Soil Variability and Biogeochemical Fluxes: Toward a Better Understanding of Soil Processes at the Land Surface

Martínez, Gonzalo; Brocca, Luca; Gerke, Horst H.; Pachepsky, Yakov

doi:10.2136/vzj2017.07.0145

Cited by 4 publications

(4 citation statements)

References 25 publications

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“…Nowadays, satellite SM products are available at different spatial and temporal resolution (e.g. References [31,32]). Two space-borne satellite missions, such as Soil Moisture and Ocean Salinity (SMOS) mission launched by the European Space Agency (ESA) in 2009 [33] and Soil Moisture Active Passive (SMAP) launched by the National Aeronautics and Space Administration (NASA) in 2015 [34], were placed in orbit and dedicated specifically to measure SM.…”

Section: Introductionmentioning

confidence: 99%

Predicting Rainfall and Runoff Through Satellite Soil Moisture Data and SWAT Modelling for a Poorly Gauged Basin in Iran

Fereidoon

Koch

Brocca

2019

Water

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Hydrological models are widely used for many purposes in water sector projects, including streamflow prediction and flood risk assessment. Among the input data used in such hydrological models, the spatial-temporal variability of rainfall datasets has a significant role on the final discharge estimation. Therefore, accurate measurements of rainfall are vital. On the other hand, ground-based measurement networks, mainly in developing countries, are either nonexistent or too sparse to capture rainfall accurately. In addition to in-situ rainfall datasets, satellite-derived rainfall products are currently available globally with high spatial and temporal resolution. An innovative approach called SM2RAIN that estimates rainfall from soil moisture data has been applied successfully to various regions. In this study, first, soil moisture content derived from the Advanced Microwave Scanning Radiometer for the Earth observing system (AMSR-E) is used as input into the SM2RAIN algorithm to estimate daily rainfall (SM2R-AMSRE) at different sites in the Karkheh river basin (KRB), southwest Iran. Second, the SWAT (Soil and Water Assessment Tool) hydrological model was applied to simulate runoff using both ground-based observed rainfall and SM2R-AMSRE rainfall as input. The results reveal that the SM2R-AMSRE rainfall data are, in most cases, in good agreement with ground-based rainfall, with correlations R ranging between 0.58 and 0.88, though there is some underestimation of the observed rainfall due to soil moisture saturation not accounted for in the SM2RAIN equation. The subsequent SWAT-simulated monthly runoff from SM2R-AMSRE rainfall data (SWAT-SM2R-AMSRE) reproduces the observations at the six gauging stations (with coefficient of determination, R² > 0.71 and NSE > 0.56), though with slightly worse performances in terms of bias (Bias) and root-mean-square error (RMSE) and, again, some systematic flow underestimation compared to the SWAT model with ground-based rainfall input. Additionally, rainfall estimates of two satellite products of the Tropical Rainfall Measuring Mission (TRMM), 3B42 and 3B42RT, are used in the calibrated SWAT- model after bias correction. The monthly runoff predictions obtained with 3B42- rainfall have 0.42 < R2 < 0.72 and−0.06 < NSE < 0.74 which are slightly better than those obtained with 3B42RT- rainfall, but not as good as the SWAT-SM2R-AMSRE. Therefore, despite the aforementioned limitations, using SM2R-AMSRE rainfall data in a hydrological model like SWAT appears to be a viable approach in basins with limited ground-based rainfall data.

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Section: Introductionmentioning

confidence: 99%

Predicting Rainfall and Runoff Through Satellite Soil Moisture Data and SWAT Modelling for a Poorly Gauged Basin in Iran

Fereidoon

Koch

Brocca

2019

Water

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show abstract

“…Nowadays, satellite SM products are available at different spatial and temporal resolution (e.g., [31,32]). Two space-borne satellite missions, such as Soil Moisture and Ocean Salinity (SMOS) mission launched by the European Space Agency (ESA) in 2009 [33] and Soil Moisture Active Passive (SMAP) launched by the National Aeronautics and Space Administration (NASA) in 2015 [34] were placed in orbit and dedicated specifically to measure SM.…”

Section: Introductionmentioning

confidence: 99%

Predicting Rainfall and Runoff Through Satellite Soil Moisture Data and SWAT Modelling for a Poorly Gauged Basin in Iran

Fereidoon¹,

Koch²,

Brocca³

2018

Preprint

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Hydrological models have been widely used for many purposes in water sector projects, including streamflow prediction and flood risk assessment. Among the input data used in such hydrological models, the spatial-temporal variability of rainfall datasets has a significant role on the final discharge estimation. Therefore, accurate measurements of rainfall are vital. On the other hand, ground-based measurement networks, mainly in developing countries, are either nonexistent or too sparse to capture rainfall accurately. In addition to in-situ rainfall datasets, satellite-derived rainfall products are nowadays available globally with high spatial and temporal resolution. An innovative approach called SM2RAIN that estimates rainfall from soil moisture data has been applied successfully to various regions. In this study, firstly soil moisture content derived from the Advanced Microwave Scanning Radiometer for the Earth observing system (AMSR-E) is used as input into the SM2RAIN algorithm to estimate daily rainfall, SM2R-AMSRE, at different sites in the Karkheh river basin (KRB), southwest Iran. Secondly, the SWAT (Soil and Water Assessment Tool) hydrological model is applied to simulate runoff using both ground-based observed rainfall and SM2R-AMSRE rainfall as input. The results reveal that the SM2R-AMSRE rainfall data are, in most cases, in good agreement with ground-based rainfall, with correlations R ranging between 0.58 and 0.88, though there is some underestimation of the observed rainfall, due to soil moisture saturation, not accounted for in the SM2RAIN equation. The subsequent SM2R-AMSRE- SWAT- simulated monthly runoff reproduces well the observations at the 6 gauging stations (with coefficient of determination, R² > 0.72), though with slightly worse performances in terms of bias (Bias) and root-mean-square error (RMSE) and, again, some systematic flow underestimation than the SWAT model with ground-based rainfall input. Furthermore, rainfall estimations of two satellite products of the Tropical Rainfall Measuring Mission (TRMM), 3B42 and 3B42RT, are used in the calibrated SWAT- model. The monthly runoff obtained with 3B42- rainfall have 0.39< R2 < 0.70 and are slightly better than those obtained with 3B42RT- rainfall, but not as good as the SM2R-AMSRE- SWAT- simulated runoff above. Therefore, in spite of the afore-mentioned limitations, using SM2R-AMSRE rainfall data in a hydrological model like SWAT, appears to be a viable approach in basins with limited ground-based rainfall data.

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“…Determining soil hydraulic parameters is a fundamental step in many soil research projects or even the final objective of the project itself. Soil hydraulic parameters are required to understand and predict soil water dynamics in soil, which is a key aspect of mass and energy cycles in soils (Martinez et al, 2017). Methods to determine soil hydraulic parameters have been and still present a challenge for soil researchers.…”

mentioning

confidence: 99%

How Critical Is the Assimilation Frequency of Water Content Measurements for Obtaining Soil Hydraulic Parameters with Data Assimilation?

Valdés-Abellán¹,

Pachepsky

Martínez³

et al. 2019

Vadose Zone Journal

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Core Ideas Ensemble Kalman filter data assimilation was used to predict soil water content. Analyzed data assimilation frequencies were 1, 2, 3, 5, 7, 9, 11, and 14 d. Assimilation of observed data every 7 d or more yielded better results. Data assimilation (DA) is a promising alternative to infer soil hydraulic parameters from soil water dynamics data. Frequency of measurements and updates are important controls of DA efficiency; however, no strict guidance exists on determining the optimal frequency. In this study, DA was performed with the ensemble Kalman filter (EnKF) with a state augmentation approach to update both model states and parameters. We analyzed updates every 1, 2, 3, 5, 7, 9, 11, and 14 d. Two soil types (sandy loam and loam) and four climates (hot semiarid [Bwh], cold semiarid [Bsk], humid continental [Dfa], and humid subtropical [Cfa]) were considered. Results demonstrate that DA with high update frequencies does not provide better results than results obtained when using low frequencies. For sandy loam soil, assimilation of data every seven or more days yields better results for whatever climate considered. For loam soil, the same is true after 9 mo of assimilation. The chosen performance metric may affect the results, but the general trend of better results with low assimilation frequencies does not change.

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Soil Variability and Biogeochemical Fluxes: Toward a Better Understanding of Soil Processes at the Land Surface

Cited by 4 publications

References 25 publications

Predicting Rainfall and Runoff Through Satellite Soil Moisture Data and SWAT Modelling for a Poorly Gauged Basin in Iran

Predicting Rainfall and Runoff Through Satellite Soil Moisture Data and SWAT Modelling for a Poorly Gauged Basin in Iran

Predicting Rainfall and Runoff Through Satellite Soil Moisture Data and SWAT Modelling for a Poorly Gauged Basin in Iran

How Critical Is the Assimilation Frequency of Water Content Measurements for Obtaining Soil Hydraulic Parameters with Data Assimilation?

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