Spatiotemporal drought analysis by the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) in Sichuan Province, China

Comparative evaluation of multiscaler drought indices under different climatic conditions in Gujarat state of India

The Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI), two drought indices, have been compared for interchangeability and reliability under various climatic conditions in Gujarat, India. As the quality of the input is crucial for the accuracy of the index, weather records from surface observatories are preferable over grid and reanalysis data. This study found that on a short timescale (1-3 months), SPEI diagnosed mild and moderate droughts more frequently than SPI, particularly in stations with relatively heavy rainfall. The indices in all timescales at all sites throughout the monsoon months displayed strong positive associations (r > 0.8). The correlation decreases but remains positive as the temporal scale is extended up to 8 months. On a 9-months or longer scales that encompassed active monsoon rainfall months at all stations, correlation coefficients were between 0.8 and 0.9 for all months of the year. During monsoon months, high fractional matches were observed on a short scale. The months after the monsoon show a generalized diagonal pattern of high fractional match with the timescale for all stations. The kappa statistic followed a broad pattern comparable to the match fractions. The instances with poor agreements (R, Match and kappa < 0.3) had proportional bias between the indices. SPEI recognized more drought events at all stations in the short time periods, while the agreements increased with longer time scales. However, SPI detects high intensities in the subhumid.

Predicting agricultural and meteorological droughts using Holt Winter Conventional 2D-Long Short-Term Memory (HW-Conv2DLSTM)

Drought is an extended shortage of rainfall resulting in water scarcity and affecting a region's social and economic conditions through environmental deterioration. Its adverse environmental effects can be minimised by timely prediction. Drought detection uses only ground observation stations, but satellite-based supervision scans huge land mass stretches and offers highly effective monitoring. This paper puts forward a novel drought monitoring system using satellite imagery by considering the effects of droughts that devastated agriculture in Thanjavur district, Tamil Nadu, between 2000 and 2022. The proposed method uses Holt Winter Conventional 2D-Long Short-Term Memory (HW-Conv2DLSTM) to forecast meteorological and agricultural droughts. It employs Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data precipitation index datasets, MODIS 11A1 temperature index, and MODIS 13Q1 vegetation index. It extracts the time series data from satellite images using trend and seasonal patterns and smoothens them using Holt Winter alpha, beta, and gamma parameters. Finally, an effective drought prediction procedure is developed using Conv2D-LSTM to calculate the spatiotemporal correlation amongst drought indices. The HW-Conv2DLSTM offers a better R2 value of 0.97. It holds promise as an effective computer-assisted strategy to predict droughts and maintain agricultural productivity, which is vital to feed the ever-increasing human population.

Examining optimized machine learning models for accurate multi-month drought forecasting: A representative case study in the USA

The Colorado River has experienced a significant streamflow reduction in recent decades due to climate change, resulting in pronounced hydrological droughts that pose challenges to the environment and human activities. However, current models struggle to accurately capture complex drought patterns, and their accuracy decreases as the lead time increases. Thus, determining the reliability of drought forecasting for specific months ahead presents a challenging task. This study introduces a robust approach that utilizes the Beluga Whale Optimization (BWO) algorithm to train and optimize the parameters of the Regularized Extreme Learning Machine (RELM) and Random Forest (RF) models. The applied models are validated against a KNN benchmark model for forecasting drought from one- to six-month ahead across four hydrological stations distributed over the Colorado River. The achieved results demonstrate that RELM-BWO outperforms RF-BWO and KNN models, achieving the lowest root-mean square error (0.2795), uncertainty (U95 = 0.1077), mean absolute error (0.2104), and highest correlation coefficient (0.9135). Also, the current study uses Global Multi-Criteria Decision Analysis (GMCDA) as an evaluation metric to assess the reliability of the forecasting. The GMCDA results indicate that RELM-BWO provides reliable forecasts up to four months ahead. Overall, the research methodology is valuable for drought assessment and forecasting, enabling advanced early warning systems and effective drought countermeasures.

Response of changes in lake area to drought and land use change

The lake area is a crucial parameter that characterizes the state of a lake. Under the dual pressures of climate change and human activity, the magnitude and frequency of changes in lake areas become more pronounced. This process poses a serious threat to the local ecological environment. In this study, we constructed a lake water extraction model (LakeNet) based on a fully convolutional neural network. We extracted and analyzed the spatiotemporal characteristics of the area of nine major lakes from 1987 to 2022, as well as the driving factors behind these changes. Our results indicate that: 1) LakeNet exhibits high extraction accuracy and can remove some clouds. 2) The area of the nine major lakes shows a fluctuating downward trend (-8.11km2/10a), with drought and land use changes identified as significant driving forces behind the changes in lake boundaries, drought events caused the lake area to decrease, and the expansion of cropland further reduced the lake area. 3) Due to variations in lake area, the impact of drought on the area of the nine major lakes exhibits a lag effect, smaller lakes are likely to respond more quickly to drought.

Assessing the responses of ecosystem patterns, structures and functions to drought under climate change in the Yellow River Basin, China

Understanding how ecosystems respond and adapt to drought has become an urgent issue as drought stress intensifies under climate change, yet this topic is not fully understood. Currently, conclusions on the response of ecosystems in different regions to drought disturbance are inconsistent. Based on long MODIS data and observed data, this study systematically explored the relationships between ecosystem patterns, structures and functions and drought, taking a typical climate change-sensitive area and an ecologically fragile area-the Yellow River Basin-as a case study. Drought assessment results revealed that the Yellow River Basin has experienced meteorological and hydrological drought during most of the last two decades, predominantly characterized by medium and slight droughts. The ecosystem patterns and structures changed dramatically as the grassland decreased and the landscape fragmentation index (F) increased with increasing wetness. The annual gross primary productivity (GPP) increased, the water use efficiency (WUE) declined and ecosystem service value (ESV) exhibited a W-shaped increase at the watershed scale, but there were significant regional differences. There were positive correlations between F, GPP, ESV and drought indices, while there was a negative correlation between WUE and drought indices at the watershed scale. Under drought stress, the ecosystem structure in the basin was disrupted, the GPP and ESV decreased, but the WUE increased. Notably, approximately 106 %, 20 %, and 1 % of the maximum reductions in F, GPP, and ESV, respectively, were caused by drought, while the maximum 4 % of WUE increased. Responses of some functions in the wetland and grassland to drought vary from those in other ecosystems. The mechanisms underlying ecosystem responses to drought were further investigated. This study enhances the understanding of these responses and will help stakeholders formulate drought mitigation policies and protect ecosystem health.

A support vector machine based drought index for regional drought analysis

The increased global warming has increased the likelihood of recurrent drought hazards. Potential links between the frequency of extreme weather events and global warming have been suggested by earlier research. The spatial variability of meteorological factors over short distances can cause distortions in conclusions or limit the scope of drought analysis in a particular region when extreme values predominate. Therefore, it is challenging to make trustworthy judgments regarding the spatiotemporal characteristics of regional drought. This study aims to improve the quality and accuracy of regional drought characterization and the process of continuous monitoring. The new drought indicator presented in this study is called the Support Vector Machine based drought index (SVM-DI). It is created by adding different weights to an SVM-based X-bar chart that is displayed with regional precipitation aggregate data. The SVM-DI application site is located in Pakistan's northern area. Using the Pearson correlation coefficient for pairwise comparison, the study compares the SVM-DI and the Regional Standard Precipitation Index (RSPI). Interestingly, compared to RSPI, SVM-DI shows more pronounced regional characteristics in its correlations with other meteorological stations, with a significantly lower Coefficient of Variation. These results confirm that SVM-DI is a useful tool for regional drought analysis. The SVM-DI methodology offers a unique way to reduce the impact of extreme values and outliers when aggregating regional precipitation data.

Spatiotemporal characteristics of meteorological drought events in 34 major global river basins during 1901-2021

Meteorological drought is a crucial driver of various types of droughts; thus, identifying the spatiotemporal characteristics of meteorological drought at the basin scale has implications for ecological and water resource security. However, differences in drought characteristics between river basins have not been clearly elucidated. In this study, we identify and compare meteorological drought events in 34 major river basins worldwide using a three-dimensional drought-clustering algorithm based on the standardised precipitation evapotranspiration index on a 12-month scale from 1901 to 2021. Despite synchronous increases in precipitation and potential evapotranspiration (PET), with precipitation increasing by more than three times the PET, 47 % (16/34) of the basins showed a tendency towards drought in over half their basin areas. Drought events occurred frequently, with more than half identified as short-term droughts (lasting less than or equal to three months). Small basins had a larger drought impact area, with major drought events often originating from the basin boundaries and migrating towards the basin centre. Meteorological droughts were driven by changes in sea surface temperature (SST), especially the El Niño Southern Oscillation (ENSO) or other climate indices. Anomalies in SST and atmospheric circulation caused by ENSO events may have led to altered climate patterns in different basins, resulting in drought events. These results provide important insights into the characteristics and mechanisms of meteorological droughts in different river basins worldwide.

Assessing long-term trends in vegetation cover change in the Xilin River Basin: Potential for monitoring grassland degradation and restoration

Under the influence of climate change and human activities, the problem of grassland degradation is becoming increasingly severe. Detection of changes in vegetation cover is crucial for a better understanding of the interaction between humans and ecosystems. This study maps changes in vegetation cover using the Google Earth Engine (GEE). We used 36 years of Landsat satellite imagery (1985-2020) in the Xilin River Basin, China, to classify grassland conditions and validated the results with field observation data. The overall classification of the model accuracy assessment was 83.3%. The Dynamic Reference Vegetation Cover Method (DRCM) was adopted to remove the effect of interannual variation of rainfall, allowing to focus on the impact of human activities on vegetation cover changes. The results identify five categories of vegetation cover changes: significantly increased, potentially increased, stable, potentially decreased, and significantly decreased. The reference level is derived from the most persistent land surface coverage across different grassland types and all years. Overall, 9.3% of the study area had a significant increase in vegetation cover, 14.2% a potential increase, 48.6% of the area showed a stable vegetation condition, 9.8% showed a potential decrease, and 18.1% a significant decrease in vegetation cover. The largest proportion of combined potential and significant reduction was 35.2% for desert grassland, where the vegetation faced the most severe reduction. This study will provide a basis for identifying grassland degradation and developing scientific management policies.

A novel framework for investigating the mechanisms of climate change and anthropogenic activities on the evolution of hydrological drought

Climate change and anthropogenic activity are the primary drivers of water cycle changes. Hydrological droughts are caused by a shortage of surface and/or groundwater resources caused by climate change and/or anthropogenic activity. Existing hydrological models have primarily focused on simulating natural water cycle processes, while limited research has investigated the influence of anthropogenic activities on water cycle processes. This study proposes a novel framework that integrates a distributed hydrological model and an attribution analysis method to assess the impacts of climate change and anthropogenic activities on hydrological drought The distributed dualistic water cycle model was applied to the Fuhe River Basin (FRB), and it generated a Nash-Sutcliffe efficiency coefficient > 0.85 with a relative error of <5 %. Excluding the year with extreme drought conditions, our analysis revealed that climate change negatively impacted the average drought duration (-105.5 %) and intensity (-23.6 %) because of increasing precipitation. However, anthropogenic activities continued to contribute positively to the drought, accounting for 5.5 % and 123.6 % of the average drought duration and intensity, respectively, because of increased water consumption. When accounting for extreme drought years, our results suggested that climate change has contributed negatively to the average duration of drought (-113.2 %) but positively to its intensity (7.8 %). Further, we found that anthropogenic activities contributed positively to both the average drought duration and intensity (13.2 % and 92.2 %, respectively). While climate change can potentially mitigate hydrological drought in the FRB by boosting precipitation levels, its overall effect may exacerbate drought through the amplification of extreme climate events resulting from global climate change. Therefore, greater attention should be paid to the effects of extreme drought.

Compensatory response of ecosystem carbon-water cycling following severe drought in Southwestern China

Climate change has increased the frequency and length of droughts, but many uncertainties remain regarding the impacts of this aridification on terrestrial ecosystem function. Vegetation water use efficiency and carbon sequestration capacity are crucial determinants that both respond to and mediate the effects of drought. However, it is important to note that the consequences of drought on these processes can persist for years. A deeper exploration of these "drought legacy effects" will help improve our understanding of how climate change alter ecosystem carbon-water cycling. Here, we investigate the spatial patterns of drought legacy effects on remotely-sensed vegetation greenness (NDVI), net primary productivity (NPP) and water use efficiency (WUE) in southwestern China, a biodiversity hotspot that was impacted by an extreme drought in 2009-2010, with a particular focus on the tradeoff between WUE and NPP. Despite widespread negative drought legacy effects in NDVI (impacting 61.26 % of the study region), there was a general increase in NPP (58.68 %) and a decrease in WUE (67.53 %) in the year following drought (2011). This drought legacy effect was most evident in forests, while drought legacies in grasslands were less common. Drought legacies were also most apparent in relatively warm and humid areas. During the study period (2002 to 2018), we found that drought impacts on WUE also lagged behind changes in NPP by 1-2 years in forests, which highlights how drought legacies may manifest differently across ecosystem processes. Our study demonstrated that severe drought conditions may significantly affect the carbon sequestration capacity and water use efficiency of vegetation in southwestern China, and that forests may compensate for the detrimental effects of water stress by increasing water use and biomass growth after drought episodes.

Assessment of multidimensional drought vulnerability using exposure, sensitivity, and adaptive capacity components

This study provides a method for analyzing the drought-vulnerability index (DVI) from a multidimensional perspective that includes biophysical and social aspects, considering the Intergovernmental Panel on Climate Change's (IPCC) assessment. The proposed method generates the "exposure index (EI)", "sensitivity index (SI)", and "adaptive capacity index (ACI)" components of the proposed DVI using nine sub-indicators and 29 proxy variables. By using it throughout all of Turkey's provinces, the performance of the developed index was evaluated. In this study, the decision matrices were built utilizing expert knowledge, and the weights of the indicators and variables were obtained by using the Analytical Hierarchy Process (AHP) technique. Moreover, the values of these four indices were classified as "very high, high, moderate, low, and very low," and their geographical distribution across the country was drawn, as well as relevant patterns retrieved. The study's major results show that 17 of the 81 provinces are classified as "very high," 16 as "high," 15 as "moderate," 17 as "low," and the remaining 16 as "very low" drought vulnerable. Another significant result is that the majority of people in the country's south, center, and southeast rely on agriculture and are thus more vulnerable to drought due to socioeconomic underdevelopment in those regions.

Spatiotemporal evaluation of drought characteristics based on standard drought indices at various timescales over Uttar Pradesh, India

Prolonged and repeated drought, as seen in India and other parts of South Asia, is a symptom of climate change, which is partially the result of human interventions. The performance of the widely used drought metrics Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) are evaluated for 18 stations in Uttar Pradesh state for the period 1971 to 2018 in this study. Drought characteristics such as intensity, duration, and frequency of different categories are estimated and compared based on SPI and SPEI. In addition, station proportion is estimated at a different timescales, providing a better insight into temporal variability drought of a specific category. Spatiotemporal trend variability of SPEI and SPI was investigated at a significance level of 0.05 using the non-parametric Mann-Kendall (MK) test. SPEI adds the effect of temperature rise and deficit change on the drought occurrences of different classes. SPEI provides a better estimation of drought characteristics due to its consideration of temperature change in the drought severity. The more significant number of drying events accounted for a timescale of 3 months and 6 months, reflecting the higher variability of the seasonal fluctuation of water balance over the state. At 9-month and 12-month timescales, SPI and SPEI fluctuate gradually with considerable differences between the duration and severity of the drought event. This study reveals that there have been a substantial number of drought events over the state during the last two decades (2000 to 2018). The results conclude that the study area is at risk of erratic meteorological drought conditions where the western part of the study is worst affected compared to the eastern part of Uttar Pradesh (India).

Characterisation of meteorological drought at sub-catchment scale in Afghanistan using station-observed climate data

Droughts have severely affected Afghanistan over the last four decades, leading to critical food shortages where two-thirds of the country's population are in a food crisis. Long years of conflict have lowered the country's ability to deal with hazards such as drought which can rapidly escalate into disasters. Understanding the spatial and temporal distribution of droughts is needed to be able to respond effectively to disasters and plan for future occurrences. This study used Standardized Precipitation Evapotranspiration Index (SPEI) at monthly, seasonal and annual temporal scales to map the spatiotemporal change dynamics of drought characteristics (distribution, frequency, duration and severity) in Afghanistan. SPEI indices were mapped for river basins, disaggregated into 189 sub-catchments, using monthly precipitation and potential evapotranspiration derived from temperature station observations from 1980 to 2017. The results show these multi-dimensional drought characteristics vary along different years, change among sub-catchments, and differ across temporal scales. During the 38 years, the driest decade and period are 2000s and 1999-2022, respectively. The 2000-01 water year is the driest with the whole country experiencing 'severe' to 'extreme' drought, more than 53% (87 sub-catchments) suffering the worst drought in history, and about 58% (94 sub-catchments) having 'very frequent' drought (7 to 8 months) or 'extremely frequent' drought (9 to 10 months). The estimated seasonal duration and severity present significant variations across the study area and among the study period. The nation also suffers from recurring droughts with varying length and intensity in 2004, 2006, 2008 and most recently 2011. There is a trend towards increasing drought with longer duration and higher severity extending all over sub-catchments from southeast to north and central regions. These datasets and maps help to fill the knowledge gap on detailed sub-catchment scale meteorological drought characteristics in Afghanistan. The study findings improve our understanding of the influences of climate change on the drought dynamics and can guide catchment planning for reliable adaptation to and mitigation against future droughts.

Uncertainty in Determination of Meteorological Drought Zones Based on Standardized Precipitation Index in the Territory of Poland

The primary aim of this work is to assess the accuracy of the methods for spatial interpolation applied for the reconstruction of the spatial distribution of the Standardized Precipitation Index (SPI). The one-month version called SPI-1 is chosen for this purpose due to the known greatest variability of this index in comparison with its other versions. The analysis has been made for the territory of the entire country of Poland. At the same time the uncertainty related to the application of such computational procedures is determined based on qualitative and quantitative measures. The public data of two kinds are applied: (1) measurements of precipitation and (2) the locations of the meteorological stations in Poland. The analysis has been made for the period 1990-2020. However, all available observations since 1950 have been implemented. The number of available meteorological stations has decreased over the analyzed period. In January 1990 there were over one thousand stations making observations. In the end of the period of the study, the number of stations was below six hundred. Obviously, the temporal scarcity of data had an impact on the obtained results. The main tools applied were ArcGIS supported with Python scripting, including generally used modules and procedures dedicated to geoprocessing. Such an approach appeared crucial for the effective processing of the large number of data available. It also guaranteed the accuracy of the produced results and brought about drought maps based on SPI-1. The methods tested included: Inverse Distance Weighted, Natural Neighbor, Linear, Kriging, and Spline. The presented results prove that all the procedures are inaccurate and uncertain, but some of them provide satisfactory results. The worst method seems to be the interpolation based on Spline functions. The practical aspects related to the implementation of the methods led to removal of the Linear and Kriging interpolations from further use. Hence, Inverse Distance Weighted, as well as Natural Neighbor, seem to be well suited for this problem.

Analysis of Future Meteorological Drought Changes in the Yellow River Basin under Climate Change

The Yellow River Basin is an important economic belt and key ecological reservation area in China. In the context of global warming, it is of great significance to project the drought disaster risk for ensuring water security and improving water resources management measures in practice. Based on the five Global Climate Models (GCMs) projections under three scenarios of the Shared Socioeconomic Pathways (SSP) (SSP126, SSP245, SSP585) released in the Sixth Coupled Model Intercomparison Project (CMIP6), this study analyzed the characteristics of meteorological drought in the Yellow River Basin in combination with SPEI indicators over 2015–2100. The result indicated that: (1) The GCMs from CMIP6 after bias correction performed better in reproducing the spatial and temporal variation of precipitation. The precipitation in the Yellow River Basin may exhibit increase trends from 2015 to 2100, especially under the SSP585 scenario. (2) The characteristics of meteorological drought in the Yellow River Basin varied from different combination scenarios. Under the SSP126 scenario, the meteorological drought will gradually intensify from 2040 to 2099, while the drought intensity under SSP245 and SSP585 scenarios will likely be higher than SSP126. (3) The spatial variation of meteorological drought in the Yellow River Basin is heterogeneous and uncertain in different combination scenarios and periods. The drought tendency in the Loess Plateau will increase significantly in the future, and the drought frequency and duration in the main water conservation areas of the Yellow River Basin was projected to increase.

Shifting of Meteorological to Hydrological Drought Risk at Regional Scale

The drought along with climate variation has become a serious issue for human society and the ecosystem in the arid region like the Soan basin (the main source of water resources for the capital of Pakistan and the Pothohar arid region). The increasing concerns about drought in the study area have brought about the necessity of spatiotemporal analysis and assessment of the linkage between different drought types for an early warning system. Hence, the streamflow drought index (SDI) and standard precipitation index (SPI) were used for the analysis of the spatiotemporal variations in hydrological and meteorological drought, respectively. Furthermore, statistical approaches, including regression analysis, trend analysis using Mann Kendall, and moving average, have been used for investigation of the linkage between these drought types, the significance of the variations, and lag time identification, respectively. The overall analysis indicated an increase in the frequency of both hydrological and meteorological droughts during the last three decades. Moreover, a strong linkage between hydrological and meteorological droughts was found; and this relationship varied on the spatiotemporal scale. Significant variations between hydrological and meteorological droughts also resulted during the past three (3) decades. These discrepancies would be because of different onset and termination times and specific anthropogenic activities in the selected basin for the minimization of hydrological drought. Conclusively, the present study contributes to comprehending the linkage between hydrological and meteorological droughts and, thus, could have a practical use for local water resource management practices at the basin scale.

Evidence for Intensification in Meteorological Drought Since the 1950s and Recent Dryness–Wetness Forecasting in China

Drought is one of the major environmental stressors; drought is increasingly threatening the living environment of mankind. The standardized precipitation evapotranspiration index (SPEI) with a 12-month timescale was adopted to monitor dry–wet status over China from 1951 to 2021. The modified Mann–Kendall (MMK) and Pettitt tests were used to assess the temporal trend and nonlinear behavior of annual drought variability. The analysis focuses on the spatio-temporal structure of the dry–wet transition and its general connections with climate change processes. In addition, the seasonal autoregressive integrated moving average (SARIMA) model was applied to forecast the dry–wet behavior in the next year (2022) at 160 stations, and the hotspot areas for extreme dryness–wetness in China were identified in the near term. The results indicate that the dry–wet climate in China overall exhibits interannual variability characterized by intensified drought. The climate in the Northeast China (NEC), North China (NC), Northwest China (NWC), and Southwest China (SWC) has experienced a significant (p < 0.05) drying trend; however, the dry–wet changes in the East China (EC) and South Central China (SCC) are highly spatially heterogeneous. The significant uptrend in precipitation is mainly concentrated to the west of 100° E; the rising magnitude of precipitation is higher in Eastern China near 30° N, with a changing rate of 20–40 mm/decade. Each of the sub-regions has experienced significant (p < 0.01) warming over the past 71 years. Geographically, the increase in temperature north of 30° N is noticeably higher than that south of 30° N, with trend magnitudes of 0.30–0.50 °C/decade and 0.15–0.30 °C/decade, respectively. The response of the northern part of Eastern China to the warming trend had already emerged as early as the 1980s; these responses were earlier and more intense than those south of 40° N latitude (1990s). The drying trends are statistically significant in the northern and southern regions, bounded by 30° N, with trend magnitudes of −0.30–−0.20/decade and −0.20–−0.10/decade, respectively. The northern and southwestern parts of China have experienced a significant (p < 0.05) increase in the drought level since the 1950s, which is closely related to significant warming in recent decades. This study reveals the consistency of the spatial distribution of variations in precipitation and the SPEI along 30° N latitude. A weak uptrend in the SPEI, i.e., an increase in wetness, is shown in Eastern China surrounding 30° N, with a changing rate of 0.003–0.10/decade; this is closely associated with increasing precipitation in the area. Drought forecasting indicates that recent drying areas are located in NWC, the western part of NC, the western part of SWC, and the southern part of SCC. The climate is expected to show wetting characteristics in NEC, the southeastern part of NC, and the eastern part of EC. The dry–wet conditions spanning the area between 30–40° N and 100–110° E exhibit a greater spatial variability. The region between 20–50° N and 80–105° E will continue to face intense challenges from drought in the near future. This study provides compelling evidence for the temporal variability of meteorological drought in different sub-regions of China. The findings may contribute to understanding the spatio-temporal effect of historical climate change on dry–wet variation in the region since the 1950s, particularly in the context of global warming.

Development and application of high resolution SPEI drought dataset for Central Asia

Central Asia is a data scarce region, which makes it difficult to monitor and minimize the impacts of a drought. To address this challenge, in this study, a high-resolution (5 km) Standardized Precipitation Evaporation Index (SPEI-HR) drought dataset was developed for Central Asia with different time scales from 1981-2018, using Climate Hazards group InfraRed Precipitation with Station's (CHIRPS) precipitation and Global Land Evaporation Amsterdam Model's (GLEAM) potential evaporation (E_p) datasets. As indicated by the results, in general, over time and space, the SPEI-HR correlated well with SPEI values estimated from coarse-resolution Climate Research Unit (CRU) gridded time series dataset. The 6-month timescale SPEI-HR dataset displayed a good correlation of 0.66 with GLEAM root zone soil moisture (RSM) and a positive correlation of 0.26 with normalized difference vegetation index (NDVI) from Global Inventory Monitoring and Modelling System (GIMMS). After observing a clear agreement between SPEI-HR and drought indicators for the 2001 and 2008 drought events, an emerging hotspot analysis was conducted to identify drought prone districts and sub-basins.

Construction and Application of Hydrometeorological Comprehensive Drought Index in Weihe River

In response to the national strategy of ecological protection in the Yellow River Basin, a more comprehensive assessment of the basin drought is made. Based on the meteorological data of 20 meteorological stations and the hydrological data of 5 hydrological stations in Weihe River from 1960 to 2010, the base flow data are obtained by digital filtering method. A new comprehensive drought index (CPBI) about base flow and precipitation is constructed based on Copula function, and the applicability of CPBI is discussed, the drought characteristics of Weihe River Basin are analyzed by using this index. The results show that CPBI can capture both meteorological and hydrological drought events and comprehensively characterize their drought characteristics; CPBI has a downward trend at all scales, and the drought situation is becoming more and more serious. After the identification of run length theory, CPBI can more accurately reflect the severe drought situation of five hydrological stations in Weihe River, and can better provide drought early warning. There is variation in CPBI. The variation on the annual scale is generally concentrated in the 1970s and 1990s, and there is a large gap in the variation on the seasonal scale. CPBI is an effective drought monitoring index in Weihe River, which can provide reference for drought early warning and response of Weihe River.

An assessment of the impact of Indian summer monsoon droughts on atmospheric aerosols and associated radiative forcing at a semi-arid station in peninsular India

A continuing increase in droughts/floods in Asian monsoon regions and worsening air quality due to aerosols are the two biggest threats to the health and well being of over 60% of the world's population. This study focuses on in-situ observations of atmospheric aerosols and their impact on shortwave direct aerosol radiative forcing (SDARF) during the southwest monsoon season (June-September) from 2015 to 2020 over a semi-arid station in Southern India. The Standardized precipitation index (SPI) is used to identify the droughts and normal monsoon years. Based on the SPI index, 2015, 2016, and 2018 were considered the drought monsoon years, while 2017, 2019, and 2020 were chosen as the normal monsoon years. During the drought monsoon years (normal monsoon years), the monthly mean black carbon (BC) was 1.17 ± 0.25 (0.72 ± 0.18), 1.02 ± 0.31 (0.64 ± 0.17), 1.02 ± 0.38 (0.74 ± 0.28), and 1.28 ± 0.35 μg/m³ (0.88 ± 0.21 μg/m³), for June, July, August and September respectively. The lower BC concentration during the normal monsoon years is mainly due to the enhanced wet-removal rates by high rainfall over the measurement location. In July, there was a high ventilation coefficient (VC) and low concentration of BC, while in September, low VC, and a high concentration of BC was observed in both the drought and the normal monsoon years. In addition, a plane-parallel radiative transfer model was used to estimate shortwave direct aerosol radiative forcing for composite and without BC at various surfaces, including the surface (SUF), atmosphere (ATM), and top of the atmosphere (TOA). During the drought monsoon years (normal monsoon years), the estimated monthly mean ATM forcing was 17.6 ± 2.4 (13.9 ± 2.1), 17.5 ± 7.5 (12.7 ± 4.4), 17.2 ± 4.0 (13.5 ± 1.9), and 17.4 ± 2.8 Wm^-2 (14.6 ± 0.7 Wm^-2) for June, July, August, and September, respectively. During the drought monsoon years, the estimated BC forcing was substantially larger (8.8 ± 2.6 Wm^-2) than that of normal monsoon years (6.0 ± 1.5 Wm^-2). It indicates the important role of absorbing BC aerosols during the drought monsoon years in introducing additional heat to the lower atmosphere, particularly over peninsular India.

The Impact of a Lack of Government Strategies for Sustainable Water Management and Land Use Planning on the Hydrology of Water Bodies: Lessons Learned from the Disappearance of the Aculeo Lagoon in Central Chile

Several studies have focused on why the Aculeo Lagoon in central Chile disappeared, with a recent one concluding that a lack of precipitation was the main cause, bringing tremendous political consequences as it supported the argument that the government is not responsible for this environmental, economic, and social disaster. In this study, we evaluated in detail the socio-economic history of the watershed, the past climate and its effects on the lagoon’s water levels (including precipitation recycling effects), anthropogenic modifications to the lagoon’s water balance, the evolution of water rights and demands, and inaccurate estimates of sustainable groundwater extraction volumes from regional aquifers. This analysis has revealed novel and undisputable evidence that this natural body of water disappeared primarily because of anthropogenic factors (mostly river deviations and aquifer pumping) that, combined with the effects of less than a decade with below-normal precipitation, had a severe impact on this natural lagoon–aquifer system.

The Study on Compound Drought and Heatwave Events in China Using Complex Networks

Compound extreme events can severely impact water security, food security, and social and economic development. Compared with single-hazard events, compound extreme events cause greater losses. Therefore, understanding the spatial and temporal variations in compound extreme events is important to prevent the risks they cause. Only a few studies have analyzed the spatial and temporal relations of compound extreme events from the perspective of a complex network. In this study, we define compound drought and heatwave events (CDHEs) using the monthly scale standard precipitation index (SPI), and the definition of a heatwave is based on daily maximum temperature. We evaluate the spatial and temporal variations in CDHEs in China from 1961 to 2018 and discuss the impact of maximum temperature and precipitation changes on the annual frequency and annual magnitude trends of CDHEs. Furthermore, a synchronization strength network is established using the event synchronization method, and the proposed synchronization strength index (SSI) is used to divide the network into eight communities to identify the propagation extent of CDHEs, where each community represents a region with high synchronization strength. Finally, we explore the impact of summer Atlantic multidecadal oscillation (AMO) and Pacific decadal oscillation (PDO) on CDHEs in different communities. The results show that, at a national scale, the mean frequency of CDHEs takes on a non-significant decreasing trend, and the mean magnitude of CDHEs takes on a non-significant increasing trend. The significant trends in the annual frequency and annual magnitude of CDHEs are attributed to maximum temperature and precipitation changes. AMO positively modulates the mean frequency and mean magnitude of CDHEs within community 1 and 2, and negatively modulates the mean magnitude of CDHEs within community 3. PDO negatively modulates the mean frequency and mean magnitude of CDHEs within community 4. AMO and PDO jointly modulate the mean magnitude of CDHEs within community 6 and 8. Overall, this study provides a new understanding of CDHEs to mitigate their severe effects.

Estimation of Daily Potential Evapotranspiration in Real-Time from GK2A/AMI Data Using Artificial Neural Network for the Korean Peninsula

Evapotranspiration (ET) is a fundamental factor in energy and hydrologic cycles. Although highly precise in-situ ET monitoring is possible, such data are not always available due to the high spatiotemporal variability in ET. This study estimates daily potential ET (PET) in real-time for the Korean Peninsula, via an artificial neural network (ANN), using data from the GEO-KOMPSAT 2A satellite, which is equipped with an Advanced Meteorological Imager (GK2A/AMI). We also used passive microwave data, numerical weather prediction (NWP) model data, and static data. The ANN-based PET model was trained using data for the period 25 July 2019 to 24 July 2020, and was tested by comparing with in-situ PET for the period 25 July 2020 to 31 July 2021. In terms of accuracy, the PET model performed well, with root-mean-square error (RMSE), bias, and Pearson’s correlation coefficient (R) of 0.649 mm day−1, −0.134 mm day−1, and 0.954, respectively. To examine the efficiency of the GK2A/AMI-derived PET data, we compared it with in-situ ET measured at flux towers and with MODIS PET data. The accuracy of the GK2A/AMI-derived PET, in comparison with the flux tower-measured ET, showed RMSE, bias, and Pearson’s R of 1.730 mm day−1, 1.212 mm day−1, and 0.809, respectively. In comparison with the in-situ PET, the ANN model produced more accurate estimates than the MODIS data, indicating that it is more locally optimized for the Korean Peninsula than MODIS. This study advances the field by applying an ANN approach using GK2A/AMI data and could play an important role in examining hydrologic energy for air-land interactions.

Analysis of Characteristics of Hydrological and Meteorological Drought Evolution in Southwest China

Based on the data of 82 meteorological stations and six representative hydrological stations in four provinces in Southwest China (Guizhou, Sichuan, Yunnan, Chongqing), this paper uses standardized precipitation evapotranspiration index (SPEI) and standardized runoff index (SRI) to analyze the spatial and temporal evolution characteristics of drought in the study area from 1968 to 2018. Combined with the Southwest monsoon index and historical drought data, the correlation of drought and the applicability of different drought indices were verified. The results show that: (1) SPEI-12 in Southwest China shows a downward trend from 1968 to 2018, with a linear trend rate of −0.074/10a, and SPEI-3 has a downward trend in four seasons, the maximum linear trend rate being −0.106/10a in autumn;(2) The change in SRI-12 and SRI-24 value directly reflected the decrease in SRI value, indicating that drought events are increasing in recent times, especially in the 21st century (3). Severe drought occurred in the south of Southwest China, as indicated by the increase of drought frequency in this area. The main reason for the variations in the frequency distribution of drought in Southwest China is the combined effect of the change of precipitation and evapotranspiration. (4) The correlation between hydrological drought index and disaster areas is stronger than the correlation between meteorological drought and disaster areas.

Spatiotemporal Drought Risk Assessment Considering Resilience and Heterogeneous Vulnerability Factors: Lempa Transboundary River Basin in The Central American Dry Corridor

Drought characterization and risk assessment are of great significance due to drought’s negative impact on human health, economy, and ecosystem. This paper investigates drought characterization and risk assessment in the Lempa River basin in Central America. We applied the Standardized Evapotranspiration Deficit Index (SEDI) for drought characterization and drought hazard index (DHI) calculation. Although SEDI’s applicability is theoretically proven, it has been rarely applied. Drought risk is generally derived from the interactions between drought hazard (DHI) and vulnerability (DVI) indices but neglects resilience’s inherent impact. Accordingly, we propose incorporating DHI, DVI, and drought resilience index (DREI) to calculate drought risk index (DRI). Since system factors are not equally vulnerable, i.e., they are heterogeneous, our methodology applies the Analytic Hierarchy Process (AHP) to find the weights of the selected factors for the DVI computation. Finally, we propose a geometric mean method for DRI calculation. Results show a rise in DHI during 2006–2010 that affected DRI. We depict the applicability of SEDI via its relationship with El Nino-La Nina and El Salvador’s cereal production. This research provides a systematic drought risk assessment approach that is useful for decision-makers to allocate resources more smartly or intervene in Drought Risk Reduction (DRR). This research is also useful for those interested in socioeconomic drought.