scholarly journals Agricultural Drought Monitoring via the Assimilation of SMAP Soil Moisture Retrievals Into a Global Soil Water Balance Model

2020 ◽  
Vol 3 ◽  
Author(s):  
Iliana E. Mladenova ◽  
John D. Bolten ◽  
Wade Crow ◽  
Nazmus Sazib ◽  
Curt Reynolds
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Soil Water ◽  
Water Balance Model ◽  
Agricultural Drought ◽  
Soil Water Balance ◽  
Drought Monitoring ◽  
Balance Model ◽  
Global Soil

scholarly journals Benchmarking a Soil Moisture Data Assimilation System for Agricultural Drought Monitoring

2014 ◽  
Vol 15 (3) ◽  
pp. 1117-1134 ◽  
Author(s):  
Eunjin Han ◽  
Wade T. Crow ◽  
Thomas Holmes ◽  
John Bolten
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Water Balance ◽  
Soil Water ◽  
Water Balance Model ◽  
Agricultural Drought ◽  
Soil Water Balance ◽  
Drought Monitoring ◽  
Balance Model ◽  
System Component

Abstract Despite considerable interest in the application of land surface data assimilation systems (LDASs) for agricultural drought applications, relatively little is known about the large-scale performance of such systems and, thus, the optimal methodological approach for implementing them. To address this need, this paper evaluates an LDAS for agricultural drought monitoring by benchmarking individual components of the system (i.e., a satellite soil moisture retrieval algorithm, a soil water balance model, and a sequential data assimilation filter) against a series of linear models that perform the same function (i.e., have the same basic input/output structure) as the full system component. Benchmarking is based on the calculation of the lagged rank cross correlation between the normalized difference vegetation index (NDVI) and soil moisture estimates acquired for various components of the system. Lagged soil moisture/NDVI correlations obtained using individual LDAS components versus their linear analogs reveal the degree to which nonlinearities and/or complexities contained within each component actually contribute to the performance of the LDAS system as a whole. Here, a particular system based on surface soil moisture retrievals from the Land Parameter Retrieval Model (LPRM), a two-layer Palmer soil water balance model, and an ensemble Kalman filter (EnKF) is benchmarked. Results suggest significant room for improvement in each component of the system.

Analysing Seasonal Differences between a Soil Water Balance Model and in Situ Soil Moisture Measurementsat Nine Locations Across Europe

2013 ◽  
Vol 19 (1) ◽  
pp. 19-34 ◽  
Author(s):  
Blaž Kurnik ◽  
Geertrui Louwagie ◽  
Markus Erhard ◽  
Andrej Ceglar ◽  
Lučka Bogataj Kajfež
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Soil Water ◽  
Water Balance Model ◽  
Soil Water Balance ◽  
Balance Model ◽  
Seasonal Differences

scholarly journals A Coupled Soil Water Balance Model for Simulating Depression-Focused Groundwater Recharge

2018 ◽  
Vol 17 (1) ◽  
pp. 170176 ◽  
Author(s):  
Saskia L. Noorduijn ◽  
Masaki Hayashi ◽  
Getachew A. Mohammed ◽  
Aaron A. Mohammed
Keyword(s):  
Water Balance ◽  
Groundwater Recharge ◽  
Soil Water ◽  
Water Balance Model ◽  
Soil Water Balance ◽  
Balance Model

Simulation of yield decline as a result of water stress with a robust soil water balance model

2006 ◽  
Vol 81 (3) ◽  
pp. 335-357 ◽  
Author(s):  
Dirk Raes ◽  
Sam Geerts ◽  
Emmanuel Kipkorir ◽  
Joost Wellens ◽  
Ali Sahli
Keyword(s):  
Water Stress ◽  
Water Balance ◽  
Soil Water ◽  
Water Balance Model ◽  
Soil Water Balance ◽  
Balance Model ◽  
Yield Decline
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