Evaluation of soil moisture data products over Indian region and analysis of spatio-temporal characteristics with respect to monsoon rainfall

2016 ◽  
Vol 542 ◽  
pp. 47-62 ◽  
Author(s):  
Anusha Sathyanadh ◽  
Anandakumar Karipot ◽  
Manish Ranalkar ◽  
Thara Prabhakaran
Keyword(s):  
Soil Moisture ◽  
Monsoon Rainfall ◽  
Indian Region ◽  
Temporal Characteristics ◽  
Soil Moisture Data ◽  
Data Products ◽  
Spatio Temporal

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 Relation of Eurasian Snow Cover and Indian Summer Monsoon Rainfall: Importance of the Delayed Hydrological Effect

2017 ◽  
Vol 30 (4) ◽  
pp. 1273-1289 ◽  
Author(s):  
Subhadeep Halder ◽  
Paul A. Dirmeyer
Keyword(s):  
Soil Moisture ◽  
Snow Cover ◽  
Summer Monsoon ◽  
Indian Summer Monsoon ◽  
Monsoon Rainfall ◽  
Indian Summer Monsoon Rainfall ◽  
Summer Monsoon Rainfall ◽  
Indian Region ◽  
Easterly Wind ◽  
Eurasian Region

Abstract This observationally based study demonstrates the importance of the delayed hydrological response of snow cover and snowmelt over the Eurasian region and Tibet for variability of Indian summer monsoon rainfall during the first two months after onset. Using snow cover fraction and snow water equivalent data during 1967–2003, it is demonstrated that, although the snow-albedo effect is prevalent over western Eurasia, the delayed hydrological effect is strong and persistent over the eastern part. Long soil moisture memory and strong sensitivity of surface fluxes to soil moisture variations over eastern Asia and Tibet provide a mechanism for soil moisture anomalies generated by anomalies in winter and spring snowfall to affect rainfall during the initial months in summer. Dry soil moisture anomalies over the eastern Eurasian region associated with anomalous heating at the surface and midtroposphere help in anchoring of an anomalous upper-tropospheric “blocking” ridge around 100°E and its persistence. This not only leads to prolonged weakening of the subtropical westerly jet but also shifts its position southward of 30°N, followed by penetration of anomalous troughs in the westerlies into the Indian region. Simultaneously, intrusion of cold and dry air from the midlatitudes can reduce the convective instability and hence rainfall over India after the onset. Such a southward shift of the jet can also significantly weaken the vertical easterly wind shear over the Indian region in summer and lead to decrease in rainfall. This delayed hydrological effect also has the potential to modulate the snow–atmosphere coupling strength for temperature and precipitation in operational forecast models through soil moisture–evaporation–precipitation feedbacks.

scholarly journals A Preliminary Assessment of the Impact of Assimilating Satellite Soil Moisture Data Products on NCEP Global Forecast System

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Weizhong Zheng ◽  
Xiwu Zhan ◽  
Jicheng Liu ◽  
Michael Ek
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Prediction Models ◽  
Precipitation Forecast ◽  
Global Forecast System ◽  
Forecast System ◽  
Numerical Weather ◽  
Soil Moisture Data ◽  
Data Products ◽  
Ncep Global Forecast System

It is well documented that soil moisture has a strong impact on precipitation forecasts of numerical weather prediction models. Several microwave satellite soil moisture retrieval data products have also been available for applications. However, these observational data products have not been employed in any operational numerical weather or climate prediction models. In this study, a preliminary test of assimilating satellite soil moisture data products from the NOAA-NESDIS Soil Moisture Operational Product System (SMOPS) into the NOAA-NCEP Global Forecast System (GFS) is conducted. Using the ensemble Kalman filter (EnKF) introduced in recent year publications and implemented in the GFS, the multiple satellite blended daily global soil moisture data from SMOPS for the month of April 2012 are assimilated into the GFS. The forecasts of surface variables, anomaly correlations of isobar heights, and precipitation forecast skills of the GFS with and without the soil moisture data assimilation are assessed. The surface and deep layer soil moisture estimates of the GFS after the satellite soil moisture assimilation are found to have slightly better agreement with the ground soil moisture measurements at dozens of sites across the continental United States (CONUS). Forecasts of surface humidity and air temperature, 500 hPa height anomaly correlations, and the precipitation forecast skill demonstrated certain level of improvements after the soil moisture assimilation against those without the soil moisture assimilation. However, the methodology for the soil moisture data assimilation into operational GFS runs still requires further development efforts and tests.

scholarly journals Spatio-temporal analysis of remotely sensed and hydrological model soil moisture in the small Jičinka River catchment in Czech Republic

2021 ◽  
Vol 69 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Vesna Đukić ◽  
Ranka Erić ◽  
Miroslav Dumbrovsky ◽  
Veronika Sobotkova
Keyword(s):  
Soil Moisture ◽  
Czech Republic ◽  
Hydrological Model ◽  
Temporal Dynamics ◽  
Remotely Sensed ◽  
Hydrological Models ◽  
Estimation Of Parameters ◽  
River Catchment ◽  
Soil Moisture Data ◽  
Spatio Temporal

Abstract The knowledge of spatio-temporal dynamics of soil moisture within the catchment is very important for rainfall–runoff modelling in flood forecasting. In this study the comparison between remotely sensed soil moisture and soil moisture estimated from the SHETRAN hydrological model was performed for small and flashy Jičinka River catchment (75.9 km2) in the Czech Republic. Due to a relatively coarse spatial resolution of satellite data, the satellite soil moisture data were downscaled, by applying the method developed by Qu et al. (2015). The sub-grid variability of soil moisture was estimated on the basis of the mean soil moisture for the grid cell and the known hydraulic soil properties. The SHETRAN model was calibrated and verified to the observed streamflow hydrographs at the catchment outlet. The good correlation between the two different soil moisture information was obtained according to the majority of applied criteria. The results of the evaluation criteria indicate that the downscaled remotely sensed soil moisture data can be used as additional criteria for the calibration and validation of hydrological models for small catchments and can contribute to a better estimation of parameters, to reduce uncertainties of hydrological models and improve runoff simulations.

scholarly journals The diagnosis about spatio-temporal characteristics and driving factors of flash drought and its prediction over typical humid and semi-arid basins in China

2021 ◽  
Author(s):  
Qian Zhu ◽  
Yushi Wang
Keyword(s):  
Soil Moisture ◽  
Agricultural Production ◽  
Rapid Onset ◽  
Driving Factors ◽  
Meteorological Variables ◽  
Support Vector ◽  
False Alarms ◽  
Temporal Characteristics ◽  
Spatio Temporal ◽  
Semi Arid

AbstractFlash drought is a rapid-onset drought, which has greatly threatened the agricultural production and economic development. However, the unclear development mechanism of flash droughts brings challenges for its monitoring, forecasting and mitigation. This study investigates the spatio-temporal characteristics, driving factors and the prediction of flash drought over typical humid and semi-arid basins. The main objectives and findings are as follows: (1) The patterns of flash drought are compared under different climate types. The results show that flash drought is more serious in the chosen humid basin than that in the semiarid basin, with more events, longer duration, larger frequency of occurrence (FOC). (2) The development mechanisms of flash drought are explored by analyzing the anomalies of seven meteorological variables in the evolution of flash drought. The results indicate that the main driving factors are the negative anomalies of precipitation and the positive anomalies of temperature, which usually occur at two pentads before the onset of flash drought. (3) The prediction of soil moisture as a key variable in flash droughts developing process is conducted using support vector machine (SVM), with meteorological variables and a remote sensing soil moisture, namely Soil Moisture Active and Passive (SMAP), as inputs. In this study, about 65%-70% of flash droughts can be captured by the prediction. However, some events are missed, and false alarms also exist in most stations in both basins. This study can provide some references for monitoring and early warning of flash drought, which is important to reduce the losses and risks in agriculture production.

scholarly journals Validation of Soil Moisture Data Products from the NASA SMAP Mission

2021 ◽  
pp. 1-1
Author(s):  
Andreas Colliander ◽  
Rolf Reichle ◽  
Wade Crow ◽  
Michael H. Cosh ◽  
Fan Chen ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Moisture Data ◽  
Data Products
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