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A Test of Two Distributed Hydrologic Models with Wsr-88D Radar Precipitation Data Input

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A Test of Two Distributed Hydrologic Models with Wsr-88D Radar Precipitation Data Input JP3.3 A TEST OF TWO DISTRIBUTED HYDROLOGIC MODELS WITH WSR-88D RADAR PRECIPITATION DATA INPUT Steven Hunter1*, Jeff Jorgeson2, Steffen Meyer1, and Baxter Vieux3 1U.S. Bureau of Reclamation, Denver, Colorado; 2U.S. Army Corps of Engineers, Vicksburg, Mississippi; 3Vieux and Associates, Inc., Norman, Oklahoma and model parameters from GIS inputs, as well as 1. INTRODUCTION AND PURPOSE visualization of model outputs, is performed using the Watershed Modeling System (WMS) version The U.S. Bureau of Reclamation 6.1, which is developed at Brigham Young (Reclamation) will test two different 2-D University in cooperation with the U. S. Army distributed-parameter hydrologic models in the Engineer Research and Development Center, case of a heavy rainfall over west-central Arizona. Coastal and Hydraulics Laboratory. The heavy rain was produced by Tropical Storm Vflo is a real-time distributed hydrologic Nora, 25-26 September 1997. The primary test model for managing precious water resources, area is the Santa Maria basin in West-central water quality management, and flood warning Arizona. The two distributed models are GSSHA systems. Improved hydrologic modeling and Vflo™. capitalizes on access to high-resolution The Gridded Surface Subsurface quantitative precipitation estimates from model Hydrologic Analysis (GSSHA) model is a forecasts, radar, satellite, rain gauges, or reformulation and enhancement of the distributed combinations in multi-sensor products. Digital runoff model CASC2D (Ogden 2000). GSSHA is maps of soils, land use, topography and rainfall a physically based, two-dimensional model that rates are used to compute and route rainfall operates on a raster (square-grid) representation excess through a network formulation based on of a watershed and is designed for long-term, the Finite Element Method (FEM) computational large-basin simulation of rainfall-runoff and base scheme described by Vieux (2001a, and 2001b). flow processes. The model solves transport Vflo is a new model implemented in Java™ to take equations using finite difference and finite volume advantage of secure servlet/applet technology for techniques and includes 2-D diffusive-wave multi-user access. Vieux and Vieux (2002), in overland flow routing and 1-D diffusive-wave these proceedings, describe the Vflo model in channel routing. GSSHA is a process-based more detail. model where the user has the option to select the The overall goal of Vflo is to provide high- specific processes to be modeled for a particular resolution, distributed hydrologic prediction from application. Among the processes that can be catchment to river basin scale. The advantage of simulated are precipitation distribution, snowfall physics-based models is that they can be setup accumulation and melting, precipitation with minimal historical data and still obtain interception by vegetation, surface water retention, meaningful results. Distributed models better infiltration, overland flow runoff, overland erosion represent the spatial variability of factors that and deposition, channel routing of water, channel control runoff enhancing the predictability of routing of sediments, channel routing of hydrologic processes (Vieux, 2002). Finite element conservative contaminants, unsaturated solution of the kinematic wave equations is an groundwater flow (Vadose zone modeling), efficient approach allowing large systems to be saturated groundwater flow, stream solved easily on single processor Intel PC’s in a recharge/discharge to groundwater, exfiltration of Windows environment, or on servers. Solution groundwater to land surface, and proceeds on a drainage network making the same evapotranspiration (ET). model scalable from small catchment to major Additional information on the GSSHA river basin. Vflo is set model and its evolution from the Cascade of up using a drainage network rather than a basin Planes, 2-Dimensional (CASC2D) model can be approach. Vflo represents an important advance in found in Downer et al. (2000b) and Downer et al. simulating rainfall-runoff using digital data (2002). To facilitate the calibration process, describing the Earth’s terrain coupled with automatic calibration using the Shuffled Complex advances in radar precipitation detection. Evolution (SCE) procedure is available for GSSHA We plan to integrate a distributed (Senerath et al. 2000). Development of input data hydrologic model such as GSSHA or Vflo into a 1 Figure 1. Representation of lowest (0.5L elevation) radar beam extent from Flagstaff AZ WSR-88D (KFSX) along azimuth 275°, which lies over the headwaters of the Santa Maria (SM) test basin. Arrow denotes location of aforesaid headwaters and line and circles above the arrow designate beam heights and width above this location (see text). generalized watershed management framework, accommodated by the model’s spatially variable such as the RiverWare modeling tool (Zagona et characteristics within an individual basin, derived al., 2001) currently used by Reclamation. from the aforementioned GIS input. Other factors RiverWare is a water resources management tool such as temporal and spatial resolution of the for operations, scheduling and planning, which input data and transformation of those data into builds water operations models and applies parameters usable by the model are also crucial decision criteria to them. This integration may be considerations in the modeling process. considered analogous to the relationship of For this preliminary comparative study, while CASC2D to WMS. both GSSHA and Vflo have some flexibility as to Both distributed models require the format of GIS data that they will accept, the Geographic Information Systems (GIS) data as most important requirement of this test is that they input, such as basin definitions, topography, soils operate from the same input data set. This and land use data. The following section will detail stipulation allows for a valid comparison of the these GIS data. models themselves instead of the quality of their input data. From these basic data sets, the 2. GIS DATA INPUT specific model parameters and inputs will be derived for the respective models. The following Distributed hydrologic models may require slightly data sets were selected for the comparison: different inputs and formats, but most need the same basic ingredients. Basin delineation, S DEM data with 30 m horizontal resolution channel network delineation, overland flow slope, from the U.S. Geological Survey (USGS) flow accumulation and drainage direction are all EROS Data Center’s National Elevation derived from topographic data, typically in the form Dataset (NED). of a Digital Elevation Model (DEM). Digital soil S Land Use Land Cover data at 30 m surveys may supply soil infiltration parameter resolution, from the USGS EROS Data estimates, and digital maps of land use/cover are Center’s National Land Cover 1992 generally used as the basis for estimates of Dataset (NLCD). surface hydraulic roughness coefficients. S Soil data from the Natural Resources Arguably the most critical component is a Conservation Service State Soil temporally and spatially variable precipitation field. Geographic (STATSGO) database for In distributed hydrologic models, such variability is Arizona. 2 S Channel cross-section data from the Williams River discharges into the mainstem of the USGS, available for this basin only at a Lower Colorado River near Lake Havasu City. single stream gauge location on the Santa The area of the Santa Maria basin is 3,727 square Maria River. km. Figs. 2 and 3 show the location, hydrography and topography of the region surrounding the 3. RADAR DATA INPUT basin. As would be expected for the arid desert soils and steep topography of this basin, response Radar data input is from the WSR-88D times in the event of heavy rains are small. (formerly NEXRAD) Doppler radar (Crum et al. An encircled red dot in Fig. 2 indicates the 1993) south of Flagstaff, Arizona (KFSX), which is single active stream gauge in the basin, namely about 150 km east of the headwaters of the test USGS 09424900 on the Santa Maria River. The basin. The data consist of reflectivities in Level II elevation of this gauge is 415 m above sea level format from the National Climatic Data Center, and is 17 km above the basin outlet, with a which have a data resolution of 0.5 dB. The beam drainage area of 2,924 square km. Mean annual width at the headwaters range is about 3.6 km and streamflow at the gauge varies widely from year to the lowest (0.5) beam center altitude is about 3.4 year - from 1967 to 1999 values ranged from zero km above ground level (Fig. 1). This location is to 232 cubic feet per second (cfs), with an average considered to be at moderate range from the radar of 66 cfs. On many days in most years there is no for precipitation estimation in the warm September flow. The same large variability is also evident in atmosphere over Arizona. annual peak streamflows, which are presented in The radar quantitative precipitation Fig. 4. estimation (QPE) is accomplished via use of Reclamation’s new Precipitation Accumulation 4.2 Synoptic and Hydrologic Characteristics of Algorithm (PAA; Hunter et al. 2001). The PAA the Storm uses Eta model soundings to distinguish rain, snow, melting snow, and virga regions and applies Tropical cyclones are rare in Arizona, but different Z-R relationships to each, producing occur occasionally as they make landfall from the precipitation accumulations at the surface. In this eastern Pacific or Gulf of California. Tropical case, the National Weather Service (NWS)- Storm (TS) Nora was an example of the latter sanctioned tropical Z-R relationship (Z = 250 R1.2) landfall location. TS Nora’s center traveled along was used for all precipitation, since its phase was the western Gulf of California and accelerated all liquid at the surface and was produced by a northward at landfall, which was near the tropical storm. Finally, a single precipitation California/Arizona border at 2100 UTC 25 gauge/radar QPE bias (G/R) for the entire radar September 1997 (Fig. 5 and Rappaport 1997).
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