SURFACE WATER HYDROLOGIC MODELING USING REMOTE SENSING DATA FOR NATURAL AND DISTURBED LANDS by MULUKEN EYAYU MUCHE B.S., Jimma University, 2002 M.S., University of Bremen, 2007 M.Eng., Boise State University, 2011 AN ABSTRACT OF A DISSERTATION submitted in partial fulfillment of the requirements for the degree DOCTOR OF PHILOSOPHY Department of Biological and Agricultural Engineering College of Engineering KANSAS STATE UNIVERSITY Manhattan, Kansas 2016 Abstract The Soil Conservation Service-Curve Number (SCS-CN) method is widely used to estimate direct runoff from rainfall events; however, the method does not account for the dynamic rainfall-runoff relationship. This study used back-calculated curve numbers (CNs) and Normalized Difference Vegetation Index (NDVI) to develop NDVI-based CNs (CNNDV) using four small northeastern Kansas grassland watersheds with average areas of 1 km2 and twelve years (2001–2012) of daily precipitation and runoff data. Analysis indicated that the CNNDVI model improved runoff predictions compared to the SCS-CN method. The CNNDVI also showed greater variability in CNs, especially during growing season, thereby increasing the model’s ability to estimate relatively accurate runoff from rainfall events since most rainfall occurs during the growing season. The CNNDVI model was applied to small, disturbed grassland watersheds to assess the model’s ability to detect land cover change impact for military maneuver damage and large, diverse land use/cover watersheds to assess the impact of scaling up the model. CNNDVI application was assessed using a paired watershed study at Fort Riley, Kansas. Paired watersheds were identified through k-means and hierarchical-agglomerative clustering techniques. At the large watershed scale, Daymet precipitation was used to estimate runoff, which was compared to direct runoff extracted from stream flow at gauging points for Chapman (grassland dominated) and Upper Delaware (agriculture dominated) watersheds. In large, diverse watersheds, CNNDVI performed better in moderate and overall flow years. Overall, CNNDVI more accurately simulated runoff compared to SCS-CN results: The calibrated model increased by 0.91 for every unit increase in observed flow (r = 0.83), while standard CN-based flow increased by 0.506 for every unit increase in observed flow (r = 0.404). Therefore, CNNDVI could help identify land use/cover changes and disturbances and spatiotemporal changes in runoff at various scales. CNNDVI could also be used to accurately estimate runoff from precipitation events in order to instigate more timely land management decisions. SURFACE WATER HYDROLOGIC MODELING USING REMOTE SENSING DATA FOR NATURAL AND DISTURBED LANDS by MULUKEN EYAYU MUCHE B.S., Jimma University, 2002 M.S., University of Bremen, 2007 M.Eng., Boise State University, 2011 A DISSERTATION submitted in partial fulfillment of the requirements for the degree DOCTOR OF PHILOSOPHY Department of Biological and Agricultural Engineering College of Engineering KANSAS STATE UNIVERSITY Manhattan, Kansas 2016 Approved by: Major Professor Stacy L. Hutchinson Copyright MULUKEN EYAYU MUCHE 2016 . Abstract The Soil Conservation Service-Curve Number (SCS-CN) method is widely used to estimate direct runoff from rainfall events; however, the method does not account for the dynamic rainfall-runoff relationship. This study used back-calculated curve numbers (CNs) and Normalized Difference Vegetation Index (NDVI) to develop NDVI-based CNs (CNNDV) using four small northeastern Kansas grassland watersheds with average areas of 1 km2 and twelve years (2001–2012) of daily precipitation and runoff data. Analysis indicated that the CNNDVI model improved runoff predictions compared to the SCS-CN method. The CNNDVI also showed greater variability in CNs, especially during growing season, thereby increasing the model’s ability to estimate relatively accurate runoff from rainfall events since most rainfall occurs during the growing season. The CNNDVI model was applied to small, disturbed grassland watersheds to assess the model’s ability to detect land cover change impact for military maneuver damage and large, diverse land use/cover watersheds to assess the impact of scaling up the model. CNNDVI application was assessed using a paired watershed study at Fort Riley, Kansas. Paired watersheds were identified through k-means and hierarchical-agglomerative clustering techniques. At the large watershed scale, Daymet precipitation was used to estimate runoff, which was compared to direct runoff extracted from stream flow at gauging points for Chapman (grassland dominated) and Upper Delaware (agriculture dominated) watersheds. In large, diverse watersheds, CNNDVI performed better in moderate and overall flow years. Overall, CNNDVI more accurately simulated runoff compared to SCS-CN results: The calibrated model increased by 0.91 for every unit increase in observed flow (r = 0.83), while standard CN-based flow increased by 0.506 for every unit increase in observed flow (r = 0.404). Therefore, CNNDVI could help identify land use/cover changes and disturbances and spatiotemporal changes in runoff at various scales. CNNDVI could also be used to accurately estimate runoff from precipitation events in order to instigate more timely land management decisions. Table of Contents List of Figures ............................................................................................................................... xii Acknowledgements ...................................................................................................................... xix Dedication ..................................................................................................................................... xx Abbreviation ................................................................................................................................ xxi Chapter 1 - Introduction .................................................................................................................. 1 1.1 General Background ....................................................................................................... 1 1.2 Research Agenda ............................................................................................................ 6 1.2.1 Statement of Problem .................................................................................................. 6 1.2.2 Study Goals and Objectives ........................................................................................ 9 References ................................................................................................................................. 12 Chapter 2 - Literature Review ....................................................................................................... 15 2.1 Modeling the Watershed ............................................................................................... 15 2.2 Land Use/Cover Changes and Management ................................................................. 17 2.3 Remote Sensing in Hydrological Modeling .................................................................. 18 2.3.1 Normalized Difference Vegetation Index and Quality Check .................................. 20 2.4 Spatiotemporal Variability in Hydrology ..................................................................... 23 2.5 Rainfall-Runoff Modeling ............................................................................................ 25 2.6 Curve Number Method ................................................................................................. 27 2.7 Data Acquisition and Uncertainty ................................................................................. 29 2.7.1 Weather and Climate Data ........................................................................................ 30 2.7.2 Satellite Data Uncertainty ......................................................................................... 31 2.8 Paired Watershed Studies ............................................................................................. 32 2.8.1 Clustering Techniques.................................................................................................. 33 References ................................................................................................................................. 34 Chapter 3 - Curve Number Development using Normalized Difference Vegetation Index ......... 43 Abstract ..................................................................................................................................... 43 3.1 Introduction ................................................................................................................... 44 3.1.1 Background of SCS-CN Method .............................................................................. 45 3.1.2 Rationale ................................................................................................................... 47 viii 3.2 Study Area .................................................................................................................... 49 3.3 Model Development...................................................................................................... 51 3.3.1 Observed CN: Back-Calculated Curve Number ....................................................... 52 3.3.2 Land cover change: Normalized Difference Vegetation Index ................................ 55 3.3.3 CNNDVI Regression Model .......................................................................................