Search

Water is a fundamental and crucial natural resource for human survival. However, the global demand for water is increasing, leading to a subsequent decrease in water availability. This study addresses the critical need for improved water resource forecasting models amidst global water scarcity concerns exacerbated by climate change. This study uses the best weather and water resource forecasting model for sustainable development. Employing a Recurrent Neural Network–Long Short-Term Memory (RNN-LSTM) approach, the research enhances drought prediction capabilities by integrating secondary data of the rainfall, temperature, and ground and surface water supplies. The primary objective is to forecast water resources under changing climatic conditions, facilitating the development of early warning systems for vulnerable regions. The results from the LSTM model show an increased trend in temperature and rainfall patterns. However, a relatively unstable decrease in rainfall is observed. The best statistical analysis result was observed with the LSTM model; the model's accuracy was 99%, showing that it was quite good at presenting the obtained precipitation, temperature, and water data. Meanwhile, the value of the root mean squared error (RMSE) was about 13, 15, and 20, respectively. Therefore, the study's results highlight that the LSTM model was the most suitable among the artificial neural networks for forecasting the weather, rainfall, and water resources. This study will help weather forecasting, agriculture, and meteorological departments be effective for water resource forecasting. [ABSTRACT FROM AUTHOR]

Drought, a highly detrimental natural disaster, poses significant threats to both human populations, wildlife, and vegetation. Traditional methods of monitoring soil moisture levels rely on ground-based measurements from meteorological stations. However, these stations often lack comprehensive coverage in certain agricultural areas, necessitating the use of alternative methods such as satellite remote sensing. This technique provides a reliable means of measuring soil moisture, a critical factor in effective agricultural management. This paper investigates variations in soil moisture and drought using data from the Cyclone Global Navigation Satellite System (CYGNSS) and the Soil Moisture Active and Passive (SMAP) system. To evaluate the accuracy of these data products, we compared both datasets with the Global Land Data Assimilation System (GLDAS) NOAH model from 2018 to 2019. Our findings reveal a strong correlation between the datasets and the model, with Pearson correlation coefficients (r) and Root Mean Square Errors (RMSE) of approximately r = 0.98 and RMSE = 0.03 for SMAP, and r = 0.97 and RMSE = 0.02 for CYGNSS, respectively. We further compared these measurement datasets with drought indicators such as the Standardized Precipitation Index over three months (SPI3), the Normalized Difference Vegetation Index (NDVI), and Total Water Storage (TWS). The correlation coefficients between SMAP and the three indicators (NDVI, SPI3, and TWS) were 0.93, 0.84, and 0.047, respectively, while the coefficients between CYGNSS and the same indicators were 0.86, 0.78, and 0.56, respectively. All the variables also exhibited significant p-values. Despite minor differences, the results demonstrate excellent agreement. Our findings underscore the sensitivity of space-based sensors to drought conditions, highlighting their effectiveness as tools for detecting and monitoring drought (e.g. agricultural drought), particularly in the short term. [ABSTRACT FROM AUTHOR]

In recent decades, water scarcity is a significant constraint for socioeconomic development and threatens livelihood in an agriculture-based developing country like Pakistan. The water crisis in the country is projected to exacerbate in the coming years, especially in the southern parts. This dire situation calls for an investigation of major droughts, associated water scarcity, and changes in teleconnection patterns over Southern Pakistan. Moderate to low Southeastern monsoon (SEM) precipitation triggered the extreme drought episode (2017--2020) over Southern Pakistan and intensified the water scarcity. This study explored the severity of the respective drought event, underlying mechanisms, and changes in water scarcity over Southern Pakistan. To investigate the future changes (1980--2050) in water scarcity, coupling models (global hydrological models (GHMs)-global climate models (GCMs)) have been used to achieve the interannual performance of water availability and total water consumption. Besides, future scenarios used in this study are the combinations of SSPs and RCPs, including middle-of-the-road (SSP1-RCP4.5) and fossil-fueled development (SSP2-RCP8.5). The findings indicated a precipitation deficit of 45% during the 4-year (2017--2020), depicting the worst events in the past 50 years. South Pakistan observed the worst SEM droughts over the last 50 years, as 2000--2005 was the worst drought (precipitation deficit of 75%), followed by 2017--2020 with a 49% of precipitation deficit. Water scarcity was exacerbated by the extreme dry spells that developed over most of southern Pakistan between 2017 and 2020 as a result of moderate-to- exceptionally low SEM precipitation. Furthermore, this drought episode was accompanied by the cool phase in the Pacific and equatorial Indian Oceans. The future changes in water scarcity over the southern regions of Pakistan present a sharp increase under the SSP2-RCP8.5 scenario and are anticipated to be intensified in already stressed regions. This research is essential for environmentalists, and water resources managers, and provided crucial information to identify the hot spot areas in the target region so that water scarcity problems could be reduced in the future. [ABSTRACT FROM AUTHOR]

Over the past few decades, South Asia (SA) has experienced an upsurge in the frequency of severe monsoonal compound drought and heatwave (CDHW) occurrences. Climate models that identify land‐atmosphere coupling as a major contributing factor for this exacerbation and anticipate an increase in the intensity and frequency of CDHW occurrences in future also represent this. For the first time, this study investigated the future evolution of monsoonal CDHW events based on new generations of the CMIP6 and population products by applying a multivariate framework. Specifically, this study explored the impacts of natural climate variability and future land‐atmosphere coupling on the monsoonal CDHW event risks and their bivariate return periods for two future time‐periods and emission scenarios across SA and its subregions. The odds of CDHW occurrences were then examined using a logistic regression model and its association with the natural and anthropogenic drivers was determined. The results indicate that the monsoonal CDHWs occurrence is anticipated to increase substantially during the late twenty‐first century (2056–2090). The 50‐year CDHW events might increase by two‐fold across most of SA by the mid‐21st century under the high emission scenario. We find that the co‐occurring dry and warm conditions rapidly strengthens with soil moisture and temperature coupling and are further exacerbated by land‐atmospheric feedback loops. Our findings show that persistent dry spells contribute significantly to heatwave events, emphasizing regional exposure to changing climates. Plain Language Summary: CDHW (compound drought and heatwave) events may have caused more severe effects on agriculture, water resources, and human society than their occurrences. Investigating future changes in CDHW is critical for anticipating and reducing the adverse effects of climate change and variability, especially in a densely populated region like South Asia (SA), where most of the population still depends on agricultural productivity. Here, we propose a novel compound and multivariate risk assessment framework to capture weekly droughts and heatwaves at a daily time scale using advanced scientific methods. With this objective, the outcomes of the current study can directly map the vulnerable areas in SA and expose the relationship between two events (drought and heatwave). The current research also explores the risk and population exposure due to CDHW events across SA under current and future warming climates by considering the joint distribution of CDHW severity and duration. The study findings will help inform stakeholders about vulnerable areas where the CDHW events are more likely to become more frequent and severe due to climate change. Key Points: CDHW occurrences are projected to increase significantly during the period mid and late 21st century under the business‐as‐usual scenarioThe population exposure from 50‐year CDHW event might increase two‐fold across 70% of the SA landmasses already by the mid‐21st centuryAnthropogenic global warming show significant positive influence on the most vulnerable climatic regions [ABSTRACT FROM AUTHOR]

The meteorological drought (MD) is the precursor of the hydrological drought (HD), and understanding the propagation process from MD to HD is of great significance for the early warning of HD. Based on the theory of run and Bayesian networks, this study uses the standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI) and standardized streamflow index (SSI) to explore the characteristics and propagation of MD and HD in the regions of eastern Gansu, China. The results indicate that: (1) The region was mainly wet (dry) and the wetness (dryness) was stronger before (after) 2000 when reflected by SSI. The frequency is higher based on SPI and SPEI than that based on SSI, while the duration and magnitude are lower than those based on SSI. (2) The propagation time from MD to HD is generally within 1–10 months. After the occurrence of a certain level of MD, it is most likely to propagate to HD events that are relatively weaker than the MD. Besides, the higher the intensity level of MD, the shorter the propagation time to HD, and the shorter for cumulative occurrence probability of HD to approach 1.0. (3) The increase in temperature and decrease in precipitation, runoff and runoff coefficient may contribute to the dryness after 2000. Moreover, the weakened propagation from MD to HD may be due to the rapid increase of water resource consumption caused by intensive anthropogenic activities in the study area. From the above results, this study found some propagation characteristics from MD to HD in a semi‐arid and semi‐humid watershed, which can provide a supplement for revealing the drought mechanism and can assist in the management and planning of water resources in China. [ABSTRACT FROM AUTHOR]

The present study uses observation-based gridded air temperature and precipitation datasets to investigate the spatiotemporal changes in meteorological drought from 1981 to 2020 in South Asia (SA). The drought characteristics i.e., duration, area, frequency, intensity, and severity are calculated using Run theory and the Standardized Precipitation Evapotranspiration Index (SPEI) on annual and seasonal timescales. SA is divided into four homogeneous subregions using the k-means clustering algorithm, while the trends were estimated using Sen's Slope estimator and modified Mann-Kendall (MMK) test. A state-of-the-art Bayesian Dynamic Linear (BDL) model was employed to assess the sub-regional drought variability and their possible links with large-scale atmospheric drivers such as the Indian Ocean Dipole (IOD), El Niño Southern Oscillation (ENSO), and the Pacific Decadal Oscillation (PDO). It is found that the SA winter season experienced a significant drying long-term trend, especially southwestern and northeastern parts of SA. Drought durations show dipolar patterns with longer drought duration in the southwestern compared to the northern parts of SA. Notably, arid and semi-arid regions have shown significant drying trends in terms of their area, frequency, and severity, implying that meteorological droughts are relatively more severe in those regions. Similarly, the relationship varies over time between drought variability and climate drivers. In particular, the IOD has an impact on drought episodes over southwest SA relative to the northern parts and is mostly affected by the Sea surface temperature (SST) variability. The study results contribute to a better understanding of the meteorological drought characteristics over SA, which is important for disaster risk managers and policymakers to mitigate drought impacts. [ABSTRACT FROM AUTHOR]

Over the last five decades, Pakistan experienced its worst drought from 1998 to 2002 and its worst flood in 2010. This study determined the record-breaking impacts of the droughts (1998–2002) and the flood (2010) and analyzed the given 12-year period, especially the follow-on period when the winter wheat crop was grown. We identified the drought, flood, and warm and cold edges over the plain of Punjab Pakistan based on a 12-year time series (2003–2014), using the vegetation temperature condition index (VTCI) approach based on Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data products. During the year 2010, the Global Flood Monitoring System (GFMS) model applied to the real-time Tropical Rainfall Measuring Mission (TRMM) rainfall incorporated data products into the TRMM Multi-Satellite Precipitation Analysis (TMPA) for the flood detection/intensity, stream flow, and daily accumulative precipitation, and presented the plain provisions to wetlands. This study exhibits drought severity, warm and cold edges, and flood levels using the VTCI drought-monitoring approach, which utilizes a combination of the normalized difference vegetation index (NDVI) with land surface temperature (LST) data products. It was found that during the years 2003–2014, the VTCI had a positive correlation coefficient (r) with the cumulative precipitation (r = 0.60) on the day of the year (D-073) in the winter. In the year 2010, at D-201, there was no proportionality (nonlinear), and at D-217, a negative correlation was established. This revealed the time, duration, and intensity of the flood at D-201 and D-217, and described the heavy rainfall, stream flow, and flood events. At D-233 and D-281 during 2010, a significant positive correlation was noticed in normal conditions (r = 0.95 in D-233 and r= 0.97 in D-281 during the fall of 2010), which showed the flood events and normality. Notably, our results suggest that VTCI can be used for drought and wet conditions in both rain-fed and irrigated regions. The results are consistent with anomalies in the GFMS model using the spatial and temporal observations of the MODIS, TRMM, and TMPA satellites, which describe the dry and wet conditions, as well as flood runoff stream flow and flood detection/intensity, in the region of Punjab during 2010. It should be noted that the flood (2010) affected the area, and the production of the winter wheat crop has consistently declined from 19.041 to 17.7389 million tons. [ABSTRACT FROM AUTHOR]

The Punjab region of Pakistan faced significant losses from flash flooding in 2010 and experienced a multiyear drought during 1998–2002. The current study illustrates the drought and flood conditions using the multi-satellite data products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) as well as the TRMM Multi-satellite Precipitation Analysis (TMPA) satellites with high-quality resolution in the region of Punjab during 2010–2014. To determine the drought and flood events, we used the Vegetation Temperature Condition Index (VTCI) drought monitoring approach combined with the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) to identify the warm and cold edges (WACE) in the provision of soil moisture as well as the VTCI imagery using the MODIS-Aqua data products. We assessed the 2010 flood effect on the four years (2011–2014) of drought conditions during winter wheat crop seasons. The obtained VTCI imagery and precipitation data were utilized to validate the drought and flood conditions in the year 2010 and the drought conditions in the years 2011–2014 during the winter-wheat-crop season. It is worth mentioning that over the four years (2011–2014) of the Julian day~D-041 year, the VTCI shows a stronger link with the accumulative precipitation anomaly (r = 0.77). It was found that for D-201 during the 2010 flood was the relationship was nonlinear, and in D-217, there was a negative relationship which revealed the flood timing, duration, and intensity. For D-281, a correlation (r = 0.97) was noted during fall 2010, which showed the drought and flood extreme conditions for the winter-wheat-crop season in the year 2010–2014. In regard to 2010, the Global Flood Monitoring System (GFMS) model employs the TRMM and TMPA data products to display the study region during the 2010 flood events and validate the VTCI results. This study's spatial and temporal observations based on the observed results of the MODIS, TRMM, and TMPA satellites are in good agreement with dry and wet conditions as well as the flood runoff stream flow and flood intensity. It demonstrates the flood events with high intensity compared with the normality of flood with the complete establishment of flood events and weather extremes during the year of 2011–2014, thereby highlighting the natural hazards impacts. Our findings show that the winter wheat harvest was affected by the 2010 monsoon's summer high rain and floods in the plain of Punjab (Pakistan). [ABSTRACT FROM AUTHOR]

Every year, millions of people are at risk due to droughts in South Asia (SA). The likely impacts of droughts are projected to increase with global warming. This study uses the new ensemble mean of 23 global climate models from the Coupled Model Intercomparison Project phase 6 (CMIP6), population and gross domestic product (GDP) projections to quantify future changes in increasing drought risks and associated socioeconomic exposure across SA and its subregions under 1.5°C and 2°C of warming. We used two shared socioeconomic pathways (SSPs), SSP2‐4.5 and SSP5‐8.5. The most likely realization copula functions are used to model the joint distribution of drought severity and duration. Simultaneously, changes in the bivariate return period are calculated under a warming climate. The frequency of 50‐year historical droughts (under a bivariate framework) might double across 80% of the SA land area under 1.5°C of warming. Conversely, 12% of SA landmasses may suffer extreme droughts under 2°C of warming. The severe drought episode frequency is expected to increase under 1.5°C (40%–75%) and 2°C (60%–90%) of warming relative to the recent climate. The largest exposure increase is projected in R2 and R4, then R1. Additionally, 75% (65%) of the SA population (GDP) could suffer from increased drought risks under the 1.5°C warmer climate, whereas the additional 0.5°C warming will lead to an unbearable regional situation. Limiting global warming to 1.5°C compared with 2°C can significantly reduce the drought risk influence in SA. These findings can help disaster‐risk managers to adopt climate‐smart management strategies. Plain Language Summary: The world has warmed rapidly since 1970, encouraging efforts to reduce climate change and to stabilize global temperatures to between 1.5°C and 2°C warming targets above preindustrial levels. With these aims, the present study explores the spatiotemporal changes in future drought events and their associated socioeconomic exposure over South Asia (SA) and its subregions under a warmer climate. We find that amplified drought risks are projected to increase over southern and southwestern SA under 1.5°C of warming for the SSP2‐4.5 and SSP5‐8.5 scenarios. As the world continues to warm, some land locations, such as the interior of South India and Pakistan, may experience a temporary emergence of a climate change signal that weakens if the climate stabilizes and the Paris Agreement goals are met. In addition, we also found that the frequency of 50‐year historical droughts might double across 80% of the SA land area under the 1.5°C warming target. In contrast, limiting global warming to 1.5°C instead of 2°C can increase the projected population exposures to increased drought risks by almost half. We believe that the study findings will help disaster‐risk managers adopt climate‐smart policies. Key Points: The frequency of 50‐year historical droughts might double across 80% of South Asia (SA) land area under 1.5°C warmingSouthwestern SA is projected to have the largest increase in exposure compared to the other regionsLimiting global warming to 1.5°C instead of 2°C can increase the projected population exposures to increased drought risks by almost half [ABSTRACT FROM AUTHOR]

South Asia is home to one of the fastest-growing populations in Asia, and human activities are leaving indelible marks on the land surface. Yet the likelihood of successive observed droughts in South Asia (SA) and its four subregions (R-1: semi-arid, R-2: arid, R-3: subtropical wet, and R-4: tropical wet and dry) remains poorly understood. Using the state-of-the-art self-calibrated Palmer Drought Severity Index (scPDSI), we examined the impact of different natural ocean variability modes on the evolution, severity, and magnitude of observed droughts across the four subregions that have distinct precipitation seasonality and cover key breadbaskets and highly vulnerable populations. The study revealed that dryness had significantly increased in R-1, R-2, and R-4 during 1981–2020. Temporal analysis revealed an increase in drought intensity for R-1 and R-4 since the 2000s, while a mixed behavior was observed in R-2 and R-3. Moreover, most of the sub-regions witnessed a substantial upsurge in annual precipitation, but a significant decrease in vapor pressure deficit (VPD) during 1981–2020. The increase in precipitation and the decline in VPD partially contributed to a significant rise in Normalized Difference Vegetation Index (NDVI) and a decrease in dryness. In contrast, a strong positive correlation was found between drought index and precipitation, and NDVI across R-1, R-2, and R-4, whereas temperature and VPD exhibited a negative correlation over these regions. No obvious link was detected with El-Niño Southern Oscillation (ENSO) events, or Indian Ocean Dipole (IOD) and drought evolution, as explored for certain regions of SA. The findings showed the possibility that the precipitation changes over these regions had an insignificant relationship with ENSO, IOD, and drought onset. Thus, the study results highlight the need for considering interactions within the longer climate system in describing observed drought risks rather than aiming at drivers from an individual perspective. [ABSTRACT FROM AUTHOR]

Long‐term drought monitoring and its assessment are of great importance for meteorological disaster risk management. The recurrent spells of heat waves and droughts have severely affected the environmental conditions worldwide, including Pakistan. The present work sought to investigate the spatiotemporal changes in drought characteristics over Pakistan during Rabi and Kharif cropping seasons. The role of large‐scale circulation and interannual mode of climate variability is further explored to identify the physical mechanisms associated with droughts in the region. Monthly precipitation and temperature data (1983–2019) from 53 meteorological stations were used to study drought characteristics, using the standardized precipitation evapotranspiration index (SPEI). The nonparametric Mann–Kendall, Sen's Slope, and Sequential Mann–Kendall tests were applied on the drought index to determine the statistical significance and magnitude of the historical trend. The state‐of‐the‐art Bayesian Dynamic Linear model was further used to analyse large‐scale climate drivers of droughts, revealed an increase in drought severity, mostly over arid to semiarid regions for both cropping seasons. While temperature played a significant role in defining droughts over dry and hot seasons, rainfall is influential over the western disturbances influenced region. The analysis of atmospheric circulation patterns revealed that large‐scale changes in wind speed, air temperature, relative humidity, and geopotential height anomalies are the likely drivers of droughts in the region. We found that Niño4, sea surface temperature, and multivariate El Niño‐Southern Oscillation (ENSO4.0) Index are the most influential factors for seasonal droughts across Pakistan. Overall, the findings provide a better understanding of drought‐prone areas in the region, and this information is of potential use for mitigating and managing drought risks. [ABSTRACT FROM AUTHOR]

This document is a corrigendum for an article titled "Recent and projected changes in water scarcity and unprecedented drought events over Southern Pakistan." The correction addresses an error in the funding statement of the article, providing the correct funding information. The authors express their gratitude for the financial support and state that the correction does not affect the scientific conclusions of the article. The document also includes a note from the publisher, stating that the claims expressed in the article are solely those of the authors and do not necessarily represent the views of their affiliated organizations or the publisher. The authors and their affiliations are listed at the end of the document. [Extracted from the article]

Drought is one of the most frequent natural disasters occurring in Pakistan and has a great influence on livelihood, agriculture, and economy. The availability of long‐term high‐quality reanalysis products over Pakistan has been of great concern in recent decades. Here, we conduct drought assessment in Pakistan based on the standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI) at 3, 6, and 12 months timescales during 1983–2018. We use long‐term in situ observations to evaluate the accuracy of reanalysis products, including Climatic Research Unit (CRU TS), National Centers for Environmental Prediction version II (NCEP‐2), European Centre for Medium‐Range Weather Forecasts Version‐5 (ERA‐5), and Modern‐Era Retrospective analysis for Research and Applications version II (MERRA‐2). The main results are summarized as follows: (a) drought indices and drought areas assessed from reanalysis products are relatively more representative of historical droughts that had occurred in southern Pakistan and overestimation is evident for drought severity in western than eastern Pakistan; (b) statistically significant increasing trends (1984–1998 and 2000–2010) in monthly drought areas and occurrence are evident by CRU TS and MERRA‐2 in dominant arid and semiarid regions; (c) climate variables and drought features of southern Pakistan are best represented by CRU TS and MERRA‐2, while that of southwestern and western parts are best represented by ERA‐5; (d) the Nash–Sutcliffe efficiency (NSE) results range from −2 to 1, where the NSE of SPEI values (−1.0) show relatively weaker than SPI values (0.5) in most parts of the regions, specifically in the southern Pakistan; (e) a strong positive linear relationship on a monthly scale is evident in CRU TS, MERRA‐2, and ERA‐5 exhibiting relatively high correlation coefficient (0.84), except for NCEP‐2. Furthermore, the SPEI results are found to be better than SPI; thus, this study suggests SPEI may be more suitable than SPI in monitoring droughts under climate change. [ABSTRACT FROM AUTHOR]

The substantial reliance of South Asia (SA) to rain-based agriculture makes the region susceptible to food scarcity due to droughts. Previously, most research on SA has emphasized the meteorological aspects with little consideration of agrarian drought impressions. The insufficient amount of in situ precipitation data across SA has also hindered thorough investigation in the agriculture sector. In recent times, models, satellite remote sensing, and reanalysis products have increased the amount of data. Hence, soil moisture, precipitation, terrestrial water storage (TWS), and vegetation condition index (VCI) products have been employed to illustrate SA droughts from 1982 to 2019 using a standardized index/anomaly approach. Besides, the relationships of these products towards crop production are evaluated using the annual national production of barley, maize, rice, and wheat by computing the yield anomaly index (YAI). Our findings indicate that MERRA-2, CPC, FLDAS (soil moisture), GPCC, and CHIRPS (precipitation) are alike and constant over the entire four regions of South Asia (northwest, southwest, northeast, and southeast). On the other hand, GLDAS and ERA5 remain poor when compared to other soil moisture products and identified drought conditions in regions one (northwest) and three (northeast). Likewise, TWS products such as MERRA-2 TWS and GRACE TWS (2002–2014) followed the patterns of ERA5 and GLDAS and presented divergent and inconsistent drought patterns. Furthermore, the vegetation condition index (VCI) remained less responsive in regions three (northeast) and four (southeast) only. Based on annual crop production data, MERRA-2, CPC, FLDAS, GPCC, and CHIRPS performed fairly well and indicated stronger and more significant associations (0.80 to 0.96) when compared to others. Thus, the current outcomes are imperative for gauging the deficient amount of data in the SA region, as they provide substitutes for agricultural drought monitoring. [ABSTRACT FROM AUTHOR]

Drought is an intricate atmospheric phenomenon with the greatest impacts on food security and agriculture in South Asia. Timely and appropriate forecasting of drought is vital in reducing its negative impacts. This study intended to explore the performance of the evaporative stress index (ESI), vegetation health index (VHI), enhanced vegetation index (EVI), and standardized anomaly index (SAI) based on satellite remote sensing data from 2002–2019 for agricultural drought assessment in Afghanistan, Pakistan, India, and Bangladesh. The spatial maps were generated against each index, which indicated a severe agricultural drought during the year 2002, compared to the other years. The results showed that the southeast region of Pakistan, and the north, northwest, and southwest regions of India and Afghanistan were significantly affected by drought. However, Bangladesh faced substantial drought in the northeast and northwest regions during the drought year (2002). The longest drought period of seven months was observed in India followed by Pakistan and Afghanistan with six months, while, only three months were perceived in Bangladesh. The correlation between drought indices and climate variables such as soil moisture has remained a significant drought-initiating variable. Furthermore, this study confirmed that the evaporative stress index (ESI) is a good agricultural drought indicator, being quick and with greater sensitivity, and thus advantageous compared to the VHI, EVI, and SAI vegetation indices. [ABSTRACT FROM AUTHOR]

• MD and HD propagations were investigated using Bayesian networks and SWAT. • Temperature and precipitation roles were highlighted in MD to HD transition. • Spatial propagation patterns were revealed in MD and HD development. In order to better understand the spatio-temporal propagation of drought and develop early warning systems for hydrological droughts under changing climate conditions, it is crucial to investigate the relationship between meteorological drought (MD) and hydrological drought (HD). There are challenges in understanding the physical process of drought propagation and limitations of current hydrological simulation technology, and the understanding of the law and mechanisms of spatio-temporal propagation of drought is also insufficient. In this study, weekly scale SPEI and SSI indexes were used to explore the propagation of drought based on the run theory and Bayesian network. SWAT model was applied to design scenarios to simulate the changes of runoff under different climate conditions and to reveal the impacts of climate conditions on the temporal propagation from MD to HD. And the spatial propagation characters were further investigated during the development and recovery period of MD and HD under different climatic conditions. The results showed that climate change had greater impacts than land-use change on Poyang Lake Basin. Increasing temperature (precipitation) led to shorter (longer) time for the cumulative occurrence probability of HD to reach 1 during the propagation from MD to HD. HD lagged behind MD in both drought development and recovery period, with time lags of approximately 35 and 10 days, respectively. Drought propagation followed typical source area and sink area characters with the recovery path being reversed compared to the development path, and the sink area recovered first, followed by the source area. Precipitation played a significant role in the recovery of drought, but its impacts were more immediate on MD areas and it took longer to affect HD areas. [ABSTRACT FROM AUTHOR]