EVALUATION OF AGRICULTURAL DROUGHT FOR DRYLAND CROP

Yavatmal is one of the drought prone districts in Maharashtra state of India and has witnessed an agricultural crisis to the extent that hundreds of its farmers have committed suicides in recent years. Satellite data based products have previously been used globally for monitoring and predicting of drought, but not for monitoring their extreme impacts that may include farmer-suicides. In this study, the performance of the Soil Water Index (SWI) derived from the surface soil moisture estimated by the European Space Agency’s Advanced Scatterometer (ASCAT) is assessed. Using the 2007-2015 data, it was found that the relationship of the SWI anomaly was bit stronger (coefficient. of correlation = 0.59) with the meteorological drought or precipitation than with the agricultural drought or crop yields of major crops (coefficient. of correlation = 0.50). The farmer-suicide rate was better correlated with the SWI anomaly averaged annually than with the SWI anomaly averaged only for the monsoon months (June, July, August, and September). The correlation between the SWI averaged annually increased to 0.89 when the averages were taken for three years, with the highest correlation occurring between the suicide rate and the SWI anomaly averaged for three years. However, a positive relationship between SWI and the suicide rate indicated that drought was not a major factor responsible for suicide occurrence and other possible factors responsible for suicide occurrence need to examine in detail.

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Spatiotemporal characteristics and risk assessment of agricultural drought disasters during the winter wheat-growing season on the Huang-Huai-Hai Plain, China

Theoretical and Applied Climatology ◽

10.1007/s00704-020-03506-8 ◽

2021 ◽

Author(s):

Zhenghua Hu ◽

Zhurong Wu ◽

A. R. M. Towfiqul Islam ◽

Xinyuan You ◽

Chao Liu ◽

...

Keyword(s):

Risk Assessment ◽

Winter Wheat ◽

Growing Season ◽

Agricultural Drought ◽

Spatiotemporal Characteristics

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Agricultural Drought Risk Assessment: A Spatial Analysis of Hazard, Exposure, and Vulnerability in Zacatecas, Mexico

Water ◽

10.3390/w13101431 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1431

Author(s):

David Ortega-Gaucin ◽

Jesús A. Ceballos-Tavares ◽

Alejandro Ordoñez Sánchez ◽

Heidy V. Castellano-Bahena

Keyword(s):

Risk Index ◽

Hazard Index ◽

Environmental Indicators ◽

The State ◽

Agricultural Drought ◽

Agricultural Activity ◽

Drought Risk ◽

Hazard Exposure ◽

The World ◽

Agricultural Drought Risk

Drought is one of the major threats to water and food security in many regions around the world. The present study focuses on the evaluation of agricultural drought risk from an integrated perspective, that is, emphasizing the combined role of hazard, exposure, and vulnerability to drought. For this purpose, we used the Mexican state of Zacatecas as a case study. This state is one of the most vulnerable to the adverse effects of agricultural drought in the country. The proposed method includes three stages: first, we analyzed the risk of agricultural drought at the municipal scale using the FAO Agricultural Stress Index System (ASIS) in its country version (Country-Level ASIS) and also determined a Drought Hazard Index (DHI). Subsequently, we conducted a municipal assessment of exposure and vulnerability to drought based on a set of socioeconomic and environmental indicators, which we combined using an analytical procedure to generate the Drought Exposure Index (DEI) and the Drought Vulnerability Index (DVI). Finally, we determined a Drought Risk Index (DRI) based on a weighted addition of the hazard, exposure, and vulnerability indices. Results showed that 32% of the state’s municipalities are at high and very high risk of agricultural drought; these municipalities are located mainly in the center and north of the state, where 75.8% of agriculture is rainfed, 63.6% of production units are located, and 67.4% of the state’s population depends on agricultural activity. These results are in general agreement with those obtained by other studies analyzing drought in the state of Zacatecas using different meteorological drought indices, and the results are also largely in line with official data on agricultural surfaces affected by drought in this state. The generated maps can help stakeholders and public policymakers to guide investments and actions aimed at reducing vulnerability to and risk of agricultural drought. The method described can also be applied to other Mexican states or adapted for use in other states or countries around the world.

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Attribution of meteorological, hydrological and agricultural drought propagation in different climatic regions of China

Agricultural Water Management ◽

10.1016/j.agwat.2021.106996 ◽

2021 ◽

Vol 255 ◽

pp. 106996

Author(s):

Yibo Ding ◽

Xinglong Gong ◽

Zhenxiang Xing ◽

Huanjie Cai ◽

Zhaoqiang Zhou ◽

...

Keyword(s):

Agricultural Drought ◽

Climatic Regions

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Use of meteorological data for identification of agricultural drought in Kumaon region of Uttarakhand

Journal of Earth System Science ◽

10.1007/s12040-021-01622-1 ◽

2021 ◽

Vol 130 (3) ◽

Author(s):

Utkarsh Kumar ◽

Sher Singh ◽

Jaideep Kumar Bisht ◽

Lakshmi Kant

Keyword(s):

Meteorological Data ◽

Agricultural Drought

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Investigating the Spatio-Temporal Variation of Soil Moisture and Agricultural Drought towards Supporting Water Resources Management in the Red River Basin of Vietnam

Sustainability ◽

10.3390/su13094926 ◽

2021 ◽

Vol 13 (9) ◽

pp. 4926

Author(s):

Nguyen Duc Luong ◽

Nguyen Hoang Hiep ◽

Thi Hieu Bui

Keyword(s):

Soil Moisture ◽

River Basin ◽

Temporal Dynamics ◽

Regional Scale ◽

Dry Season ◽

Red River ◽

Agricultural Drought ◽

Severe Drought ◽

Red River Basin ◽

Spatio Temporal

The increasing serious droughts recently might have significant impacts on socioeconomic development in the Red River basin (RRB). This study applied the variable infiltration capacity (VIC) model to investigate spatio-temporal dynamics of soil moisture in the northeast, northwest, and Red River Delta (RRD) regions of the RRB part belongs to territory of Vietnam. The soil moisture dataset simulated for 10 years (2005–2014) was utilized to establish the soil moisture anomaly percentage index (SMAPI) for assessing intensity of agricultural drought. Soil moisture appeared to co-vary with precipitation, air temperature, evapotranspiration, and various features of land cover, topography, and soil type in three regions of the RRB. SMAPI analysis revealed that more areas in the northeast experienced severe droughts compared to those in other regions, especially in the dry season and transitional months. Meanwhile, the northwest mainly suffered from mild drought and a slightly wet condition during the dry season. Different from that, the RRD mainly had moderately to very wet conditions throughout the year. The areas of both agricultural and forested lands associated with severe drought in the dry season were larger than those in the wet season. Generally, VIC-based soil moisture approach offered a feasible solution for improving soil moisture and agricultural drought monitoring capabilities at the regional scale.

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Deep Learning for Monitoring Agricultural Drought in South Asia Using Remote Sensing Data

Remote Sensing ◽

10.3390/rs13091715 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1715

Author(s):

Foyez Ahmed Prodhan ◽

Jiahua Zhang ◽

Fengmei Yao ◽

Lamei Shi ◽

Til Prasad Pangali Sharma ◽

...

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

South Asia ◽

Model Performance ◽

Remote Sensing Data ◽

Agricultural Drought ◽

Gradient Boosting ◽

Complex Nature ◽

Sensing Data

Drought, a climate-related disaster impacting a variety of sectors, poses challenges for millions of people in South Asia. Accurate and complete drought information with a proper monitoring system is very important in revealing the complex nature of drought and its associated factors. In this regard, deep learning is a very promising approach for delineating the non-linear characteristics of drought factors. Therefore, this study aims to monitor drought by employing a deep learning approach with remote sensing data over South Asia from 2001–2016. We considered the precipitation, vegetation, and soil factors for the deep forwarded neural network (DFNN) as model input parameters. The study evaluated agricultural drought using the soil moisture deficit index (SMDI) as a response variable during three crop phenology stages. For a better comparison of deep learning model performance, we adopted two machine learning models, distributed random forest (DRF) and gradient boosting machine (GBM). Results show that the DFNN model outperformed the other two models for SMDI prediction. Furthermore, the results indicated that DFNN captured the drought pattern with high spatial variability across three penology stages. Additionally, the DFNN model showed good stability with its cross-validated data in the training phase, and the estimated SMDI had high correlation coefficient R2 ranges from 0.57~0.90, 0.52~0.94, and 0.49~0.82 during the start of the season (SOS), length of the season (LOS), and end of the season (EOS) respectively. The comparison between inter-annual variability of estimated SMDI and in-situ SPEI (standardized precipitation evapotranspiration index) showed that the estimated SMDI was almost similar to in-situ SPEI. The DFNN model provides comprehensive drought information by producing a consistent spatial distribution of SMDI which establishes the applicability of the DFNN model for drought monitoring.

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