The Tibetan Plateau, often referred to as Asia\'s water tower, is a focal point for studying spatiotemporal changes in water resources amidst global warming. Precipitation is a crucial water resource for the Tibetan Plateau. Precipitation information holds significant importance in supporting research on the Tibetan Plateau. In this study, we estimate the performance and applicability of Climate Prediction Center Merged Analysis of Precipitation (CMAP), Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG), Global Land Data Assimilation System (GLDAS), and Global Precipitation Climatology Project (GPCP) precipitation products for estimating precipitation and different disaster scenarios (including extreme precipitation, drought, and snow) across the Tibetan Plateau. Extreme precipitation and drought indexes are employed to describe extreme precipitation and drought conditions. We evaluated the performance of various precipitation products using daily precipitation time series from 2000 to 2014. Statistical metrics were used to estimate and compare the performances of different precipitation products. The results indicate that (1) Both CMAP and IMERG showed higher fitting degrees with gauge precipitation observations in daily precipitation. Probability of detection, False Alarm Ratio, and Critical Success Index values of CMAP and IMERG were approximately 0.42 to 0.72, 0.38 to 0.56, and 0.30 to 0.42, respectively. Different precipitation products presented higher daily average precipitation amount and frequency in southeastern Tibetan Plateau. (2) CMAP and GPCP precipitation products showed relatively great and poor performance, respectively, in predicting daily and monthly precipitation on the plateau. False alarms might have a notable impact on the accuracy of precipitation products. (3) Extreme precipitation amount could be better predicted by precipitation products. Extreme precipitation day could be badly predicted by precipitation products. Different precipitation products showed that the bias of drought estimation increased as the time scale increased. (4) GLDAS series products might have relatively better performance in simulating (main range of RMSE: 2.0-4.5) snowfall than rainfall and sleet in plateau. G-Noah demonstrated slightly better performance in simulating snowfall (main range of RMSE: 1.0-2.1) than rainfall (main range of RMSE: 2.0-3.8) and sleet (main range of RMSE: 1.5-3.8). This study\'s findings contribute to understanding the performance variations among different precipitation products and identifying potential factors contributing to biases within these products. Additionally, the study sheds light on disaster characteristics and warning systems specific to the Tibetan Plateau.

Drought is deemed a major natural disaster that can lead to severe economic and social implications. Drought indices are utilized worldwide for drought management and monitoring. However, as a result of the inherent complexity of drought phenomena and hydroclimatic condition differences, no universal drought index is available for effectively monitoring drought across the world. Therefore, this study aimed to develop a new meteorological drought index to describe and forecast drought based on various artificial intelligence (AI) models: decision tree (DT), generalized linear model (GLM), support vector machine, artificial neural network, deep learning, and random forest. A comparative assessment was conducted between the developed AI-based indices and nine conventional drought indices based on their correlations with multiple drought indicators. Historical records of five drought indicators, namely runoff, along with deep, lower, root, and upper soil moisture, were utilized to evaluate the models\' performance. Different combinations of climatic datasets from Alice Springs, Australia, were utilized to develop and train the AI models. The results demonstrated that the rainfall anomaly drought index was the best conventional drought index, scoring the highest correlation (0.718) with the upper soil moisture. The highest correlation between the new and conventional indices was found between the DT-based index and the rainfall anomaly index at a value of 0.97, whereas the lowest correlation was 0.57 between the GLM and the Palmer drought severity index. The GLM-based index achieved the best performance according to its high correlations with conventional drought indicators, e.g., a correlation coefficient of 0.78 with the upper soil moisture. Overall, the developed AI-based drought indices outperformed the conventional indices, hence contributing effectively to more accurate drought forecasting and monitoring. The findings emphasized that AI can be a promising and reliable prediction approach for achieving better drought assessment and mitigation.

Agricultural drought affects the regional food security and thus understanding how meteorological drought propagates to agricultural drought is crucial. This study examines the temporal scaling trends of meteorological and agricultural drought data over 34 Indian meteorological sub-divisions from 1981 to 2020. A maximum Pearson\'s correlation coefficient (MPCC) derived between multiscale Standardised Precipitation Index (SPI) and monthly Standardised Soil Moisture Index (SSMI) time series was used to assess the seasonal as well as annual drought propagation time (DPT). The multifractal characteristics of the SPI time series at a time scale chosen from propagation analysis as well as the SSMI-1 time series were further examined using Multifractal Detrended Fluctuation Analysis (MF-DFA). Results reveal longer average annual DPT in arid and semi-arid regions like Saurashtra and Kutch (~ 6 months), Madhya Maharashtra (~ 5 months), and Western Rajasthan (~ 6 months), whereas, humid regions like Arunachal Pradesh, Assam and Meghalaya, and Kerala exhibit shorter DPT (~ 2 months). The Hurst Index values greater/less than 0.5 indicates the existence of long/short-term persistence (LTP/STP) in the SPI and SSMI time series. The results of our study highlights the inherent connection among drought propagation time, multifractality, and regional climate variations, and offers insights to enhance drought prediction systems in India.

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

Drought is a natural and complex climatic hazard. It has both natural and social connotations. The purpose of this study is to use machine learning methods (MLAs) for drought vulnerability (DVM) in Uttar Pradesh, India. There were 18 factors used to determine drought vulnerability, separated into two groups: physical drought and meteorological drought. The study found that the eastern part of Uttar Pradesh is high to very highly prone to drought, which is approximately 31.38% of the area of Uttar Pradesh. The receiver operating characteristic curve (ROC) was then used to evaluate the machine learning models (artificial neural networks). According to the findings, the ANN functioned with AUC values of 0.843. For policy actions to lessen drought sensitivity, DVMs may be valuable. Future exploration may involve refining machine learning algorithms, integrating real-time data sources, and assessing the socio-economic impacts to continually enhance the efficacy of drought resilience strategies in Uttar Pradesh.

The division and evaluation of data series used in monitoring drought into different time intervals is a practical approach to detecting the spatial and temporal extent of drought spread. This study aimed to determine meteorological drought\'s spatial and temporal distribution using overlapping and consecutive periods and cycles of the standardized precipitation index (SPI) time series in the Mediterranean region, Turkey. In the scope of the research, SPI values for the SPI12, SPI6 (1), and SPI6 (2) seasons were calculated for consecutive and overlapping hydrological years (1978-1998/21 years, 1978-2008/31 years, and 1978-2018/41 years) at 28 meteorological stations. Autocorrelation, Mann-Kendall, and Sen slope trend tests were applied at a 5% significance level for each season (SPI12, SPI6 (1), and SPI6 (2)) and different time scales (21, 31, and 41 years). For each season and period, maps of the SPI drought class, average formation of drought class, Mann-Kendall (MK) trend, and Sen\'s slope (SS) trend test statistics for the Mediterranean region were obtained, and the spatial distribution rate of trends was determined by drawing hypsometric curves. Changes in drought occurrence at different time scales were thoroughly evaluated with the changing length of data recording. Consequently, it was determined that the mild wet (MIW) and mild drought (MID) classes dominate the study area in the Mediterranean region. Significant and nonstationary changes detected in extreme wet and drought occurrences (extreme wet, EW; severe wet, SW; extreme drought, ED; severe drought, SD) were found to pose a risk in the study area. It was observed that there were spatially and temporally insignificant decreasing drought trends in the Mediterranean basin, considering that the time scales of these trends slowed down. Despite a nonsignificant trend from the MID drought class to the MIW drought class, it is predicted that the MIW and MID classes will maintain their dominance in the Mediterranean region. The central part of the study area (central Mediterranean basin) is the region with the highest drought risk.

Solar-induced chlorophyll fluorescence (SIF) has been used since its discovery to characterize vegetation photosynthesis and is an effective tool for monitoring vegetation dynamics. Its response to meteorological drought enhances our comprehension of the ecological consequences and adaptive mechanisms of plants facing water scarcity, informing more efficient resource management and efforts in mitigating climate change. This study investigates the spatial and temporal patterns of SIF and examines how vegetation SIF in the Yellow River Basin (YRB) responds to meteorological drought. The findings reveal a gradual southeast-to-northwest decline in SIF across the Yellow River Basin, with an overall increase-from 0.1083 W m-2μm-1sr-1 in 2001 to 0.1468 W m-2μm-1sr-1 in 2019. Approximately 96% of the YRB manifests an upward SIF trend, with 75% of these areas reaching statistical significance. The Standardized Precipitation Evapotranspiration Index (SPEI) at a time scale of 4 months (The SPEI-4), based on the Liang-Kleeman information flow method, is identified as the most suitable drought index, adeptly characterizing the causal relationship influencing SIF variations. As drought intensified, the SPEI-4 index markedly deviated from the baseline, resulting in a decrease in SIF values to their lowest value; subsequently, as drought lessened, it gravitated towards the baseline, and SIF values began to gradually increase, eventually recovering to near their annual maximum. The key finding is that the variability of SIF with SPEI is relatively pronounced in the early growing season, with forests demonstrating superior resilience compared to grasslands and croplands. The responsiveness of vegetation SIF to SPEI can facilitate the establishment of effective drought early warning systems and promote the rational planning of water resources, thereby mitigating the impacts of climate change.

Lacking of available water quality data causes the limited understanding of the coupled dynamics of hydrologic and nutrient cycles in lakes and reservoirs and along river streams. This study conducts the rotated Principal Component Analysis (rPCA) of water volume and total organic carbon (TOC) concentration data from ∼2200 agricultural reservoirs in South Korea to extract the major modes of their spatiotemporal variability. Over 2020-2022, the total TOC load in the reservoirs ranges between 1,165 and 1,492 tons (289 and 360 Mtons of water storage volume; 3.54 and 4.60 mg/L of TOC concentration). The first rPCA mode is assoicated with a decreasing trend of water level (38 % of the explained variance) and increasing trend of TOC concentration (27 %) over the southern Korea region, where the TOC concentration increased during the 2022 drought. The second rPCA mode is associated with interannual variability of water level (25 %) and TOC concentration (18 %) over the central Korea region. This study found a marginal relationship between paddy field area and TOC concentration and their regime shift to high TOC concentration during the 2022 drought, which was a potential cause of the increased TOC concentration in 2022. This study provided observational evidence of interactions between water volume and TOC concentration during a severe drought, suggesting a possible shift of the role of agricultural reservoirs to carbon source.

This research was conducted on North Wollo, South Wollo, and Oromia special zones, in Ethiopia. The study aimed to analyze the temporal and spatial variability of meteorological and hydrological drought trends using the selected drought indices and to predict its future trend in the selected areas. To achieve these objectives, meteorological and hydrological data were collected from the Ethiopian Meteorology Institute and the Ministry of Water and Energy respectively. The historical and future drought condition was analyzed by using the standardized precipitation index (SPI), reconnaissance drought index (RDI), and streamflow drought index (SDI) from the drought indicator calculator (DrinC) software. Based on the availability of the data, for historical drought analysis, ten meteorological stations with thirty-two years of daily data were selected. For the future scenario, RCP 4.5 was used to downscale the future climate data and to forecast SPI and RDI values. Also, an artificial neural network (ANN) was applied to forecast the future streamflow data using Python software, then the future hydrological drought was determined using the forecasted streamflow data. The result indicates that all zones were historically affected by severe to extreme droughts, especially 1984, 1986, 1987, 1989, 1991, 1992, 2003, 2007, 2010, 2013, and 2014 years. From 1984 to 1992 the probability of severe to extreme drought occurrence was on average of two years intervals and from 1992 to 2003 there is a huge gap. From the future drought analysis results, the probability of severe to extreme drought occurrence will be at five-year intervals on average. Based on the analyzed results, the frequency of severe to extreme drought occurrence of historical drought which was two and three years was increased to five years for the future conditions on average. But, these are short intervals and the magnitude of the event is very high. So, the regional water and energy office and other concerned bodies in the area have to plan a good drought mitigation mechanism and should develop a drought early warning system for the communities in and around the study area.

Understanding the spatiotemporal patterns of drought is crucial for planning, disaster preparedness, vulnerability assessment, impact evaluation, and policy formulation to mitigate drought-induced effects. The purpose of this study was to assess rainfall trends and spatiotemporal patterns of meteorological drought using geospatial techniques in Menna watershed. The Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) rainfall, and station-based observed rainfall were the datasets used. The station-based rainfall was used to confirm the accuracy of CHIRPS rainfall data. The Mann-Kendall (MK) test and Sen\'s slope estimator were utilized to assess trends and ascertain the extent of change. To characterize meteorological droughts, percent of normal (PN), standardized anomaly index (SAI), and standardized precipitation index (SPI) were computed during the crop growing seasons (2000-2022). The validation result confirmed a strong agreement between the observed and CHIRPS rainfall data (R2 = 0.88). Based on the MK test, an increasing trend has been observed in annual (3.7 mm/year) and belg (3.4 mm/year) rainfall, which was significant at p < 0.05. But the kiremt season was slightly decreasing (-0.7 mm/year). The PN, SAI, and SPI values detected that 2002, 2004, 2009, 2011, 2014, 2015, and 2019 were drought years in the area. Even only 1.4, 0.2, and 0.5% of the watershed were free from drought in 2009, 2014, and 2015, respectively, due to extremely high rainfall deficiency. Conversely, 2001, 2010, and 2016 were notable for having the highest amounts of rainfall compared to the other years. Generally, the region could be classified as an area highly susceptible to meteorological drought in northwestern Ethiopia. There was no even a single year free from drought in the entire study period. To that extent, about 86% of it had repeatedly encountered extreme rainfall deficit (7-23 times) during the study period. Thus, the population has always been repeatedly smashed down by the frequent droughts. To tackle existing challenges and mitigate upcoming risks, continual droughts monitoring and implementation of efficient early warning systems are vital for the region.