Drought-prone Regions Research Articles

Assessing the vulnerability to drought of the farm households in Ninh Thuan province, Vietnam

Drought is the cause of severe water shortages and losses to the livelihood of farmers in the central provinces of Vietnam. This study aims to assess the drought vulnerability of farm households in Ninh Thuan province using two vulnerability assessment indices, the livelihood vulnerability index, and the IPCC vulnerability index. Data for the analysis were gathered from a survey of 231 randomly selected households across three drought-prone regions of the province. The data collected included information on the household’s sociodemographic profile, livelihood strategies, social networks, financial capital, water security, health status, and drought conditions. The research reveals that the poor farmers from ethnic minority groups with limited access to water sources, financial capital, and social networks are the most vulnerable farm households, requiring more support from the local community, and government to adopt an integrated drought risk management approach.

Water and Handwashing in a Drought-Prone Region of Southern Niger: How Environment, Household Infrastructure, and Exposure to Social and Behavior Change Messages Interact.

This study aims to inform multisectoral development programs by exploring the extent to which social and behavior change (SBC) messages, environment, and household infrastructure are associated with knowledge and practice of handwashing behaviors. A cross-sectional survey of 2,708 households in the Maradi and Zinder districts of Niger was collected in April 2021. Household data were integrated with two local environmental measures: 1) water level at the nearest waterhole point, and 2) anomalous rainfall for the previous rainy season derived from climate hazards infrared precipitation with station rainfall (CHIRPS) data. Logistic regression models were constructed to explore how environment, household infrastructure, and exposure to SBC messages were associated with two hygiene-related outcomes: 1) observed water and soap available at household handwashing stations, a behavior, and 2) knowledge of critical moments for handwashing, a behavioral determinant. We find that in households near a water point with higher water depth, households were statistically significantly more likely (odds ratio [OR] = 1.25); (confidence interval [CI] = 1.12-1.49) to have water and soap observed at the handwashing station. Women in households near a water point with increased water depth (more water) were more likely to know three or more critical handwashing moments (OR = 1.07; CI = 1.03-1.11). Exposure to messages about the importance of handwashing was significantly associated with knowledge of critical handwashing moments and having water and soap observed at a handwashing station. Multisectoral programming should consider layering efforts so that development projects that increase access to water sources are complemented with SBC approaches focused on hygiene.

Copula-Based Assessment and Regionalization of Drought Risk in China.

Droughts are widespread in China and have brought considerable losses to the economy and society. Droughts are intricate, stochastic processes with multi-attributes (e.g., duration, severity, intensity, and return period). However, most drought assessments tend to focus on univariate drought characteristics, which are inadequate to describe the intrinsic characteristics of droughts due to the existence of correlations between drought attributes. In this study, we employed the standardized precipitation index to identify drought events using China's monthly gridded precipitation dataset from 1961 to 2020. Univariate and copula-based bivariate methods were then used to examine drought duration and severity on 3-, 6-, and 12-month time scales. Finally, we used the hierarchical cluster method to identify drought-prone regions in mainland China at various return periods. Results revealed that time scale played an essential role in the spatial heterogeneity of drought behaviors, such as average characteristics, joint probability, and risk regionalization. The main findings were as follows: (1) 3- and 6-month time scales yielded comparable regional drought features, but not 12-month time scales; (2) higher drought severity was associated with longer drought duration; (3) drought risk was higher in the northern Xinjiang, western Qinghai, southern Tibet, southwest China, and the middle and lower reaches of the Yangtze River, and lower in the southeastern coastal areas of China, the Changbai Mountains, and the Greater Khingan Mountains; (4) mainland China was divided into six subregions according to joint probabilities of drought duration and severity. Our study is expected to contribute to better drought risk assessment in mainland China.

Spatio-temporal characteristics of meteorological and agricultural drought indices and their dynamic relationships during the pre-monsoon season in drought-prone region of Bangladesh

The global water crisis has become a rising concern against the background of climate change, which is responsible for increasing drought frequency, a devastating natural hazard that severely affects the agricultural sector of a nation. Due to the geographical position, spatial variation in seasonal precipitation patterns, and groundwater table, the northwestern part of Bangladesh is one of the most drought-prone regions with extensive irrigation practices. In recent decades, the frequency of meteorological drought has significantly increased, but its dynamic relationship with agricultural drought is still unclear. In this research, we aim to study the meteorological and agricultural drought conditions and their dynamic relationships during the pre-monsoon season (March-May) in northwest Bangladesh. For this, we analyzed the 3-month standardized precipitation index (SPI), Palmer drought severity index (PDSI), and 3-month standardized precipitation evapotranspiration index (SPEI) to study the meteorological drought, and the normalized difference vegetation index (NDVI), land surface temperature (LST), vegetation condition index (VCI), temperature condition index (TCI), and vegetation health index (VHI) using MODIS to study the agricultural drought. Results revealed that SPI-3 and SPEI-3 during the pre-monsoon increased, while PDSI decreased. Agricultural drought indices showed a better VHI status in 2021. The Pearson correlation among these indices represented no correlation between meteorological and agricultural drought. This research identified that the relationship between VHI and SPI-3 might not be positive in all cases, especially in extensively irrigated regions. Therefore, it is important to consider hydrological drought indices to improve the understanding and accuracy of drought assessment.

A regional model for the variability of Agricultural development: evidence from a drought-prone region of Rarh Bengal, Eastern India

A regional model for the variability of Agricultural development: evidence from a drought-prone region of Rarh Bengal, Eastern India

A new methodological approach to the establishment of sustainable agricultural ecology in drought vulnerable areas of eastern India

Rice (Oryza sativa L.), the staple food for much of the world, requires gallons of water during its growth phase from tillage to harvest through conventional farming and emits significant greenhouse gases. Hence, modified cultivation techniques are felt to be needed to increase rice productivity, minimize water and fertilizer consumption effectively, and mitigate greenhouse gas emissions to a certain extent. The present experiment was based on the Purulia district, identified as a drought-prone zone through the RS-GIS technique. Two methods, the conventional method (CVN) and the system of rice intensification (SRI), were studied with four varieties of rice, viz. var. MTU 1010, var. IET 4786, var. IET 17430 and var. IET 9947 on water resource vulnerability, associated greenhouse gas emissions (CH4, N2O) and global warming potential (GWP). The results indicated that SRI cultivation is the smart method compared to CVN in the drought-prone Purulia District due to its lower water requirements and greenhouse gas (GHG) emissions. There was higher CH4 flux among the CVN plots than in the SRI plots (p < 0.05). The N2O flux in the rice field plots of CVN (Rabi-1.54 ± 0.04 kg ha−1, Kharif 1.51 ± 0.10 kg ha−1) was lower than the SRI (Rabi-2.08 ± 0.07, Kharif- 1.83 ± 0.02 kg ha−1) during Rabi season. The CH4 flux for CVN (Rabi-94.32 ± 2.71 kg ha−1; Kharif 110.33 ± 3.90 kg ha−1) was higher than the SRI (Rabi-68.16 ± 2.37, Kharif- 61.72 ± 2.41 kg ha−1) during Rabi season. The CO2-eq emission (pGWP) and yield-scaled CO2-eq emission (YpGWP) from the SRI were significantly lower when compared with CVN (p < 0.05). The result shows that the SRI technique is an effective agricultural method that farmers should be encouraged to follow in the drought-prone region to ensure less water consumption and a sustainable future for generations.

Modelling of groundwater potential zone in hard rock-dominated drought-prone region of eastern India using integrated geospatial approach

Modelling of groundwater potential zone in hard rock-dominated drought-prone region of eastern India using integrated geospatial approach

White Lupin Drought Tolerance: Genetic Variation, Trait Genetic Architecture, and Genome-Enabled Prediction.

White lupin is a high-protein crop requiring drought tolerance improvement. This study focused on a genetically-broad population of 138 lines to investigate the phenotypic variation and genotype × environment interaction (GEI) for grain yield and other traits across drought-prone and moisture-favourable managed environments, the trait genetic architecture and relevant genomic regions by a GWAS using 9828 mapped SNP markers, and the predictive ability of genomic selection (GS) models. Water treatments across two late cropping months implied max. available soil water content of 60-80% for favourable conditions and from wilting point to 15% for severe drought. Line yield responses across environments featured a genetic correlation of 0.84. Relatively better line yield under drought was associated with an increased harvest index. Two significant QTLs emerged for yield in each condition that differed across conditions. Line yield under stress displayed an inverse linear relationship with the onset of flowering, confirmed genomically by a common major QTL. An adjusted grain yield computed as deviation from phenology-predicted yield acted as an indicator of intrinsic drought tolerance. On the whole, the yield in both conditions and the adjusted yield were polygenic, heritable, and exploitable by GS with a high predictive ability (0.62-0.78). Our results can support selection for climatically different drought-prone regions.

Spatial monitoring of meteorological drought characteristics based on the NASA POWER precipitation product over various regions of Iran.

Drought directly impacts the human economy and society, so a proper understanding of its spatiotemporal characteristics in different time scales and return periods can be effective in its evaluation and risk warning. In this research, the spatiotemporal variation of drought characteristics in 70 investigated stations in Iran during 1981-2020 was examined, evaluated, and compared. The Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) have been used on time scales of 1, 3, 6, 9, 12, and 24months to calculate the meteorological drought. Drought characteristics have been calculated through the run theory method, and the correlation between these characteristics has been checked. Statistical distribution functions have been used to calculate drought characteristics for the 10-, 20-, 50-, and 100-year return periods. Results show that the duration, severity, and peak of the drought in rainy areas increase as the return period increases. The drought features obtained from the SPI and SPEI show that the average value of severity obtained based on the SPI (43.5) is higher than that of the SPEI (40.9) while the average values of the peak are 3.9 and 2.6 for SPI and SPEI, respectively. Extreme drought was identified in 1990 in all regions of Iran. The highest severity in the current study is from 1999 to 2003. At the end of this period, Iran faced wet years. These results are evident on all time scales. The results obtained in this study can identify drought-prone regions and the beneficial use of water resources in the region.

Rainfall Trend in Drought Prone Region of Ahmednagar District of Maharashtra in India: A Geographical Study

Rainfall Trend in Drought Prone Region of Ahmednagar District of Maharashtra in India: A Geographical Study

Assessment and prediction of index based agricultural drought vulnerability using machine learning algorithms

The consequences of droughts are far-reaching, impacting the natural environment, water quality, public health, and accelerating economic losses. Applications of remote sensing techniques using satellite imageries can play an influential role in identifying drought severity (DS) and impacts for a broader range of areas. The Barind Tract (BT) is a region of Bangladesh located northwest of the country and considered one of the hottest, semi-arid, and drought-prone regions. This study aims to assess and predict the drought vulnerability over BT using Landsat satellite images from 1996 to 2031. Several indices, including Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), Soil Moisture Content (SMC), Temperature Condition Index (TCI), Vegetation Condition Index (VCI), and Vegetation Health Index (VHI). VHI has been used to identify and predict DS based on VCI and TCI characteristics for 2026 and 2031 using Cellular Automata (CA)-Artificial Neural Network (ANN) algorithms. Results suggest an increasing patterns of DS accelerated by the reduction of healthy vegetation (19 %) and surface water bodies (26 %) and increased higher temperature (>5 °C) from 1996 to 2021. In addition, the VHI result signifies a massive increase in extreme drought conditions from 1996 (2 %) to 2021 (7 %). The DS prediction witnessed a possible expansion in extreme and severe drought conditions in 2026 (15 % and 13 %) and 2031 (18 % and 24 %). Understanding the possible impacts of drought will allow planners and decision-makers to initiate mitigating measures for enhancing the communities preparedness to cope with drought vulnerability.

Comparison of Novel Hybrid and Benchmark Machine Learning Algorithms to Predict Groundwater Potentiality: Case of a Drought-Prone Region of Medjerda Basin, Northern Tunisia

Water scarcity is a severe problem in Tunisia, particularly in the northern region crossed by the Medjerda River, where groundwater is a conjoint water resource that is increasingly exploited. The aim of this study is to delineate the groundwater potential zones (GWPZs) in the Lower Valley of the Medjerda basin by using single benchmark machine learning models based on artificial neural network (ANN), random forest (RF), and support vector regression (SVR), and by developing a novel hybrid method, NB-RF-SVR, to reach the highest accuracy of groundwater potential prediction. Each model produced a spatial groundwater potential map (GPM) with the input of 26 groundwater-related factors (GRF) selected by the frequency ratio model and 70% of the transmissivity training data. The models’ effectiveness was assessed using the AUC-ROC curve, sensitivity, specificity, MAE, and RMSE metric indicators. The validation findings revealed that all the models performed successfully for the GWPZ mapping, where the AUC values for the ANN, RF, SVR, and NB-RF-SVR models were estimated as 71%, 79%, 87%, and 92%, respectively. The relative importance of the GWPZs revealed that land use followed by geology and elevation were the most important factors. Finally, these outcomes can provide valuable information for decision makers to effectively manage groundwater in water-stressed regions.

Farmers Perception On Land Use Pattern

In drought prone Rayalaseema region agriculture is the principal occupation of the people and is influencing the land use pattern. The study of Farmer’s perception on land use and its planning has a considerable importance. To find out the farmers perception on the agricultural land use pattern in Chittoor district of Rayalaseema region, farmer's opinion on land use pattern and its related aspects have been collected. For this, a field survey was conducted through questionnaire with different parameters like total agriculture land, land under different crops, total cultivated area, area under food crops, area under commercial crops, land under fruit gardens, land under fallow land. etc. For this 6 mandals have been selected for sample field survey. The sample mandals are selected on the basis of factors like mandals which are located in drought prone area, located in high rain fall receiving area. located in irrigation source area, located in fruit growing dominance and areas with high intensity of commercial cropping pattern. Apart from these factors, distribution of mandals based on nature of topography like plain, valley and plateau land forms were also taken in to account. To know the farmers perception on cropping pattern. 6 mandals have been selected for sample field survey. The study has indicated agricultural land use, cropped area, crops cultivated, shifting from traditional cropping system to commercial and cash crops in different farming communities. Keywords: Perception, Holdings Commercial Cropping, Mechanization, Drought Prone Area

Unlocking the potential of agroforestry as a nature-based solution for localizing sustainable development goals: A case study from a drought-prone region in rural India

Agroforestry enhances farmers’ ability to adapt to climate change and delivers multiple ecological, social, and economic benefits. However, scientific evidence linking agroforestry as a Nature-based Solution (NbS) to the achievement of the Sustainable Development Goals (SDGs), and in particular the localization of these goals, is limited. Using a case study from a drought-prone region of southern India, this paper uses a qualitative research methodology to demonstrate how agroforestry offers NbS that localize 10 of the 17 SDG targets. In doing so, it identifies farmers' intrinsic motivations, barriers to the adoption of agroforestry practices as means to adapt to climate hardships, and the role of the carbon market in rewarding environmental stewardship.This case study focuses on the farmers’ narratives, and puts their perspectives at the forefront, emphasizing on basic needs of the poorest of the rural poor, illustrating the “real world” setting of developing countries. The information presented in this paper will be of interest to practitioners, researchers, and policymakers working on community-based NbS in developing countries, as well as those interested in agroforestry as a strategy for advancing the SDGs and its scope under global initiatives as UN Decade for Ecosystem Restoration.

Delaying drought-driven leaf cell damage may be the key trait of invasive trees ensuring their success in the Mediterranean basin.

Invasive alien species (IAS) threaten the biodiversity richness of the Mediterranean basin, a drought-prone region. However, our knowledge on the adaptive strategies of IAS for facing Mediterranean drought summers is still incomplete. The aim of the present study is to compare the water relations and the critical relative water content (RWC) values leading to loss of cell rehydration capacity of two Mediterranean basin IAS (i.e., Ailanthus altissima (Mill.) Swingle and Robinia pseudoacacia L.) versus two co-occurring native species (i.e., Fraxinus ornus L.and Quercus pubescensWilld.). Study IAS showed higher values of water potential at turgor loss point and osmotic potential at full turgor, lower values of modulus of elasticity and leaf mass area but higher photosynthesis rate, even during the summer, with respect to the Mediterranean native species. These findings supported the hypothesis that IAS are characterized by a resource acquisitive strategy coupled with a safety-efficiency trade-off, compared with Mediterranean native species. However, similar leaf RWC thresholds leading to loss of cell rehydration capacity were recorded in the two groups of species. Moreover, IAS showed higher saturated water content and capacitance values compared with the co-occurring species. Overall, our results suggest that the success of Mediterranean IAS is driven by their ability to delay dehydration damage of mesophyll cells during Mediterranean summer drought, thereby supporting their distinctive high carbon assimilation rate.

Seasonal weight loss effect in the hepatic fatty acid composition in Australian Merino, Damara and Dorper sheep

Seasonal weight loss (SWL) is one of the major limitations in small ruminant production in drought-prone regions. The study of breeds with higher tolerance to the effects of SWL is particularly important to define breed selection strategies. In this work we evaluated the effect of SWL in the hepatic fatty acids profile in three ovine breeds with different levels of tolerance: the Merino (susceptible to SWL), the Dorper (intermediate tolerant to SWL), and Damara (tolerant to SWL). Animals from each breed were divided into two experimental groups: a growth group and a restricted fed group. The more representative fatty acids in the three breeds were stearic acid, oleic acid and palmitic acid. Univariate analysis revealed interaction between breed and feed-restriction in eighteen of the total fatty acids identified. Multivariate analysis showed separation between growth and restricted animals in each of the three breeds. Variations observed in the three breeds are in general related with fatty acid mobilization from the adipose tissue. Dorper and Damara breed showed exclusive variations, related with muscular tissue maintenance and hepatic adaptations respectively. Generally, Damara and Dorper breeds performed better under nutritional restriction, in accordance with results previously obtained following other approaches.

Thinning improves growth and resilience after severe droughts in Quercus subpyrenaica coppice forests in the Spanish Pre-Pyrenees

During the past years, growth and productivity of different oak species have been constrained by water shortage in seasonally dry regions such as the Mediterranean Basin. Thinning could improve oak radial growth in these drought-prone regions through the reduction of tree competition for soil water in summer. However, we still lack adequate, long-term assessments on how lasting are thinning treatments effects and to what extent they contribute to oak growth recovery after drought. Here we aim: (i) to study the radial growth sensitivity to drought of Quercus subpyrenaica in the Spanish Pre-Pyrenees, and (ii) to verify if thinning represents a suitable option to enhance growth resistance to drought and post-drought growth recovery. We analysed basal area increment (BAI) trends in the period 1960–2020 of formerly coppiced oak stands thinned in 1984 and compared them with unthinned plots and also with coexisting Scots pine (Pinus sylvestris) growing in thinned plots. We used the Standardized Precipitation Evapotranspiration Index (SPEI) to estimate the severity of droughts and we also assessed climate-growth relationships. Oaks in thinned plots showed higher BAI (369 mm2) than those in unthinned plots (221 mm2). Growth rates remained higher in thinned than in unthinned plots also under severe drought stress. A severe summer drought in 1986 caused abrupt BAI reductions in both oaks (- 40.5%) and pines (- 40.1%). The positive effect of thinning on growth lasted for over 20 years and slightly declined as canopies closed. In the thinned plots, trees with smaller diameter showed the greatest growth release. Oaks in unthinned plots and Scots pine were more sensitive to short-term droughts in terms of growth reduction than oaks in thinned plots, while long term droughts have similar effects on oaks from both thinned and unthinned plots. Oaks were resilient to drought, showing recovery periods lasting from 1 to 2 years in both thinned and unthinned plots. However, intense and prolonged droughts could strongly reverse the expected growth enhancement of thinned plots, and a greater frequency of droughts would limit coppice growth and productivity thus lengthening the rotation periods.

Modeling maize evapotranspiration using three types of canopy resistance models coupled with single-source and dual-source hypotheses—A comparative study in a semi-humid and drought-prone region

The reliability and accuracy of canopy resistance (rc) are crucial for the evapotranspiration (ET) estimation in sparsely vegetated ecosystems, but most studies are still limited to the single-source Penman-Monteith (PM) model. In this study, we coupled three types of rc models (three-way model: Jarvis, JA; two-way model: Kelliher-Leuning, KL; Farias, FA; one-way model: Katerji-Perrier, KP; Massman, MA) to the PM and Shuttleworth-Wallace (SW) models to assess their applicability and stability under single-source and dual-source hypotheses in a maize field in northwest China. The models were calibrated and validated against the observed data from a three-year (2011–2013) field experiment based on genetic algorithms (GA). The results indicated that the rc coupled models optimized by GA greatly alleviated the overestimation of the original PM and SW models with mean absolute error (MAE) decreased by 13.85%-40.77% and determination coefficient (R2) increased by 0.40%-7.37%. The SW-type models achieved overall high performance to simulate ET at a diurnal timescale over the entire crop season compared to the equivalent PM-type models. The three-way JA model including crop, soil and meteorological factors tended to perform more accurately than the other rc models, and the coupled SW-JA model was optimal for predicting maize ET over the entire growing season. The two-way KL model produced lower errors than the FA model, whereas the FA model had the potential to be improved due to the highest R2 with the field-based ET for both PM- and SW- hypotheses. In addition, the sensitivity analysis to environmental variables showed the effect of leaf area index (LAI) and vapor pressure deficit (VPD) was less on ET compared with net radiation (Rn) and soil moisture (θ). This study contributes to the knowledge in the area of actual ET modeling over sparse vegetation, which provides a reference for agricultural and environmental applications.

Risk perception, impact, and management by farmer households in Rajasthan (India)

ABSTRACT The increasing frequency of droughts and floods is undermining sustainable agricultural development at the global level. The concerns are more profound in countries like India, where agriculture the primary source of livelihood is mainly rainfed. Based on the primary survey of 180 rural farm households in the highly natural disaster-prone state of India, this study elucidates how farm households perceive and respond to manage climatic shocks and associated risks. Results show that the majority of the households perceived the occurrence of adverse weather events, but a sizeable proportion of them in the drought-prone regions appear to be neglecting or downplaying the risk outcomes. The households in flood prone region were proactive in controlling and monitoring risks and resorted to multiple risk mitigation measures like diversification of crop and enterprises, changing agronomic practices, etc. The economic position of the households played the most significant role in influencing the adoption of a particular strategy. The weak empirical relationship between the communication variables and management of weather-induced risks was reflective of the lack of quality information available to the farmers through various sources.

SM2RAIN-ASCAT satellite-based spatiotemporal drought monitoring using multiscale WT-VMD-ENERGY method

Drought is one of the severe natural disasters that has devastating effects on various parts of the environment. Therefore, spatiotemporal monitoring of drought using reliable methods is very important. In this study, using a new Discrete Wavelet Transform (DWT)-Variational Mode Decomposition (VMD)-Energy multi-scale approach, the efficiency of the new Soil Moisture to Rain-Advanced SCATterometer (SM2RAIN-ASCAT) precipitation product was investigated in monitoring the spatiotemporal patterns of short- to long-term droughts for the Northwest part of Iran. In this regard, after validating the accuracy of the SM2RAIN-ASCAT datasets, a multi-scale method was developed based on the serial decomposition of the drought signals. The mean energy amounts of the obtained subseries were imposed into the K-means (K signifies the number of clusters) to identify the drought-prone regions. Results showed that the highest Correlation Coefficient (R) between SM2RAIN-ASCAT and in-situ observations are found at monthly time scales, in which 73% of the stations had R higher than 0.7. Investigating the reliability of this product for drought monitoring showed that for short- and mid-term timescales in 75% of the stations, the values of R were higher than 0.7. The applied methodology successfully showed the drought-prone regions and a direct relationship was found between energy and drought intensity. The capability of the applied methodology was verified via the CPC Merged Analysis of Precipitation (CMAP) and Moderate Resolution Imaging Spectroradiometer (MODIS) products and the obtained results proved the desirable performance of the SM2RAIN-ASCAT datasets. It was found that SOI, Nino3, and Nino3.4 had major effects on the drought index in the selected area, while AO had the least effect. The relationship between the LST and SPIs was negative, while there was a positive relationship between NDVI and SPIs.