Estimating Runoff for Ungauged Watersheds Using Curve Number Method Piero Cauptoa, Hadia Akbara, Tyler Leea a Department of Civil and Environmental Engineering, Utah State University, 4110 Old Main Hill, Logan, Utah,84321 ([email protected], [email protected], [email protected]) Abstract: This study presents a model for estimation of monthly streamflow volume in ungauged watersheds in South Eastern Utah, using ArcMap (10.5.1). The model uses physical and metrological parameters including watershed area, soil type, monthly precipitation, climate and land use classification. Monthly average precipitation was collected by the National Oceanic and Atmospheric Administration (NOAA) and interpolated using the Thiessen Polygon method. Curve number raster was created using landcover and soil data. Streamflow for the gaged basins was estimated using curve number and precipitation for May, June and July. The calculated flow was compared with the streamflow data from the gaged systems. The estimates were within 50% of the observed values for 9 out of 28 watersheds for at- least for two months out of three. Keywords: Watershed; Stream Flow; Analysis; Geographic Information System (GIS); Ungauged Streams; 1 INTRODUCTION 1.1 Background Streamflow data is one of the prime sources of information for a watershed. It can help identify the flow patterns, precipitation patterns or serve as an indicator of water availability for various analyses. This data can be used for planning and resource allocation. Though the United State Geologic Survey (USGS) has a very successful stream gauging network all across the US, the stream-gauging stations are relatively few given the size and number of streams. Utah alone has a gauge density of 1 gauge per 314 km2. This means that there are many areas in the state where there is no estimation of streamflow. Stream flow estimation for un-gauged basin has been a challenge in hydrology. In the absence of gauged data, often records from nearby gauges are used to synthesize flow record for an un-gauged area/stream. However, this might not be applicable all the time if flow records are not available or there is not enough data for validation. Even if streamflow records are not available, climate data is readily available for the entire state. Additional data such as land cover and land use data, soil type and characteristics are also available that can be indicators of the streamflow patterns. The purpose of our study is to create a model that predicts streamflow for an ungauged watershed based on data that is readily available or can be conveniently manipulated. The model we created is data dependent. Developing such models requires finding and acquiring data from various sources. The 1 analysis was also dependent on Geographical Information System (GIS) tools and the data required as input to those tools to predict streamflow. 1.2 Prediction of Runoff Researchers have used some established methods to predict runoff of watersheds theoretically. Rainfall observations from gauged areas to predict flood probability in an assumed homogeneous river basin in Italy were used by (Boni et al, 2007). (Razavi & Coulibaly, 2016) used different regionalization models to estimate daily streamflow data by transferring hydrologic data from gauged to ungauged watersheds. Using the measured streamflow data by USGS, (Palanisamy, 2010) developed flow transferring characteristics of watersheds in Kentucky River Basin that helps predict streamflow at locations where streamflow is not recorded. A list of statistical methods for the prediction of runoff has been provided by (Blöschl et al, 2013). The application of these approaches depends on the data available and the scope of the study. The two “fundamental methods” can be statistical, or based. The statistical methods use relationships between runoff and the properties of the catchment, those correlations can be linear or non-linear. On the other hand, the based methods use a “combination of balance equations of mass, momentum and energy”. In this study, similar approach to the Index statistical method was used. (Blöschl et al., 2013) described that in this method the runoff will be predicted by the “usage of a scaled property of the catchment” to predict the total runoff of the studied basin. The scaled properties of the catchment used to analyze the watersheds were the Soil Conservation Service (SCS) Curve Number. SCS curve number method was chosen because of its simplicity and time constraints for the study. 1.3 SCS Curve Number method Curve number (CN) is an empirical perimeter that is used to predict runoff depth from rainfall. Curve number is a function of soil type, land use and soil hydrologic condition. Since this method is very simple. it does not take into account the complex parameters such as spatial or temporal variability of infiltartion and other abstarctive losses. Rather it represents these losses in a constant (Ia) (Eq. 2). Using land use type and hydrologic soil class, runoff curve number is calculated which is a represntative number for the amount of runoff geenated in that area. The detials of specifics are mentioned in the USGS manual (NRCS, 1986). CN can range from 30-100, where 100 indicates maximum runoff/waterbodies. The curve number is related to soil moisture rentention(S) using (Eq. 1) 1000 � = – 10 (1) �� S is the used to calculate runoff for any potential rainfall event (P) (Eq. 2 ) (�−Ia)^2 � = (2) �−��+� Where: Q =runoff (in) P = rainfall (in) S = potential maximum soil moisture retention after runoff begins (in) Ia = initial abstraction (in) Assumption, Ia=0.2S (NRCS, 1986) 2 2 METHODS Methods developed in the analysis of ungauged watersheds in South Eastern Utah. The majority of the analysis took place using ArcGIS 10.5.1. 2.1 Study Area We chose to develop the model for area in Southeastern Utah as this area is very inadequately gaged for stream measurement (Fig. 1). The major river flowing through the area is Colorado River. Figure 1. Location of USGS stream gauges in Utah. 2.2 Data The data for the study was acquired from a number of sources. The elevation raster was downloaded from the 3D Elevation Program at USGS. Precipitation data was acquired from NOAA. Soil characteristics data was downloaded from State Soil Geographic Data (STATSGO). 2011 Land Cover and Land use raster data were acquired from Multi-Resolution Land Characteristics Consortium (MRLC). 2.3 Delineation of watersheds The following steps were followed to delineate watersheds in the study area. A Digital Elevation Model (DEM) raster for the entire study area was created using the Mosaic To New Raster tool in ArcMap. The DEM layer was then used to create flow direction using the Flow Direction tool. Following the flow direction raster generation the Flow accumulation, Set Null and Stream to Feature tools were used to generate the stream network of the study areas (Horsburgh, 2018). The watersheds were delineated using the stream gages as the downstream points using Watershed tool in ArcMap. The workflow of the model is shown in 3 (Appendix A, Figure 1). Prior to delineation, the stream gages’ location were compared to National Hydrography Dataset (NHD) stream network and Topographic base map in ArcMap. The locations of gages were slightly adjusted to match approximate location and on actual stream network. The stream gages that were present downstream of the reservoir were eliminated from the analysis as the streamflow may be regulated at those stream gages. 2.4 Curve Number Analysis Curve number is an empirical perimeter that is used to predict runoff from rainfall. HEC-GeoHMS toolbar was used to create a composite curve number that represents runoff potential base on soil properties and landuse (Merwade, 2012). The tool requires DEM, land use, soil hydrologic groups and table for curve number lookup 2.4.1. Data Preparation and generating composite curve number raster Since the tool uses soil hydrologic groups, we needed to extract that from STATSGO database. Complete procedure on how to do that is given in Appendix B. Curve numbers for certain land use classes for corresponding hydrologic soil group are derived as shown in (Appendix A ,Table A1) (NRCS, 1986). Each hydrologic soil group was given a curve number from 30 to 100 based on runoff on surface, 100 being the highest. Having a value of 100 means that all precipitation will contribute as runoff. Each soil class was converted to a percentage of soil present for each map unit in soil layer, this is used later, to find out percentage of soil present in each watershed. Land use raster was converted to polygon to be merged with the land use data. The land use and soil features were merged together using Union tool. The resultant layer had all the characteristics of the land use and soil data. The polygons that did not have characteristics of either layers were deleted from the attributes table as they were marginal polygons. These data were used in the Generate CN Grid tool in Hec GeoHMS tool to create a composite curve number grid raster for the entire area. 2.5 Precipitation Analysis Precipitation data was collected from 57 National Oceanic and Atmospheric Administration (NOAA) stations in southern Utah. Each of these stations was evaluated to determine the average precipitation received in each month for year 2011. Once the data was obtained, the next task was associating it with vector data. The Join tool in ArcMap 10.5.1 was used to combine the table of average monthly values with the vector data's attribute table. Once the data was obtained the precipitation data for each month was interpolated and extrapolated using the Create Thiessen Polygon Tool. The resulting vector data was then converted to 12 precipitation rasters for each month.