Soil moisture variations in remotely sensed and reanalysis datasets during weak monsoon conditions over central India and central Myanmar

Shrivastava, Sourabh; Kar, S. C.; Sharma, Anu Rani

doi:10.1007/s00704-016-1792-z

Cited by 25 publications

(14 citation statements)

References 28 publications

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“…The ESA-CCI soil moisture has already been extensively validated over the India continent as well [30][31][32][33]. In particular, the ESA-CCI product merging active and passive sensors that is used in this study is in good agreement (i.e., high Pearson correlation coefficients) with the soil moisture time-series produced by both the land surface reanalysis and the high-resolution precipitation data [30,32] in India. ESA-CCI has been evaluated also with the available direct soil moisture observations in India, providing similar correlations coefficients with respect to reanalyzed soil moisture datasets [31].…”

Section: Soil Moisture Observationssupporting

confidence: 56%

Surface Freshwater Limitation Explains Worst Rice Production Anomaly in India in 2002

et al. 2018

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Abstract:India is the second-most populous country and the second-most important producer of rice of the world. Most Indian rice production depends on monsoon timing and dynamics. In 2002, the lowest monsoon precipitation of the last 130+ years was observed. It coincided with the worst rice production anomaly recorded by FAOSTAT from 1961 to 2014. In that year, freshwater limitation was blamed as responsible for the yield losses in the southeastern coastal regions. Given the important implication for local food security and international market stability, we here investigate the specific mechanisms behind the effects of this extreme meteorological drought on rice yield at the national and regional levels. To this purpose, we integrate output from the hydrological model, surface, and satellite observations for the different rice cropping cycles into state-of-the-art and novel climate indicators. In particular, we adopt the standardized precipitation evapotranspiration index (SPEI) as an indicator of drought due to the local surface water balance anomalies (i.e., precipitation and evapotranspiration). We propose a new indicator of the renewable surface freshwater availability due to non-local sources, i.e., the standardized river discharge index (SDI) based on the anomalies of modelled river discharge data. We compare these indicators to the soil moisture observations retrieved from satellites. We link all diagnostics to the recorded yields at the national and regional level, quantifying the long-term correlations and the best match of the 2002 anomaly. Our findings highlight the need for integrating non-local surface freshwater dynamics with local rainfall variability to determine the soil moisture conditions in rice fields for yields assessment, modeling, and forecasting.

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Section: Soil Moisture Observationssupporting

confidence: 56%

Surface Freshwater Limitation Explains Worst Rice Production Anomaly in India in 2002

et al. 2018

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“…Similarly, Shrivastava, Kar, and Sharma (2016), Chakravorty, Chahar, Sharma, and Chanya (2017), and Sathyanadh, Karipot, Ranalkar, and Prabhakaran (2017) have validated this dataset over Indian region, and therefore, it could be used as a proxy for agricultural drought studies in the absence of continuous observed data for the mid-Mahanadi region. The ECV-SM dataset at different grid locations at daily time step is aggregated to monthly time series for use in the current research.…”

Section: Introductionmentioning

confidence: 89%

“…Padhee, Nikam, Dutta, and Aggarwal () had downscaled this dataset to obtain finer resolution soil moisture that showed good agreement with meteorological drought index and vegetation condition index computed over a study region in Central India. Similarly, Shrivastava, Kar, and Sharma (), Chakravorty, Chahar, Sharma, and Chanya (), and Sathyanadh, Karipot, Ranalkar, and Prabhakaran () have validated this dataset over Indian region, and therefore, it could be used as a proxy for agricultural drought studies in the absence of continuous observed data for the mid‐Mahanadi region. The ECV‐SM dataset at different grid locations at daily time step is aggregated to monthly time series for use in the current research.…”

Section: Study Area and Datamentioning

confidence: 99%

Drought hotspot analysis and risk assessment using probabilistic drought monitoring and severity–duration–frequency analysis

Samantaray

Singh

Ramadas

et al. 2018

Hydrological Processes

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Drought hotspot identification requires continuous drought monitoring and spatial risk assessment. The present study analysed drought events in the agriculture‐dominated mid‐Mahanadi River Basin in Odisha, India, using crop water stress as a drought indicator. This drought index incorporated different factors that affect crop water deficit such as the cropping pattern, soil characteristics, and surface soil moisture. The drought monitoring framework utilized a relevance vector machine model‐based classification that provided the uncertainty associated with drought categorization. Using the proposed framework, drought hotspots are identified in the study region and compared with indices based on precipitation and soil moisture. Further, a bivariate copula is employed to model the agricultural drought characteristics and develop the drought severity–duration–frequency (S–D–F) relationships. The drought hotspot maps and S–D–F curves are developed for different locations in the region. These provided useful information on the site‐specific drought patterns and the characteristics of the devastating droughts of 2002 and 2012, characterized by an average drought duration of 7 months at several locations. The site‐specific risk of short‐ and long‐term agricultural droughts are then investigated using the conditional copula. The results suggest that the conditional return periods and the S–D–F curves are valuable tools to assess the spatial variability of drought risk in the region.

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“…However, Shrivastava et al . () attempted to evaluate ESA‐CCI soil moisture data with soil moisture data from the ECMWF Reanalysis‐Interim over India. This data is also validated across the globe using ground data (Dorigo et al ., ).…”

Section: Introductionmentioning

confidence: 99%

Characterizing satellite‐derived soil moisture and its relationship with rainfall over India

Vezhapparambu

Madhusoodanan

Sharma

et al. 2019

Intl Journal of Climatology

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Understanding the spatial and temporal variations of soil moisture is important since the feedback between soil moisture and precipitation is a key control of the climate. This is also important as soil moisture data is a key input in regional climate models. The inter‐annual and inter‐decadal variability of soil moisture has been investigated here using the European Space Agency's Climate Change Initiative active and passive combined soil moisture product over different climate regimes of India. The observed soil moisture and rainfall are positively correlated and their relationship in this study suggests that both positive and negative feedback occur. The analysis showed an increasing soil moisture trend with a significant increase in the monsoon and autumn seasons for the past two decades over central India, where the frequency of extreme precipitation increased in the same period. An overall increase in soil moisture over the western arid region is also significant. A decreasing trend in soil moisture prevailed throughout the monsoon season over eastern India, where monsoon depressions make landfall, signalling the recent climate trends over the Indian subcontinent.

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Soil moisture variations in remotely sensed and reanalysis datasets during weak monsoon conditions over central India and central Myanmar

Cited by 25 publications

References 28 publications

Surface Freshwater Limitation Explains Worst Rice Production Anomaly in India in 2002

Surface Freshwater Limitation Explains Worst Rice Production Anomaly in India in 2002

Drought hotspot analysis and risk assessment using probabilistic drought monitoring and severity–duration–frequency analysis

Characterizing satellite‐derived soil moisture and its relationship with rainfall over India

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