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• Central Asia drought propagation time were longer early and late in the growing season. • Drought thresholds in Central and Western Central Asia were often below 30 mm during 1948–2022. • Soil moisture drought intensity was positively correlated with drought propagation characteristics. • VPD and AI are the main drivers of accelerated drought propagation. Drought propagation time (PT) and threshold (TR) play crucial roles in real-time monitoring of drought progression and severity under climate change, for which there is currently no unified method or standard for quantitative characterization. Central Asia (CA), a prototypical arid region, is frequently plagued by droughts that profoundly impact its ecology and socioeconomic framework, leaving a lack of comprehension about the transition from meteorological to soil moisture drought as for the complex impact of ground-atmosphere interaction. Therefore, this study revealed high-resolution PT and TR across CA based on precipitation and soil moisture datasets by employing a Bayesian causal framework for drought propagation modeling, in which the sliding window with the random forest method was integrated to investigate the dynamic changes and driving factors of drought propagation over recent decades. The conclusions drawn are as follows: (1) in the southeastern hilly and eastern plateau, a prolonged PT (i.e., longer than 100 days) was experienced during the extremities of the growing season, while most areas had a shorter mid-season PT (i.e., less than 40 days), in which the soil moisture drought intensity was positively correlated with longer PTs; (2) from 1948 to 2022, the central and western regions in CA generally displayed lower TR values than those in the northeast, seldom exceeding 30 mm, reflecting the regional precipitation gradient and the positive relationship between TR and severe soil moisture droughts; and (3) the increase in vapor pressure deficit (VPD), the decrease in aridity index (AI) for the reduced precipitation, and the considerable impact of vegetation transpiration on soil moisture consumption contributed to accelerated drought propagation. Overall, this study reveals the complex dynamics and mechanisms of drought propagation amid environmental changes, laying the foundation for innovative drought early warning systems and emergency management approaches in CA. [ABSTRACT FROM AUTHOR]

[Display omitted] • An improved general unit hydrograph model for dam-break hydrograph was proposed. • The RMSE of proposed model was reduced by 65 % during the rising stage. • Successfully applied the model to 1818 Giétro Glacial Lake and Cancano Dam outburst flood hydrographs. • This model outperforms Gamma and Log-normal models. Dam breaks pose significant risks, necessitating simple and effective predictive models for disaster management. This study aims to enhance the understanding of dam-break flood hydrographs, an aspect less emphasized in traditional research focusing mainly on peak discharge and flow depth values. Employing a series of experiments, an improved general unit hydrograph model was used to analyze both discharge and flow depth hydrographs. This model is then critically compared with the standard gamma and log-normal asymmetric distribution models. The results reveal that the improved unit hydrograph model significantly outperforms these traditional models, particularly in predicting the rising stage of discharge hydrographs, evidenced by a 65 % reduction in root mean square error. Comparison of this model with real dam failures in natural river valleys shows its potential to be applied to changes in river slope and different dam-break rates. These advancements present a more precise methodology for predicting dam-break flood hydrographs, offering benefits for disaster management strategies. [ABSTRACT FROM AUTHOR]

[Display omitted] • Three series of pairwise rainfall and river flow were extracted to capture their dependence. • Pairs of rainfall and river flow were proportionally sampled from their bivariate joint distributions. • Expected annual damage was employed to characterize compound flood risk. • The relationship between flood damage and rainfall and river flow is positively stepped. • Future compound flood risk is expected to increase in most of the Yangtze River Basin. Compound floods induced by co-occurring multiple drivers may exacerbate the flood impacts and lead to larger flood damage. Exploring future changes in compound flood risk is imperative for flood management and disaster reduction. This study attempts to investigate future changes in compound flood risk across the Yangtze River Basin during 2030 ∼ 2100. Future river flow was projected using an improved hydrological model and pairwise series of extremes of rainfall and river flow were extracted from both observed and projected series. Subsequently, stochastic pairs of rainfall and river flow characterizing compound floods were proportionally sampled from their bivariate joint distributions. The damage from each compound flood was obtained from the flood damage function constructed by Random Forests (RF). Further, the expected annual damage (EAD) was calculated to investigate future changes in compound flood risk. Results show that: (1) Future annual maximums of rainfall and river flow are expected to increase by 14.51 % ∼ 66.13 % and 1.72 % ∼ 55.73 % in the mainstream and northern tributaries, while future annual peak discharge in the southern tributaries (except for the Dongting Lake Basin) is expected to decrease by 4.18 % ∼ 12.30 %. A similar spatial distribution of future changes is also found in the bivariate joint distribution of rainfall and river flow. (2) The high coefficient of determination (R 2) of 0.84 indicates the satisfactory simulation and projection capacity of the constructed flood damage function. The positive stepped relationship between flood damage and rainfall or river flow reflects the superposition of multiple flood damage processes. (3) The Han River Basin, the Jialing River Basin, and the two-lakes (the Dongting and Poyang Lakes) area face great threat from compound floods in both historical and future periods. Future compound flood risk is expected to increase by 13.43 % ∼ 46.04 % in these regions except for the Poyang Lake Basin, while future risk is expected to increase by 2.03 % ∼ 46.04 % in the whole basin. The findings help improve the understanding of future flood risk variations in the Yangtze River Basin and provide essential information for damage reduction. [ABSTRACT FROM AUTHOR]

• IMERG-E and IMERG-L is evaluated and compared with GSMaP-MVK during the record-breaking Meiyu event in 2020. • IMERG-L can generally reproduce the diurnal cycle of precipitation amount, with a two-hour earlier peak. • The bias of IMERG products in the afternoon is higher than in the morning for all precipitation intensities. • The performance of IMERG in reproducing rainstorm events fluctuates significantly throughout the day, influenced by the use of varying data sources. • IMERG-L (14 h, time lag) is recommended for relevant near-real time applications during the extrme Meiyu season, provided that the time lag is permissible. The extreme Meiyu season, characterized by continuous heavy precipitation, is often leads to numerous flood-related disasters. Accurate and timely monitoring of extreme Meiyu precipitation is crucial for the effective prevention and mitigation of these disasters. The near-real-time (NRT) products from Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), specifically IMERG-E and IMERG-L products, have high potential utility to acquire timely precipitation data. Therefore, it is important to quantify their errors and uncertainties during the extreme Meiyu season. In this study, we assess and compare the performance of IMERG NRT products with Global Satellite Mapping of Precipitation Motion Vector Kalman product (GSMaP-MVK) using hourly gauge observations during the recor-breaking Meiyu event in 2020. The main conclusions are as follows: all three satellite products generally capture the spatial–temporal evolution of Meiyu precipitation, with IMERG-L showing the closest agreement with gauge observations, particularly for the precipitation center. In terms of the statistical and volumetric categorical metrics, IMERG-L performs best among the three products, while IMERG-E does not exhibit superior performance compared to GSMaP-MVK. Regarding the diurnal cycle, IMERG-L generally reproduces the diurnal cycle of observed precipitation amount with a peak time occurring two hours earlier. In contrast, both IMERG-E and GSMaP-MVK exhibit notably poorer performance. When it comes to the diurnal cycle of precipitation frequency and intensity, both IMERG products display bad performance in reproducing their diurnal variations. Furthermore, all three satellite products show a wet biases for light precipitation and a dry biases for heavy precipitation, which is consistent with previous evaluation studies. However, a novel phenomenon is noticed that theses biases tend to worsen significantly in the afternoon compared to the morning. Although this study is limited to a case study, the findings can serve as a valuable reference for the NRT applications of IMERG products during the extreme Meiyu seasons and provide insights for the improvement of IMERG algorithm. [ABSTRACT FROM AUTHOR]

• A data-driven waterlogging risk mapping method was proposed based on long-tail learning and game theory. • A novel F (α , β) score was defined to both consider precision and recall and trade-off equity and efficiency in public emergency resource allocation. • A framework for evaluating and enhancing urban waterlogging risk mitigation capabilities was proposed, targeting nonprofit operations and government bailouts. Because disaster reduction resources are always constrained, risk mitigation measures and policies need to be weighted toward efficiency or equity. Greater efficiency focuses on high-risk areas, and greater equity focuses on identifying additional potential risk areas. To address this efficiency-equity tradeoff decision, a data-driven urban waterlogging risk mitigation capability evaluation and improving framework was proposed for nonprofit operations and government bailouts. First, a novel evaluation index was defined, the F (α , β) score, to consider both precision and recall in the binary classification tasks and to seek an effective efficiency-equity trade-off, after which a two-person, zero-sum matrix game model was developed to calculate the α and β parameters and select the highest valued optimal model. Then, using this model, a risk probability map was generated for Wuhan, China, and data envelopment analysis was used to conduct a risk mitigation capability evaluation and identify the needed policy improvements. It was found that: (1) vulnerability was the primary risk-related factor, followed by adaptability and restorability; (2) more importance should be attached to equity than efficiency when implementing risk reduction capability evaluations, that is, authorities should reduce resource investments in extremely high-risk DEA-inefficient areas and identify a greater number of potential risk areas; (3) there were around 1.55% of extremely high-risk areas, which were mainly concentrated in the Jianghan and Wuchang districts and around 9% of high-risk areas, mainly located in the Hongshan district; and (4) northeastern and southwestern Jianghan district could be a valuable reference for urban waterlogging management and disaster reduction. However, excessive investment in transportation infrastructure and construction in DEA inefficient areas should be appropriately reduced. [ABSTRACT FROM AUTHOR]

[Display omitted] • Short term drought (SPI-3) in central Europe for more than 200 years was investigated. • 4 ML (BG, DT, M5P, RF) algorithms and 3 scenarios were used for drought prediction. • Drought frequency increased since 1970 and projected to be increased (ssp245, ssp460). • More frequent droughts are projected under ssp460. • ML algorithms varied in their ability for SPI-3 drought prediction. • RF outperformed other algorithms for accurate prediction. Water availability for agricultural practices is dynamically influenced by climatic variables, particularly droughts. Consequently, the assessment of drought events is directly related to the strategic water management in the agricultural sector. The application of machine learning (ML) algorithms in different scenarios of climatic variables is a new approach that needs to be evaluated. In this context, the current research aims to forecast short-term drought i.e., SPI-3 from different climatic predictors under historical (1901–2020) and future (2021–2100) climatic scenarios employing machine learning (bagging (BG), random forest (RF), decision table (DT), and M5P) algorithms in Hungary, Central Europe. Three meteorological stations namely, Budapest (BD) (central Hungary), Szeged (SZ) (east south Hungary), and Szombathely (SzO) (west Hungary) were selected to forecast short-term agriculture drought i.e., Standardized Precipitation Index (SPI-3) in the long run. For this purpose, the ensemble means of three global circulation models GCMs from CMIP6 are being used to get the projected (2021–2100) time series of climatic indicators (i.e., rainfall R , mean temperature T , maximum temperature T m a x , and minimum temperature T m i n under two scenarios of socioeconomic pathways (SSP2-4.5 and SSP4-6.0). The results of this study revealed more severe to extreme drought events in past decades, which are projected to increase in the near future (2021–2040). Man-Kendall test (Tau) along with Sen's slope (SS) also revealed an increasing trend of SPI-3 drought in the historical period with Tau = −0.2, SS = −0.05, and near future with Tau = −0.12, SS = −0.09 in SSP2-4.5 and Tau = −0.1, SS = −0.08 in SSP4-6.0. Implementation of ML algorithms in three scenarios: SC1 (R + T + T max + T min), SC2 (R), and SC3 (R + T)) at the BD station revealed RF-SC3 with the lowest RMSE RFSC3 - TR = 0.33, and the highest NSE RFSC3 - TR = 0.89 performed best for forecasting SPI-3 on historical dataset. Hence, the best selected RF-SC3 was implemented on the remaining two stations (SZ and SzO) to forecast SPI-3 from 1901 to 2100 under SSP2-4.5 and SSP4-6.0. Interestingly, RF-SC3 forecasted the SPI-3 under SSP2-4.5, with the lowest RMSE = 0.34 and NSE = 0.88 at SZ and RMSE = 0.34 and NSE = 0.87 at SzO station for SSP2-4.5. Hence, our research findings recommend using SSP2-4.5, to provide more accurate drought predictions from R + T for future projections. This could foster a gradual shift towards sustainability and improve water management resources. However, concrete strategic plans are still needed to mitigate the negative impacts of the projected extreme drought events in 2028, 2030, 2031, and 2034. Finally, the validation of RF for short-term drought prediction on a large historical dataset makes it significant for use in other drought studies and facilitates decision making for future disaster management strategies. [ABSTRACT FROM AUTHOR]

• The drought indices SDCI, VHI and IDCI were constructed by four weighting methods. • Determination of weights was validated by comparing against scPDSI, SMCI and CY. • New classification schemes were executed by cumulative frequency curve of scPDSI. • Reliability of schemes was verified by drought affected area and frequencies. With the continuous development of remote sensing technology, an increasing number of drought indices are proposed to assess the drought process in different regions. However, most of the studies used the empirical coefficient method and standard drought classification scheme, without considering regional applicability, which may overestimate or underestimate drought intensity. Therefore, the GLDAS soil moisture dataset was firstly verified in the study area, Wei River basin, China, and then the response of precipitation accumulation to NDVI and the lagged effect were analyzed. Secondly, the Scaled Drought Condition Index (SDCI), Vegetation Health Index (VHI), and Integrated Drought Condition Index (IDCI) were constructed and the optimal weights of the drought indicators were determined against the scPDSI, SMCI and CY based on four weighting methods. Finally, the new classification schemes of the three indicators were executed based on the cumulative frequency curve of the self-calibrating Palmer Drought Severity Index (scPDSI) from June to September during the period 2000–2019, and the reliability of the new schemes was verified by comparing with drought frequency and drought affected area. Furthermore, the variabilities of drought frequency of the new classification scheme based on the three indicators were analyzed. The results showed that the correlation coefficients of 2-month accumulation of precipitation and NDVI produced the best value and no lagged effect was detected. The SDCI and VHI determined by the AHP weighting method and the IDCI determined by the entropy weighting method showed better performance. The new classified schemes of the three drought indicators demonstrated better performance than the original classified scheme with more similar frequency distributions to scPDSI and higher correlation coefficients against the drought affected area. Additionally, the three indicators based on the revised classification scheme all indicated a decreasing trend in the frequency of drought occurrence for all the different drought levels; the SDCI and VHI were better at capturing drought events than IDCI. Overall, the results of this study proved that the optimal weighting methods and new classification schemes could effectively improve the precision of drought assessment and may provide helpful information for agricultural drought disaster prevention. [ABSTRACT FROM AUTHOR]

• A validated two-dimensional numerical model is adopted to simulate the process of flood entering buildings through doors and windows. • The phenomenon of water flooding a cluster of buildings is simulated with this model, and the interaction of the flows in the buildings and on the streets are analyzed and discussed. • A dynamic assessment of pedestrian's risk is undertaken, providing insights and recommendations for urban flood prevention and mitigation. In recent years, the study of urban floods has gained significant importance due to the impacts of climate change and rapid urbanization. A crucial task in accurately reproducing real-world urban flood events lies in enhancing the modeling capability to incorporate the entry of floodwaters into buildings through openings. This study presents a validated two-dimensional (2D) model based on the Shallow Water Equations (SWEs) to extend the numerical simulations beyond the analysis of water entering a single building to a cluster of buildings. The results confirm the feasibility of incorporating floodwater entering buildings processes in future urban flood simulations, thereby enhancing the realism of the outcomes. Moreover, this research delves into the characteristics of water depth and flow patterns not only on the streets but also inside the buildings. The investigation reveals that building openings play a vital role in the transportation and redistribution of floodwaters within the street network. Furthermore, variations in the rates of water level rise and recession inside buildings are observed, highlighting the influence of opening configurations and building locations. Additionally, a dynamic assessment of pedestrian risk throughout the flood duration is conducted, providing valuable insights and recommendations for disaster prevention and mitigation strategies during urban flood events. It serves as a valuable resource for urban planners and policymakers involved in the development of resilient cities in the face of increasing urban flood challenges. [ABSTRACT FROM AUTHOR]

• Increases in extreme precipitation occurred in fragmented patches across India. • Significant increase in extreme precipitation observed in less than 10% basin area. • Frequency and spatial extent of extreme precipitation is projected to increase. • A robust increase in widespread floods projected in the seven major river basins. Widespread floods simultaneously affect several subbasins in a large river basin and pose severe challenges to disaster management and flood mitigation efforts. India is highly susceptible to widespread flooding as the country receives more than 70% of annual rainfall in the four-month period from June to September. However, the potential impacts of climate change on the drivers and frequency of widespread floods in India remain unexplored. We use observations, climate model simulations, and a hydrological model to examine the drivers and changes in widespread floods under a projected future climate. The observed increase in extreme precipitation does not translate to flooding in most river basins in India due to its fragmented nature. Translation of extreme precipitation to flood depends on the catchment area. We examined the changes in the widespread floods in Indian river basins using a hydrological model and climate projections. Robust increases in intense precipitation area and frequency are projected under the warming climate. Furthermore, hydrological model simulations indicate a profound increase in widespread floods in Indian river basins under a warming climate. The increase in widespread floods in a warming climate can pose challenges for flood mitigation and management in the future. Consideration of spatial extent of extreme precipitation and catchment area can enhance flood monitoring and prediction efforts. In addition, increases in extreme precipitation may not always lead to rise in flooding. [ABSTRACT FROM AUTHOR]

• Flood volume evolution under climate change is evaluated on 1403 catchments. • Multiple climate models, hydrological models and bias correction methods are used. • Uncertainty analysis showed climate and hydrological models as key contributors. • Approximately 80% of basins will see increased extreme flood volumes in the future. • Changes seem to follow a pattern that can help predict flood volume evolution. Climate change is expected to increase the frequency and intensity of extreme events, such as droughts and floods. Assessing the impacts of climate change on flood volumes is crucial to provide better management of flooding disasters given the devastating consequences they can cause. Using flood volume instead of flood peak is critical because the latter focuses on the highest possible discharge observed during a flood event, while flood volume also considers flow duration, which is an important factor in terms of the hazard caused to the surrounding environment. This study aims to evaluate the overall impact of climate change on floods caused by long-duration flows exceeding synthetic flooding thresholds. These flows are used to compute flood storage with various flow thresholds increasing from the 50th to 95th percentile of annual maximum observed discharge over a large sample of 1403 North American catchments. This study also aims to evaluate the contribution of each uncertainty source of the ensemble approach (climate models, bias-correction methods, and hydrological models) on future flood volumes. The results show that flood volumes are expected to decrease in western mountainous areas, the Great Lakes region, and the Maritimes, while increases are expected over most of eastern North America. The study finds that climate models contribute the most to the variance of flood volume uncertainty, followed by hydrological models. Overall, this study provides projected flood volume changes for North American catchments from a comprehensive ensemble that includes eleven climate models driven by two RCP scenarios and four hydrological models of varying complexity. This leads to a large-sample assessment of future flood volumes that could be useful to policymakers for making better-informed decisions in flood risk management. [ABSTRACT FROM AUTHOR]

[Display omitted] • The cyclones occurred over BoB show their highest rainfall within 300 km radius. • The highest rainfall is for the cyclone Akash (54 mm/day), within the 300 km radius. • The average rainfall on land within the 300 km radius is 12.6 mm/day. • The cyclone-induced rainfall is higher in the right-top quadrant followed by the left-top. Cyclone-induced rainfall analysis plays a significant role in cyclone-related disaster mitigation and management. Here, we present a comprehensive analysis of tropical cyclone (TC)-induced rainfall in Bay of Bengal (BoB) and its adjacent land regions by the land falling TCs for the past two decades (2000–2020). An average of 12.6 mm/day of land precipitation and 28.75 mm/day of oceanic precipitation are found within 300 km radius of the cyclones, as analysed from the 54 cyclones occurred during the study period. It is observed that the highest TC rainfall contribution to the total rainfall is in the winter months of January and February (40%), whereas the smallest is in the monsoon months of June, July, August and September (0.85%). The TC rainfall contributes about 10% to the annual rainfall over the land regions. The quadrant analysis shows that the TC rainfall depends on the season of TC formation and its direction. About 40.47% of BoB cyclones has the highest rainfall in the right-top quadrant, 35.15% in the left-top quadrant and 14.18% in the left-bottom quadrant. Our study provides key information on land falling TCs and their potential to inflict disasters and thus, this information can be used to provide better safety measures for TC-related hazards such as heavy rainfall and floods. This is particularly important, as the TCs can be more intense and frequent in the context of climate change. [ABSTRACT FROM AUTHOR]

• An exploratory framework to urban flood collaborative mitigation strategy is proposed. • A tracer-aided urban flood model is employed to examine the synergistic effect of flood volume. • Optimal allocation model of flood volume for urban flood mitigation strategy is developed. • A case study is presented to illustrate the disaster reduction efficacy of collaborative mitigation strategy. As a result of climate change and urbanization, the occurrence of urban flooding has become a common and severe natural hazard that can have devastating consequences on both human society and urban infrastructure. The increasing incidence of urban floods emphasizes the importance of implementing reliable strategies aimed at minimizing losses caused by these disasters. In this regard, an exploratory framework to urban flood collaborative mitigation strategy is developed to optimize the benefits of disaster management and economic investments in flood-prone areas. The proposed framework begins by applying an urban flood model coupled with the source tracking method to simulate hydrological and hydrodynamic process during a storm event, and effectively identifies the synergistic effect of flood volume between different catchments. Subsequently, based on the simulation data from the source tracking model, a method for the comprehensive analysis of spatial prioritization in urban flood management has been developed by employing the combined weight analysis approach. Furthermore, a drainage system dynamic cooperative allocation model is formulated within a comprehensive mathematical programming framework, aiming to effectively reduce the flood volume by considering the synergistic effect. In this comprehensive model, cooperative game theory is utilized to investigate how the flood volume can be effectively mitigated to achieve optimal economic allocation of local municipality. The application of this framework to the Longkungou drainage system of Haikou City demonstrates its potential as a tool for maximizing the economic benefits of disaster mitigation within a drainage system, while minimizing the potential damage caused by urban floods. [ABSTRACT FROM AUTHOR]

• The integrated model has been used to simulate the compound flood. • The nonlinear superposition characteristics has been shown with the effect of sea level rise and typhoon wind intensity. • The multiple hazards significantly increases the inundation of compound flood, seawall defense can effectively reduce the inundation impact. Coastal cities are affected by the compound effects of flood hazard systems, including the ocean, river, and coastal land, within which the disaster-induced factors interact complexly. To explore the combined effects of multiple disaster-causing factors under changing environment, this study first constructed univariate distribution by maximum daily precipitation of the year at Zhuhai Station from 1962 to 2020 and maximum daily average river discharge of the year at Makou Station from 1951 to 2005. Based on the integrated model of the validated Delft3D-FLOW and HEC-RAS hydrodynamic models, the river level process of compound floods was simulated for 24 h, and the effects of extreme compound flood events involving storm surge, rainfall and river flood under future scenarios is analyzed. In this study, the Modaomen waterway in Zhuhai City of China is used as the study area. The results show: (1) The Lognormal distribution is selected as the optimal distribution, and the design values of precipitation and discharge in different recurrence periods are calculated based on Lognormal distribution. The precipitation and discharge for the 100-year return period were 450.3 mm and 51000 m3, respectively, which could be used in the compound flood simulation; (2) The application of Delft3D-FLOW model ensures the accurate simulation of the compound flood process. In the different future scenarios of rising sea levels and increasing wind intensity (WI), the effect of multiple disaster-causing factors results to a more serious inundation than that of a single disaster-causing factor, with the increase of maximum storm surge level from 8.09 % to 20.31 %; and (3) Fluvial flooding and storm surge became the major causes of compound flood when the city coastal protection is vulnerable, while in the city with robust coastal protection, precipitation serves as a main disaster-causing factor. Taking the inundation under scenario S1 as an example, when the seawall elevation is 2 m, the composite flooding causes significant inundation in the riparian area (the total inundation area is 15.29 km2), whereas when the seawall elevation is raised to 2.5 m, the inundation area in the riparian area caused by the compound flood decreases significantly (the total inundation area is 0.03 km2). This study is expected to provide scientific references on disaster prevention planning, compound disaster risk management and urban resilience construction along the coastline, as well as have a significant impact on modern economic growth, social stability, and the security in coastal cities. [ABSTRACT FROM AUTHOR]