Agricultural Drought Characteristics Research Articles

Dynamic Variations of Agricultural Drought and Its Response to Meteorological Drought: A Drought Event‐Based Perspective

AbstractThe dynamic variations of agricultural drought can reflect the water shortage situation of the agricultural system, and there is a progressive relationship in the response of agricultural drought to meteorological drought on a spatiotemporal scale. In this study, the vegetation health index and the standardized precipitation evapotranspiration index were adopted as agricultural and meteorological drought indicators, respectively. Additionally, using the three‐dimensional spatiotemporal clustering technology, the dynamic evolutions of typical drought events were clarified, and the spatiotemporal response characteristics of agricultural drought to meteorological drought were revealed. The results indicated that: (a) there were 81 agricultural drought events in the North China Plain (NCP) during 1982–2020, with a largest drought severity (12.82 × 104 month km2), a 6‐month duration, and a 23.24 × 104 km2 affected area occurring in the No. 4 event; (b) from the 1980s to the 2010s, the agricultural drought gradually decreased and large‐scale droughts mainly concentrated in the border areas of Hebei, Shandong, and Henan; and (c) a total of 13 drought event pairs were successfully matched in the NCP, including 7 pairs of “one‐to‐one,” 4 pairs of “one‐to‐many,” 1 pair of “many‐to‐one,” and 1 pair of “many‐to‐many.” The spatiotemporal responses of agricultural drought were elucidated in a three‐dimensional perspective, which can propose a new approach for establishing drought propagation model, predicting future agricultural drought conditions, improving ecological environment quality, and can also be applied for the investigation of other drought types.

Enhancing a machine learning model for predicting agricultural drought through feature selection techniques

This study aims to determine the crucial variables for predicting agricultural drought in various climates of Iran by employing feature selection methods. To achieve this, two databases were used, one consisting of ground-based measurements and the other containing six reanalysis products for temperature (T), root zone soil moisture (SM), potential evapotranspiration (PET), and precipitation (P) variables during the 1987–2019 period. The accuracy of the global database data was assessed using statistical criteria in both single- and multi-product approaches for the aforementioned four variables. In addition, five different feature selection methods were employed to select the best single condition indices (SCIs) as input for the support vector regression (SVR) model. The superior multi-products based on time series (SMT) showed increased accuracy for P, T, PET, and SM variables, with an average 47%, 41%, 42%, and 52% reduction in mean absolute error compared to SSP. In hyperarid climate regions, PET condition index was found to have high relative importance with 40% and 36% contributions to SPEI-3 and SPEI-6, respectively. This suggests that PET plays a key role in agricultural drought in hyperarid regions because of very low precipitation. Additionally, the accuracy results of different feature selection methods show that ReliefF outperformed other feature selection methods in agricultural drought modeling. The characteristics of agricultural drought indicate the occurrence of drought in 2017 and 2018 in various climates in Iran, particularly arid and semi-arid climates, with five instances and an average duration of 12 months of drought in humid climates.

Assessing meteorological and agricultural drought characteristics and drought propagation in Guangdong, China

Study regionGuangdong, China Study focusDroughts have resulted in significant crop failure in Guangdong province, where annual crop yield exceed 12 million tons. However, there is a lack of a comprehensive assessment of both meteorological drought and agricultural drought in Guangdong, particularly regarding the influencing factors and drought propagation. This study aims to address this gap by utilizing the standardized precipitation evapotranspiration index (SPEI) and soil water deficit index (SWDI) to represent meteorological drought and agricultural drought, respectively. To examine the characteristics of drought in the past 50 years (1973–2022), the year is divided into flood and non-flood season based on the temporal distribution of precipitation. New hydrological insights for the regionOver the last five decades, the number of meteorological and agricultural drought days decreases from coastal to inland areas. Notably, agricultural droughts have become more pronounced in the last two decades. More agricultural drought days appears during the non-flood season than the flood season. Precipitation emerges as the most influential factor for meteorological drought, while potential evapotranspiration, actual evapotranspiration and temperature are identified as the primary factors for agricultural drought. The drought propagation time ranges from 10 to 45 days which is shorter in central Guangdong than other regions, and it is longer in non-flood season than in flood season.

Spatial and temporal patterns of agricultural drought in China during 1960–2020 characterized by use of the crop water deficit Abnormal Index

Agricultural drought intensifies crop water stress and limits photosynthetic productivity, thereby threatening food security and socio-economic development. However, current drought indices from model simulations or remote sensing have limitations with regard to fully considering the complex interactions between various climate factors and crop water demands. Furthermore, estimates from remote sensing-based drought indices are subject to significant uncertainties due to the coarse spatiotemporal resolution of the data sources, inherent systematic errors in methodologies, and the limited number of ground observations. This study aimed to bridge these gaps by employing the nationwide Agricultural Drought Monitoring Network covering over 2000 in-situ observations and the Crop Water Deficit Abnormal Index (CWDIa) to quantify the spatiotemporal characteristics of agricultural drought from 1960 to 2020. The duration and severity of drought in northern China were significantly greater than in the southern regions, especially in the North China Plain, and the northern Tibetan Plateau. The occurrence of severe drought in southwestern China and in the southern Tibetan Plateau during the spring and winter seasons was driven by persistent water deficits. Over the past 60 years, China has experienced an overall alleviation in the severity and duration of agricultural drought. However, the drought situation has continued to worsen in certain regions, such as in southwestern China and the southern North China Plain. Multiscale decomposition of the ensemble empirical mode has further strengthened this finding (i.e., the drying trend in southwestern China since 1990). As seen in authoritative drought statistics, the integration of CWDIa and dense observations in the drought monitoring framework have demonstrated robust efficacy in capturing over 80 % of the actual drought-covered and drought-affected area. These findings highlight the importance of incorporating climate and crop water demand in agricultural drought monitoring to provide reliable and long-term spatiotemporal estimates of drought characteristics based on a nationwide observation network.

Spatio-Temporal Analysis and Contribution of Agricultural Drought in Daling River Basin: A VIC Model-Based Soil Moisture Simulation and SMAPI Evaluation

Soil moisture is a crucial factor that directly influences agricultural drought. As such, investigating drought-monitoring methods utilizing soil moisture data is of significant importance for accurately evaluating and predicting agricultural drought. However, the current soil moisture data for the Daling River Basin is insufficient. Therefore, the variable infiltration capacity (VIC) hydrological model was utilized to simulate soil moisture in the Daling River Basin. The simulated data were then analyzed in conjunction with the standardized moisture anomaly index (SMAPI) to analyze and evaluate the spatio-temporal characteristics of agricultural drought in the Darling River Basin. The results indicate that the frequency of drought occurrence in the basin follows a seasonal pattern of winter > spring > autumn > summer. Between 1981 and 2019, 24 out of 39 years experienced slight or greater drought, 15 years experienced moderate or more severe drought, and 4 years experienced severe drought. Drought conditions have become exceptionally severe in the 21st century. Specifically, the frequency of drought occurrence from 2001 to 2019 was nearly 10 times higher compared to the period from 1981 to 2000. The droughts were most severe in the southeast and southwest of the Daling River Basin, while the northeast and northwest experienced relatively mild drought. Agricultural drought is influenced by numerous complex factors. The contribution of climate change (CC) and other factors (OF) to agricultural drought was quantified by using a partial derivative under six different scenarios. Results showed that SMAPI was positively correlated with precipitation and solar radiation, while negatively correlated with temperature. From 1981 to 2000, SMAPI exhibited an increasing trend that accounted for 61.66% of variability, while a decreasing trend accounted for 38.34%. From 2001 to 2019, SMAPI exhibited a significant decreasing trend that accounted for 93.53% of the variability, while the increasing trend only accounted for 6.47%. CC was the dominant factor in most of the areas with increased SMAPI. OF was the main controlling factor for areas with decreased SMAPI.

Identifying the spatio-temporal pattern of drought characteristics and its constraint factors in the Yellow River Basin

The Yellow River Basin (YRB) constitutes an important grain-producing area, as well as an economic core area in China. However, drought and water shortage have always been important factors restricting the agricultural sustainable development of this region. The intensity of agricultural drought in the YRB and the factors affecting it need to be urgently clarified. Based on this, this study attempts to assess the spatio-temporal characteristics of agricultural drought in the YRB using the improved traditional temperature vegetation dryness index (ITVDI) and to identify the type and intensity of constraint effects of human activities (population, GDP, land use type), climate change (temperature, precipitation) and topography (elevation, slope) on drought in the YRB with the constraint line method. Results appeared that from 2001 to 2015, the drought intensity in the YRB exhibited fluctuating changes, while it decreased after 2015. Spatially, the drought characteristics of the YRB existed large heterogeneous, with drought showing a significant increase in the northwestern and downstream areas and a significant decrease in the central part of the basin. There were eight types of constraining effects of human activities, climate change, and topographic factors on drought intensity in the YRB, including U-shaped curve, positive linear, negative linear, logarithmic, negative convex, positive convex, exponential, and hump-shaped. Precipitation and temperature were the most direct factors affecting drought intensity and would exert a binding effect on both the upper and lower boundaries of drought intensity. The constraint effect of precipitation on drought varied in space. In the upstream and midstream, precipitation constrained the upper and lower boundaries of ITVDI, and the constraint effect increased with the increase of precipitation. In the downstream, the constraint effect of precipitation did not change significantly, and the maximum value of ITVDI was always less than 0.8. Temperature had upper and lower boundary constraint effects at three scales. The constraining effects of elevation, slope, GDP, and population were stable across scales. It should be noted that the constraint effect of land use type on drought exhibited U-shaped, and the threshold point of minimum values appeared in the areas covered by grassland and water. The constraint line method can improve the drought monitoring ability of ITVDI, as well as help to clarify the constraint factors and mechanism of drought intensity in the YRB and provide scientific decision-making support to mitigate regional drought and promote regional sustainable development.

Precipitation and vegetation transpiration variations dominate the dynamics of agricultural drought characteristics in China

Agricultural drought posing a significant threat to agricultural production is subject to the complex influence of ocean, terrestrial and meteorological multi-factors. Nevertheless, which factor dominating the dynamics of agricultural drought characteristics and their dynamic impact remain equivocal. To address this knowledge gap, we used ERA5 soil moisture to calculate the standardized soil moisture index (SSI) to characterize agricultural drought. The extreme gradient boosting model was then adopted to fully examine the influence of ocean, terrestrial and meteorological multi-factors on agricultural drought characteristics and their dynamics in China. Meanwhile, the Shapley additive explanation values were introduced to quantify the contribution of multiple drivers to drought characteristics. Our analysis reveals that the drought frequency, severity and duration in China ranged from 5–70, 2.15–35.02 and 1.76–31.20, respectively. Drought duration is increasing and drought intensity is intensifying in southeast, north and northwest China. In addition, potential evapotranspiration is the most significant driver of drought characteristics at the basin scale. Regarding the dynamic evolution of drought characteristics, the percentages of raster points for drought duration and severity with evapotranspiration as the dominant factor are 30.7 % and 32.7 %, and the percentages with precipitation are 35.3 % and 35.0 %, respectively. Precipitation in northern regions has a positive effect on decreasing drought characteristics, while in southern regions, evapotranspiration dominates the dynamics in drought characteristics due to increasing vegetation transpiration. Moreover, the drought severity is exacerbated by the Atlantic Multidecadal Oscillation in the Yangtze and Pearl River basins, while the contribution of the North Atlantic Oscillation to the drought duration evolution is increasing in the Yangtze River basin. Generally, this study sheds new insights into agricultural drought evolution and driving mechanism, which are beneficial for agricultural drought early warning and mitigation.

Accuracy of agricultural drought indices and analysis of agricultural drought characteristics in China between 2000 and 2019

Accuracy of agricultural drought indices and analysis of agricultural drought characteristics in China between 2000 and 2019

Agricultural drought characteristics in a typical plain region considering irrigation, crop growth, and water demand impacts

Agricultural drought characteristics in a typical plain region considering irrigation, crop growth, and water demand impacts

The climate change effects on agricultural drought in the Be River Basin

Drought is one of the extreme weather events that has been occurring with increasing frequency and complexity as well as having negative effects on water resources and agricultural production. The focus of the present study is to investigate the climate change effects on agricultural drought in the Be River Basin. The SWAT model was applied to simulate the soil moisture content and Standardized Soil Water Index (SSWI) was utilized to estimate the characteristics of agricultural drought. In addition, the future climate conditions for the three periods (2022–2040, 2042–2060, and 2062–2080) were generated by the delta change method based on the outputs of five global climate models. The results show that agricultural drought is anticipated to increase in the frequency, intensity, and duration (up to 168.82%, depending on time and emission scenarios). Moreover, drought events and water shortage in the dry season tend to be more likely to happen soon in the Be River Basin. These results are consistent with the changing trends of related soil moisture. Besides, the results contribute reliable scientific evidence to help managers and policy makers having appropriate plans in the future.

Copula based analysis of meteorological, hydrological and agricultural drought characteristics across Indian river basins

AbstractDuration and severity are the two most important parameters used for drought characterization. In this study, we used a bivariate copula‐based approach to understand the joint dependence of drought duration and severity of three different drought types. Three types of bivariate copulas (Gumbel, Frank and Plackett) are estimated for modelling and the best fit copula is selected over 1,162 grid points (at a resolution of 0.5° × 0.5°) of India. Further, the joint dependence of drought duration and severity are analysed to infer important properties in terms of exceedance probability and return periods. Finally, conditional probability and conditional return periods of drought characteristics are also derived, which could be useful for proper planning and management of the water resource system. From the investigation, it is observed that drought events in the Western and Central India are longer and more severe whereas the ones in the south Indian river basins are more frequent but less severe. Moreover, similar results were also obtained for the conditional probability and conditional return periods. This study provides information regarding the severe and longer drought event hotspots all over the study area and thus helpful for the policymakers in developing effective drought prevention and mitigation strategies comprehensively at a national scale.

Sensitivity of the Evapotranspiration Deficit Index to Its Parameters and Different Temporal Scales

Sound estimates of drought characteristics are very important for planning intervention measures in drought-prone areas. Due to data scarcity, many studies are increasingly using less data-intensive approaches, such as the evapotranspiration deficit index (ETDI), in estimations of agricultural droughts. However, little is known about the sensitivity of this specific ETDI formula to its parameters, and to data at different temporal scales. In this study, a general ETDI formula, homologous to the specific ETDI formula, was introduced and used to test the sensitivity of the ETDI to its parameters and to data at different temporal scales. The tests used time series of remotely sensed evapotranspiration data in the Ruvu River basin in Tanzania. The parameter sensitivity tests revealed that ETDI is sensitive to its parameters, and different parameter combinations resulted in different drought characteristics. The temporal scale sensitivity test showed that drought characteristics, such as the number of drought events and the total drought durations, decreased as the temporal scale increased. Thus, an inappropriate temporal scale may lead to the misrepresentation of drought characteristics. To reduce uncertainty and increase the accuracy of ETDI-based agricultural drought characteristics, ETDI requires parameter calibration and the use of data with small temporal scales, respectively. These findings are useful for improving estimations of ETDI-based agricultural droughts.

Drought assessment on barley and millet production in Karnali Province, Nepal

Spatio-temporal characteristics of agricultural drought and its impact on the yield of winter barley and summer millet crops in Karnali Province of Nepal were evaluated. For this purpose, precipitation data from 1988 to 2018 of 18 stations and agricultural data from 2003 to 2018 were used for the analysis. Standardized Precipitation Indices (SPI-3 and SPI-4) were used for measuring drought intensity. Based on drought intensity, SPI<-1 was considered for drought occurrence. Similarly, Standardized Residual Yield Series (SYRS) was also calculated for crop yield change, eliminating other factors responsible for crop yield. The result shows that Salyan, Surkhet, and Jumla districts have more occurrence of summer and winter drought in comparison to other districts. Similarly, 1999, 2002, 2004-05, 2008-09, 2012, and 2015 were considered as drought years in most stations. Moreover, the province experienced more winter drought events than summer and winter droughts are increasing after 2015. Concerning the impact of drought occurrence on cereal yield loss, barley seems to be more affected. SPI-3 and SYRS (barley) are significantly correlated in Jumla, Humla, Mugu, Jajarkot, Dailekh, Surkhet, and Salyan districts at 95% confidence. With the exception, drought occurrence has a larger impact on barley yield loss than drought severity in the study area. However, millet yield loss doesn't seem to be affected due to drought occurrence and intensity in all districts except Rukum, as millet is a drought-tolerant crop. Therefore, for sustainable agriculture practices, drought-tolerant crops like millet could be suitable in Karnali Province, where drought frequency is very high.

Identification of Seasonal Sub-Regions of the Drought in the North China Plain

Regional climate variability assessment is of great significance in decision-making such as agriculture and water resources system management. The identification of sub-regions with similar drought variability can provide a basis for agricultural disaster reduction planning and water resource distribution. In this research, a modified daily Standardized Precipitation Evapotranspiration Index (SPEI) was used to monitor the spatial and temporal variation characteristics of agricultural drought in the North China Plain from 1960 to 2017, which was studied by using the rotated empirical orthogonal functions (REOF). Through the seasonal REOF process, 7–9 seasonal drought sub-regions are confirmed by applying time series and the correlation relationship of SPEI original data. The strong correlation of these sub-regions indicates that the climate and weather conditions causing the drought are consistent and the drought conditions are independent for the regions that show no correlation. In general, the results of the seasonal trend analysis show that there has been no significant trend value in most areas since 1960. However, it is worth noting that some regions have the positive and negative temporal trends in different seasons. These results illustrate the importance of seasonal analysis, particularly for agro-ecosystems that depend on timely rainfall during different growing seasons. If this trend continues, seasonal drought will become more complex, then a more elaborate water management strategy will be needed to reduce its impact.

Performance and relationship of four different agricultural drought indices for drought monitoring in China's mainland using remote sensing data

Increasing frequency and intensity of extreme drought events have harmed the environment, ecosystem, and agricultural productivity. However, the characteristics of agricultural drought in China have not been well understood. The remote sensing (RS) based gridded monthly precipitation, soil moisture, land surface temperature (LST), and normalized difference vegetation index (NDVI) datasets over 1982–2018 were utilized to derive standardized precipitation index (SPI), standardized soil moisture index (SSI), multivariate standardized drought index (MSDI), and vegetation health index (VHI). The variation patterns and trends of SPI, SSI, and MSDI at the 1-, 3-, and 6-month timescales against monthly VHI anomaly were compared to identify the best agricultural drought index in China. The drought variations in the four sub-regions (northwest, north, Qinghai-Tibet area, and south area) were also investigated. The results showed that: (1) Temporal patterns of VHI anomaly were similar to relative soil moisture and slightly different from precipitation. The spatial patterns of MSDI matched with VHI the best than SPI and SSI. (2) All three indices showed positive correlations with VHI at the three timescales. The highest correlation coefficients (r) between MSDI and VHI ranged from 0.25 to 0.67, 0.22 to 0.78, 0.23 to 0.69, and 0.19 to 0.74 in northwest China, north China, Qinghai-Tibet Plateau, and south China, respectively. (3) The connections between monthly VHI and the three drought indices were weaker at the 1-month timescale (0.16 < r < 0.25) than the 3-month (0.39 < r < 0.78) and 6-month (0.26 < r < 0.68) timescales. (4) The VHI significantly increased in most of China except north China. Overall, MSDI showed better performance for monitoring agricultural drought in China's mainland.

Agricultural drought mitigating indices derived from the changes in drought characteristics

Droughts can cause tremendous losses to agricultural and economic development; humans have explored many anti-drought measures to mitigate the influence of drought and accordingly altering the characteristics of actual agricultural drought. In this study, a method is proposed to detect the spatiotemporal changes in drought characteristics, which show the effects of anti-drought measures on drought mitigation. Two agricultural drought mitigation evaluation indices are proposed, the agricultural drought frequency change (ADFC) and agricultural drought area change (ADAC), which are calculated by combining the Palmer drought severity index (PDSI) and vegetation health index (VHI), two widely used drought monitoring indices. The PDSI and VHI represent the natural and actual agricultural drought severity under natural and actual conditions respectively, and their differences in drought frequency and affected area reflect the level of anti-drought measures in mitigating agricultural drought. The feasibility of using ADFC and ADAC to quantify the effects of anti-drought measures for agriculture is explored using data from six typical agricultural provinces in the North China Plain and Northeast China. The results show that ADFC and ADAC could reflect both the spatiotemporal changes in agricultural drought characteristics and the influence of anti-drought measures on agricultural drought. The trend of the drought mitigation index is consistent with agricultural activity statistics. These two indices could be further used to evaluate the effects of different anti-drought methods and aid in defeating agricultural drought across many countries.

Application of LISS III and MODIS-derived vegetation indices for assessment of micro-level agricultural drought

Drought in recent years has crippled the livelihoods of millions of people living in India and has also been the cause of many deaths. Puruliya district, India with more than one-third of its population belonging to the backward community has no proper agricultural drought management system. In this study, spatial and temporal characteristics of agricultural drought were examined using indices derived from Indian Remote Sensing (IRS) Linear Imaging Self Scanning (LISS III) sensor and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) sensor satellite images. MODIS Normalized Difference Vegetation Index (NDVI) results almost match with the results obtained from LISS III NDVI analysis. Vegetation Condition Index (VCI) was prepared from MODIS for a period of 16 years (2000–2016) and subsequently blockwise drought severity maps were generated from MODIS-derived VCI for Kharif and Rabi season. MODIS VCI analysis shows that nearly 34.1% and 76.5% of the study area for Kharif and Rabi season respectively faces drought conditions during the recent year 2015–16. It also shows severe and extreme drought situations for the years 2010–11 and 2005–06 respectively. Blockwise drought severity analysis reveals that Jaipur, Purulia I, Purulia II and Para blocks were chronically drought prone areas. The results indicate significant agreement between NDVI anomaly obtained from MODIS sensor and foodgrain anomaly obtained from crop yield statistics. The outcome of the research may be used for the district’s drought preparedness programme so that proper crop planning and management can be carried out to help in agricultural production.

Bivariate analysis of drought duration and severity for irrigation planning

In this study, the effectiveness of a bivariate analysis of agricultural drought characteristics for irrigation planning was evaluated. The case study was conducted in locations in central Italy and was based on olive crops which are widely grown in that area under rainfed or deficit irrigation regimes. For each locality, the available time series of daily precipitation and maximum and minimum temperatures were used to simulate the daily soil water dynamics (SWt) for olive crops. The simulation was performed assuming 10 irrigation strategies, different for both the volume and date of the interventions. By applying the Theory of Runs to SWt, with a threshold corresponding to the readily available water, the agricultural drought events in the time series were identified and characterised by their duration D (days) and severity S (i.e. the cumulative evapotranspiration deficit in mm) for each locality and strategy.A 2-parameter Gamma distribution was fitted to both D and S, whilst their dependence structure was modelled by a Gumbel copula. The evaluation of the best irrigation strategy in each locality was obtained by comparing the contour lines of the pairs (d,s) having a 10-year return period for the condition D ≥ d AND S ≥ s. Results showed a great influence of both climate and irrigation strategy on the joint distribution of D and S. Some strategies, despite a very similar behaviour in the severity, differed a lot in terms of the associated values of duration, thus denoting different dynamics of water stress (more or less intense). Among the single-irrigation strategies, the optimal one was usually represented by an application at the end of June. Among the double-irrigation strategies, the optimal one involved two interventions, one at the end of June and one at the end of July. These results were also discussed taking into account the corresponding probability of consecutive severe drought events.

Evaluating the Spatiotemporal Characteristics of Agricultural Drought in Bangladesh Using Effective Drought Index

This study aims to assess the spatiotemporal characteristics of agricultural droughts in Bangladesh during 1981–2015 using the Effective Drought Index (EDI). Monthly precipitation data for 36 years (1980–2015) obtained from 27 metrological stations, were used in this study. The EDI performance was evaluated for four sub-regions over the country through comparisons with historical drought records identified by regional analysis. Analysis at a regional level showed that EDI could reasonably detect the drought years/events during the study period. The study also presented that the overall drought severity had increased during the past 35 years. The characteristics (severity and duration) of drought were also analyzed in terms of the spatiotemporal evolution of the frequency of drought events. It was found that the western and central regions of the country are comparatively more vulnerable to drought. Moreover, the southwestern region is more prone to extreme drought, whereas the central region is more prone to severe droughts. Besides, the central region was more prone to extra-long-term droughts, while the coastal areas in the southwestern as well as in the central and north-western regions were more prone to long-term droughts. The frequency of droughts in all categories significantly increased during the last quinquennial period (2011 to 2015). The seasonal analysis showed that the north-western areas were prone to extreme droughts during the Kharif (wet) and Rabi (dry) seasons. The central and northern regions were affected by recurring severe droughts in all cropping seasons. Further, the most significant increasing trend of the drought-affected area was observed within the central region, especially during the pre-monsoon (March–May) season. The results of this study can aid policymakers in the development of drought mitigation strategies in the future.

AGRICULTURAL DROUGHT AND SOIL MOISTURE ANALYSIS USING SATELLITE IMAGE BASED INDICES

Abstract. The deficiency in rainfall leads to meteorological droughts. Its manifestations are visible both in the vegetation cover and soil moisture. The present study assessed the characteristics of agricultural drought following meteorological droughts. The study also assessed the severity of meteorological droughts and their manifestation on the agriculture and soil moisture in a semi-arid area. The study has been carried out for the Malaprabha sub-basin which partly covers three districts of North Interior Karnataka, India. India Meteorological Department’s (IMD) criteria have been used to identify the drought years, and its severity has been assessed through the Standard Precipitation Index (SPI). The IMD’s monthly rainfall data were used to identify the drought years and periods for the region. Among the drought years, the mild, moderate, and severe drought along with deficit and excess rainfall years were considered to assess and characterize the soil moisture conditions and the agricultural drought. The satellite image based indices for these selected years were constructed to determine the soil moisture conditions and the agricultural drought severity. The Temperature-Vegetation Dryness Index (TVDI) was used to determine the soil moisture conditions. The indices employed to determine the agriculture drought are NDVI, Thermal Condition Index (TCI), Vegetation Condition Index (VCI), and Vegetation Health Index (VHI). These satellite-based indices were calculated using the Landsat images of the selected drought and non-drought years. The results showed that the seasonal and annual drought are frequent in the study area. There are spatial and temporal variations in the drought years and their severity. The satellite-based indices clearly indicate the spatial variation in the agriculture droughts and its intensity. It has been found that the impact of drought on agriculture has significantly reduced due to the development of well-irrigation in the sub-basin. VHI is more appropriate in determining the agricultural drought and its characteristics.