scholarly journals Usefulness of Global Root Zone Soil Moisture Product for Streamflow Prediction of Ungauged Basins

2021 ◽  
Vol 13 (4) ◽  
pp. 756
Author(s):  
Jeonghyeon Choi ◽  
Jeongeun Won ◽  
Okjeong Lee ◽  
Sangdan Kim
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Stream Flow ◽  
Root Zone ◽  
Predictive Performance ◽  
Ungauged Basins ◽  
Flow Data ◽  
Stream Flows ◽  
Soil Moisture Data ◽  
Global Soil

Using modelling approaches to predict stream flow from ungauged basins requires new model calibration strategies and evaluation methods that are different from the existing ones. Soil moisture information plays an important role in hydrological applications in basins. Increased availability of remote sensing data presents a significant opportunity to obtain the predictive performance of hydrological models (especially in ungauged basins), but there is still a limit to applying remote sensing soil moisture data directly to models. The Soil Moisture Active Passive (SMAP) satellite mission provides global soil moisture data estimated by assimilating remotely sensed brightness temperature to a land surface model. This study investigates the potential of a hydrological model calibrated using only global root zone soil moisture based on satellite observation when attempting to predict stream flow in ungauged basins. This approach’s advantage is that it is particularly useful for stream flow prediction in ungauged basins since it does not require observed stream flow data to calibrate a model. The modelling experiments were carried out on upstream watersheds of two dams in South Korea with high-quality stream flow data. The resulting model outputs when calibrated using soil moisture data without observed stream flow data are particularly impressive when simulating monthly stream flows upstream of the dams, and daily stream flows also showed a satisfactory level of predictive performance. In particular, the model calibrated using soil moisture data for dry years showed better predictive performance than for wet years. The performance of the model calibrated using soil moisture data was significantly improved under low flow conditions compared to the traditional regionalization approach. Additionally, the overall stream flow was also predicted better. In addition, the uncertainty of the model calibrated using soil moisture was not much different from that of the model calibrated using observed stream flow data, and showed more robust outputs when compared to the traditional regionalization approach. These results prove that the application of the global soil moisture product for predicting stream flows in ungauged basins is promising.

A multi-layer soil moisture data assimilation using support vector machines and ensemble particle filter

2012 ◽  
Vol 475 ◽  
pp. 53-64 ◽  
Author(s):  
Zhongbo Yu ◽  
Di Liu ◽  
Haishen Lü ◽  
Xiaolei Fu ◽  
Long Xiang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Support Vector Machines ◽  
Data Assimilation ◽  
Particle Filter ◽  
Support Vector ◽  
Soil Moisture Data ◽  
Vector Machines

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

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.

scholarly journals Web Geoprocessing Services for Disseminating and Analyzing SMAP Derived Soil Moisture Data Products

2020 ◽  
Author(s):  
Chen Zhang ◽  
Zhengwei Yang ◽  
Liping Di ◽  
Eugene Yu ◽  
Li Lin ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Moisture Data ◽  
Data Products ◽  
Geoprocessing Services

scholarly journals Potential of bias correction for downscaling passive microwave and soil moisture data

2015 ◽  
Vol 120 (13) ◽  
pp. 6460-6479 ◽  
Author(s):  
Kurt C. Kornelsen ◽  
Michael H. Cosh ◽  
Paulin Coulibaly
Keyword(s):  
Soil Moisture ◽  
Bias Correction ◽  
Passive Microwave ◽  
Soil Moisture Data
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