Drought Index Research Articles

Robust drought forecasting in Eastern Canada: Leveraging EMD-TVF and ensemble deep RVFL for SPEI index forecasting

Drought stands as a highly perilous natural catastrophe that impacts numerous facets of human existence. Drought data is nonstationary and noisy, posing challenges for accurate forecasting. This study proposes a novel hybrid framework integrating TVF-EMD preprocessing, LASSO feature selection and Ensemble Deep RVFL modeling for improved multistep ahead drought prediction. Using decomposed SPEI12 values, six machine-learning techniques (Support Vector Regression (SVR), Simple RVFL, Ensemble Deep RVFL, and Recurrent Neural Network (RNN), XGBoost, Random Forest (RF)) were applied to forecast the SPEI12 drought index. The present study involved forecasting drought in two Canadian stations located in the eastern region (Charlottetown in Prince Edward Island and Fredericton in New Brunswick), where agriculture is rainfed and mostly affected by drought. The statistical period of 1980–2022 was considered for analysis. Following the decomposition of drought data with TVF-EMD, lagged data was generated using the TVF-EMD results. Training time was decreased by utilizing the Lasso regression feature selection algorithm to select effective inputs. Various statistical measures, including the root mean square error (RMSE) and correlation coefficient (R), were employed to assess the precision of the models. The research findings indicated that the TVF-ED-RVFL model achieved the highest level of precision in forecasting multistep ahead (1,3,6 and 12) SPEI12 drought index for both Charlottetown and Fredericton stations. During testing, the TVF-ED-RVFL model predicted 1-month SPEI12 for Charlottetown (R = 0.9995, RMSE = 0.0352) and Fredericton (R = 0.9974, RMSE = 0.0560). For multistep ahead forecasting, the R-values range from 0.9924 for 3-months ahead to 0.9242 for 12-months ahead in Charlottetown and range from 0.9846 for 3-months ahead to 0.8293 for 12-months ahead in Fredericton. By increasing the forecasting horizon, the accuracy of models decreased. The present study’s outcomes can contribute to enhancing water management practices during periods of drought.

Neural Hierarchical Interpolation for Standardized Precipitation Index Forecasting

In the context of climate change, studying changes in rainfall patterns is a crucial area of research, remarkably so in arid and semi-arid regions due to the susceptibility of human activities to extreme events such as droughts. Employing predictive models to calculate drought indices can be a useful method for the effective characterization of drought conditions. This study applies two type of machine learning methods—long short-term memory (LSTM) and Neural Hierarchical Interpolation for Time Series Forecasting (N-HiTS)—to develop and deploy artificial neural network models with the aim of predicting the regional standardized precipitation index (SPI) in four regions of Zacatecas, Mexico. The predictor variables were a set of climatological time series data spanning from 1964 to 2020. The results suggest that the N-HiTS model outperforms the LSTM model in the prediction and forecasting of SPI time series for all regions in terms of performance metrics: the Mean Squared Error, Mean Absolute Error, Coefficient of Determination and ξ correlation coefficient range from 0.0455 to 0.5472, from 0.1696 to 0.6661, from 0.9162 to 0.9684 and from 0.9222 to 0.9368, respectively, for the regions under study. Consequently, the outcomes revealed the successful performance of the N-HiTS models in accurately predicting the SPI across the four examined regions.

Trend analysis of precipitation and drought characteristics over Churu district of northeast Rajasthan, India

ABSTRACT Drought is one of the most destructive natural disasters, becoming more extreme and less predictable due to climate change. Drought directly affects a region's water resources, leading to inadequate water availability and harming crops, animals, and humans. The objective of the present study is to assess the long-term trends of precipitation and drought characteristics for the Churu district of northeast Rajasthan, India. For this purpose, mean monthly precipitation data are collected through the India-WRIS from 1901 to 2022. The trend analysis uses statistical methods (M-K test & amp; SS estimator), and the results are compared with a graphical method (innovative trend analysis). Drought events are found using precipitation-based drought indices, i.e., standardized precipitation index, Z-score index, and percentage of normal precipitation index. Interestingly, the results of the statistical method are well matched with the results of the graphical method. The study concludes that during the annual precipitation a rising trend is observed, which can help to mitigate the severity of drought. Also, it is found that moderately dry droughts occur most frequently compared with other drought events. This study will help policy-makers and local administrators to take necessary action to mitigate the severity of drought.

Meteorological Drought Analysis and Regional Frequency Analysis in the Kızılırmak Basin: Creating a Framework for Sustainable Water Resources Management

Drought research is needed to understand the complex nature of drought phenomena and to develop effective management and mitigation strategies accordingly. This study presents a comprehensive regional frequency analysis (RFA) of 12-month meteorological droughts in the Kızılırmak Basin of Turkey using the L-moments approach. For this purpose, monthly precipitation data from 1960 to 2020 obtained from 22 meteorological stations in the basin are used. In the drought analysis, the Standard Precipitation Index (SPI), Z-Score Index (ZSI), China-Z Index (CZI) and Modified China-Z Index (MCZI), which are widely used precipitation-based indices in the literature, are employed. Here, the main objectives of this study are (i) to determine homogeneous regions based on drought, (ii) to identify the best-fit regional frequency distributions, (iii) to estimate the maximum drought intensities for return periods ranging from 5 to 1000 years, and (iv) to obtain drought maps for the selected return periods. The homogeneity test results show that the basin consists of a single homogeneous region according to the drought indices considered here. The best-fit regional frequency distributions for the selected drought indices are identified using L-moment ratio diagrams and ZDIST goodness-of-fit tests. According to the results, the best-fit regional distributions are the Pearson-Type 3 (PE3) for the SPI and ZSI, generalized extreme value (GEV) for the CZI, and generalized logistic distribution (GLO) for the MCZI. The drought maps obtained here can be utilized as a useful tool for estimating the probability of drought at any location across the basin, even without enough data for hydrological research.

Drought trend and its association with land surface temperature (LST) over homogeneous drought regions of India (2001–2019)

Droughts pose significant challenges to food security, affecting millions of people and hectares of land in India. Despite their widespread impact, assessing drought patterns at a high spatial resolution remains a challenge, particularly in regions with poor data availability. Standardised Precipitation Evapotranspiration Index (SPEI) is one of the most widely accepted drought indices. The publicly available SPEI reanalysis dataset has a poor spatial resolution for regional drought studies. Land Surface Temperature (LST) has the potential to act as an effective proxy for SPEI and can be used for high-resolution drought studies. The study considered six homogeneous drought regions (NWH, CPR, NEI, NCP, NWI, and SPR) over India, and the recent trend in LST and SPEI over these regions was estimated using modified Mann-Kendell (MMK) trend and Sen’s slope estimator at monthly, seasonal, and annual time scales. The correlation between SPEI and LST was also checked at a 95% confidence level. Results indicate statistically significant decreasing annual LST trends in NWH (slope = −0.10), CPR (slope = −0.09), and NWI (slope = −0.11) regions, while increasing SPEI trends were observed in NWI (slope = 0.06) and NCP (slope = 0.01) regions during 2001–2019. Additionally, NCP (p = 0.025), CPR (p = 0.002), and NWI (p = 0.020) exhibited significantly decreasing LST trends during winter, with a notable post-monsoon decrease observed in NWI. Significant correlations between annual LST and SPEI were observed only in CPR (r =−0.527) and NWI (r = −0.601), with seasonal associations found predominantly in winter across CPR, NEI, NCP, and NWI regions. Notably, a significant correlation was observed in all months except July over NWI, with consistent correlations during most of the winter months in NWH, CPR, NCP, and NWI regions. The observed trends and correlations can provide valuable insights for policymakers and stakeholders in formulating effective drought mitigation strategies.

Characteristics of groundwater drought and its propagation dynamics with meteorological drought in the Sanjiang Plain, China: Irrigated versus nonirrigated areas

Study regionSanjiang Plain, China Study focusThe characterization of groundwater drought is inadequate, particularly in terms of the differences in the drought propagation dynamics between cropland and other regions. In this study, we identified groundwater drought events and characteristics in the Sanjiang Plain, using the standardized groundwater drought index (SGDI); we also quantified the mechanism of dynamic factors influencing drought propagation characteristics based on the geodetector method and wavelet coherence analysis and discussed the correlation between the NDVI and SGDI.New Hydrological Insights for the Region: The drought propagation time was shorter during summer and longer during spring and autumn in irrigated areas than in nonirrigated areas. The groundwater system exhibited greater resistance to drought during summer, and spatially nonirrigated area was more vulnerable to meteorological drought. The factor that best explained the variation in propagation time and threshold differed across seasons. A significant correlation time-frequency domain existed between all the factors and SGDI in nonirrigated area, with the SGDI exhibiting the fastest response to soil moisture. The relationship between the SGDI and factors was characterized by a discontinuous response of less than 8 months and a continuous response of more than 30 months. The correlations between the SGDI and NDVI in irrigated and nonirrigated area exhibit different changes due to groundwater exploitation and differences in plant physiology between artificial crops and natural vegetation.

Integrated GNSS-derived precipitable water vapor and remote sensing data for agricultural drought monitoring and impact analysis

Agricultural drought is a natural disaster that impacts soil water deficiency, plant water stress, and yield loss. It has several effective drought indices to monitor the impact on agriculture, particularly the evapotranspiration deficit index (ETDI). However, this index has exposed the inconsistency of spatial potential evapotranspiration (PET) because of the restricted spatial distribution of meteorological stations and the influence of spatial heterogeneity. The present study aims to develop the fine spatial PET using the Global Navigation Satellite System-derived Precipitable Water Vapor (GNSS-PWV) and remote sensing data for enhancing the ETDI and determining the impacts of drought on sugarcane yield. The grid PET (GPET) model is developed by the correlation between the land surface temperature from Moderate Resolution Imaging Spectroradiometer (MODIS LST) and the PET from the Revised Potential Evapotranspiration (RPET) model as the ground observations to estimate daily PET at 30-m spatial resolution using spatial extrapolation technique. In addition, the actual evapotranspiration (AET) was evaluated using the Surface Energy Algorithms for Land (SEBAL) algorithm. Both spatial PET and AET were utilized to compute the ETDI as an agricultural drought index. Then, the ETDI was correlated with sugarcane yield to investigate the impact of drought on yield. The results indicated that the GPET model had a strong correlation with the RPET model (R2 = 0.73 and RMSE = 0.84 mm) and relatively good accuracy (RSR = 0.57 and NSE = 0.68). This proposed model could be applied to compute the ETDI with fine spatial resolution. Moreover, the normalized yield of sugarcane exhibited a negative correlation with ETDI in the period from March to April 2020 with a strong relationship (r = −0.83). Therefore, the ETDI is an appropriate index for drought monitoring and determining the effects of drought on yield. These findings are useful for supporting the decision-makers to enhance the national policies for water management in agriculture.

Characteristics of Changes in Typical Mountain Wetlands in the Middle and High Latitudes of China over the Past 30 Years

Analysis of the driving mechanisms of wetland change can help identify spatial differences in the mechanisms affecting various elements, enabling a more scientific approach to the conservation and utilization of wetlands. This study investigated the impacts of natural and anthropogenic factors on the spatiotemporal evolution of the Altay and Greater and Lesser Khingan Mountains areas using Landsat satellite image data from 1980 to 2018 and fieldwork data from 2019 to 2020. A transfer matrix, correlation analysis, and dynamic characteristics were applied to calculate and analyze the transformation types and areas of wetland resources across all consecutive periods. Finally, the dominant factors influencing the spatiotemporal evolution of the wetland were explored and revealed using the drought index (Standardized Precipitation Index, SPEI) and statistical almanacs. The results showed: (1) From 1980 to 2018, the wetlands area in the Altay Mountains exhibited a decreasing trend, whereas the wetlands area in the Greater and Lesser Khingan Mountains showed an increasing trend. The primary type of wetland transfer in the Altay Mountains was grassland, whereas in the Greater and Lesser Khingan Mountains regions, the primary types of wetland transfer were grassland and forestland. The wetlands area transferred out of the Altay Mountain region was larger than the area of wetland types transferred into during 2010–2018, whereas the wetland areas of the Greater and Lesser Khingan Mountain areas showed the opposite trend. (2) From 1980 to 2018, the wetland ecosystem types in the Altay Mountains exhibited the highest dynamic and conversion degrees of the channels. Similarly, the mountain areas of the Greater Khingan Mountains showed the highest dynamic and conversion degrees of marshes and channels among the wetland types. In addition, the mountainous areas of the Lesser Khingan Mountains showed the highest dynamic and conversion degrees for reservoirs and rivers. (3) Natural driving factor analysis revealed that the SPEI values in the Altay Mountains and the Greater and Lesser Khingan Mountains areas exhibited an increasing trend, indicating that the climate has been warm and humid over the past 30 years and that the expansion of cropland and human-made wetland areas has been significantly influenced by human activities. Therefore, the wetland areas of the Greater and Lesser Khingan Mountains in the northeast are strongly influenced by human activities, whereas the wetland in the Altay Mountains in the northwest is strongly influenced by the climate.

Regional Climate Change and Drought Dynamics in Tunceli, Turkey: Insights from Drought Indices

Global warming and climate change are causing temperatures to rise, which is having a negative impact on water resources. Climate change is a worldwide problem that affects and will continue to affect the frequency and intensity of natural disasters in many regions of the world. Tunceli region in Turkey, which until 10 years ago was known as an environmentally friendly city with abundant water resources and frequent rainfall, is experiencing a decrease in precipitation during the snowy winter season. This situation has made the investigation of climate change impacts an important issue in the region. Therefore, effective climate change adaptation strategies need to be developed. To determine these strategies, in this study, we assessed long-term drought conditions using multiple drought indices such as Standardized Precipitation Index (SPI), Reconnaissance Drought Index (RDI), Normal Precipitation Index (PNI), and Aridity Index (AI). The SPI and RDI analyses were performed in annual reference periods on a time scale of 3, 6, 9, and 12 months using temperature, precipitation, and evaporation data. Consequently, the SPI and RDI results were compared, and both indices show similar behavior in dry, wet, and normal seasons. Nevertheless, RDI shows less variation between different time scales, which is an advantage over SPI and is probably due to the inclusion of potential evapotranspiration in RDI. The variations in PNI between humid and dry sub-humid categorizations throughout the years, combined with the AI results, indicate that the Tunceli region predominantly experiences a climate ranging from dry sub-humid to semi-arid. This study could help decision-makers take effective measures to become more resilient to climate change in temperate climate regions and take important steps toward sustainable water resources management.

Impacts of climate change and variability on drought characteristics and challenges on sorghum productivity in Babile District, Eastern Ethiopia

ABSTRACT Examining the characteristics of drought indices in the context of climate variability and change, particularly in semi-arid water-stressed regions, requires adaptation. Observed climate data of the Babile station from 1980 to 2009 were used as a baseline for climate projection. Future climate projection was established under two Representative Concentration Pathway (RCP4.5 and RCP8.5) climate scenarios for the 21st century. Two drought indices, namely standard precipitation index and standard evapotranspiration index (SPI and SPEI) were employed based on temperature and rainfall to characterize droughts. Our study revealed that drought severity and intensity are more likely to increase under RCP4.5 climate forcing in the middle of the 21st century. The average drought severity (S) was 1.1, 1.53, 1.55, and 1.8 in SPEI 3-month time scale; 1.51, 2.1, 2.38, and 2.29 in SPEI 4-month time scale; and 2.15, 2.77, 3.44, and 2.91 in SPEI 6-month time scale, whereas, the drought severity (S) was 1.33, 1.37, and 1.79 in SPEI 3-month time scale; 1.79, 2.05, and 2.19 in SPEI 4-month time scale; and 2.47, 3.19, and 2.69 in SPEI 6-month time scale in observed, near, mid and end of the 21st century under RCP4.5 and 8.5 scenarios, respectively. Increase in drought frequency and severity occurrences under RCP4.5 scenario would have a negative impact on sorghum crop productivity. We recommended further study on practical soil water conservation to drought management in the study area.

Refined Identification and Spatiotemporal Characteristics of Drought and Flood Abrupt Alternation Events in the Poyang Lake Basin from 1965 to 2023

This paper utilizes actual observation data from 75 precipitation stations in the Poyang Lake Basin (PLB), combined with the daily scale drought and flood abrupt alternation (DFAA) index, to analyze the occurrences of DFAA in the basin from 1965 to 2023. This includes the frequency and spatiotemporal variation of events, as well as the spatial variation in the intensity of DFAA events. The results indicate that there is no apparent temporal pattern in the frequency and intensity of events in the PLB, with spatial concentration mainly in the Fuhe, Xinjiang, and Raohe river basins. The analysis of a typical drought-to-flood event (DTF) in 1994 and a typical flood-to-drought event (FTD) in 2022 shows that the 1994 event progressed from the northeast to the south, and the prolonged drought following the 2022 event exacerbated the losses caused by the DFAA.

New secondary decomposition ensemble support vector regression for monthly drought index forecasting

Drought prediction on a monthly timescale is conducive to the timely identification of drought characteristics and provides a reference for drought mitigation. Current research is generally based on drought indices on timescales of three months or more for drought prediction, whereas the presence of highly nonlinear and drastic changes in the characteristics of drought indices leads to suboptimal drought prediction for drought index sequences on monthly timescales. In this study, a secondary decomposition model based on extreme-point symmetric mode decomposition (ESMD), variational mode decomposition (VMD), and support vector regression (SVR) was investigated to predict a monthly-scale drought index. To evaluate the predictive ability of the model, it was applied to 100 meteorological stations in the Pearl River Basin of China and compared and analyzed using four comparative models. The results showed that the model proposed in this study has better performance index values, and the prediction accuracies of all stations are improved compared with the four comparative models, with the most significant improvement in the Nash–Sutcliffe efficiency (NSE) index value, followed by the root mean square error (RMSE) and mean absolute error (MAE) index values, and the smallest improvement in the Wilmot consistency index (WIA) index value. The monthly-scale drought quadratic decomposition (ESMD-VMD) combined prediction model established in this study provides a novel method for drought prediction.

Nonlinear effects of agricultural drought on vegetation productivity in the Yellow River Basin, China

Agricultural drought (AD) is the main environmental factor affecting vegetation productivity (VP) in the Yellow River Basin (YRB). In recent years, the nonlinear effects of AD on VP in the YRB have attracted much attention. However, it is still unclear whether fluctuating AD will have complex nonlinear effects on VP in the YRB, and there are scant previous studies at large scale on whether there is a threshold for nonlinear effects of AD on VP in the YRB. Therefore, this study used a newly developed agricultural drought index to explore nonlinear effects on VP revealing the nonlinear effects of AD on VP in the YRB. First, we developed a kernel temperature vegetation drought index (kTVDI) based on kernel normalized difference vegetation index (kNDVI) and land surface temperature data to study the spatiotemporal variation of AD in the YRB. Second, we used GPP data from solar-induced chlorophyll fluorescence inversion as an indicator to explore the spatiotemporal variation of VP in the YRB. Finally, we used several statistical indicators and a distributed lag nonlinear model (DLNM) to analyze the nonlinear effect of AD on VP in the YRB. The results showed that AD decreased significantly during 2000–2020, mainly in the southeast of the Loess Plateau, while GPP increased significantly in 80.93 % of the YRB. Meanwhile, moderate and severe AD stress limited VP growth, with the negative effects gradually decreasing, while mild AD had an increasingly positive promoting effect on VP. AD stress resulted in a VP decrease of 69.78 %, and severe AD stress resulted in a VP decrease of 65.52 %, mainly distributed in the northern Loess and Ordos Plateau. AD had significant nonlinear effects on VP. The effects of moderate and severe AD on the sustained nonlinear lag of vegetation were more obvious, and those of moderate and severe AD on the nonlinear lag of VP were the largest when the lag was approximately 1 month and 7 months. The effect of AD on the nonlinear hysteresis of VP in YRB was significantly different under different vegetation types, and forests were more able to withstand longer and more severe droughts than grasslands and croplands. The results of the study provide a theoretical basis for evaluating AD and analyzing the nonlinear impact of AD on VP. This will provide scientific basis for studying the mechanism of drought effect on vegetation in other regions.

DFEAT: A multifaceted yearly Drought FEature Assessment Tool from daily soil water content

The characterization of yearly drought events represents a key information for conducting impact assessments and forecasting future threats, and usually relies on a single index of duration or severity. Mostly based on daily soil or atmospheric water balances, the derivation of key drought facets is not yet standardized or embedded in a single tool, thus limiting intercomparisons between studies. We developed DFEAT as a fully-automated tool designed to characterize yearly drought features, based on any simulated/observed/remotely sensed soil water content time series. We provide here an application assessment performed with the Keetch-Byram Drought Index (KBDI) as a standard daily soil water model over a 60-year period and covering the Mediterranean aridity gradient in Lebanon (Middle-East) experiencing humid to semi-arid climate conditions. We computed and tested 19 drought features related to duration, severity, onset, offset, drying and wetting rates, driest peak day, and rainfall pulses across three soil water desiccation thresholds. For our study area, we revealed the uncorrelated specificities of 6 features, allowing to regionally discriminate between mountainous Mediterranean climate experiencing shorter drought duration (45 days), later onset (Day of the Year = 198), less rainfall pulses intensities (3.35 mm), and slower drying (1.68 mm/day) and wetting (−2.72 mm/day) rates, but similar offset date (Day of the Year = 356) compared to the coast. DFEAT also captured regions with prolonged Multi-Year Droughts not fully refilling field capacity under arid bioclimate reaching up to 22 years. We demonstrate here the applicability of DFEAT across water-limited bioclimates and the non-correlation between drought features with contrasted agro-ecological impacts.

Identifying High‐Yielding and Drought‐Tolerant Wheat Cultivars Based on Ideotypic Traits and Yield Responses to Stress

ABSTRACTDrought is one of the most adverse factors affecting plant growth and productivity. Identifying elite genotypes and their ideotypic traits conferring high yield potential and drought tolerance is critical in selecting and breeding drought‐tolerant wheat cultivars. In this study, we conducted field experiments at the Xinxiang Agricultural Comprehensive Experimental station in the North China Plain from 2018 to 2020 and assessed 209 wheat cultivars released since the 1940s under irrigated and nonirrigated conditions. Then, we selected drought‐tolerant cultivars by classifying them into four groups based on yield response to drought stress and several drought indices. Finally, the key ideotypic traits associated with high yield potential and drought tolerance were identified. Results indicated that the grain yield of the 209 cultivars decreased on average by 10.4% and 9.4% under nonirrigated treatment in 2018–2019 and 2019–2020, respectively, relative to full irrigation. The high‐yielding cultivars under both irrigation treatments are characterised by a compact plant type, larger thousand‐grain weight, larger chlorophyll content, higher leaf photosynthesis, shorter plant height and stay‐green traits. The stomatal and nonstomatal limitations are strongly associated with genotype yield performance, elucidating a potential mechanism underlying drought tolerance. Drought tolerance and yield stability of wheat cultivars have been improved through breeding over the past 70 years. Our findings enhance understanding of drought tolerance and identify genotypes and traits beneficial for breeding high‐yielding and drought‐tolerant cultivars.

Assessing global drought conditions under climate change: A comparison of stationary and non-stationary approaches and identification of hotspot regions

Droughts pose significant threats to both human society and the surrounding environment. Because of the anticipated intensification of drought conditions owing to climate change, assessing their impacts using dynamic and non-stationary methodologies is considerably required. This study employed temperature and precipitation data derived from a comprehensive set of 15 global climate models (GCMs) under two shared socioeconomic pathways (SSP2-4.5 and SSP5-8.5) within the framework of the Coupled Model Intercomparison Project Phase 6 (CMIP6) dataset to address crucial inquiries related to drought. Specifically, we explored the transformations that drought conditions may undergo with increasing temperature and precipitation levels. The study identified geographical areas likely to become hotspots of global warming and compared drought indices that incorporate non-stationarity with those that do not in assessing the evolution of global drought conditions. The findings revealed that indices using stationary approaches with fixed distribution parameters underestimate or overestimate future drought conditions because they overlook trends such as decreasing precipitation and increasing temperatures in drought variables. This results in an average misestimation of approximately 10 %–20 % in duration and severity in the far future in the sub-regions. Specifically, underestimations were detected in the high latitudes of the Northern Hemisphere (Alaska and Northern Asia), northwestern China, India, and northern and eastern Africa. Conversely, the stationary drought index exhibited maximum overestimations in the Amazon, Western Australia, the Mediterranean region, and South Africa regions for future drought projections. Combining the results of the multi-drought characterization, including frequency, duration, severity, and spatial extent, we identified Northern North America, the Amazon region, the Mediterranean region, south-central Africa, and southeastern Asia as hotspot regions for drought responses to global warming. In future drought warnings, particular attention should be directed toward regions where drought is underestimated.

Machine Learning Methods for Predicting Argania spinosa Crop Yield and Leaf Area Index: A Combined Drought Index Approach from Multisource Remote Sensing Data

Machine Learning Methods for Predicting Argania spinosa Crop Yield and Leaf Area Index: A Combined Drought Index Approach from Multisource Remote Sensing Data

Economic Impact of the Drought in Spain: Measurement for the Adoption of Measures

This paper aims to evaluate the economic implications of meteorological drought in Spain. The study seeks to provide decision-makers with crucial insights into the macroeconomic effects of drought, enabling them to devise mitigation strategies and minimize its impact on economic activity. The Partial Hypothetical Extraction Method (HEM) is employed within the Input-Output analysis framework extended to a Social Accounting Matrix (SAM) of Spain to achieve this goal. The database utilized for this analysis is the FNAM for Spain in 2017, in millions of euros, obtained from the Full International and Global Accounts for Research in Input-Output Analysis (FIGARO) project, a collaboration between Eurostat and the European Commission. The study aims to estimate the economic impact of drought on the productive sectors of the Spanish economy in terms of sectoral production and GDP. This involves simulating the partial reduction in value-added resulting from variations in average water productivity per gross value added, based on the drought indicator SPI-24. Three scenarios are generated: (1) drought, (2) moderate drought and (3) severe drought. In quantitative terms, the simulated drought scenarios could lead to a drop in GDP of 0.88% for the drought scenario, 1.61% for the moderate drought scenario, and 1.76% for the severe drought scenario. Additionally, it is important to recognize that water scarcity hampers the social and economic development of cities and regions beyond the results in quantitative terms.

Impacts of multiple temporal and spatial scale drought on grassland vegetation dynamics in the Tibetan Plateau region of China, 1982–2015

The Tibetan Plateau is a sensitive area to global climate change, and the special natural environmental conditions have nurtured extremely fragile vegetation and its ecosystems, making it one of the ideal regions to study the response of vegetation to climate change. In this study, we used the drought index (SPEI) and the vegetation index (NDVI) as drought and green indicators, respectively. The spatial and temporal variation patterns of vegetation greenness on the Tibetan Plateau from 1982 to 2015 were analysed, and the response of vegetation greenness to dry and wet changes was investigated. The results showed that the grassland vegetation improved overall (slope = 0.00015, P < 0.05) and degraded locally during 34 years. The NDVI of grassland vegetation showed an increasing trend from 1982 to 2008 (slope = 0.00005, P = 0.86), and a significant decreasing trend from 2009 to 2015 (slope = −0.002, P < 0.05). The degraded areas of grassland NDVI were mainly distributed in wet areas. Meanwhile, the Tibetan Plateau as a whole showed a non-significant trend of increasing drought, with a mitigating trend of drought in dry areas and an increasing trend of drought in wet areas. The probability of vegetation growth loss was higher with increasing drought. The vulnerability of grassland vegetation to drought events was significantly higher in the humid region than in the arid region. Under the current drought intensification, the risk of loss of grassland vegetation to drought in the humid zone will increase. This suggests that we need to pay more attention to the stability of vegetation in the humid region and drought trends.

Assessing the dynamics and trigger factors of drought propagation in the China-Pakistan economic corridor: A three-dimensional perspective

Effectively managing drought in the China-Pakistan Economic Corridor (CPEC) region requires a precise understanding of the three-dimensional characteristics of meteorological drought (MD) and agricultural drought (AD), as well as the factors that trigger their propagation. This study employed non-stationary drought indices (NSPEI and SSMI) to develop a cutting-edge 3-dimensional drought identification model. This model was used to detect MD and AD patterns from 1981 to 2022 in the CPEC region and was integrated with binomial logistic regression to identify the critical factors that drive drought propagation. This study's key findings include: 1) Between 1981 and 2022, droughts in Xinjiang, China, exhibited a discernible southward migration trend, while in Pakistan, droughts showed a northward migration pattern. Drought frequency and extent have increased over time, with affected regions becoming more widespread in CPEC. Notably, drought events with higher preceding drought contagion indices (DCI) were more likely to evolve into extreme, long-term droughts. 2) Drought area emerged as a significant positive triggering factor for drought propagation in the CPEC region. Conversely, snowmelt in Xinjiang and the leaf area index for low vegetation in Pakistan acted as triggering elements affecting negatively. 3) Various factors played a pivotal role during drought propagation process, including geographical coordinates of drought centroids, DCI, and temperature variations. Additionally, snowmelt and snow evaporation significantly impacted drought propagation in Xinjiang, while vegetation cover in Pakistan played a crucial role during the drought propagation process. By utilizing four regression models and conducting comprehensive attribution analysis, this study sheds light on the characteristics of drought propagation and the factors influencing it. These findings are valuable for enhancing early warning systems and implementing effective drought mitigation strategies in the CPEC region.