Search for: All records

This study examined eight Great Plains moderate-sized (832 to 4892 km2) watersheds. The Soil and Water Assessment Tool (SWAT) autocalibration routine SUFI-2 was executed using twenty-three model parameters, from 1995 to 2015 in each basin, to identify highly sensitive parameters (HSP). The model was then run on a year-by-year basis, generating optimal parameter values for each year (1995 to 2015). HSP were correlated against annual precipitation (Parameter-elevation Regressions on Independent Slopes Model—PRISM) and root zone soil moisture (Soil MERGE—SMERGE 2.0) anomaly data. HSP with robust correlation (r > 0.5) were used to calibrate the model on an annual basis (2016 to 2018). Results were compared against a baseline simulation, in which optimal parameters were obtained by running the model for the entire period (1992 to 2015). This approach improved performance for annual simulations generated from 2016 to 2018. SMERGE 2.0 produced more robust results compared with the PRISM product. The main virtue of this approach is that it constrains parameter space, minimizesing equifinality and promotesing modeling based on more physically realistic parameter values. 

Root zone soil moisture (RZSM) is one of the least-monitored variables within the hydrologic cycle. Given the importance of RZSM to agriculture, more effort is needed to understand the potential impacts of the El Niño southern oscillation (ENSO), Pacific decadal oscillation (PDO), and Atlantic multidecadal oscillation (AMO) on this critical variable. This study focused on the CONtiguous United States (CONUS) RZSM (0 to 40 cm depth) over nearly three decades (1992 to 2018). Basic trend analysis with the Mann–Kendall test and wavelet transform coherence (WTC) was utilized. The RZSM product examined was Soil MERGE (SMERGE 2.0). More CONUS pixels exhibited drying (56 to 75%) versus wetting (25 to 44%) trends between 1992 and 2018. Seasonal wetting trends were observed particularly during winter in the Southwest and Northwest regions associated with El Nino and La Nina episodes, respectively. The noted long-term RZSM trends are more clearly attributable to oceanic-atmospheric teleconnections than global climate change. The most significant result was the strong drying trend in central CONUS reflected a shift to La Nina and cool PDO conditions during the 2000s, further amplified by a change to positive AMO corresponding with this period. 

Over the last decade, autocalibration routines have become commonplace in watershed modeling. This approach is most often used to simulate a streamflow at a basin’s outlet. In alpine settings, spring/early summer snowmelt is by far the dominant signal in this system. Therefore, there is great potential for a modeled watershed to underperform during other times of the year. This tendency has been noted in many prior studies. In this work, the Soil and Water Assessment Tool (SWAT) model was auto-calibrated with the SUFI-2 routine. A mountainous watershed from Idaho was examined (Upper North Fork). In this study, this basin was calibrated using three estimates of evapotranspiration (ET): Moderate Resolution Imagining Spectrometer (MODIS), Simplified Surface Energy Balance, and Global Land Evaporation: the Amsterdam Model. The MODIS product in particular, had the greatest utility in helping to constrain SWAT parameters that have a high sensitivity to ET. Streamflow simulations that utilize these ET parameter values have improved recessional and summertime streamflow performances during calibration (2007 to 2011) and validation (2012 to 2014) periods. Streamflow performance was monitored with standard objective metrics (Bias and Nash Sutcliffe coefficients) that quantified overall, recessional, and summertime peak flows. This approach yielded dramatic enhancements for all three observations. These results demonstrate the utility of this approach for improving watershed modeling fidelity outside the main snowmelt season.