Using SWAT to Model Streamflow in Two River Basins With Ground and Satellite Precipitation Data1

Abstract: Both ground rain gauge and remotely sensed precipitation (Next Generation Weather Radar – NEXRAD Stage III) data have been used to support spatially distributed hydrological modeling. This study is unique in that it utilizes and compares the performance of National Weather Service (NWS) rain gauge, NEXRAD Stage III, and Tropical Rainfall Measurement Mission (TRMM) 3B42 (Version 6) data for the hydrological modeling of the Middle Nueces River Watershed in South Texas and Middle Rio Grande Watershed in South Texas and northern Mexico. The hydrologic model chosen for this study is the Soil and Water Assessment Tool (SWAT), which is a comprehensive, physical‐based tool that models watershed hydrology and water quality within stream reaches. Minor adjustments to selected model parameters were applied to make parameter values more realistic based on results from previous studies. In both watersheds, NEXRAD Stage III data yields results with low mass balance error between simulated and actual streamflow (±13%) and high monthly Nash‐Sutcliffe efficiency coefficients (NS > 0.60) for both calibration (July 1, 2003 to December 31, 2006) and validation (2007) periods. In the Middle Rio Grande Watershed NEXRAD Stage III data also yield robust daily results (time averaged over a three‐day period) with NS values of (0.60‐0.88). TRMM 3B42 data generate simulations for the Middle Rio Grande Watershed of variable qualtiy (MBE = +13 to ?16%; NS = 0.38‐0.94; RMSE = 0.07‐0.65), but greatly overestimates streamflow during the calibration period in the Middle Nueces Watershed. During the calibration period use of NWS rain gauge data does not generate acceptable simulations in both watersheds. Significantly, our study is the first to successfully demonstrate the utility of satellite‐estimated precipitation (TRMM 3B42) in supporting hydrologic modeling with SWAT; thereby, potentially extending the realm (between 50°N and 50°S) where remotely sensed precipitation data can support hydrologic modeling outside of regions that have modern, ground‐based radar networks (i.e., much of the third world).     

Physically Based,Hydrologic Model Results Based on Three Precipitation Products1

Abstract: The main objective of the study is to examine the accuracy of and differences among simulated streamflows driven by rainfall estimates from a network of 22 rain gauges spread over a 2,170 km² watershed, NEXRAD Stage III radar data, and Tropical Rainfall Measuring Mission (TRMM) 3B42 satellite data. The Gridded Surface Subsurface Hydrologic Analysis (GSSHA), a physically based, distributed parameter, grid‐structured, hydrologic model, was used to simulate the June‐2002 flooding event in the Upper Guadalupe River watershed in south central Texas. There were significant differences between the rainfall fields estimated by the three types of measurement technologies. These differences resulted in even larger differences in the simulated hydrologic response of the watershed. In general, simulations driven by radar rainfall yielded better results than those driven by satellite or rain‐gauge estimates. This study also presents an overview of effects of land cover changes on runoff and stream discharge. The results demonstrate that, for major rainfall events similar to the 2002 event, the effect of urbanization on the watershed in the past two decades would not have made any significant effect on the hydrologic response. The effect of urbanization on the hydrologic response increases as the size of the rainfall event decreases.     

EVALUATION OF NEXRAD STAGE III PRECIPITATION DATA OVER A SEMIARID REGION1

This study examines NEXRAD Stage III product (hourly, cell size 4 km by 4 km) for its ability in estimating precipitation in central New Mexico, a semiarid area. A comparison between Stage III and a network of gauge precipitation estimates during 1995 to 2001 indicates that Stage III (1) overestimates the hourly conditional mean (CM) precipitation by 33 percent in the monsoon season and 55 percent in the nonmonsoon season; (2) overestimates the hourly CM precipitation for concurrent radar‐gauge pairs (nonzero value) by 13 percent in the monsoon season and 6 percent in the nonmonsoon season; (3) overestimates the seasonal precipitation accumulation by 11 to 88 percent in monsoon season and underestimates by 18 to 89 percent in the nonmonsoon season; and (4) either overestimates annual precipitation accumulation up to 28.2 percent or underestimates it up to 11.9 percent. A truncation of 57 to 72 percent of the total rainfall hours is observed in the Stage III data in the nonmonsoon season, which may be the main cause for both the underestimation of the radar rainfall accumulation and the lower conditional probability of radar rainfall detection in the nonmonsoon season. The study results indicate that the truncation caused loss of small rainfall amounts (events) is not effectively corrected by the real‐time rain gauge calibration that can adjust the rainfall rates but cannot recover the truncated small rainfall events. However, the truncation error in the monsoon season may be suppressed due to the larger rainfall rate and/or combined effect of overestimates by bright band and hail contaminations, virga, advection, etc. In general, improvement in NEXRAD performance since the monsoon season in 1998 is observed, which is consistent with the systematic improvement in the NEXRAD network.     

Towards Real‐Time Continental Scale Streamflow Simulation in Continuous and Discrete Space

The National Weather Service (NWS) forecasts floods at approximately 3,600 locations across the United States (U.S.). However, the river network, as defined by the 1:100,000 scale National Hydrography Dataset‐Plus (NHDPlus) dataset, consists of 2.7 million river segments. Through the National Flood Interoperability Experiment, a continental scale streamflow simulation and forecast system was implemented and continuously operated through the summer of 2015. This system leveraged the WRF‐Hydro framework, initialized on a 3‐km grid, the Routing Application for the Parallel Computation of Discharge river routing model, operating on the NHDPlus, and real‐time atmospheric forcing to continuously forecast streamflow. Although this system produced forecasts, this paper presents a study of the three‐month nowcast to demonstrate the capacity to seamlessly predict reach scale streamflow at the continental scale. In addition, this paper evaluates the impact of reservoirs, through a case study in Texas. Validation of the uncalibrated model using observed hourly streamflow at 5,701 U.S. Geological Survey gages shows 26% demonstrate PBias ≤ |25%|, 11% demonstrate Nash‐Sutcliffe Efficiency (NSE) ≥ 0.25, and 6% demonstrate both PBias ≤ |25%| and NSE ≥ 0.25. When evaluating the impact of reservoirs, the analysis shows when reservoirs are included, NSE ≥ 0.25 for 56% of the gages downstream while NSE ≥ 0.25 for 11% when they are not. The results presented here provide a benchmark for the evolving hydrology program within the NWS and supports their efforts to develop a reach scale flood forecasting system for the country.     

Evaluation of Gauge‐Radar Merging Methods Using a Semi‐Distributed Hydrological Model in the Upper Thames River Basin,Canada

Gauge‐radar merging methods combine rainfall estimates from rain gauges and radar to capitalize on the strengths of the individual instruments. The performance of four well‐known gauge‐radar merging methods, including mean field bias correction, Brandes spatial adjustment, local bias correction using kriging, and conditional merging, are examined using Environment Canada radar and the Upper Thames River Basin in southwestern Ontario, Canada, as a case study. The analysis assesses the effect of gauge‐radar merging methods on: (1) the accuracy of predicted rainfall accumulations; and (2) the accuracy of predicted streamflows using a semi‐distributed hydrological model. In addition, several influencing factors (i.e., gauge density, storm type, basin type, proximity to the radar tower, and time‐step of adjustment) are analyzed to determine their effect on the performance of the rainfall estimation techniques. Confirming results of previous studies, the merging methods provide an increase in the accuracy of both rainfall accumulation estimations and predicted streamflows. The results also indicate specific factors such as gauge density, rainfall intensity, and time‐step of adjustment can reduce the accuracy of merging methods and play a key role in the examination of its use for operational purposes. Results provide guidance for hydrologists and engineers assessing how best to apply corrected radar products to improve rainfall estimation and hydrological modeling accuracy.     

MODIS Biophysical States and NEXRAD Precipitation in a Statistical Evaluation of Antecedent Moisture Condition and Streamflow1

Abstract: The potential of remotely sensed time series of biophysical states of landscape to characterize soil moisture condition antecedent to radar estimates of precipitation is assessed in a statistical prediction model of streamflow in a 1,420 km² watershed in south‐central Texas, Moderate Resolution Imaging Spectroradiometer (MODIS) time series biophysical products offer significant opportunities to characterize and quantify hydrologic state variables such as land surface temperature (LST) and vegetation state and status. Together with Next Generation Weather Radar (NEXRAD) precipitation estimates for the period 2002 through 2005, 16 raw and deseasoned time series of LST (day and night), vegetation indices, infrared reflectances, and water stress indices were linearly regressed against observed watershed streamflow on an eight‐day aggregated time period. Time offsets of 0 (synchronous with streamflow event), 8, and 16 days (leading streamflow event) were assessed for each of the 16 parameters to evaluate antecedent effects. The model results indicated a reasonable correlation (r² = 0.67) when precipitation, daytime LST advanced 16 days, and a deseasoned moisture stress index were regressed against log‐transformed streamflow. The estimation model was applied to a validation period from January 2006 through March 2007, a period of 12 months of regional drought and base‐flow conditions followed by three months of above normal rainfall and a flood event. The model resulted in a Nash‐Sutcliffe estimation efficiency (E) of 0.45 for flow series (in log‐space) for the full 15‐month period, ?0.03 for the 2006 drought condition period, and 0.87 for the 2007 wet condition period. The overall model had a relative volume error of ?32%. The contribution of parameter uncertainties to model discrepancy was evaluated.     

AutoRAPID: A Model for Prompt Streamflow Estimation and Flood Inundation Mapping over Regional to Continental Extents

This article couples two existing models to quickly generate flow and flood‐inundation estimates at high resolutions over large spatial extents for use in emergency response situations. Input data are gridded runoff values from a climate model, which are used by the Routing Application for Parallel computatIon of Discharge (RAPID) model to simulate flow rates within a vector river network. Peak flows in each river reach are then supplied to the AutoRoute model, which produces raster flood inundation maps. The coupled tool (AutoRAPID) is tested for the June 2008 floods in the Midwest and the April‐June 2011 floods in the Mississippi Delta. RAPID was implemented from 2005 to 2014 for the entire Mississippi River Basin (1.2 million river reaches) in approximately 45 min. Discretizing a 230,000‐km² area in the Midwest and a 109,500‐km² area in the Mississippi Delta into thirty‐nine 1° by 1° tiles, AutoRoute simulated a high‐resolution (~10 m) flood inundation map in 20 min for each tile. The hydrographs simulated by RAPID are found to perform better in reaches without influences from unrepresented dams and without backwater effects. Flood inundation maps using the RAPID peak flows vary in accuracy with F‐statistic values between 38.1 and 90.9%. Better performance is observed in regions with more accurate peak flows from RAPID and moderate to high topographic relief.     

Assessment of the Extreme Rainfall Event at Nashville,TN and the Surrounding Region on May 1–3, 2010

This paper analyzes the May 1–3, 2010 rainfall event that affected the south‐central United States, including parts of Mississippi, Tennessee, and Kentucky. The storm is evaluated in terms of its synoptic setting, along with the temporal distributions, and spatial patterns of the rainfall. In addition, the recurrence interval of the storm is assessed and the implications for hydrologic structure designs are discussed. The event was associated with an upper‐level trough and stationary frontal boundary to the west of the rainfall region, which remained quasi‐stationary for a period of 48 h. Heavy rainfall was produced by two slow‐moving mesoscale convective complexes, combined with abundant atmospheric moisture. Storm totals exceeding 330 mm occurred within a large elongated area extending from Memphis to Nashville. Isolated rainfall totals over 480 mm were reported in some areas, with NEXRAD weather radar rainfall estimates up to 501 mm. An extreme value analysis was performed for one‐ and two‐day rainfall totals at Nashville and Brownsville, Tennessee, as well as for gridded rainfall estimates for the entire region using the Storm Precipitation Analysis System. Results suggest maximum rainfall totals for some durations during the May 1–3, 2010 event exceeded the 1,000‐year rainfall values from National Oceanic and Atmospheric Administration Atlas 14 for a large portion of the region and reached up to 80% of the probable maximum precipitation values for some area sizes and durations.     

Effects of Spatial and Temporal Data Aggregation on the Performance of the Multi‐Radar Multi‐Sensor System

The objectives of this study were to (1) evaluate the performance of the Multi‐Radar Multi‐Sensor (MRMS) system in capturing precipitation compared to gauge data, and (2) assess the effects of spatial (1–50 km) and temporal (15–120 min) data aggregation scales on the performance of the MRMS system. Point‐to‐grid comparisons were conducted between 215 rain gauges and the MRMS system. The MRMS system at 1 km spatial and 15 min temporal resolutions captured precipitation reasonably well with average R², root mean square error (RMSE), and percent bias (PBIAS) values of 0.65, 0.5 mm, and 11.9 mm; whereas Threat Score, probability of detection, and false alarm ratio were 0.57, 0.92, and 0.40, respectively. Decreasing temporal resolution from 15 min to two hours resulted in an increase in R² and a decrease in RMSE, whereas PBIAS was not affected. Reducing spatial resolution from 1 to 50 km resulted in increases in R² and PBIAS, whereas RMSE was decreased. Increasing spatial aggregation scale from 1 to 50 km resulted in an R² increase of only 0.08. Similarly, improvement in R² was only modest (0.17) compared to an eightfold reduction in temporal resolution (from 15 min to two hours). While aggregating data at coarser temporal resolutions resolved some of the under/overestimation issues of the MRMS system, it was apparent even at coarser spatial and temporal resolutions the MRMS system inherently overestimated smaller precipitation events while underestimated bigger events.     

iTree‐Hydro: Snow Hydrology Update For The Urban Forest Hydrology Model1

Yang, Yang, Theodore A. Endreny, and David J. Nowak, 2011. iTree‐Hydro: Snow Hydrology Update for the Urban Forest Hydrology Model. Journal of the American Water Resources Association (JAWRA) 47(6):1211–1218. DOI: 10.1111/j.1752‐1688.2011.00564.x Abstract: This article presents snow hydrology updates made to iTree‐Hydro, previously called the Urban Forest Effects—Hydrology model. iTree‐Hydro Version 1 was a warm climate model developed by the USDA Forest Service to provide a process‐based planning tool with robust water quantity and quality predictions given data limitations common to most urban areas. Cold climate hydrology routines presented in this update to iTree‐Hydro include: (1) snow interception to simulate the capture of snow by the vegetation canopy, (2) snow unloading to simulate the release of snow triggered by wind, (3) snowmelt to simulate the solid to liquid phase change using a heat budget, and (4) snow sublimation to simulate the solid to gas phase via evaporation. Cold climate hydrology routines were tested with research‐grade snow accumulation and weather data for the winter of 1996‐1997 at Umpqua National Forest, Oregon. The Nash‐Sutcliffe efficiency for open area snow accumulation was 0.77 and the Nash‐Sutcliffe efficiency for under canopy was 0.91. The USDA Forest Service offers iTree‐Hydro for urban forest hydrology simulation through http://www.iTreetools.org .     

Mechanistic Simulation of Tree Effects in an Urban Water Balance Model1

Abstract: A semidistributed, physical‐based Urban Forest Effects – Hydrology (UFORE‐Hydro) model was created to simulate and study tree effects on urban hydrology and guide management of urban runoff at the catchment scale. The model simulates hydrological processes of precipitation, interception, evaporation, infiltration, and runoff using data inputs of weather, elevation, and land cover along with nine channel, soil, and vegetation parameters. Weather data are pre‐processed by UFORE using Penman‐Monteith equations to provide potential evaporation terms for open water and vegetation. Canopy interception algorithms modified established routines to better account for variable density urban trees, short vegetation, and seasonal growth phenology. Actual evaporation algorithms allocate potential energy between leaf surface storage and transpiration from soil storage. Infiltration algorithms use a variable rain rate Green‐Ampt formulation and handle both infiltration excess and saturation excess ponding and runoff. Stream discharge is the sum of surface runoff and TOPMODEL‐based subsurface flow equations. Automated calibration routines that use observed discharge has been coupled to the model. Once calibrated, the model can examine how alternative tree management schemes impact urban runoff. UFORE‐Hydro model testing in the urban Dead Run catchment of Baltimore, Maryland, illustrated how trees significantly reduce runoff for low intensity and short duration precipitation events.     

Utility of Gridded Rainfall for IHACRES Daily River Flow Predictions in Southern California Watersheds1

Abstract: Studies to regionalize conceptual hydrologic models generally require rainfall and river flow data from multiple watersheds. Besides the considerable time (cost) to assemble and process rainfall data for many watersheds, investigators often need to choose from a number of candidate gauges, subjectively weighing the relative importance of proximity and elevation to select a representative rainfall dataset. The Unified Raingauge Dataset (URD) is a gridded daily rainfall dataset that covers the conterminous United States at 0.25 × 0.25 degrees spatial resolution and is available from 1948 to present. The objective of this study was to determine whether uncertainty in daily river flow predictions using the conceptual hydrologic model IHACRES in small to moderate size watersheds (50‐400 km²) in southern California would increase if URD gridded rainfall data were used in place of single rain gauge data to calibrate the model. Rain gauge data were obtained from the gauge nearest the watershed centroid and the gauge closest in elevation to the watershed mean elevation. Results from 20 randomly selected watersheds indicated that IHACRES calibration performance was similar using rainfall data from the URD grids and rain gauge data. There was some evidence of greater uncertainties associated with the URD calibrations in areas where topography may affect rainfall amounts. In contrast to the URD data, monthly gridded data produced by the Parameter‐Elevation Regressions on Independent Slopes Model (PRISM) includes adjustments for elevation and produces gridded values at a finer spatial resolution (4 km²). A limited test on two watersheds demonstrated that scaling the URD daily rainfall estimates to match the PRISM monthly values may improve rainfall estimates and model simulation performance.     

Identifying Urban Features from LiDAR for a High‐Resolution Urban Hydrologic Model

Light Detection and Ranging (LiDAR), is relatively inexpensive, provides high spatial resolution sampling at great accuracy, and can be used to generate surface terrain and land cover datasets for urban areas. These datasets are used to develop high‐resolution hydrologic models necessary to resolve complex drainage networks in urban areas. This work develops a five‐step algorithm to generate indicator fields for tree canopies, buildings, and artificial structures using Geographic Resources Analysis Support System (GRASS‐GIS), and a common computing language, Matrix Laboratory. The 54 km² study area in Parker, Colorado consists of twenty‐four 1,500 × 1,500 m LiDAR subsets at 1 m resolution with varying degrees of urbanization. The algorithm correctly identifies 96% of the artificial structures within the study area; however, application success is dependent upon urban extent. Urban land use fractions below 0.2 experienced an increase in falsely identified building locations. ParFlow, a three‐dimensional, grid‐based hydrological model, uses these building and artificial structure indicator fields and digital elevation model for a hydrologic simulation. The simulation successfully develops the complex drainage network and simulates overland flow on the impervious surfaces (i.e., along the gutters and off rooftops) made possible through this spatial analysis process.     

Spatiotemporal Evaluation of Simulated Evapotranspiration and Streamflow over Texas Using the WRF‐Hydro‐RAPID Modeling Framework

This study assesses a large‐scale hydrologic modeling framework (WRF‐Hydro‐RAPID) in terms of its high‐resolution simulation of evapotranspiration (ET) and streamflow over Texas (drainage area: 464,135 km²). The reference observations used include eight‐day ET data from MODIS and FLUXNET, and daily river discharge data from 271 U.S. Geological Survey gauges located across a climate gradient. A recursive digital filter is applied to decompose the river discharge into surface runoff and base flow for comparison with the model counterparts. While the routing component of the model is pre‐calibrated, the land component is uncalibrated. Results show the model performance for ET and runoff is aridity‐dependent. ET is better predicted in a wet year than in a dry year. Streamflow is better predicted in wet regions with the highest efficiency ~0.7. In comparison, streamflow is most poorly predicted in dry regions with a large positive bias. Modeled ET bias is more strongly correlated with the base flow bias than surface runoff bias. These results complement previous evaluations by incorporating more spatial details. They also help identify potential processes for future model improvements. Indeed, improving the dry region streamflow simulation would require synergistic enhancements of ET, soil moisture and groundwater parameterizations in the current model configuration. Our assessments are important preliminary steps towards accurate large‐scale hydrologic forecasts.     

Coastal Flood Inundation Monitoring with Satellite C‐band and L‐band Synthetic Aperture Radar Data

Satellite Synthetic Aperture Radar (SAR) was evaluated as a method to operationally monitor the occurrence and distribution of storm‐ and tidal‐related flooding of spatially extensive coastal marshes within the north‐central Gulf of Mexico. Maps representing the occurrence of marsh surface inundation were created from available Advanced Land Observation Satellite (ALOS) Phased Array type L‐Band SAR (PALSAR) (L‐band) (21 scenes with HH polarizations in Wide Beam [100 m]) data and Environmental Satellite (ENVISAT) Advanced SAR (ASAR) (C‐band) data (24 scenes with VV and HH polarizations in Wide Swath [150 m]) during 2006‐2009 covering 500 km of the Louisiana coastal zone. Mapping was primarily based on a decrease in backscatter between reference and target scenes, and as an extension of previous studies, the flood inundation mapping performance was assessed by the degree of correspondence between inundation mapping and inland water levels. Both PALSAR‐ and ASAR‐based mapping at times were based on suboptimal reference scenes; however, ASAR performance seemed more sensitive to reference‐scene quality and other types of scene variability. Related to water depth, PALSAR and ASAR mapping accuracies tended to be lower when water depths were shallow and increased as water levels decreased below or increased above the ground surface, but this pattern was more pronounced with ASAR. Overall, PALSAR‐based inundation accuracies averaged 84% (n = 160), while ASAR‐based mapping accuracies averaged 62% (n = 245).     

RAINGAGE NETWORK DESIGN USING NEXRAD PRECIPITATION ESTIMATES1

ABSTRACT: A general framework is proposed for using precipitation estimates from NEXRAD weather radars in raingage network design. NEXRAD precipitation products are used to represent space time rainfall fields, which can be sampled by hypothetical raingage networks. A stochastic model is used to simulate gage observations based on the areal average precipitation for radar grid cells. The stochastic model accounts for subgrid variability of precipitation within the cell and gage measurement errors. The approach is ideally suited to raingage network design in regions with strong climatic variations in rainfall where conventional methods are sometimes lacking. A case study example involving the estimation of areal average precipitation for catchments in the Catskill Mountains illustrates the approach. The case study shows how the simulation approach can be used to quantify the effects of gage density, basin size, spatial variation of precipitation, and gage measurement error, on network estimates of areal average precipitation. Although the quality of NEXRAD precipitation products imposes limitations on their use in network design, weather radars can provide valuable information for empirical assessment of rain‐gage network estimation errors. Still, the biggest challenge in quantifying estimation errors is understanding subgrid spatial variability. The results from the case study show that the spatial correlation of precipitation at subgrid scales (4 km and less) is difficult to quantify, especially for short sampling durations. Network estimation errors for hourly precipitation are extremely sensitive to the uncertainty in subgrid spatial variability, although for storm total accumulation, they are much less sensitive.     

Regional Blue and Green Water Balances and Use by Selected Crops in the U.S.

The availability of freshwater is a prerequisite for municipal development and agricultural production, especially in the arid and semiarid portions of the western United States (U.S.). Agriculture is the leading user of water in the U.S. Agricultural water use can be partitioned into green (derived from rainfall) and blue water (irrigation). Blue water can be further subdivided by source. In this research, we develop a hydrologic balance by 8‐Digit Hydrologic Unit Code using a combination of Soil and Water Assessment Tool simulations and available human water use estimates. These data are used to partition agricultural groundwater usage by sustainability and surface water usage by local source or importation. These predictions coupled with reported agricultural yield data are used to predict the virtual water contained in each ton of corn, wheat, sorghum, and soybeans produced and its source. We estimate that these four crops consume 480 km³ of green water annually and 23 km³ of blue water, 12 km³ of which is from groundwater withdrawal. Regional trends in blue water use from groundwater depletion highlight heavy usage in the High Plains, and small pockets throughout the western U.S. This information is presented to inform water resources debate by estimating the cost of agricultural production in terms of water regionally. This research illustrates the variable water content of the crops we consume and export, and the source of that water.     

Development of the Spatial Rainfall Generator (SRGEN) for the Agricultural Policy/Environmental Extender Model

Accurate spatial representation of climatic patterns is often a challenge in modeling biophysical processes at the watershed scale, especially where the representation of a spatial gradient in rainfall is not sufficiently captured by the number of weather stations. The spatial rainfall generator (SRGEN) is developed as an extension of the “weather generator” (WXGEN), a component of the Agricultural Policy/Environmental eXtender (APEX) model. SRGEN generates spatially distributed daily rainfall using monthly weather statistics available at multiple locations in a watershed. The spatial rainfall generator as incorporated in APEX is tested on the Cowhouse watershed (1,178 km²) in central Texas. The watershed presented a significant spatial rainfall gradient of 2.9 mm/km in the lateral (north‐south) directions based on four rainfall gages. A comparative analysis between SRGEN and WXGEN indicates that SRGEN performs well (PBIAS = 2.40%). Good results were obtained from APEX for streamflow (NSE = 0.99, PBIAS = 8.34%) and NO₃‐N and soluble P loads (PBIAS ≈ 6.00% for each, respectively). However, APEX underpredicted sediment yield and organic N and P loads (PBIAS: 24.75‐27.90%) with SRGEN, although its uncertainty in output was lower than WXGEN results (PBIAS: ?13.02 to ?46.13%). The overall improvement achieved in rainfall generation by SRGEN is demonstrated to be effective in the improving model performance on flow and water quality output.     

Assessing Satellite and Ground‐Based Potential Evapotranspiration for Hydrologic Applications in the Colorado River Basin

This study evaluates a remotely sensed and two ground‐based potential evapotranspiration (PET) products for hydrologic application in the Upper Colorado River Basin (UCRB). The remotely sensed Moderate Resolution Imaging Spectroradiometer product (MODIS‐PET) is a continuous, daily time series with 250 m resolution derived using the Priestley‐Taylor (P‐T) equation. The MODIS‐PET is evaluated against regional flux tower data as well as a synthetic pan product (Epan; 0.125°, daily) derived from the North American Land Data Assimilation System (NLDAS) and a Hargreaves PET derived from DAYMET variables (DAYMET‐PET; 1 km, daily). Compared to point‐scale PET computed using regional flux tower data, the MODIS‐PET had lower errors, with RMSE values ranging from 2.24 to 2.85 mm/day. Epan RMSE values ranged from 3.70 to 3.76 mm/day and DAYMET‐PET RMSE values ranged from 3.55 to 4.58 mm/day. Further investigation showed biases in temperature and radiation data contribute to uncertainty in the MODIS‐PET values, while bias in NLDAS temperature, downward shortwave (SW↓), and downward longwave (LW↓) propagate in the Epan estimates. Larger discrepancies between methods were observed in the warmer, drier regions of the UCRB, however, the MODIS‐PET was more responsive to landcover transitions and better captured basin heterogeneity. Results indicate the satellite‐based MODIS product can serve as a viable option for obtaining spatial PET values across the UCRB.