundary of predictability are the deterministic models. In these models, given

values of initial and boundary conditions, a set of input values will always produce exactly the

21 same output values. The generating processes contain no random components. Operationally, however, deterministic models link with the stochastic models, since to predict outputs for the future, the possible future inputs must be stochastically generated. Recently, deterministic methods of modeling hydrologic behavior of a watershed have become popular because deterministic simulation describes the behavior of the hydrologic cycle in terms of mathematical relations outlining the interactions of various phases of the hydrologic cycle. Frequently, the models are structured to simulate a streamflow value, hourly or daily, from given rainfall amounts within the watershed boundaries.

An example of a typical conceptual representation of large-scale hydrological processes including upper and lower soil zones in the draining streams is shown on Figure 1.2. The variables Xu and Xl represent upper and lower soil water (Lettenmaier and Wood, 1993). In conceptual representation the interflow, baseflow and surface runoff all contribute to streamflow, while in addition to the evapotranspiration loss, there is leakage to deep groundwater layers.

Typically subsurface parameters and processes are not directly observable with adequate sampling intervals in space (if at all).

Figure 1.2 Conceptual representation of watershed model

It is common hydrologic practice to estimate key model parameters from historical data of precipitation, evapotranspiration, and streamflow. The challenge to present day hydrologic science is to devise methodologies to estimate such parameter values from observable physical characteristics by taking into consideration the large spatial variability that several properties of the land surface exhibit (e.g., soil water texture, soil hydraulic conductivity). Long-range streamflow prediction is valuable for the management of water resources. For example, the management of large multi-objective reservoirs requires reliable streamflow forecasts with at least daily resolution and with forecast lead times that may extend to a few months into the future.

A methodology which is commonly used to produce such streamflow forecasts is to run the

23 hydrologic model up to the present time with observed historical data, and then to force it with future data corresponding to the same within year period as the forecast horizon, but taken from past years (Lettenmaier and Wood, 1993).

1.1.1.3 Advantages and limitations of simulation models

The development of more sophisticated hydrological models in recent years has been a cause of a mixture of optimism and debate amongst hydrologists (Bardossy, ECLAT-2 KNMI

Workshop). It was mentioned above that computer models have been developed from those of the lumped, conceptual type, through distributed models to the latest generation of physically based distributed models. In order to model changing/changed conditions, models must have the capability to reproduce hydrological processes within a basin, rather than relying on empirical or statistical relationships. This task requires that the models have a physical basis (VAHMPIRE,

1995). For example, Yang et al. (2000) argues that distributed physically based hydrological models give a detailed and potentially more correct description of hydrological processes in the catchments than other model types. An example of a typical grid-based distributed hydrological model is MIKE SHE (European hydrological model). It is “a dynamic, user friendly modeling tool for the analysis, planning and management of a wide range of water resources and environmental problems related to surface water and groundwater, in particular when the effect of human interference is to be assessed”. TOPMODEL, which is “a rainfall-runoff model that bases its distributed predictions on an analysis of catchment topography that predicts saturation excess and infiltration excess surface runoff and subsurface storm flow” is another example. Other examples of digital simulation models of hydrological processes are: (a) HSPF - Hydrological

Simulation Program-Fortran, designed by EPA in late 60s to simulate continuous streamflow; (b)

USGS Rainfall-Runoff model, designed to simulate rainfall-runoff events; (c) STORM – quantity and quality runoff of urban runoff model and others.

However, according to Christians and Feyen (2000) one of the disadvantages is the huge amount of input variables and parameters necessary to run the model. Vogel, (1999) introduces a

24 new, unified view on hydrologic modeling stating that stochastic models are derived to assure that certain properties of the output (streamflow) are reproduced such as its probability distribution or perhaps its mean, variance, skewness and serial correlation. Such models are also derived to insure that basic relationships between inputs and outputs are reproduced such as the cross correlation between P (precipitation) and Q (discharge) and between PE

(evapotranspiration) and Q. Since these properties are often the basis of the model derivation, stochastic models can constantly reproduce these properties, unless the model is either theoretically flawed or improperly verified. On the other hand, deterministic models are derived to reproduce certain physical processes inherent to the process being modeled. For example, a watershed model of streamflow may contain physical models of evapotranspiration, infiltration, groundwater, snowmelt, etc. Therefore, deterministic models are designed to represent internal physical processes, enabling a wide range of model applications which stochastic models are unable to accomplish. Yet deterministic modelers often lament that modeled streamflows tend to have lower variance than observed streamflow. Deterministic modelers rarely examine such properties as the skewness or serial correlation of streamflows. That is why, Vogel, (1999) concludes that a unified approach to modeling is critical and has numerous advantages from combining stochastic and deterministic “world views” when deriving, testing, and applying models.

Along with listed applications of some hydrological simulation models, there is a series of models developed for: (1) river/reservoir hydraulics purposes: unsteady, one-dimensional simulation of surface flows in a branching river network (GVUFP), SHP (Stream hydraulic

Package) which performs dynamic streamflow routing in a complex river network; (2) hydrology purposes: STORM (Storage, treatment, overflow runoff model) that simulates the precipitation runoff processes for a single, usually urban basin; (3) stochastic hydrology: HEC-4 (monthly streamflow simulation model) analyzes streamflows at a number of interrelated station to determine their statistical characteristics and generate multi-year synthetic monthly streamflow

25 series; (4) flood damage purpose: EAD (Expected Annual Flood Damage Computation), model computes flood damage for the economic evaluation of flood control and floodplain management plans.

However, because simulation entails a mathematical abstraction of real-world systems, some degree of misinterpretation of system behavior can occur. The extent to which the model and system outputs vary depends on many factors. The test of a developed simulation model consists of verification by demonstrating that behavior is consistent with known behavior of the physical system. One limitation of computer simulation hydrological model is the difficulty encountered in using the models to generate optimal plans for development and management. They will allow the performance assessments of specific schemes but cannot be used efficiently to generate options for stated objectives. Another limitation of many simulation models involves the inflexibility of changing the operating procedures for potential or existing components of the system being modeled. It requires considerable reprogramming of the software to generate new operating procedures (Viesmann and Welty, 1985 and Viesmann et. al. 1989).

1.1.2 Water quality assessment

In the United States and many other countries, the quality of waters is determined by their suitability for use by cities, industries, and individuals. In view of the complexity of factors determining water quality, and the large choice of variables used to describe the status of water bodies in quantitative and qualitative terms, it is difficult to provide a simple definition of water quality. Furthermore, our understanding of water quality has evolved over the past century with the expansion of water use requirements and ability to measure and interpret water characteristics

(Chapman, 1996). Generally, the quality of the aquatic environment is determined by set of concentrations and physical partitions of inorganic and organic substances, composition and state of aquatic biota in the water body and description of temporal and spatial variations due to factors internal and external to water body.

26 1.1.2.1 Purposes of Water Quality Assessment

As it was noted before, water quality assessment being as a part of regional hydrological assessment is an overall process of evaluation of the physical, chemical and biological nature of water in relation to natural water quality, human effects and intended uses, particularly uses which may affect human health and the health of the aquatic system itself. Another important issue when considering water quality assessment, is water quality monitoring, that is the actual collection of information at set locations and at regular intervals in order to provide the data, which may be used to define current conditions, establish trends etc. Water quality assessment includes the use of monitoring to define the condition of water to provide the basis for detecting trends and to provide the information enabling the establishment of cause-effect relationships.

Under U.S legislation, for example, each body of surface water must be put into one or another quality class. The U.S Environmental Protection Agency (EPA) publishes criteria that specify the conditions deemed consistent with specified uses. State authorities then establish broad water use classifications (water supplies, swimming, indigenous aquatic life, etc) and apply the federal criteria in determining standards that must be met within each class. With the enactment of the

Clean Water Act in 1972, the nation rejected past practices that had resulted in widespread pollution of rivers, lakes, and coastal waters and made a new commitment to restore and maintain the chemical, physical, and biological integrity of the nation's waters.

1.1.2.2 Types of water pollution

Pont-source pollution

By definition, a point source is a pollution input that can be related to a single outlet.

Untreated, or inadequately treated, sewage disposal is probably still the major point source of pollution to natural waters. Other important point sources include industrial effluents. A sewage treatment plant serving a fixed population delivers a continuous load of nutrients to a receiving water body.

Non-point source pollution

Non-point source pollution is produced by the transport runoff of constituents from diffuse sources on the land to streams (Browne 1990, Huber 1993). At present time, it is considered as the most problematic and largest agent of pollution because of its geographic scale and increasing technogenic activity on watersheds, especially urbanization and agricultural development.

Groundwater Pollution

Groundwater is a major source of water supply in many parts of the United States. 32 states use ground water as a major drinking water source. Agriculture and industry use ground extensively for different technological purposes. Severe problems related to both the quantity and the quality of ground water are arising. Many groundwater resources are being contaminated by poor waste management practices and other are being rapidly depleted.

1.1.2.3 Water Quality modeling

Watersheds are important as integrators of effects of many forces, including land use and climate. Their natural boundaries and hierarchical structure represent an appropriate structure for environmental impact analysis and modeling (Krysanova et al, 1998).

Because water quality is inextricably linked to water quantity, it is important for the hydrologist to understand the significance of developing modeling techniques that can accommodate both features. A water quality model is a mathematical statement or set of statements that equate water quality at a point of interest to causative factors. In general, water quality models are designed to: (1) accept as input, constituent concentration versus time at points of entry to the system, (2) simulate the mixing and reaction kinetics of the system, and (3) synthesize a time-distributed output at the system outlet. Either stochastic or deterministic approaches may be taken in developing methods for predicting pollution loads. Stochastic technique is based on determining the likelihood (frequency) of a particular output quality response by statistical means (McCutcheon, 1989 and Viesmann et. al. 1989). This is similar to

28 frequency analysis of floods or low flows in water quantity modeling. The deterministic approach

(output explicitly determined for a given input) requires that a model be developed to relate water quality loading to a known or assumed hydrologic input. Such a model can range from empirical centration discharge relation to a physical equation representing the hydrochemical cycle. The ultimate modeling technique is to best define the actual mechanism triggering the water quality response. In general, water quality models should permit acceptance of inputs in terms of pollutant concentration versus time at points of entry into the system, description of the mixing and reaction kinetics in the stream element or groundwater element of concern, and synthesis of a time-distributed output indicating pollutant concentration at the outlet of the element (segment) being modeled. The stochastic approach in water quality modeling is often ruled out because actual records of water quality parameters are unavailable for long enough periods to permit frequency methods to be used. The deterministic approach to water quality modeling requires that relations between water quality loading and the flow or hydraulic features of the system be established and that appropriate chemical and/or biological reactions be tractable for solutions.

1.1.2.4 Types of water quality models

Most water quality models in use today are designed to trace the movement of pollutants through streams, rivers, lakes, estuaries and other open bodies of water. Point-source water quality models generally deal only with confined bodies (e.g. channels) of water while non-point models also take into consideration other phases of the hydrological cycle. Since non-point pollutants are moved to streams, estuaries in overland flows, interflow, and groundwater flow processes, the representative models must include these phases of the hydrologic cycle in addition to the channel phase. As a result, non-point models are often thought of as “loading models”, which act to trace the movement of pollutants from their originating locations to water courses.

Once a water course is reached, these loads can be handled by stream quality models or groundwater quality models as the case may be. Since such non-point models represent the introduction of pollutants to water courses from land surfaces, they are strongly associated with

29 the occurrence of precipitation events. Point source models, on the other hand, usually represent continuous inputs of pollutants, primarily from discharge of waste treatment works and other sources (Viesmann and Welty, 1985, Viesmann et. al. 1989 and Chapman, 1996).

Water quality models are usually classified according to model complexity, type of receiving water and the water quality parameters (dissolved oxygen, nutrients, etc.) that the model can predict. The more complex the model is, the more difficult its application will be in a given situation. Model complexity, in general, can be characterized by a set of four factors:

- The number and type of water quality indicators (in general, the more indicators

included, the more complex the model)

- The level of spatial detail (with increasing the number of pollution sources and water

quality monitoring points so do the size of the model and input datasets).

- The level of temporal detail (It is easier to predict long-term static averages than short-

term dynamic changes in water quality).

- Water Body complexity (that is characterized by the size of the body, number of

tributaries and others).

In selecting parameters for the model, care should be taken to choose pollutants that are a concern in themselves and are also representative of the broader set of substances which cannot all be modeled in detail (Pollution Prevention Handbook, 1998).

Water quality models may be steady or time varying in design. Steady state models can be used where the principal variables are not time dependent or can be assumed to be so within a given stream reach or segment. In general, steady state models are more suited to long-range planning while time-varying models fit the need for setting policies for event management.

Categorically, water quality models are often classified into simulation and optimization ones.

Simulation models serve mostly for the purpose of understanding, research, operation and control. In contrast, optimization is primarily employed for planning and management. In several cases the two methods are used in sequence: optimization is applied for the screening of

30 alternatives, while simulation is utilized for the final selection. During the past few decades, there has been plenty of development in water quality modeling. This applies to methodologies, as well as software and hardware. The time requirement for model developments/applications has been significantly reduced (Somlyody, 1997). Watershed simulation models describe physical, hydrological and biogeochemical processes in a dynamic way. Conceptually, such models describe mathematically water fluxes and associated mass fluxes from the land surface and soil profile to the closest river cross section (Krysanova et al, 1998). On the other hand, optimization models are mathematical representations of an economic problem applied onto natural system and include mathematical programming, calculus of variations, and optimal control models.

Finally, similar to water quantity modeling, both lumped and distributed parameter approaches may be taken. Lumped parameter models are especially suited to large-scale system analysis, while distributed parameter models can provide a greater level of detail where localized decision must be made. In either case, the models may be operated continuously or tailored to the simulation of specific events.

Among widely used water quality models are: (1) EPA SWMM (Storm Water Management

Model) with capability for modeling the movement of certain quality constituents in urban areas.

The model can simulate runoff from an area for any prescribed rainfall pattern as well as determine storm water flows and water quality at various locations in a storm water system and receiving body of water; (2) EPA QUAL2E (The Enhanced Stream Water Quality Model) model, which is a comprehensive stream quality model that can simulate several water quality constituents in branching stream systems using a finite-difference solution of the advective- dispersive mass transport and reaction equation (DeVries and Hromadka 1993). A stream reach is divided into a number of subreaches, and for each subreach, a hydrologic balance in terms of discharge, a heat balance in terms of temperature, and a materials balance in terms of concentration is written. QUAL2E is the latest in a series of water quality management models initially developed by the Texas Water Development Board in the 1960's. At present, the model is

31 supported by the Center for Exposure Assessment Modeling (CEAM) of the EPA (Basins 3.0

User’s manual)

(3) HSPF (Hydrologic simulation program – fortran) permits the continuous simulation of a broad range of hydrologic and water quality processes. The Hydrologic Simulation Program -

FORTRAN (HSPF) simulates watershed hydrology and water quality (DeVries and Hromadka

1993, Al-Abed and Whiteley 1995). It allows an integrated simulation of land contaminant runoff processes with in-stream hydraulic and sediment-chemical interactions. HSPF computes a continuous hydrograph of stream flow at the basin outlet based on continuous record of precipitation and evaporation data. HSPF also simulates transport of sand, clay and silt sediments, and a single organic chemical and the transformation products of that chemical. Transfer and reaction products modeled are hydrolysis, oxidation, biodegradation, volatilization, and sorption.

Being HSPF a lumped model, the spatial variability of the watershed is considered by partitioning it into subwatersheds and applying the lumped model to each of them. The water quantity routines in HSPF are a FORTRAN version of the Hydrocomp Simulation Program which was developed from the Stanford Watershed Model (SWM) originally developed in 1959 (HSPF

User’s Manual, Release 9.0); (4) The EPA approved Water Quality Analysis Simulation Program

(WASP5) developed at the Manhattan College, New York, in the early 1980's, which is used to account for kinetic transformations of the pollutants as well as for diffusion and dispersion processes in aquatic systems. With respect to non-point source pollution modeling, WASP could be used for analyzing the fate of the constituents in the receiving water bodies, but it does not simulate overland flow, which is the main mechanism of non-point source pollution generation.

Among other applications, WASP has been used to model eutrophication and pollution of the

Great Lakes, pollution of the James River Estuary, volatile organic pollution of the Delaware

Estuary, and heavy metal pollution of the Deep River, North Carolina, all water bodies with long residence times in which the constituent kinetics, diffusion and dispersion processes are significant;

32 (5) The Agricultural Non-Point Source Model (AGNPS) is an event-based model that simulates runoff water quality from agricultural watersheds (DeVries and Hromadka 1993). The model uses geographic data cells of 0.4 to 16 hectares to represent land surface conditions, and, within the framework of these cells, runoff characteristics and transport processes for sediment, nutrients, and chemical oxygen demand are simulated. Flows and pollutants are routed through the channel system to the basin outlet. Runoff volume is calculated by the Soil Conservation Service curve number procedure, and peak flow is determined by using an empirical formula that takes into account drainage area, channel slope, runoff volume, and watershed length-width ratio. AGNPS was developed by the Agricultural Research Service in Cooperation with the Minnesota Pollution

Control Agency and the Soil Conservation Service.

1.1.2.5 Reliability and limitations of water quality models

Although water quality models have become recognized tools to aid planners and managers, it must be understood that their usefulness, reliability and applicability are quite varied. The introduction of assumptions in the modeling process creates an element of uncertainty in the modeling output. The degree of uncertainty depends on the nature of the model and the conditions specified (initial conditions, boundary condition etc.).

1.2 LAND USE CHANGE AND HYDROLOGICAL ASSESSMENT

As it was briefly noted above, the relative magnitudes of components of the hydrological cycle are sensitive to changes in the environmental system within which they operate. Changing land use and land management practices are affecting the hydrologic system, often leading to deterioration in the resource baseline. For instance, the portion of total precipitation that infiltrates into the soil versus that which remains on the surface as runoff is essentially controlled by land surface characteristics or land use. Consequently, changes in land-use will alter the hydrologic regime of a watershed that in turn, will impact water quality in view of changes in hydrochemical and hydrobiological regimes. Hydrologic impacts associated with land-use change have a variety of adverse effects on the environment including river channel erosion and

33 widening, decreasing ecological diversity in aquatic communities, loosing groundwater recharge, all of which can have negative impacts on human health and welfare (Gosselink and Turner,

1978).

At present, 2/3 of the US population lives in urban areas and these urban areas are constantly expanding to support the growing urban population (Chinitz, 1991). Furthermore, urban and agricultural areas have been recognized by the United States Environmental Protection Agency

(EPA) as the major source of contaminated runoff (Browne 1990). Constituents in urban runoff include suspended solids, bacteria, heavy metals, oxygen demanding substances, nutrients, oil and grease, which are derived from construction sites, developed urban lands, streets and parking lots.

Runoff from agricultural areas are enriched with pesticides, sediments, nutrients, organic materials and pathogens. Significant amounts of nutrient pollution (nitrogen and phosphorus) is produced by urban and agricultural areas. However, according to Huber (1993), land use has a strong influence only on the amount of runoff (volume of water per unit time), while its effect on the runoff concentration (mass of pollutant per unit volume of water) is less important. Still, land use affects the pollutant load (mass of pollutant per unit time). Typically, land use change, particularly in the form of urbanization, increases impervious areas and significantly alters the spatial and temporal patterns of surface runoff. An increase in surface runoff volume not only contributes to downstream flooding but also reduces local groundwater recharge. The low vegetation density and the extensive impermeable surfaces reduce the infiltration capacity.

Precipitation collected by rooftops and roads is diverted through the drainage systems into the nearby streams. Consequently, there is an increase in the volume of flood flows and the rate of runoff immediately downstream of the urban area. Moreover, there is degradation of surface water quality because urban areas produce significant amounts of non-point source (NPS) pollution. The influx of pollutants is partly caused by anthropogenic activities and partly by accelerated denudation as flood characteristics are modified by urbanization (Tong, 1990).

Another example of such studies, Fisher, et al, (2000) addresses the issues of land use practices

34 and their interrelationships with water quality by relating datasets representing surface water quality at selected sites within the survey area to the predominant land use in each portion of watershed with application of spatio-temporal analysis to reveal the tendencies of each group of pollutants related to different land use types.

With regard to water quality monitoring, especially non-point source pollution, there is a need for sampling high-flow conditions (Browne, 1990). Traditionally, streams were sampled only during low-flows because, in dry season, they are more vulnerable to point-source pollution.

However, now that the importance of non-point sources of pollution has been recognized, streams are monitored also during the wet season, when most of the runoff takes place and higher pollutant concentrations often occur.

The actions taken towards decreasing the effects of non-point source pollution are called runoff quality controls. For instance, factors involved in controlling urban runoff quality include:

(1) preventing or reducing pollutant deposition in urban areas; (2) preventing pollutant contact with runoff; (3) minimizing Directly Connected Impervious Areas (DCIA); (4) designing controls for small storms (usually less than 1 in rainfall) and (5) using the treatment train concept, which assumes source controls, individual building lot controls, group of lots controls, and regional controls in sequence. Treatment practices are grouped into two broad categories: infiltration and detention practices. Infiltration practices include swales and filter strips, porous pavement, percolation trenches, and infiltration basins; while detention practices include extended detention basins and retention ponds (Urbonas and Roesner 1993).

Urban development and land use can be looked upon as a multidimensional process, which consequently poses many difficulties for proper description and classification. Land use is determined by the interaction in space and time of biophysical factors (constraints) such as climate, soils, topography, vegetation etc. and human factors like population, socio-economic infrastructure, technology etc. Urbanization of watershed and changes in land use practices poses significant risk to aquatic ecosystem health, these risks result from a variety of stressors,

35 including physical (removal of vegetative cover, in-stream modifications etc.), chemical

(discharges from industrial operations, atmospheric deposition, diffuse non-point sources from various land-uses, accidents and spills) and biological (pathogens from human and animal waste, introduced species) (Vieux, 1991).

Considering these concerns and facts, and due to significant importance of surface and ground water resources to most urban as well as non-urban communities, it is critical to understand and manage the hydrological impacts of land-use change over a range of spatial and temporal scales using integrated methods.

1.3 CLIMATE CHANGE AND HYDROLOGICAL ASSESSMENT

1.3.1 Brief discussion of climate change

The Earth’s climate is maintained through interactions between the atmosphere, oceans, ice and the land surface. Studies of sediments and ice cores show that the climate of the distant past has swung between cold and warm conditions. Sometimes the transitions from one state to another have been abrupt, at other times the rate of change has been slow, but in all cases change has been driven by natural processes.

But now evidence is gathering that human activities are themselves changing climate, or perhaps accelerating naturally occurring climate change. The Earth has warmed over the past 100 years. Can we distinguish human effects on climate change from effects due to natural processes?

More importantly, can we predict the rate and extent of future changes and their impacts on our lives? Presently, scientists have the following accepted facts about climate change (according to different sources: Arnell and Reynard, 1996; Bardossy, 1998; Hulme, 1998; Gleick, 1986; IPCC report, 1997; Viner et al, 1996 and others):

36 Carbon dioxide, methane and nitrous oxide, together with water vapor, are all greenhouse gases which trap radiation emitted from the Earth’s surface, keeping the Earth about 30°C warmer than it otherwise would be. Carbon dioxide levels in the atmosphere have risen by about

32% in the last 200 years, increasing from about 280 parts per million to 370 parts per million today. Methane levels in the atmosphere have doubled over the last 100 years. Nitrous oxide levels are rising at about 0.25% each year. Carbon dioxide, methane and nitrous oxide levels are rising mainly as a result of human activities connected with energy generation, transport and agriculture. The order of importance in contributing to human-induced greenhouse effect is carbon dioxide (60%), methane (20%), nitrous oxide and other gases (20%).

Although increases in greenhouse gases result in warming, this is offset to some extent by other factors such as tiny particles in the atmosphere. Examples are sulphates resulting from industrial activities, or dust from volcanic eruptions. These can reflect sunlight and produce a cooling effect. Average global surface temperature has risen by 0.6°C in the last 140 years and global sea level has risen by between 10 and 25 cm over the past 100 years. The oceans are particularly important in controlling when and where climate change occurs for two reasons.

They act as giant heat reservoirs and redistribute heat globally via their circulation patterns.

The consequences that are likely to happen due to global climate change could be very serious. For example, if greenhouse gas emissions continue on the existent current basis, models predict that carbon dioxide levels will double or perhaps even triple from pre-industrial levels by the end of this century. When the effect of other factors such as increased water vapor is added, the estimated average global temperature rise by 2100 will be between 1.5 and 5.5°C. The rise in temperature, in turn, will produce an impact on a wide range of climate-related factors. Global sea levels are likely to rise by between 20 and 90 cm over this century, and will continue to rise further in the future. Low-lying coasts will flood, affecting many human settlements, including some major cities, and some habitats such as saltmarshes will be lost. We might expect more extreme weather events, such as heat waves, floods, droughts and storms, but intense cold events

37 will become more rare. Rainfall is likely to increase in many regions and tropical cyclones may become more severe. The world’s vegetation zones may undergo major changes, in particular, boundary shifts between grasslands, forests and shrublands. Forests are growing faster with rising

CO2 and warmer temperatures, but towards the end of this century, there is a risk that some tropical rainforests may die in hot, dry spells. Some regions and seasons will become wetter, others drier. Summer droughts are likely to intensify in the interiors of continents. Half the world’s mountain glaciers could melt and Arctic ice would be reduced in both extent and thickness. Freshwater systems will experience changes in temperature, flows and levels, affecting biodiversity, water supplies and probably quality. Human conflict over access to water resources may increase. Agricultural productivity is likely to vary across regions. Although global productivity may stay about the same, there may be increased risk of famine in arid and semi-arid regions. Marine fisheries are likely to be affected in ways we cannot predict in any detail. Cold water species such as cod and haddock are likely to be reduced, but will probably be replaced by warmer water species such as mullets, soles and breams. Mass movements of people away from flooded or arid regions could cause conflicts and health problems and so on. As it is seen from this brief overview, the feedback mechanisms might be predicted, but only up to some levels.

Some feedbacks could be much more serious in terms of scale and consequences.

At the same time, some uncertainties about climate change facts should be stressed. These are mainly associated with (1) carbon budget (It is not completely clear what determines how much carbon is in which part of the earth’s systems and the rate at which it moves between the parts. (2) temperature records and trend analysis (this is mainly refer to the question of systematic and instrumental errors in temperature record analysis since temperature fluctuates annually and over much longer time scales associated with the natural variability of the climate. Accurate records using instruments have only been made for about 150 years. Past records are inferred from other evidence. Identifying small warming trends against this background variation is difficult. (3)

Solar radiation varies due to physical changes in the sun, the best known being the 11-year sun-

38 spot cycle. Variations detected using satellites over the last 20 years are very small, less than

0.1%. It is not clear whether variations over a long time scale might be more significant and what effect any of this variation has on the warming of the Earth. (4) Tiny particles in the atmosphere, from both natural sources and human activities, can have positive and negative effects. For example, soot particles absorb heat and cause warming while sulphates reflect heat, resulting in cooling. The different contributions to climate change are uncertain. (5) Ozone has a complex effect on climate change. Close to the Earth’s surface, increased ozone levels contribute to warming, but ozone depletion in the stratosphere has a cooling effect. (6) Clouds can reflect incoming solar radiation back into space, keeping heat out. But clouds can also prevent radiation from the earth’s surface escaping, thus keeping heat in. So the effects can be positive or negative depending on the height, temperature and properties of the clouds, all of which vary in time and from place to place. The effects of clouds and how these might respond to climate change are poorly understood and they remain one of the biggest uncertainties. (7) A warmer atmosphere can hold more water vapour, which is a powerful greenhouse gas, thus amplifying the warming by positive feedback. (8) Plant growth may increase if carbon dioxide rises, thus absorbing more carbon from the atmosphere creating a negative feedback, but at the same time increasing in vegetation density may cause parallel increase in carbon dioxide consumption by vegetation and rise of oxygen levels in the atmosphere. (9) Polar ice sheets will melt to some extent as temperatures rise, but melting will be partially balanced by greater snowfall over polar areas.

Arctic ice sheets will melt faster than snow will accumulate, therefore adding to sea level rises.

But in the Antarctic, recent studies suggest that the interactions of ice shelves (the parts of the

Antarctic Ice Sheet which extend out over the ocean) with the waters beneath are complex, and that warmer temperatures will not necessarily result in thinner ice sheets and shelves in the southern hemisphere. (10) Reflectance - changes in the distribution of vegetation in warmer climates may alter the reflectance and thus the capacity of the Earth to absorb heat. Less snow

39 cover over the continents of the northern hemisphere in warmer conditions will mean that more solar radiation is absorbed by the darker surface.

1.3.2 Climate change and water resources

It is important to emphasize that climate change is just one of a number of stresses facing the hydrological system and water resources. One of the most pressing issues of global change research is the interaction of land cover changes with global climate (Veldkamp et al, 1996) as well as their effects on hydrological, hydrobiological and water quality regimes. As it was discussed before, according to The Intergovernmental Panel on Climate Change (IPCC) reports

(IPCC 1997), a rise in the order of 0.3 to 0.6oC n the mean surface temperature per decade could be felt in the next century. This in turn, will modify water cycle, disturb atmospheric water vapor content and hence the precipitation and evapotraspiration rates. Based on projections made by the

IPCC, for instance, temperatures for Midwest region of the US could increase by 2 to 4oC and precipitation could fluctuate in a range of –20% to +20% by the year 2010 (IPCC report 1997).

Over the last decade there have been numerous studies (Arnell, 1999; Arnell et al 1996; Ponce et al, 1995; Bultot et al, 1988, 1992, Chiew et al 1995) investigating the sensitivity of hydrological regimes to climatic changes associated with global warming, in a wide range of environments and using many different models and scenarios. These investigations employ a variety of models, ranging from the simple water balance models to evaluate the annual and seasonal streamflow variation to the complex distributed-parameter models that simulate a wide range of hydrological and biogeochemical processes. Arnell et al, (1999) investigates the effect of climate change over a large geographic domain such as Europe, East Africa and southern Africa.

Reynard, et al, (1997) introduces a macro-scale water-balance hydrological model “Macro-PDM” reflecting an interest among hydrologists and water managers to simulating hydrological behavior over a large geographic domain due to (1) water resources managers responsible for large regions need to estimate the spatial variability in recourses over that large area, at a spatial resolution finer than can be provided by observed data alone; (2) hydrologists and water managers are

40 interested in the effects of catchment and climate variability and change over large geographic domain and (3) hydrologists and atmospheric modelers are very interested in developing generalized models of land surface processes that simulate these processes accurately, but which work over large areas and can be incorporated into climate simulation models.

Gleick, (1986) reviews and introduces approaches for evaluating the regional hydrologic impacts of global climatic changes and presents a series of criteria for choosing among different methods.

He focuses special attention on using modified water-balance models because they offer significant advantages over other methods in accuracy, flexibility and ease of use. Especially water balance models are useful when identifying the regional hydrologic consequences of change in temperature, precipitation and other climatic variables. Climate change scenarios impacts on streamflow and its surface and underground components are examined and simulated in Gellens, Roulin, (1998) with detailed discussion of such components as groundwater flow, number of flood days, number of low flow days under the baseline climate and climatic change conditions. Mimikou et al, (1991) investigate regional hydrological effects of climate change arguing that according to their estimations, increase of the annual temperature due to the increasing concentration of atmospheric CO2 and other trace gases would significantly affect the regional hydrologic resources in many Greek watersheds, more specifically, the spatial and temporal redistribution of the region’s water resources. Seidel et al, (1998) develop algorithms to quantitatively estimate snow cover and resulting snowmelt runoff and how, to what degree it will be affected by climatic change, based on a case study in Alpine catchments.

1.4. GIS AND REMOTE SENSING IN WATER QUALITY ANALYSIS

Geographical distributions on the earth surface has been inherently complex, revealing more information at higher spatial resolution apparently without limit. Modeling these distributions into geographical reality is a process of discretization that converts a finite number of database records or objects (GIS development staff, 1998). The environmental processes in the real world are computer based, mathematical models that simulate spatially distributed and time dependent,

41 which are increasingly recognized as fundamental requirements on the reliable, quantitative assessment of complex environmental issues of local regional and global concern. There are three basic issues in Hydrology, which are necessary to model to understand the problems faced by the environmental system. Summarizing, these issues are:

• Pollution Control and mitigation for both groundwater and surface water.

• Water Utilization for water supply for municipalities, agriculture and industry and the

competing demands for instream water use and wildlife habitat.

• Flood Control and mitigation

For almost two decades in the 1960s and 1970s, geographic information systems (GIS) and hydrological modeling developed in parallel with few interactions. Major research efforts toward the integration of GIS with hydrological modeling did not take place until late 1980s, as a part of the GIS community’s efforts to improve the analytical capabilities of GIS and hydrologist’s new demand for accurate digital representation of terrain (Sui, and Maggio, 1999).

Nowadays, both GIS users and hydrologists have increasingly recognized the mutual benefits of such integration from the success of the past 10 years. Various hydrological modeling techniques have enabled GIS users to go beyond the data inventory and management stage to conduct sophisticated modeling and simulation. For hydrologic modeling efforts, GIS, especially through their powerful capabilities to process DEM (Digital Elevation Models) data have provided modelers with new platforms for data management and visualization.

Remote Sensing has already demonstrated its capabilities to provide information on natural resources such as crop, land use, soils, forest etc. Although few remotely sensed data can be directly applied in hydrology, such information is of great value since many hydrologically relevant data can be derived from remote sensing information (Seith, Jain, Jain, 1998). One of the greatest advantages of using Remote Sensing data for hydrological modeling and monitoring is its ability to generate information in spatial and temporal domain, which is very crucial for successful model analysis, prediction and validation. However, the use of Remote Sensing

42 technology involves large amount of spatial data management and requires an efficient system to handle such data. The GIS/RS technology provides suitable alternatives for efficient management of large and complex databases (Seith et al, 1998). Taking into consideration Chow, Maidment and Mays (1988) taxonomy of hydrological models, several possible applications of linking GIS with the hydrological models can be specified:

• Hydrological Assessment to represent hazard or vulnerability (through weighted and

summed influences of significant factors rather than through physical laws)

• Hydrological Parameter Determination, whereby the GIS provides inputs to the model in

terms of parameters such as surface slope, channel length, land use and soil

characteristics.

• Hydrological Modeling within the GIS, provides feasible time snapshots or temporal

averages are involved, not time – series.

• Linking the GIS and hydrological models to utilize the GIS as an input and display

device, including real time process monitoring if the necessary (remotely sensed)

observations are available.

Schematically, a conceptual structure for GIS/Remote Sensing relations to Hydrology is shown on Figure 1.3 (based on Burrough, P.A. and McDonnell, R.A. (1998):

Figure 1.3 A conceptual structure of GIS/Remote Sensing relations to Hydrology

GIS Hydrology

AM (Automated Mapping) Hydraulic modeling, runoff FM(Facilities Management) formation modeling, duration flow RM(Remote Sensing) curves, sediment discharge, thermal LIS(Land Information System) regime etc. Spatial Statistics etc.

Hydro GIS

Management Information System

Sui and Maggio, 1999 address the functional properties in embedding GIS in hydrological modeling, arguing that four different approaches methodologically describe the interrelationship between GIS and hydrological modeling:

(1) embedding GIS in hydrological mapping Hydrological This approach aims to embed GIS functionalities modeling in hydrological modeling packages, and has adopted primarily by hydrologists who think of GIS essentially GIS as a mapping tool and conceptually irrelevant to the fundamental of hydrological modeling.

(2) embedding hydrological modeling in GIS GIS Hydrological GIS software vendors improved analytical and modeling modeling capabilities of their products. Example are ESRI’s ArcStorm and ArcGrid, Intergraphs’s InRoads and others.

GIS (3) Loose coupling

This approach usually involves a standard GIS package (e.g. Arc/Info) and hydrological/hydraulic modeling programs (STORM etc) and statistical tools (SAS, SPSS etc) Hydrological Statistical tools modeling

(4) Tight coupling GIS This approach embeds certain hydrological models within Hydrological Statistical tools a commercial GIS software package via either GIS macro modeling or conventional programming. Examples are ESRI Avenue etc.

These four general approaches have resulted in abundant empirical studies in various regions in the world, most of which rely on a combination of loose- and tight-coupling (Sui and Maggio,

1999). The study conducted by Zanderbergen, (1998) develops a generic conceptual model and a set of key indicators such as impervious areas, riparian habitat, pollutant loadings, water quality, sediment quality, fish health and public health to mitigate ecological risk of urbanization on the health of local streams. Information on each indicator is transformed into a single dimensionless score. Two indicators (impervious areas and water quality) are selected for a detailed evaluation of spatial and temporal patterns with use of GIS. The author specifies and pays special attention to this limited set of key indicators linked to GIS being as an integrated approach in decision- making process. Yang et al, (1999) highlight and address the issues using remote sensing data in water quality modeling (QUAL2E) for the reasons of developing an integrative approach in water

45 quality modeling with extensive usage of remote sensing and GIS techniques for the final goal – to improve practical water management. Remotely sensed data application techniques to hydrological models is described in Kite, et al, 1996, where the authors discuss advantages, disadvantages and perspectives for methods of soil moisture estimation from SAR data, snow cover mapping using LANDSAT, SPOT and GOES satellite imagery, vegetation and leaf area indices LAI and NDVI techniques, assessment of evapotraspiration using AVHHR and flood estimation tasks.

Although ESRI’s Arc/Info and the US Army Corps of Engineers HEC series dominated these modeling works, a variety of other GIS and modeling software tools have been used, such as

GRASS and TOPMODEL (Chariat and Delleur, 1993). Integrated a physically based hydrological model with GRASS), SPAN, SWAT, HSPF and others.

1.5 BIOLOGICAL INTEGRITY CONCEPT IN IMPACT ANALYSIS

The concept of ecological integrity is firmly entrenched in the regulatory structure of water law in the USA. The Clean Water Act (CWA) of 1972 identified as the fundamental, long-term goal of environmental protection for aquatic resources, the restoration and maintenance of ecological integrity, which is expressed in the law as physical, chemical and biological integrity (Figure 1.4)

(Barbour, et al, 2000):

Physical Integrity

Ecological Integrity

Chemical Biological Integrity Integrity

46 Alternative (dynamic) model of biological integrity was introduced by Yoder, (1995) (Figure

1.5) that shows the overlapping influence of the three major components is both disproportionate and dynamic. The term biological integrity originates from the Federal Water Pollution Control

Act (FWPCA) amendments of 1972 and has remained a part of the subsequent reauthorizations.

Biological integrity can be defined as “the ability of an aquatic ecosystem to support and maintain a balanced, integrated, adaptive community of organisms having a species composition, diversity, and functional organization comparable to that of the natural habitats of a region”. (Yoder and

Rankin, 1998). The concept of biological integrity is all encompassing and, in essence, is the underpinnings of ecological integrity.

Figure 1.5: New concept of ecological integrity

Area of Biological Integrity Physical

Chemical

A principle objective of the CWA is to restore and maintain the physical, chemical and biological integrity of the nation’s surface waters (CWA Section 101). Although, this goal is fundamentally ecological, the specific methods by which regulatory agencies have attempted to reach this goal have been predominated by such non-ecological measures as chemical/physical water quality (Karr et al. 1986). The basic idea of this process is to apply developed chemical criteria through toxicological laboratory studies of selected aquatic organisms serve as surrogates for determining attainment of the ecologically-based goals of CWA. Until recently, chemical criteria were effective in reducing pollutant loadings (chemical sources) to the nation’s surface waters that made a concept of ecological integrity narrowly be interpreted to mean chemically

47 clean water. According to Karr (1993), water resources, particularly their biological components continued to be degraded at alarming rates primarily due to non-point sources of pollution.

Traditional methods (i.e water quality sampling of storm events) have remained largely inadequate due to the transient and unpredictable nature of non-point source pollution (Barbour, et al 2000). Therefore, the need to accurately measure and characterize the cumulative impacts of multiple stressors, not just individual pollutants, became a priority in water resource management.

Biological communities integrate the effects of different stressors, such as reduced oxygen, excess nutrients, increased temperature, excessive sediment loading and habitat degradation. This integration occurs through time, a dimension difficult to measure with only chemical information

(Barbour, et al, 2000). Compared to chemical and physical criteria, biological criteria potentially provide a more comprehensive and sensitive set of indicators of water quality and ecological integrity and an indirect means of detecting physical and chemical impairment (Jacobson, 2000).

Yoder and Rankin, (1998) states that if the real progress towards restoration and protection of aquatic ecosystems is to occur, a broader focus on the water resource as a whole is needed.

According to them, biological criteria and the attendant monitoring and assessment design provides a means to incorporate the broader concept of water resource integrity while preserving the appropriate roles of the traditional chemical/physical and toxicological approaches developed over the past three decades. Yoder, (1995) points out that biological assessment and criteria is not a substitute for, but, rather complements other methods of water quality assessment and, thus, measures of biological condition presents a critically needed tool for water resource agencies to detect and evaluate the aggregate impact of the stressors on water quality (Jacobson, 2000).

Schematically, five major factors that influence water resource integrity in streams are shown on

Figure 1.6.

48 Figure 1.6 Five major factors influencing water resource integrity in streams (Karr et. al 1986)

Chemical Variables Biotic Factors Energy Source

Solutibilities, Adsorption, Disease, Reproduction, Nutrients, Sunlight, Nutrients, Organics, Feeding, Predation, Organic Matter inputs, Alkalinity, Temperature, DO Competition Seasonal Cycles etc. (dissolved oxygen), pH,

Turbidity, Hardness

Water Resource Integrity

Flow Regime Habitat Structure

Water discharge/velocity, Riparian Vegetation, High/Low extremes, Siltation, Current, Precipitation, Ground Width/Depth, Gradient,

Water Channel Morphology, Bank Stabilty, Canopy etc

Biocriteria are narrative descriptions or numerical values adopted into state water quality standards that can be used to factually and quantitatively describe a desired condition for the aquatic life in waters with a designated aquatic life use (USEPA, 1998). The purpose of biocriteria is to establish enforceable standards based on biological characteristics related to biological integrity that can be used to direct water quality management and biocriteria are developed by biologists and other natural scientists using accepted scientific principles to characterize the regional reference conditions for the different water bodies within a state

(Barbour et al, 2000). Ohio EPA adopted biological criteria in its water quality standards regulations in May, 1990. These criteria, such as species richness, key taxonomic groupings, environmental tolerance, and evidence of stress are structured in the Ohio Water Quality

Standards (WQS) regulations within a system of tiered aquatic life uses. The resulting numerical

49 expressions essentially reflect the health and well-being of reference aquatic communities and are the end product of an ecologically complex and structured derivation process (Yoder and Rankin,

1998).

New methods and approaches to assessing aquatic community data have been developed over the past 10-15 years which have provided a significant advancement in being able to utilize biological community information. These include such as the Index of Biotic Integrity (IBI) and its recent modifications (Ohio EPA and others), the Invertabrate Community Index (Ohio EPA) and most recently the Benthic IBI for macroivertabrate assemblages (BIBI, Kerans and Karr,

1993).

In summary, the biological components of NAWQA consist of ecological surveys

(characterizations of fish, benthic invertebrate, algal communities, physical habitat etc.) and tissue contaminant studies. As it was mentioned above, biological components are important to an integrated assessment of water quality because of factors such as (1) sensitivity to a wide variety of natural and human environmental influences (for example, chemical constituents, hydrologic modifications, sedimentation, and thermal enhancement); (2) increased analytical sensitivity due to bioconcentration of certain contaminants; (3) integration of exposure to environmental influences over multiple temporal and spatial scales (for example, algae integrate exposure over several millimeters and for periods of several weeks, whereas fish may integrate exposure over many kilometers and for a decade or more); and (4) a high degree of public interest and concern, particularly for endangered species. Ecological surveys as part of NAWQA are designed to characterize fish, benthic invertebrate, and algal communities and associated instream and riparian habitats. It should be emphasized the importance of study of fish communities among all of them as an essential component of many water-quality assessment programs (Karr and others,

1986; Ohio EPA, 1987) because fish are particularly sensitive indicators of water-quality conditions (Karr et al, 1986). Human influences, such as changes in water chemistry or physical habitat modifications, can alter fish communities by disrupting their structures. Changes in fish

50 community structure can be detected through changes in size components of the community, functional groups, species diversity, and relative abundance (Karr et al, 1986). Along with fish communities, benthic invertebrates (insects, mollusks, crustaceans, and worms) are important elements of ecological surveys too because they tend to (1) live in, on, or near streambed sediments; (2) have, with the exception of most mollusks, life cycles (months to a few years) that are intermediate to fish (years to decades) and algae (days to weeks) and (3) be relatively sessile compared to larger organisms, such as fish. This combination of characteristics ensures that benthic invertebrates (1) respond to natural and anthropogenic environmental conditions that physically or chemically alter streambed sediments (for example, sedimentation, xenobiotics, eutrophication, or hydrologic modifications), (2) integrate effects over an approximately annual time period, and (3) characterize effects over a relatively small spatial area in contrast with fish, which may travel over long distances. These factors make benthic invertebrates well suited for use in assessing site-specific water quality and comparing spatial patterns of water quality at multiple sites, and for integrating effects that represent 6 months to a year of exposure at a site.

Benthic invertebrates also are particularly useful for monitoring cumulative effects imparted to a site by conditions in the entire upstream landscape (Harrington, 1999; Ohio EPA 1997).

1.6 COMBINED CLIMATE AND LAND-USE CHANGE EFFECTS IN HYDROLOGICAL ASSESSMENT

Water balance models were developed and applied by Thornthwaite (1948) and Thornthwaite and Mather (1955) describing the spatio-temporal water dynamics within a watershed. This type of models is known quite well due to their relative simplicity. This is mainly because they are fundamentally based on conceptual representation and understanding of hydrological cycle. A family of conceptual lumped-parameter models were developed and employed in different studies

(Moor, 1986, Valdes et al, 1994). Recently, process based distributed-parameter models have been introduced as a complex integrative tool in water quality management and modeling. The distinguishing properties of this group of models are the ability to integrate hydrological

51 processes with biogeochemical processes into one system describing the watershed ecosystem behavior mechanisms and feedbacks. SWAT (Soil- Water Assessment Tool) (Arnold et al, 1993),

MATSALU (model developed for the agricultural watershed of the Matsalu Bay (Baltic Sea) for evaluating different management scenarios for eutrophication control of the Bay (Krysanova et al,

1998), SWIM (Krysanova et al, 1998) that integrates weather, hydrology, erosion, vegetation and nitrogen dynamics at the watershed scale are some examples of flow simulation and water quality models (discussed further).

For the last 5-6 decades, due to rapid technological progress, we observe increased levels of technogenic activity within river watersheds of any sizes. As it was mentioned couple times before, by far the most important factor is land cover modification and conversion by human activity. Gaining a better understanding of the ways that land cover and land-use practices are evolving is a priority concern of the global change research community. The characteristics of land cover have important impacts on mezo and microclimate, biogeochemistry, hydrology, and the diversity and abundance of terrestrial species. Hence being able to project future states of land cover is a requirement for making numerical predictions about other global changes.

Understanding the significance of land-cover changes for climate, biogeochemistry, or ecological complexity is not possible, however, without additional information on land use. This is because most land-cover change is now driven by human use and because land-use practices themselves also have major direct effects on environmental processes and systems. Land-use is obviously determined by environmental factors such as soil characteristics, climate, topography, and vegetation, but it also reflects land's importance as a fundamental factor of production.

Therefore, understanding past changes in land use and projecting future land-use trajectories requires understanding the interactions of the basic human forces that motivate production and consumption. Combining this with climate change effect studies has a potential to significantly change the approach, more precisely, to veer it towards more integrative and comprehensive basis. Research on how human factors interact in driving land use will improve projections of

52 land use and our comprehension of human responses to environmental changes. For the economic, social, and behavioral sciences, it will also provide an opportunity for basic research into the factors that shape individual and group behavior. While much land use takes place at the scale of small individual units of production, its impact is global and cumulative. Changes in land cover cannot be understood therefore, without a better knowledge of the land use changes that drive them, and their links to human causes (Veldkamp and Fresco, 1996). On the other hand, from the literature related to climatic change effect studies, it is revealed the sensitivity of water resources to changes in major components of hydrologic cycle and their regimes with a number of negative feedback mechanisms of these impacts.

Liu et al, (2000) uses an integrative approach to study the water quality impacts of future global climate change and land use changes through analyzing changes in land use as a mitigation strategy to reduce the adverse impacts of global climate change on water resources using two watersheds in Ohio River Basin.

Whitehead et al, (1995) used QUASAR (Quality Simulation Along Rivers) water quality model that has been applied to the River Don system in North East Scotland. This mass balance model for nitrogen has been used to estimate the impacts of land use change and climate change on river water quality. Changing agricultural patterns have been simulated and indicate increased nitrogen levels in the river as catchment land use changes. According to simulations, climate change will alter flow regimes, temperature and nitrogen mineralization patterns. Simulation runs for a range of scenarios illustrate the impacts of climate change with the most significant effect being mineralization of nitrogen in the soil feeding through into the river system. Chang, H, (2001) studied climate and land-use effects on freshwater quantity and quality. The study examines the complex interactions of climate change and land-use change in selected watersheds of the

Susquehanna River Basin with application of Generalized Watershed Loading Function (GWLF) model. The effects of climate change on streamflow and nitrogen and phosphorus loading are analyzed for six watersheds with different land use characteristics. She reports that (1) climate

53 change alone increases spring streamflow by 4.8 to 18.1%, leading to increases in nutrient loads in streams. (2) Urban expansion exacerbates the adverse effects of climate change by increasing annual nitrogen loads up to 50%. (3) The magnitude and direction of changes in nutrient loads are sensitive to watershed size, climate and land use scenarios used. Smaller sub-basins exhibit greater effects than the entire watershed.

Unfortunately, there is not much literature on research work discussing and examining the combined effects of land use and climate change effects on hydrologic on water quality and quantity and more specifically on aquatic biota. Based on the facts from the above discussions about the nature, types and basic elements of hydrological assessment, and, more specifically, accenting special attention on the issue of integrated approach in studying and assessing water quantity and quality problems on a watershed, this study is designed to enhance the level of understanding the system land-use changes-climate-hydrology, with a special emphasis in mitigating the combined effects of climate and land-use change on hydraulic and water quality characteristics of the Great Miami River. For the purposes of study, a comprehensive US EPA

BASINS 3.0 will be used to build a water quality simulation model.

54 CHAPTER II: METHODOLOGY Overview

Firstly, this chapter introduces the environmental settings of the study area, characterizes the natural and human processes and factors that take place within the watershed boundaries and describe the physical, chemical and biological quality of surface and ground water in the area.

These factors such as climate, physiography, geology, soil and ecology also influence long and short term trends in water quality: ambient water quality and richness and diversity of aquatic ecosystems. Other agents which significantly alter water quantity and quality are land use and water use or waste disposal activities. Obviously, agricultural activities and urban development represent the most significant sources of non-point sources contamination, the former result in sediment loss as well as pesticide and nutrient runoff and the latter result in removal of riparian vegetation and increasing in urban runoff. Point sources of contamination include industrial and wastewater discharges of toxic substances, pathogens and nutrients. Preliminary analysis of flow and precipitation is made to illustrate the changes in these components over the last 80 years.

Subsequent sections describe and discuss the rationale and methodological structure which is used in water quality and quantity modeling under different future climatic and land use change scenarios, criteria for model (HSPF) selection, HSPF model structure and data sources, including the rationale for future climate and land use change scenarios development.

2.1 STUDY AREA AND ENVIRONMENTAL SETTINGS

The Great Miami River (GMR) basin was used to be inhibited by the Native Americans until the arrival of European settlers. The name Miami was originally given to the native tribe

“Tewightewee”. Miami comes from the Ojibwe name, Oumnami, which translates into the

“people of the peninsula”. The Miami valley was known for its lush vegetation, abundant water resources, Ohio-Erie canal, and rich archeological past. Today, the Valley is known for its water supply, recreation, industries, productive farmland and high quality tributaries. The GMR watershed is located in southwestern Ohio and drains an area of about 5,385 square miles. The

55 watershed has a maximum width of approximately 70 miles and a length of approximately 120 miles. It contains portions of sixteen Ohio and Indiana counties. Indian Lake in Logan County,

Ohio, marks the headwaters of the Great Miami River (Figures 2.1-2.2). The GMR and its tributaries drain four large cities – Dayton, Hamilton, Middletown and Springfield (Figure 2.3).

Figure 2.1 Location of the region of study

56 Figure 2.2 The Great Miami River Basin (GMR) with Counties

HA RDIN GREAT MIAMI RIVER

AUGLAIZE basin

INDIAN LAKE ME RCE R LOGAN

R SHELBY I M A I M

T CHA MP AIGN A E R G

RANDOLPH DA RKE D

S A

T MIAMI M I L L W A T Indian Lake E R CLA RK

WAYNE

W MONTGOMERY

I N GREE NE

R PREBLE

S E R V I E UNION N M A M I IL M E IN T C A D R IA E N R C G WARREN R BUTLE R Legend FRANKLIN Streams

Major roads and highways HA MILTON DE ARBORN County boundaries

Basin boundary BOONE

050100Miles

57 Figure 2.3 The GMR basin and major urban areas

N Major Urban areas within

HARDIN the GMR basin

AUGLAIZE IN DI AN L AKE MERCER LOGAN

R SHELBY I M IA

M T A CH AM PA IGN E R G

RANDOLP H DA RKE D S A

T MIAMI M IL L W A T E R CL ARK

R Springfield

WAYNE T MONTGOMERY W Dayton I N GRE E NE

R PREBLE

S E R V I UNI ON E M N Legend M IA IL M E IN T D C A Middletown R IA E N R C G WARREN R BUT LE R County boundaries FRANKL IN Hamilton Streams

Urban areas HAMILTON DEA RBORN Cincinnati Major roads and highways

BOONE Basin boundary

04080Miles

58 2.1.1 Climate

The climate of the GMR watershed plays an important role in determining the availability of water for a variety of uses as well as influences the nature of the region’s landscapes by governing the rates of soil and bedrock weathering. Climate controls such factors as temperature, humidity and precipitation. Climatic factors influence rates of physical and chemical weathering of rocks and soils; constituents from the breakdown of these media are carried into streams and lakes by runoff and into ground water through infiltration. Temperature determines growing seasons and governs evapotranspiration. Precipitation, carrying airborne contaminants, influences water chemistry.

The study area has a temperate continental climate, characterized by well-defined winter and summer seasons that are accompanied by large annual temperature variations. Seasonal temperature variations reflect the dominance of polar continental air masses in the fall and winter and tropical maritime air masses in the late spring, summer and early fall. The main sources of moisture are tropical maritime air masses from the Gulf of Mexico and the western Atlantic

Ocean. The area experiences frequent cyclonic disturbances caused by tropical air masses moving northeast from the Gulf of Mexico. These storms interact with arctic air masses moving south- southeast and can transport considerable amounts of moisture. In the spring and summer, most precipitation is associated with thunderstorms produced by daytime convection or passing cold fronts. The mean annual temperature ranges from 49o to 55oF. Mean monthly temperatures range from 68oF to 77oF in the summer and from 26oF to 33oF during the winter. Mean annual precipitation is 39 in. in the study area. Precipitation within the region of study ranges from less than 36 to more than 42 in. (Debrewer, et. al. 2000).

59 Figure 2.4 represents the annual precipitation variation at Dayton Airport Weather Station for the period 1951-2000:

Applying Spearman’s criteria for trend analysis to the precipitation data:

2 2 r*s = 12/(n – 1){ 1/n Σt*Rt – ((n+1)/2) }

where X1, ….Xn as members of the records are substituted with their ranks R1, …Rn: Rt = 1, if Xt is a minimal member of the record; Rt = 2 if Xt – is the second minimal member and so forth;

Rt = n if Xt is a maximum of the row.

For the realization of “white noise”, value SQRT (n-1)* r*s is approximately close to normal distribution with N (0,1), then: SQRT (n-1)* r*s >= t(α/2) where t(α/2) is a quintile of normal distribution N(0,1) = 1.96 when α = 5% Î there is a statistically significant tendency toward increasing X(t) with increasing t, i.e – positive trend. If SQRT (n-1)* r*s <= -t(α/2) then there is a statistically determined negative trend.

60 SQRT(n-1)* r*s for summary precipitation at Dayton, OH is equal to 2.254, indicating trend toward increasing for the period 1951-2000.

Using number of extremes criteria that is defined by:

Te = (ne+0.5 –me)/SQRT (De) where Me = (2n-1)/3 and De = (16n-29)/90; ne is a sum of minimums and maximums in variable realization. If Te <= -t(α/2), then X(t) graph has a small number of extremes for the “white noise” meaning gentle fluctuation pattern with higher existence of in-between points. And, in opposite, if Te >= t(α/2) then X(t) graph has a large number of extremes and sharp fluctuation pattern. For the precipitation record at Dayton Intl. Airport for the 1951-2000 period, Te is equal to (–3.25) indicating that statistically the number of extremes is significantly smaller than it should be for independent series, and, hence, the variables show high positive correlation between adjacent years.

Monthly variation in precipitation is presented in the Figure 2.5 and Table 2.1:

Z-value is the coefficient describing the departure of average monthly precipitation to the annual mean, and INVij is the coefficient reflecting degree of seasonal semi-annual variation.

Zij = (Pij-P j-average)/P j-average where Pij is a monthly average of a j-year, i =1,….12, j= 1,…50 and P j-average is annual average of j -year.

INVij = max{Zij} – min(Zij)} shows the difference between maximum and minimum monthly Zij for the whole period j=1,…50 (from 1951 until 2000).

61 Figure 2.5 Seasonal precipitation variations, Dayton, OH

The highest variation in precipitation for the last 50 years seems to happen in March and June months (INV=3.04 and 3.02 respectively). Relatively low variations were found in February (1.5) and October (1.6). Generally, it may be speculated that variations of precipitation (catastrophic events vs droughts) in low (June-July) and high flow (March-April) seasons for the GMR affect the volumes of flow for some degree, and influence hydrograph shapes.

63 2.1.2 Hydrology and Hydrography

The water budget of the study area is dependent on three major natural components: precipitation, streamflow and evapotranspiration. In general, it can be said that for the area, a little more than 2/3 of the precipitation that falls on the land surface is returned to the atmosphere by evapotraspiration. The remaining 1/3 flows into the Ohio River as streamflow (UC Geology department, Midwestern Climate Center, based on written and verbal communication).

The GMR drains an area of about 5,330 sq. miles. It includes the Upper GMR basin (2480 sq. miles) and Lower GMR basin (1390 sq. miles). Headwater streams originate in northern and eastern parts of the study area in agricultural areas consisting of rolling hills and steep-walled but shallow valleys.

Streamflow is regulated by reservoirs, dams or intrabasin transfers in some parts of the study area. Dams associated with large reservoirs were constructed in the period of 1960’s and 1970’s, partly for flood control and partly for recreation and water supply purposes (Figure 2.6). In addition, there are five “dry” dams and associated retarding basins that were constructed for the sole purpose of flood control in the Great Miami River Valley; these structures do not regulate streamflow, except during floods. There are eight low dams along the main stem of the Great

Miami River, starting at Dayton. The low dams were constructed to provide pooled areas for recreation or to provide water to power plants for cooling or stream generation. There are about

60 USGS streamflow gage stations within the basin area (Figure 2.6).

Indian Lake is the headwaters of the GMR. The length of the GMR channel is 170.3 miles.

Land surface elevations range from 1550 feet above mean sea level in the northern portions of the watershed to 400 feet and less at the confluence of the GMR with the Ohio River in Hamilton

County. The GMR and its tributaries drain four large cities – Dayton, Hamilton, Middletown and

Springfield (Figure 2.3). Significant tributaries in the Upper Great Miami River Basin are the

Stillwater River (676 mi2), designated as a Scenic River in the State of Ohio and the Mad River

(657 mi2). The confluence of the Stillwater and Mad Rivers with the Great Miami River is near

64 Dayton, Ohio. Major tributaries in the Lower Great Miami Basin include Twin Creek (316 mi2) and Four Mile Creek (315 mi2). The Great Miami River joints the Ohio River west of Cincinnati.

The total drainage area is about 3,870 mi2. (Miami Conservancy Group, Debrewer et al, 2000).

Figure 2.6 Dams within the GMR basin

Location of Dams and N AUGLAIZE MERCER USGS Gage stations LOGAN

GMR watershed SHELBY

CHAMPAIGN RANDOLPH DARKE MIAMI

CLARK

WAYNE MONTGOMERY GREENE PREBLE

UNION Legend WARREN USGS Gage stations BUTLER FRANKLIN Dams

County boundaries HAMILTON Streams DEARBORN Basin boundary

BOONE 03060Miles

2.1.3 Reservoirs and flood control

The study area contains many man-made reservoirs that are used for flood control, water supply, and recreation. The effects of natural and human influences on water quality in reservoirs depend on land use in the surroundings areas, use of the water bodies and management of wastes and other by-products of human activities.

The study area contains one large lake – Indian Lake (5,800 acres) (Figure 2.2) that was originally a cluster of small natural lakes formed by Pleistocene glacial activity. Indian Lake

65 (formerly called Lewiston Reservoir) was constructed in 1860 to supply water for the Miami and

Erie Canal system. In 1898, as use of the canal system declined, the lake was opened to the public

for recreational use. In 1923, flood control dams and associated retarding basins were completed

in the GMR basin area: Lockington Dam, Germantown Dam, Huffman Dam, Taylorsville Dam

and Englewood Dam. The dams were designed with conduits that allowed water to pass through

the dam during the periods of normal flow. Retarding basins behind the dams are dry normally,

but during floods, excess water is stored in those basins; subsequently, the water is released in a

controlled manner that greatly reduces the magnitude of flood peaks (US Army Corp of

Engineers, 1998 and Indiana Department of Natural Resources, 1988). Table 2.2 include the list

of flood-control structures in the GMR basin (Debrewer et al, Environmental Setting and Effects

on Water quality in the GMR and LMR basins, Ohio and Indiana, 2000):

Table 2.2 Some of the reservoirs and flood control structures within the GMR basin

Name of reservoir or flood control Name of Normal Surface Drainage Year Use structure stream capacity area area (sq. completed (acre-feet) (acres) miles) Indian Lake Great Miami river 45900 5800 100 1860 Recreational Lockington Reserve Loramie Creek 70000 4020 261 1922 Flood control Germantown reserve Twin creek 106000 3600 275 1922 Flood control Huffman reserve Mad river 167000 9180 635 1922 Flood control Brookville Lake East fork, 5280 379 1974 Recreational, Whitewater 184008 Flood control river and water supply Taylorsville reserve Great Miami river 186000 11000 1155 1922 Flood control Englewood reserve Stillwater river 312000 7900 650 1922 Flood control

66 2.1.4 Wetlands

An estimated 40 sq. miles of the study area are wetlands. Wetlands have numerous environmental functions: regulate water quality as vegetation filters sediment, nutrients and toxic chemical from water entering a stream or lake; decrease land erosion and increase stability; decrease the velocity of floodwaters, capturing water and releasing it gradually; and support rich biotic communities by providing unique habitats for waterfowl, fish, plants and other terrestrial and aquatic animals. The Whitewater River basin contains approximately 28 sq. miles of wetlands. Natural wetland areas also can be found along the East Fork, Beavercreek Rivers.

2.1.5 Geology and Physiography

Geology of the study area is dominated by Quaternary glacial deposits that overlie a thick sequence of Ordovician, Silurian and Devonian sedimentary rocks (Figure 2.7). Geologic characteristics of the unconsolidated glacial deposits and underlying bedrock affect the physical characteristics of the land (topography, soil type, runoff, land use). The distribution of various types of geologic materials in the subsurface governs the transport and storage of ground water in aquifers. Reactions between water, soil and aquifer materials can influence the concentration of major ions, trace elements and synthetic organic chemicals in ground water and surface water.

This underlying bedrock, mostly in southern portions of the Great Miami River basin consists of interbedded limestone and shale, the result of sediment deposition during Ordovican Age (465-

510 million years ago). In general, the Ordovican Age bedrock found throughout Hamilton,

Warren and Butler counties is not a productive aquifer. Only limited quantities of groundwater are available for domestic use.

Pure limestone and dolostone deposits are located on top of the Ordovican interbedded limestone and shale bedrock in the more northerly portions of the Great Miami River drainage basin. Limestone and dolostone differ only slightly in their chemical compositions. Deposited during the Silurian Age (415 – 465 million years ago), these rocks contain significantly less shale and are not nearly as thick as the underlying Ordovican bedrock. These Silurian rocks comprise

67 the youngest bedrock unit within the Miami Valley Region, and they can provide significant quantities of groundwater for industrial, municipal and residential use. The best locations to observe bedrock are usually found along the sides of streams or highway road cuts where the overlying glacial deposits have been removed by erosion or construction activities.

The watershed is located along the margin of the Laurentide Ice sheet during Pleistocene and has been greatly influenced by glaciations (Figure 2.8). Current topographic and drainage conditions across the Miami Valley Region are the result of massive glaciers that covered the entire region as recently as 20,000 years ago. Several glacial advances into southwest Ohio occurred during the time span of 20,000 to 300,000 years ago. As glaciers advanced into the

Miami Valley region, they transported and deposited great quantities of silt and clay over the existing landscape. These deposits of silt and clay are referred to as till. The glacial deposits that overlie shale and limestone vary from a few meters thick to more than 100m. Coarser deposits of sand and gravel are commonly interlayered with the till. The modern Great Miami River channel is largely coincident with ancient drainage meltwater stream ways beneath or on top of glaciers.

The study area is almost within the Till Plains section of the Central Lowland physiographic province with exception of the southernmost parts of the basin. Within a Central Lowland

Province the study area covers Southern Ohio Loamy Till Plain region, Illinoian Till Plain region

(Hamilton, Warren Counties) and Central Ohio Clayey Till Plain region in the upper portion of

Great Miami River basin (Figure 2.9). The Till Plains subecoregiones drain from north to south- southwest toward Cincinnati; they are characterized by high lime, late Wisconsinan glacial till with a well-developed drainage network and fertile soils.

In the southern part of the study area, glacial deposits are thin or absent, and erosion of less- resistant shale has produced a dissected hilly terrain of higher stream density. The general topographic gradient is from north to south. The study area contains the highest and the lowest elevations: 472 m (1,550 feet) above sea level – Campbell Hill in Logan County and areas along

68 the banks of the Ohio River in Hamilton County with elevations of 130-140 m (425-460 feet)

(Figure 2.10).

Figure 2.7 Geologic map of Ohio (State of Ohio, Division of Geologic Survey)

69 Figure 2.8 Glacial map of Ohio (State of Ohio, Division of Geologic Survey)

70 Figure 2.9 Physiographic regions of Ohio (State of Ohio, Division of Geologic Survey)

71 Figure 2.10 DEM map, GMR basin

N Digital Elevation Model HA RDIN GMR basin AUGLAIZE

INDIAN LAKE ME RCER LOGAN

R SHELBY I Legend M A I M

T CHA MP AIGN A E County boundaries R G

Streams

RANDOLPH DARKE D

S A

T MIAMI M I L L W DEM Great Miami river A T E R CLARK

R 151 - 185

186 - 219 WAYNE T MONTGOMERY W

I N GREE NE 220 - 253

R PREBLE 254 - 287

S E R V I E 288 - 321 UNION N M A M I IL M E I T N C A D R 322 - 355 IA E N R C G WARREN R BUTLE R 356 - 389 FRANKLIN 390 - 423

424 - 457

HA MILTON No Data DEARBORN

BOONE

04080Miles

2.1.6 Groundwater

Aquifers in the study area are considered to be a part of the Midwestern basins and Arches

Glacial and Carbonate Regional Aquifer System (Casey, 1996). The hydrologic setting of aquifers can broadly be characterized by two major categories: (1) unconsolidated glaciofluvial sediments that fill buried valleys that often underlie present-day stream valleys; (2) upland areas where ground water is withdrawn from unconsolidated and consolidated aquifers.

Buried valley aquifers produce the significant amount of water within the region. The sand and gravel deposits within the buried valley aquifer transmit large quantities of groundwater due to their permeable nature. As a result, many municipalities have their well fields located on top of

72 the buried aquifer in order to maximize the amount of water that can be produced. Buried aquifers tend to be coincident with the modern Great Miami River drainage channel and its tributaries.

The Silurian bedrock system also provides a valuable aquifer resource. The Silurian bedrock aquifer system is capable of producing ample supplies of water in the upland regions of the Great

Miami River drainage basin.

2.1.7 Soils

Soil characteristics influence ground-and surface water quality. Soils are classified by composition of parent material, native vegetation, texture, structure, depth and thickness of horizons (Ulrich, 1966). Physical properties of soils influence runoff, sedimentation and infiltration rates. Chemical properties controlled by organic material, microorganisms and gases available in soils influence dissolution, precipitation, adsorption and oxidation-reduction reactions.

Soil regions within the study area can be grouped into four types: (1) developed from loess or glacial till (close to 80%); (2) along flood plains (about 10%); (3) from bedrock and (4) from lake sediments. Soils developed from loess or glacial till are the most common and cover large area.

These soils mostly comprise Wisconsin-age loamy and clayey glacial till that is often are overlain by thin to moderately thick loess. These soils typically have poor to moderate drainage, high base content and high fertility. Soils developed along flood plains of major streams and tributaries include alluvial and outwash deposits. These soils are generally well drained and fertile and have high base contents. Till and outwash soils support intensive row-crop agriculture and livestock farming (Woods and others, 1998).

The soil map in Figure 2.11 shows the GMR watershed’s distinctive pattern of soils, relief and drainage. This soil map is quite useful for estimating the suitability of large areas for general land uses.

73 Figure 2.11 Soils of the study region

SOIL MAP N GMR watershed AUGLAIZE MERCER LOGAN Legend SHELBY Co unt y bo undaries

Streams

Soi l category name BLOUNT-GLYNWOOD-MORLEY (IN004) CHAMPAIGN BLOUNT-GLYNWOOD-MORLEY (OH021) BLOUNT-PEWAMO-GLYNWOOD (OH022) RANDOLPH DARKE BROOKS TON-CROSBY-CELINA (OH023) MIAMI CINCINNATI-BONNELL-ROSSMOYNE (IN083) CROSBY-MIAMI AN-BROOKSTO N (O H025) EDEN-CARMEL-SWITZERLAND (OH055) CLARK

EDEN-SWITZERLAND-EDENTON (IN089) ELDEAN-OCKLEY-SLEETH (OH028) ELDEAN-WESTLAND-PATTON (OH029) WAYNE FI NCASTLE-BROO KSTON-MIA MIAN (IN037) FI NCASTLE-BROO KSTON-MIA MIAN (OH038) MONTGOMERY GREENE HUNTINGTON-NEWARK-WOODMERE (IN031) PREBLE KOKO MO-CROSBY-MIAMIA N (OH031)

LATTY-FULTON-NAPPANEE (OH009) MERMILL-MILLGROVE-HASKINS (OH014) MIAMI-MIAMIAN-XENI A (IN039) UNION MIAMI-MIAMIAN-XENI A (OH040)

MIAMIAN-BROOKSTON-CROSBY (OH032) MIAMIAN-CELINA-CROSBY (IN054) BUTLER WARREN MIAMIAN-CELINA-CROSBY (OH033) MIAMIAN-ELDEAN-CRO SBY (O H034) FRANKLIN MILTON-MILLSDALE-RANDOLPH (OH036) MONTGOMERY-MILFORD-MCGARY (OH015) NAPPANEE-ST. CLAIR-PAULDING (OH016)

REESVILLE-FI NCASTLE-RAGSDALE (IN057) REESVILLE-FI NCASTLE-RAGSDALE (OH039) HAMILTON ROSSMOYNE-EDEN-CI NCINNATI (OH052)

RUSSELL-MIAMI-XENIA (IN042) DEARBORN SAWMILL-GENESEE-LAWS ON (OH030) SAWMILL-LAWS ON-GE NE SEE (IN029) 03060Miles

URBA N LAND -HUNTINGTON-E LK INSVIL LE (O H1 33 ) WATER (OHW )

Soils developed from bedrock are less common in the study area. These soils comprise discontinuous loess over weathered limestone and shale, mainly in the southernmost unglaciated parts and loamy till over limestone bedrock, mainly in portions of Miami County adjacent to the

Stillwater and GMR.

2.1.8 Land Use

Historically, streams and aquifers of the study area have played a significant role in the development of the region’s economy and land use patterns. Approximately 80% (about 2,900 mi2) of the land within the basin is used for agricultural activities. Typical livestock include

74 swine, cattle, and poultry. Hogs, pigs and cattle were the main livestock raised in the study area.

Based on 1995 county-level data, an estimated 720,000 hogs and pigs and about 300,000 cattle were raised in the counties within the study area. Residential commercial and industrial land makes up approximately 10% of the area (close to 410 mi2) with the remaining area consisting of forests –8-9% (450-500 sq. miles) and water bodies or wetlands (less than 1% or 40 mi2) (Figure

2.12-2.15, Land cover map LULC GIRAS 1980s and MRLC, 1992 and Tables 2.3-2.4).

Table 2.3 Land use by category, GMR basin, USGS LULC GIRAS dataset, 1980s

mi2 of total Land cover category UPPER GMR LOWER GMR TOTAL GMR area Total basin area 2480 mi2 1390 mi2 3870 mi2 Urban Built-up % % % mi2 residential 4.45 10.16 4.9 192.7 commercial services 1.67 2.72 1.9 74.9 industry 0.33 0.55 0.4 17.0 transportation 0.75 0.93 0.8 32.5 mixed urban 0.03 0.01 0.0 0.8 Total 317.9 Agricultural land cropland/pasture 88.65 75.5 85.2 3301.0 Total 3301.0 Forest land decidious 2.49 7.07 4.8 185.0 coniferous 0.03 0.0 0.6 mixed 0.14 0.56 0.4 13.5 Total 199.1 Water stream/canals 0.04 0.35 0.2 7.5 lakes 0.34 0.2 6.6 reservoirs 0.32 0.38 0.4 13.5 Total 27.7 Wetlands forested 0.01 0.02 0.0 0.6 non-forested 0.01 0.0 0.2 Total 0.8 Barren land 1.5 strip mines/gravel pits 0.21 0.49 0.4 13.5 transitional lands 0.04 0.22 0.1 5.0 Total 18.6

75 Table 2.4 Land use by category, GMR basin, USGS MRLC dataset, 1992

mi2 of LOWER TOTAL total Land cover category UPPER GMR GMR GMR area Total basin area 2480 mi2 1390 mi2 3870 mi2 % % % mi2 Open Water 0.73 0.72 0.7 28.1 Total 28.1 Urban Land Low Density Residential 2.97 7.16 7.1 276.0 High Density Residential 0.65 1.17 1.6 61.4 Commercial/Industrial/Transportation 1.42 2.4 1.9 73.9 Total 411.3 Barren land Quarries/Strip Mines, Gravel Pits 0.03 0.11 0.1 2.7 Transitional 0.03 0.06 0.0 1.7 Total 4.5 Forest land Deciduous Forest 9.36 16.1 12.7 492.7 Evergreen Forest 0.09 0.59 0.3 13.2 Mixed Forest 0.02 0.04 0.0 1.2 Total 507.0 Agricultural Land Pasture/Hay 12.89 20.81 16.9 652.1 Row Crops 68.9 45.1 57.0 2205.9 Recreational Grasses 1.32 1.3 1.3 50.7 Total 2908.7 Wetlands Woody wetlands 0.24 0.11 0.2 6.8 Emergent Herbaceous Wetlands 0.1 0.08 0.1 3.5 Total 10.3

Major industries that are located along Dayton-Cincinnati corridor produce automobile parts, computer equipment, chemicals, household goods, paper products and processed foods and beverages. While urban land use has increased from 11% in the 1970s to almost 15% in 1990, other uses have remained relatively constant. Forest lands are located mainly in the southern part of the study area. Very little barren lands are located within the study area (less than 0.1%). Strip mines and exposed bedrock are considered as “barren land”.

76 Most land in the study area is used for agricultural activities. More than 90 % of the cropland in the study area was planted in corn, soybeans and wheat, with highest production in Darke County in 1997 (Indiana Agricultural Statistics Service, 1996). Corn and soybeans are planted in spring and early summer and harvested in summer.

Intensive activities associated with agricultural practices often imply the usage of fertilizers and chemical pesticide, that in turn, degrade surface and ground water quality. Among them are commercial fertilizers such as nitrogen, phosphate and others. Micronutrients, livestock manure, sulfur and lime also are applied alone or in combination with commercial fertilizers (Ohio

Agricultural Statistics Service, 1995). Fertilizer and manure associated with row-crop farming and livestock production are major sources of nutrients in surface and ground water. Nutrients applied in quantities exceeding the amount used by crops and held by soil are carried to surface water through runoff and subsurface drainage systems and to ground water through infiltration

(Nokes and Ward, 1997). Estimated nitrogen-fertilizer application rates are about 17-19 tons per mi2 with the lowest in Butler, Warren, Hamilton and Montgomery Counties (less than 10 t/sq.mile). Phosphorus-fertilizer application rates vary from 5 (Butler, Hamilton, Warren,

Montgomery Counties) to 10 and greater (Mercer, Auglaze), the average being as 7-8-t/sq. mile

(based on data from Battaglin and Goolsby, 1994; Puckett and others, 1998). Pesticides are applied to agricultural croplands to enhance crop production and to control weeds (herbicides), insects (insecticides) and fungi (fungicides). These chemicals enter streams by way of runoff and eroded soil, wind transport to water bodies and subsurface tile drains in areas of poor drainage. In areas underlain permeable glacial deposits, leaching of pesticides to shallow ground water and subsequent discharge to local streams is another potential mechanism. Pesticides are applied mainly on spring and early summer by ground application.

Sedimentation from poor agricultural practices is the largest non-point source threat to stream and river integrity (Rankin and others, 1997).

77 Figure 2.12 Land cover of the study region (USGS LULC GIRAS data, 1980s)

N Land Use Map ,GMR basin

HA RDIN

AUGLAIZE INDIAN LAKE MERCER LOGAN

SHELBY R I M A I M

T A E R G CHAMPAIGN

R DARKE RANDOLPH D

A S MIAMI M T I L L W A T E CLARK R

WAYNE Legend

T W MONTGOMERY I N GREENE

PREBLE R County boundaries

Streams S E R V I UNION E N M A M I I L M E T Land Cover type IN C D A R E IA N R C G WARREN Urban or Built-up Land R BUTLER FRANKLIN Agricultural Land Rangeland Forest Land HAMILTON Water DEA RBORN Wetland 04080MilesBarren Land

78 Figure 2.13 N LAND USE MAP,

Upper GMR portion HARDIN

AUGLAIZE INDIAN LAKE MERCER

LOGAN

SHELBY R

I M A I Legend M

T A E County boundaries R G CHAMPAIGN Streams

R PH DARKE

D Land Cover type MIAMI A

S M T IL Urban or Built-up Land L CLARK W A Agricultural Land T E R Rangeland R Springfield Forest Land Water YNE

T Wetland W MONTGOMERY

I N Dayton

Barren Land PREBLE C R GREENE

02040Miles

R RANDOLPH DARKE Figure 2.14 D MIAMI A S M T I L L W A T E CLARK R

LAND USE MAP, R Lower GMR portion WAYNE

T W MONTGOMERY I Dayton N

PREBLE C R GREENE

S E R V I UNION E N M A M I I M LE T Middletown IN C A D R E IA R N WARREN C G Legend R BUTLER Hamilton FRANKLIN County boundaries

Streams

Land Cover type HAMILTON Urban or Built-up Land Cincinnati Agricultural Land DEARBORN

Rangeland Forest Land BOONE Water Wetland 02040Miles Barren Land

79 Figure 2.15 Land cover of the study region (USGS MRLC data, 1992)

Land cover map, GMR, MRLC dataset, 1992

AUGLAIZE MERCER LOGAN

SHELBY

CHAMPAIGN RANDOLPH DARKE MIAMI

CLARK

WAYNE MONTGOMERY GREENE PREBLE Legend

UNION Streams

County boundaries BUTLER WARREN FRANKLIN MRLC Lan d Cover Open Water

Low Density Residential HAMI LTON High Density Residential DEARBORN Commercial/Industrial/Transportation

Quarries/Strip Mines, Gravel Pits

BOONE Trans iti onal

Deciduous Forest

Coniferous Forest

Mixed Forest

Pasture/Hay

Row Crops

Recreational/Urban Grasses

Woody Wetlands

Wetlands

No Data

050100Miles

80 2.1.9 Demographics, Urban development and Industry

Since the 1970s the percentage of urban land within the study area has increased about 3%, approximately 177 sq. miles (Buchberger et al, 1997). Urbanization is the result of conversion of agricultural land to new suburban and residential developments and is clustered to zones bordering the major metropolitan areas.

Estimated population in the study area in 2000 was around 2.5 million. Major urban areas in the study area include Cincinnati and Dayton, Ohio. In 1990, the Cincinnati Metropolitan Area was the 31st largest in the United States, the Dayton-Springfield Metropolitan area was 54th, and the Hamilton-Middletown Metropolitan area was the 146th largest (American Map Corporation,

1993). Table 2.5 represents the population in individual sub-basins of the GMR basin, including major cities and towns (with population greater than 35,000) (taken from U.S Bureau of the

Census).

Table 2.5 Population of the GMR basin with its major urban areas

Basin Population, 1995

Upper Great Miami 584,041

Lower Great Miami 784,916

Cities

Cincinnati, OH 349,027

Dayton, OH 174,459

Middletown, OH 47,931

Hamilton, OH 62,117

Springfield, OH 67,951

81 The population density by counties within the GMR basin is presented in Figure (based on US

Census data for Ohio, 1980-2000):

Hamilton, Montgomery, Butler, Greene and Clark counties represent the highest population densities. In fact, approximately 60 percent of the total population lives in Hamilton,

Montgomery and Butler counties. These counties, in rough estimation, represent only about 30 percent of the total study area.

Population in the study area increased approximately 45 percent from 1940s, with the most rapid rise occurring in the period from 1950s to 1970s. From 1980 to 2000, there is a slight increase in total population but not significant – about 5% (from 2,382,582 to 2,510,502). But, there is a positive trend in migration of city residents into suburban areas that surround Cincinnati and Dayton. Such migration usually is accompanied by the conversion of agricultural land to

82 residential and commercial areas, resulting in development that adversely affect water quality and aquatic ecosystems. These effects may include increased erosion and channel instability, sediment loads, loss of aquatic habitat, runoff and flooding frequency and intensity, reductions in groundwater recharge, urban contaminants discharges to streams and aquifers. Residential and commercial development will increase the amount of treated sewage effluent discharged into streams so that treated wastewater may become the dominant source of water to a stream during periods of low flow (Buchberger et al, 1997).

Figure 2.12-2.13 show the changes in population, number of farms and acres of land in farms by counties located within the GMR watershed boundary. In the period 1980-2000 population in

Lebanon (Warren County) almost doubled, exhibiting a 55% increase (from 9,636 to 16,962).

Butler county (Middletown, Hamilton) showed almost a 30% increase in population size,

Champaign county (Urbana) – 18%, Greene county – 15%, Logan county ~20%, Miami (Troy) and Preble counties~10-11% and 12% accordingly and, finally, Shelby county increase by 12%.

At the same time, Clark (Springfield), Darke, Hamilton (Cincinnati), Montgomery (Dayton) counties reflect negative change in population size for the same period, most likely due to the

“urban sprawl”/migration of city residents to suburbs. Number of households reflect similar pattern: decrease in Metropolitan areas (Springfield, Dayton – in upper portion of GMR watershed) and Cincinnati in an average of 9-10 percent. In opposite, non-metropolitan counties show increase in the number of households for the same period 1980-2000.

83 Figure 2.12 Population, number of farms and acres of land in farms dynamic in the study area, mapped for the Counties located within the GMR basin

Figure 2.13 Upper and Lower GMR percentage change in number of households

84 Major industries in the study area produce automobile and aircraft parts, business and computer development, steel, chemicals, household goods and appliances, paper products and processed foods and beverages (Rowe and Baker, 1997). Industrial facilities generally are within or near the large urban areas of Dayton and Cincinnati. Liquid and solid wastes produced by industrial processes are a significant source of contamination as treated wastewater and cooling waters are discharged to streams and solid wastes are disposed of in landfills or by incineration.

Industrial minerals mining take place in the study area. They are mined for commercial and industrial uses. Consolidated and unconsolidated geologic materials (limestone, shale, sand and gravel) are mined for their economic value. Thus, there are approximately 150 active stone quarries within the area. Active quarries include sand and gravel, limestone and dolomite, clay and shale operations. These industries require considerable amounts of water to process their products that explains mining-related water withdrawals from streams. Withdrawals can reduce the streamflow and alter the water quality not only quality of the surface waters but also ground water resources.

2.1.10 Water Use and Water Quality

Water from streams and aquifers in the GMR basins is used for municipal, industrial and rural water supplies and irrigation. Estimated water withdrawals from streams and aquifers within the study area are approximately 600-650 Mgal/day (mega-gallons per day) (data from USGS for

1995).

In terms of overall water use, the largest use categories in the study area were public supply

(about 50%). Public supply consumes 65% (~200 Mgal/day) of total groundwater withdrawals.

Ground water is the primary source of drinking water in the study area. Most well fields are located near major streams and their tributaries. Water use is generally greater near largely populated or industrial areas. Freshwater withdrawals are greatest in the counties including and surrounding Dayton and Cincinnati (Lloyd and Lyke, 1995). Only a few suppliers in the Ohio part of the study area rely on surface water instead of ground water: Piqua (Miami County),

85 Greenville (Darke County) and couple others (Ohio-Kentucky-Indiana Regional Council of

Governments, 1991).

Surface water quality in the study area is affected by a variety of natural and human factors.

Natural factors include climate, physiography, geology, soil type and ecology. Human factors can be represented by population density, land use, industrial discharges (both air and water), municipal wastewater treatment plans and urban and agricultural non-point sources.

Excessive nutrient loading in a waterbody can alter the water quality and ecosystem integrity through the eutrophication process. An excessive amount of nutrients in the water stimulates the growth of algae. In addition to being an aesthetic nuisance, the increased algal population can lead to harmful effects to the aquatic ecosystem. The growth of dense algal blooms prevents sunlight from penetrating into water depths, thus affecting the plant life along the bottom.

Decaying algae exert significant oxygen demand, thus, depleting the oxygen levels in the stream.

As a result, fish and shellfish populations, as well as other aquatic species are stressed due to the oxygen depletion and loss of plant life habitat. Increased nutrients can also lead to the growth of unfavorable, toxic algal species that can be lead to fish kills or toxic contamination of aquatic species.

The dissolved oxygen (DO) water quality criterion to protect aquatic life requires that instream dissolved oxygen levels remain greater than 5.0 mg/l with an instantaneous low of 4 mg/l.

Although nutrient enrichment has been identified as one of the leading causes for stream impairment, currently there are no water quality standards for nitrogen and phosphorous. The healthy or naturally occurring level of nutrients within an aquatic ecosystem varies regionally and seasonally. Due to the variation, the U.S EPA is currently in the process of developing nutrient criteria (standards). Water quality standards, established by the U.S EPA are in general designed to protect water quality for the designated stream uses. The water quality standards identify specific beneficial uses for each waterbody and establish water quality criteria (usually pollutant concentrations) for a variety of pollutant parameters that must be met to support the designated

86 uses. The water quality criteria are then used as a basis for developing permit limits for point source discharges. Typically, in order to assess the water quality in terms of nutrients, target levels are widely used as a scientific “rules of thumb”. The following table lists typical target levels for nutrients, oxygen demands, pH, dissolved oxygen, temperature and fecal coliforms in streams (taken from US. EPA Water Quality Criteria table and from the literature):

Water quality parameter Units Target Level

6.5 (chronic) pH - 9.0 (acute)

T Co Co 32.2 (acute) DO Mg/l 5.0 (chronic) BOD Mg/l <5 (7.0-acute)

NO2 +NO3 Mg/l as N <1.5

NH4 (Ammonia) Mg/l as N <0.5 Total P Mg/l as P <0.2 (1.0 – acute) Fecal Coliforms #/100ml 2000 (acute)

The Ohio EPA collects nutrient and major-ion samples at 4 sites on a routine (monthly) basis within the GMR watershed. These sites are: the Stillwater river at Pleasant Hill, Mad river at

Eagle City, GMR at Dayton and Miamisburg. ORSANCO (Ohio River Valley Water Sanitation

Commission) collects monthly water quality samples as well. The data have been collected since

1975 as part of ORSANCO’s manual sampling program and include determinations of pH, alkalinity, sulfate, major metals, nutrients, bacteria and selected trace elements (ORSANCO,

1990). Water quality samples were collected routinely by the USGS from 1970s through 1993 as a part of NASQAN (National Stream Quality Accounting Network) at these locations: GMR at

New Baltimore and Whitewater river near Alpine, Indiana.

The GMR data show a high percentage of sulfate and chloride relative to other sites, possibly caused by the influence of treated wastewater. Measurements of pH indicate slightly alkaline water, with recorded pH values generally between 7 and 8. The following figures illustrate some

87 of the important water quality criteria (based on U.S EPA STORET data – period 1971-1980, low streamflow July month, GMR at Hamilton, Columbia rd.):

Nitrogen ammonia for the same period of 1974-1981 fluctuates from minimum magnitudes of

0.11-0.19 to almost 1.0 mg/l that satisfies the target levels. Sum of Nitrites and Nitrates typically fall in a range between 1.0 and up to 7.3 mg/l, exceeding the target levels for the parameter, total phosphorus concentrations are recorded from less than 0.5 to 3 mg/l showing relatively substantial difference compared to target levels. Dissolved oxygen for this period stays fairly steady during the period (6-9 mg/l), which is in compliance with the criteria. Water temperature in July was recorded and ranged from 22 to 33 degrees during this period. 5-day BOD ranged from 5 and higher up to 8 mg/l, slightly higher in some years to reference value. These characteristics are quite typical to the GMR and for the most its tributaries.

91 2.1.11 Stream sediments

Trace elements and hydrophobic organic compounds that persist in the environment and that have the potential to bioaccumulate in fish and other wildlife can degrade the quality of fish for human consumption, disrupt the normal endocrine function of fish and wildlife and make cleanup and remediation of contaminated river sediments difficult and costly. Sedimentation increases turbidity of waters, which reduces light penetration, alters oxygen availability and reduces food supply for aquatic organisms. Sediments also cover spawning beds, reducing fish populations and increasing nutrient levels. Thus, phosphorus attached to soil particles is carried into the water during erosion (Hill and Mannering, 1995) and increased nutrient concentrations may cause algal blooms. Areas with high concentrations of trace elements and organic compounds are found include selected reaches of the lower portion of GMR and its tributaries, especially within Dayton area (Ohio EPA 1994, 1995). Also, Ohio EPA conducted several sediment samples in GMR as a part of its 5-year watershed assessment programs. All sediment samples were analyzed for a suite of heavy metals (cadmium, copper, chromium, lead, mercury, nickel and zinc) and selected samples were analyzed for VOC’s (volatile organic compounds) including polycyclic aromatic hydrocarbons (PAH’s), and polychlorinated byphenils (PCB’s). In the GMR basin, 2/3 of the sites sampled had metal concentrations at background or slightly above background levels. The concentrations of persistent hydrophobic organic compounds such as PCBs, PAHs and various organochlorine pesticides are generally low and, in the absence of specific point sources, are found at levels being optimal for aquatic organisms health (Ohio EPA, 1995,1996, 1997).

2.2 MODEL SELECTION AND DEVELOPMENT

2.2.1 Selection Criteria

When selecting a model, there are several problem-specific factors to be considered. One significant element in the process of model selection is the type of water body to which the model will be applied. There is a family of hydrodynamic simulation models, which can be applied

92 either to river systems or lake systems. Another important factor is to determine the pollutant parameters of interest and the potential sources to be included in the modeling effort. The selected model must have the ability to simulate the pollutant of interest with the level of complexity appropriate to the application

If non point sources are to be included in the modeling effort, the model must have the capability to simulate hydrologic and water quality processes over pervious and impervious land segments as well as in the streams. An additional factor in model selection is the determination of an appropriate spatial and temporal scale. The size of the watershed to be modeled is also important in choosing a model. Many models are designed for small, site-specific problems, whereas others are designed for large watersheds. Determining whether to use a steady state model versus a dynamic, time-dependent model is necessary as well. Many of these selection criteria are dependent on circumstances such as the amount and form of available data, the complexity of the system to be modeled and the intended use of the model.

For this project, in addition to the listed criteria, several other basic considerations were used:

(1) Model availability in public domain; (2) adequate technical support available not only for model development but also for model application and (3) popularity of model and degree of support by the governing regulatory agencies such as U.S EPA and Ohio EPA.

2.2.2 Model selection and compatibility

Through the preliminary analysis and literature review of the watershed models, as well as projects associated with water quality and quantity simulations, it was determined that the most suitable and compatible model for the current project would be WinHSPF (Hydrological

Simulation Program Fortran designed for Windows interface) integrated into the GIS ArcView based U.S EPA BASINS 3.0 software package. This combination provides a comprehensive and powerful tool in modeling nutrients and other major water quality parameters, especially from non-point source loads from the watershed. Along with this, WinHSPF can simulate instream processes, allowing the model to estimate the water quality and quantity, especially hydraulic

93 simulations with different hypothetical climate change scenarios. The ability of HSPF to model non-point sources from mixed land use scenarios is necessary when applying a model to a fairly large watershed such as the GMR. Another positive aspect of HSPF is the continuous simulation, versus steady-state assumptions that some other models utilize. Continuous simulation provides a more realistic outlook on hydrology and water quality in a system, allowing seasonal variation to be accounted for.

2.2.3 U.S EPA BASINS 3.0 and HSPF

For the purpose of developing a hydrologic and water quality model for the Great Miami River

Basin, assessing the current water quality conditions of flow and nutrients and simulating the changes in water quality and quantity associated with climate and land-use changes, U.S EPA

BASINS 3.0 comprehensive software package is be used in this project.

Better Assessment Science Integrating Point and Non-point Sources (BASINS) is a multipurpose environmental analysis system for use by regional, state, and local agencies in performing watershed- and water-quality-based studies. It was developed by the U.S EPA, Office of Water, to address three objectives:

• To facilitate examination of environmental information

• To support analysis of environmental systems

• To provide a framework for examining management alternatives

Because many state and local agencies are moving toward a watershed-based approach, the

BASINS system is configured to support environmental and ecological studies in a watershed context. The system is designed to be flexible. It can support analysis at a variety of scales using tools that range from simple to sophisticated.

A geographic information system (GIS) provides the integrating framework for BASINS. GIS organizes spatial information so it can be displayed as maps, tables, or graphics. Through the use of GIS, BASINS has the flexibility to display and integrate a wide range of information (e.g. land

94 use, point source discharges, water supply withdrawals) at a scale chosen by user. The conceptual structure of BASINS 3.0 is shown on Figure 2.14.

Figure 2.14 BASINS 3.0 Systems overview (courtesy of Tetra Tech Inc.)

BASINS comprises a suite of interrelated components for performing the various aspects of environmental analysis. The components include: (1) nationally derived databases with Data

Extraction tools and Project Builders; (2) assessment tools (TARGET, ASSESS, and Data

Mining) that address large and small scale characterization needs; (3) utilities to facilitate organizing and evaluating data; (4) tools for Watershed Delineation; (5) utilities to facilitate compilation and output of information on selected watersheds; (7) an instream water quality model, QUAL2E; (8) two watershed loading and transport models, Hydrological Simulation

Program-Fortran (HSPF) and Soil and Water Assessment Tool (SWAT); and (9) PLOAD, a simplified GIS based model that estimates non-point loads (NPS) of pollution on an annual average basis (Basins User’s Manual, 2000) .

95 As it was mentioned above, HSPF (Hydrologic Simulation Program – Fortran) model in

BASINS 3.0 will be used in analysis. Figure 2.15 displays the general mechanisms of linking

U.S EPA BASINS 3.0 and HSPF being into one integrated assessment tool:

Figure 2.15 Schematic representation of basic stages in integrated analysis with BASINS 3.0 and

HSPF

B. Land Use and pollutant C. Meteorological Data specific data

A. LANDSACAPE DATA D. Windows Interface, WinHSPF GUI Point Sources

Land Use distribution

Stream Data GIS ArcView

F. Post Processing and HSPF decision-making process E.

Generally, the modeling process is divided into six fundamental stages (detailed information associated with types and sources of data is given and discussed below in “Data availability summary”): Stage A, includes collecting, preprocessing, formatting etc, of spatially distributed landscape data such as land-cover grids and streams, including location of point sources within the region of study. Stage B involves introducing site-specific land-use and pollutant data. Stage

96 C is responsible for collection and processing reliable meteorological data. All these three stages are combined into Stage D, which consists of final examining and processing of available data package into the form, ready for model execution. Stage E itself represents the HSPF model execution and Stage F is the post-processing analysis.

HSPF is a comprehensive, conceptual, continuous watershed simulation model designed to simulate all the water quantity and water quality processes that occur in a watershed, including sediment transport and movement of contaminants. Although it is usually classified as a lumped model, it can reproduce spatial variability by dividing the basin in hydrologically homogeneous land segments and simulating runoff for each land segment independently, using different meteorologic input data and watershed parameters.

HSPF has its origin in the Stanford Watershed Model developed by Crawford and Linsley

(1966). This model, frequently cited in the literature as being one of the first comprehensive watershed models, has been widely used and has undergone numerous modifications and additions. For example, the Kentucky Model is a modified version of the Stanford Model.

Crawford and Linsley further developed the original model and created HSP (the Hydrocomp

Simulation Program), which included sediment transport and water quality simulation.

Recognizing the limitations of most existing simulation models, in terms of data management and compatibility with other models, in 1976 EPA commissioned Hydrocomp, Inc. to develop a system of simulation modules in standard Fortran that would handle essentially all the functions performed by HSP, ARM (Agricultural Runoff Management Model) and NPS (Nonpoint

Source Pollutant Loading Model), as such that they would be easy to maintain and modify.

The result was HSPF, which consists of a set of computer codes that can simulate the hydrologic and associated water quality processes on pervious and impervious land surfaces and in streams and well-mixed impoundments. The model can be applied to most watersheds using existing meteorologic and hydrologic data. Although data requirements are extensive and learning to

97 correctly use the model requires some time, U.S EPA recommends its use as the most accurate and appropriate management tool available for the continuous simulation of hydrology and water quality in watersheds. The result of this simulation is a time history of the runoff flow rate, sediment load, and nutrient and pesticide concentrations, along with a time history of water quantity and quality at any point in a watershed. HSPF simulates three sediment types (sand, silt, and clay) in addition to a single organic chemical and its transformation products.

HSPF has been widely used for non-urban watershed modeling, although the program does include simplified runoff modeling options for urban areas as well. The capability of HSPF to effectively model all types of land uses and pollution sources has resulted in wide application of this model, especially in medium-large size watersheds as well as for flood mapping, urban drainage studies, river basin planning, studies of sedimentation and water erosion problems and in-stream water quality planning.

One of the most attractive features of HSPF is its ability to effectively simulate loading from agricultural lands on a broad scale. This factor is especially useful in this study, since about 80% of the total GMR basin presently is covered by agricultural land. The in-stream nutrient processes modeled include DO, BOD, nitrogen and phosphorous reactions and some other processes and parameters.

Also, HSPF has a history of successful application. The model has been applied in multiple hydrologic and water quality studies, including analysis of best management practice implementation on agricultural lands, pesticide exposure assessments in surface waters, pesticide runoff testing and so on (Donigian, 1991). One of the most important and prominent applications of HSPF has been in the Chesapeake Bay system. HSPF was used to develop the Chesapeake Bay watershed model to estimate nutrient inputs to the Chesapeake Bay and to mitigate the effectiveness of agricultural best management practices within the drainage area (Donigian, et al,

1994).

98 2.2.4 Overview of HSPF Structure for Non-point Source Modeling

In HSPF, the various hydrologic processes are represented mathematically as flows and storages initially based on the concept of hydrologic cycle (Figure 2.16)

Figure 2.16 Conceptual representation of Hydrological cycle

In general, each flow is an outflow from storage, usually expressed as a function of the current storage amount and the physical characteristics of the subsystem. Thus the overall model is physically based, although many of the flows and storages are represented in a simplified or conceptual manner. Although this requires the use of calibrated parameters, it has the advantage of avoiding the need for giving the physical dimensions and characteristics of the flow system.

This reduces input requirements and gives the model its generality.

For simulation with HSPF, the basin has to be represented in terms of land segments and reaches/reservoirs. A land segment is a subdivision of the simulated watershed. The boundaries

99 are established according to the user's needs, but generally, a segment is defined as an area with similar hydrologic characteristics. For modeling purposes, water, sediment and water quality constituents leaving the watershed move laterally to a down slope segment or to a reach/reservoir

(RCHRES). A segment of land that has the capacity to allow enough infiltration to influence the water budget is considered pervious (PERLND). Otherwise it is considered impervious

(IMPLND). The two groups of land segments are simulated independently (Figures 2.17-2.18).

Figure 2.17 Conceptual structure of HSPF (U.S EPA source)

100 Figure 2.18 Basic modules of HSPF

RCHRES

PERLND IMPLND

These three fundamental modules are used to describe hydrologic, sediment and water quality processes on the land surface and through subsurface pathways (Bicknell et al, 2000). In pervious land segments HSPF models the movement of water along three paths: overland flow, interflow and groundwater flow. Each of these three paths experiences differences in time delay and differences in interaction between water and its various dissolved constituents (Donigina, et. al,

1991). A variety of storage zones are used to represent the storage processes that occur on the land surface and in the soil horizons. Snow accumulation and melt are also included, so that the complete range of physical processes affecting the generation of water and associated water quality constituents can be approximated. The structure of the PERLND module and associated subroutines is displayed in Figure 2.19.

101 Figure 2.19 Structure of HSPF PERLND Module (Bicknell, et al, 2000)

PERLND Pervious land segment

ATEMP SNOW MSTLAY PEST Correct air Simulate snow and Estimate solute Simulate pesticides temperature ice transport

PWATER SEDMNT NITR PHOS Simulate water Simulate sediment Simulate nitrogen Simulate budget phosphorous

PSTEMP PQUAL Estimate soil TRACER Simulate Simulate a temperature general quality conservative i

PWTGAS Estimate water Agrichemical Modules temperature and gas

HSPF offers both a simplified and detailed approach to simulating water quality and quantity.

The number of modules active in the simulation depends upon the tasks to achieve and input data available. The primary functions within the PERLND module include simulation of snow accumulation and melt (SNOW), the water budget (PWATER), sediment produced by land surface erosion (SEDMNT), air temperature correction (ATEMP), estimation of soil temperature

(PSTEMP), estimation of water temperature and gas production (PWTGAS) and water quality constituents utilizing a simplified (PQUAL) method or more comprehensive (Agrichemical) simulation method. The PQUAL section simulates the outflow of pollutants from pervious land segments using simple relationships with water and sediment yield (Bicknell eta al. 2000). Using

PQUAL, the quantity of water quality constituent in surface outflow can be represented by association with sediment removal or as a function of the storage of the constituent on the land surface and the water flow (Bicknell et al, 2000). The quantity of a water quality constituent in

102 subsurface flow is based on user input concentrations. Any constituent can be simulated using the simplified approach of PQUAL. However, more detailed modules (SEDMNT, PSTEMP,

PWTGAS and agrichemical modules) are often utilized to simulate parameters such as sediment, heat, DO, nutrients and pesticides.

The agrichemical sections of the PERLND module offer a more detailed approach to simulating the biological and chemical processes involved with the movement of nutrients and pesticides within a land segment. These sections include estimating soil transport (MSTLAY), simulating pesticide transport (PEST), simulating nitrogen transport (NITR), simulating phosphorous transport (PHOS) and simulating a conservative tracer (TRACER). These sections were originally developed as a part of NPSM to simulate the processes on agricultural lands.

However, the agrichemical sections can also be utilized in modeling other lands where pesticides and plant nutrients are important, such as orchards, parks and forests.

The more detailed approach of the agrichemical modeling routines allows for a more thorough simulation of the chemical and biochemical processes that typically occur within cropland soil.

When modeling nutrients, these processes include plant uptake, mineralization, nitrification and denitrification, immobilization, sorption and desorption and volatilization. Simulation of these processes is affected by factors such as hydrologic conditions, soil type, soil moisture and temperature, as well as the type of agricultural practices implemented. The detailed nutrient simulation requires more data and input parameters to model water quality than the simplified approach.

The IMPLND module of HSPF simulates hydrodynamic and water quality processes on impervious land segments with little or no infiltration. The structure of the IMPLND module is presented on Figure 2.20:

103 Figure 2.20 Structure of HSPF IMPLND Module (Bicknell et al., 2000)

IMPLND Impervious land segment

ATEMP SNOW Correct air Simulate snow temperature and ice

IWATER Simulate water SOLIDS budget for Accumulate and impervious water remove solids

IWTGAS IQUAL Simulate water Simulate quality temperature and constituents using dissolved gas simple relationships with solids and/or

Most of the sections within IMPLND module are quite similar to those of the PERLND module.

The main difference between the modules is in the level of the complexity since the IMPLND module does not consider infiltration or subsurface flow from land segments.

The hydraulic and water quality processes that occur in the river channel network are simulated by reaches/reservoir module in the model (RCHRES). The outflow from a reach or completely mixed lake may be distributed across several targets to represent normal outflow, diversions and multiple gates on a lake or reservoir. Evaporation, precipitation and other fluxes that take place in the surface are also represented. Routing is done using a modified version of the kinematic wave equation. Figure 2.21 illustrates the RCHRES module structure (Donigian et al,

2000 for details):

104 Figure 2.21 Conceptual scheme of RCHRES module

RCHRES Reach or mixed reservoir segment

HYDR ADCALC CONS HTRCH SEDTRN GQUAL Sim ulate Simulate Simulate Simulate Simulate Simulate hydraulic transport conservativ heat inorganic general behavi or behavior of e exchange sediment quality

RQUAL Simulate biochemical transformations of

OXRX NUTRX PLANK PHCARB Simulate Simulate Simulate Simulate DO and inorganic phytoplankt pH and BOD nitrogen on inorganic

Because HSPF offers versatility in the methods used to simulate the hydrology and water

quality of non-point source runoff within a watershed, a combination of methods was applied to

this study. The matrices (Figures 2.22-2.23) show the hydrologic process or pollutant to be

simulated with HSPF along the top and the HSPF modules and sections along the left for each

basic module in HSPF. A set of notes, at the end and in the manual itself, provides additional

details about the conditions under which a section is required, recommended, or optional (based

on HSPF manual, Donigian, et al, 2000).

105

Figure 2.22 Matrix of HSPF sections required versus pollutants and processes for PERLND and IMPLND

Figure 2.23 Matrix of HSPF sections required versus pollutants and processes for RCHRES section

106 2.2.5 Modeling approach

In this study, the BASINS/HSPF model will be used to simulate flow and transport processes.

This process begins with rainfall and the associated hydrological cycle components of runoff, infiltration, and evapotranspiration before exiting the watershed. The fate of the rainfall is determined by the watershed’s land use type. Depending on the land use types, the rain either infiltrates into the ground, is lost by evapotranspiration, or becomes surface runoff. The runoff generated by the various land use practices makes its way through the watershed via the stream channel network. This runoff from the watershed may carry various pollutants being generated by man made practices within different portions of the watershed (known as sub-watersheds).

Additionally the point sources (e.g., wastewater treatment or stormwater discharges, etc.) with their flow and pollutant loads are also discharged into the stream channels. The various sub- watersheds with their associated land use practices and tributary network then carry the load into the main stream of the river, which is the total flow being generated from the watershed.

The modeling approach used in this study can be summarized as follows:

• Development of representative model input data that reflect the land use characteristics of

the sub-watersheds

• Calibration and validation of the HSPF model using historical and observed data from the

Great Miami River

• Application of the HSPF model for simulation of pollutant concentrations or pollutant

loadings carried by each source (overland, interflow, and baseflow) from pervious and

impervious land segments of each land use type

• Identification of the relationship between pollutant type and pollutant source within each

sub-basin, identification of impact of land use/cover on pollutant loadings, and

assessment of the impact of these pollutant loadings on receiving water bodies

107 • Evaluation of the impact of future scenarios such as land use changes and climate

changes on generation of runoff and pollutant loadings from the study area watersheds

and recommendations for best management practices (BMPs) as a basis for

environmentally sound watershed management program.

Table 2.6 displays the HSPF sections that were used to simulate runoff and water quality for each land use in this study with respect to data availability and specifics of the GMR watershed. To simulate the dissolved oxygen in the runoff, PWTGAS and IWTGAS were utilized. Simulation of nutrients was performed using the simplified PQUAL for all land uses. The SEDMNT section of the PERLND module that simulates erosion processes was also utilized on agricultural lands since nutrients are often transferred to surface water through adsorption to sediment.

Table 2.6 HSPF Non-point Source Simulation summary

R ATEMP PWATE IWATER SEDMNT PSTEMP PWTGAS IWTGAS PQUAL IQUAL MSTLAY NITR PHOS ■ ■ ■ ■ ■ ■ ■ Agricultural Land

■ ■ ■ ■ ■ Forest Land

■ ■ ■ ■ ■ ■ ■ ■ Urban build-up

■ ■ ■ ■ ■ Barren land

■ ■ ■ ■ ■ Wetlands

108 2.2.6 Database Summary

Successful application of the HSPF model requires obtaining and formatting various datasets so as to properly represent the non-point and point sources within a watershed. The following dataset are required by the model:

(1) Meteorological data

(2) Land use data

(3) Point source data

(4) Channel cross-sections and profiles

(5) Observed flow and water quality data

Several data sources have been used to extract data in order to accomplish research tasks:

• Land-use/land cover. The use of accurate land use data is essential for correctly estimating impacts of NPS pollution on a watershed. Such data are also used in calculating impervious surface percentages. In addition, these data function as key model inputs and are the basis for calculating future pollutant loads. Land use information was obtained from USGS and converted to ARC/INFO by the U.S EPA (LULC GIRAS data) as well as MRLC dataset (National Land Cover Dataset Classification).

• Urbanized areas. This dataset was created to show the location and extent of urbanized areas in the United States. This type of coverage is useful in determining areas, which may have high population densities, significant water quality monitoring information, and significant discharges.

• Populated areas. This data set provides the location of populated places as represented on USGS topographic maps. It includes a collection of populated place names derived from USGS Geographic Names Information System II (GNISII) Topographic Names data. The relationship between populations potentially affected by water quality problems can then be established. It will also aid in the determination of the sources of water quality problems.

• Reach files (RF3) and NHD (National Hydrography Dataset). The U.S. EPA Reach Files are a series of hydrographic databases of the surface waters of the continental United States and Hawaii. River channels and tributaries are the major pathways that transport pollutants from the watershed. It is therefore important to accurately represent and characterize the channel network in the HSPF model. The watershed delineation, along with the observed flow and water quality locations, will serve as a guideline for channel segmentation. Accurate characterization is needed to provide a sound basis for routing of streamflow, sediment, and water quality constituents through the channel system to reproduce field data and measurements. Comparison of the simulated streamflow results with measured streamflow data, as part of the calibration process, will help to evaluate the extent to which HSPF adequately represents the channel network. Therefore, NHD data is used in this study because it contains more accurate information and hydraulic characteristic of the main streams and their tributaries.

109 • DEM elevation. Digital Elevation Model (DEM) is the terminology adopted by the USGS to describe terrain elevation data sets in a digital raster form. The standard DEM consists of a regular array of elevations cast on a designated coordinate projection system. 1-degree DEMs (3- by 3-arc second data spacing) provides coverage in 1- by 1-degree blocks. DEM's can be used as source elevation data for digital orthophotos, and, as layers in geographic information systems, for earth science analysis. DEM's can also serve as tools for volumetric analysis, for site location of towers, or for drainage basin delineation. The ESRI shapefile format allows for analysis involving elevational data and also provides for the possibility of contour generation.

• State Boundaries. This coverage containesstate boundaries of the conterminous United States. It was derived from the U.S. Geological Survey State Boundaries, which were derived from Digital Line Graph (DLG) files representing the 1:2,000,000-scale map in the National Atlas of the United States.

• USGS hydrologic unit boundaries (HUC) Watershed delineations are based on the Hydrologic Unit Maps published by the U.S. Geological Survey Office of Water Data Coordination, together with the list descriptions and name of region, subregion, accounting units, and cataloging units. These data sets are intended to support watershed analysis in BASINS. • Water Quality Observation Data. This data set provides a limited set of raw water quality observation data for the conterminous United States. The data are extracted from the U.S. EPA Storage and Retrieval of US Waters Parametric Data (STORET-STOrage and RETrieval), which are contributed, by a number of organizations including federal, state, interstate agencies, universities, contractors, individuals and water laboratories as well as from USGS National Water Information System Web site (NWISWeb).

• USGS Gaging Stations and Meteorological data. A key element of watershed model calibration is comparison of simulated results to observed values. For flow data, the primary source of observed values is the USGS. Recorded values from their extensive network of stream gages are available on the web (USGS NWISWeb). Daily values of streamflow were downloaded for USGS gage station at Dayton and Hamilton. Climotological records (min./max./mean monthly and air temperatures, daily precipitation values, wind direction, clod cover, etc.) were acquired from NCDC (National Climatic Data Center) for Dayton Airport Weather Station (1950-2000). The meteorological input data and units required for BASINS/HSPF are shown in Table 2.7. BASINS requires meteorological data collected at hourly intervals for non-point source modeling.

Table 2.7 Basins/HSPF Meteorological Input Data Data Description Units

Measured air temperature Degrees Fahrenheit

Measured precipitation Inches/hour

Measured dewpoint temperature Degrees Fahrenheit

Measured wind movement Miles/hour

Measured solar radiation Langleys/hour

Measured cloud cover Tenths of sky dome

Potential evapotranspiration (ET) Inches/hour

Potential surface evaporation Inches/hour

110

• Point Source data (PCS) Point source inputs to the watershed are required for application of the HSPF model. These data represent all significant loadings from municipal and industrial wastewater treatment plants that discharge to the channel reaches.

All currently available time-series data will be obtained and placed in a set of Watershed Data

Management (WDM) files, the time-series data repository used by HSPF. This includes meteorological, streamflow, and point source data.

2.3 DEVELOPMENT CLIMATE CHANGE SCENARIOS

2.3.1 Brief overview

Over the last century, the average temperature near Columbus, Ohio, has increased 0.30F, and precipitation has increased by up to 10% in this and other parts of the state, and declined by up to

10% in the southern part of the state (IPCC report, 2000).

Over the next century, climate in Ohio may experience additional changes. For example, based on projections made by the Intergovernmental Panel on Climate Change and results from the

United Kingdom Hadley Centre’s climate model (HadCM2), a model that accounts for both greenhouse gases and aerosols, by the year 2100, temperature in Ohio could increase by 30F

(1.6oC) in winter, spring, and summer with range of 1-60F (0.5-3.3oC), and 40F(2oC) in fall with a range of 2-70F (1-4oC). Precipitation is estimated to increase by 15% in winter and spring (with a range of 5-25%), 20% in fall (with a range of 10-35%), and 25% (with a range of 10-40%) in summer. The frequency of extreme hot days in summer is expected to increase along with the general warming trend. The frequency and intensity of summer thunderstorms is possible as well

(EPA, report, 1998).

111 2.3.2 Climate scenarios

This study incorporates the following climate change scenarios for simulation in the model

(scenarios selection is based on model scenarios from the IPCC and the United Kingdom Hadley

Centre’s climate simulation models (HadCM2)) (Figure 2.24):

Figure 2.24 Climate change scenarios for the HSPF model simulations

Hot Scenario Group

Hot and Wet scenario Hot and Dry scenario (T+4oC, P+20%) (T+4oC, P-20%) HW HD

Base Scenario (Current Temperature and Precipitation)

Warm and Wet scenario Warm and Dry scenario (T+2oC, P+20%) (T+2oC, P-20%) WW WD

Warm Scenario Group

The “hot group” displays scenarios for future change in temperature within the region of study by

+4oC increase along with amount of precipitation (including seasonal patterns) changed by -20% and +20%. This group characterizes the extreme warming of the atmosphere that might happen during the next century according to the GCM’s output results with opposite trends in precipitation. On the other hand, the “warm group” represents moderate warming with the same range in precipitation amounts. Probably, this simulation group sounds more realistic, given the technological progress that may yield new ways in preventing global warming and the associated

112 ‘greenhouse effect”. And, finally, “base case” group depicts current temperature and seasonal precipitation amounts and acts as a reference point in assessing changes of hydrologic and water quality regimes under given hypothetical future climate change scenarios.

2.4 DEVELOPMENT OF LAND USE CHANGE SCENARIOS

2.4.1 Brief overview

Land use change has been identified as a major driving force for global change. The meaning of the term ‘land use’ has been evolving. Initially, ‘land use’ referred to descriptions of the magnitudes and spatial distributions of activities influencing travel patterns expressed as areas of different land use designations (such as residential, commercial, industrial). These descriptions were used in the early versions of the standard transportation modeling. The term land use has now expanded to include any component of the spatial activity system not directly represented in a transport model or in the transport or environmental components of an integrated land use and transport model, such as:

• population composition, magnitude and spatial distribution;

• employment composition, magnitude and spatial distribution;

• land use and zoning rules;

• floorspace and land markets and prices;

• developer actions and the resulting state and evolution of floorspace in terms of composition,

magnitude and distribution;

• production and consumption behavior of both population (households) and industry (firms),

including input-output models and related technology (technical coefficients); and

• labor and commodity markets and prices.

113 There are two basic forms of land use modeling:

(1) Forecasting – where the intent is to establish an estimate or range of estimates regarding the future state of the system of interest, which typically involves simulations of the system of interest using forecast model inputs to obtain model outputs.

(2) Policy analysis – where the intent is to evaluate alternative courses of action, which typically involves multiple simulations of the system of interest using model inputs that reflect the alternative courses of action and the comparison of the results of different simulations among themselves.

Understanding past and future impacts of changes in land use and land cover is central to the study of environmental change and its human driving forces and impacts.

2.4.2 Land use scenarios

Since land-use change dynamic assessment and modeling are very complex, integrative criteria should be applied in order to, firstly, characterize land use and, secondly, create hypothetical scenarios of its future changes. Generally, to obtain information on future land use for a particular region, the factors acting as driving forces must be identified and their relative importance evaluated. As a rule, these factors can be summed up as:

1. Former land-use structure and change

2. General economic environment: (a) GDP share: agriculture, forestry, industry, other; (b) Share in employment (agriculture, forestry, industry, other); (c) Market development (agriculture or forestry)

3. Agriculture: (a) Percentages of GDP and employment in agriculture (b) Size of farms (d)

Ownership structure; (d) Policies (subsides, agricultural pricing policies etc.);

4. Environmental conditions: (a) climate, topography, soil characteristics, water availability; (b)

Environmental pollution (acidification or other pollution loads)

114 5. Social content: (a) demographic factors (population density, migration etc.); (b) Markets for agricultural products (local and interstate); (c) Traditional land-use; (d) Attitudes and values

(towards the landscape, cultural heritage and nature conservation);

6. Policies related to land-use planning: (a) development plans; (b) Legal framework (land-use planning, land-use policy)

The analysis of these factors provides basic information for developing scenarios of future land use for the region of interest. The importance of a given driving force varies depending on the part of the region.

It should be noted that the approach used in the development of land-use change scenarios for the GMR basin is fairly subjective. A more precise approach to develop land use change scenarios would be the use of land-use models. Land-use models often incorporate a variety of land-use categories as inputs, thereby, they can account for different subclassifications of urban and non-urban land-use, such as commercial, industrial, and agricultural, and even more detailed subclassifications, such as density of residential use and type of commercial/industrial development. Many of the more user-friendly models are integrated with GIS’s to become spatially explicit decision-support systems with relational database technology (Schock, 2000).

There are several specific land-use change models (e.g. SLEUTH (Slope, Land Use,

Exclusion, Urban, Transportation, Hillshading), LUCAS (Land-Use Change Analysis System),

UPLAN Urban Growth Model and some others). They are capable of producing more accurate results, but they are complex, require a huge datasets and are difficult to calibrate and validate. To illustrate the natural complexity of the land use change models, let’s briefly consider LUCAS model and its structure.

LUCAS (Land-Use Change Analysis System) is a computer-based application specifically designed to integrate current and future information for (1) providing a multidisciplinary modeling environment for addressing research questions concerning land use and its impacts, (2) applying adaptive management approaches in order to address management questions concerning

115 landscape-impact assessment, and (3) designing a tool for workstations supporting the Unix operating system, X-Windows, and Motif user libraries. LUCAS was developed in 1994 to examine the impact of human activities on land use and subsequent impacts on environmental and natural resource sustainability. LUCAS stores, displays and analyses map layers derived from remotely-sensed images, census and ownership maps, topographical maps and outputs from econometric models using the Geographic Resources Analysis Support System (GRASS), a public domain GIS. Simulations using LUCAS generate new maps of land cover representing the amount of land-cover change. Output of the LUCAS includes a time series of projected land uses in the watershed at user specified time steps. For each land cover type, the following output information is provided: Area, Amount of Edge, Mean patch size, Number of patches, Proportion of land cover, Amount of total edge, Size of largest patch and others. Information needed to run the model is: (1) Transportation network (access and transportation costs); (2) Slope and elevation (indicators of land-use potential); (3) Ownership (landholder characteristics); (4) Land

Cover (vegetation); (5) Population density. Format of inputs is GRASS grids. According to

Schock, (2000), model has two major strengths: (1) it provides a graphical user interface that is intuitive and easily understood by users with a wide range of technical abilities and experience and (2) the system provides a flexible and interactive computing environment for landscape management studies. The limitations of the model are: presence of some bugs and the model requires training and experience to calibrate.

The structure adopted for LUCAS consists of three subject modules linked by a common database (Figure 2.25).

116 Figure 2.25 LUCAS modules

Socioeconomic Model Module

Transition

probability matrix Census data

Landscape Change Model Database Module

Land-use, Land cover maps Maps

Tabular data Impacts Model Module

Impact Maps, Graphs

The first LUCAS module contains the socioeconomic models that are used to derive transition probabilities associated with changes in land cover. These probabilities are computed as a function of socioeconomic driving variables including (1) transportation networks (access and transportation costs), (2) slope and elevation (indicators of land-use potential), (3) ownership

(landholder characteristics), (4) land cover (vegetation), and (5) population density.

The landscape-change model resides in the second LUCAS module. This module receives as its input the transition matrix produced in the socioeconomic models (Module 1), and accesses the same spatial database of driving variables. A single iteration of the landscape-change model

117 produces a map of land cover that reflects socioeconomic motivations behind human land-use decision-making (represented in the transition probability matrix).

The impact models defined in the third module of LUCAS utilize the land-cover maps produced by the landscape-change module to estimate impacts to selected environmental and resource-supply variables. These environmental variables include the amount and spatial arrangement of habitat for selected species and changes in water quality caused by human land use (Schock, 2000).

As it was mentioned before, land use change scenarios in this study will be qualitatively estimated based on “educated guess” after examining the following factors: former land-use structure and change, general economic development, agriculture (number of farms, area for farmlands), demographic factors (population density) and policies related to land-use planning

(development plans and land use policy). Unfortunately, there is not much detailed information on counties land-use development plans within the GMR basin boundaries, so generalized development policies in land use will be taken into consideration (based on research work conducted by the Department of Agricultural, Environmental and Development Economics and the Department of Human and Community Resource Development at the Ohio State University on analysis of urban sprawl and exurban change project in Ohio as well as OKI (Ohio, Kentucky,

Indiana Regional Council of Governments).

In subchapters 2.18 (Land Use) and 2.19 (Demographics, Urban development and Industry) not only land use characterization of the region of study was performed but also a brief analysis of region’s demographics, urbanization magnitudes and its trend were presented based on US

Census data for the Ohio.

118 Figure 2.26 illustrates the major urban areas in Ohio (courtesy of the Ohio Department of

Development):

Southwest of Ohio (the boundaries of GMR basin) contains three major metropolitan urban areas:

Cincinnati (OH, KY, IN), Dayton (OH) and Springfield (OH). These urban areas are comparable in size to another urban metropolitan areas in Ohio: Cleveland (OH), Akron (OH) and Canton

(OH).

119 In order to describe the change dynamic and aerial extent of the land cover types within the region of study, temporal analysis of land cover changes by type of land use in counties situated within the GMR basin was conducted (Figures 2.27-2.34).

Figures 2.27-2.34 Changes in land cover for some urban counties within the GMR basin for the

period of 1982-1997 (Source: National Resource Inventory):

Hamilton County

Figure 2.34 Land cover changes by Type of land use, 1982-1997

120 Butler County

Figure 2.28 Land cover changes by Type of land use, 1982-1997

Montgomery County

Figure 2.29 Land cover changes by Type of land use, 1982-1997

121 Preble County

Figure 2.30 Land cover changes by Type of land use, 1982-1997

Clark County

Figure 2.31 Land cover changes by Type of land use, 1982-1997

122 Champaign County

Figure 2.32 Land cover changes by Type of land use, 1982-1997

Miami County

Figure 2.33 Land cover changes by Type of land use, 1982-1997

123 Logan County

Figure 2.34 Land cover changes by Type of land use, 1982-1997

The phenomenon of urbanization in the GMR basin is the result of conversion of agricultural land to new suburban and residential developments and is clustered to zones bordering the major metropolitan areas.

Complementing the analysis on population trends in subchapter 2.1.9 for counties within the study area, the land cover changes in Hamilton County show increase in area of urban land by 75 km2 (18,700 acres) for the period of 1982-1997 (based on National inventory Resource data).

Forest cover decreased by 40 km2 (9,900 acres) as well as crop land by 21 km2 (5,200 acres) for the same period. In Butler County urban land sector experienced increase by 103 km2 (25,300 acres) when crop and pasture lands decreased by 14 km2 (3,500 acres) and 86 km2 (21, 400 acres) accordingly. Noticeable decrease in cropland happened in the period of 1987-1997. For this 10 years, the area decreased by 74 km2 (18,500 acres). In Montgomery County, the same trend for urban land sector is observed: increase by 57 km2 (14,100 acres) with slight diminishing of crop and pasture lands. Cropland area in Preble County dropped by 65 km2 (16,200 acres) for the

124 period of 1982-1997 with rise of urban land area. Significant contraction of cropland cover happened in Logan County (decline by 140 km2 (34,700 acres)). The following table displays the tendencies in population dynamics for most counties located within the GMR basin (based on US

Census data and Ohio Department of Development):

Population projections for counties located within the GMR basin

% change by 2015 (1990 as a reference County 1990 1995 2000 2005 2010 2015 year) Auglaize 44,585 46,800 47,300 49,300 50,200 52,200 +17 Butler 291,479 314,750 335,560 361,610 386,360 418,040 +43 Champaign 36,019 37,200 38,600 39,100 40,000 40,200 +12 Clark 147,548 148,500 149,600 149,300 150,900 151,800 +3 Darke 53,619 53,800 53,500 53,800 53,900 54,400 +1 Greene 136,731 141,700 147,300 150,200 155,300 158,400 +16 Hamilton 866,228 871,200 873,300 875,800 882,300 896,200 +3 Logan 42,310 44,620 47,230 49,500 52,450 54,970 +30 Miami 93,182 96,300 99,200 102,900 106,100 109,200 +17 Montgomery 573,809 581,300 588,600 589,100 597,000 607,000 +6 Preble 40,113 41,800 42,900 44,500 45,800 47,600 +19 Warren 113,909 128,010 137,060 152,030 163,500 183,290 +61

Based on the results from the analysis on population dynamics, former land use and changes by counties, general economic development in the region of study, including land use development plans, in average 30% increase in urban area segment (+23% for Upper GMR basin and +32% for the Lower GMR basin) is taken and incorporated to the HSPF model as the future land use scenario (details in Chapter 4). Urban extent is, mostly characterized by growing suburban and low/high residential areas clustered to major cities in the study area (Cincinnati, Dayton,

Middletown and Hamilton). As it was discussed, IMPLND module in HSPF counts the impervious area in hydraulic and water quality simulations. To run the future land use scenario, changes in IMPLND module are made in respect to each contributing sub-watershed within the whole GMR basin area. Proportionally to urban growth, deductions in areas representing

PERLND segment, mostly agricultural lands in HSPF are introduced as well, in order to complete

125 the scenario. However, some areas that contain sub-basins situated very close to major urban centers (Sidney, Piqua, Troy, Greenville, Urbana, Springfield, Dayton, Miamisburg, Middletown and Hamilton) are increased in urban aerial extent in the range of 10-90%.

2.5 APPROACHES FOR SCENARIO ANALYSIS

(1) Existing Conditions (Base case scenario): for the analysis of the existing conditions, the hydrologic regime, NPS loads will be estimated using the 1980 to 1995, meteorological data and

1992 land use data.

(2) Future Climate conditions (Hot scenario group and Warm scenario group: HW, WW and HD,

WD, Figure 2.24): the hydrologic regime and water quality will be estimated using generated meteorological records of future climatic changes in respect to each scenario along with current land use data.

(3) Future Land use conditions (in general, +34% increase in IMPLND segment): the hydrologic regime, NPS loads will be estimated using current meteorological data.

(4) Future Climate and Future Land use conditions: the hydrologic regime, NPS pollutants loads will be estimated using future climate and land use scenarios.

126 CHAPTER III: MODEL CALIBRATION AND VALIDATION

3.1 HYDROLOGY CALIBRATION/VALIDATION 3.1.1 Brief analysis of flow regime

In order to quantitatively and qualitatively characterize the flow regime of the GMR and its major tributaries (Mad river and Stillwater river), statistical analysis was performed using the data from USGS NWIS (National Water Information System) and NOAA NWS (National Weather

Service). Flow and precipitation change dynamics for the GMR at Dayton, OH is shown on

Figures 3.1-3.3.

Figure 3.1 Precipitation and discharge, GMR at Dayton, OH and annual flow dynamics at

Hamilton, OH

127 Figure 3.2

Figure 3.3

128 Coefficient of flow variation equals to 0.34 for the period of 1914-2000. Flow fluctuations at

Dayton USGS station on GMR show similar pattern in tendencies with precipitation dynamics

(Dayton Airport weather station): decreases in average annual precipitation causes decreases in volumes of flow for most years (1953, 1954, 1955, 1956, 1957 and others). However, in some years, i.e. 1959,1960, this pattern was not evident. There were some delays in volume of flow increase with elevated annual precipitation values.

Spearman’s trend criteria analysis (discussed above) gives a coefficient r*s of 0.061, and criteria

* statistics (r s *SQRT (n-1)) of 0.565 with t(α/2) being as 1.96 (α = 1% interval), implying that there is no trend in the annual means in flow of the GMR at Hamilton for the period of 1917-

* 2000. For GMR at Dayton 1914-2000, r s *SQRT (n-1))= -0.662 with t (α/2) being as -1.96 (α =

1% interval), indicating rejection of the null hypothesis – no tendency either to decrease or to increase. Absence of positive trend, as it is in the case with annual precipitation (above) could be partially explained by the high level of flood control management and channelization of Lower

Great Miami River and some of its tributaries.

Flow duration curve and years representing different volumes of flow for GMR at Dayton

USGS station as well as seasonal variations of flow are presented on the Figures 3.4-3.8 and

Tables 3.1-3.2:

129 Figure 3.4 Flow duration curve, GMR at Dayton, OH

Table 3.1 Years by magnitudes of flow, GMR at Dayton, OH

Very High Flow (VHF) seasons are characterized by probability to exceed the discharge being in the range of 0-20% (Flow duration curve has annual means on y-axis). There were years (

1916, 1920, 1950 etc.), that had discharge magnitudes of 3,000 cfs and higher. High Flow

Regime (HF) (P=20-40%) is shown by mean annual discharges of 2,400-3,000 cfs, Medium Flow

(MF) years (P=40-60%) - 2,000-2,400 cfs, Low Flow (LF) years (P=60-80%) – 1,500-2,000 cfs and Very Low Flow (VLF) is characterized by magnitudes in the range of 700-1,500 cfs.

130 Seasonal flow variations of GMR at Dayton were analyzed for the period 1914-2000 (Table and

Figures). The whole period was divided into three 30-year segments (1914-1944, 1945-1975 and

1976-2000) to track the seasonal variations in flow using the same coefficient as in precipitation dynamics analysis (INVij = max{Zij} – min(Zij)} where Zij = (Qij-Q j-average)/Q j-average (Qij is a monthly average of a j-year, i =1,….12, j= 1,…50 and P j-average is annual average of j –year) and

INVij is a difference between maximum and minimum Zij for the whole period n on monthly step.

Table 3.2 Seasonal changes of flow, GMR, Dayton, OH

131 Figures 3.5 Graphs showing flow variation, GMR at Dayton, OH

Figure 3.6

132 Figure 3.7

Figure 3.8

It is seen that during the 1914-1944, seasonal flow experienced quite substantial variations.

The highest magnitude of variation in flow was detected in January (5.65), fairly moderately flow varied in February and summer months (2.19-3.43) with local high extreme in April. The lowest for the period is 0.78 and it refers to August as a low flow regime. Period for 1945-1975 is characterized by really high variation in March (6.73) and relatively low variances for the rest of

133 the year’s seasons (3.20 for April (generally high flow regimes) down to 0.34-0.61 for September and October variations). The last thirty years (1976-2000) are characterized by quite moderate variations in seasonal flow, from 4.10 (December) as the highest to 0.72 (September) as the lowest. Probably, it’s worthwhile to note, that the difference between maximum extremes during the period of 1976-2000 is the lowest (3.45= March (max) – September (max)) compared to

1945-1976 being as 6.84 (March and September again) and 4.97 (difference in maximums of

January and August). Possibly, this fact might be explained by flood control constructions and projects that took place in 30s, 40s and 70s within the GMR basin with respect to system’s adaptation time.

Overall, the whole period 1914-2000 in terms of seasonal flow variation might be characterized as relatively high in high flow season (March, April) and relatively low in low flow season (July,

August, September, October). Winter flow varied relatively high for the analysis period (4.13 to

6.54). Precipitation variations were fairly close to the extremes in flow variations. (Figures 3.1-

3.3).

Long-term fluctuations of Stillwater river flow at Pleasant Hill, OH for the period 1917-2000

* didn’t reflect trends toward increase or decline: (r s *SQRT(n-1)) = -0.0523). Stillwater river at

* Englewood, OH has (r s *SQRT(n-1)) equal to 0.232, meaning no trends over the whole period of

1926-2000. However, Mad river flow changes at Urbana, OH, signals some tendency to increase

* over 57 years of analysis (1940-1997): r s *SQRT(n-1)) = 2.48 (Figures 3.9-3.11)

134 Figure 3.9 Fluctuations of flow, Stillwater river at Pleasant Hill, OH

Figure 3.10 Fluctuations of flow, Stillwater river at Englewood, OH

135 Figure 3.11 Fluctuations of flow, Mad river at Urbana, OH

Summarizing the results from the analysis of flow, it seems that long-term flow dynamics of

GMR is characterized by high cyclicity with tendencies to frequent changes and alterations between high and low flows. There is no grouping among adjacent high and low flow years.

There is no clear and steady tendencies for increase or decrease over the period of some 80 years

(GMR at Dayton, OH and Hamilton, OH).

136 3.1.2 Summary of Hydrologic Simulation

HSPF utilizes routines within PWATER section to calculate the processes of the hydrologic cycle from pervious land segments. Processes such as precipitation, interception, evaporation, surface runoff, evapotranspiration, interflow and ground water flow are all included in the simulation of the water budget. The processes represented in hydrologic simulation are not only important for estimating flow within the stream network, but also have a large effect on the transport of pollutants, especially nutrients, by means of overland flow or infiltration.

As it was discussed earlier, simulation of basic hydrology in HSPF requires at least having

PWATER (Water Budget Pervious) tables in PERLND section, IWATER (Water Budget

Impervious) in IMPLND section and HYDR (Hydraulic Behavior) in RCHRES section active.

Among optional tables are ATEMP and SNOW in PERLND and IMPLND sections.

3.1.3 Brief Overview: Calibration and Validation

Calibration is a test of the model with known input and output information that is used to adjust or estimate factors for which data are not available. Validation is a comparison of model results with numerical data independently derived from experiments or observations of the environment. According to French, R., (1989), model calibration and validation are critical steps in a model application. For HSPF, and virtually all watershed models, calibration is an iterative procedure of parameter evaluation and refinement, as a result of comparing simulated and observed values of interest. This process is required for parameters that cannot be deterministically, and uniquely, evaluated from topographic, climatic, or physical/chemical characteristics of the watershed. Fortunately, the large majority of HSPF parameters do not require calibration (Pathwardhan et. al, 2001). Calibration is generally based on several years of simulation (at least 3 to 5 years) in order to evaluate parameters under a variety of climatic, soil moisture, and water quality conditions.

137 A proper calibration of hydrology and water quality parameters compares both monthly and annual values, and individual storm events, whenever sufficient data are available for these comparisons. In addition, when a continuous observed record is available, such as for streamflow, simulated and observed values should be analyzed on a frequency basis and their resulting cumulative distributions (e.g., flow duration curves) compared to assess the model behavior and agreement over the full range of observations. Calibration should result in parameter values that produce the best overall agreement between simulated and observed values.

Calibration is a hierarchical process beginning with hydrology calibration of both runoff and streamflow, followed by sediment erosion and sediment transport calibration, and finally calibration of water quality constituents. When land surface processes are being modeled, hydrologic calibration must precede water quality calibration since runoff is the transport mechanism by which NPS pollution occurs. Likewise, adjustments to the in-stream hydraulic simulation must be completed before in-stream sediment and water quality and transport processes are calibrated.

Model validation is actually an extension of the calibration process. Its purpose is to assure that the calibrated model properly assesses all the variables and conditions that can affect model results. While there are several approaches to validate a model, perhaps the most effective procedure is to use only a portion of the available record of observed values for calibration; once the final parameter values are developed through calibration, simulation is performed for the remaining period of observed values and goodness-of-fit between recorded and simulated values is reassessed. For hydrology, specific comparisons of simulated and observed values include:

(a) Annual and monthly runoff (means or volumes) (cfs or inches)

(b) Daily time-series of flow (cfs)

In addition to the above comparisons, the water balance components (observed and simulated) may be reviewed. This involves displaying model results for individual land use types for such

138 water balance components as precipitation, total runoff (overland flow, interflow, base flow), total actual ET (interception ET, upper zone ET, lower zone ET, base flow ET, active groundwater ET), and deep groundwater recharge/losses (Bicknell, et. al, 2000). Although observed values are not available for each of these water balance components, the average annual values must be consistent with expected values for the region, as impacted by the individual land use types. This is a separate consistency, or reality, check with data independent of the modeling

(except for precipitation) to ensure that land use categories and the overall water balance reflect local conditions.

In BASINS, general calibration/validation tolerance levels have been provided to model users as part of HSPF training workshops and manuals (Donigian, 2000). The values in Table 3.3 provide a general guidance for HSPF users, in terms of the percent mean errors or differences between simulated and observed values, so that accuracy of model could be estimated.

Table 3.3 Calibration/Validation targets for HSPF. The figures shown are the % difference between simulated and observed values. (Donigian, 2000)

Very Good Good Fair Hydrology/Flow <10 10-15 15-25 Sediment <20 20-30 30-45 Water temperature <7 8-12 13-18 Water Quality/Nutrients <15 15-25 25-35 Pesticides/Toxics <20 20-30 30-40

3.1.4 Calibration and Validation of the Hydrological Model

Model performance and calibration/validation are evaluated through qualitative and quantitative measures, involving both graphical comparisons and statistical tests. For flow simulations where continuous records are available, all of these techniques may be used, and the same comparisons performed, during both the calibration and validation phases. Initially, for hydrology calibration, two USGS gage stations were used: GMR at Dayton, OH (USGS

#03270400) and GMR at Hamilton, OH (USGS #03274000), with daily flow records from 1980-

1995. Comparisons of simulated and observed state variables may be performed for daily,

139 monthly, and annual values, in addition to flow-frequency duration assessments. Statistical procedures often include error statistics, correlation and model-fit efficiency coefficients, and goodness-of-fit tests, as appropriate. Figures 3.12-3.13 display the Upper and Lower portions of the GMR basin. These two portions are divided in BASINS using automatic delineation tool into sub-basins. This is essential in running the hydrologic model.

140

Figure 3.12 Upper GMR and sub-basins.

N Upper GMR basin divided into sub-basins

Legend

Cataloging Unit Boundaries

Streams

$ USGS Gage Stations

Permit Compliance Syst em 1 I Watershed ND I AN 2 L Subbasins

A $

R R C 4

G IE M A A * R O L 6 $ 3 $

S $ $ W L 10 A 8 E M A M P T O 9 H S C 5 Q MUDDY CR R E U R IT W O R C C O R GS $ O 7 KIN D A R N C C N D R E 13 E 11 G T T R N L I E S

S $ R C O$ T P R R HARRIS CR N I S C L C L C R D W H R R Y C 14 A I A N P D O LE CR $ T VIL T M 18 EN D A B GRE S R $ E I A M O A N C R 15 $ L N H C K 19 R $ O R $ C R C $ N U C R $ R $ C $ E B D T 17 Y U U PAINTER CR C A 12 M R R $ K $ $ $$ $ LU $ $ BEAVER D R C LOW $$$ R C C R 27 16 $$ S

22 E

N 20

O Springfield $$ D $ 26 23 21 $ 24 $$25 28 Dayton 03060Miles Scale

141

Figure 3.13 Lower GMR and sub-basins.

Lower GMR basin

N divided into sub-basins

M i T lle w 2 rs in F 4 C o r rk ee k 1

3 P 10 W r o 7ic lf e $ C Dayton C 5 re r e 8 e k e $$ k 6 B 14 e T a r o m C 12 r s e 13 H e o R L l $ k es i u t 16 C n t r l e e ek

F $ 11 o u 20 29 9 $ r

21M r e i 15 l $ 18 iv e R C i

r 17 m e El ia e $ k Clear Cre k C M ek re at 24 ek e S r 23 19 e 22G Middletown ve $ nm $i 28 le C Dicks Creek re Legend ek 25 32 26 $ 27G r $ 30 e g o 36 r L y itt Subbasins le In 31C di $ r an $ Hamiltone C e reek 33 k Permit Compliance System

34 $ USGS Gage Stations 35 $ Streams 37 $ Watershed k e re C r lo y a T 02040Miles Scale

142 As in the other hydrologic models, calibration in HSPF is an iterative process: making parameters changes, running the model, producing the comparisons between simulated and observed values and interpreting the results. WinHSPF contains a shell, named GenScn (Scenario generator), which allows the user to picture the adjusted parameters of the hydrologic model and graphically or statistically compare them. These post-processing capabilities of GenScn (plots, listings, stats) are very helpful during the calibration and validation.

The time period of 1985-1990 was selected to calibrate the hydrologic model, and the period of

1990-1995 to validate it. After building and running the hydrologic model, with the illustrated above basin boundaries, the results showed a high error rate between the simulated and observed values (about 20-23% error in daily and annual flows that corresponds to a “fair” calibration and validation). To achieve more accurate results in hydrology calibration, the Upper portion of the

GMR basin was subdivided into three sub-basins: Stillwater river sub-watershed, Mad river sub- watershed and Great Miami river sub-watershed. The Great Miami river sub-watershed includes

Loramie creek watershed, Muchinippi creek watershed, Honey creek watershed, Lost creek watershed, and Mosquito creek watershed. For each of the three sub-basins, the reference points at Englewood USGS gage station #03266000 for the Stillwater river, USGS gage station

#03270000 for the Mad river near Dayton and USGS gage #03263000 at Taylorsville for the

Great Miami river, were chosen for comparative analysis between simulated and observed flows.

The Lower GMR basin was not divided into smaller watersheds, and the USGS #03274600 gage station at Hamilton, OH was chosen for calibration/validation purposes (Figures 3.14-3.15).

Each of the selected sub-basins was incorporated into HSPF for the construction of their own hydrologic model. Sum of flows from the Stillwater river, Mad and GMR creates an outlet flow of Upper GMR basin at Dayton, OH. Later on, in building the hydrologic model for Lower GMR, the simulated flow at Dayton was added to the Great Miami reach (subbasin #13, reach #13) as a point source inflow, using available option in HSPF. During the numerous calibration runs, each sub-basin’s hydrological model had to be adjusted individually. This was done many times until

143 the results showed a good agreement. To minimize the difference between observed and simulated values in calibration procedure, DEEPR (Fraction of GW inflow to deep recharge),

LSUR (Length of overland flow), INTFW (Interflow), INFILT (Index of infiltration capacity), and LZETP (Lower zone ET) parameters were adjusted, in respect to the conditions in the watershed (type of soils, percentage of urbanized areas, slopes, mean elevation of pervious and impervious areas etc.). Validation, or verification, was performed for hydrological model of each stream in order to verify the accuracy of the parameters.

144 Figure 3.14 Sub-basins and their boundaries used in calibration and validation analysis

Subdivided Upper GMR basin N for hydrology

k e e r

i p p calibration 1 i n i h c u 2

M and validation $

M $ ek re i C 4 a m ie m i a or R

e Creek L i Mil 6 v $e 3 $r

u M r $ t $ o l s e 8 qu 10 Legend i C to C r 9 e re 5 e ek k $ USGS Gage Stations $ 7

13 un11 n R $ ga S $u t D Subbasins i l l k w ee a 14 r k t C e e

e e ll r $ 18 i r R v $ M GMR BASIN n C e i 15 re v t $a k 19 e s d $e G o r e R r $ L H C $ i $ $ o v k MAD RIVER BASIN 17 e n c r e u 12 y B $ C $ $ $ $$r $ e STILLWATER RIVER BASIN $ e $$k 27 16 $$ 22 20 Permit Compliance Syst em

$ un $26 R $ ud 21 Streams M 23

er 24 iv $ R ad Digitized streams M $$25 28 Removed streams 02040Miles

Figure 3.15

M Boundaries of Lower GMR il T le w 2rs in F 4 C o N r rk ee used in calibration k 1 3 P 10 W 7ri o and validation c lf e C C 5 $ re 8 r ek ee $ $ k

6 B 14 e T a r o m C 12 r s e 13 H e o $ R k l L es u i C t 16 n t re l e e k

F $ 11 o 9 u 20 29 $ r

21 M r 15 e i l $ 18iv e R

C 17 i r E am e l i e k $ M Clear Creek k C t re a 24 ek re 19 S 23 e 22G v en $ m $i 28 le C r Dicks Creek ee 25 k 32 26 $ 27G 30 r $ e g o 36 r L y it tl C e In 31 d $ r ian $ e Cr e Legend eek 33 k

Streams 34 35

Subbasins $ 37 LOWER GMR - area, incorporated to HSPF $

k $ USGS Gage Stations e r e C r l o y a Watershed T

Permit Compliance Syst em 02040Miles

145 Table 3.4 and Figures 3.17-3.28 illustrate the results from hydrologic model calibration and validation steps with graphical comparisons of simulated and observed daily flows for the GMR river, the flow duration curves, and statistical relationships between simulated and observed magnitudes with each plot showing both calibration and validation time periods. Auxiliary plots demonstrating the results of calibration and validation procedures are shown in Appendix A.

For the GMR flow at Taylorsville, OH, the results from calibration shows good agreement between simulated and observed values (9.8%) with correlation coefficient of 0.78. Validation results yielded even lower (3.2%) difference between simulated and observed values. Hydrologic models of the Mad and Stillwater rivers demonstrate good agreement as well (2.8% for calibration, 14.0% for validation periods for Mad river and 2.9% and 14.5% respectively). The simulated annual flows of these sub-watersheds, exhibit good agreement with observed flow at

USGS gage station #03272000 at Dayton, OH – 7.5% difference in calibration runs and 9.8% for validation time period. The results indicate, that the hydrologic model can simulate the flow very accurately, placing it into a ”very good” category in terms of HSPF model efficiency targets for calibration and validation procedures (Table 3.4). Correlation coefficients in the range of 0.78-

0.80 are quite common in HSPF hydrologic simulation runs and, usually, are considered as acceptable for further analysis (Wicklein and Schiffer, 2002). Simulation of flow in the Lower

GMR at Hamilton, OH, provided the results that can be considered as a “good” model efficiency: the percent error in mean annual flows between simulated and observed values are 17.7% and

15.0% for calibration and validation time periods accordingly. For the Upper GMR, correlation coefficients are consistently higher, indicating more accurate simulation of storm peaks and low flows. The hydrologic model of the Upper GMR tends to constantly over predict the high flows and significantly under predict the base flows. (Flow duration curves for Upper and Lower

GMR).

146 Table 3.4 Hydrology Calibration and Validation Summary (Mean annual observed and simulated flows, %Error between simulated and observed and correlation coefficients) UPPER GMR Upper GMR at Taylorsville, OH (USGS #03263000) MAD river near Dayton, OH (USGS #03270000)

Base Case Mean observed Mean simulated % Error: Average Mean observed Mean simulated % Error: Average Scenario annual flow, annual flow, simulated and daily annual flow, annual flow, simulated and daily (cfs) (cfs) observed correlation (cfs) (cfs) observed correlation coefficient coefficient 1980-1985 1070 880 17.0 0.79 706 614 13.0 0.77 Calibration period Calibration period 1985-1990 1104 995 9.8 0.78 674 655 2.8 0.77 Validation period Validation period 1990-1995 1214 1253 3.2 0.79 702 801 14.0 0.76 Whole period Whole period 1980-1995 1097 1008 8.1 0.78 677 703 3.8 0.75

STILLWATER R at Englewood, OH (USGS #03266000) SUM OF FLOWS: GMR, MAD R and STILLWATER river * Base Case Mean observed Mean simulated % Error: Average Mean observed Mean simulated % Error: Average Coefficient of Scenario annual flow, annual flow, simulated and daily annual flow, annual flow, simulated and daily runoff ** (cfs) (cfs) observed correlation (cfs) (cfs) observed correlation Pavr=39 inches coefficient coefficient obs/sim 1980-1985 639 582 8.9 0.79 2487 2034 18.0 0.78 Calibration period Calibration period 1985-1990 641 660 2.9 0.78 2494 2305 7.5 0.79 Validation period Validation period 1990-1995 646 740 14.5 0.79 2659 2922 9.8 0.79 Whole period Whole period 1980-1995 620 675 8.8 0.78 2472 2347 5.0 0.78 0.34/0.27 * Creates a flow of Great Miami River below Dayton, OH (mean observed annual flow from Dayton USGS #03270500) ** Coefficient of runoff = Y/X, where Y (mm or inches) is a surface runoff - amount of water coming from the watershed surface area per time and which equals to the thickness of the layer equally distributed basin, in terms of area. Y = (W*10^6)/F, where W - is the annual runoff volume (cubic kilometers) and F is the basin area (sq.km) and X is a mean annual precipitation (mm). Table 3.4 (Continued)

LOWER GMR Lower GMR at Hamilton,OH (USGS #03274000) Mean observed Mean simulated % Error: Average Coefficient of Base Case annual flow, annual flow, simulated and daily runoff ** Scenario (cfs) (cfs) observed correlation Pavr=39 inches coefficient obs/sim 1980-1985 3736 2870 23.0 0.96 Calibration period 1985-1990 3646 2998 17.7 0.97 Validation period 1990-1995 3728 3150 15.0 0.96 Whole period 1980-1995 3576 2984 16.5 0.96 0.78/0.62 ** Coefficient of runoff = Y/X, where Y (mm or inches) is a surface runoff - amount of water coming from the watershed surface area per time and which equals to the thickness of the layer equally distributed basin, in terms of area. Y = (W*10^6)/F, where W - is the annual runoff volume (cubic kilometers) and F is the basin area (sq.km) and X is a mean annual precipitation (mm). Figures 3.16-3.21 Results of hydrological model calibration and validation made for Upper GMR basin

Great Miami River at Dayton (Joined flows from Mad, Stillwater and upstream GMR rivers):

Precipitation graph, Simulated and Observed flow, flow duration curves and model agreement

plots.

Calibration period: 1985-1990

Figure 3.16

Calibration

147

Figure 3.17

Calibration

Figure 3.18

Calibration

148

Validation period: 1990-1995

Figure 3.19

Validation

149 Figure 3.20

Validation

Figure 3.21

Validation

150 Figures 3.22-3.27 Results of hydrological model calibration and validation made for Lower GMR basin Great Miami River at Hamilton, OH: Precipitation graph, Simulated and Observed flow, flow duration curves and model agreement plots.

Calibration period: 1985-1990

Figure 3.22

Calibration

151 Figure 3.23

Calibration

Figure 3.24

Calibration

152 Validation period: 1990-1995

Figure 3.25

Validation

153 Figure 3.26

Validation

Figure 3.27

Validation

154 Based on the results from calibration and validation procedures, the following conclusions are derived:

(1) The daily correlation coefficients for calibration and validation periods are consistently

greater than 0.78. Great Miami river at Dayton, OH resulted in r = 0.79 for both calibration

and validation runs. Simulations of the Lower portion of GMR provided r as being 0.97 and

0.96. The mean annual volumes are within 15% of calibration/validation targets for the

hydrology/flow simulation, except for the Lower GMR at Hamilton (17%). The hydrologic

model of the Upper GMR at Dayton demonstrated higher accuracy – 7.5% and 9.8% for

calibration and validation time periods, respectively. This result is considered to be ”very

good” in terms of model efficiency. The Lower GMR hydrologic model is acceptable as well

and might be considered as “good”.

(2) Calculated coefficients of runoff have quite good agreement for the Upper GMR

simulations (0.34/0.27), whereas for the Lower GMR, it may be considered as fair. Also, it is

noted that the Upper GMR portion runoff coefficients are significantly lower than those for

the Lower portion at Hamilton. This may be attributed to the fact that in the Lower GMR

basin, substantial amount of precipitation becomes surface runoff and only a little amount of

moisture is trapped via infiltration, ground water aquifer recharges due to the greater amount

of impervious land (urban areas) in the Lower GMR (12-14%). The upper portion of the

basin has only 5-6%. Also, the Lower GMR is highly channelized, which might have an

effect on the volumes of streamflow.

(3) As shown graphically in daily time series plots, the daily storm peaks and volumes

matched fairly well, except for the Upper GMR hydrologic model. The Upper GMR model

tends to over predict high flow events and significantly under predict base streamflow. Daily

hydrographs and flow frequency curves, drawn for the Upper GMR, illustrate this type of

model behavior. Regression equation for Upper GMR at Dayton was derived as: Y=0.7X

+417, where Y is a simulated flow. However, graphical results for the Lower GMR show

155 greater accuracy in representing flow seasonal dynamics. The model simulates very well the general pattern in high-low, with slight under prediction for all seasons. Regression equation derived for the Lower GMR at Hamilton is: Y = 1.166X + 257, where Y is a simulated values of flow.

(4) Based on the full range of the hydrologic model results, the hydrological model is confirmed to be calibrated and validated. It can provide “very good – good” simulations of the flow conditions of the Upper and Lower GMR basins. Successfully calibrated and validated hydrological model provide a further basis for the water quality analysis.

156 3.2 WATER QUALITY CALIBRATION/VALIDATION

3.2.1 Analysis of the current Water Quality conditions and summary of Water Quality simulation

For water quality calibration, four stations were used in comparing the simulated records with observed data: Ohio EPA water quality station at Englewood, OH (Stillwater river), Ohio EPA water quality station near Dayton for Mad river, USGS station #03274600 at Hamilton, OH for the Lower Great Miami River, and USGS # 3270500 and Ohio EPA water quality station at

Dayton, Monument Ave., (ID# 610060) for the Upper Great Miami river. The available records were downloaded from U.S EPA STORET database and from USGS Water Quality database for the period of 1980-1991. Water quality constituents for Stillwater, Mad River and Upper sub- basin of Great Miami River were calibrated and validated using 1980-1986 time frame. Figures

3.28 – 3.35 illustrate annual and monthly variations of water quality constituents at Dayton and at

New Baltimore, OH. This preliminary analysis was performed to characterize the current spatial and temporal patterns of water quality for the selected stations.

157 Figure 3.28 Annual changes of water quality constituents at Dayton, OH (Upper GMR)

Figure 3.29 Seasonal changes in water quality constituents at Dayton, OH (Upper GMR)

158

Figure 3.30 Annual changes of water quality constituents at New Baltimore, OH

Figure 3.31 Seasonal changes in water quality constituents at New Baltimore, OH

159

Figure 3.32 Annual changes of water quality constituents, Mad River near Dayton, OH

Figure 3.33 Seasonal changes in water quality constituents, Mad River near Dayton

160 Figure 3.34 Annual changes of water quality constituents, Stillwater at Englewood, OH

Figure 3.35 Seasonal changes in water quality constituents, Stillwater at Englewood, OH

161 As it is shown on the above plots, semiannual DO, NH4 variations were within the target criteria as recommended by the U.S EPA Water Quality Criteria (5 and 1.3 mg/l, respectively) for the indicated period at all stations. However, mean annual concentrations of total phosphorus slightly exceeded the threshold of 0.2-0.3 mg/l in 1980, 1981 and 1988 at New Baltimore station, and 0.31mg/l at Mad River water quality station. Sum of nitrites and nitrates (NO2+NO3) at these stations remained relatively high for this period of time in respect to water quality targets: at

Dayton the average concentrations ranged from 2.8 (1988) up to 4.84 (1999) with annual mean of

3.65 mg/l, that is higher than the target level concentrations. At New Baltimore, NO2 +NO3 fluctuated from 3.0 to 4.6 mg/l with average of 4.1 for the same period. Mad and Stillwater

Rivers exhibit nearly identical pattern of semiannual variation in NO2+NO3 concentrations, ranging about 3-4 mg/l. The general character of variation in the total sum of nitrates and nitrites is fairly uniform, without noticeable extremes. Mean annual concentrations of total phosphorus reflect slightly greater levels for 1980-1991 period at Dayton and New Baltimore water quality stations. At Dayton, total P ranged very close to target level of 0.2 mg/l, with an average of 0.3 mg/l. The Lower GMR at New Baltimore had even higher levels of total P: 0.56 mg/l in 1982 and

1988 as local maximums, and 0.29 mg/l as a minimum, happened in 1990, with annual average of

0.47 mg/l. Concentrations of total P in the Mad and Stillwater Rivers remained stable, fluctuating around 0.2-mg/l.

In terms of seasonal pattern, it is noticeable how NH4 concentrations vary within the year. The period of low flow (late spring, early fall) is characterized by reduced NH4 (0.06-1.0 mg/l) relative to other flow seasons. NO2+NO3 concentrations stay fairly steady during the seasons of flow – (3.1-3.8 mg/l), with local minimums during Fall seasons. DO ranges fairly uniformly during the year (around 8 mg/l during Summer up to 12 mg/l during Fall and Winter seasons).

Phosphorus concentrations tend to slightly decrease during high flow season (March-April) - 0.2-

0.3mg/l, as opposed to 0.35-0.6 mg/l for the rest of the year. This may be caused by dissolution during high flow events.

162 3.2.2 Summary: Water quality model calibration and validation

For water quality constituents, model performance is often based primarily on visual and graphical presentations, as the frequency of observed data is often inadequate for accurate statistical measures. The following water quality constituents are simulated:

-Water temperature T, (F)

-DO

-Ammonia as NH4

-Nitrite-nitrate as N (NO2+NO3)

-Total phosphorus as P

Sources of these constituents will include NPS and point sources. NPS loadings are calculated by considering accumulation and depletion/removal, and a first-order wash off rate of the available constituent removed by overland flow. Quantities in the subsurface outflow are simulated using a monthly varying concentration. The resulting NPS loadings, calculated separately for each land use, is the input to the channel reaches represented in the model, along with the identified point sources, in each channel reach the fate, transport, and delivery of the constituent loads will be simulated.

Point source inputs data represent all significant loadings from municipal and industrial wastewater treatment plants that discharge to the channel reaches and are required for application of the HSPF model. The point source loading data were downloaded from the Permit Compliance

System (PCS) portion of the U.S EPA Envirofacts web site, then the list of facilities was compiled and added to the initial data records that came with the BASINS PCS file. This step was necessary, because the BASINS file is not complete and accurate. Point Source Editor built in

HSPF was used to accomplish this step. The pollutant constituents from point sources that were modeled include ammonia (NH4), nitrate (NO3), and orthophosphate (PO4).

163 Once all constituent contributions from all land use types are estimated, the modeled hydrologic and hydraulic processes will be superimposed to illustrate transport mechanisms, and water quality modeling will then be performed to allow adjustments in parameters and evaluation of sources as part of the calibration process. Water quality calibration is an iterative process. The model predictions are the integrated result of all the assumptions used in developing the model input and representing the modeled processes. Differences in model predictions and observations require the modeler to re-evaluate these assumptions, in terms of both the estimated model input and parameters, and consider the accuracy and uncertainty in the observations. At the current time, water quality calibration is more of an art than a science, especially for comprehensive simulations of NPS and point sources and their impacts on in-stream water quality.

3.2.2.1 Water Temperature and Dissolved Oxygen Simulation

Water temperature and dissolved oxygen concentrations of the runoff from pervious and impervious segments are simulated using PSTEMP and PWTGAS sections of the HSPF. The

PSTEMP section calculates the soil temperature for each of the four defined soil layers: surface, upper zone, lower zone and the groundwater zone. Water flowing through these zones is assumed to be of equal temperature. Therefore, the temperature of flow on the land surface is assumed to be at the same temperature as the surface soil, and the temperature of the groundwater is assumed to be at the same temperature as that of the soil within the groundwater zone.

The PWTGAS section of the HSPF is used to simulate dissolved oxygen levels in overland flow, interflow and ground flow. The surface soil temperature (and water temperature) calculated within the PSTEMP section is used in simulation of the dissolved oxygen levels in the overland flow. The dissolved oxygen concentration of the surface runoff is assumed to be at saturation.

The temperature dependent equation used to calculate the DO saturation is:

DO = [14.652+T(-0.41022+T*(0.007991-0.000077774*T)]*ELEV,

Where, DO – dissolved oxygen saturation in surface flow (mg/l); T – is surface outflow temperature, (C); ELEV – is the correction factor for elevation above sea level.

164 The water temperature (WT) and DO concentration of the overland flow from impervious land segments is calculated in the IWTGAS section of HSPF. The outflow temperature is estimated using the regression equation:

SOTMP = AWTF + (BWTF*AIRTC),

Where, SOTMP is the impervious surface temperature (C); AWTF is surface temperature when the air is 0 C; BWTF is the slope of the water temperature regression equation and AIRTC is the air temperature.

3.2.2.2 Nutrient Simulation

For simulation of nutrient losses from pervious and impervious land segments of watershed, the simplified approach available within the HSPF was used. This approach simulates each water quality constituent independently based on simple relationships with water and/or sediment transport mechanisms. The PQUAL and IQUAL sections of HSPF, for pervious and impervious land, respectively, were employed to model the loading from agricultural and urban areas for the following constituents: total ammonia (NH4), nitrates (NO3) and nitrites (NO2), and orthophosphates (PO4). The basic algorithms used in the PQUAL and IQUAL sections of HSPF to simulate water quality constituents are the combination of methods from pervious models, such as NPS Model (Donigian and Crawford, 1976).

In the PQUAL and IQUAL modules, water quality constituents in the surface outflow can be simulated by two methods: one is to simulate the water quality concentration as a function of sediment removal, and the other, is to use atmospheric deposition and/or basic accumulation and depletion rates together with depletion by washoff (Bichnell, et al., 2000). Simulation of sediment removal from the land surfaces requires fine resolution input data that is why, the water quality was simulated based on accumulation and removal rates. The data, representing the atmospheric deposition of nitrogen was obtained via NADP/NTN (National Atmospheric Deposition

Program/National Trends Network) for site OH09 at Oxford, OH. Wet deposition data (NO3) and

165 ammonium NH4 has been collected, and incorporated on monthly basis into HSPF PQUAL module.

The storage of constituents on the land surface is calculated with the following equation

(Bicknell et al, 2000): SQO = ACQUP + SQOS*(1-REMQOP), where ACQOP is the accumulation rate of the constituent on the land surface (lb/ac-day); SQOS is the SQO at the start of the interval and REMQOP is the unit removal rate of the stored constituent per day. The amount of a constituent in runoff is a function of the pollutant storage, the surface outflow of water and the susceptibility of the quality constituent to wash off:

SOQO = SQO*(1.0 – e(-SURO*WSFAC)), where SOQO is the washoff of the quality constituent from the land surface, lb/ac-time; SQO is the storage of available water quality constituent on the land surface; SURO and WSFAC are surface outflow of water, inch/time and susceptibility of the quality constituent to washoff. The accumulation rate of water quality constituents on the land surface for each land use category was calculated based on recommendations from US EPA, 1999 and, partially, from the study of

TMDLs for Little Miami River (Ohio EPA, 2002).

In addition to computing the quantity and quality of runoff from land segments within a watershed, HSPF also simulates instream processes. Flow and water quality constituents from upstream reaches, nonpoint source contributions, point sources, precipitation are combined to simulate water quality within each stream reach. The responsible module in HSPF for this is

RCHRES. The input of water and water quality constituents to a stream reach includes contributions from upstream reaches, point sources and non-point sources. The RCHRES module itself is composed of individual sub-modules, each performing a designated function, which might be activated or deactivated (Bicknell et al, 2000).

166 3.2.3 Water Quality Calibration

The calibration involved numerous model runs and iterations at each of the selected water quality calibration sites: Stillwater River at Englewood, OH, Mad River near Dayton, OH, confluence point of upstream sub-basin of the Great Miami River with Stillwater and Mad River near Dayton, OH, and the Lower Great Miami River with reference point at Hamilton, OH.

The time period of the water quality calibration was chosen to be as 1980-1983, and for verification purposes – 1983-1986. It should be noted, that hydrology calibration was performed for different time period, but the hydrologic model for the time period of 1980-1986 is acceptable in terms of model efficiency (within 15% difference between simulated and observed values).

The following steps were performed to calibrate water quality:

- Estimate all model parameters, including land-use specific accumulation/removal rates,

wash off rates and sub-surface concentrations.

- Calibrate instream water temperature

- Analyze the simulation runs and determine appropriate instream or non-point source

parameter adjustments.

The fundamental idea of water quality calibration is to obtain acceptable agreement between observed and simulated concentrations, while maintaining the instream water quality parameters within physically realistic boundaries. The non-point source loading rates, sometime referred to as “export coefficients” are highly variable, with value ranges sometimes up to an order of magnitude, depending on local and site conditions of soils, slopes, topography, climate etc

(Pathwardhan et al, 2001).

Figures 3.36-3.40, 3.41-3.60, 3.61-3.78 and Tables 3.5-3.11 (including the Auxiliary Plots in

Appendix B) represent the results of water quality calibration in graphical and table formats. The simulation runs for water quality constituents were produced on a daily basis, and observed concentrations are presented on monthly basis. The constituents include Ammonia (as N), Nitrite-

Nitrate as N, orthophosphates-P, Water Temperature and Dissolved Oxygen (DO).

167 Water Temperature and Dissolved Oxygen

For the Upper GMR basin at Dayton, OH, simulation of Water Temperature produced fair to good results: 540 F and 440 F for observed and simulated runs, accordingly, with 18.4% difference between them and Correlation Coefficient as 0.95 (Figure 3.44). For the calibration time period, Dissolved Oxygen was simulated with better quality (9.7% difference between simulated and observed values), Total Phosphorus – 2.9% difference, Nitrogen Ammonia –

11.1% and Total Nitrites and Nitrates – 10.8% (Table 3.5). Simulation runs for the validation period resulted in similar agreements between monthly simulated and observed values: 16.1% for

Water Temperature, 8.7% for Dissolved Oxygen, 6.4% for Phosphorus, 11.1% for Nitrogen

Ammonia and 15.7% for Nitrites and Nitrates. Simulated Water Temperature and Dissolved

Oxygen relatively accurately represent the real magnitudes and the dynamical seasonal patterns

(Figures 3.41-3.49). Dissolved Oxygen has clear tendencies to have higher concentrations in late

Fall and Winter times and lower concentrations during the Summer (Figures 3.45 and 3.47). This is explained by the oxygen demand for biomass productivity, which has its peak activity during the warm season. The Correlation Coefficient between observed and simulated values is not very high (0.77) for the Dissolved Oxygen, because there is quite a significant range between extremes and daily-simulated values tend to show under-prediction of the minimum values (Figure 3.45).

Tables 3.36-3.40 demonstrate monthly observed and simulated values, the averages and standard deviation values for calibration and validation periods. Standard deviations from observed records and simulated runs of Water temperature are close (14.2 and 12.0), indicating relatively significant departure from the mean (Figure 3.36). However, standard deviations calculated for

Dissolved Oxygen concentrations show good agreement between simulated and observed values

(Figure 3.37).

168 Table 3.5 Water Quality Calibration results (Upper GMR)

Base case scenario Calibration Period (1980-1983) Validation Period (1983-1986) (Current Land Use) % Difference Monthly % Difference Monthly Constituent (Units) Observed Simulated between Correlation Observed Simulated between Correlation observed and coefficient observed and coefficient STILLWATER RIVER AT ENGLEWOOD, OH simulated simulated Water temperature (F) 54.4 48.0 11.7 0.91 56.4 49.0 13.1 0.96 Dissolved Oxygen (MG/L) 10.3 11.4 10.7 0.85 10.0 11.5 14.9 0.75 Total Phosphorus (MG/L) 0.31 0.36 16.0 - 0.34 0.38 11.7 - Total Nitrogen Ammonia (MG/L) 0.12 0.1 16.1 0.86 0.12 0.11 8.3 0.82 Sum of Nitrites and Nitrates (MG/L) 3.7 3.53 4.6 0.81 4.0 3.4 14.8 0.77

Base case scenario Calibration Period (1980-1983) Validation Period (1983-1986) (Current Land Use) % Difference Monthly % Difference Monthly Constituent (Units) Observed Simulated between Correlation Observed Simulated between Correlation observed and coefficient observed and coefficient MAD RIVER NEAR DAYTON, OH simulated simulated Water temperature (F) 52.0 44.0 15.2 0.95 53.0 45.0 15.0 0.95 Dissolved Oxygen (MG/L) 10.9 11.5 5.5 0.79 10.8 11.5 6.5 0.81 Total Phosphorus (MG/L) 0.26 0.27 3.8 - 0.26 0.28 7.7 - Total Nitrogen Ammonia (MG/L) 0.12 0.11 8.3 0.72 0.09 0.1 11.1 0.89 Sum of Nitrites and Nitrates (MG/L) 3.35 2.89 13.7 0.82 3.3 2.91 11.8 0.71 Table 3.5 (Continued)

Base case scenario Calibration Period (1980-1983) Validation Period (1983-1986) (Current Land Use) % Difference Monthly % Difference Monthly Constituent (Units) Observed Simulated between Correlation Observed Simulated between Correlation observed and coefficient observed and coefficient GMR AT TAYLORSVILLE, OH simulated simulated Water temperature (F) 54.2 43.2 20.3 0.90 54.4 43.0 20.1 0.91 Dissolved Oxygen (MG/L) 10.1 10.9 7.9 0.89 10.3 10.9 5.8 0.73 Total Phosphorus (MG/L) 0.34 0.32 5.8 - 0.32 0.31 3.1 - Total Nitrogen Ammonia (MG/L) 0.19 0.17 10.5 0.75 0.15 0.16 6.6 0.71 Sum of Nitrites and Nitrates (MG/L) 3.4 2.92 14.1 0.70 3.6 3.05 15.3 0.70

Table 3.5 (Continued)

Base case scenario Calibration Period (1980-1985) Validation Period (1985-1991) (Current Land Use) % Difference Monthly % Difference Monthly Constituent (Units) Observed Simulated between Correlation Observed Simulated between Correlation observed and coefficient observed and coefficient JOINED: STILLWATER, MAD AND simulated simulated GMR AT DAYTON, OH Water temperature (F) 54.0 44.0 18.4 0.92 53.4 44.8 16.1 0.96 Dissolved Oxygen (MG/L) 10.3 11.3 9.7 0.77 10.3 11.2 8.7 0.87 Total Phosphorus (MG/L) 0.34 0.33 2.9 - 0.31 0.33 6.4 - Total Nitrogen Ammonia (MG/L) 0.18 0.16 11.1 0.77 0.09 0.1 11.1 0.71 Sum of Nitrites and Nitrates (MG/L) 3.5 3.12 10.8 0.79 3.8 3.2 15.7 0.92 Table 3.6 Water Quality Calibration results (Lower GMR)

Water temperature (F) 57.2 56.3 1.6 0.90 57.2 56.8 1.0 0.89 Dissolved Oxygen (MG/L) 9.9 9.4 5.0 0.89 10.5 9.3 11.4 0.87 Total Phosphorus (MG/L) 0.5 0.44 12.0 - 0.44 0.41 6.8 - Total Nitrogen Ammonia (MG/L) 0.3 0.27 10.0 0.97 0.18 0.16 11.1 0.98 Sum of Nitrites and Nitrates (MG/L) 4.18 3.61 13.6 0.80 3.9 3.44 11.7 0.90 Nutrients

The comparison of mean concentrations for Total Phosphorus and the ratios of simulated to observed demonstrate that simulated values are lower for the calibration time period run (0.33 and 0.34 mg/l accordingly), as the % difference being very small (2.9% for 1980-1985 and 6.4% for 1985-1991) (Table 3.5). In terms of seasonal distribution of Phosphorus concentrations, generally, the model over-predicts winter and spring seasons (January, February and March-May months) (Tables 3.7, 3.8, 3.9, 3.10 and Figures 3.49, 3.50). In the period 1985-1991, the model consistently over-predicted the minimum concentrations of Phosphorus, even though the annual means are within a very good agreement (6.4% difference). The calculated standard deviations of phosphorus concentrations for the Upper GMR show close agreement with the observed values

(0.08 and 0.02 for calibration and 0.07 and 0.02 for validation periods) (Figure 3.38).

The comparison of mean annual concentrations for Nitrogen Ammonia and the ratios of simulated to observed (Table 3.5) demonstrate that simulated values are 11.1% lower than observed for 1980-1985 (0.16mg/l and 0.18mg/l), and 11.1% higher for 1985-1991 (0.1 and 0.09 mg/l). The Figures 3.51, 3.53 complement these numbers. There are many observed records for

Nitrogen Ammonia of 0.05 mg/l, especially during 1985-1991. STORET reported these nitrogen ammonia concentrations marked with the letter “K”, which means - “Off scale low. Actual value is not known”. This may have caused the model to over-predict nitrogen ammonia concentrations for the validation runs (Figure 3.53). The correlation coefficient between mean monthly observed and simulated concentrations of Nitrogen Ammonia is 0.77 (Figure 3.52). Overall, except for the period of 1985-1991, the model simulated nitrogen ammonia well, describing the seasonal pattern with higher concentrations during cold seasons, and lower in warm seasons.

However, it failed to represent the extreme concentrations, which could be caused by point source pollution inflow.

When comparing mean concentrations for Nitrate and Nitrite (as N) and the percentage difference between simulated and observed (Figures 3.55, 3.56, 3.57 and Tables 3.5 and 3.7-

169 Table 3.7 Water Quality Calibration Results (Monhtly values) Base case scenario Calibration Period (1980-1983) Validation Period (1983-1986) (Current Land Use)

Constituent (Units) Standard Standard and month Observed Simulated Deviation Observed Simulated Deviation STILLWATER RIVER (Obs/Sim) (Obs/Sim) AT ENGLEWOOD, OH Water Temperature (F) January 33.8 32.1 33.4 32.1 February 37.2 32.2 38.5 32.3 March 42.0 33.8 43.5 33.8 April 53.0 43.8 57.0 43.8 May 57.6 56.7 65.2 56.2 June 70.9 69.8 72.6 60.9 July 75.7 71.4 75.4 65.7 August 74.3 55.2 70.7 62.3 September 60.6 43.4 62.4 47.4 October 51.4 36.5 55.2 40.5 November 46.2 33.3 46.7 33.2 December 41.5 32.5 40.6 32.2 TOTAL 54.4 48 14.4/14.7 56.4 49 14.4/13.1 Dissolved Oxygen (MG/L) January 13.1 13.6 12.5 13.6 February 12.8 13.5 11.7 13.5 March 12.0 13.0 11.1 13.0 April 10.5 11.1 10.1 11.2 May 10.8 9.2 10.8 9.3 June 7.7 8.5 8.2 8.5 July 7.4 8.3 8.0 8.5 August 7.5 9.3 7.0 9.9 September 9.1 11.3 7.6 11.8 October 10.1 12.7 10.5 12.6 November 11.1 13.3 11.2 13.2 December 11.3 13.4 11.7 13.4 TOTAL 10.3 11.4 1.98/2.08 10.0 11.5 1.85/2.01 Total Phosphorus (MG/L) January 0.25 0.33 0.31 0.32 February 0.23 0.34 0.29 0.34 March 0.24 0.36 0.17 0.36 April 0.42 0.38 0.18 0.35 May 0.18 0.37 0.16 0.36 June 0.32 0.37 0.33 0.36 July 0.31 0.36 0.29 0.39 August 0.40 0.37 0.43 0.41 September 0.36 0.38 0.42 0.48 October 0.37 0.35 0.41 0.43 November 0.25 0.37 0.31 0.39 December 0.24 0.36 0.21 0.35 TOTAL 0.3 0.36 0.07/0.01 0.34 0.38 0.09/0.04 Table 3.7 (Continued)

Base case scenario Calibration Period (1980-1983) Validation Period (1983-1986) (Current Land Use)

Constituent (Units) Standard Standard Observed Simulated Deviation Observed Simulated Deviation STILLWATER RIVER (Obs/Sim) (Obs/Sim) AT ENGLEWOOD, OH Total Nitrogen Ammonia (MG/L) January 0.25 0.12 0.22 0.14 February 0.30 0.14 0.30 0.14 March 0.13 0.13 0.15 0.13 April 0.09 0.10 0.06 0.09 May 0.07 0.08 0.09 0.08 June 0.16 0.07 0.08 0.06 July 0.06 0.06 0.05 0.06 August 0.10 0.06 0.05 0.05 September 0.10 0.08 0.08 0.05 October 0.11 0.10 0.16 0.09 November 0.10 0.12 0.10 0.12 December 0.07 0.14 0.12 0.14 TOTAL 0.12 0.1 0.07/0.03 0.12 0.11 0.07/0.04 Sum of Nitrites and Nitrates (MG/L) January 3.7 2.68 4.05 2.28 February 3.6 2.54 3.86 2.38 March 4.5 2.56 5.33 2.29 April 4.3 2.69 4.21 2.50 May 3.3 3.52 3.84 3.58 June 5.6 4.43 2.66 4.67 July 4.0 4.42 2.54 4.92 August 2.6 4.68 3.80 5.42 September 1.9 4.24 3.20 4.19 October 3.7 4.25 3.91 3.44 November 3.9 3.67 5.53 3.11 December 3.30 2.71 4.83 2.01 TOTAL 3.7 3.53 0.92/0.85 4.0 3.4 0.93/1.17 Table 3.8 Water Quality Calibration Results (Monhtly values)

Base case scenario Calibration Period (1980-1983) Validation Period (1983-1986) (Current Land Use)

Constituent (Units) Standard Standard and month Observed Simulated Deviation Observed Simulated Deviation MAD RIVER (Obs/Sim) (Obs/Sim) NEAR DAYTON, OH Water Temperature (F) January 41.8 32.1 38.3 32.1 February 42.2 32.2 40.8 32.3 March 41.5 33.9 42.8 33.9 April 53.6 44.0 55.9 44.1 May 55.4 57.0 57.7 56.5 June 65.4 62.1 68.8 61.4 July 68.2 62.4 70.5 61.4 August 64.3 54.7 67.7 54.2 September 55.8 46.2 59.9 52.5 October 47.9 37.6 50.6 42.4 November 47.3 33.4 47.8 33.6 December 40.1 32.5 37.4 32.3 TOTAL 52.0 44.0 10.05/12.14 53.0 45.0 12.04/11.9 Dissolved Oxygen (MG/L) January 12.2 13.7 12.9 13.5 February 11.7 13.5 12.9 13.5 March 13.8 13.1 13.4 13.0 April 11.6 11.3 10.8 11.2 May 11.7 9.3 10.3 9.3 June 8.2 8.6 9.6 8.7 July 8.2 8.6 8.8 8.9 August 8.1 9.7 8.6 10.2 September 8.9 11.3 8.6 11.1 October 11.4 12.8 9.6 12.0 November 12.8 13.4 11.6 13.2 December 11.8 13.5 12.2 13.5 TOTAL 10.9 11.5 1.96/2.04 10.8 11.5 1.78/1.87 Total Phosphorus (MG/L) January 0.20 0.24 0.25 0.24 February 0.24 0.25 0.25 0.26 March 0.86 0.28 0.19 0.27 April 0.22 0.29 0.16 0.27 May 0.29 0.29 0.13 0.28 June 0.24 0.27 0.21 0.27 July 0.18 0.27 0.25 0.29 August 0.27 0.26 0.41 0.29 September 0.21 0.27 0.38 0.32 October 0.14 0.27 0.36 0.31 November 0.15 0.28 0.28 0.31 December 0.16 0.28 0.22 0.27 TOTAL 0.26 0.27 0.19/0.02 0.26 0.28 0.08/0.02 Table 3.8 (continued)

Base case scenario Calibration Period (1980-1983) Validation Period (1983-1986) (Current Land Use)

Constituent (Units) Standard Standard Observed Simulated Deviation Observed Simulated Deviation MAD RIVER (Obs/Sim) (Obs/Sim) NEAR DAYTON, OH Total Nitrogen Ammonia (MG/L) January 0.2 0.14 0.16 0.15 February 0.32 0.14 0.13 0.14 March 0.17 0.13 0.11 0.13 April 0.09 0.10 0.05 0.10 May 0.08 0.09 0.05 0.09 June 0.07 0.08 0.05 0.07 July 0.10 0.07 0.06 0.07 August 0.09 0.07 0.05 0.05 September 0.08 0.09 0.05 0.07 October 0.08 0.11 0.11 0.10 November 0.09 0.12 0.14 0.12 December 0.09 0.14 0.15 0.15 TOTAL 0.12 0.11 0.07/0.03 0.09 0.1 0.04/0.03 Sum of Nitrites and Nitrates (MG/L) January 3.54 1.87 3.72 1.86 February 3.41 1.78 3.32 1.85 March 3.16 1.93 3.83 1.73 April 3.19 2.04 4.22 2.03 May 3.56 2.64 3.65 2.68 June 3.71 3.18 2.94 3.40 July 3.38 3.18 2.62 3.17 August 3.05 3.11 2.53 2.94 September 3.04 2.53 2.17 2.43 October 3.23 2.35 2.51 2.15 November 3.18 2.20 3.88 2.12 December 3.90 1.88 4.30 1.61 TOTAL 3.35 2.89 0.27/0.53 3.3 2.91 0.73/0.59 Table 3.9 Water Quality Calibration Results (Monhtly values)

Base case scenario Calibration Period (1980-1983) Validation Period (1983-1986) (Current Land Use)

Constituent (Units) Standard Standard and month Observed Simulated Deviation Observed Simulated Deviation GMR AT (Obs/Sim) (Obs/Sim) TAYLORSVILLE, OH Water Temperature (F) January 33.8 32.1 32.5 32.1 February 37.6 32.2 37.2 32.3 March 42.8 33.7 42.1 33.7 April 54.4 43.5 57.0 43.5 May 59.7 56.0 62.4 55.6 June 69.4 60.9 72.0 60.2 July 73.9 61.2 74.2 60.2 August 73.9 53.6 70.9 51.8 September 63.8 42.8 62.6 42.6 October 53.8 36.1 56.3 36.9 November 46.0 33.2 46.7 33.2 December 41.0 32.5 39.2 32.2 TOTAL 54.2 43.2 14.1/11.8 54.4 43.0 14.6/11.3 Dissolved Oxygen (MG/L) January 12.8 12.9 13.0 12.9 February 12.8 12.9 12.9 12.9 March 12.3 12.5 11.7 12.6 April 10.4 10.8 10.8 10.8 May 10.1 8.9 10.5 8.9 June 7.7 8.2 9.5 8.1 July 7.3 8.0 7.9 8.0 August 7.3 8.9 7.6 9.1 September 8.8 10.6 8.0 10.7 October 9.8 11.9 8.6 11.8 November 10.9 12.5 11.1 12.6 December 11.4 12.8 11.7 12.9 TOTAL 10.1 10.9 2.02/1.94 10.3 10.9 1.93/1.94 Total Phosphorus (MG/L) January 0.30 0.29 0.28 0.30 February 0.33 0.32 0.28 0.33 March 0.30 0.35 0.17 0.37 April 0.35 0.39 0.16 0.36 May 0.21 0.39 0.14 0.36 June 0.35 0.38 0.30 0.32 July 0.48 0.32 0.30 0.31 August 0.30 0.29 0.45 0.22 September 0.37 0.27 0.42 0.20 October 0.40 0.24 0.41 0.25 November 0.35 0.29 0.30 0.34 December 0.30 0.33 0.20 0.35 TOTAL 0.34 0.32 0.06/0.05 0.32 0.31 0.10/0.06 Table 3.9 (Continued)

Base case scenario Calibration Period (1980-1983) Validation Period (1983-1986) (Current Land Use)

Constituent (Units) Standard Standard Observed Simulated Deviation Observed Simulated Deviation GMR AT (Obs/Sim) (Obs/Sim) TAYLORSVILLE, OH Total Nitrogen Ammonia (MG/L) January 0.49 0.21 0.42 0.23 February 0.52 0.23 0.36 0.24 March 0.21 0.21 0.13 0.22 April 0.08 0.18 0.05 0.17 May 0.08 0.15 0.08 0.14 June 0.19 0.14 0.08 0.10 July 0.10 0.10 0.05 0.09 August 0.11 0.10 0.05 0.05 September 0.08 0.11 0.07 0.06 October 0.14 0.13 0.16 0.12 November 0.19 0.17 0.10 0.19 December 0.26 0.22 0.17 0.25 TOTAL 0.19 0.17 0.15/0.1 0.15 0.16 0.12/0.09 Sum of Nitrites and Nitrates (MG/L) January 3.76 2.78 4.08 2.95 February 3.41 2.97 4.06 3.04 March 4.39 3.02 5.47 3.20 April 3.94 3.25 4.30 3.30 May 3.86 3.35 3.78 3.37 June 4.40 3.22 2.47 3.05 July 2.73 2.47 2.39 2.27 August 1.66 1.58 1.88 1.13 September 2.06 1.37 1.99 0.83 October 3.52 1.26 3.65 1.16 November 3.78 1.92 5.25 2.22 December 4.13 2.85 4.98 3.15 TOTAL 3.4 2.92 0.88/0.77 3.6 3.05 1.25/0.94 Table 3.10 Water Quality Calibration Results (Monhtly values)

Base case scenario Calibration Period (1980-1985) Validation Period (1985-1991) (Current Land Use)

Constituent (Units) Standard Standard and month Observed Simulated Deviation Observed Simulated Deviation JOINED: STILLWATER R (Obs/Sim) (Obs/Sim) MAD R AND GMR AT DAYTON, OH Water Temperature (F) January 33.5 32.1 34.2 32.3 February 35.8 32.2 35.8 32.2 March 41.0 33.7 45.2 34.3 April 55.8 43.9 53.6 44.3 May 60.8 56.4 65.0 57.3 June 70.3 61.1 69.1 61.5 July 73.0 61.1 71.6 62.7 August 71.5 53.8 68.9 55.0 September 63.4 46.0 64.1 47.8 October 55.9 38.3 54.4 40.0 November 46.7 33.4 42.1 35.1 December 40.7 32.5 37.5 32.3 TOTAL 54.0 44.0 14.2/11.7 53.4 44.8 14.1/12.0 Dissolved Oxygen (MG/L) January 12.6 13.3 12.7 13.3 February 13.1 13.3 13.0 13.3 March 12.3 12.9 11.2 12.7 April 10.9 11.1 9.7 10.9 May 10.2 9.1 9.3 9.0 June 9.0 8.5 8.6 8.5 July 7.7 8.5 8.4 8.3 August 7.6 9.7 7.9 9.6 September 8.6 11.3 8.2 11.0 October 8.9 12.3 9.9 12.1 November 11.0 12.9 11.5 12.4 December 11.6 13.2 12.7 13.3 TOTAL 10.3 11.3 1.91/1.92 10.3 11.2 1.90/1.92 Total Phosphorus (MG/L) January 0.29 0.30 0.24 0.32 February 0.33 0.32 0.36 0.34 March 0.26 0.35 0.17 0.34 April 0.30 0.36 0.14 0.36 May 0.19 0.36 0.22 0.35 June 0.31 0.34 0.25 0.36 July 0.43 0.33 0.27 0.35 August 0.49 0.33 0.36 0.33 September 0.40 0.35 0.31 0.33 October 0.43 0.34 0.33 0.38 November 0.35 0.35 0.32 0.38 December 0.27 0.34 0.31 0.37 TOTAL 0.34 0.33 0.08/0.02 0.31 0.33 0.07/0.02 Table 3.10 (Continued)

Base case scenario Calibration Period (1980-1985) Validation Period (1985-1991) (Current Land Use)

Constituent (Units) Standard Standard Observed Simulated Deviation Observed Simulated Deviation JOINED: STILLWATER R (Obs/Sim) (Obs/Sim) MAD R AND GMR AT DAYTON, OH Total Nitrogen Ammonia (MG/L) January 0.43 0.17 0.20 0.17 February 0.54 0.17 0.17 0.18 March 0.19 0.16 0.07 0.16 April 0.07 0.13 0.07 0.13 May 0.08 0.11 0.10 0.10 June 0.14 0.09 0.06 0.07 July 0.08 0.07 0.05 0.07 August 0.09 0.07 0.05 0.05 September 0.07 0.08 0.05 0.08 October 0.15 0.10 0.06 0.11 November 0.17 0.14 0.06 0.13 December 0.23 0.17 0.10 0.15 TOTAL 0.18 0.16 0.15/0.09 0.09 0.1 0.05/0.04 Sum of Nitrites and Nitrates (MG/L) January 4.03 3.59 5.46 4.50 February 3.56 3.49 4.93 4.80 March 4.90 2.90 5.56 4.60 April 4.18 3.80 3.26 3.72 May 4.01 3.82 4.03 3.67 June 3.70 3.75 3.69 3.80 July 2.40 2.99 3.19 2.93 August 1.72 1.93 2.31 1.84 September 1.99 1.83 2.49 2.31 October 3.02 2.80 2.68 2.25 November 4.15 3.97 3.54 3.16 December 4.39 3.78 3.95 3.20 TOTAL 3.5 3.1 1.0/0.74 3.8 3.2 1.1/0.97 3.10) a good simulation of seasonal dynamic with lower concentrations during low streamflow months (usually Fall) and higher concentrations in peak flow months produced by the model. The model calibration demonstrated that simulated values are nearly 10% lower than observed, indicating a very good calibration and model accuracy (3.5 mg/l (observed) and 3.12mg/l

(simulated) for calibration and 3.8mg/l (observed) and 3.2 mg/l (simulated) for validation period).

The correlation coefficient between observed and simulated NO2+NO3 concentrations is 0.79 for calibration and 0.92 for validation period (Figure 3.57).

Calibration and validation of the water quality parameters in the Lower GMR at

Hamilton, OH demonstrated better results, as it did for hydrology calibration/validation except, perhaps, Total Phosphorus. Mainly, the model handled extreme concentrations more accurately.

Higher correlation coefficients are found for Water temperature – 0.89-0.9; 57.2F-observed annual, 56.3F –simulated annual (1.6% difference), Dissolved Oxygen –0.89-0.87; 9.9-10.5mg/l – observed annual, 9.3-9.4 mg/l – simulated annual; about 8% difference, Nitrogen Ammonia –

0.97-0.98; 0.18-0.3 mg/l -observed annual, 0.16-0.27 mg/l –simulated annual, about 10% difference, Nitrites plus Nitrates – 0.8-0.9, 3.9-4.18mg/l – observed annual, 3.44-3.61 mg/l – simulated annual values; about 12% difference between simulated and observed (Tables 3.6 and

3.11).

Water Temperature and Dissolved Oxygen

Simulated water temperature and Dissolved Oxygen concentrations have a good agreement with the observed monthly records (Figures 3.58-3.61, 3.62-3.65, 3.64, 3.65 and Tables 3.6, 3.11).

Simulated values of Dissolved Oxygen follow the seasonal dynamic pattern of observed values well, representing low DO months (June, July, August) and high DO months (Winter months).

Calculated correlation coefficients between monthly observed and simulated values of water temperature and DO are: 0.90 for WT and 0.89 for DO (for calibration time period) (Figures 3.59 and 3.61).

170 Table 3.11 Water Quality Calibration Results (Monhtly values)

Base case scenario Calibration Period (1980-1985) Validation Period (1985-1991) (Current Land Use)

Constituent (Units) Standard Standard and month Observed Simulated Deviation Observed Simulated Deviation LOWER GMR (Obs/Sim) (Obs/Sim) AT HAMILTON, OH Water Temperature (F) January 39.5 42.82 37.4 42.94 February 42.2 42.95 41.6 42.94 March 44.0 44.68 44.2 45.44 April 54.8 56.78 58.1 57.40 May 64.1 71.88 65.2 72.87 June 72.1 77.35 69.5 77.40 July 74.8 77.07 77.4 78.13 August 76.5 68.32 74.5 68.29 September 70.1 57.57 69.7 58.20 October 56.9 48.98 59.9 49.84 November 49.6 44.38 50.0 45.33 December 42.4 43.20 39.7 43.01 TOTAL 57.2 56.3 13.8/13.9 57.2 56.8 14.3/14.0 Dissolved Oxygen (MG/L) January 11.58 11.05 12.81 11.07 February 11.15 11.10 11.54 11.20 March 10.60 10.87 12.29 10.79 April 11.23 9.38 11.30 9.31 May 9.33 7.73 10.03 7.60 June 8.45 7.10 9.07 6.97 July 7.63 6.93 8.70 6.87 August 7.98 7.77 9.14 7.76 September 8.82 9.00 9.21 8.90 October 9.78 9.98 10.04 9.90 November 11.20 10.75 10.87 10.50 December 11.92 11.05 11.59 11.09 TOTAL 9.9 9.4 1.5/1.6 10.5 9.3 1.4/1.7 Total Phosphorus (MG/L) January 0.55 0.45 0.44 0.41 February 0.50 0.42 0.36 0.31 March 0.38 0.34 0.27 0.29 April 0.37 0.37 0.39 0.38 May 0.33 0.35 0.34 0.30 June 0.55 0.46 0.47 0.45 July 0.49 0.39 0.42 0.43 August 0.57 0.48 0.50 0.48 September 0.65 0.50 0.58 0.41 October 0.73 0.52 0.67 0.45 November 0.58 0.45 0.49 0.47 December 0.33 0.38 0.41 0.39 TOTAL 0.5 0.44 0.12/0.07 0.44 0.41 0.10/0.07 Table 3.11 Water Quality Calibration Results (Monhtly values)

Base case scenario Calibration Period (1980-1985) Validation Period (1985-1991) (Current Land Use)

Constituent (Units) Standard Standard Observed Simulated Deviation Observed Simulated Deviation LOWER GMR (Obs/Sim) (Obs/Sim) AT HAMILTON, OH Total Nitrogen Ammonia (MG/L) January 0.51 0.38 0.40 0.32 February 0.84 0.67 0.36 0.28 March 0.58 0.59 0.30 0.21 April 0.22 0.13 0.19 0.15 May 0.14 0.11 0.12 0.10 June 0.12 0.09 0.09 0.07 July 0.10 0.07 0.07 0.07 August 0.23 0.17 0.16 0.13 September 0.16 0.15 0.06 0.08 October 0.22 0.17 0.10 0.11 November 0.29 0.19 0.13 0.13 December 0.23 0.24 0.19 0.15 TOTAL 0.3 0.27 0.23/0.20 0.18 0.16 0.11/0.08 Sum of Nitrites and Nitrates (MG/L) January 4.17 4.02 6.34 4.68 February 3.67 3.50 4.91 3.94 March 3.12 2.90 4.67 3.75 April 4.35 4.15 3.56 3.29 May 6.40 4.54 3.16 3.10 June 5.65 4.50 4.50 4.26 July 4.08 3.65 3.55 3.14 August 4.13 3.90 2.76 2.51 September 3.30 3.10 3.07 2.84 October 4.13 2.98 3.20 2.87 November 3.80 2.66 3.71 2.63 December 3.33 3.37 4.44 4.23 TOTAL 4.18 3.61 0.96/0.63 3.9 3.44 1.02/0.71 Nutrients

The comparison of mean concentrations for Total Phosphorus for calibration period shows % error between simulated and observed values as 12% (Table 3.6). The model under-estimates phosphorus loads: 0.44-0.5 mg/l (observed annual concentrations) and 0.41-0.44 mg/l

(simulated). As in the case with the Upper GMR, there is no clear seasonal pattern for Phosphorus concentrations fluctuations.

Comparison of mean concentrations for Nitrogen Ammonia and the ratios of simulated to observed demonstrate that simulated values are lower than the observed ones, indicating an underestimation by the model. Validation runs improved the Phosphorus model performance by

6-7% (Table 3.6 and 3.11). Correlation coefficients between observed and simulated records remain to be high: 0.97 and 0.98 for calibration and validation runs, respectively (Figure 3.69 and 3.71).

As it is seen from Tables 3.6, 3.11 and Figures 3.72, 3.74, the sum Nitrates and Nitrites is under-simulated by the model (3.9-4.18 mg/l (observed annual concentrations) and 3.44-3.61 mg/l (simulated annual concentrations). During validation, the % difference between simulated and observed declined from 13.6% down to 11.7%. These results can be considered as a very good calibration and validation for Nitrates and Nitrites. High correlation coefficients (0.8 and

0.90) prove a good accuracy of the simulation (Figure 3.73 and 3.75).

171 Figure 3.36-3.40 Water Quality Validation Plots for Upper GMR Basins (1985-1991),

showing annual means and standard deviations for simulated and observed values.

Figure 3.37

172 Figure 3.38

Figure 3.39

Figure 3.40

173 Upper GMR at Dayton (joined water quality constituent flows from Stillwater, Mad and GMR rivers, which were calibrated individually. Calibration and validation results for Stillwater, Mad and Great Miami Rivers are shown as Auxiliary Plots in Appendix B).

Figures 3.41-3.57 Upper Great Miami River at Dayton, OH. Water Quality Calibration/Validation and Correlation Coefficients between Simulated and Observed values. Water Temperature

Figure 3.41

Calibration

174 Figure 3.42

175

Figure 3.43

Validation

176 Figure 3.44

177 Dissolved Oxygen

Figure 3.45

Calibration

178 Figure 3.46

179

Figure 3.47

Validation

180 Figure 3.48

181 Total Phosphorus

Figure 3.49

Calibration

182

Figure 3.50

Validation

183 Total Nitrogen Ammonia

Figure 3.51

Calibration

184 Figure 3.52

185

Figure 3.53

Validation

186 Figure 3.54

187 Total Nitrites and Nitrates

Figure 3.55

Calibration

188

Figure 3.56

Validation

189 Figure 3.57

190 Lower GMR at Hamilton, OH

Figures 3.58-3.75Lower Great Miami River at Hamilton, OH. Water Quality Calibration and Validation and Correlation Coefficients between Simulated and Observed values. Water Temperature

Figure 3.58

Calibration

191

Figure 3.59

192

Figure 3.60

Validation

193

Figure 3.61

194 Dissolved Oxygen

Figure 3.62

Calibration

195

Figure 3.63

196

Figure 3.64

Validation

197 Figure 3.65

198 Total Phosphorus

Figure 3.6

Calibration

199

Figure 3.67

Validation

200 Total Nitrogen Ammonia

Figure 3.68

Calibration

201 Figure 3.69

202

Figure 3.70

Validation

203 Figure 3.71

204 Nitrites plus Nitrates

Figure 3.72

Calibration

205 Figure 3.73

206

Figure 3.74

Validation

207 Figure 3.75

208 The results from water quality calibration and validation procedures provided the following conclusions:

• Water Temperature and DO – the statistical comparison shows that the model results

closely represent the observation Water Temperature values and DO concentrations in

each of the basin. The model under-predicts the Water Temperature by 10-15% in

average, over-predicts DO by 7% for the Upper GMR basin and slightly under-predicts

for the Lower GMR basins by 6% in average. In terms of calibration target criteria, these

results may be interpreted as fair for Water Temperature and good for DO parameter.

• Total Phosphorus - the model tend to under-predict mean annual loads of phosphorus

for both basins by an average of 7%, which can be considered as a very good calibration

(“Very Good” target criteria for nutrients is <15% difference between simulated and

observed values).

• Ammonia - the mean Ammonia concentrations simulated by the model were generally

close to the observed values. Overall, the model under-predicts the annual concentrations

of Ammonia. The average % difference between simulated and observed values yielded

as 11% that results to be a very good calibration as well. The only problem encountered

with Ammonia, when biased observed records (mostly in 1985-1991) produced over-

estimation of ammonia.

• Sum of Nitrates and Nitrites – The simulated and observed mean NO2+NO3 values

were reasonably close in each of the basin. In each basin, the model under-predicts NO2+

+NO3 mean annual concentrations by an average of 13%. The results are considered as

very good for the purposes of calibration.

209 Overall, the comparison of simulated results with available water quality data from across the study area indicates that for the existing conditions the model adequately represents the conditions observed in the Upper and Lower GMR basins. Therefore, the model can be used to evaluate land-use change effects on water quality, basins management practices, and effects of climatic changes on water quality in further analysis.

210 CHAPTER IV: CLIMATE SCENARIOS AND SIMULATION RESULTS

Overview

This Chapter reviews and describes some details in the procedures of climate scenarios constructions, including preparation of meteorological WDM file using WDMUtil program. It also contains the material, which was not covered in the Methodology chapter.

The effects of climate change under various hypothetic scenarios are examined and discussed in respect to the hydrology and water quality regimes of the GMR.

4.1 REVIEW OF CLIMATE SCENARIOS

Recent model calculations made by the Intergovernmental Panel on Climate Change and results from the United Kingdom Hadley Centre’s climate model (HadCM2) suggest that global surface temperature could increase an average of 1.6-6.3 0F by 2100, with significant regional variation. In the Unites States, it is projected that the temperature will be warmer than the global average. The models suggest that the rate of evaporation will increase as the climate warms, which will increase global precipitation. They also suggest increased frequency of intense rainfall as well as marked decrease in soil moisture over some mid-continental regions during the summer.

Calculations of regional climate change are much less reliable than global ones, and it is unclear yet whether regional climate will become more variable. It is expected that it will be an increase in frequency and intensity of some extreme weather, which is of critical importance to ecological systems (droughts, floods, frosts, cloudiness and so on) (U.S EPA, 1998).

According to the United Kingdom Hadley Centre’s climate model (HadCM2) results, temperature in Ohio could increase by 30F (1.6oC) in winter, spring, and summer with range of 1-

60F (0.5-3.3oC) and 40F(2oC) in fall with a range of 2-70F (1-4oC). Precipitation is estimated to increase by 15% in winter and spring (with a range of 5-25%), 20% in fall (with a range of 10-

35%), and 25% (with a range of 10-40%) in summer. The frequency of extremely hot days in

211 summer is expected to increase along with the general warming trend. The frequency and intensity of summer thunderstorms is possible as well.

The GMR Basin, located in the SW part of Ohio, experienced decrease in precipitation by 5% for the last 100 years in the lower part of the basins, and about 5% increase in the northern portion of the basin area. Precipitation is very important in the processes of runoff formation.

This study presents hypothetical climate scenarios that cover the whole possible range in precipitation variation in Ohio for the next 100 years. Along with precipitation, evaporation has an effect on the hydrologic and water quality regimes. Increased evaporation rates due to atmospheric warming are reflected in the hypothetical climate scenarios in this study.

Summarizing, the following climate change scenarios were used in this study:

• Base Case scenario (current air temperature and precipitation)

• Hot and Dry Scenario (T+4Co(7F) and P-20%)

• Hot and Wet Scenario (T+4Co(7F) and P+20%)

• Warm and Dry Scenario (T+2Co(3.5F) and P-20%)

• Warm and Wet Scenario (T+2Co(3.5F) and P+20%)

WDMUtil program is a utility, which allows users to import available meteorological data into

WDM files and performed needed operations with meteorological parameters (e.g. editing, aggregation/disaggregation, filling missing data, etc.) in order to create the input time-series data for the HSPF model. The compilation of meteorological data includes: (1) reading time-series data from standard and user-defined formats; (2) summarizing periods of missing or faulty data for a time series; (3) listing time-series values for viewing, printing and saving to a file; (4) editing time series; (5) computing time-series data using existing data; (6) disaggregating existing time-series data from daily to hourly values and (7) writing time series data to WDM time series data sets.

212 HSPF requires the following input meteorological data:

-Measured air temperature (F)

-Measured precipitation (in)

-Measured dewpoint temperature (F)

-Measured wind movement (mph)

-Measured solar radiation (Ly/hr)

-Cloud cover (0-10)

-Potential evapotraspiration (in)

-Potential surface evaporation (in)

Downloaded meteorological WDM file for the Dayton WSO Airport contains several meteorological parameters: PREC (hourly precipitation), EVAP (hourly evaporation), ATEM

(hourly temperature), WIND (hourly wind speed), SOLR (hourly solar radiation), PEVT (hourly potential evapotraspiration), DEWP (hourly dewpoint temperature), CLOU (hourly cloud cover),

TMAX (daily maximum temperature), TMIN (daily minimum temperature), DWND (daily windspeed), DCLO (daily cloud cover), DPTP (daily dewpoint temperature), DSOL (daily solar radiation), DEVT (daily evapotraspiration), and DEVP (daily evaporation).

Hypothetical climate change scenarios were developed and saved as meteorological WDM files. Each file contains primary parameters such as ATEMP, TMIN, TMAX and PREC. Current parameters were modified according to individual scenario specifics. Secondary parameters had to be recalculated with respect to changed air temperature and precipitation. Daily solar radiation for the period of simulation (1980-1991) was computed in METCMP using daily cloud cover

(DCLO) as an input. The daily solar radiation time series was placed in the DSOL data set. The

METCMP, which is a disaggregate function, was used to convert daily solar radiation into hourly values using additional input information such as latitude. These hourly solar radiation values were input into the SOLR data set.

213 Daily pan evaporation was computed using Penman Method in METCMP. Required inputs were: daily maximum (TMAX) and daily minimum (TMIN) temperatures, daily dewpoint temperature (DPTP), daily wind movement (DWND), and daily solar radiation (DSOL). Daily dewpoint temperatures were recalculated from the initial data sets in respect to changed

AIRTEMP, TMAX and TMIN, using the regression equation derived from data records:

DPTP = 0.8213*TMAX –8.3021 (correlation coefficient = 0.99)

Daily potential evapotranspiration was input into the DEVAP data set. Daily evaporation was distributed into hourly values using the disaggregate function. The hourly evaporation records were compiled in the EVAP data set.

Daily potential evapotranspiration was computed using Hamon Method and additional inputs of latitude and monthly coefficients. The required inputs were: daily maximum (TMAX) and daily minimum (TMIN) temperatures. Daily evapotraspiration was placed in the DEVT data set.

Daily potential evapotraspiration was converted into hourly values using the disaggregate function. Hourly potential evapotranspiration values were placed in the PEVT data set.

As a result of the above operations, four climate scenario WDM files has been produced and to be imported into HSPF: hd.wdm (Hot and Dry scenario), hw.wdm (Hot and Wet scenario), wd.wdm (Warm and Wet Scenario) and ww.wdm (Warm and Wet Scenario).

4.2 FLOW REGIME UNDER CLIMATE CHANGE

Hot and Dry (HD) Scenario

The results from simulating of the Upper Great Miami River flow under Hot and Dry climate scenario showed a dramatic decrease of mean annual flow from 2347 ft3/s (2.1 km3/year) to 928 ft3/s (0.83 km3/year) by a magnitude of 1412 ft3/s (1.23 km3/year) or 60% decline from the Base

Case scenario. Coefficient of runoff changed from 0.33 in Base Case scenario to 0.16. The

Lower Great Miami River flow experienced similar changes: it decreased from 2984 ft3/s (2.66 km3/year) to 1119 ft3/s (0.99 km3/year) that is a 62% reduction. Coefficient of runoff changed

214 from 0.74 to 0.35, characterizing the significance of mean annual flow reduction (Table 4.2.1 and Figures 4.2.1 – 4.2.9).

This substantial decrease of flow is driven by higher evapotraspiration rates and significant precipitation deficit in the system. In relatively small watershed’s (mid-size by classification) area

- 3870 mi2 (10023 km2), the response of the system to changes in climate temperature and precipitation will be more rapid and considerable, compared to larger basins, (e.g Mississippi or

Ohio River basins. To test the reaction of the hydrologic system to changes in precipitation only

(-20%) (Dry Only scenario), the simulation runs were performed using specially prepared meteorological WDM file, where precipitation (PREC) parameter was changed without modifying air temperature (ATEMP, TMIN and TMAX). The flow reacted in the same manner, but in smaller magnitude: it experienced a decrease of mean annual flow by 1082 ft3/s or 46% and by 1485 ft3/s or 50% from the Base Case Scenario in the Upper GMR and Lower GMR basins accordingly. Therefore, the part of evapotranspiration role in the water balance was estimated to be as much as 15-20% (Table 4.2.1). This also confirms the leading role of precipitation in the runoff formation mechanisms in the GMR basin. The deficit of precipitation in the system would significantly affect the water balance and reduce runoff, as well as potentially cause reduction in volumes of water, recharging the aquifers, which themselves have significant importance in terms of hydraulic connectivity with the river and for water supply purposes.

Figures 4.2.1 through 4.2.9 graphically demonstrate the resulted changes in flow under HD climate scenario. It should be noted that when comparing Base Case and HD scenario, the hydrographs look similar. It is because, the seasonal precipitation pattern during meteorological scenarios preparation step was not changed. Only magnitudes of daily and hourly precipitation have been changed and placed into the new data sets.

Comparing the duration curves of the Upper GMR and Lower GMR, it is seen, that in the Upper

GMR the decrease in high (peak) and low flow (base flow) seasons has similar rate. However, in

215 Table 4.2.1 Hydrologic Modeling results under HD Climate Scenario

HD Upper GMR at Taylorsville, OH (USGS #03263000) (Hot and Dry) Mean Base Case Mean simulated % Difference: Mean observed % Difference: Mean simulated % Difference: Scenario simulated annual flow, annual flow, simulated HD annual flow, simulated HD annual flow, simulated P-20% ONLY (T+4C, P-20%) (cfs) HD Scenario and Base case (cfs) and Observed with P-20% ONLY and Base case (current land (cfs) (cfs) use) 1980-1985 880 331 -62.0 1070 -69.0 455 -48.0 1985-1990 995 396 -60.0 1104 -64.0 526 -47.0 1990-1995 1253 488 -61.0 1214 -60.0 681 -45.0 1980-1995 1008 393 -61.0 1097 -64.0 536 -47.0

HD MAD river near Dayton, OH (USGS #03270000) (Hot and Dry) Mean Base Case Mean simulated % Difference: Mean observed % Difference: Mean simulated % Difference: Scenario simulated annual flow, annual flow, simulated HD annual flow, simulated HD annual flow, simulated P-20% ONLY (T+4C, P-20%) (cfs) HD Scenario and Base case (cfs) and Observed with P-20% ONLY and Base case (current land (cfs) (cfs) use) 1980-1985 614 248 -59.0 706 -65.0 330 -46.0 1985-1990 655 292 -55.0 674 -56.0 377 -42.0 1990-1995 801 357 -55.0 702 -49.0 482 -40.0 1980-1995 703 291 -58.0 677 -57.0 384 -45.0

HD STILLWATER R at Englewood, OH (USGS #03266000) (Hot and Dry) Mean Base Case Mean simulated % Difference: Mean observed % Difference: Mean simulated % Difference: Scenario simulated annual flow, annual flow, simulated HD annual flow, simulated HD annual flow, simulated P-20% ONLY (T+4C, P-20%) (cfs) HD Scenario and Base case (cfs) and Observed with P-20% ONLY and Base case (current land (cfs) (cfs) use) 1980-1985 582 201 -65.0 639 -68.0 289 -50.0 1985-1990 660 246 -63.0 641 -61.0 338 -48.0 1990-1995 740 310 -58.0 646 -52.0 446 -40.0 1980-1995 675 244 -64.0 620 -65.0 345 -49.0 Table 4.2.1 (Continued)

HD SUM OF FLOWS: GMR, MAD R and STILLWATER river (Great Miami River below Dayton) (Hot and Dry) Mean Base Case Mean simulated % Difference: Mean observed % Difference: Mean simulated % Difference: Scenario simulated annual flow, annual flow, simulated HD Coefficient of annual flow, simulated HD annual flow, simulated P-20% ONLY (T+4C, P-20%) cfs (km3/year), (mm) HD Scenario and Base case runoff * (cfs, km3/year) and Observed with P-20% ONLY and Base case (current land (inches). (cfs, km3/year, mm Base case/ (cfs) use) inches). sim.HD 1980-1985 2034 (1.81) 781 (0.69) -61.0 Pavr = 39 inches 2487 (2.21) -68.0 1074 -47.0 1985-1990 2305 (2.05) 937 (0.83) -59.0 P-20% =31 inches 2494 (2.22) -62.0 1241 -46.0 1990-1995 2922, (2.60) 1154 (1.03) -60.0 2659 (2.37) -56.0 1609 -45.0 1980-1995 2347 (2.1),(327),(12.8) 928 (0.83),(129),(5.0) -60.0 0.33/0.16 2472 (2.20) -62.0 1265 -46.0

HD Lower GMR at Hamilton,OH (USGS #03274000) (Hot and Dry) Mean Base Case Mean simulated % Difference: Mean observed % Difference: Mean simulated % Difference: Scenario simulated annual flow, annual flow, simulated HD Coefficient of annual flow, simulated HD annual flow, simulated P-20% ONLY (T+4C, P-20%) cfs (km3/year), (mm) HD Scenario and Base case runoff * (cfs, km3/year) and Observed with P-20% ONLY and Base case (current land (inches). (cfs, km3/year, mm Base case/ (cfs) use) inches). sim.HD 1980-1985 2870 (2.56) 951 (0.85) -67.0 Pavr = 39 inches 3736 (3.3) -74.0 1283 -55.0 1985-1990 2998 (2.67) 1127 (1.0) -62.0 P-20% =31 inches 3646 (3.2) -69.0 1571 -47.0 1990-1995 3150 (2.80) 1378 (1.22) -56.0 3728 (3.3) -56.0 1892 -40.0 1980-1995 2984 (2.66),(738),(29.0) 1119 (0.99),(275),(10.8) -62.0 0.74/0.35 3576 (3.1) -69.0 1500 -50.0 * Coefficient of runoff = Y/X, where Y (mm or inches) is a surface runoff - amount of water, coming from the watershed area per time and equals to the thickness of the layer of equally distributed, in terms of area, basin. Y=(W*10^6)/F, where W - is the annual runoff volume (cubic kilometers) and F is the basin area (sq. km), when X is precipitation (mm). the Lower GMR, the low flow reduced a little greater than extreme high flow regimes (2% chance to exceed or discharges in the range of 7,000 ft3/s and higher).

Smaller sub-basins (Mad River, Stillwater River and GMR at Taylorsville sub-basin) experienced similar changes in flow regime: Mad River – decrease in annual mean flow from

1008 ft3/s down to 393 ft3/s (61%); Stillwater River – drop from 675 ft3/s to 244 ft3/s (64%) and

GMR sub-basin from 703 ft3/s to 291 ft3/s (58%). Similar simulations but without temperature modification (Dry Only scenario) was performed for these smaller watersheds. The results revealed the same 10-20% evaporation losses in the water balance between Hot and Dry with Dry

Only scenario (Table 4.2.1).

Hot and Wet (HW) Scenario

The comparison of the simulated HW and Base case annual mean flows and their ratios for the

Upper GMR basin demonstrates that simulated HW scenario flow is 28% higher than the Base case flow (3017 ft3/s (2.7 km3/year) and 2347 ft3/s (2.1 km3/year), respectively. The Lower GMR simulation under HW scenario produced increased mean annual flow from 2984 ft3/s (2.66 km3/year) in the Base case scenario to 3494 ft3/s (3.11 km3/year) or a 17% raise. Running the

Wet Only scenario (analogy of Dry Only scenario, except P is +20%) produced higher annual flow for both Upper and Lower GMR – 3633 and 4189 ft3/s, accordingly. Once again, it shows about 20% losses of moisture in water balance are accounted to evapotraspiration/evaporation loss caused by the 4 degrees Centigrade increase in air temperature (Table 4.2.2).

However, comparing the results of the Hot group scenarios, it seems that precipitation has much more important effects in the processe of runoff formation. When precipitation increased by 20%

(Wet Scenario) as opposed to the Dry Scenario (drop by 20%), the model predicted significantly higher annual flows in Wet than it did in Dry Scenario: Upper GMR -928 ft3/s (0.83 km3/year) in the Dry Case and 3017 ft3/s (3.11 km3/year) for the Wet Case Scenarios, and Lower GMR -1119 ft3/s (0.99 km3/year) in the Dry Case and 3494 ft3/s (3.11 km3/year) in the Wet Case, which accounts for 70% and 68 % of difference, accordingly.

216 Table 4.2.2 Hydrologic Modeling results under HW Climate Scenario

HW Upper GMR at Taylorsville, OH (USGS #03263000) (Hot and Wet) Mean Base Case Mean simulated % Difference: Mean observed % Difference: Mean simulated % Difference: Scenario simulated annual flow, annual flow, simulated HW annual flow, simulated HW annual flow, simulated P+20% ONLY (T+4C, P+20%) (cfs) HW Scenario and Base case (cfs) and Observed with P+20% ONLY and Base case (current land (cfs) (cfs) use) 1980-1985 880 1104 +25.0 1070 +3.0 1355 +54.0 1985-1990 995 1269 +27.0 1104 +15.0 1512 +52.0 1990-1995 1253 1565 +25.0 1214 +30.0 1863 +49.0 1980-1995 1008 1272 +26.0 1097 +16.0 1532 +52.0

HW MAD river near Dayton, OH (USGS #03270000) (Hot and Wet) Mean Base Case Mean simulated % Difference: Mean observed % Difference: Mean simulated % Difference: Scenario simulated annual flow, annual flow, simulated HW annual flow, simulated HW annual flow, simulated P+20% ONLY (T+4C, P+20%) (cfs) HW Scenario and Base case (cfs) and Observed with P+20% ONLY and Base case (current land (cfs) (cfs) use) 1980-1985 614 774 +26.0 706 +10.0 940 +53.0 1985-1990 655 884 +35.0 674 +31.0 1040 +58.0 1990-1995 801 1084 +35.0 702 +54.0 1278 +59.0 1980-1995 703 887 +26.0 677 +31.0 1057 +50.0

HW STILLWATER R at Englewood, OH (USGS #03266000) (Hot and Wet) Mean Base Case Mean simulated % Difference: Mean observed % Difference: Mean simulated % Difference: Scenario simulated annual flow, annual flow, simulated HW annual flow, simulated HW annual flow, simulated P+20% ONLY (T+4C, P+20%) (cfs) HW Scenario and Base case (cfs) and Observed with P+20% ONLY and Base case (current land (cfs) (cfs) use) 1980-1985 582 740 +27.0 639 +16.0 920 +58.0 1985-1990 660 853 +29.0 641 +33.0 1024 +55.0 1990-1995 740 1062 +43.0 646 +64.0 1042 +54.0 1980-1995 675 858 +27.0 620 +38.0 1173 +58.0 Table 4.2.2 (Continued)

HW SUM OF FLOWS: GMR, MAD R and STILLWATER river (Great Miami River below Dayton) (Hot and Wet) Mean Base Case Mean simulated % Difference: Mean observed % Difference: Mean simulated % Difference: Scenario simulated annual flow, annual flow, simulated HW Coefficient of annual flow, simulated HW annual flow, simulated P+20% ONLY (T+4C, P+20%) cfs (km3/year), (mm) HW Scenario and Base case runoff * (cfs, km3/year) and Observed with P+20% ONLY and Base case (current land (inches). (cfs, km3/year, mm Base case/ (cfs) use) inches). sim.HW 1980-1985 2034 (1.81) 2619 (2.33) +29.0 Pavr=39 inch 2487 (2.21) +5.0 3219 +58.0 1985-1990 2305 (2.05) 3006 (2.7) +30.0 P+20% =47 inch 2494 (2.22) +21.0 2580 +55.0 1990-1995 2922, (2.60) 3711 (3.3) +27.0 2659 (2.37) +40.0 4414 +51.0 1980-1995 2347 (2.1),(327),(12.8) 3017 (2.7),(420),(16.5) +28.0 0.32/0.35 2472 (2.2) +22.0 3633 +55.0

HW Lower GMR at Hamilton,OH (USGS #03274000) (Hot and Wet) Mean Base Case Mean simulated % Difference: % Difference: Mean simulated % Difference: Scenario simulated annual flow, annual flow, simulated HW Coefficient of simulated HW annual flow, simulated P+20% ONLY (T+4C, P+20%) cfs (km3/year), (mm) HW Scenario and Base case runoff * and Observed with P+20% ONLY and Base case (current land (inches). (cfs, km3/year, mm Base case/ (cfs) use) inches). sim.HW 1980-1985 2870 (2.56) 3042 (2.7) +6.0 Pavr=39 inch 3720 +30.0 1985-1990 2998 (2.67) 3481 (3.10) +16.0 P+20% =47 inch 4129 +38.0 1990-1995 3150 (2.80) 4282 (3.8) +36.0 5077 +61.0 1980-1995 2984 (2.66),(738),(29.0) 3494 (3.11),(863),(34.0) +17.0 0.74/0.72 4189 +40.0 * Coefficient of runoff = Y/X, where Y (mm or inches) is a surface runoff - amount of water, coming from the watershed area per time and equals to the thickness of the layer of equally distributed, in terms of area, basin. Y=(W*10^6)/F, where W - is the annual runoff volume (cubic kilometers) and F is the basin area (sq. km), when X is a mean annual precipitation (mm). The plots in Figures 4.2.10-4.2.18 present the seasonal fluctuations in flow between Base case and HW scenario. The flow frequency curves created for the Lower GMR at Hamilton, OH shows that simulated values, as supposed to be, are higher in high flow regime, but, interestingly enough, the model predicted lower values for base-flow hydrological regime. Perhaps, the reason of this- is caused by changed hydraulic connectivity mechanisms between the stream and underlying aquifers when aquifers stop discharging the water into the river during low flow seasons.

Warm and Dry (WD) Scenario

Simulation of the flow under Warm and Dry Scenario produced a significant reduction of flow in the Upper and Lower GMR basins: from 2347 ft3/s (2.1 km3/year) to 1144 ft3/s (1.0 km3/year)

– 51% decline for the Upper GMR and 2984 ft3/s (2.66 km3/year) to 1363 ft3/s (1.21 km3/year)-

54% drop for the Lower GMR.

Comparing the results from HD scenario simulation with WD, it is seen that the difference between mean annual flows is not very high: 928 ft3/s (0.83 km3/year) – HD and 1144 ft3/s (1.0 km3/year) or 51% reduction - WD for the Upper GMR and 1119 ft3/s (0.99 km3/year) or 54 % reduction – HD and 1363 ft3/s (1.21 km3/s) – WD for the Lower GMR flow (Table 4.2.3). On the average, it is about 10% difference between the scenarios. From this comparison, it may be concluded that an increase in temperature by 2 degrees Centigrade (from 2 up to 40C), intensifies the evaporation/evapotraspiration rates and causes the flow to decrease by 10-12% in average due to additional losses of moisture in the hydrological cycle (water balance) of the GMR basin.

However, the amount of precipitation acts as a primary dynamic agent of the hydrological system.

By examining the seasonal and annual fluctuations of flow (Figures 4.2.10 – 4.2.18), it is worthwhile to note, that the flow duration curves for the Lower GMR at Hamilton, OH demonstrate relatively small change in extreme peak discharges (less than 1% to exceed or between 10,000-20,000 ft3/s).

217 Table 4.2.3 Hydrologic Modeling results under WD Climate Scenario

WD Upper GMR at Taylorsville, OH (USGS #03263000) (Warm and Dry) Mean Base Case Mean simulated % Difference: Mean observed % Difference: Scenario simulated annual flow, annual flow, simulated WD annual flow, simulated WD (T+2C, P-20%) (cfs) WD Scenario and Base case (cfs) and Observed (current land (cfs) use) 1980-1985 880 409 -53.0 1070 -53.0 1985-1990 995 480 -52.0 1104 -56.0 1990-1995 1253 613 -51.0 1214 -49.0 1980-1995 1008 484 -52.0 1097 -55.0

WD MAD river near Dayton, OH (USGS #03270000) (Warm and Dry) Mean Base Case Mean simulated % Difference: Mean observed % Difference: Scenario simulated annual flow, annual flow, simulated WD annual flow, simulated WD (T+2C, P-20%) (cfs) WD Scenario and Base case (cfs) and Observed (current land (cfs) use) 1980-1985 614 300 -51.0 706 -57.0 1985-1990 655 347 -47.0 674 -48.0 1990-1995 801 439 -45.0 702 -37.0 1980-1995 703 350 -50.0 677 -48.0

WD STILLWATER R at Englewood, OH (USGS #03266000) (Warm and Dry) Mean Base Case Mean simulated % Difference: Mean observed % Difference: Scenario simulated annual flow, annual flow, simulated WD annual flow, simulated WD (T+2C, P-20%) (cfs) WD Scenario and Base case (cfs) and Observed (current land (cfs) use) 1980-1985 582 257 -56.0 639 -59.0 1985-1990 660 305 -54.0 641 -52.0 1990-1995 740 399 -46.0 646 -38.0 1980-1995 675 309 -54.0 620 -50.0 Table 4.2.3 (Continued)

WD SUM OF FLOWS: GMR, MAD R and STILLWATER river (Warm and Dry) (Great Miami River below Dayton) Scenario Mean Base Case Mean simulated % Difference: Mean observed % Difference: (T+2C, P-20%) simulated annual flow, annual flow, simulated WD Coefficient of annual flow, simulated WD (current land cfs (km3/year), (mm) WD Scenario and Base case runoff * (cfs, km3/year) and Observed use) (inches). (cfs, km3/year, mm Base case/ inches). sim.WD 1980-1985 2034 (1.81) 969 (0.86) -52.0 Pavr = 39 inches 2487 (2.21) -61.0 1985-1990 2305 (2.05) 1132 (1.0) -50.0 P-20% =31 inches 2494 (2.22) -55.0 1990-1995 2922, (2.60) 1450 (1.3) -50.0 2659 (2.37) -45.0 1980-1995 2347 (2.1),(327),(12.8) 1144 (1.0),(156),(6.14) -51.0 0.32/0.20 2472 (2.2) -54.0

WD Lower GMR at Hamilton, OH (USGS #03274000) (Warm and Dry) Mean Base Case Mean simulated % Difference: Mean observed % Difference: Scenario simulated annual flow, annual flow, simulated WD Coefficient of annual flow, simulated WD (T+2C, P-20%) cfs (km3/year), (mm) WD Scenario and Base case runoff * (cfs, km3/year) and Observed (current land (inches). (cfs, km3/year, mm Base case/ use) inches). sim.WD 1980-1985 2870 (2.56) 1160 (1.0) -59.0 Pavr = 39 inches 3736 (3.3) -68.0 1985-1990 2998 (2.67) 1350 (1.2) -55.0 P-20% =31 inches 3646 (3.2) -63.0 1990-1995 3150 (2.80) 1713 (1.53) -45.0 3728 (3.3) -54.0 1980-1995 2984 (2.66),(738),(29.0) 1363 (1.21),(336),(13.2) -54.0 0.74/0.42 3576 (3.1) -62.0 * Coefficient of runoff = Y/X, where Y (mm or inches) is a surface runoff - amount of water, coming from the watershed area per time and equals to the thickness of the layer of equally distributed, in terms of area, basin. Y=(W*10^6)/F, where W - is the annual runoff volume (cubic kilometers) and F is the basin area (sq. km), when X is precipitation (mm). Warm and Wet (WW) Scenario

Table 4.2.4 lists the results from simulation under Warm and Wet Scenario. The Upper GMR gained 46% of the mean annual flow: the flow increased from 2347 ft3/s (2.1 km3/year) up to

3436 ft3/s (3.06 km3/year). Simulated flow for the Lower GMR resulted in 33% increase, from

2984 ft3/s (2.66 km3/year) to 3968 ft3/s (3.54 km3/year) under WW scenario. Comparing the results from WW and HW scenarios reveals that increasing the temperature by 20C, the flow decreased by the same 10-15% in average due to moisture losses arriving from elevated evaporation/evapotraspiration rates. Lower than Base case scenario discharges are predicted for the base-flow GMR river regime at Hamilton, similar to HW scenario results (Figures 4.2.19 –

4.2.27).

218 Table 4.2.4 Hydrologic Modeling results under WW Climate Scenario

WW Upper GMR at Taylorsville, OH (USGS #03263000) (Warm and Wet) Mean Base Case Mean simulated % Difference: Mean observed % Difference: Scenario simulated annual flow, annual flow, simulated WW annual flow, simulated WW (T+2C, P+20%) (cfs) WW Scenario and Base case (cfs) and Observed (current land (cfs) use) 1980-1985 880 1278 +45.0 1070 +19.0 1985-1990 995 1433 +44.0 1104 +30.0 1990-1995 1253 1769 +41.0 1214 +45.0 1980-1995 1008 1450 +44.0 1097 +32.0

WW MAD river near Dayton, OH (USGS #03270000) (Warm and Wet) Mean Base Case Mean simulated % Difference: Mean observed % Difference: Scenario simulated annual flow, annual flow, simulated WW annual flow, simulated WW (T+2C, P+20%) (cfs) WW Scenario and Base case (cfs) and Observed (current land (cfs) use) 1980-1985 614 887 +44.0 706 +25.0 1985-1990 655 992 +51.0 674 +37.0 1990-1995 801 1217 +52.0 702 +73.0 1980-1995 703 1003 +43.0 677 +48.0

WW STILLWATER R at Englewood, OH (USGS #03266000) (Warm and Wet) Mean Base Case Mean simulated % Difference: Mean observed % Difference: Scenario simulated annual flow, annual flow, simulated WW annual flow, simulated WW (T+2C, P+20%) (cfs) WW Scenario and Base case (cfs) and Observed (current land (cfs) use) 1980-1985 582 863 +48.0 639 +35.0 1985-1990 660 969 +47.0 641 +51.0 1990-1995 740 1207 +63.0 646 +86.0 1980-1995 675 984 +46.0 620 +58.0 Table 4.2.4 (Continued)

WW SUM OF FLOWS: GMR, MAD R and STILLWATER river (Warm and Wet) (Great Miami River below Dayton) Scenario Mean Base Case Mean simulated % Difference: Mean observed % Difference: (T+2C, P+20%) simulated annual flow, annual flow, simulated WW Coefficient of annual flow, simulated WW (current land cfs (km3/year), (mm) WW Scenario and Base case runoff * (cfs, km3/year) and Observed use) (inches). (cfs, km3/year, mm Base case/ inches). sim.WW 1980-1985 2034 (1.81) 3028 (2.7) +48.0 Pavr=39 inch 2487 (2.21) +22.0 1985-1990 2305 (2.05) 3394 (3.03) +47.0 P+20% =47 inch 2494 (2.22) +36.0 1990-1995 2922, (2.60) 4193 (3.7) +43.0 2659 (2.37) +57.0 1980-1995 2347 (2.1),(327),(12.8) 3436 (3.06),(476),(18.7) +46.0 0.33/0.40 2472 (2.2) +39.0

WW Lower GMR at Hamilton, OH (USGS #03274000) (Warm and Wet) Mean Base Case Mean simulated % Difference: % Difference: Scenario simulated annual flow, annual flow, simulated WW Coefficient of simulated WW (T+2C, P+20%) cfs (km3/year), (mm) WW Scenario and Base case runoff * and Observed (current land (inches). (cfs, km3/year, mm Base case/ use) inches). sim.WW 1980-1985 2870 (2.56) 3504 (3.12) +22.0 Pavr=39 inch 1985-1990 2998 (2.67) 3918 (3.5) +30.0 P+20% =47 inch 1990-1995 3150 (2.80) 4827 (4.3) +53.0 1980-1995 2984 (2.66),(738),(29.0) 3968 (3.54),(983),(38.7) +33.0 0.74/0.82 * Coefficient of runoff = Y/X, where Y (mm or inches) is a surface runoff - amount of water, coming from the watershed area per time and equals to the thickness of the layer of equally distributed, in terms of area, basin. Y=(W*10^6)/F, where W - is the annual runoff volume (cubic kilometers) and F is the basin area (sq. km), when X is precipitation (mm). The following results came out from the running HSPF hydrologic model under the hypothetical climate change scenarios:

HD scenario (T+4C, P-20%)

Figures 4.2.1 – 4.2.3 Flow of Mad River near Dayton, OH, Stillwater River at Englewood, OH and Great Miami River at Taylorsville, OH under Hot and Dry Scenario (HD)

Figure 4.2.1

1980-1995 HD

Figure 4.2.2

Monthly discharges HD

219 Figure 4.2.3

Flow duration curves HD

Figures 4.2.4-4.2.6 Observed and Simulated Flows of Upper Great Miami River at Dayton, OH

Figure 4.2.4

1980-1995 HD

220 Figure 4.2.5

Monthly discharges HD

Figure 4.2.6

Flow duration curves HD

221 Figures 4.2.7 – 4.2.9 Observed and Simulated Flows of Lower Great Miami River at Hamilton, OH

Figure 4.2.7

1980-1995 HD

Figure 4.2.8

Monthly discharges HD

Figure 4.2.9

Flow duration curves HD

222

HW scenario (T+4C, P+20%)

Figures 4.2.10 – 4.2.12 Flow of Mad River near Dayton, OH, Stillwater River at Englewood, OH and Great Miami River at Taylorsville, OH under Hot and Wet Scenario (HW) Figure 4.2.10 1980-1995 HW

Figure 4.2.11 HW Monthly discharges

223 Figure 4.2.12

Flow duration curves HW

Figures 4.2.13 – 4.2.15 Observed and Simulated Flows of Upper Great Miami River at Dayton, OH Figure 4.2.13 1980-1995 HW

Figure 4.2.14

HW Monthly discharges

224 Figure 4.2.15

Flow duration curves HW

Figures 4.2.16-4.2.18 Observed and Simulated Flows of Lower Great Miami River at Hamilton, OH Figure 4.2.16 1980-1995 HW

Figure 4.2.17

Monthly discharges HW

225 Figure 4.2.18

Flow duration curves

226 WD scenario (T+2C, P-20%)

Figures 4.2.19-4.2.21 Flow of Mad River near Dayton, OH, Stillwater River at Englewood, OH and Great Miami River at Taylorsville, OH under Warm and Dry Scenario (WD) Figure 4.2.19 1980-1995 WD

Figure 4.2.20

Monthly discharges WD

227 Figure 4.2.21

Flow duration curves WD

Figures 4.2.22-4.2.24 Observed and Simulated Flows of Upper Great Miami River at Dayton, OH Figure 4.2.22

WD 1980-1995

Figure 4.2.23

Monthly discharges WD

228 Figure 4.2.24

WD Flow duration curves

Figures 4.2.25-4.2.27 Observed and Simulated Flows of Lower Great Miami River at Hamilton, OH Figure 4.2.25

1980-1995 WD

Figure 4.2.26

Monthly discharges WD

229 Figure 4.2.27

WD Flow duration curves

230 WW scenario (T+2C, P+20%)

Figures 4.2.28-4.2.30 Flow of Mad River near Dayton, OH, Stillwater River at Englewood, OH and Great Miami River at Taylorsville, OH under Warm and Wet Scenario (WW) Figure 4.2.28 1980-1995 WW

Figure 4.2.29

WW Monthly discharges

231 Figure 4.2.30

Flow duration curves WW

Figures 4.2.31-4.2.33 Observed and Simulated Flows of Upper Great Miami River at Dayton, OH Figure 4.2.31

1980-1995 WW

Figure 4.2.32

WW Monthly discharges

232 Figure 4.2.33

Flow duration curves WW

Figures 4.2.34-4.2.36 Observed and Simulated Flows of Lower Great Miami River at Hamilton, OH Figure 4.2.34 1980-1995 WW

Figure 4.2.35

Monthly discharges WW

233 Figure 4.2.36

Flow duration curves WW

234 Summary Summarizing the results from the analysis of the Upper and Lower GMR flow regimes, it is apparent that: (1) Under Hot group Scenarios (HD and HW) the mean annual flow of the Upper GMR significantly decreased by 60% under HD climate scenario (from 2347 ft3/s (2.1 km3/year) down to 928 ft3/s (0.83 km3/year) and increased by 28% under HW climate scenario (from 2347 ft3/s

(2.7 km3/year) to 3017 ft3/s (2.1 km3/year) as compared to Base Case Scenario. The mean annual flow of the Lower GMR decreased by 62% (from 2984 ft3/s (2.66 km3/year) to 1119 ft3/s (0.99 km3/year) under HD climate scenario and increased by 17% from 2984 ft3/s (2.66 km3/year) to

3494 ft3/s (3.11 km3/year) as compared to Base Case Scenario.

(2) Under Warm Group Scenarios (WD and WW), the model predicted a reduction of annual flow by 51% (from 2347 ft3/s (2.1 km3/year) to 1144 ft3/s (1.0 km3/year) under WD climate scenario and significantly increased by 46% (from 2347 ft3/s (2.1 km3/year) up to 3436 ft3/s (3.06 km3/year) under WW climate scenario for the Upper GMR basin. Simulation results for the

Lower GMR under WD climate scenario demonstrate that annual flow is reduced by 54% (from

2984 ft3/s (2.66 km3/year) to 1363 ft3/s (1.21 km3/year). The flow under WW climate scenario increased by 33% compared to Base case scenario (from 2984 ft3/s (2.66 km3/year) to 3968 ft3/s

(3.54 km3/year).

(3) Analyzing the Dry Only and Wet Only climate scenarios produced by the HSPF, the difference between simulated and Base case has come out lower than the complete HW, HD, WD and WW scenarios. From the results, it seems that evaporation/evapotraspiration, as a function of air temperature, is more effective in the water balance and can decrease annual flow by 15-20% due to losses of moisture. However, to some degree, it is an oversimplification of the processes within the hydrological cycle, because, increased evaporation rates and precipitation amounts, might affect micro or mezo-climatic patterns, producing intensive rainfall events, which may

235 significantly affect peak discharges during the summer-fall seasons, altering the seasonal flow regime.

(4) In cross-comparisons of the produced results from Wet Scenarios (HW-WW) and Dry

Scenarios (HD-WD), it is revealed that, when air temperature is increased by 2 degrees

Centigrade with no change in precipitation, the model annual flow will be reduced by 10-15% in average for both Upper and Lower GMR. This confirms the capability of the model to assess evaporation losses.

(5) The model was not parameterized to simulate seasonal flow distribution in details, due to the reasons explained above in this subchapter. Therefore, seasonal patterns of flows simulated, generally look similar to the Base Case, except observable shifts in shapes of recession curves and shapes of peaks. However, taking into consideration the fact, that the flow, especially in the

Lower part of GMR basin is highly regulated by flood control systems and channelization, it may be assumed, that, in general, real future seasonal flow variation would most likely stay close to the ones simulated in this study.

Table 4.2.5 demonstrates summary results from the modeling result of flow regime under climate change scenarios:

UPPER GMR AT DAYTON, OH Mean annual flow Difference between Base Case Scenario ft3/s (km3/year) and Simulated Climate Scenario Base Case Scenario 2347 (2.1) - Hot and Dry Scenario (HD) 928 (0.83) -60% Hot and Wet Scenario (HW) 3017 (2.7) +28% Warm and Dry Scenario (WD) 1144 (1.0) -51% Warm and Wet Scenario (WW) 3436 (3.06) +46%

LOWER GMR AT HAMILTON, Mean annual flow Difference between Base Case Scenario OH ft3/s (km3/year) and Simulated Climate Scenario Base Case Scenario 2984 (2.66) - Hot and Dry Scenario (HD) 1119 (0.99) -62% Hot and Wet Scenario (HW) 3494 (3.11) +17% Warm and Dry Scenario (WD) 1363 (1.21) -54% Warm and Wet Scenario (WW) 3968 (3.54) +33%

236 4.3 WATER QUALITY AND CLIMATE CHANGE Hot Group Scenarios The results from water temperature simulations under Hot Group Climate scenarios show an increase in average by 2% compared to the base case scenario (for both, Upper and Lower GMR)

(Table 4.3.1, Figures 4.3.1, 4.3.6, 4.3.11, 4.3.16). Practically there is no difference between changes in water temperature simulated under Dry vs. Wet scenario. Dissolved oxygen under Hot group simulations declined by approximately 4% (from 10 to close to 9 mg/l) for both basins

(Table 4.3.1 and Figures 4.3.2, 4.3.7, 4.3.12, 4.3.17). Mean annual concentrations of total

Phosphorus slightly climbed by 5% in average under HD scenario and by 8% under HW scenario. (Table 4.3.1 and Figures 4.3.3, 4.3.8, 4.3.13, 4.3.18). Total Nitrogen Ammonia simulated under HD scenario resulted in decrease by 5% in average, whereas HW scenario produced close to 10% increase (Table 4.3.1 and Figures 4.3.4, 4.3.9, 4.3.14, 4.3.19). Simulated mean annual concentrations of the Sum of Nitrites and Nitrates decreased by 8% (from 3.16 to

2.90 mg/l) under HD scenario in Upper GMR and insignificantly increased by 2.3% (from 3.52 to 3.60 mg/l) in Lower GMR. Wet case scenario simulations demonstrated an increase in

NO2+NO3 by 3.8% (from 3.16 down to 3,28 mg/l) for Upper GMR and by 6.8% (from 3.52 to

3.76 mg/l) for Lower GMR (Table 4.3.1 and Figures 4.3.5, 4.3.10, 4.3.15, 4.3.20). This fact

(increase of constituent concentrations in Wet case scenario) might be explained by elevated rates of nutrient loads from different land use types due to increased surface runoff reflected in Wet climate scenario.

237 Table 4.3.1 Water quality modeling results from simulations under Climate Change Scenarios (Hot group Scenarios)

Water Quality Scenario Upper GMR Lower GMR % difference % difference Scenario Upper GMR Lower GMR % difference % difference Parameter HOT AND DRY at Dayton at Hamilton Upper to Lower to HOT AND WET at Dayton at Hamilton Upper to Lower to (HD) OH OH Base Case Base Case (HW) OH OH Base Case Base Case T (F) Base Case 44.4 56.5 Base Case 44.4 56.5 Hot and Dry only (HD) 46.0 57.4 +2.4 +1.6 Hot and Wet only (HW) 45.3 57.2 +2.0 +1.2 DO Base Case 11.2 9.3 Base Case 11.2 9.3 (mg/l) Hot and Dry only (HD) 10.9 9.0 -2.7 -3.2 Hot and Wet only (HW) 10.5 9.0 -6.2 -3.2 Total P Base Case 0.33 0.42 Base Case 0.33 0.42 (mg/l) Hot and Dry only (HD) 0.34 0.45 +3.0 +7.1 Hot and Wet only (HW) 0.36 0.45 +9.1 +7.1 NH4 Base Case 0.13 0.21 Base Case 0.13 0.21 (mg/l) Hot and Dry only (HD) 0.12 0.20 -7.9 -2.3 Hot and Wet only (HW) 0.14 0.24 +4.6 +14.2 NO2+NO3 Base Case 3.16 3.52 Base Case 3.16 3.52 (mg/l) Hot and Dry only (HD) 2.9 3.60 -8.0 +2.3 Hot and Wet only (HW) 3.28 3.76 +3.8 +6.8 Upper GMR at Dayton, OH (Climate ONLY Scenarios) HD SCENARIO Water Temperature Figure 4.3.1

DO Figure 4.3.2

238 Total P Figure 4.3.3

NH4 Figure 4.3.4

239 NO2+NO3 Figure 4.3.5

LOWER GMR at Hamilton, OH (Climate ONLY Scenarios) HD SCENARIO Water Temperature Figure 4.3.6

240 DO Figure 4.3.7

Total P Figure 4.3.8

241 NH4

Figure 4.3.9

NO2+NO3 Figure 4.3.10

242 Upper GMR at Dayton, OH (Climate ONLY Scenarios) HW SCENARIO Water Temperature Figure 4.3.11

DO Figure 4.3.12

243 Total P Figure 4.3.13

NH4 Figure 4.3.14

244 NO2+NO3 Figure 4.3.15

Lower GMR at Hamilton, OH (Climate ONLY Scenarios) HW SCENARIO Water Temperature Figure 4.3.16

245 DO Figure 4.3.17

Total P Figure 4.3.18

246 NH4 Figure 4.3.19

NO2+NO3 Figure 4.3.20

247

Warm Group Scenarios Water temperature simulations under Warm Group scenarios produced approximately 1% increase compared to base case scenario for both, Upper and Lower GMR (Table 4.3.2 and

Figures 4.3.21, 4.3.26, 4.3.31, 4.3.36). Modeling DO concentrations demonstrated that it reduced by 2-4% in average for the Upper and Lower GMR (Table 4.3.2 and Figures 4.3.22, 4.3.27,

4.3.32, 4.3.37). Simulations of Total Phosphorus under WD climate scenario resulted in 15.1% increase (from 0.33 to o.38 mg/l) for the Upper GMR and 12% (from 0.42 to 0.47 mg/l) for the

Lower GMR. Total Phosphorus concentrations simulated under WW climate scenario demonstrate in average a 23% increase (from 0.33-0.42 to 0.41-0.51 mg/l for the Upper and

Lower GMR respectively) (Table 4.3.2 and Figures 4.3.23, 4.3.28, 4.3.33, 4.3.38). The simulations of Total Nitrogen Ammonia under Dry vs. Wet scenario groups came out with different results: WD scenario resulted in 7.7% decrease for the Upper GMR and 4.7% decrease for the Lower GMR and about 4% increase in WW scenario (Table 4.3.2 and Figures 4.3.24,

4.3.29, 4.3.34, 4.3.39). NO2+NO3 resulted in overall increase by approximately 3% (Table 4.3.2 and Figures 4.3.25, 4.3.30, 4.3.35, 4.3.40).

248 Table 4.3.2 Water quality modeling results from simulations under Climate Change Scenarios (Warm Group Scenarios)

Water Quality Scenario Upper GMR Lower GMR % difference % difference Scenario Upper GMR Lower GMR % difference % difference Parameter WARM AND DRY at Dayton at Hamilton Upper to Lower to WARM AND WET at Dayton at Hamilton Upper to Lower to (WD) OH OH Base Case Base Case (WW) OH OH Base Case Base Case T (F) Base Case 44.4 56.5 Base Case 44.4 56.5 Warm and Dry only (WD) 45.2 57.0 +1.1 +1.0 Warm and Wet only (WW) 44.9 56.7 +1.1 +1.0 DO Base Case 11.2 9.3 Base Case 11.2 9.3 (mg/l) Warm and Dry only (WD) 10.7 9.0 -4.4 -3.2 Warm and Wet only (WW) 11.0 9.1 -1.7 -2.1 Total P Base Case 0.33 0.42 Base Case 0.33 0.42 (mg/l) Warm and Dry only (WD) 0.38 0.47 +15.1 +12.0 Warm and Wet only (WW) 0.41 0.51 +24.2 +21.1 NH4 Base Case 0.13 0.21 Base Case 0.13 0.21 (mg/l) Warm and Dry only (WD) 0.12 0.20 -7.7 -4.7 Warm and Wet only (WW) 0.13 0.22 +1.0 +4.7 NO2+NO3 Base Case 3.16 3.52 Base Case 3.16 3.52 (mg/l) Warm and Dry only (WD) 3.22 3.60 +1.9 +2.3 Warm and Wet only (WW) 3.24 3.55 +2.5 +1.0

Upper GMR at Dayton, OH (Climate ONLY Scenarios) WD SCENARIO Water Temperature Figure 4.3.21

DO Figure 4.3.22

249 Total P Figure 4.3.23

NH4 Figure 4.3.24

250 NO2+NO3 Figure 4.3.25

LOWER GMR at Hamilton, OH (Climate ONLY Scenarios) WD SCENARIO Water Temperature Figure 4.3.26

251

DO Figure 4.3.27

Total P Figure 4.3.28

252

NH4 Figure 4.3.29

NO2+NO3 Figure 4.3.30

253

Upper GMR at Dayton, OH (Climate ONLY Scenarios) WW SCENARIO Water Temperature Figure 4.3.31

DO Figure 4.3.32

254

Total P Figure 4.3.33

NH4 Figure 4.3.34

255 NO2+NO3

Figure 4.3.35

LOWER GMR at Hamilton, OH (Climate ONLY Scenarios) WW SCENARIO Water Temperature Figure 4.3.36

256 DO Figure 4.3.37

Total P Figure 4.3.38

257

NH4 Figure 4.3.39

NO2+NO3 Figure 4.3.40

258 Concluding the discussion, it may be said that Wet case scenarios resulted in slightly higher mean annual concentrations of nutrients, which could be explained by increased nutrient loads into the stream with the surface runoff. Dry case scenarios demonstrated general tendency toward reduction in nutrient content, except NO2+NO3 concentrations that resulted in slight increase for the Lower GMR. There is a general tendency toward decreasing of DO concentrations in the water by 2-6%.

259 CHAPTER V: LAND USE SCENARIO AND SIMULATION RESULTS

Overview

This Chapter discusses typical impacts of land use development on hydrology and water quality. This includes consequences of increasing the impervious cover within the watershed on streams’ hydrology, geomorphology, and water quality.

In addition, this Chapter provides details in the construction of land use change scenario for the

GMR basin. This includes more detailed procedure of modifying sub-basins and reaches in terms of IMPLND area cover, the magnitudes of change and spatial extent.

The results of Hydrologic and Water Quality modeling under Land Use Change scenario are also summarized and discussed.

5.1 IMPACTS OF LAND USE DEVELOPMENT

During urbanization, pervious spaces, including vegetated and open forested areas are converted to land uses that usually have increased areas of impervious surface, resulting in increased runoff volumes and pollutant loadings (U.S EPA, 1998).

Imperviousness represents the imprint of land development on the landscape. It is composed of two primary components: the rooftops and the transport system (roads, driveways, parking lots etc.) As it happens, the transport component often exceeds the rooftop component in terms of total impervious area created. For example, transport-related imperviousness comprised 63 to

70% of total impervious cover at the residential, commercial and industrial areas. This phenomenon is observed most often in suburban areas and reflects the recent ascendancy of the automobile in both our culture and landscape. The sharp increases in per capita vehicle ownership, trips taken, and miles traveled forced local planners to increase the relative size of the transport component of imperviousness over the last two decades (Schueler, 1999). Another aspect of imperviousness is associated with high/medium/low density residential areas and could range from 20 to nearly 60%, depending on the character of urbanization.

260 While urbanization may enhance the use of property under a wide range of environmental conditions, it typically results in changes to the physical, chemical and biological characteristics of the watershed (U.S EPA, 1993). As population density increases, there is a corresponding increase in pollutant loadings generated from human activities. These pollutants typically enter surface waters via runoff without undergoing treatment.

The effects of urbanization on aquatic resources can be organized into four categories. Figure

5.1 demonstrates these categories:

Hydrology Geomorphology

IMPACTS OF LAND USE DEVELOPMENT (URBANIZATION)

Water Habitat Quality

Hydrologic Effects of Urbanization:

One of the major impacts of urbanization on streams is the effect on stream hydrology.

Hydrologic and hydraulic changes occur in response to site cleaning, grading and the addition of impervious surfaces and maintained landscapes (Schueler, 1999). Hydrologic effects of urbanization could be summarized as follows: (1) Disruption of natural water balance; (2)

Increased flood peaks; (3) Increased stormwater runoff; (4) More frequent flooding and (5)

Increased bankfull flows.

261 In a developed setting, compared to pre-developed state, when much of the rainfall is absorbed by the surrounding vegetation, soil and ground cover, the water balance changes and a disproportionate amount of rainfall becomes surface runoff. Impervious surfaces such as rooftops, roads, parking lots and sidewalks, decrease the infiltration capacity of the ground and result in significantly increased volumes of runoff, changes in volumetric runoff coefficients, increased flood peaks and frequency of bankfull flows, decreasing the time needed for runoff to reach the stream and increasing the runoff velocity during storms due to the combined effects of higher peak discharges and the smoother hydraulic surfaces that occur as a result of development (EPA,

1993, Leopold, 1968, Schueler, 1987).

Geomorphologic Effects of Urbanization: Geomorphologic changes due to urbanization include: (1) Stream widening and erosion - urban streams begin to enlarge as impervious land exceeds 10-15%. It is characterized by downcutting, decreased channel stability, channel widening and embeddedness. Very often, when impervious cover exceeds 45-50%, engineers are called to channelize or stabilize stream channels; (2)

Reduced fish passage; (3) Degradation of habitat structure; (4) Fragmentation of riparian tree canopy; (5) Decreased substrate quality.

Effects of Urbanization on Water Quality: In addition to hydrologic and geomorphologic changes to the stream, urbanization directly impacts the quality of the receiving water. The pollutants that occur in urban areas vary widely, from common organic material to highly toxic metals. Many studies (Klein, 1985, Livingston and

McCarron, 1992) confirm that urbanization leads to degradation of urban waterways. Livingston and McCarron, (1992) concluded that urban runoff was the major source of pollutants loading to

Florida’s lakes and streams. Urbanization increases the amount of pollutants entering water bodies, such as sediment, nutrients, organic matter, trace metals, pesticides, hydrocarbons and others. During storm events, the quality of urban stormwater declines sharply, which adversely affects human and aquatic uses of downstream waters. For instance, Schueler, (1987)

262 demonstrates that a one-acre parking lot sends about 15 pounds (5.6 kg) of nitrogen and 2 pounds

(~1 kg) of phosphorus into the nearby waterway each year, compared with 2 pounds of nitrogen and 1/2 pound of phosphorus from a one-acre meadow. Excessive nutrient loading to aquatic ecosystems can result in eutrophication and depressed DO levels due to elevated phytoplankton populations. Eutrophication-induced hypoxia and anoxia can result in fish kills and widespread destruction of bethic habitats (Harper and Gullient, 1989).

Increased water temperature is another indicator of impacts of urbanization on water quality.

Impervious surfaces act as heat collectors, heating urban runoff as it passes over the impervious surface. Galli and Dubose, (1990) studied the effects of urbanization on water temperature and concluded that intensive urbanization of the watershed can increase temperature as much as 2-10 degrees F in urban streams. Stream temperature is a very important parameter for fish populations, and temperature variability can dictate the growth of aquatic insects and timing of migration and molts. Thermal loading disrupts aquatic organisms that have their temperature limits. Table 5.1 illustrates examples of pollutant loadings from urban areas. Table 5.2 describes potential sources of urban runoff pollutants.

Table 5.1 Estimated Mean Runoff concentrations for land uses (based on Nationwide urban

Runoff Program, Whalen and Cullum, 1989 and U.S EPA, 1993).

Parameter Residential Commercial Industrial

NO2 +NO3 (mg/l) 0.23 1.5 1.6 Total P (mg/l) 1.8 0.8 0.93 Copper (µg/l) 56 50 32 Zinc (µg/l) 254 418 1063 BOD (mg/l) 13 14 62

263 Table 5.2 Sources of urban Runoff Pollutants (source: USEPA, 1993, Woodward-Clyde, 1990) Source Pollutants of concern Erosion Sediment and attached soil nutrients, organic matter Atmospheric Deposition Hydrocarbons emitted from automobiles, metals and other chemicals from industrial and commercial activities Construction materials Metals flashing from gutters, galvanized pipes and metal plating, paint and wood Manufactured products Heavy metals, PAHs, other volatiles and pesticides and phenols from automobile use, industrial use and other uses Plant and animals Plant debris and animal excrement Non-storm water Discharges from sanitary sewage and industrial wastewater to storm drainage connections systems Onsite disposal systems Nutrients and pathogens from failing or improperly sited systems

Effects of Urbanization on Habitat: Along with changes in hydrology, geomorphology and water quality, the habitat value of urban streams diminishes with increased impervious cover. There are numerous impacts to the aquatic habitat as well as the riparian corridor, particularly along the streamside zone: (1) Decline in habitat value of streams; (2) Loss of buffer zones; (3) Creation of fish barriers; (4) Increased algae growth and others. Consequences of urbanization impacts on habitat include decline in aquatic insect diversity, decline in fish habitat quality and diversity, loss of sensitive coldwater species, decline in wetland plant and animal community diversity, and so forth.

264 5.2 LAND USE SCENARIO DEVELOPMENT FOR GMR BASIN

As it was discussed in Chapter 2, the Land Use Change scenarios for the Upper and Lower

GMR basins were developed after the analysis of population dynamics, former land use changes by counties, general economic development in the region of study, including land use development plans. The IMPLND section of the HSPF was increased by an average of 30%.

Taking into consideration the potential variability of urban area development within the Upper and Lower basins, some sub-basins were modified at a larger extent in terms of acres of impervious land, some at smaller extent. The decisive roles when performing these changes were

(1) the major transportation routes crossing the urban areas, (2) rates of low/high residential developments for the past 10 years and (3) general economic development of the regions on

County levels. Therefore, for example, some sub-basins urban area segments were increased by a magnitude of about 10% (being as slowly developing areas) and some gained as much as 70% compared to current conditions. However, detailed plans of land use development were not available for each county located within the area of study. Lack of this kind of information weakened the validity and accuracy of constructed Land Use scenario for this study, but, overall, it is acceptable to achieve the major goal - to quantitatively and qualitatively assess the effects of urbanization on hydrology and water quality of the GMR. Summarizing the results from Tables

5.3 and 5.4, urban area of the Upper GMR basin was increased overall by 33%, and the Lower

GMR by 56% (lower portion of the basin is more urbanized than the upper one), compared to

Current Land Use. However, it does not necessarily mean that every sub-basin’s urban area had been changed. Many urban areas located within the GMR basin boundaries were not modified due to certain reasons, e.g remoteness from major transportation routes, low population density etc.

265

Table 5.3 demonstrates the sub-basins and reaches, which were modified in terms of urban area coverage with affiliated urban regions, centers and types of urbanization.

Upper GMR Basin

Reach, Urbanized Urbanized Change Sub-watershed area of total area of total from Urban Type of # in HSPF area area (Future) Current Region/Center Urbanization (Current) (acres, km2, to Future Effect acres, (km2, %) (% of %) total area) Stillwater River sub-basin Greenville creek, 2172 (8.8, 2) 15230 (61.6, +10% Greenville, OH Low density #12 12) residential Dayton, OH, Low/high density Stillwater River, 4300 (17.4, 3) 60341 (244, +37% Vandalia, OH residential, #27 40) and commercial, Englewood, industry and OH transportation Mad River sub- basin Dugan Run, #11 1182 (4.7, 7) 5025 (20.3, +23% Urbana, OH Low/high density 30) residential, commercial Mad river, #18 3711 (15, 4) 22100 (89.4, +21% Urbana, OH Low/high density 25) residential, commercial Buck creek, #19 5690 (23.0, 7) 36570 (148, +38% Springfield, Low/high density 45) OH residential Dayton, OH, Low/high density Huber Heights, residential, Mud Run, #21 981 (3.9, 6) 5584 (22.6, +29% OH, Fairborn, commercial, 35) OH, transportation, Springfield, industry OH Dayton, OH, Low/high density Huber Heights, residential, Mud Run, #23 608 (2.4, 19) 2580 (10.4, +61% OH, Fairborn, commercial, 80) OH, transportation, industry Dayton, OH, Low/high density Mad River, #22 3123 (12.6, 7) 22662 (91.7, +43% Huber Heights, residential, 50) OH commercial, transportation Low/high density Mad River, #28 937 (3.8, 72) 1233 (5.0, 95) +23% Dayton, OH residential, commercial,

266

Low/high density residential, Mad River, #24 6475 (26.2, 13525 (54.7, +47% Dayton, OH commercial, 43) 90) transportation, industry

GMR sub-basin at Taylorville, OH South Fork, #2 2032 (8.22, 2) 14306 (57.9, +13% Indian Lake Residential areas 15) shore Turtle creek, #8 500 (2.02, 2558 (10.3, 7) +5.5% Sidney, OH Low density 1.5) residential Great Miami 3097 (12.5, 2) 24031 (97.2, +13% Sidney, OH Low density river, #6 15) residential Great Miami 5258 (21.3, 6) 46740 (190, +49% Piqua, OH, Low density river, #15 55) Troy, OH residential, commercial, transportation Great Miami 1300 (5.3, 9158 (37.0, +51.5 Sidney, OH Low/high density river, #9 8.5) 60) residential, commercial, transportation Great Miami 5553 (22.4, 22451 (90.8, +68% Dayton, OH, Low/high density river, #26 22) 90) Vandalia, OH residential, commercial, transportation, industry

Table 5.4 Lower GMR Basin (sub-basins and reaches, which were modified in terms of urban area coverage with affiliated urban regions, centers and types of urbanization).

Reach, Urbanized Urbanized Change Sub-watershed # area of total area of total from Urban Type of in HSPF area area (Future) Current to Region/Center Urbanization (Current) (acres, km2, Future (% Effect acres, (km2, %) of total %) area) High/low residential, Wolf creek, #10 12305 (49.8, 31472 (127.3, +40% Dayton, OH industry, 25) 65) commercial, transportation High/low residential, Holes creek, #11 6025 (24.4, 43) 12670 (51.3, +47% Dayton, OH industry, 90) commercial, transportation Bear creek, #12 1038 (4.2, 5) 21077 (85.3, +65% Dayton, OH High/low density 70) residential, commercial

267

High/low density residential, Great Miami river, 3250 (13.1, 28) 10146 (41.0, +62% Dayton, OH commercial, #13 90) industry, transportation Clear creek, #19 2479 (10.0, 5) 20990 (125.4, +50% Middletown, High/low density 65) OH residential, commercial, transportation Dayton, OH, High/low density Great Miami river, Middletown, residential, #18 2768 (11.2, 16) 15955 (64.5, +74% OH commercial, 90) industry, transportation Great Miami river, 70 (0.28, 12) 350 (1.4, 72) +60% Middletown, High/low density #23 OH residential, commercial Dicks creek, #26 3714 (15.0, 22) 6590 (26.7, 40) +18% Middletown, High/low density OH residential, commercial Twin creek, #17 1388 (5.6, 10) 3870 (15.6, 70) +60% Dayton, OH, High/low density Middletown, residential, OH commercial Great Miami river, 115 (0.46, 17) 596 (2.4, 90) +73% Middletown, High/low density #25 OH, Hamilton, residential, OH commercial High/low density Great Miami river, 1700 (6.8, 10) 13116 (53.0, +75% Middletown, residential, #30 85) OH, Hamilton, commercial, OH industry, transportation Sevenmile creek, 450 (1.82, 5) 4437 (17.9, 45) +40% Hamilton, OH High/low density #32 residential High/low density residential, Great Miami river, 4689 (18.9, 31) 13418 (54.3, +59% Hamilton, OH commercial, #33 90) industry, transportation

268 Figures 5.2 – 5.5 graphically illustrate base case land use scenario and hypothetically created

Future Land Use scenarios that were produced based on MRLC 1990-s reference land use cover map.

Figure 5.2 Current (Base Case) Land Use for the Upper GMR Basin

Current Land Use k I-75 e e r

i p

p i for the UPPER GMR 1 n i h c u 2 Basin (Base Case) M BELLEFONTAINE

ek M e i Cr 4 a m ie m i a or R reek L i Mile C 6 v

e Legend 3 r T SID NEY

r t l

8 10 C

Ma jor Roa ds r e 9 5 e k UR BANA Streams 7 I-70

13 un11 R PIQUA an S ug t MRLC Land Use i D l l GREeEk NVILLE w e a r k 14 t

C e e

le r e 18 Urban il r v R M n C e i 15 a re v t k s d 19 e e

G o r e R r H L C Barren or Mining i v o k TROY17 e n c e r u 12 y B

Transitional C r e e 27 16 k Agriculture - Cropland 22 20 DAYTON Agriculture - Pasture un SPRINGFIELD R 26 ud 21 Forest M 23 r 24 ive Upland Shrub Land R ad 25M 28 Grass Land

Water Wetlands

02040Miles Scale

269 Figure 5.3 Hypothetically created Future Land Use scenario for Upper GMR Basin.

Future Hypothetical N

Land Use Scenario k e e r

I-75 i p

p i for the UPPER GMR 1 n i h c u 2 Basin M BELLEFONTAINE

ek M e i Cr 4 a m ie m i a or R reek L i Mile C 6 v

e 3 r T SID NEY

r LEGEND t l I-70 e

8 10 C

r e 9 5 e Major Roads k UR BANA 7 Streams 13 un11 R PIQUA an S ug t i D l l FUTURE LAND USE SCENARIO GREeEk NVILLE w e a r k 14 t

C e e

le r e 18 il r Urban v R M n C e i 15 a re v t k s d 19 e e

G o r e R r H L C i Barren or Mining v o k TROY17 e n c e r u 12 y B

Transitional C r e e 27 16 k Agriculture - Cropland 22 20 Agriculture - Pasture DAYTON un SPRINGFIELD R 26 ud Forest M 23 21

Upland Shrub Land r 24 ive R ad Grass Land 25M 28 Water

Wetlands

02040Miles Scale

270 Figure 5.4 Current (Base Case) Land Use for the Lower GMR Basin

Current Land Use 4 for the LOWER GMR 1 N 3 10 Basin (Base Case) 7 5 8 6 14 DAYTON

12 13 16 11 20 29 9 21 15 Legend 18 17

Ma jor Roa ds 24 23 19 22 MIDDLETOWN Streams 28 25 32 26 MRLC Land Use 30 27 36 Urban 31 Barren or Mining 33 Transitional HAMILTON Agriculture - Cropland 34 35 Agriculture - Pasture Forest 37

Upland Shrub Land Grass Land

Water Wetlands

02040Miles Scale

271 Figure 5.5 Hypothetically created Future Land Use scenario for Lower GMR Basin.

Future Hypothetical N 12 13 Land Use Scenario 16 DA11 YTON for the LOWER GMR 20 29 9 21 15 Basin 18 17

24 23 19 22 MIDDLETOWN LEGEND 28 25 Major Roads 32 27 26 Streams 30 36 FUTURE LAND USE SCENARIO 31 Urban 33

Barren or Mining

Transitional HAMILTON Agriculture - Cropland 34 35

Agriculture - Pasture

Forest 37 Upland Shrub Land

Grass Lan d

Water

Wetlands

02040Miles Scale

272 5.3 HYDROLOGY UNDER FUTURE LAND USE SCENARIO

Urbanization has significantly affected flow regime of the Upper and Lower GMR. Results from hydrology simulation under Future Land Use Scenario produced an increase of mean annual flow for both, Upper GMR and Lower GMR basins (Figures 5.6-5.9 through 5.10-5.13,Tables

5.5 – 5.6 and Auxiliary plots in Appendix C). The flow of Upper GMR resulted in 31% increase compared to Base Case Scenario (from 2347 ft3/s (2.1 km3) to 3077 ft3/s (2.74 km3)). Mean annual flow of Lower GMR experienced an increase by 43% (from 2984 ft3/s (2.66 km3) to 4322 ft3/s (3.8 km3)) (Table 5.5). Streams in smaller sub-watersheds within the Upper GMR basin such as Stillwater and Mad river watersheds demonstrated increase in mean annual flow by 21%

(Stillwater R and Greenville Creek) and by 26% (Mad river).

Increase in the proportion of urban area in the watershed disrupts and changes the natural water balance that causes alterations in seasonal flow regime. Primarily it is reflected in increased flood peaks due to increased stormwater runoff. Also, increased bankfull flows are typical for urbanized watersheds. For example, comparing the hydrographs for the Upper GMR at Dayton,

OH composed for Future and Current land Use (Figures 5.6-5.9), it is seen that in some months flood peaks increased almost twice in magnitudes. Numerous additional flood peaks appeared on the hydrograph as a result of simulation under Future Land Use scenario. Flow duration curves supplement these results and indicate an overall increase of base flow (flow 80% chance exceeded) by 5 to 10% (in average from 28,000 to about 31,000 ft3/s), along with higher discharges during high flow seasons (flow 5-7% chance exceeded) by about 20% to more than 5 times.

Hydrographs and flow duration curves resulted from hydrologic simulation of the Lower GMR at Hamilton, OH reflect the same patterns in seasonal flow alterations, but greater difference in magnitudes (Figures 5.10-5.13). Flood peaks increased by more than 35%, and base flow discharge values more than 2-8 times (from around 40-700 ft3/s to 300-1100 ft3/s).

273 Table 5.5 Hydrologic modeling results under Future Land Use Change Scenario

Upper GMR basin Upper GMR at Taylorsville, OH (USGS #03263000) MAD river near Dayton, OH (USGS #03270000) FUTURE Mean simulated Mean simulated % Difference: Mean simulated Mean simulated % Difference: LAND USE annual flow, annual flow, Base Case and annual flow, annual flow, Base Case and CHANGE Base Case Scenario under Land Use change Land use change (cfs) under Land Use change Land use change SCENARIO (cfs) (cfs) (cfs) 1985-1990 995 1402 +41% 655 838 +28% 1990-1995 1253 1652 +32% 801 1001 +25% 1980-1995 1008 1415 +40% 703 884 +26%

Upper GMR basin STILLWATER R at Englewood, OH (USGS #03266000) SUM OF FLOWS: GMR, MAD R and STILLWATER river * FUTURE Mean simulated Mean simulated % Difference: Mean simulated Mean simulated % Difference: LAND USE annual flow, annual flow, Base Case and annual flow, annual flow, Base Case and CHANGE Base Case Scenario under Land Use change Land use change (cfs, km3) under Land Use change Land use change SCENARIO (cfs) (cfs) (cfs, km3) 1985-1990 660 805 +22% 2305 3045 +32% 1990-1995 740 987 +33% 2922 3640 +24% 1980-1995 675 816 +21% 2347 (2.1) 3077 (2.74) +31% * Creates a flow of Great Miami River below Dayton, OH

Lower GMR basin Lower GMR at Hamilton,OH (USGS #03274000) FUTURE Mean simulated Mean simulated % Difference: LAND USE annual flow, annual flow, Base Case and CHANGE Base Case Scenario under Land Use change Land use change SCENARIO (cfs, km3) (cfs, km3) 1985-1990 2998 4284 +43% 1990-1995 3150 4963 +57% 1980-1995 2984 (2.66) 4322 (3.8) +43%

UPPER GMR AT DAYTON, OH Figures 5.6-5.9 Results of Hydrology simulation under Current and Future Land Use Scenarios, Upper GMR at Dayton, OH Flow Regime

Figure 5.6

Figure 5.7

Flow duration curves

274

Illustration of seasonal flow (increased flood peaks and bankfull discharges) Figure 5.8

Figure 5.9

Flow duration curves

275 LOWER GMR AT HAMILTON, OH Figures 5.10-5.13 Results of Hydrology simulation under Current and Future Land Use Scenarios, Upper GMR at Dayton, OH

Flow regime

Figure 5.10

Figure 5.11

Flow duration curves

276 Illustration of seasonal flow (increased flood peaks and bankfull discharges) Figure 5.12

Figure 5.13

Flow duration curves

277 5.4 WATER QUALITY UNDER FUTURE LAND USE SCENARIO

Water temperature

Simulation of water temperature under Future Land Use scenario produced the results that are slightly higher when compared with the Base Case scenario. Mean annual water temperature in the Upper GMR at Dayton, OH resulted in 2.7% increase (from 44.5 to 45.7 F), and +1.6% increase (56.5 to 57.4 F) for the Lower GMR at Hamilton, OH (Figures 5.14-5.23 and Table

5.6). The model predicted no change in the pattern of water temperature seasonal fluctuation.

Increased water temperature is a result of thermal loadings into the stream via surface runoff from urbanized areas, which has higher water temperature than, for instance, the surface runoff from pervious land segments.

Dissolved Oxygen

The model produced lower mean annual DO concentrations for the Upper and Lower GMR runs: drop from 11.2 to 10.2 mg/l or 8.9% in the Upper GMR and reduced DO concentrations from 9.3 to 9.1 (2.1% reduction) in the Lower GMR (Table 5.6). However, it is not a very sharp decline in DO concentrations, considering the significant increase in proportion of urban area introduced in Future Land Use scenario. The critical level of chronic DO concentrations is listed by US.EPA Water Quality Criteria for freshwater bodies to be above 5 mg/l. At the same time, the model resulted close to 5 mg/l concentrations on a daily basis (Figures 5.15 and 5.20).

Generally these minimums are associated with summer months (June, July, August).

Excessive nutrient loading to aquatic system of the GMR, could enhance euthrophication and result in depressed DO levels.

278 Table 5.6 Water Quality modeling results under Future Land Use Change Scenario

FUTURE LAND USE SCENARIO Upper GMR at Taylorsville, OH (USGS #03263000) MAD river near Dayton, OH (USGS #03270000) Mean simulated Mean simulated % Difference: Mean simulated Mean simulated % Difference: concentrations, concentrations, Base Case and concentrations, concentrations, Base Case and Constituent (Units) Base Case Scenario under Land Use Change Land use change Base Case Scenario under Land Use Change Land use change (1980-1986) Scenario (1980-1986) Scenario Scenario (1980-1986) (1980-1986) Water temperature (F) 43.2 44.0 +2% 44.0 46.0 +5% Dissolved Oxygen (MG/L) 10.9 10.8 -1% 11.5 11.8 +2.6% Total Phosphorus (MG/L) 0.32 0.5 +56% 0.27 0.43 +59% Total Nitrogen Ammonia (MG/L) 0.17 0.17 - 0.10 0.13 +30% Nitrites and Nitrates (MG/L) 2.98 3.06 +3% 2.90 3.00 +3.5%

FUTURE LAND USE SCENARIO STILLWATER R at Englewood, OH (USGS #03266000) SUM OF FLOWS: GMR, MAD R and STILLWATER river * Mean simulated Mean simulated % Difference: Mean simulated Mean simulated % Difference: concentrations, concentrations, Base Case and concentrations, concentrations, Base Case and Constituent (Units) Base Case Scenario under Land Use Change Land use change Base Case Scenario under Land Use Change Land use change (1980-1986) Scenario (1980-1988) Scenario Scenario (1980-1986) (1980-1988) Water temperature (F) 48.0 51.0 +6.2% 44.5 45.7 +2.7% Dissolved Oxygen (MG/L) 11.4 11.2 -1.7% 11.2 10.2 -8.9% Total Phosphorus (MG/L) 0.37 0.53 +43% 0.33 0.45 +36% Total Nitrogen Ammonia (MG/L) 0.10 0.11 +10% 0.13 0.14 +7.7% Nitrites and Nitrates (MG/L) 3.51 3.58 +2% 3.15 3.5 +11% * Creates a flow of Great Miami River below Dayton, OH

FUTURE LAND USE SCENARIO Lower GMR at Hamilton,OH (USGS #03274000) Mean simulated Mean simulated % Difference: concentrations, concentrations, Base Case and Constituent (Units) Base Case Scenario under Land Use Change Land use change (1980-1988) Scenario Scenario (1980-1988) Water temperature (F) 56.5 57.4 +1.6% Dissolved Oxygen (MG/L) 9.3 9.1 -2.1% Total Phosphorus (MG/L) 0.42 0.58 +38% Total Nitrogen Ammonia (MG/L) 0.21 0.25 +19% Nitrites and Nitrates (MG/L) 3.52 4.10 +16.5% Nutrients

The most dramatic results produced by simulation of nutrients under the future Land Use scenario are demonstrated by Total Phosphorus mean annual concentrations change. Urbanization significantly increased mean annual total Phosphorus concentrations in the streams (Table 5.6,

Figures 5.16 and 5.21). For the Upper GMR at Dayton, OH this increase is as much as 36%

(from 0.33 to 0.45 mg/l). The simulation results for the Lower GMR at Hamilton, OH produced a

38% increase (from 0.42 to 0.58 mg/l). The target (critical) levels for Phosphorus concentrations in the stream are set to 0.2 mg/l for chronic and up to 1 mg/l for acute (US EPA, 1993). As it is seen, urbanization has a profound effect on phosphorus loads. Moreover, in most cases,

Phosphorus tends to be the limiting nutrient in fresh water ecosystems. That is why, the significant increase in Phosphorus concentrations, as simulated by the model, could have very harmful effect on the ecosystems of the Upper and Lower GMR basins.

Simulated mean annual Total Nitrogen Ammonia concentrations demonstrate overall increase compared to the Base Case scenario. The Upper GMR at Dayton, OH experienced a 7.7% raise

(from 0.13 to 0.14 mg/l), when in the Lower GMR at Hamilton, OH the mean annual value increased by 19% (from 0.21 to 0.25 mg/l) (Figures 5.17 and 5.22, Table 5.6). However, these values are still within the U.S EPA Water Quality Criteria (for Nitrogen Ammonia, concentrations higher than 0.2-0.5 mg/l are considered as toxic to freshwater aquatic life).

In comparisons of NO2+NO3 mean annual concentrations, the modeling results indicate an increase in the Upper and Lower GMR basins, by 11% (from 3.15 to 3.5 mg/l) and 16.5% (from

3.52 to 4.10), accordingly (Figures 5.18 and 5.22, Table 5.6). Concentrations of Nitrate and

Nitrate nitrogen are greater than 10 mg/l is considered unsafe for human consumption (US EPA,

1993). Therefore, in terms of safety for water supply purposes, the simulated results are considered to be good. At the same time, elevated levels of NO2+NO3 in the water body could have variable effects on living aquatic species and might contribute to the processes of eutrophication.

279 UPPER GMR AT DAYTON, OH

Water Temperature (F) Figure 5.14

Dissolved Oxygen (mg/l)

Figure 5.15

280

Total Phosphorus (mg/l)

Figure 5.16

Total Nitrogen Ammonia (mg/l)

Figure 5.17

281 NO2+NO3, mg/l

Figure 5.18

282 LOWER GMR AT HAMILTON, OH

Water Temperature (F) Figure 5.19

DO (mg/l) Figure 5.20

283 Total Phosphorus (mg/l) Figure 5.21

Total Nitrogen Ammonia (mg/l) Figure 5.22

284 NO2 +NO3 (mg/l) Figure 5.23

285 Summary Summarizing the results from the hydrology and water quality simulations under hypothetical

Future Land Use Scenario, the following conclusions are drawn:

(1) Urbanization of the Upper and Lower GMR basins has shown a significant effect on flow

regime. The mean annual flow of the Upper GMR under Future Land Use conditions

increased by 31% (from mean annual discharge values of 2347 to 3077 ft3/s or 2.1 to

2.74 km3, by volumes), compared to Current conditions. The simulated values of flow in

the Lower GMR resulted in a 43% increase (from 2984 ft3/s to 4322 ft3/s or 2.66 km3 to

3.8 km3, by volumes). The change in flow was not only by magnitude, but also by

alterations in seasonal regimes. Urbanization of the area will increase flood peaks

resulted from increased stormwater runoff, as well as it will increase bankfull flows,

especially in the Lower GMR basin.

(2) Water Quality of the GMR experienced significant effect of Urbanization as well. Water

temperature simulations under Future Land use scenario produced an increase by 2.7%

and 1.6% for the Upper and Lower GMR, respectively. Elevated water temperature is

explained by surface runoff coming from newly introduced urban areas, which has

greater water temperature than the surface runoff from pervious lands (agricultural or

pasture). Increase in ambient water temperature might produce a negative impact on fish,

causing loss of sensitive coldwater species, affecting general health of species, and their

reproduction activity.

(3) Concentrations of DO produced by the model are slightly lower than the ones in the Base

Case scenario. Overall, for both Upper and Lower GMR basins, the mean annual DO

concentrations were reduced by 5.5%. The mean annual values of DO concentrations

resulted in the simulation run are much greater than the ones listed by the U.S EPA.

However, the model produced minimum daily-based concentrations, which are close to

286 the critical target (5 mg/l). Generally, it is a summer limiting period (June, July and

August), when phytoplankton and biomass growth rates are in the most active stage.

(4) Simulation of nutrients revealed quite significant changes. The mean annual

concentrations of Phosphorus increased by 36% (from 0.33 to 0.45 mg/l) and 38% (from

0.42-0.58 mg/l) for the Upper and Lower GMR, accordingly. It is a significant increase,

and it could have a harmful effect on the ecosystem of the Great Miami River and its

tributaries.

(5) Total Nitrogen Ammonia mean annual concentrations, produced by the model,

demonstrate an increase by 7.7% (from 0.13 to 0.14 mg/l) for the Upper GMR, and by

19% (from 0.21 to 0.25 mg/l) for the Lower GMR. Generally, the concentrations of

Ammonia in water increases with increasing pH and temperature. The produced results

do not contradict with limits set by the U.S EPA. However, combined effect of

dramatically increased Phosphorus concentrations along with increased Nitrogen

Ammonia concentrations in the streams, would impair water quality of the GMR and

enhance the processes of eutrophication resulting in increased algae growth, decline in

aquatic insect diversity, decline in fish habitat quality, and fish diversity.

(6) Expansion of urbanized areas resulted in a relatively significant increase of NO2+NO3

mean annual concentrations for the Upper and Lower GMR by 11% (from 3.15 to 3.5

mg/l) and 16.5% (from 3.52 to 4.10 mg/l), accordingly. Elevated levels of NO2+NO3 in

freshwater ecosystems could contribute to the process if eutrophication with known

adverse consequences to aquatic habitat.

287 CHAPTER VI: SIMULATING THE COMBINED EFFECTS OF CLIMATE AND LAND USE CHANGES

6.1 INTEGRATED ASSESSMENT OF COMBINED CLIMATE AND LAND USE CHANGES

It is important for policymakers to be able to put climate change impacts in the context of other social, economic, and technological conditions, such as: (1) Demographic change; (2) Land-use change; (3) Land degradation; (4) Air and water pollution; (5) Economic and social change and development (including technological change) (IPCC, 1998).

All of the potential impacts of climate and land use change may vary regionally. Variations in the regional distribution of impacts within the U.S. need to be clearly articulated for policymakers

(IPCC, 1998). Currently, most of the GCM models operate on a cell size of 200 by 200 km2.

Therefore, it is difficult to accurately predict regional climatic changes for the next 50-100 years using these models. At the same time, urbanization and processes related to land use changes significantly differ within the U.S. regions: some areas exhibit active and increasing pattern of urban sprawl, some regions do not. Figure 6.1 illustrates the complex and integrative nature among the relationships of climate, natural and human systems (IPCC, 1998):

288 Figure 6.1 The integrative structure and interrelationships of changes in the climate system with the natural and human systems.

As it is shown on the Figure 6.1, climate is a primary system in our biosphere. Any changes of climate may cause direct effects on the physical, biological and socioeconomic systems. Some consequences are attributed to the indirect links between climate-sensitive systems and related social and economic activities. Some result from feedbacks between human activities that affect the climate system, which in turn can lead to further impacts (e.g., human activities affecting the climate system—which, in turn, could lead to further impacts on human health, the environment, and socioeconomic systems). Land use changes, being as a significant part of human activities, generate second and higher order impacts on the natural system (e.g., hydrologic system).

The primary goal of this research work is to examine the complex interrelationships and impacts of human activities in the forms of non-point source pollution (land use changes), point- source pollution and global climate changes on the natural system (hydrological regime and water quality) in the Great Miami River by simulating hypothetical scenarios and analyzing the responses.

289 6.2 FLOW REGIME UNDER COMBINED FUTURE CLIMATE AND LAND USE

SCENARIOS

Hot and Dry Scenario plus Future Land Use (HD+LU)

The results from the flow simulation of the Upper GMR under combined Hot and Dry (HD) with Future Land Use (LU) scenarios produced a decrease in the mean annual flow from 2347 ft3/s (2.1 km3/year) down to 1688 ft3/s (1.5 km3/year) which presents a 28% decline from the

Base Case Scenario. The Lower GMR flow simulations revealed in 11% reduction in the mean annual flow (from 2984 ft3/s (2.66 km3/year) down to 2653 ft3/s (2.36 km3/year) (Figures 6.2-6.9,

Table 6.1). However, the magnitudes of decrease in mean annual flows for both, Upper and

Lower GMR are lower than the model produced under Hot and Dry (HD) climate scenario only

(60% decline for the Upper GMR and 62% reduction for the Lower GMR). This is attributed to the effect of the sub-basins urbanization (increased areas of imperviousness). With a deficit of moisture and high evapotranspiration rates in the hydrological cycle (HD scenario), and introduction of large areas of impervious lands, most of the precipitation would likely to evaporate from the ground or become surface runoff shortly after the precipitation event, affecting the shape of hydrographs (e.g., making it more “flashy”, but in most cases, smaller in magnitudes) (Figures 6.2, 6.4, 6.6 and 6.8). The remaining portion of precipitation would likely to recharge the aquifer systems. The hydraulic connectivity between streams and underlying aquifers would play a more significant role in the conditions of moisture deficit. This is shown on flow duration curves constructed for the Upper and Lower GMR (Figures 6.3, 6.5, 6.7 and 6.9).

The flow frequency curves show increased volumes of bankfull flows under combined HD and

LU scenarios: in the case of Upper GMR at Dayton, OH the discharges of low-flow regime increased 2-3 times, and even more dramatically in the Lower GMR (more than 10 times in magnitude).

290 Table 6.1 Hydrologic modeling results from simulating the combined effects of climate and future land-use changes on flow regime

% Difference % Difference GMR R % Difference Joined Flow % Difference Lower GMR % Difference Scenario Stillwater R compared to Mad R compared to at Taylorsville compared to GMR at compared to at compared to Base Case Base Case OH Base Case Dayton, OH Base Case Hamilton, OH Base Case

Base Case 675 703 1008 2347 2984 Hot and Dry only (HD) 244 -64.0 291 -58.0 393 -61.0 928 (0.83) -60.0 1119 (0.99) -62.0 Land Use only (LU) 816 +21.0 884 +26.0 1415 +40.0 3077 (2.74) +31.0 4322 (3.8) +45.0 HD+LU 394 -41.0 467 -33.0 828 -18.0 1688 -28.0 2653 -11.0 Hot and Wet only (HW) 858 +27.0 887 +26.0 1272 +26.0 3017 (2.7) +28.0 3494 (3.11) +17.0 Land Use only (LU) 816 +21.0 884 +26.0 1415 +40.0 3077 (2.74) +31.0 4322 (3.8) +45.0 HW+LU 1022 +51.0 1042 +48.0 1711 +69.0 3775 +61.0 5080 +70.0 Warm and Dry only (WD) 309 -54.0 350 -50.0 484 -52.0 1144 (1.0) -51.0 1363 (1.21) -54.0 Land Use only (LU) 816 +21.0 884 +26.0 1415 +40.0 3077 (2.74) +31.0 4322 (3.8) +45.0 WD+LU 450 -33 508 -28.0 905 -10.0 1863 -21.0 2928 -2.0 Warm and Wet only (WW) 984 +46.0 1003 +42.0 1450 +44.0 3436 (3.06) +46.0 3968 (3.54) +33.0 Land Use only 816 +21.0 884 +26.0 1415 +40.0 3077 (2.74) +31.0 4322 (3.8) +45.0 WW+LU 1129 +67.0 1127 +60.0 1857 +84.0 4112 +75.0 5464 +83.0 UPPER GMR AT DAYTON, OH HD (Hot and Dry) SCENARIO+FUTURE HYPOTHETICAL LAND USE SCENARIO Figures 6.2-6.5 Results of Hydrologic simulation under combined Hot and Dry and Future Hypothetical Land Use Scenarios, Upper GMR at Dayton, OH Flow Regime

Figure 6.2

HD+LU

Figure 6.3

HD+LU

Flow duration curves

291 Illustration of seasonal flow (increased flood peaks and bankfull discharges) Figure 6.4

HD+LU

Figure 6.5

HD+LU

Flow duration curves

292 LOWER GMR AT HAMILTON, OH HD (Hot and Dry) SCENARIO+FUTURE HYPOTHETICAL LAND USE SCENARIO Figures 6.6-6.9 Results of Hydrologic simulation under combined Hot and Dry and Future Hypothetical Land Use Scenarios, Lower GMR at Hamilton, OH Flow Regime

Figure 6.6

HD+LU

Figure 6.7

HD+LU

Flow duration curves

293

Illustration of seasonal flow (increased flood peaks and bankfull discharges) Figure 6.8

Figure 6.9

HD+LU

Flow duration curves

294 Hot and Wet Scenario plus Future Land Use (HW+LU)

The comparison of the simulated mean annual flows under Hot and Wet (HW) and

Hypothetical Future Land Use with Base Case scenario and the ratios between them for the Upper

GMR basin, demonstrates that simulated flow under HW+LU scenario is 61% higher than the

Base case (3775 ft3/s (3.36 km3/year) and 2347 ft3/s (2.1 km3/year). The hydrologic model of the

Lower GMR produced a 70% increase in the mean annual flow under HW+LU scenario (from

2984 ft3/s (2.66 km3/year) to 5080 ft3/s (4.53 km3/year) (Table 6.1). Similar to HD+LU scenario, the hydrologic system responded dramatically to urbanization of the watershed area. Thus, for instance, simulation run under “HW only” scenario produced 17% increase in mean annual flow for the Lower, and 27 % increase for the Upper GMR basins, which is 30 to 40% smaller than in

HW+LU scenario. Increased imperviousness of the area would cause less amounts of moisture to percolate and infiltrate, become throughflow, and recharge the aquifers resulting in “flashy” hydrograph shapes and increased bankfull flows. The model predicted the hydrograph of the

Lower GMR to be more “flashy”, which could be explained by a larger percent of imperviousness in the lower portion of the GMR basin (Figures 6.10, 6.12, 6.14 and 6.16). Comparing the flow duration curves constructed for HD+LU and HW+LU scenarios, it is seen, that in Wet case scenario, the model simulated larger peak flows than for the Dry scenario (Figures 6.3, 6.5, 6.7,

6.9, 6.11, 6.13, 6.15 and 6.17).

295 UPPER GMR AT DAYTON, OH HW (Hot and Wet) SCENARIO+FUTURE HYPOTHETICAL LAND USE SCENARIO Figures 6.10-6.13 Results of Hydrologic simulation under combined Hot and Wet and Future Hypothetical Land Use Scenarios, Upper GMR at Dayton, OH Flow Regime

Figure 6.10

HW+LU

Figure 6.11

HW+LU

Flow duration curves

296

Illustration of seasonal flow (increased flood peaks and bankfull discharges) Figure 6.12

HW+LU

Figure 6.13

HW+LU

Flow duration curves

297 LOWER GMR AT HAMILTON, OH HW (Hot and Wet) SCENARIO+FUTURE HYPOTHETICAL LAND USE SCENARIO Figures 6.14-6.17 Results of Hydrologic simulation under combined Hot and Wet and Future Hypothetical Land Use Scenarios, Lower GMR at Hamilton, OH Flow Regime

Figure 6.14

HW+LU

Figure 6.15

HW+LU

Flow duration curves

298 Illustration of seasonal flow (increased flood peaks and bankfull discharges) Figure 6.16

HW+LU

Figure 6.17

HW+LU

Flow duration curves

299 Warm and Dry Scenario plus Future Land Use (WD+LU)

Examining the results from Warm group scenarios, the model produced a decrease of mean annual flow under WD+LU scenario for the Upper GMR by 21% (from 2347 ft3/s (2.1 km3/year) to 1863 ft3/s (1.66 km3/year) and relatively small (2%) decline for the Lower GMR (from 2984 ft3/s (2.66 km3/year) to 2928 ft3/s (2.6 km3/ear) (Table 6.1). It is a fairly small reduction of mean annual flow for the Lower GMR, indicating the resultant effect of watershed urbanization and smaller losses of moisture due to evapotraspiration in the hydrological cycle. The Upper GMR basin is less urbanized in the Hypothetical Land Use scenario, so more moisture would be trapped due to infiltration and would not reach the stream as a surface flow. Flow duration curve drawn for the Upper GMR indicates that under WD+LU scenario the flow regime would be altered, which is reflected in decreased peak flows and increased bankfull flows (Figures 6.19, 6.21, 6.23 and 6.25).

Comparing WD+LU and HD+LU scenarios (Dry group), it is seen, that in the conditions of moisture deficit and losses in moisture surplus caused by increased evapotraspiration rates (2 degrees Centigrade air temperature increase), which is eventually reflected in the volumes of flow. The Dry case scenario simulations for the Upper GMR revealed a 7% difference between

Warm and Hot cases, and 9% for the Lower GMR (Table 6.1).

300 UPPER GMR AT DAYTON, OH WD (Warm and Wet) SCENARIO+FUTURE HYPOTHETICAL LAND USE SCENARIO Figures 6.18-6.21 Results of Hydrologic simulation under combined: Warm and Dry and Future Hypothetical Land Use Scenarios, Upper GMR at Dayton, OH Flow Regime

Figure 6.18

WD+LU

Figure 6.19

WD+LU

Flow duration curves

301 Illustration of seasonal flow (increased flood peaks and bankfull discharges) Figure 6.20

WD+LU

Figure 6.21

WD+LU

Flow duration curves

302 LOWER GMR AT HAMILTON, OH Warm and Dry SCENARIO+FUTURE HYPOTHETICAL LAND USE SCENARIO Figures 6.22-6.25 Results of Hydrologic simulation under combined: Warm and Dry and Future Hypothetical Land Use Scenarios, Lower GMR at Hamilton, OH Flow Regime

Figure 6.22

WD+LU

Figure 6.23

WD+LU

Flow duration curves

303 Illustration of seasonal flow (increased flood peaks and bankfull discharges) Figure 6.24

WD+LU

Figure 6.25

WD+LU

Flow duration curves

304 Warm and Wet Scenario plus Future Land Use (WW+LU)

The results from hydrologic simulation under combined Warm and Wet climate and Future land use hypothetical scenario demonstrate 75% increase in mean annual flow for the Upper

GMR basin (from 2347 ft3/s (2.1 km3/year) to 4112 ft3/s (3.6 km3/year)) and 83% increase for the

Lower GMR basin (from 2984 ft3/s (2.66 km3/year) to 5464 ft3s (4.8 km3/year)) compared to Base

Case scenario (Table 6.1). The total increase in mean annual flow under WW+LU is very significant and represents the maximum magnitudes among the simulated results under all realizations of constructed Climate and Land use scenarios. The hydrographs drawn from simulations demonstrate the changes of the hydrological regime: volumetric increase not only in stream’s low flow regime, but also in high flow regimes as well as a distinctive “flashy” character of the hydrographs under WW+LU scenario (Figures 6.26-6.31).

Crosschecking the simulation results from Wet group scenarios (HW+LU and WW+LU) indicates the overall increase of the mean annual discharge of the GMR by 70%, confirming the losses of moisture due to increased evapotraspiration rates in HW+LU scenario in average by

14% compared to WW+LU (Table 6.1).

305 UPPER GMR AT DAYTON, OH Warm and Wet (WW) SCENARIO+FUTURE HYPOTHETICAL LAND USE SCENARIO Figures 6.26-6.29 Results of Hydrologic simulation under combined Warm and Wet and Future Hypothetical Land Use Scenarios, Upper GMR at Dayton, OH Flow Regime

Figure 6.26

WW+LU

Figure 6.27

WW+LU

Flow duration curves

306 Illustration of seasonal flow (increased flood peaks and bankfull discharges) Figure 6.28

WW+LU

Figure 6.29

WW+LU

Flow duration curves

307 LOWER GMR AT HAMILTON, OH Warm and Wet (WW) SCENARIO+FUTURE HYPOTHETICAL LAND USE SCENARIO Figures 6.30-6.33 Results of Hydrologic simulation under combined Warm and Wet and Future Hypothetical Land Use Scenarios, Lower GMR at Hamilton, OH Flow Regime

Figure 6.30

WW+LU

Figure 6.31

WW+LU

Flow duration curves

308 Illustration of seasonal flow (increased flood peaks and bankfull discharges) Figure 6.32

WW+LU

Figure 6.33

WW+LU

Flow duration curves

309

6.3 WATER QUALITY UNDER COMBINED FUTURE CLIMATE AND LAND USE SCENARIOS

Hot and Dry Scenario plus Future Land Use (HD+LU)

Water temperature

Simulations of water temperature under HD+LU scenario produced an increase in mean annual water temperature by 7.6% (from 44.4 F to 47.8 F) compared to the Base Case scenario for the

Upper GMR basin and by 5.5% (from 56.5 F to 59.6 F) for the Lower GMR (Table 6.2). The results are two-three times higher than the magnitudes produced from the simulation under “LU- only” scenario, indicating the effect of global (regional) atmospheric warming on physical conditions of aquatic system is 2-3 times larger compared to the effect of Urbanization of the watershed, i.e. increased percentage of imperviousness. In terms of seasonal water temperature regime, the model simulated larger peaks occurred during the summer months (Figures 6.34 and

6.39).

Dissolved Oxygen (DO)

Under HD+LU scenario, the model simulated a decrease of the mean annual DO concentrations for both Upper and Lower GMR basins: decline by 14% for the Upper GMR (from 11.2 to 9.6 mg/l) and by 11% in the Lower GMR (from 9.3 to 8.3 mg/l) (Table 6.2). Lower levels of DO in the water is the result of not only increased impervious area introduced by the hypothetical Future

Land use scenario causing increase in nutrients loads into the water body and decreasing the DO concentrations, but also increased water temperature due to warmer atmospheric temperatures which enhance the process of biomass development. The simulations show changes in seasonal pattern of DO fluctuations, mainly it is reflected in smaller minimum and maximum DO concentrations (summer and winter seasons respectively) (Figures 6.35 and 6.40).

310 Table 6.2 Water quality modeling results from simulating combined effects of climate and future land-use changes

Water Quality Scenario Upper GMR Lower GMR % difference % difference Scenario Upper GMR Lower GMR % difference % difference Parameter HOT AND DRY WITH at Dayton at Hamilton Upper to Lower to HOT AND WET WITH at Dayton at Hamilton Upper to Lower to FUTURE LAND USE OH OH Base Case Base Case FUTURE LAND USE OH OH Base Case Base Case Base Case 44.4 56.5 Base Case 44.4 56.5 T (F) Hot and Dry only (HD) 46.0 57.4 +2.4 +1.6 Hot and Wet only (HW) 45.3 57.2 +2.0 +1.2 Land Use only (LU) 45.7 57.4 +2.7 +1.7 Land Use only (LU) 45.7 57.4 +2.7 +1.7 HD+LU 47.8 59.6 +7.6 +5.5 HW+LU 46.8 58.2 +5.4 +3.0 DO Base Case 11.2 9.3 Base Case 11.2 9.3 (Dissolved Hot and Dry only (HD) 10.9 9.0 -2.7 -3.2 Hot and Wet only (HW) 10.5 9.0 -6.2 -3.2 Oxygen) Land Use only (LU) 10.2 9.1 -8.9 -2.1 Land Use only (LU) 10.2 9.1 -8.9 -2.1 (mg/l) HD+LU 9.6 8.3 -14.0 -11.0 HW+LU 10.1 9.0 -9.8 -3.0 Total Base Case 0.33 0.42 Base Case 0.33 0.42 Phosphorus Hot and Dry only (HD) 0.34 0.45 +3.0 +7.1 Hot and Wet only (HW) 0.36 0.45 +9.1 +7.1 (mg/l) Land Use only (LU) 0.45 0.58 +36.0 +38.0 Land Use only (LU) 0.45 0.58 +36.0 +38.0 HD+LU 0.54 0.65 +63.0 +55.0 HW+LU 0.47 0.59 +42.0 +39.0 Nitrogen Base Case 0.13 0.21 Base Case 0.13 0.21 Ammonia Hot and Dry only (HD) 0.12 0.20 -7.9 -2.3 Hot and Wet only (HW) 0.14 0.24 +4.6 +14.2 (mg/l) Land Use only (LU) 0.14 0.25 +7.7 +19.0 Land Use only (LU) 0.14 0.25 +7.7 +19.0 HD+LU 0.14 0.26 +9.0 +16.6 HW+LU 0.16 0.27 +19.6 +27.5 NO2+NO3 Base Case 3.16 3.52 Base Case 3.16 3.52 (mg/l) Hot and Dry only (HD) 2.9 3.60 -8.0 +2.3 Hot and Wet only (HW) 3.28 3.76 +3.8 +6.8 Land Use only (LU) 3.50 4.10 +10.7 +16.5 Land Use only (LU) 3.50 4.10 +10.7 +16.5 HD+LU 3.57 4.20 +12.9 +19.3 HW+LU 3.88 4.79 +22.8 +30.7 Table 6.2 (Continued)

Water Quality Scenario Upper GMR Lower GMR % difference % difference Scenario Upper GMR Lower GMR % difference % difference Parameter WARM AND DRY WITH at Dayton at Hamilton Upper to Lower to WARM AN WET WITH at Dayton at Hamilton Upper to Lower to FUTURE LAND USE OH OH Base Case Base Case FUTURE LAND USE OH OH Base Case Base Case Base Case 44.4 56.5 Base Case 44.4 56.5 T (F) Warm and Dry only (WD) 45.2 57.0 +1.1 +1.0 Warm and Wet only (WW) 44.9 56.7 +1.1 +1.0 Land Use only (LU) 45.7 57.4 +2.7 +1.6 Land Use only (LU) 45.7 57.4 +2.7 +1.6 WD+LU 46.0 57.7 +3.6 +2.1 WW+LU 46.1 57.8 +3.8 +2.3 DO Base Case 11.2 9.3 Base Case 11.2 9.3 (Dissolved Warm and Dry only (WD) 10.7 9.0 -4.4 -3.2 Warm and Wet only (WW) 11.0 9.1 -1.7 -2.1 Oxygen) Land Use only (LU) 10.2 9.1 -8.9 -2.1 Land Use only (LU) 10.2 9.1 -8.9 -2.1 (mg/l) WD+LU 10.2 9.0 -8.9 -3.2 WW+LU 11.1 9.2 -1.0 -1.0 Total Base Case 0.33 0.42 Base Case 0.33 0.42 Phosphorus Warm and Dry only (WD) 0.38 0.47 +15.1 +12.0 Warm and Wet only (WW) 0.41 0.51 +24.2 +21.1 (mg/l) Land Use only (LU) 0.45 0.58 +36.0 +38.0 Land Use only (LU) 0.45 0.58 +36.0 +38.0 WD+LU 0.50 0.60 +51.0 +43.0 WW+LU 0.46 0.54 +39.0 +29.0 Nitrogen Base Case 0.13 0.21 Base Case 0.13 0.21 Ammonia Warm and Dry only (WD) 0.12 0.20 -7.7 -4.7 Warm and Wet only (WW) 0.13 0.22 +1.0 +4.7 (mg/l) Land Use only (LU) 0.14 0.25 +7.7 +19.0 Land Use only (LU) 0.14 0.25 +7.7 +19.0 WD+LU 0.14 0.21 +7.8 - WW+LU 0.13 0.22 +3.8 -4.9 NO2+NO3 Base Case 3.16 3.52 Base Case 3.16 3.52 (mg/l) Warm and Dry only (WD) 3.22 3.60 +1.9 +2.3 Warm and Wet only (WW) 3.24 3.55 +2.5 +1.0 Land Use only (LU) 3.50 4.10 +10.7 +16.5 Land Use only (LU) 3.50 4.10 +10.7 +16.5 WD+LU 3.45 3.9 +9.2 +10.7 WW+LU 3.60 4.23 +12.4 -2.2 Total Phosphorus

The model produced a significant increase in mean annual concentrations of Total Phosphorus in the Upper and Lower GMR. The concentrations of Total Phosphorus in the Upper GMR increased 63% compared to Base Case scenario (from 0.33 to 0.54 mg/l) and by 55% in the

Lower GMR (from 0.42 to 0.65 mg/l) (Table 6.2). This dramatic increase is a product of combined effects of elevated levels of nutrient loads (supply) into the streams from the impervious areas in the basin and more favorable and comfortable conditions for biomass growth enhancing eutrophication process in the streams.

Total Ammonia Nitrogen (NH4)

The results of Total Ammonia Nitrogen simulations under HD+LU scenario produced an increase in mean annual NH4 concentration by 9% for the Upper GMR and by 16.6% for the

Lower GMR (from 0.13 mg/l to 0.14 mg/l and from 0.21 mg/l to 0.25 mg/l, respectively) (Table

6.2).

Sum of Nitrites and Nitrates (NO2+NO3)

The water quality model generated an increase in the mean annual concentrations of the

NO2+NO3 under HD+LU scenario for both Upper and Lower GMR basins. In the Upper GMR, of the mean annual NO2+NO3 concentration increased by 12.9% (from 3.16 to 3.57 mg/l), whereas in the Lower GMR the increase came out to 19.3% (3.52 to 4.20 mg/l), compared to Base Case scenario Table 6.2, Figures 6.38 and 6.43).

311 UPPER GMR AT DAYTON, OH Hot and Dry (HD) SCENARIO+FUTURE HYPOTHETICAL LAND USE SCENARIO Figures 6.34-6.38 Results of Water Quality simulations under combined Hot and Dry and Future Hypothetical Land Use Scenarios, Upper GMR at Dayton, OH Water Temperature

Figure 6.34

HD+LU

Figure 6.35

HD+LU

312 Total Phosphorus

Figure 6.36 HD+LU

Total Nitrogen Ammonia (NH4)

Figure 6.37

HD+LU

313 Sum of Nitrites and Nitrates (NO2+NO3) Figure 6.38

HD+LU

314 LOWER GMR AT HAMILTON, OH Hot and Dry (HD) SCENARIO+FUTURE HYPOTHETICAL LAND USE SCENARIO Figures 6.39-6.43 Results of Water Quality simulations under combined Hot and Dry and Future Hypothetical Land Use Scenarios, Lower GMR at Hamilton, OH Water Temperature

Figure 6.39

HD+LU

Figure 6.40 HD+LU

315 Total Phosphorus

Figure 6.41 HD+LU

Total Nitrogen Ammonia (NH4)

Figure 6.42

HD+LU

316 Sum of Nitrites and Nitrates (NO2+NO3) Figure 6.43

HD+LU

317 Hot and Wet Scenario plus Future Land Use (HW+LU)

Water temperature

Simulations of water temperature under HW+LU scenario produced the results close to

HD+LU scenario for both Upper and Lower GMR basins. Thus, mean annual water temperature simulated for Upper GMR increased by 5.4% (from 44.4 to 46.8 F) and by 3.0% in the Lower

GMR (from 56.5F to 58.2F) (Table 6.2 and Figures 6.44 and 6.49). The increase is still 2-3 times higher compared to the results of water temperature simulations under “LU-only” scenario.

Dissolved Oxygen (DO)

The water quality model produced a decline in mean annual concentrations of dissolved oxygen under HW+LU scenario: DO concentration for the Upper GMR declined 9.8% compared to Base

Case scenario (from 11.2 to 10.1 mg/l) and slightly decreased 3.0% for the Lower GMR (from

9.3 to 9.0 mg/l) (Table 6.2). DO’s seasonal regime has changed as well. Mostly, it is reflected in downshifts of peak concentrations occurred during late Fall-Winter seasons as well as in lower minimum concentrations during Spring-Summer seasons (Figures 6.45 and 6.50).

Total Phosphorus

The results from modeling Total Phosphorus concentrations revealed an increase by 42%

(from 0.33 to 0.46 mg/l) for the Upper GMR and by 39% (from 0.42 to 0.59 mg/l) for the Lower

GMR (Table 6.2, Figures 6.46 and 6.51). The increase is significantly smaller than the model produced for Dry scenario (HD+LU): 63% and 55% for Upper and Lower GMR accordingly. The reason for this could be due to the effects of dissolution, which occurs during high volume flows, e.g. hydrologic regime modeled under HW climate scenario. The surplus in moisture content within the system plays a fairly distinctive role, minimizing the nutrient concentrations in the water body that in turn, affects biomass production and eutrophication rates etc.

Total Nitrogen Ammonia (NH4)

The simulations of NH4 under HW+LU scenario demonstrated an increase in mean annual constituent concentration by 19.6% (from 0.13 to 0.16 mg/l) for the Upper GMR and by 27.5%

318 (from 0.21 to 0.27 mg/l) for the Lower GMR (Table 6.2,Figures 6.47 and 6.52). In comparison to Dry case scenario, it is about 10% lower and it could be explained by the mechanisms of dissolution introduced by moisture surplus to the hydrological system simulated in Wet case scenario.

Sum of Nitrites and Nitrates (NO2+NO3)

Mean annual concentrations of NO2+NO3 under HW+LU scenario increased by 22.8% (from

3.16 to 3.88 mg/l) for the Upper GMR and by 31% (from 3.52 to 4.79 mg/l) for the Lower GMR

(Table 6.2, Figures 6.48 and 6.53). Smaller local peaks and minimums are shown on the plots of annual NO2+NO3 fluctuations simulated under HW+LU scenario. Comparing HD+LU and

HW+LU scenarios results, the difference in model prediction in NO2+NO3 concentrations is noticeable in the Wet case scenario, which produced approximately 10% lower concentrations than the model did for Dry case.

319 UPPER GMR AT DAYTON, OH Hot and Wet SCENARIO+FUTURE HYPOTHETICAL LAND USE SCENARIO Figures 6.44-6.48 Results of Water Quality simulations under combined Hot and Wet and Future Hypothetical Land Use Scenarios, Upper GMR at Dayton, OH Water Temperature

Figure 6.44

HW+LU

DO Figure 6.45

HW+LU

320 Total Phosphorus

Figure 6.46

HW+LU

Total Nitrogen Ammonia (NH4)

Figure 6.47

HW+LU

321 Sum of Nitrites and Nitrates (NO2+NO3) Figure 6.48

HW+LU

322 LOWER GMR AT HAMILTON, OH Hot and Wet SCENARIO+FUTURE HYPOTHETICAL LAND USE SCENARIO Figures 6.49-6.53 Results of Water Quality simulations under combined Hot and Wet and Future Hypothetical Land Use Scenarios, Lower GMR at Hamilton, OH Water Temperature

Figure 6.49

HW+LU

Figure 6.50 HW+LU

323 Total Phosphorus

Figure 6.51

HW+LU

Total Nitrogen Ammonia (NH4)

Figure 6.52

HW+LU

324

Sum of Nitrites and Nitrates (NO2+NO3) Figure 6.53

HW+LU

325 Warm and Dry Scenario plus Future Land Use (WD+LU)

Water temperature

The comparison of the simulated water temperature under WD+LU with the Base Case scenario demonstrated an increase in the magnitudes by 3.6% (from 44.4F to 46.0F) for the

Upper GMR and by 2.1% (from 56.5 to 57.7F) for the Lower GMR (Table 6.2,Figures 6.54 and

6.59). The average increase is about 2 times smaller than the model produced for the HD+LU scenario for the Upper and Lower GMR (Table 6.2). Therefore, these results indicate that an increase of the atmospheric temperature by 2 degrees Centigrade produce an increase of water temperature in average by 3-4% (or 1.5-1.9 degrees Centigrade).

Dissolved Oxygen (DO)

The results from the modeling DO concentrations under WD+LU scenario showed a reduction of mean annual DO concentrations in both Upper GMR and Lower GMR basins. Simulations for the Upper GMR demonstrate a decrease in mean annual DO concentration by 8.9% (from 11.2 to

10.2 mg/l) and by 3.2% (from 9.3 to 9.0 mg/l) for the Lower GMR compared to Base Case

Scenarios (Table 6.2, Figures 6.55 and 6.60). The reduction of DO concentration simulated under WD+LU scenario is about 7% greater than the results came out from the simulation under

HD+LU scenario (Table 6.2).

Total Phosphorus

Modeling Total Phosphorus concentrations under WD+LU produced a significant increase by

51% (from 0.33 to 0.5 mg/l) for the Upper GMR and by 43% (from 0.42 to 0.60 mg/l) for the

Lower GMR compared to Base Case scenarios (Table 6.2, Figures 6.56 and 6.61). The reduction is about 8% smaller than the simulated results produced for the HD+LU scenario (Table 6.2).

Total Nitrogen Ammonia (NH4)

In terms of NH4 annual concentrations, the model simulated slight increase of concentration for

Upper GMR by 7.8% (from 0.13 to 0.14 mg/l) (Table 6.2, Figures 6.57 and 6.62). For the

326 Lower GMR, the model predicted no change in NH4 mean annual concentration, compared to the results under HD+LU scenario, which resulted in an increase by 16.6%.

Sum of Nitrites and Nitrates (NO2+NO3)

During the simulation of NO2+NO3 in the Upper and Lower GMR basins under WD+LU scenario, the water quality model produced an increase in mean annual concentrations of Nitrites and Nitrates by 9.2% (from 3.16 to 4.45 mg/l) and 11.0% (from 3.52 to 3.90 mg/l) for the Upper and Lower GMR, respectively (Table 6.2, Figures 6.58 and 6.63). The model predicted higher peak concentrations mostly in summer seasons, that could be explained by flushing of nitrates from increased urban areas and potentially smaller effect of dissolution during high flow hydrological regime due to deficit of moisture, created in the Dry climate scenario (Figures 6.58 and 6.63).

UPPER GMR AT DAYTON, OH Warm and Dry SCENARIO+FUTURE HYPOTHETICAL LAND USE SCENARIO Figures 6.54-6.58 Results of Water Quality simulations under combined Warm and Dry and Future Hypothetical Land Use Scenarios, Upper GMR at Dayton, OH Water Temperature

Figure 6.54

WD+LU

327 DO

Figure 6.55 WD+LU

Total Phosphorus

Figure 6.56

WD+LU

328 Total Nitrogen Ammonia (NH4)

Figure 6.57

WD+LU

Sum of Nitrites and Nitrates (NO2+NO3) Figure 6.58

WD+LU

329 LOWER GMR AT HAMILTON, OH Warm and Dry SCENARIO+FUTURE HYPOTHETICAL LAND USE SCENARIO Figures 6.59-6.63 Results of Water Quality simulations under combined Warm and Dry and Future Hypothetical Land Use Scenarios, Lower GMR at Hamilton, OH Water Temperature

Figure 6.59

WD+LU

DO Figure 6.60

WD+LU

330 Total Phosphorus

Figure 6.61

WD+LU

Total Nitrogen Ammonia (NH4)

Figure 6.62 WD+LU

331 Sum of Nitrites and Nitrates (NO2+NO3) Figure 6.63

WD+LU

332 Warm and Wet Scenario plus Future Land Use (WW+LU)

Water temperature

The model predicted a slight increase by 3.8% (from 44.4 to 46.1F) and 2.3% (from 56.5 to

57.8F) in water temperature under WW+LU scenario for the Upper and Lower GMR, accordingly

(Table 6.2, Figures 6.64 and 6.69). This increase is close to the results produced under WD+LU scenario. Semi-annual fluctuations of water temperature are described, mainly, by higher peaks during warm seasons (Figures 6.64 and 6.69).

Dissolved Oxygen (DO)

Dissolved oxygen simulated under WW+LU scenario experienced practically no changes compared to Base Case Scenario: insignificant decrease by 1.0% for both Upper and Lower

GMR (Table 6.2, Figures 6.65 and 6.70).

Total Phosphorus

Mean annual concentrations of phosphorus, simulated under WW+LU scenario demonstrate an increase by 39% (from 0.33 to 0.46 mg/l) for the Upper GMR and by 29% (from 0.42 to 0.54 mg/l) for the Lower GMR (Table 6.2, Figures 6.66 and 6.71). These results are slightly lower than the results derived from simulation of phosphorus under HW+LU scenario (Table 6.2). In the meantime, comparing the results from the Warm Group scenarios (WW+LU with WD+LU), it is seen that concentration of phosphorus increased greater in the Dry Case scenario.

Total Nitrogen Ammonia (NH4)

Simulations of NH4 concentrations under WW+LU scenario demonstrate increase in mean annual NH4 concentrations by 3.8% for the Upper GMR and a slight decrease by 4.9% (from

0.21 to 0.20 mg/l) for the Lower GMR, respectively (Table 6.2, Figures 6.67 and 6.72). Much more dramatic increase in NH4 concentrations has been produced by the model under HW+LU scenarios- approximately 15% in average (Table 6.2).

333 Sum of Nitrites and Nitrates (NO2+NO3)

The mean annual concentrations of NO2+NO3, simulated under WW+LU scenario demonstrated an increase by 15.4% (from 3.16 to 3.60 mg/l) for the Upper GMR. The Lower

GMR simulations showed very insignificant reduction by 2% (Table 6.2, Figures 6.68 and

6.73). Interestingly, the model predicted reductions in NH4 and NO2+NO3 concentrations for the

Lower GMR as opposed to results under HW+LU scenario. Perhaps, it might be explained by the hydrology of the Lower GMR, which streams are more channelized and, therefore, the effect of dissolution from stormwater runoffs is taking place at higher volumes and times.

UPPER GMR AT DAYTON, OH Warm and Wet SCENARIO+FUTURE HYPOTHETICAL LAND USE SCENARIO Figures 6.64-6.68 Results of Water Quality simulations under combined Warm and Wet and Future Hypothetical Land Use Scenarios, Upper GMR at Dayton, OH Water Temperature

Figure 6.64

WW+LU

334 DO

Figure 6.65 WW+LU

Total Phosphorus

Figure 6.66

WW+LU

335 Total Nitrogen Ammonia (NH4)

Figure 6.67

WW+LU

Sum of Nitrites and Nitrates (NO2+NO3) Figure 6.68

WW+LU

336 LOWER GMR AT HAMILTON, OH Warm and Wet SCENARIO+FUTURE HYPOTHETICAL LAND USE SCENARIO Figures 6.69-6.73 Results of Water Quality simulations under combined Warm and Wet and Future Hypothetical Land Use Scenarios, Lower GMR at Hamilton, OH Water Temperature

Figure 6.69

WW+LU

DO Figure 6.70

WW+LU

337 Total Phosphorus

Figure 6.71

WW+LU

Total Nitrogen Ammonia (NH4) Figure 6.72

WW+LU

338 Sum of Nitrites and Nitrates (NO2+NO3) Figure 6.73

WW+LU

339 Summary of the results

This subchapter presents the analysis of the results produced from the hydrological and water quality modeling in order to answer the central question of this research study - to evaluate the combined effects of Future Climate and Land Use changes, as postulated by a set of hypothetical scenarios, on hydrologic and water quality regimes of the Great Miami River.

HYDROLOGICAL REGIME OF GREAT MIAMI RIVER UNDER COMBINED FUTURE CLIMATE AND LAND USE SCENARIOS

(1) Hot Climate Group scenarios combined with Hypothetical Land Use scenario (HD+LU and

HW+LU)

As it was discussed, the hydrological simulations under HD+LU scenario demonstrated a reduction in mean annual discharge by 28% and by 11%, compared to the Base Case scenario for the Upper and Lower GMR accordingly. It might be considered as a moderate change, especially if comparing these results against HD-only scenario, that conceptually represents conditions of moisture deficit in the system, produced an average 60% reduction in flow (Table 6.1). This kind of moderate change in hydrologic regime might be explained by urbanization of the watershed presented in Hypothetical Land use scenario. Simulation under LU-only scenario revealed a 35% in average surplus in volume of flow that significantly affected the behavior of hydrologic system of the GMR modeled under combined HD+LU scenario.

Simulations under HW+LU scenario produced a 61% increase in mean annual flow for the

Upper GMR and 70% for the Lower GMR. It is approximately 35% higher than the model simulated for HW-only scenario.

(2) Warm Climate Group scenarios combined with Hypothetical Land Use scenario (WD+LU and WW+LU)

Quite different results came out from the simulations under WD+LU scenario. For Upper

GMR, the mean annual flow is reduced by 21%, whereas the flow of the Lower GMR is decreased only by 2%. Perhaps this difference might be explained by the larger percentage of

340 urbanized area in the lower portion of the GMR basin, when in the conditions of moisture deficit, major portion of moisture from precipitation events become a surface flow and rapidly enters the streams.

However, regarding to flooding and its consequences, hydrologic simulations demonstrated that

WW+LU scenario might be the most dangerous for communities located nearby or along major streams. The model produced an average 80% increase in mean annual flow, compared to the

Base Case scenario. This is roughly 20-30% greater than the model produced under WW-only scenario.

(3) Dry vs. Wet scenarios (HD+LU, WD+LU vs. HW+LU, WW+LU)

In general, the wet case scenarios produced much higher flow than the dry case scenarios did.

Thus, Wet case combined with Land use scenarios on average produced 70% increase in volumes of flow compared to the Base Case scenario. A 70% increase in mean annual flow is very significant and potentially could cause a series of devastating floods with severe economic and even human life damage (possibly much more severe than we have recently seen on June 14-15th,

2003 in the Tri-State area counties, and other counties located within the Great Miami River basin). Results from Dry cases show moderate decrease in mean annual flow – about 20% for the whole GMR basin. It is seen from these facts that precipitation amount (i.e increase by 20%) plays a significant role in the water budget of the Great Miami River.

The hydrologic model responded to temperature increase as well. Thus, in average, comparing the scenarios with the same moisture content but different temperatures (WW+LU, HW+LU with

WD+LU, HD+LU), it is seen that by increasing temperature by 2 degrees Centigrade, the flow is reduced by 8-9%.

341 Table 6.3 Summary of results from the hydrologic modeling under combined Climate and Land

Use scenarios:

UPPER GMR AT DAYTON, OH Mean annual flow Difference between Base Case Scenario ft3/s (km3/year) and Simulated combined Climate and Hypothetical Land Use Scenario Base Case Scenario 2347 (2.1) - Hot and Dry Climate Scenario + Land Use Scenario (HD+LU) 1688 (1.5) -28% Hot and Wet Climate Scenario + Land Use Scenario (HW+LU) 3775 (3.36) +61% Warm and Dry Climate Scenario + Land Use Scenario (WD+LU) 1863 (1.66) -21% Warm and Wet Climate Scenario + Land Use Scenario (WW+LU) 4112 (3.6) +75%

LOWER GMR AT HAMILTON, Mean annual flow Difference between Base Case Scenario OH ft3/s (km3/year) and Simulated combined Climate and Hypothetical Land Use Scenario Base Case Scenario 2984 (2.66) - Hot and Dry Climate Scenario + Land Use Scenario (HD+LU) 2653 (2.36) -11% Hot and Wet Climate Scenario + Land Use Scenario (HW+LU) 5080 (4.53) +70% Warm and Dry Climate Scenario + Land Use Scenario (WD+LU) 2928 (2.6) -2% Warm and Wet Climate Scenario + Land Use Scenario (WW+LU) 5464 (4.8) +83%

342 WATER QUALITY OF GREAT MIAMI RIVER UNDER COMBINED FUTURE CLIMATE AND LAND USE SCENARIOS

(1) Water temperature and Dissolved Oxygen

Simulation of water temperature under combined Hot climate group and Land Use scenarios produced in average a 5% increase compared to the Base case scenario. Therefore, in response to

4 degrees Centigrade increase of air temperature, the mean annual water temperature of the Great

Miami River would increase by 30F (1.7 0C). Results from Warm group combined with Land Use scenarios demonstrate an average 2-3% increase in water temperature (~1.30F or close to 1 degree Centigrade).

Generally speaking, it is not a significant increase. However, considering the seasonal water temperature fluctuations, the model produced considerably higher daily maximums during late

Spring-Summer months that might affect certain temperature-sensitive fish species, like coldwater species. It is likely that increased temperatures may affect their health, migration patterns, spawning, and so forth.

As a general rule, dissolved oxygen concentrations in the water tend to diminish when water temperature increases and with introducing larger amounts of nutrients into the water body. The water quality model depicted these processes quite well. Thus, under HD+LU and HW+LU scenarios, DO concentrations in the GMR decreased by 9%, with larger decline in the HD+LU scenario (~13%). WD+LU and WW+LU scenarios produced in average a 3% reduction in mean annual DO concentrations.

(2) Nutrients

Simulations of Phosphorus under each scenario produced an increase in mean annual phosphorus concentrations. Thus, Hot group combined with Land Use scenario produced in average a 50% increase, compared to the Base Case scenario. Warm group scenarios (WD+LU and WW+LU) in average resulted in close to 40% increase in mean Phosphorus concentrations.

343 Analysis of the results from Dry (HD+LU, WD+LU) vs. Wet scenarios (HW+LU, WW+LU) indicate that total phosphorus concentrations increased by 53% and 37%, accordingly, ranging between 0.46 to 0.65 mg/l, which is well beyond the US. EPA Water Quality Criteria established limits for freshwaters (for Phosphorus it is limited to 0.2-0.3 mg/l). Such an increase (2-3 times) would have a negative effect on aquatic ecosystem of the GMR, and more specifically, it would activate and enhance the processes of eutrophication in the water body, which in turn, would depress dissolved oxygen levels and lead to fish health distractions and fish kills.

Simulations of Total Nitrogen Ammonia under Dry Case scenarios (HD+LU and WD+LU) resulted in increase of mean annual concentrations in average by 11%. In Wet Case scenarios the concentration increased by 17% in Wet Case scenarios (HW+LU and WW+LU), except

WW+LU scenario for the Lower GMR, where Nitrogen Ammonia concentration reduced by 5%.

Potentially, it indicates the process of dissolution in action, dissolving and reducing daily or seasonal concentrations of Total Nitrogen Ammonia in the water. In general, the modeled results exceed the U.S EPA recommendations for aquatic life (typically ranges from 0.02 mg/l to 0.10 mg/l, depending on aquatic life present and other water quality factors).

The results from modeling the Sum of Nitrites and Nitrates under Dry case scenarios (HD+LU and WD +LU) showed an increase in mean annual concentrations in average by 13% compared to the Base Case scenario. It appears, this increase is a direct function of increased imperviousness effect. Wet Case scenarios revealed a 19% increase in NO2+NO3 mean annual concentrations for the Upper GMR. Different results came out from simulations for the Lower

GMR: HW+LU scenario produced a 31% increase in Nitrites+Nitrates in the water, whereas

WW+LU resulted in insignificant decline by 2.2%. Typically, the complexity of the interactions of nutrient concentrations and flow make it important to examine both point sources and non- point sources of nutrients and wet weather (high flow) and dry weather (low flow) stream conditions to verify nutrient sources and concentrations in multiple flow conditions. However, the water quality model seems to describe the general mechanisms of the system fairly well. For Wet

344 case scenario group, it is expected more nutrient loads from non-point sources due to increased stormfloods from urban areas, and, hence, immediate lifting the daily concentrations of nutrient in the water. At the same time, peak discharges tend to lower dissolved pollutants concentrations.

This interplay is well-depicted in the case of WW+LU scenario, when hydrologic model produced a volumetric increase in flow by approximately 80% compared to Base case but the concentrations of nutrients (NH4 and NO2+NO3) generated by water quality model did not show as high increase as they show under HW+LU scenario. The fact that hydrologic modeling results under HW+LU scenario resulted in 65% increase in the mean annual flow, which is 15% smaller comparing to WW+LU, might explain the difference in NH4 and NO2+NO3 simulations results.

Simulations of NH4 and NO2+NO3 under Dry case group seem to be largely controlled by the effects of urbanization represented in LU scenario.

345 Table 6.4 Summary results from water quality modeling for Great Miami River under combined climate and land-use scenarios:

Difference between Base Case Scenario Base Case and Simulated combined Climate and UPPER GMR AT DAYTON, OH (Simulated) Hypothetical Land Use Scenario (Mean annual values) Hot and Dry Climate Scenario + Land Use Scenario (HD+LU): Water temperature (F) 44.4 (47.8) +7.6% DO (Mg/l) 11.2 (9.6) -14.0% Total Phosphorus (Mg/l) 0.33 (0.54) +63.0% Total NH4 (Mg/l) 0.13 (0.16) +9.0% NO2+NO3 (Mg/l) 3.16 (5.40) +12.9% Hot and Wet Climate Scenario + Land Use Scenario (HW+LU): Water temperature (F) 44.4 (46.8) +5.4% DO (Mg/l) 11.2 (10.1) -9.8% Total Phosphorus (Mg/l) 0.33 (0.47) +42.0% Total NH4 (Mg/l) 0.13 (0.10) +19.6% NO2+NO3 (Mg/l) 3.16 (2.51) +22.8%

Warm and Dry Climate Scenario + Land Use Scenario (WD+LU): Water temperature (F) 44.4 (46.0) +3.6% DO (Mg/l) 11.2 (10.2) -8.9% Total Phosphorus (Mg/l) 0.33 (0.50) +51.0% Total NH4 (Mg/l) 0.13 (0.14) +7.8% NO2+NO3 (Mg/l) 3.16 (4.90) +9.2% Warm and Wet Climate Scenario + Land Use Scenario (WW+LU): Water temperature (F) 44.4 (46.1) +3.8% DO (Mg/l) 11.2 (11.1) -1.0% Total Phosphorus (Mg/l) 0.33 (0.46) +39.0% Total NH4 (Mg/l) 0.13 (0.12) +3.8% NO2+NO3 (Mg/l) 3.16 (3.10) +12.4%

346 Table 6.4 (Continued)

Difference between Base Case Scenario Base Case and Simulated combined Climate and LOWER GMR AT HAMILTON, OH (Simulated) Hypothetical Land Use Scenario (Mean annual values) Hot and Dry Climate Scenario + Land Use Scenario (HD+LU): Water temperature (F) 56.5 (59.6) +5.5% DO (Mg/l) 9.3 (8.3) -11.0% Total Phosphorus (Mg/l) 0.42 (0.65) +55.0% Total NH4 (Mg/l) 0.21 (0.26) +16.6% NO2+NO3 (Mg/l) 3.52 (5.69) +19.3% Hot and Wet Climate Scenario + Land Use Scenario (HW+LU): Water temperature (F) 56.5 (58.2) +3.0% DO (Mg/l) 9.3 (9.0) -3.0% Total Phosphorus (Mg/l) 0.42 (0.59) +39.0% Total NH4 (Mg/l) 0.21 (0.18) +27.5% NO2+NO3 (Mg/l) 3.52 (3.10) +30.7%

Warm and Dry Climate Scenario + Land Use Scenario (WD+LU): Water temperature (F) 56.5 (57.7) +2.1% DO (Mg/l) 9.3 (9.0) -3.2% Total Phosphorus (Mg/l) 0.42 (0.60) +43.0% Total NH4 (Mg/l) 0.21 (0.21) - NO2+NO3 (Mg/l) 3.52 (4.50) +10.7% Warm and Wet Climate Scenario + Land Use Scenario (WW+LU): Water temperature (F) 56.5 (57.8) +2.3% DO (Mg/l) 9.3 (9.2) -1.0% Total Phosphorus (Mg/l) 0.42 (0.54) +29.0% Total NH4 (Mg/l) 0.21 (0.20) -4.9% NO2+NO3 (Mg/l) 3.52 (3.40) -2.2%

347 In conclusion, it seems that the above analysis extracted three scenarios that might be considered as negative in terms of their effects on water quality- HD+LU, HW+LU and

WW+LU. For these scenarios, the water quality model predicted not only the highest reductions in Dissolved Oxygen concentrations, which is a critical chemical parameter in respect to aquatic plants and animal health, but also the highest increase in nutrient content in the GMR waters that would negatively impact aquatic and, potentially, human health. Increases in nutrient contents in the waters of the Upper and Lower GMR, especially in Total Phosphorus concentrations, would likely enhance the rates of eutrophication that would further depress DO levels and cause degradation of fish life conditions. It also has a potential to alter water quality criteria for drinking water, recreation and so forth. Noticeably, HW+LU scenario produced the largest increase among all scenarios tested. Results of nutrient simulations under Dry Case scenarios indicate that LU

(i.e. % of imperviousness) is acting as a dominant controlling factor in nutrient loads and regime.

Hydrologic modeling results under Wet group scenarios show higher and more frequent flood peaks (maximum discharges) that would result in higher probability for severe economic damages from extensive floods.

348 CHAPTER VII: APPLICATION OF BMPs AND WATER QUALITY

7.1 BMP: GENERAL PRINCIPLES

Recent reports acknowledge that a principal water quality problem in the United States is non- point source (NPS) pollution. The U.S. EPA defines NPS pollution as precipitation driven stormwater runoff, generated by land-based activities, such as agriculture, construction, mining, or silviculture (Illinois EPA Urban Manual, 1995, U.S EPA, 1993). These activities result in diffuse runoff, seepage or percolation of pollutants from the land surface to ground and surface waters. Because of the diverse nature of NPS pollution, approaches to addressing the sources must also be diverse. It requires numerous programs and involves a number of state, local and federal agencies, and the private sector. These programs include: (1) Erosion and sediment control; (2) Stormwater management; (3) Nutrient management; (4) Agricultural best management practices; (5) Floodplain management; (6) Dam safety; (7) Public beaches conservation; (8) Technical and financial support of soil and water conservation districts and some others (Illinois EPA, 1995).

Typically, the control of NPS pollution requires the use of two primary strategies: the prevention of pollutant loadings and the treatment of unavoidable loadings. By definition, BMP

(Best Management Practice) is a device, practice or method for removing, reducing, retarding or preventing targeted stormwater runoff constituents, pollutants and contaminants from reaching receiving waters. Therefore, speaking of long-term interests, the primary goal for watershed managers/planners is to develop and apply a “watershed approach” based on several principles, including development and implementation of BMPs, which focuses on pollution prevention or source reduction practices (U.S EPA, 1993, Illinois Urban Manual, 1995).

349 7.1.1 Overview of Watershed Protection Planning As it is discussed in Rapid Watershed Planning Handbook, (1998) and Kwon, et. al., there are eight basic tools of Watershed Protection, representing the “watershed approach”:

(1) (2) Watershed Land Planning Conservation

(8) (3) Watershed Aquatic Stewardship Buffers programs TOOLS OF WATERSHED PROTECTION (7) (4) Non- Better Site Stormwater Design Discharges (6) (5) Stormwater Erosion and Management Sediment Practices Control

- Watershed Planning is probably the most important stage in Watershed Protection plan

because it provides the initial framework for further program development. Typically,

Watershed Planning includes: (a) Preparing a Land Use Plan (estimate what would

happen to water resources with future land use changes, develop future land use pattern

for the subwatersheds that can meet the most important water resource goals, select the

most acceptable and effective land use planning techniques to reduce or shift impervious

cover etc.); (b) Developing and managing Land Use planning techniques such as

watershed based zoning (estimate impervious cover, verify impervious cover with stream

water quality relationships, classify subwatersheds and so forth), delineating urban

growth boundaries (analyze areas appropriate for urban and suburban development, for

agricultural development etc.)

350 - Land Conservation stage refers to the activities directed to the decision about which

natural or cultural resources must be conserved. This issue usually is addressed by

considerations and analysis of critical habitats (wetlands etc.), aquatic corridors,

hydrologic reserve areas (undeveloped areas that maintain the predevelopment

hydrologic response of a subwatershed: forests, wetlands, crops, pastures and so on), land

use activities that create a greater risk of potential water pollution (septic systems,

landfills, impervious cover, stormwater vulnerable places, pesticide application areas,

industrial sites, roads and others). The next step is to develop a list of Land Conservation

Techniques, which could be used to conserve land. These include land acquisition,

regulations of land alteration and so forth.

- Site planning for Aquatic Buffers (buffers that physically protect and separate a stream

from future disturbance) which could serve as effective tool in removing sediment,

nutrients and bacteria from stormwater, helping to stabilize and protect the streambank

and improving the habitat for aquatic plants and animals. Generally, the structure of a

stream buffer in urban setting consists of three zones: streamside (mature forest with

strict limitations to clearing), the middle zone (could be managed forest with some

allowable cleaning) and the outer zone (forest, turf etc.).

- Better Site Design refers to techniques to reduce impact of site development: urban

development, driveways, sidewalks, parking lots etc.).

- Erosion and Sediment Control measures assist in assessing the impacts of development

and includes issues like protecting steep slopes and cuts, employment of advanced

sediment setting controls, stabilizing exposed soils and drainage ways, install perimeter

controls to filter sediments and some others.

- Stormwater Management Practices are the techniques used to delay, store, treat or

infiltrate stormwater runoff. The primary scopes of SMP are to maintain groundwater

recharge and quality, to reduce stormwater pollutant loads and to protect stream channels,

351 prevent increased overbank flooding. Typical SMP techniques include: (a) Ponds (create

permanent polls of water); (b) Stormwater wetlands; (c) Infiltration trenches (allow

stormwater to percolate slowly into the soil); (d) Filtering systems (bioretention areas that

often used in parking lots) and (e) Open channels (used to convey and infiltrate

stormwater).

- Non-Stormwater Discharges measures can contribute significant pollutant loads to

receiving waters. Key program elements include inspection of private Septic systems (on-

site sewage disposal systems) that are used to treat and discharge wastewaters from

toilets, washbasins, washing machines and other sources, which could contribute to

pollutant loads. Other elements are sanitary sewers that collect wastewater in a central

sewer pipe and send it to a municipal treatment plant and urban “return flows” from such

activities as car washing or watering lawns.

- Watershed Stewardship Programs refers to public involvement and community’s

awareness about watershed management efforts and other things associated with public

participation.

7.1.2 Methodological Aspects and BMP Planning Principles for the Great Miami River

This research study is not intended to develop of full-scale BMP program, it rather attempts, in the first approximation, to estimate the potential effects of Stormwater Management Practices on water quality (in terms of NPS pollution management) using HSPF model and built-in BMP module.

HSPF model contains the BMP Editor, which allows users adding/modifying/creating BMPs in a simulation. The procedure includes selecting a certain stream from the delineated basin and adding percentages of BMP to a desired land use group or reach (e.g. Forest Land, Agricultural

Land, Urban Built-up Land etc.). Also, the user can edit Removal Efficiency Rates in types of

BMPs. The database on Removal Efficiency rates was constructed by BASINS development team. However, several additional sources were used in order to estimate the fractions of removal

352 rates for a particular BMP type. These sources include articles and other publications by Winer,

2000, Watershed Management Institute (WMI), 1997, Center for Watershed Protection (CWP),

1998 and web-published materials from SMRC (Stormwater Manager’s Resource Center). There are several types of BMPs, available in HSPF for simulations and some of them are (based on materials from Stormwater Manager’s Resource Center):

• Dry detention ponds (dry ponds, extended detention basins, detention ponds, extended

detention ponds) are basins whose outlets are designed to detain the stormwater runoff

from a water quality "storm" for some minimum duration (e.g., 24 hours) which allow

sediment particles and associated pollutants to settle out. Unlike wet ponds, dry extended

detention ponds do not have a permanent pool. However, dry extended detention ponds

are often designed with small pools at the inlet and outlet of the pond, and can also be

used to provide flood control by including additional detention storage above the

extended detention level.

• Wet detention ponds (stormwater ponds, retention ponds, wet extended detention ponds)

are constructed basins that have a permanent pool of water throughout the year (or at

least throughout the wet season). Ponds treat incoming stormwater runoff by settling and

algal uptake. The primary removal mechanism is settling while stormwater runoff resides

in the pool. Nutrient uptake also occurs through biological activity in the pond. Wet

ponds are among the most cost-effective and widely used stormwater treatment practices.

While there are several different versions of the wet pond design, the most common

modification is the extended detention wet pond, where storage is provided above the

permanent pool in order to detain stormwater runoff in order to provide greater settling.

• Infiltration basins and trenches. Infiltration basins are shallow impoundments that are

designed to infiltrate stormwater into the soil. Infiltration basins are believed to have a

high pollutant removal efficiency, and can also help recharge the groundwater, thus

restoring low flows to stream systems. Infiltration basins can be problematic at many

353 sites because of stringent soils requirements. In addition, some studies have relatively

high failure rates compared with other stormwater treatment practices. Infiltration

trenches are rock-filled trenches with no outlets that receive stormwater runoff.

Stormwater runoff passes through some combination of pretreatment measures, such as a

swale or sediment basin, before entering the trench. Runoff is then stored in the voids of

the stones, slowly infiltrated through the bottom and into the soil matrix over a few days.

The primary pollutant removal mechanism of this practice is filtering through the soil.

• Constructed Wetlands are structural practices similar to wet ponds that incorporate

wetland plants in a shallow pool. As stormwater runoff flows through the wetland,

pollutant removal is achieved by settling and biological uptake within the practice.

Wetlands are among the most effective stormwater practices in terms of pollutant

removal, and also offer aesthetic value.

• Porous Pavement is a permeable pavement surface with an underlying stone reservoir that

temporarily stores surface runoff before infiltrating into the subsoil. This porous surface

replaces traditional pavement, allowing parking lot runoff to infiltrate directly into the

soil and receive water quality treatment. There are several pavement options, including

porous asphalt, pervious concrete, and grass pavers.

• Bioretention areas are landscaping features adapted to treat stormwater runoff on the

development site. They are commonly located in parking lot islands or within small

pockets in residential land uses. Surface runoff is directed into shallow, landscaped

depressions. These depressions are designed to incorporate many of the pollutant removal

mechanisms that operate in forested ecosystems

354 Table 7.1 illustrates the typical pollutant removal rates (%) for a certain type of BMP for

Stormwater management (source Stormwater Manager’s Resource Center):

Type of BMP Total Phosphorus NO2+NO3 Total Nitrogen Total Nitrogen Ammonia Porous Pavement 65 NA NA 82 Infiltration Basin 60-70 NA NA 55-60 Bioretention 29 38 NA 49 Dry Detention ~21 ~10 ~35 ~30 Wet detention ~51 ~43 ~40 ~33 Infiltration trench ~80 ~80 NA ~30 Wetlands 50-60 30-70 ~30 20-40

Initially, this research study was intended to test BMPs on the whole Great Miami River basin’s major tributaries and their watersheds such as Mad River, Upper Great Miami, Stillwater River and Lower Great Miami River. But due to timing constraints, only Stillwater River watershed was picked up for BMPs testing. Also, it appears that there is a methodological problem on this stage, which does not allow testing BMPs on each major sub-basin of the Great Miami River. The problem is that at the beginning, the Upper and Lower Great Miami River basins were delineated into 37 sub-basins using automatic delineation tool in BASINS. It turned out to be a coarse delineation for BMPs analysis. For BMPs simulations finer delineation is required in order to simulate common BMP types for each land-use type, i.e. wet/dry detention ponds for agricultural land segment upstream, bioretention and porous pavement for urban built-up land segments, aquatic buffers for particular reaches and so on. Therefore, it needs a more refined approach based on detailed preliminary analysis of the sub-watersheds, their locations, and examination of removal efficiency rates etc.

355 7.2 BMPs FOR THE STILLWATER RIVER

Stillwater river basin has a drainage area of approximately 650 square miles. In 1975, the

Stillwater River and Greenville Creek (tributary to Stillwater river) became Ohio's eighth scenic river (Ohio Department of natural Resources). The river enters the Great Miami River at Dayton,

OH (Figure 7.1) According to the Ohio EPA, the Stillwater River watershed is impaired by nutrient enrichment, ammonia, metals, and other habitat alterations. Similar to many of the other watersheds, the majority of the river miles are impacted by nutrient enrichment. Such severe river and stream impairments are commonly caused by human development, improper agricultural practices and land use changes in the surrounding area. In the future land-use scenario developed for the Stillwater river basin, the urban growth rate would be significant - about 7-12% for Shelby county over the next 40-50 years (Ohio EPA) with no or minimal control. The future land-use scenario simulated a 10% future increase in urban areas in forms of high/low density residential areas for Greenville creek. The major contributing urban center is Greenville, OH with current population over 15,000. Dayton, OH, Vandalia, OH and Englewood, OH would control the urbanization processes in the lower portions of the Stillwater river basin. According to the future land-use scenario, lower Stillwater river basin would increase in urban area by 13-15% compared to current conditions. Obviously, this degree of uncontrolled urbanization would likely to have a negative impact on water quality.

Therefore, it is important to estimate the effects of common BMPs applied to the upper and lower portions of Stillwater River.

356 Figure 7.1 Location of the Stillwater River basin

N Upper GMR basin divided into sub-basins

Legend

Cataloging Unit Boundaries

Streams

$ USGS Gage Stations

Permit Compliance System 1 I Watershed ND I AN 2 L Subbasins

A $

R R C 4

G IE M A A * R O 6 L $ 3 $

S $ $ W L 10 A 8 E M A M P T O 9 H S C 5 Q MUDDY CR R E U R IT W O R C C O R GS $ O 7 IN A K D R N C C N D R E 13 E 11 G T T R N L I E S

S $ R C O$ T P R R HARRIS CR N I S C L C L C R D W H R R Y C 14 A I A N P D O LE CR $ T 18 VIL T M EN D A B GRE S R $ E I A M O A N C R 15 $ L N H C K 19 R $ O R $ C R C $ N U C R $ R $ C $ E B D T 17 Y U U PAINTER CR C A 12 M R R $ K $ $ $$ $ LU $ $ BEAVER D R C LOW $$$ R C C R 27 16 $$ S

22 E

N 20

O $ D $ 26 23 21 Springfield $ 24 Stillwater river basin $ 25 28 Dayton 03060Miles Scale

357 The general principles of BMPs application to the Stillwater river basin were based on preliminary analysis of land-uses within the Stillwater river basin. According to current land-use conditions, the Stillwater watershed is predominantly agricultural (93%). Urban area is about 5% of the total basin area. As it was mentioned above, the future land-use scenario imply a 14% increase in urban area, leaving agricultural are to be ~75%. For BMPs simulations, wet/dry detention ponds, wetlands, and infiltration trenches were selected for agricultural land uses

(mostly upper portions of the watershed). Porous pavement structures and infiltration basins were chosen for urban land areas (lower part of the basin). Aquatic buffers were simulated in all reaches within the basin. The results of simulations are presented in Table 7.2.

Table 7.2 Summary results from simulating BMPs in the Stillwater river watershed

With BMPs and No BMPs and With BMPs No BMPs future land-use current and current and future scenario conditions conditions land use (% change to “No (base case) (% change to scenario BMPs and future base case) land use”) Annual flow (ft3/s) at 675 612 (-9.3%) 816 715 (-12.3%) Englewood, OH Annual total phosphorus 0.37 0.30 (-19.0%) 0.53 0.38 (-28.3%) (mg/l) Annual ammonia 0.10 0.10 (no change) 0.11 0.10 (-10.0%) nitrogen (mg/l) Annual sum of nitrites and 3.50 3.30 (-6.0%) 3.58 3.0 (-16.2%) nitrates (mg/l)

Summarizing the results of simulating BMPs, it seems that application of BMPs in forms of constructing wet and dry detention ponds, wetlands, infiltration trenches, infiltration basins and porous pavements impacted the water quantity and quality of the Stillwater river. As the results in the table show, when comparing two scenarios “no BMPs with future land use change” and “with

BMPs and land-use change scenario”, the annual flow had been reduced by 12%, annual phosphorus concentrations by 28%, total ammonia nitrogen annual concentrations by 10% and by

16% for the annual nitrites and nitrates concentrations.

358 CHAPTER VIII: DISSCUSSION OF THE RESULTS

One of the most pressing issues of global change research is the interaction of land cover changes with global climate (Veldkamp et al, 1996).

The aim of climate change impacts assessment studies is to increase the understanding of the regional and global effects of future climate change on natural and managed ecosystems, agriculture and other human activities and to provide information for the development of adaptation and mitigation strategies (Viner et al, 1995). The impacts of climate change on hydrology usually are estimated from hydrologic modeling based on climate change scenarios generated from the output of general circulation models (GCMs) (IPCC, 1998). The major developments in studies to estimate the climate change impacts on hydrological system are (1) constructing scenarios that are suitable for hydrological impact assessments; (2) developing and using realistic hydrological models; and (3) understanding better the linkages and feedbacks between climate and hydrological systems. Over the last decade there have been numerous studies (e.g., Arnell, 1999; Arnell et al 1996; Ponce et al, 1995; Bultot et al, 1988, 1992, Chiew et al 1995, Mimikou, M, et al, 1991) investigating the sensitivity of hydrological regimes to climatic changes associated with global warming, in a wide range of environments and using many different models and scenarios. These investigations employ a variety of models, ranging from the simple water balance models to evaluate the annual and seasonal streamflow variation to the complex distributed-parameter models that simulate a wide range of hydrological and biogeochemical processes.

The impacts of climate change on water quality have received less attention than the impacts on water quantity, but current research studies show a growing interest in this problem (Mimikou, et al, 2000, Arnell, 1998). Potential negative implications of climate change on water quality include reductions in dilution flows, higher water temperatures, increased agricultural and urban pollutants wash off into streams.

359 No projection of the future state of the aquatic ecosystem can be made without taking into account past, present, and future human land-use patterns (IPCC, 1998). Land-use is known to have significant impacts on water quantity and quality. A number of studies have been done to estimate the impacts of land-use changes and urbanization on water quality (e.g., Gardi, C, 2001,

Ha, S, Bae, M, 2001, Brun, S, Band, L., 1999, Klein, 1978).

However, not many studies have investigated the combined effects of land-use and climate changes on hydrology and water quality. There is a growing interest among water resource planners and hydrologists to study the coupled effects of future climate and land use changes on aquatic ecosystems. Some studies, Chang, H, (2001), Whitehead et al, (1995), prove the effectiveness of this approach.

Therefore, it is important to consider the integrative approach in the studies associated with evaluating the impacts of climate and land-use changes on hydrology and water quality.

Integrative approach includes a consideration and analysis of combined effects of future climate and land use changes on the behavior of aquatic ecosystem, e.g. development of different scenarios and simulating hydrological, hydrochemical and hydrobiological regimes using water quantity and quality models. This approach will improve our understanding of the interrelationships between land-use-climate-hydrology-water quality. Ultimately, it could become a useful practical tool in water resource planning which would help us to analyze, evaluate and predict the possible impacts of climate and land-use changes on water resources.

8.1 HYDROLOGY AND TESTED SCENARIOS

Hydrology and climate change scenarios

Simulating hydrologic regime of the Great Miami River under climate change scenarios demonstrated that under Dry case climate scenarios (precipitation is decreased by 20% and temperature increase by 2 or 4 degrees Centigrade) the model predicted a reduction in the mean annual flow of the Great Miami River in average by 55% (Table 4.2.1 and 4.2.3) If Dry case scenario happened in the next 50-70 years, the mean annual flow of the Upper GMR at Dayton,

360 OH would have decreased from 2,347 ft3/s to about 1,040 ft3/s, which is more than two times. For the Lower GMR the impact of “dryness” would reduce the mean annual flow at Hamilton, OH from 2,984 ft3/s to approximately 1,241 ft3/s. That is again, more than 50% reduction.

Interestingly to note, these magnitudes of flow reduction are higher than those reported by Liu,

(2002), when simulating flow regime of the Little Miami River under Dry climate scenario using

SWAT model. She reported a 34% decrease in the mean annual flow (Liu, 2001). The deficit of moisture in the system would reduce the volumes of water entering the streams from surface runoff and runoff from impermeable areas. Moreover, along with reduction of surface flow, the model predicted corresponding decrease in interflow and baseflow. Examining the monthly and daily hydrographs simulated by the hydrologic model under moisture deficit, it is seen that the numbers and daily magnitudes of stormflow events decreased significantly, and baseflow regime has been changed toward lowering daily magnitudes. Shapes of flow recession curves would change as well, becoming smaller and smoother. Limited precipitation under Dry climate scenario would affect ground water aquifers. Less moisture would pass through the soil profile deeper, zone of percolation and reach the ground water table. Therefore, less amounts of moisture would be available to recharge ground water aquifers. And, since the baseflow regime of the GMR during low flow seasons is primarily controlled by hydraulic connections with groundwater aquifers, the smaller amounts of water would recharge the streamflow during these times, potentially reducing the daily baseflow and monthly discharges.

In opposite, Wet case climate scenarios simulations produced an increase in the mean annual flow by approximately 31%. Thus, with +20% precipitation surplus in the next 50-70 years, the mean annual flow of the Upper GMR would increase roughly by 40%, changing from 2,347 ft3/s

(or 2.1 km3/year) to 3,250 ft3/s (2.88 km3/year). The mean annual flow of the Lower GMR would increase by 25%, changing from 2,984 ft3/s (2.66 km3/year) to 3,730 ft3/s (3.3 km3/year) (Table

4.2.2 and 4.2.4). The results are close to those, reported by Liu, et. al. (2002). She reported a 35% increase in the mean annual flow of the Little Great Miami River under wet scenario

361 (precipitation +20%). Increased amounts of precipitation would bring more moisture into the system and would increase the volumes and frequencies of stormwater flows (e.g. flows from impermeable areas and surface runoff). Simulated hydrographs for wet scenarios demonstrate that increase in volumes of stormflows is reflected in increased daily maximum discharge magnitudes, sometimes more than 5-6 times, increased interflow due to faster and shorter time for ground saturation. However, the hydrologic model predicted a slight decrease in baseflow discharges for the Lower GMR.

Another interesting issue that has been investigated in this research work is the ability of

HSPF to quantitatively estimate the evapotraspiration losses and how it would impact the flow regime and water budget of the Great Miami River. For this reason, two new groups of scenarios have been developed: dry scenario (precipitation –20%) and current temperature and wet scenario

(precipitation +20%) and current temperature. Simulation results from these two scenarios and the comparison between the results from HD (hot and dry) and HW (hot and wet) scenarios

(where the temperature was increased by 4 degrees Centigrade) revealed that by increasing the air temperature by 4 degrees Centigrade, the hydrological model predicted 10-25% lower or higher mean annual flows, depending whether it dry or wet scenario (Table 4.2.1 and 4.2.2). For instance, comparing the results between Hot and Dry (HD) and “P minus 20% and current temperature” scenarios, the simulated mean annual flow in the Upper GMR under HD scenario was reduced by 60% compared to base case, whereas under “P minus 20% and current temperature” was reduced only by 46%, meaning that evapotraspiration losses due to increased atmospheric temperature are reflected in 14% losses in streamflow volumes. The comparisons of the results between “Hot and Wet--Warm and Wet” and “Hot and Dry –Warm and Wet” support the above discussion as well. Increasing the temperature by 2 degrees Centigrade (going from

Warm to Hot group with the same precipitation amounts) resulted in decrease in the mean annual flow by 9-10% in average, for both, Upper and Lower Great Miami River.

362 Hydrology and land-use change scenario

As it was discussed earlier in the Chapters 2 and 4, land-use changes in the form of urbanization or increasing areas of imperious and pervious urban segments tend to increase runoff volumes. In this research, the hypothetical land-use scenario for the Upper and Lower GMR was developed based on several factors such as former land-use changes by counties (about 5-6 % growth in urban area over last 20 years), general economic development in the region, population dynamics and proximity to major roads and interstates. According to hypothetical land-use scenario, the urban area (both pervious and impervious segments of urban area) would increase by roughly 25% in the Upper GMR basin, and by 30-45% in the Lower GMR basin. It should be noted, that this rate of urbanization is likely to be exaggerated, but considering perspective of 100 years ahead, it is also likely that it would happen or be close to the hypothetical numbers. In fact, this land-use scenario might be considered as the “worst case” scenario, in terms of urbanization of the Great Miami River basin.

Simulating hydrologic regime under future land-use scenario proved that urbanization would significantly affect the flow regime of the Upper and Lower Great Miami Rivers. The hydrologic modeling results demonstrate that the mean annual flow of the Upper GMR would increase by

31%, from 2,347 ft3/s to 3,077 ft3/s, compared to current conditions. The flow in the Lower GMR at Hamilton, OH would increase by 43%, from 2,984 ft3/s to 4,322 ft3/s (Table 5.5). Such a gain in flow volumes is primarily explained by the following reasons: (1) increased flood peaks; (2) increased stormwater runoff and (3) increased bankfull flows. With high rates of urban development, previously pervious areas like agricultural lands and other types of vegetated cover turn to impervious surfaces such as rooftops, roads, parking lots, sidewalks, and so forth. These actions would result in decreasing the infiltration capacity of the ground and, therefore, it would increase the surface runoff (in types of overland flows, flows from water collecting channels and pipes) from impermeable areas. Eventually, the frequency and the magnitudes of flood peaks would become higher. For example, analysis of the hydrograph and flow duration curve of the

363 Upper Great Miami River basin at Dayton, OH under future land-use scenario demonstrates 2-8 times higher daily peak discharges compared to base case scenario along with larger number of stormwater flow peaks. Hydrograph and flow duration curve drawn for the Lower Great Miami

River at Hamilton, OH under land-use scenario show even larger increase in daily maximum discharges (5-10 times higher) as well as in a larger number of stormflow peaks. The hydrologic model predicted an increase in baseflow volumes for both Upper and Lower Great Miami Rivers.

Potentially, more frequent and higher by magnitudes of stormflows would increase the risk of damaging floods to the areas located in close proximities to the streams and tributaries of the

Great Miami River.

It is worthwhile to note, that the results of Little Miami River hydrologic modeling under future land-use scenario for the Little Miami River basin (which is set to be 50% low-density residential and 35% being as agricultural) using SWAT model produced reverse results in volumes of flow compared to the result of this study. Liu, et. al. (2002) reports a 20% reduction in annual flow when compared to the current land-use scenario for the Little Miami River basin. The reasons for these differences might be different.

Firstly, it could be the parameter estimations of the IMPLND module. In HSPF the user needs to specify certain parameters for each land-use category in order to model surface runoff. The land cover map that was used in this study contains several categories of urban areas such as residential, commercial, industry and transportation. For the final input into the HSPF, these categories were generalized into Urban Built-up areas with two categories: pervious and impervious urban segments. Future land-use scenario was constructed by increasing % of urban area of each urban segment clustered to a corresponding reach number. Later, each segment was parameterized within IMPLND category IWATER module. The major parameters to adjust are

LSUR (length of overland flow plane), SLSUR (slope of overland flow plane), NSUR

(Manning’s roughness coefficient n for overland flow), RETSC (retention (interception) storage capacity of the IMPLND surface), RETS (the initial retention storage) and SURS (the initial

364 surface overland flow storage). Those parameters were estimated and adjusted according to the

HSPF technical memos on hydrologic calibrations as well as the HSPF parameter Database program (HSPFParm).

To simulate surface runoff, SWAT model uses curve numbers for each land-use/soil type combinations provided by Soil Conservation Service (SCS Engineering Division). Liu, et. al.,

(2002) found that there is a significant decrease in surface runoff from low-density residential area.

However, many studies (Brun and Band, 1999, Dunne and Leopold, 1978, Pett and Foster, 1985,

Lazaro, 1990, Brown, 1988, Keng et al. 1998) with applications of HSPF model confirm that watersheds with large amounts of impervious cover may experience overall increases in annual, stormflow volumes and flood frequency and there is a positive correlation between storm runoff volume and the amount of impervious cover.

The second reason might be in the different rates and areas of urban development between the

Little and Great Miami River basins. The Great Miami River basin is more urbanized and contains larger amounts of pervious and impervious urban areas in types of high, low residential, commercial, industrial and transportation networks. Therefore, it is reasonable that there is a an overall increase in stormflow peaks and flood frequencies as the HSPF model simulated for the

Great Miami River under future land-use scenario.

Hydrology and combined climate and land-use changes

One of primary goals and central focus of this research is to quantitatively estimate the changes in hydrological regime of the Great Miami River under combined climate and future land-use changes. In the next 50-80 years it is likely to expect the combination of climatic and land-use changes within the region of study rather than individual effect of each factor.

Hydrologic simulation under combined climate and land-use changes demonstrated that the mean annual flow of the Great Miami River would decrease in Dry case climate and land-use change scenarios in average by 20% (Table 6.1 and 6.3). The simulated reduction of flow is

365 larger in the Upper Great Miami River – 25% decrease, from 2,347 ft3/s to 1,775 ft3/s. In the

Lower Great Miami River the annual flow reduced by 7%, from 2,984 ft3/s to approximately

2,790 ft3/s. Unlike the results from simulation under “climate change only” scenarios (Dry case group: precipitation minus 20%), the factor of increased impermeable area (e.g. decreasing the infiltration capacity) would contribute to enlargements in surface runoff volumes. With limited precipitation but larger amounts of impervious areas in the watersheds, it is likely that significant portion of precipitation after ground saturation would be involved in surface runoff and tiny portions would pass through the zone of percolation, recharge the aquifers and support the baseflow. Daily hydrographs drawn for the Dry group of combined climate and land-use scenarios show the decreased daily magnitudes of stormflow events but the peaks are not as low as it is in climate change only simulation results. However, the hydrologic model predicted higher baseflows for both the Upper and Lower Great Miami Rivers. The overall decrease (by 20%) in the volume of simulated annual flow of the Great Miami River is smaller when comparing to the

SWAT results from Liu, et. al., (2002) study on Little Miami River, where they reported approximately 45% decrease under combined dry climate (precipitation minus 20%) and land-use future scenarios.

In contrast, the hydrologic simulations of the Great Miami River flow under combined climate and land-use scenarios, but Wet case group, produced an overall increase in the annual flow on an average by 70% compared to current conditions scenario. With 20% precipitation surplus and close to 30% increase in urban area, the annual flow of the Upper GMR would increase by roughly 68%, changing from 2,347 ft3/s (or 2.1 km3/year) to 3,943 ft3/s (3.48 km3/year). The mean annual flow of the Lower GMR under the same conditions would increase by 75%, changing from 2,984 ft3/s (2.66 km3/year) to 5,270 ft3/s (4.66 km3/year) (Table 6.1 and

6.3). Analyzing the simulated hydrographs for the combined wet-climate and land-use change scenarios, it seems that not only the peak daily discharges would increase (up to 10 times) compared to current conditions scenario but also interflow runoff and baseflow discharges show

366 higher magnitudes. The results from Liu, et. al., (2002) SWAT simulations of the Little Miami

River flow report a 20% increase in annual flow. Such a difference between the results in flow simulations in these two studies, perhaps, might be explained by the reasons that were discussed above in the “Hydrology and land-use change scenario” section.

The ultimate consequences of the increased number and magnitudes of stormflows in the Great

Miami River, simulated under combined wet-climate and land-use change scenarios could be significant in terms of economic damages. Although, the flow of the Great Miami River is highly regulated by flood control systems and reservoirs, according to the modeling results it is very likely to expect higher floods in the next 20 and more years. Higher peak discharges can lead to more frequent and severe floods, which could impact various urban infrastructure including private properties and bring significant economic damages.

Changes in water quantity can significantly affect the water quality parameters as well. Changes in hydrological regime can affect the stability of water temperature and chemistry. It also affects what size and species of fish will be found in water body.

8.2 WATER QUALITY UNDER TESTED SCENARIOS

The second primary goal of this research work is to evaluate the effects of combined climate and land-use changes on water quality of the Great Miami River. Surface runoff, especially under the first flush phenomena, is an important source of non-point source pollution (Tong and Chen,

2002). The increased amount of precipitation along with larger amounts of impervious areas in the watershed will increase the surface flow and wash away more contaminants, including nutrients, from the land surfaces into the receiving waters.

The following water quality constituents have been modeled in this research study: water temperature, dissolved oxygen, total phosphorus, total ammonia nitrogen and sum of nitrites and nitrates. The importance of these water quality constituents is discussed next.

367 8.2.1 BRIEF OVERVIEW OF WATER QUALITY PARAMETERS AND THEIR IMPORTANCE TO AQUATIC ECOSYSTEM

As it was previously mentioned, water temperature is very important in determining what fish species will thrive in the water body. Temperature affects how active fish are and what areas they will use. Certain temperatures favor some species and exclude others. Most fish spawning is also temperature dependent. Temperature not only affects when spawning will occur, but also how successful it will be.

Dissolved Oxygen is oxygen that is held in between water molecules and is available to fish and other aquatic species. DO is one of the most critical chemical parameters. Both, aquatic plants and animals depend on dissolved oxygen for survival. Generally, dissolved oxygen levels decrease in warmer waters or when waters are filled with excessive amounts of nutrients. When plants decay, they consume more oxygen, leaving less for fish. It is not rare when considerable losses of oxygen result in fish kills (Wisconsin Department of Natural Resources, 1999).

Nutrients are very important for aquatic plants and animal’a life. All plants and animals need a balance of nutrients to grow and reproduce. Nitrogen is recycled continually by plants and animals, creating a Nitrogen Cycle (Figure 8.1) (Murphy, 2000).

368 Figure 8.1 Conceptual scheme of Nitrogen Cycle

Most organisms cannot use nitrogen in the gaseous form N2 for their nutrition, so they are dependent on other organisms to convert nitrogen gas to nitrate, ammonia, or amino acids.

"Fixation" is the conversion of gaseous nitrogen to ammonium or nitrate. The most common kind of fixation is "biological fixation" which is carried out by a variety of organisms, including blue- green algae, the soil bacteria Azobacter, and the association of legume plants and the bacteria

Rhizobium. Additionally, nitrogen can be fixed by some inorganic processes. For example, "high- energy fixation" occurs in the atmosphere as a result of lightning, cosmic radiation, and meteorite trails. Atmospheric nitrogen and oxygen combine to form nitrous oxides (NOx), which fall to the earth as nitrate (Murphy, 2000).

When plants and animals die, proteins (which contain organic nitrogen) are broken down by bacteria to form ammonia (NH3). This process is called "ammonification." Ammonia is then broken down by other bacteria (Nitrosomonas) to form nitrite (NO2), which is then broken down

369 by another type of bacteria (Nitrobacter) to form nitrate (NO3). This conversion of ammonia to nitrate and nitrite is called "nitrification. Nitrates can then be used by plants for growth.

Completing the nitrogen cycle, nitrates are reduced to gaseous nitrogen by the process of

"denitrification." (Figure 8.1) This process is performed by organisms such as fungi and the bacteria Pseudomonas. These organisms break down nitrates to obtain oxygen (Murphy, 2000).

Therefore, two of the most important nutrients are nitrogen (in the form of Nitrates, Nitrites and

Nitrogen Ammonia) and Phosphorus. Nitrates (NO3) are needed by the plants to make proteins to grow and reproduce.

Nitrates are highly soluble and stable over a wide range of environmental conditions. Nitrates are taken up by plankton, aquatic plants, algae, which are then eaten by fish. Nitrite (NO2) is relatively short-lived in water because it is quickly converted to nitrate by bacteria. Excessive concentrations of nitrate and nitrites could be very harmful not only for aquatic life but also to humans and wildlife. For instance, when nitrates are broken down in animal or humans intestines to become nitrites. In human organisms, nitrite reacts with hemoglobin in blood to produce methemoglobin, which limits the ability of red cells to carry oxygen. This condition is called methemoglobinema and could be very serious for humans. High nitrate and nitrite levels can also cause methemoglobinema in livestock and other animals (Murphy, 2000).

Ammonia is another inorganic form of nitrogen. It results from the breakdown of wastes.

Ammonia is easily transformed to nitrate in waters that contain oxygen and can be transformed to nitrogen gas in waters that are low in oxygen. Nitrogen Ammonia is present in waters in two

+ forms: ionized (NH4 ) and un-ionized (NH3). Total Nitrogen Ammonia is the sum of both forms.

The un-ionized Ammonia concentration is a function of pH and temperature. The reaction between the two forms is shown by this equation:

+ - NH3 + H2O ÅÆ NH4 + OH

Toxic concentrations of ammonia in humans may cause loss of equilibrium, convulsions, coma, and death. Ammonia concentrations can affect hatching, the growth rates of fish as well as the

370 changes in tissues of gills, liver, and kidneys during structural development (Murphy, 2000, Ohio

EPA, 1997, Pollution Prevention Handbook, 1998).

Phosphorus is also needed for plant growth. Phosphorus exists in water in either a particulate phase or a dissolved phase. Particulate matter includes living and dead plankton, precipitates of phosphorus, phosphorus adsorbed to particulates, and amorphous phosphorus. The dissolved phase includes inorganic phosphorus and organic phosphorus. Phosphorus in natural waters is

-3 usually found in the form of phosphates (PO4 ). Phosphates can be in inorganic form (including orthophosphates and polyphosphates), or organic form (organically-bound phosphates).

Inorganic phosphate is phosphate that is not associated with organic material. Types of inorganic phosphate include orthophosphate and polyphosphates. Orthophosphate is sometimes referred to as "reactive phosphorus." Orthophosphate is the most stable kind of phosphate, and it is the form used by plants. Orthophosphate is produced by natural processes and is found in sewage.

Polyphosphates (also known as metaphosphates or condensed phosphates) are strong complexing agents for some metal ions. Polyphosphates are used for treating boiler waters and in detergents.

In water, polyphosphates are unstable and will eventually convert to orthophosphate (U.S EPA,

1999, French, 1989).

Besides the nitrogen/phosphorus, which are naturally present in waters, a water body can get additional nutrients from external anthropogenic sources. Major factors (sources) affecting nutrients concentrations in natural waters are: (1) Wastewaters and septic systems (increase both nitrogen and phosphorus concentrations); (2) Fertilizer runoffs (contains phosphorus, nitrogen ammonia and nitrates); (3) Detergents (orthophosphates as a major constituent); (4) Animal wastes (phosphate and nitrogen runoffs from cattle feedlots, hog farms, dairies, and barnyards);

(5) Industrial discharges; and other human influences.

371 8.2.2 WATER QUALITY MODELING RESULTS

Water quality and climate change scenarios

Water quality modeling under climate change scenarios produced diverse results. Wet-climate- group scenarios results are characterized by reduced annual concentrations of dissolved oxygen by an average of 3%, increased total phosphorus concentrations by 16%, increased total ammonia nitrogen concentrations by 6% and increased concentrations of NO2+NO3 by 4%, in both the

Upper and Lower Great Miami Rivers (Table 4.3.1) Dry-climate group scenarios output results show about 5% reduction in dissolved oxygen annual concentrations in the waters, 9% increase in total phosphorus annual concentrations, 5% reduced total ammonia nitrogen concentrations and an overall 2% increase in NO2+NO3 annual concentrations. There is an indication that the changes of runoff control the loads of nutrients into the streams. Larger surface runoff volumes simulated in wet case scenarios reflect slightly higher concentrations in the water for total phosphorus, total ammonia nitrogen, and NO2 +NO3, compared to dry climate scenarios. The concentrations of phosphorus in every scenario simulated exceed the suggested EPA’s limit (0.2 mg/l) 2-4 times. It could be a problem because phosphorus is a significant factor limiting plant growth. Increasing phosphorus concentrations in the water would result in heavy algae blooms and excess aquatic plants and will enhance the process of eutrophication. As this process continues, more and more algae and aquatic plants die and decay, consuming more dissolved oxygen, thereby altering fish health.

Water quality and land-use change scenario

Results of modeling under land-use change scenario indicate that the future land-use would significantly impact the water quality of the Great Miami River. Under future land-use, the water temperature will increase by 2% (or by 1.5 degrees Fahrenheit) (Table 5.6). Elevated water temperatures might be a result of warmer surface runoff coming from larger urban areas presented in the future land-use scenario. Annual dissolved oxygen concentrations in the water are reduced by 2-9% under future land-use. The projected values are still within the EPA’s limits

372 for freshwaters (5 mg/l). However, simulated daily dissolved oxygen concentrations during summer months are often very close to critical 5 mg/l. Simulation of nutrients under future land- use scenario revealed quite significant changes compared to current conditions. Annual concentrations of phosphorus increased by 36% (from 0.33 to 0.45 mg/l) and 38% (from 0.42-

0.58 mg/l) for the Upper and Lower GMR, accordingly (Table 5.6). It is 2-3 times higher than the

EPA limits for phosphorus concentrations in the water. Expansion of urban areas resulted in a relatively significant increase of NO2+NO3 annual concentrations – by 15%. Annual concentrations of nitrogen ammonia increased on average by 15% as well. Still, the actual annual and daily magnitudes do not exceed the EPA’s limits (U.S EPA, 1990) for NO2+NO3 and nitrogen ammonia. Excessive amounts of nutrients in the water as a result of future land-use changes will contribute and enhance the processes of eutrophication of the Great Miami Rover waters.

Water quality and combined climate and future land-use change scenario

After analyzing the individual impacts of climate and land-use changes on water quality, it sounds logical to investigate the combined effects of climate and land-use changes on water quality of the Great Miami River. Simulation of water temperature under combined hot-climate and land-use scenarios produced in average a 5% increase compared to current conditions.

Therefore, a 4 degree Centigrade increase in air temperature corresponds to 1.7 0C increase in annual water temperature of the Great Miami River. Results from warm-climate combined with land-use scenario demonstrate in average approximately 2-3% increase in water temperature

(~1.3 F or close to 1 degree Centigrade) (Table 6.2 and 6.4). Generally speaking, it is not a significant increase. However, considering the seasonal water temperature fluctuations, the model produced considerably higher daily maximums during late Spring-Summer months that might affect certain temperature-sensitive fish species, like cold-water species. It is likely that under these simulated conditions, increased temperatures may affect their health, migration patterns, spawning, and so forth.

373 Simulated dissolved oxygen concentrations under combined climate and land-use scenario decreased by 3-9% in both Upper and Lower Great Miami Rivers (Table 6.2 and 6.4). Dissolved oxygen is controlled by water temperature and, also, by the amount of nutrients in the water.

Simulations of total phosphorus, total nitrogen ammonia and sum of nitrites and nitrates for each constructed combined scenario resulted in an overall increase in concentrations. Thus, the greatest increase showed phosphorus concentrations – for all scenarios in average by 45% (0.46 –

0.65 mg/l). This is again 2-3 times higher than the limits suggested by EPA. Daily phosphorus concentrations are even up to 11-12 times the EPA’s limit for phosphorus. Total ammonia nitrogen annual concentrations increased by 11% and nitrites and nitrates by 16%. Simulations of ammonia and NO2+NO3 in the Lower Great Miami River under warm and wet climate and land- use scenarios produced slight reduction in their annual concentrations by 2% for the sum of nitrites and nitrates and by 5% for total ammonia nitrogen (Table 6.2 and 6.4). This might be explained by the effect of runoff, because warm and wet climate scenario combined with land-use scenario produced the greatest increase in surface and annual runoff, so despite the increase in pollutants wash off rates, the processes of dilution might take place, lowering the daily peak concentrations during stormflows. Typically, the complexity of the interactions of nutrient concentrations and flow make it important to examine both point sources and non-point sources of nutrients and wet weather (high flow) and dry weather (low flow) stream conditions to verify nutrient sources and concentrations in multiple flow conditions. Overall, it seems that the model predicted more nutrient loads from pervious and impervious urban areas in the future combined wet climate and land use change scenarios. These results contradict with the results of SWAT nutrient simulations for the Little Miami River (Liu et al., 2002). In fact, they seem to be opposite in trends. Liu et. al., (2002) reported an overall reduction in phosphorus and nitrogen concentrations/loads by 50-60% for phosphorus and by close to 40% for nitrogen, whereas HSPF model produced an overall increase in the annual concentrations of total phosphorus, ammonia and the sum of nitrites and nitrates. Again, these differences, might be explained by the fact that

374 the Great Miami River basin contains larger urban areas in the future land-use scenario.

Therefore, potentially, greater volumes of surface runoff flow from impervious and pervious urban areas would enter the major streams, carrying larger amounts of pollutants, which would eventually increase the mean annual concentrations of nutrients in the water. However, the daily concentrations of nutrients will vary depending on the season. For instance, during stormwater events, the concentrations of pollutants in the water tend to decrease, primarily, due to dilution processes.

8.3 BMPs AND THEIR EFFECTS ON HYDROLOGY AND WATER QUALITY OF STILLWATER RIVER

Chapter 7 of this research has been targeted to investigate how Best Management Practices

(BMPs) can be used in the management of urban stormwater runoff and how they can improve water quality of the Stillwater River.

Rapid urbanization impacts natural water ways and affects their quality and quantity. As the area develops, undisturbed pervious surfaces become impervious due to the construction of parking lots, buildings, homes, streets and other structures (Best management Practices for South

Florida Urban Stormwater Management Systems, 2002). As it was mentioned before, the increase in impervious surfaces results in increased stormwater runoff. Increase in impervious areas reduces the infiltration of water into the ground and causes pollution of aquatic systems.

Stormwater BMPs could provide effective methods in minimizing the non-point source pollution from stormwater runoff and, hence, improving water quality of the stream.

The results of stormwater BMPs simulations (Table 7.2) indicate that after applying

(constructing) wet and dry detention ponds, wetlands, infiltration trenches, infiltration basins, porous pavements and aquatic buffers in the upper and lower portions of the Stillwater river basin, water quality and quantity have changed. Thus, when comparing the two scenarios (“no

BMPs with future land use change” and “with BMPs and land-use change”), the annual flow was reduced by 12% (from 816 ft3/s to 715 ft3/s), annual phosphorus concentrations declined by 28%

375 (from 0.53 mg/l to 0.38 mg/l), total ammonia nitrogen annual concentrations by 10% (from 0.11 mg/l to 0.10 mg/l) and by 16% (from 3.58 mg/l to 3 mg/l) in the sum of nitrites and nitrates annual concentrations. From this first estimation, the resultant reductions in the magnitudes of the above parameters prove the effectiveness of the BMPs for Stillwater river basin.

As it was discussed in Chapter 7, at the beginning of this research study, it was planned to estimate BMPs effects on water quality of all major tributaries of the Great Miami River.

Unfortunately, only the Stillwater River has been studied to evaluate BMPs. Therefore, the potential effects of a variety of BMPs on the water quality of the Mad River, Upper and Lower

Great Miami needs further investigation.

376 CONCLUSION

In the next 50-100 years, it is likely to expect changes in climate and land use in the Great

Miami River basin. These changes would occur in combination rather than individually. The ultimate goal of this research work was to examine the reaction of the hydrological system of the

Great Miami River to land-use activity changes in the context of climate change. How and at what degree these combined changes would affect the Great Miami River ecosystem are the central questions in this research.

Firstly, this study investigates the individual impacts of climate and land-use changes on the hydrologic regime and water quality of the Great Miami River. This step is needed in order to evaluate the separate effects of climate and land-use change on the hydrologic and water quality conditions of the Great Miami River. After analyzing the individual effects, the effects of combined impacts of climate and land-use changes on hydrology and water quality were simulated.

The modeling results indicate that the combination of future climate and land-use changes would significantly impact the hydrology and water quality of the Great Miami River basin. The rates and magnitudes of changes in the hydrologic regime and water quality conditions vary with the type of future scenario. Generally, simulations under dry climate scenarios paired with future land-use change scenario demonstrated a reduction in annual flow of the Great Miami River and a slight increase in the annual concentrations of phosphorus, total ammonia nitrogen and the sum of nitrites and nitrates. The results from the combined wet climate scenario and land-use change scenario show a significantly larger increase in annual flow as well as a higher level of nutrients in the water. Nutrient enrichments would be larger in the wet case scenario group due to higher nutrient loads carrying into the streams from urban areas by surface runoff. The study shows extremely high enrichments of phosphorus in the waters regardless whether it is a dry or wet scenario.

377 The significance of these results lie in the fact that the assessment is performed on an integrative basis, considering the complex interplay between future land-use and climate changes and their impacts on water quantity and quality.

It is likely that uncontrolled urbanization and climate changes would affect water quality and quantity of the Great Miami River in the ways this study has portrayed. Continuing urban sprawl would contribute to nutrient enrichments in the streams of the Great Miami River basin.

Overabundance of nutrients (especially phosphorus) in the waters of the Great Miami River basin poses a great problem to the stream ecosystems. It could accelerate the rates of euthrophication causing impairment of water quality and risks to habitat health. There is a probability that in the next several decades, the climate in the southwest of Ohio would be wet rather than dry. This speculation is based on the positive trend in precipitation over the last 50 years at Dayton, OH, weather station. If the climate becomes warmer and precipitation increases by 20% in the southwest of Ohio, a substantial increase in the number and magnitudes of stormflows is likely to happen. This would result in a greater number of floods. The more frequent floods will cause significant economic damages to the populated areas adjacent to the streams.

One important contribution of the present study is that it has demonstrated the effectiveness of chosen integrated approach when considering the combined effects of future climate and land-use changes on water resources. The results of this research will contribute to our existing knowledge on estimating the behavior of hydrological system and water quality conditions under future climate and land-use changes. This approach can provide a sound basis for water quality and quantity assessment and it will be an effective tool for defining and predicting the long-term impacts of land-use and climate changes on water resources. Application of such a technique and information derived from this dissertation work can be useful to environmental scientists, state and local agencies, watershed managers and planners, assisting them in identifying and assessing the combined impacts of possible climate and land-use changes in the implementations of full-

378 scale long-term water resources management development programs in the Great Miami River basin.

Another significant issue that has been investigated in this research work is the different measures, which could be applied in attempt to alleviate and minimize the consequences of predicted negative impacts on water quality of the Great Miami River. Development of a full- scale sound watershed protection program has a potential to minimize the adverse impacts of non- point source pollution on water quality. Frequently, such a project includes numerous programs and involves a number of state, local and federal agencies. The programs are directed to and include erosion and sediment control, stormwater management, agricultural best management practices, floodplain management, and others. Since stormwater runoff is a large contributor of nutrients into the streams, it makes sense to investigate the effects of a variety of stormwater

BMPs in attempt to reduce and prevent targeted stormwater runoff constituents, pollutants and contaminants from reaching receiving waters.

Modeling structural BMPs in HSPF for the Stillwater river basin has showed that construction of wet/dry detention ponds in agricultural lands upstream and bioretention and porous pavement for urban built-up land segments downstream can be an effective tool in reducing the non-point source pollution from stormwater runoff and, hence, improving water quality of the stream.

Potentially, these results could contribute to the on-going research studies and projects associated with the analysis of the effects of BMPs constructions on the water quality of the Great

Miami River. The results can be useful for local agencies, involved in the development and management of conservation programs in the Stillwater River basin. BMP modeling results received in this study provide not only a first estimation of the effects of a variety of BMPs on water quantity and quality of the Stillwater River, but also could serve as a preliminary guide for further investigation of BMPs and their effects on hydrology and water quality conditions.

This dissertation research has also demonstrated that application of the GIS-based U.S. EPA

BASINS multipurpose environmental analysis system and HSPF model in concert is a very

379 comprehensive water quality and quantity analysis tool. Integrated into BASINS, HSPF model is capable to model current and future hydrologic regime, nutrients and other major water quality parameters, especially from non-point source loads, with acceptable degree of accuracy. Although

HSPF model requires relatively significant efforts to calibrate its hydraulic and water quality parameters, at the end, when the model is properly calibrated, it can very accurately characterize the current hydrologic regime and water quality conditions in the Great Miami River basin and is capable to perform future scenario analysis.

Future research might include more detailed analysis of different BMPs upstream and their net effects on water quality and quantity downstream for the Mad River basin, several portions of the

Upper Great Miami River basin and in the sub-basins of the Lower Great Miami River.

Also, special and more detailed attention should be paid to the analysis of land-use and climate change impacts on seasonal changes in the hydrologic and water quality regimes of the Great

Miami River. Aquatic biota responses to future land-use and climate changes could be investigated.

Finally, future research might include examining the effectiveness of the application of

LUCAS (Land-Use Change Analysis System), the U.S EPA BASINS, and HSPF/SWAT models in concert in estimating the impacts of climate and land-use changes on the hydrology and water quality of the Great Miami River. Application of LUCAS model has a potential to develop a more accurate future land-use change scenario (land-cover map) for the Great Miami River basin, which can be used in further simulations in HSPF or SWAT models.

380

APPENDICES

381

APPENDIX A. (Auxiliary Plots showing the flow simulations of upstream GMR, Mad and Stillwater rivers)

Great Miami River at Taylorsville, OH: Simulated and Observed flows

1980-1995

1980-1985

382 1985-1990

1985-1990

Mad river near Dayton, OH: Simulated and Observed flows

1980-1995

383 1980-1985

1985-1990

1990-1995

384 Stillwater river at Englewood, OH: Simulated and Observed flows

1980-1995

1980-1985

1985-1990

385 1990-1995

Joined Mad river, Stillwater and upstream GMR flows a tDayton, OH: Simulated and Observed flows

1980-1995

386 APPENDIX B.

(Auxiliary plots showing water quality constituents simulations of upstream GMR, Mad and Stillwater rivers) Stillwater river at Englewood, OH Water tem-re: (1980-1986) – observed: 54.2 F; sim: 48F %Err: 11% Calibration (1980-83) obs – 53.7; sim – 47; ERR: 12%

387 Validation (1983-1986) observed: 56.4F sim: 49F; Err: 12%

388 DO (1980-1986) observed- 10.2 MG/L; simulated –11.4 MG/L; ERR: 12% Calibration period: 1980-1983; obs: 10.3 sim: 11.4; ERR: 11%

389 Validation: 1983-1986; obs: 10.0 sim: 11.5; ERR: 15%

390 Phosphorus: sim – 0.34; observed at Englewood – 0.31; Whole ERR: 10% Calibration period: 1980-1983; obs: 0.30 sim: 0.33; ERR: 10%

391 Validation: 1983-1986; obs: 0.29 sim: 0.32; ERR: 10%

392 Total Nitrogen Ammonia: sim – 0.10; observed at Englewood – 0.12; Whole ERR: 17% Calibration period: 1980-1983; obs: 0.12 sim: 0.10; ERR: 16%

393 Validation: 1983-1986; obs: 0.12 sim: 0.11; ERR: 10%

394 Nitrites + Nitrates: sim – 3.50; observed at Englewood – 3.80; Whole ERR: 8% Calibration period: 1980-1983; obs: 3.70 sim: 3.53; ERR: 5%

395 Validation: 1983-1986; obs: 4.0; sim: 3.40; ERR: 15%

396 (1980-1986) NO2+NO3 ANNUAL LOADS (TONNES): obs – 1976; sim – 1855.

397 Mad River near Dayton, OH Water temperature Observed- 53F; simulated –45F; ERR: 15% Calibration period: 1980-1983; obs: 52 sim: 44; ERR: 15%

398

Validation: 1983-1986; obs: 53F sim: 45F ; ERR: 15%

399

DO (Dissolved Oxygen) Observed- 10.7 MG/L; simulated –11.5 MG/L; ERR: 7% Calibration period: 1980-1983; obs: 10.9 sim: 11.5; ERR: 5%

400 Validation: 1983-1986; obs: 10.8 sim: 11.5; ERR: 6.5%

401

Phosphorus: sim – 0.28; observed near Dayton– 0.27; Whole ERR: 5% Calibration period: 1980-1983; obs: 0.26 sim: 0.27; ERR: 3.8%

402

Validation: 1983-1986; obs: 0.26; sim: 0.28; ERR: 8%

403

Total Nitrogen Ammonia: sim – 0.10; observed near Dayton – 0.11; Whole ERR: 9% Calibration period: 1980-1983; obs: 0.12 sim: 0.11; ERR: 8.3%

404 Validation: 1983-1986; obs: 0.09 sim: 0.10; ERR: 11%

405 Nitrites + Nitrates: sim – 2.5; observed near Dayton – 3.30; Whole ERR: 24% Calibration period: 1980-1983; obs: 3.35 sim: 2.59; ERR: 22%

406 Validation: 1983-1986; obs: 3.31; sim: 2.53; ERR: 23%

407 Great Miami River at Taylorsville, OH Water temperature Observed 54F; simulated 43F; ERR: 20% Calibration period: 1980-1983; obs: 54.2 sim: 43; ERR: 20%

408 Validation: 1983-1986; obs: 54.4F sim: 43F ; ERR: 21%

409

DO (Dissolved Oxygen) Observed- 10.2 MG/L; simulated –10.9 MG/L; ERR: 7% Calibration period: 1980-1983; obs: 10.1 sim: 10.9; ERR: 8%

410

Validation: 1983-1986; obs: 10.3 sim: 10.9; ERR: 6%

411

Phosphorus: sim – 0.31; observed at Taylorsville 0.32; Whole ERR: 5% Calibration period: 1980-1983; obs: 0.34 sim: 0.32; ERR: 6%

412 Validation: 1983-1986; obs: 0.32; sim: 0.31; ERR: 3%

413

Total Nitrogen Ammonia: sim – 0.16; observed at Taylorsville– 0.17; Whole ERR: 6% Calibration period: 1980-1983; obs: 0.19 sim: 0.17; ERR: 10%

414 Validation: 1983-1986; obs: 0.15 sim: 0.16; ERR: 6%

415 Nitrites + Nitrates: sim – 2.7; observed at Taylorsville, OH – 3.50; Whole ERR: 23% Calibration period: 1980-1983; obs: 3.4 sim: 2.8; ERR: 18%

416 Validation: 1983-1986; obs: 3.6; sim: 2.5; ERR: 29%

417 APPENDIX C. (Auxiliary Plots showing Hydrology and Water quality simulation results under Future Land Use Scenario - Upstream GMR, Mad and Stillwater rivers) Flow

418 WT

419 Total P

NH4

420 NO2+NO3

421 Mad River near Dayton, OH Flow

422 WT

423 Total P

NH4

424 NO2+NO3

425 GMR River at Taylorsville, OH Flow

426 WT

427

Total P

NH4

428 NO2+NO3

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