Examination of aggregation effects in watershed hydrological modeling using microwave-derived soil moisture data

Hydrological simulation models have proven to be an important tool for managing and planning water resources, enabling the assessment of the impacts of rainfall on surface runoff and soil moisture. This work therefore aimed to apply the SWAT model for the analysis of hydrological processes in the Experimental Basin of the Jatobá Stream, in the semiarid region of the State of Pernambuco, Brazil, considering the calibration and validation of the model from streamflow and soil moisture data. Moreover, the study investigated hydrological effectiveness in a recovery scenario in areas of higher topographic elevation of the arborescent Caatinga and the behavior of the hydrological components under an agricultural expansion scenario. Events which occured between 2009 and 2010 were used to calibrate and validate streamflow and soil moisture data. The calibration and validation of streamflow exhibited efficiency coefficients (NSE) of 0.58 and 0.42, respectively, and 0.53 and 0.46 for soil moisture. The adjustment of the parameters was considered adequate for representing streamflow recession periods. It was also verified that the alternative process of calibration and validation with soil moisture reduced uncertainty. Regeneration of the vegetative cover over 21% of the hilltop areas of arborescent Caatinga led to a significant increase in percolation (42%) and a decrease of 34% in soil moisture (due to water consumption by plants), thus contributing to the recovery of headwaters, increasing resilience to water scarcity. On the other hand, the 38% expansion of agriculture caused an increase of 11% in surface runoff and, consequently, an increase of 10% in soil moisture.

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The value of ASCAT soil moisture and MODIS snow cover data for calibrating a conceptual hydrologic model

Hydrology and Earth System Sciences ◽

10.5194/hess-25-1389-2021 ◽

2021 ◽

Vol 25 (3) ◽

pp. 1389-1410

Author(s):

Rui Tong ◽

Juraj Parajka ◽

Andreas Salentinig ◽

Isabella Pfeil ◽

Jürgen Komma ◽

...

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Snow Cover ◽

Hydrological Modeling ◽

Hydrologic Model ◽

Catchment Scale ◽

Data Set ◽

Water Index ◽

Soil Moisture Data ◽

Moderate Resolution Imaging Spectroradiometer

Abstract. Recent advances in soil moisture remote sensing have produced satellite data sets with improved soil moisture mapping under vegetation and with higher spatial and temporal resolutions. In this study, we evaluate the potential of a new, experimental version of the Advanced Scatterometer (ASCAT) soil water index data set for multiple objective calibrations of a conceptual hydrologic model. The analysis is performed in 213 catchments in Austria for the period 2000–2014. An HBV (Hydrologiska Byråns Vattenbalansavdelning)-type hydrologic model is calibrated based on runoff data, ASCAT soil moisture data, and Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover data for various calibration variants. Results show that the inclusion of soil moisture data in the calibration mainly improves the soil moisture simulations, the inclusion of snow data mainly improves the snow simulations, and the inclusion of both of them improves both soil moisture and snow simulations to almost the same extent. The snow data are more efficient at improving snow simulations than the soil moisture data are at improving soil moisture simulations. The improvements of both runoff and soil moisture model efficiencies are larger in low elevation and agricultural catchments than in others. The calibrated snow-related parameters are strongly affected by including snow data and, to a lesser extent, by soil moisture data. In contrast, the soil-related parameters are only affected by the inclusion of soil moisture data. The results indicate that the use of multiple remote sensing products in hydrological modeling can improve the representation of hydrological fluxes and prediction of runoff hydrographs at the catchment scale.

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Micrometeorological, evapotranspiration, and soil-moisture data at the Amargosa Desert Research site in Nye County near Beatty, Nevada, 2006-11

Data Series ◽

10.3133/ds725 ◽

2012 ◽

Author(s):

Jonathan M. Arthur ◽

Michael J. Johnson ◽

C. Justin Mayers ◽

Brian J. Andraski

Keyword(s):

Soil Moisture ◽

Soil Moisture Data ◽

Research Site ◽

Nye County ◽

Desert Research

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Global soil moisture data derived through machine learning trained with in-situ measurements

Scientific Data ◽

10.1038/s41597-021-00964-1 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Sungmin O. ◽

Rene Orth

Keyword(s):

Machine Learning ◽

Soil Moisture ◽

Large Scale ◽

Short Term Memory ◽

Temporal Dynamics ◽

Soil Moisture Data ◽

Wide Range ◽

Global Soil

AbstractWhile soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture derived through machine learning trained with in-situ measurements, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0–10 cm, 10–30 cm, and 30–50 cm) at 0.25° spatial and daily temporal resolution over the period 2000–2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses.

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Drought Monitoring using High Spatial Resolution Soil Moisture Data over Australia in 2015-2019

Journal of Hydrology ◽

10.1016/j.jhydrol.2021.125960 ◽

2021 ◽

pp. 125960

Author(s):

Bin Fang ◽

Prakrut Kansara ◽

Chelsea Dandridge ◽

Venkat Lakshmi

Keyword(s):

Soil Moisture ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Drought Monitoring ◽

Soil Moisture Data

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Analysis of an Extreme Weather Event in a Hyper Arid Region Using WRF-Hydro Coupling, Station, and Satellite data

10.5194/nhess-2018-226 ◽

2018 ◽

Author(s):

Youssef Wehbe ◽

Marouane Temimi ◽

Michael Weston ◽

Naira Chaouch ◽

Oliver Branch ◽

...

Keyword(s):

Soil Moisture ◽

United Arab Emirates ◽

Hydrological Modeling ◽

Flash Flood ◽

Pearson Correlation ◽

Global Radiation ◽

Wrf Model ◽

Extreme Weather ◽

Extreme Weather Event ◽

Weather Event

Abstract. This study investigates an extreme weather event that impacted the United Arab Emirates (UAE) in March 2016 using the Weather Research and Forecasting (WRF) model version 3.7.1 coupled with its hydrological modeling extension package (Hydro). Six-hourly forecasted forcing records at 0.5o spatial resolution, obtained from the NCEP Global Forecast System (GFS), are used to drive the three nested downscaling domains of both standalone WRF and coupled WRF/WRF-Hydro configurations for the recent flood-triggering storm. Ground and satellite observations over the UAE are employed to validate the model results. Precipitation, soil moisture, and cloud fraction retrievals from GPM (30-minute, 0.1o product), AMSR2 (daily, 0.1o product), and MODIS (daily, 5 km product), respectively, are used to assess the model output. The Pearson correlation coefficient (PCC), relative bias (rBIAS) and root-mean-square error (RMSE) are used as performance measures. Results show reductions of 24 % and 13 % in RMSE and rBIAS measures, respectively, in precipitation forecasts from the coupled WRF/WRF-Hydro model configuration, when compared to standalone WRF. The coupled system also shows improvements in global radiation forecasts, with reductions of 45 % and 12 % for RMSE and rBIAS, respectively. Moreover, WRF-Hydro was able to simulate the spatial distribution of soil moisture reasonably well across the study domain when compared to AMSR2 satellite soil moisture estimates, despite a noticeable dry/wet bias in areas where soil moisture is high/low. The demonstrated improvement, at the local scale, implies that WRF-Hydro coupling may enhance hydrologic forecasts and flash flood guidance systems in the region.

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