Meteorological Drought Research Articles

Health vulnerability mapping for drought: a case study in a predominantly rural, agricultural state

Abstract Drought is considered among the most devastating climate hazards impacting civilizations, including human health, throughout history. It causes cumulative damages based on the five generally categorized meteorological, hydrological, agricultural, socioeconomic, and ecological droughts. Reducing damages at the local, regional, and global levels requires a better understanding of human (health) vulnerability to drought. While there are different studies to distinguish and measure vulnerabilities for the five aforementioned conditions, there is minimal effort to identify vulnerabilities to health impacts from drought. Our study aims to develop an analysis of vulnerability for Nebraska based on the established health effects associated with drought. We considered vulnerability as the interaction between exposure to drought and different sensitivity measures for a timespan, including the highest drought levels in the 21st century. To calculate the total sensitivities, we extracted ten initial variables and applied two well-known methods of dimensionality reduction and (Weighted) Additive Overlays of percentile-ranked values. The result showed the inadequacy of the former method for our study. We also grouped the sensitivity variables into socioeconomic, environmental, and water-related intervention categories and developed related intensity maps showing different spatial patterns. We calculated the drought exposure levels by adding the intensity, duration, and frequency of drought over the study period (2012–2016) and developed total vulnerability maps to determine the ten most vulnerable counties, of which nine are rural. The resulting three intervention category maps can help related experts find priority areas within Nebraska, and the final vulnerability maps can help distinguish the areas of concern for general state-wide planning. While the results and some sensitivity variables are unique to Nebraska, the provided framework and the inclusion of two different methods can guide other regions in similar studies.

Spatiotemporal analysis of tropical vegetation ecosystems and their responses to multifaceted droughts in Mainland Southeast Asia using satellite-based time series

ABSTRACT Drought ranks among the costliest of all climate-related phenomena and manifests in various forms, posing significant challenges in understanding its influence on agriculture and natural ecosystems. Mainland Southeast Asia (MSEA), a significant region of tropical agriculture and vegetation ecosystems, has become increasingly susceptible to drought hazards. In this study, we characterized and assessed vegetation dynamics and their drought impacts using correlation analysis and explainable machine learning methods under different vegetation types and elevation zones during the dry growing seasons from 2000 to 2022. Specifically, we characterized the vegetation dynamics and their trend in space and time. Next, we assessed vegetation-drought responses in consideration of meteorological, hydrological, and agricultural droughts under different land cover types and elevation characteristics. Lastly, we used an explainable machine learning method to quantify the drivers and impacts of multifaceted droughts on natural and undisturbed vegetation ecosystems. Our findings revealed that nearly 70% of the MSEA region experienced a greening trend despite large areas of vegetative damage during the drought years. Vietnam witnessed increasing vegetation condition in most observed years while the declining trend was mainly found in Cambodia and southern Laos. Vegetation-drought responses showed that tropical vegetation had a high sensitivity to drought conditions, and stronger responses were observed in rainfed crop, mixed forest, and deciduous forest at lower altitude areas. In natural and undisturbed ecosystems, short-term meteorological and agricultural drought disturbances accounted for nearly 93% of variations in tropical vegetation. Among the different examined drought indices, the 3-month Standardized Precipitation Evapotranspiration (SPEI-3) and Temperature Condition Index (TCI) were identified as the factors having the largest influence, together explaining about 55% of the observed natural undisturbed vegetation variations. These findings deepen our understanding of tropical vegetation-drought responses and the underlying drivers of natural and undisturbed ecosystems. Such insights could provide valuable information to assist national and local governments in the MSEA region in developing effective drought management and adaptation programs to safeguard tropical agricultural production and natural ecosystems amidst growing climate challenges.

Multi-year climate memory in shallow lake water levels

Landscape hydrologic memory of meteorological cycles can have an important impact on catchment hydrological responses by propagating clustering of wet or dry conditions into extreme events. The non-linear and hysteretic hydrologic response functions driven by memory are often only studied at shorter temporal scales (event, season) despite larger interannual hydrologic responses evident in some systems. Within the Canadian Boreal, lakes and lake water levels provide an important indicator that can be used to assess the role of landscape memory on catchment hydrological function. Landscape memory has also been hypothesised to control the hydrological dynamics of shallow lake ecosystems that are also important biogeochemically and ecologically. Here we combine measurements of lake water levels in 26 lakes of varying type at varying temporal frequencies within the glaciated sub-humid Boreal Plain, to examine the impact and variability of interdecadal, decadal, multi-year, and intra-annual memory on lake water levels. We show multi-annual hysteresis of precipitation-lake water levels with varied characteristics in space and time. These spatial variations in landscape memory are driven by differences in storage capacities controlled by heterogeneity in glacial landforms, wetland-forest landcover and lake properties. Thus, the propensity for drought years or wet years to persist or accumulate into extreme landscape drying or wetting varies significantly between different lake-landscape characteristics. We show how landscape memory is crucial to project lake water levels by defining spatial variability of the impact of periods of meteorological drought and deluge vital for understanding system sensitivity, duration of recovery and in turn infer resilience on Boreal Plain hydrology.

Dynamic relationships and propagation characteristics between meteorological drought and vegetation drought based on a three-dimensional identification algorithm

Drought is a regional phenomenon and progressive in specific dimensions of time and space, with significant continuity and dynamic characteristics on a spatiotemporal scale. The meteorological drought acts as a driving factor for vegetation drought, and studying the response characteristics of vegetation drought to meteorological drought is crucial for comprehending the mechanisms of drought evolution. In this study, based on a three-dimensional spatiotemporal clustering algorithm, meteorological and vegetation drought events in the Yellow River Basin (YRB) from 1982 to 2020 were identified, and the dynamic characteristics of typical drought events were depicted, elucidating the inherent correlations and propagation features between meteorological and vegetation drought events. Additionally, according to the spatiotemporal matching criteria of drought events, the meteorological-vegetation drought event pairs were successfully matched, which can reveal the lag time between meteorological drought and vegetation drought. From a three-dimensional perspective, we revealed the dynamic propagation characteristics of meteorological and vegetation drought events, which could provide an effective way for monitoring and mitigating regional vegetation drought, restoring ecosystem functionality, and promoting sustainable development of the ecological environment.

The Impact of Meteorological Drought at Different Time Scales from 1986 to 2020 on Vegetation Changes in the Shendong Mining Area

The Shendong Mining Area, being the largest coal base in the world, has significant challenges in the intensive development and utilization of coal resources, as well as the impact of a dry climate, which can have serious negative effects on the growth of flora in the region. Investigating the spatial and temporal patterns of how meteorological drought affects vegetation in the Shendong Mining Area at various time scales can offer a scientific foundation for promoting sustainable development and ecological restoration in the region. This study utilizes the Standardized Precipitation Evapotranspiration Index (SPEI) and Normalized Difference Vegetation Index (NDVI) data from 1986 to 2020 in the Shendong Mining Area. It employs Slope trend analysis, a Mann–Kendall test, a Geographic Detector, and other methods to examine the spatiotemporal distribution characteristics of meteorological drought at various time scales. Additionally, the study investigates the influence of these drought patterns on vegetation growth in the Shendong Mining Area. Across the mining area, there was a general decrease in the monthly average SPEI on an annual basis. However, on a seasonal, semi-annual, and annual basis, there was a gradual increase in the annual average SPEI, with a higher rate of increase in the southern region compared to the northern region. When considering the spatial variation trend in different seasons, both positive and negative trends were observed in winter and summer. The negative trend was mainly observed in the western part of the mining area, while the positive trend was observed in the eastern part. In spring, the mining area generally experienced drought, while in autumn, it generally experienced more precipitation. The mining area exhibits a prevailing distribution of vegetation, with a greater extent in the southeast and a lesser extent in the northwest. The vegetation coverage near the mine is insufficient, resulting in a low NDVI value, which makes the area prone to drought. Over the past few years, the mining area has experienced a significant increase in vegetation coverage, indicating successful ecological restoration efforts. Various forms of land use exhibit distinct responses to drought, with forests displaying the most positive correlation and barren land displaying the strongest negative correlation. Various types of landforms exhibit varying responses to drought. Loess ridge and hill landforms demonstrate the most pronounced positive association with monthly-scale SPEI values, whereas alluvial and floodplain landforms display the poorest positive correlation with yearly scale SPEI values. The general findings of this research can be summarized as follows: (1) The mining area exhibits a general pattern of increased humidity, with the pace of humidity increase having intensified in recent times. Seasonal variations exhibit consistent cyclic patterns. (2) There are distinct regional disparities in NDVI values, with a notable peak in the southeast and a decline in the northwest. The majority of the mining area exhibits a positive trend in vegetation recovery. (3) Regional meteorological drought is a significant element that influences changes in vegetation coverage in the Shendong Mining Area. Nevertheless, it displays complexity and is more obviously impacted by other factors at a small scale. (4) It should be noted that forests and barren land exert a more significant influence on SPEI values, despite their relatively lesser spatial coverage. The predominant land use type in most locations is grasslands; however, they have a relatively minor influence on SPEI. (5) A shorter time period, higher elevation, and steeper slope gradient all contribute to a larger correlation with drought.

Exploring the sensitivity of river flow patterns to meteorological drought using multifractal and cross-correlation applications

Exploring the sensitivity of river flow patterns to meteorological drought using multifractal and cross-correlation applications

Separating the impact of climate change and human activities on the connection between meteorological and hydrological drought

AbstractUnderstanding the transition from meteorological to hydrological drought is essential for accurately predicting hydrological droughts. However, the factors driving this transition are intricate, and a comprehensive understanding of how direct human activities influence this shift in drought is lacking. In this study, we initially explored the spatiotemporal correlation between the occurrence of meteorological and hydrological droughts. Subsequently, we formulated multiple hydrological replenishment scenarios using the soil and water assessment tool (SWAT) model to assess the environmental impact of the transition from meteorological to hydrological droughts. The Xijiang River Basin (XRB), the primary tributary of the Pearl River basin, was selected as the study area. Our results identified 91 meteorological droughts in the XRB, and only 66 hydrological droughts from 1978 to 2018. The transition rate from meteorological to hydrological drought demonstrated large spatial variability, with a basin‐average rate of 56% and the lowest transition rate of 45% in the headstream The transition from meteorological to hydrological drought was mostly rapid between November and December (~2 months), but can be prolonged during spring (about 3–5 months) and winter (around 7–9 months). Additionally, analysis of regeneration scenarios indicated that human activity has mitigated drought severity over recent decades. The primary driver affecting drought duration and frequency during the transition from meteorological to hydrological droughts shows conspicuous spatial disparities in densely populated areas. Although human activity significantly contributes to drought duration and severity compared to climate change during the transition in the headstream, its effects are more pronounced downstream in terms of drought duration and frequency. One plausible explanation is that increased water consumption downstream has considerably prolonged drought progression, whereas water management has counteractive effects on drought progression due to climate change in the headstream. Our findings offer valuable insights into the transition process from meteorological to hydrological droughts in the presence of extensive human activities.

The changing characteristics of propagation time from meteorological drought to hydrological drought in a semi‐arid river basin in India

AbstractThis study employs an event‐based approach to analyse drought propagation from meteorological to hydrological drought via agricultural drought in the semi‐arid Krishna River basin of India. The Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Soil Moisture Index (SSMI) and Standardized Streamflow Index (SSI) representing meteorological, agricultural and hydrological drought, respectively, were estimated. Two different cases of drought propagation are analysed: meteorological‐to‐agricultural (SPEI‐to‐SSMI) and agricultural‐to‐hydrological (SSMI‐to‐SSI). The drought propagation is analysed using three‐time matrices, namely the time difference between initiation (), peak () and termination () at multiple timescales of 1, 3, 6, 9 and 12 months using different drought threshold values 0, −0.5, −1 and − 1.5, respectively, to delineate shifts from mild to extreme drought conditions in detail. The results indicate that the propagation time from SPEI‐to‐SSMI drought decreased for most of the tributaries using multiple timescales at different threshold values, whereas it increased significantly for SSMI‐to‐SSI drought. The drought propagation changes with respect to time as well as magnitude (intensity and severity). The propagation factor (PF), defined as the ratio of the average value of succeeding drought to preceding drought characteristics, has also been studied. For SPEI‐to‐SSMI drought, the duration PF shrinks across all tributaries using multiple timescales at different threshold values, whereas it expands for SSMI‐to‐SSI drought. On the other hand, the severity and intensity PF magnify for SPEI‐to‐SSMI drought, whereas it experiences dampening effects for SSMI‐to‐SSI drought. Thus, the proposed study provides valuable insights into drought propagation dynamics, aiding in managing and mitigating droughts in the semi‐arid river basin and elsewhere.

Drought risk in Moldova under global warming and possible crop adaptation strategies.

This study analyzes the relationship between drought processes and crop yields in Moldova, together with the effects of possible future climate change on crops. The severity of drought is analyzed over time in Moldova using the Standard Precipitation Index, the Standardized Precipitation Evapotranspiration Index, and their relationship with crop yields. In addition, rainfall variability and its relationship with crop yields are examined using spectral analysis and squared wavelet coherence. Observed station data (1950-2020 and 1850-2020), ERA5 reanalysis data (1950-2020), and climate model simulations (period 1970-2100) are used. Crop yield data (maize, sunflower, grape), data from experimental plots (wheat), and the Enhanced Vegetation Index from Moderate Resolution Imaging Spectroradiometer satellites were also used. Results show that although the severity of meteorological droughts has decreased in the last 170 years, the impact of precipitation deficits on different crop yields has increased, concurrent with a sharp increase in temperature, which negatively affected crop yields. Annual crops are now more vulnerable to natural rainfall variability and, in years characterized by rainfall deficits, the possibility of reductions in crop yield increases due to sharp increases in temperature. Projections reveal a pessimistic outlook in the absence of adaptation, highlighting the urgency of developing new agricultural management strategies.

A deep learning perspective on meteorological droughts prediction in the Mun River Basin, Thailand

A deep learning perspective on meteorological droughts prediction in the Mun River Basin, Thailand

Enhanced autumn phenology model incorporating agricultural drought

The impacts of various drought types on autumn phenology have yet to be extensively explored. We address the influence of pre-season agricultural and meteorological droughts on autumn phenology in the Northern Hemisphere. To this end, enhanced autumn phenology models incorporating drought factors was developed, contributing to a deeper understanding of these complex interactions. The study reveals that there was no significant trend of advancement or delay in the End of Season (EOS) across the Northern Hemisphere based on SIF estimates from 2001 to 2020. The cumulative and delayed impacts of pre-season agricultural drought on EOS were found to be more pronounced than those associated with meteorological drought. The analysis of various evaluation indexes shows that the performance of the Cooling Degree Days (CDD) model incorporating the Standardized Soil Moisture Drought Index (CDDSSMI) in simulating EOS in the Northern Hemisphere is >14 % higher than that of the standard CDD model. Additionally, the performance of the CDD model with the Standardized Precipitation Index (CDDSPI) in simulating EOS in the Northern Hemisphere is improved by >5.6 % compared to the standard CDD model. A comparison of future EOS projections across various models reveals that the CDD model significantly overestimates EOS in different scenarios (SSP245 and SSP585). The CDDSSMI model projects EOS approximately 7 days earlier than the CDD model, and the CDDSPI model projects EOS approximately 5 days earlier than the CDD model. This study highlights the diverse impacts of drought types on plant autumn phenology and underscores the significance of parameterizing drought impacts in autumn phenology models.

Disentangling the spatially combined and temporally lagged influences of climate oscillations on seasonal droughts in the East Asian monsoon influenced Poyang Lake Basin

Large-scale climate oscillations are the main forcings affecting regional meteorological droughts and being relevant to sources of their predictability. However, the physical mechanism of atmospheric teleconnections with respect to regional droughts is still not fully understood. In this study, a univariate-to-multivariate analysis framework is proposed to disentangle the spatially combined and temporally lagged effects of multiple oceanic-atmospheric oscillations on meteorological droughts at regional scale. Our study focuses on the largest freshwater lake basin of China, the Poyang Lake basin (PLB). Pearson's correlation coefficient and cross-wavelet transform are used to analyze the pair-wise linear and non-linear correlations between droughts and each climate oscillation. Random forests model is used to reveal the combined influences of multiple climate oscillations. The associated atmospheric mechanism for the identified combination of climate indices with changing lags is explored by performing composite analysis. Regarding the spatially combined influences, the concurrence of El Niño-Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO) are the most important drought precursors. Regarding the temporally lagged influences, ENSO with lag of 11 months and NAO with lag of 2–3 months trigger meteorological droughts. The combined effect of preceding winter El Niño and late-summer negative NAO is the primary cause for triggering autumn droughts. The positive Eurasian teleconnection pattern, triggered by ENSO and NAO and favorable for anomalous northerly currents, is the main drought-prone circulation pattern for the PLB. These findings contribute to improved understanding of joint effects of lagged teleconnections for meteorological droughts, which could eventually lead to more skillful seasonal drought forecasting.

Identifying regional hotspots of heatwaves, droughts, floods, and their co-occurrences

In this paper we present a framework to aid in the selection of optimal environmental indicators for detecting and mapping extreme events and analyzing trends in heatwaves, meteorological and hydrological droughts, floods, and their compound occurrence. The framework uses temperature, precipitation, river discharge, and derived climate indices to characterize the spatial distribution of hazard intensity, frequency, duration, co-occurrence, and dependence. The relevant climate indices applied are Standardized Precipitation Index, Standardized Precipitation and Evapotranspiration Index (SPEI), Standardized Streamflow Index, heatwave indices based on fixed (HWIS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_\ extrm{S}$$\\end{document}) and anomalous temperatures (HWIE\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_\ extrm{E}$$\\end{document}), and Daily Flood Index (DFI). We selected suitable environmental indicators and corresponding thresholds for each hazard based on estimated extreme event detection performance using receiver operating characteristics (ROC), area under curve (AUC), and accuracy, which is defined as the proportion of correct detections. We assessed compound hazard dependence using a Likelihood Multiplication Factor (LMF). We tested the framework for the case of Sweden, using daily data for the period 1922–2021. The ROC results showed that HWIS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_\ extrm{S}$$\\end{document}, SPEI12 and DFI are suitable indices for representing heatwaves, droughts, and floods, respectively (AUC > 0.83). Application of these indices revealed increasing heatwave and flood occurrence in large areas of Sweden, but no significant change trend for droughts. Hotspots with LMF > 1, mostly concentrated in Northern Sweden from June to August, indicated that compound drought-heatwave and drought-flood events are positively correlated in those areas, which can exacerbate their impacts. The novel framework presented here adds to existing hydroclimatic hazard research by (1) using local data and historical records of extremes to validate indicator-based hazard hotspots, (2) evaluating compound hazards at regional scale, (3) being transferable and streamlined, (4) attaining satisfactory performance for indicator-based hazard detection as demonstrated by the ROC method, and (5) being generalizable to various hazard types.

Spatio-temporal analysis of meteorological drought return periods in a Mediterranean arid region, the center of Morocco

ABSTRACT Morocco has recently witnessed a surge in drought occurrences, leading to considerable socioeconomic damages. The standardized precipitation index (SPI) at 3- and 12-month timescales was employed to assess spatiotemporal drought characteristics across the Marrakech-Safi arid region. Mann-Kendall and Sen's slope methods were adopted to assess SPI trends. Furthermore, the Gumbel Copula was employed to construct the joint distribution function of three drought variables: duration, severity, and intensity. The spatial distribution of drought return period for different scenarios was carried out. Results revealed notable interannual fluctuations of wet and drought cycles, with the driest years being 1983 and 2005 and a mixture of positive and negative trends of SPI-12 and SPI-3 over the 1972–2018 period. Nevertheless, statistical significance was observed in only 50% of the SPI-12 trends and 27% of the SPI-3 trends. The drought severity in the region was highest in the plain area, with droughts lasting 6–20 months for 75% of all drought events for SPI-12. The coastal strip exhibited the lowest drought recurrences. Furthermore, dry periods exhibited greater temporal variability over shorter timescales. Finally, trivariate return periods of drought events were the longer, averaging 93 months for SPI-12.

Forecasting Meteorological Drought Conditions in South Korea Using a Data-Driven Model with Lagged Global Climate Variability

Drought prediction is crucial for early risk assessment, preventing negative impacts and the timely implementation of mitigation measures for sustainable water management. This study investigated the relationship between climate variations in three seas and the prediction of December meteorological droughts in South Korea, using the Standardized Precipitation Evapotranspiration Index (SPEI). Climate indices with multiple time lags were integrated into multiple linear regression (MLR) and Random Forest (RF) models and evaluated using Pearson’s correlation coefficients (PCCs) and the Root Mean Square Error (RMSE). The results indicated that the MLR model outperformed RF model in the western inland region with a PCC of 0.52 for predicting SPEI-2. On the other hand, the RF model effectively predicted drought states of ‘moderate drought’ or worse (SPEI < −1) nationwide, achieving an average hit rate of 47.17% and Heidke skill score (HSS) of 0.56, particularly excelling in coastal areas. Nino 3.4 turned out to be the most influential factor for short-period extreme droughts (SPEI-2) with a three-month lag, contributed by the Pacific, Atlantic, and Indian Oceans. For periods of four months or longer, climate variations had a lower predictive value. However, integrating autocorrelation functions to account for the previous month’s drought status improved the accuracy. A HYBRID model, which blends linear and nonlinear approaches, further enhanced reliability, making the proposed model more applicable for drought forecasting in neighboring countries and valuable for South Korea’s drought monitoring system to support sustainable water management.

Meteorological drought in the upper Noteć catchment area (Central Poland) in the light of NDVI and SPI indicators

The currently used standardised precipitation index (SPI) does not allow for a reliable assessment of the impact of drought due to the small and unevenly distributed network of meteorological stations. Hopes for developing methods to assess the impact of droughts are pinned on remote data acquisition and the use of analysis of aerial photographs and satellite scenes. The aim of this study is to assess the occurrence of drought based on the normalised difference vegetation index (NDVI) and SPI at multiple time scales (1-, 3-, and 6-, 9- and 12-month). NDVI values do not simply reflect meteorological drought. However, the spatial co-occurrence of meteorological drought with drought defined on the basis of the NDVI index was demonstrated. The study presents a new approach to identifying drought characterized by SPI and NDVI based on the bivariate choropleth map method, which can indicate the actual places of drought occurrence. The study was carried out for the upper Noteć catchment located in Central Poland.

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.

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.

Evolution Characteristics of Meteorological and Hydrological Drought in an Arid Oasis of Northwest China

In the context of global warming, the acceleration of the water cycle increases the risk of meteorological drought (MD) and hydrological drought (HD) in the arid region of Northwest China. The Manas River Basin is a typical agricultural oasis and the largest oasis farming area in Xinjiang, Northwest China. Droughts in this basin have significant implications for both agricultural production and the livelihoods of inhabitants. To evaluate the MD and HD and provide information for drought relief in the MRB, the standardized precipitation evapotranspiration index (SPEI) and standardized runoff index (SRI) were calculated using long-term rainfall and runoff data. Subsequently, combined with ArcGIS 10.3 software and the trend analysis method, the SPEI and SRI characteristics were evaluated at different time scales (1-, 3-, 6-, and 12-month). There were three main findings. First, both MD and HD were alleviated, with significantly more HD alleviation. MDs in spring and autumn exhibited a trend of aggravation. The SRIs in summer, autumn, and winter increased significantly at a confidence level of p < 0.01, with an insignificant decline in spring. In the 2010s, the frequency of light drought of MD was stable at 10% to 20%, while severe and extreme droughts increased. The frequency of HDs has decreased since the 1990s. Second, on annual and seasonal scales, MDs occurred mainly as light and moderate droughts. The highest frequency of MD was 24% of moderate droughts in winter. Spatially, the northern region of the MRB was characterized by more frequent light and extreme droughts. Third, runoff in the Manas River Basin increased significantly during the 1990s, which may have been related to the acceleration of glacial retreat in the Tianshan Mountains. This study can effectively reveal the changes in meteorological and hydrological drought in NWC and provide the basis for risk decision-making and management for watershed managers.

The Influence of Dynamical Downscaling and Boundary Layer Selection on Egypt’s Potential Evapotranspiration using a Calibrated Version of the Hargreaves-samani Equation: RegCM4 Approach

Potential Evapotranspiration (PET) is an important variable for monitoring daily agricultural activity as well as meteorological drought. Therefore, it is necessary to investigate the influence of different options of the physical dynamical downscaling and boundary layer schemes on the simulated PET. Using the RegCM4 regional climate model, four simulations were conducted (two for each case) in the period 1997 to 2017. In all simulations, the RegCM4 was configured with 25 km resolution and downscaled by the ERA-Interim reanalysis dataset. To ensure a reliable estimation of the PET, a calibrated version of the Hargreaves-Samani equation was adopted. A high-resolution product of the ERA5 was used as the observational dataset. Results showed that the simulated PET is insensitive either to the dynamical downscaling or the boundary layer options. Concerning the annual climatological cycle, the RegCM4’s performance varies with month and location. Quantitatively, a root mean square error lies between 1 mm and 1.6 mm day-1, the Nash-Sutcliffe efficiency between 0.2 and 0.6, and the coefficient of determination between 0.5 and 0.75. Additionally, the Linear Scaling (LS) method showed its added value in the evaluation/validation periods. In conclusion, the RegCM4 can be used to develop a regional PET map of Egypt using the LS either in the present climate or under different future scenarios.