Satellite Soil Moisture Validation Using Hydrological SWAT Model: A Case Study of Puerto Rico, USA

Nilawar, Aditya P.; Calderella, Cassandra P.; Lakhankar, Tarendra; Waikar, Milind L.; Muñoz, Jonathan

doi:10.3390/hydrology4040045

Cited by 17 publications

(9 citation statements)

References 33 publications

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“…Another approach to minimize equifinality involves the incorporation of remote sensing observations to directly constrain model parameter values [4][5][6][7][8][9][10][11][12][13]. Obviously, the utility of this approach relies on the robustness of the remote sensing data applied.…”

Section: Discussionmentioning

confidence: 99%

“…During recent years, hydrologists have begun to leverage remote sensing observations to improve model calibration and achieve a more accurate picture of processes at a watershed scale. Examples of such studies that utilized the SWAT model span diverse aspects of the hydrologic cycle and include quantifying total terrestrial water [4,5], soil moisture [6][7][8], evapotranspiration [9][10][11], and groundwater recharge [12,13].…”

Section: Introductionmentioning

confidence: 99%

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Improving SWAT Model Calibration Using Soil MERGE (SMERGE)

Tobin

Bennett

2020

Water

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving SWAT Model Calibration Using Soil MERGE (SMERGE)

Tobin

Bennett

2020

Water

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“…Several hydrologic studies have been conducted in the Añasco watershed, including [42][43][44][45][46]. Prieto [42] developed a surface water/groundwater MIKESHE model for the Añasco, Yagüez, and Guanajibo watersheds.…”

Section: Previous Hydrologic Studies Conducted In Puerto Ricomentioning

confidence: 99%

“…The study showed a clear connection between coffee farms and their practices and urbanization with large sediment plumes in the coastal waters near the mouth of the Añasco River. Nilawar et al [46] estimated soil moisture in the Añasco and Guanajibo watersheds using the SWAT model, comparing the AMSR2 soil moisture product results. The SWAT model was calibrated for 2010-2012 using USGS stream gauge data from the two watersheds.…”

Section: Previous Hydrologic Studies Conducted In Puerto Ricomentioning

confidence: 99%

Flood Impacts on Critical Infrastructure in a Coastal Floodplain in Western Puerto Rico during Hurricane María

et al. 2021

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Flooding during extreme weather events damages critical infrastructure, property, and threatens lives. Hurricane María devastated Puerto Rico (PR) on 20 September 2017. Sixty-four deaths were directly attributable to the flooding. This paper describes the development of a hydrologic model using the Gridded Surface Subsurface Hydrologic Analysis (GSSHA), capable of simulating flood depth and extent for the Añasco coastal flood plain in Western PR. The purpose of the study was to develop a numerical model to simulate flooding from extreme weather events and to evaluate the impacts on critical infrastructure and communities; Hurricane María is used as a case study. GSSHA was calibrated for Irma, a Category 3 hurricane, which struck the northeastern corner of the island on 7 September 2017, two weeks before Hurricane María. The upper Añasco watershed was calibrated using United States Geological Survey (USGS) stream discharge data. The model was validated using a storm of similar magnitude on 11–13 December 2007. Owing to the damage sustained by PR’s WSR-88D weather radar during Hurricane María, rainfall was estimated in this study using the Weather Research Forecast (WRF) model. Flooding in the coastal floodplain during Hurricane María was simulated using three methods: (1) Use of observed discharge hydrograph from the upper watershed as an inflow boundary condition for the coastal floodplain area, along with the WRF rainfall in the coastal flood plain; (2) Use of WRF rainfall to simulate runoff in the upper watershed and coastal flood plain; and (3) Similar to approach (2), except the use of bias-corrected WRF rainfall. Flooding results were compared with forty-two values of flood depth obtained during face-to-face interviews with residents of the affected communities. Impacts on critical infrastructure (water, electric, and public schools) were evaluated, assuming any structure exposed to 20 cm or more of flooding would sustain damage. Calibration equations were also used to improve flood depth estimates. Our model included the influence of storm surge, which we found to have a minimal effect on flood depths within the study area. Water infrastructure was more severely impacted by flooding than electrical infrastructure. From these findings, we conclude that the model developed in this study can be used with sufficient accuracy to identify infrastructure affected by future flooding events.

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“…The Soil and Water Assessment Tool (SWAT) is widely used among quantitative hydrological models at the catchment scale to estimate SWC. It has been extensively tested for various watershed scales and environmental conditions worldwide to simulate SWC and generate long-term SWC series [17][18][19][20][21][22][23][24]. However, these studies have not investigated the effects of climate change on SWC in western Canada, and particularly, using highresolution remote sensing data and ground truthing data.…”

Section: Introductionmentioning

confidence: 99%

Impact of Climate Change on Soil Water Content in Southern Saskatchewan, Canada

Zare

Azam

Sauchyn

2022

Water

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The main objective of this research was to understand the effects of climate change on soil water content (SWC) across the Qu’Appelle River basin in southern Saskatchewan, Canada. The Soil and Water Assessment Tool (SWAT) and output from 11 high-resolution (0.22°) regional climate models (RCM) were used over three 30-year periods: the near future (2021–2050) and distant future (2051–2080) and the historical reference (1975–2005). The RCM data are from the CORDEX North American domain, which employs the RCP8.5 high-emission scenario. SWC was modeled at the hydrological response units (HRU) level and at daily and monthly (warm season only) intervals for 2015–2020. The model was calibrated and validated using SUFI-2 in SWAT-CUP based on observations for streamflow and SWC, including measured data and Soil Moisture Active Passive (SMAP) Level 4 for surface (up to 5 cm deep) soil moisture. Values of the Nash–Sutcliffe model efficiency (NS) ranged from 0.616 and 0.784 and the coefficient of determination (R2) was 0.8 for calibration and 0.82 for validation. Likewise, the correlation coefficients between ground measurements and SWAT were 0.698 and 0.633, respectively. Future climate forcing of the calibrated SWAT model revealed that warmer and drier growing seasons will prevail in the region. Similarly, the ensemble of all RCMs indicated that the mean temperature will increase by 2.1 °C and 3.4 °C for the middle and late periods, respectively, along with a precipitation increase of 10% and 11.2%. SWC is expected to decrease with an increase in potential evapotranspiration, despite an increase in precipitation. Likewise, the annual SWC is expected to decrease by 3.6% and 4% in the middle and late periods, respectively. The monthly SWC changes showed the highest decreases (5.4%) in April in the late period. The spatial pattern of SWC for 11 RCMs was similar such that the northwest and west of the river basin are wetter than the south and east. SWC projections suggest that southern Saskatchewan could experience significant SWC deficiencies in the summer by the end of this century.

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Satellite Soil Moisture Validation Using Hydrological SWAT Model: A Case Study of Puerto Rico, USA

Cited by 17 publications

References 33 publications

Improving SWAT Model Calibration Using Soil MERGE (SMERGE)

Improving SWAT Model Calibration Using Soil MERGE (SMERGE)

Flood Impacts on Critical Infrastructure in a Coastal Floodplain in Western Puerto Rico during Hurricane María

Impact of Climate Change on Soil Water Content in Southern Saskatchewan, Canada

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