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• Out-of-sample prediction of extreme precipitation using nonstationary models. • Significant trends in data of extreme precipitation are insufficient to warrant the selection of a nonstationary model. • Large prediction uncertainty reduces the support for nonstationary models of extreme precipitation. • (Non)stationary model selection should explicitly account for model complexity. Knowledge of the recurrence intervals of precipitation extremes is vital for infrastructure design, risk assessment, and insurance planning. However, trends and shifts in rainfall patterns globally pose challenges to the application of extreme value analysis (EVA) which relies critically on the assumption of stationarity. In this paper, we explore: (1) the suitability of nonstationary (NS) models in the presence of statistically significant trends, and (2) their potential in modeling out-of-sample data to improve frequency analysis of extreme precipitation. We analyze the benefits of using a nonstationary Generalized Extreme Value (GEV) model for annual extreme precipitation records from Southern Brazil. The location of the GEV distribution is allowed to change with time. The unknown GEV model parameters are estimated using Bayesian techniques coupled with Markov chain Monte Carlo simulation. Next, we use GAME sampling to compute the evidence (and their ratios, the so-called Bayes factors) for stationary and nonstationary models of annual maximum precipitation. Our results show that the presence of a statistically significant trend in annual maximum precipitation alone does not justify the use of a NS model. The location parameter of the GEV distribution must also be well defined, otherwise, stationary models of annual maximum precipitation receive more support by the data. These findings reiterate the importance of accounting for GEV model parameters and predictive uncertainty in frequency analysis and hypothesis testing of annual maximum precipitation data records. Furthermore, a meaningful EVA demands detailed knowledge about the origin and persistence of observed changes. [ABSTRACT FROM AUTHOR]

• Nationwide streamflow projections for Australia informed by CMIP5 and CMIP6. • Vast majority of CMIP5 and CMIP6 GCMs project less winter rainfall across Australia. • Lower winter rainfall translates to a significant reduction in annual runoff. • Modelled median projection of runoff shows a 50% reduction in far southwest Australia. • Modelled median projection of runoff shows a 20% reduction in far southeast Australia. Projections of streamflow under future climate are essential for developing adaptation strategies in the water and related sectors. This paper presents nationwide streamflow projections for Australia informed by climate change signals in CMIP6 GCMs and compares the projections with those from CMIP5 GCMs. The modelled future runoff projections driven by CMIP5 and CMIP6 GCMs are relatively similar for far southern Australia. The median projection is a 50% reduction in mean annual runoff in far southwest Australia and 20% reduction in far southeast Australia by 2046–2075 relative to 1976–2015 under the high SSP5-8.5/RCP8.5 global warming scenario. The vast majority of CMIP5 and CMIP6 GCMs project less winter rainfall across Australia. As most of the runoff in far southern Australia occurs in winter and spring, the lower winter rainfall translates to a significant reduction in annual runoff. It is therefore prudent to plan and manage for a reduction in future water resources, particularly in the densely populated and important agricultural regions in southeast Australia. There is less agreement between GCMs in the summer rainfall projection, with the CMIP6 GCMs generally projecting a wetter future (or smaller decrease in rainfall) than the CMIP5 GCMs. The modelled median projection for mean annual runoff is a 10% reduction in the south-east coast, 5% reduction in the north-east coast and little change in northern Australia. More GCMs project an increase rather than a decrease in the interannual variability of rainfall, and this would further amplify multi-year hydrological droughts. [ABSTRACT FROM AUTHOR]

[Display omitted] • Climate change affects the stormwater quality due to variation in climatic parameters. • Scientific interventions with strong database should be geared up in developing nations. • Emerging pollutants needs more attention by undertaking systematic research. • Groundwater recharge with stormwater may raise groundwater level and reduce pollution. The problem of water logging and deteriorating water quality of surface water bodies has been witnessed in and around the urban centers owing to rapid population growth, urbanization, industrialization and climate variability. The stormwater runoff management is the need of the hour, and with increasing public and political awareness and rapid economic growth, it is receiving due attention. In the climate-change scenario, it needs to be addressed with scientific interventions as changes in pattern of precipitation affects the runoff volume and movement of pollutants including nutrients. This paper discussed and critically reviewed the stormwater runoff characterization, modelling, relationship between runoff and water quality, first flush phenomena, runoff treatment in changing climate scenarios. Researchers have used regional climate models (RCMs) to project future precipitation and then incorporated in various hydrologic and hydraulic models such as SWMM, SWAT, HSPF, SUSTAIN and MUSIC etc to study the impact of climate change and its adaptation. The flooding and water quality problem can be combatted with adopting green infrastructure practices. Many works on stormwater management have been carried out in developed countries, but efforts need to be geared up in the developing countries, particularly for runoff characterization and effective management. Emerging pollutants need to be paid more attention by undertaking systematic research. Future studies should include measurements and quantification of ecosystem services and integration of stormwater management into environmental policy. This review suggests intriguing future research avenues at the nexus of stormwater management and ecological services. [ABSTRACT FROM AUTHOR]

• We analyzed trends and breakpoints for three streamflow signatures of drying patterns in 540 non-perennial streams over CONUS • Nonlinear changes in streamflow are more common than linear changes for all three stream intermittency signatures. • The past behavior of non-perennial streams might be a poor predictor of future drying patterns Stream drying patterns – including duration, timing, and dry-down rates – affect aquatic ecosystems and nutrient exports in non-perennial streams. Because hydrologic processes are often nonlinear, changes in drying may also be nonlinear, but analyses of historical changes in stream drying to date have not characterized the frequency or functional forms of nonlinear change. Understanding the extent of nonlinear change in non-perennial streams is essential for advancing our fundamental knowledge of hydrological processes, aquatic ecosystems, and watershed functioning under a warming climate. This paper uses a polynomial-based trend detection technique (PolyTrend) to analyze the linear and nonlinear trend behaviors of three intermittency signatures (annual no-flow days specifying longer or shorter drying duration, day of first no-flow occurrence specifying timing of stream drying, and days from peak to no-flow specifying dry-down rates) at 540 non-perennial gage stations over 38 years (1980–2017) across the continental United States (CONUS). Additionally, we carried out a breakpoint analysis to characterize the discontinuities in the time series of each intermittency signature. Analysis of annual no-flow days shows that about 37 % of the total streamflow stations are drying for longer each year, whereas about 22 % are wetter for longer than in the past. The day of first no-flow occurrence analysis shows that 10 % of the streams are drying earlier, and 19 % are drying later. On the other hand, analysis of days from peak to no-flow shows that 14 % of streams are drying faster, and 17 % are drying more slowly. For all these metrics, among the significant trends, at least half of the relationships were nonlinear. For annual no-flow days , the breakpoint analysis shows more discontinuities in the second half of the analysis period (1999 to 2017) than in the first half, with more discontinuities in the Southern Great Plains than in other regions. The other two signatures demonstrate less frequent discontinuities in the second half of the analysis period, suggesting decreased nonlinear dynamics in recent years. Nonlinear no-flow duration trends are common in Mediterranean California, and the dry-down rate has increased in recent decades. Our findings indicate that nonlinear change in stream drying is widespread and must be accounted for in watershed planning and management. [ABSTRACT FROM AUTHOR]

• Transition zones are the hotspots of bioclimatic changes under global warming. • Global warming extends growing season lengths but increases heat and water stresses. • Sustainable grasslands in China shrink and shift northwestward with global warming. Bioclimatic response to global warming is a key linkage between anthropogenic climate change and ecosystem sustainability, particularly for the impact-prone climate transition zones. This paper examines changes in heat-water conditions from 2006 to 2100 across China, using the fifth generation climate reanalysis from the European Centre for Medium-Range Weather Forecasts (ERA5) and bias-adjusted climate projections by the Coupled Model Intercomparison Project Phase 5 (CMIP5). We then assess the effects of these changes on habitat suitability for responsive ecosystems using a species distribution model. The results reveal significant changes in bioclimatic environments across dry–wet climate transition zones under the present-day climate and the RCP8.5 scenario. By the end of the 21st century, local growing season length (GSL) could extend by 60 days and 30 % of the GSL could be under heat stress. Water conditions worsen overall in both the air and the soil, despite some improvements in the middle parts of transition zones. With altered heat-water conditions, sustainable grasslands shift northwestward, with a reduction of approximately 131 million km2 (46.6 % relative to 2006–2010) in total area. These changes primarily arise from alterations in winter and spring heat-water conditions. Our findings highlight the climate transition zones as hotspots of ecosystem vulnerability demanding particular attention in impact assessment and adaptation studies. [ABSTRACT FROM AUTHOR]

• Integrating an agent-based model into a large-scale distributed hydrologic model. • Evaluating the co-evolution of the CNHS at multiple spatial scales. • Demonstrating the trade-off among FEW sectors under climate change. Managing water resources to meet increasing energy and food demands while maintaining environmental sustainability under climate change is a major challenge, especially when this nexus occurred in a coupled natural–human system (CNHS), where heterogeneous human activities affect the natural hydrologic cycle and vice versa. The relevant research has been limited by the lack of models that can effectively integrate human dynamics and hydrologic conditions with spatial details to examine co-evolutionary systems. To address this challenge, this paper develops a modeling framework that integrates an agent-based model (ABM; human behavior model) into a large-scale, process-based distributed hydrologic model to simulate human decisions endogenously in the hydrologic cycle. We then apply the Decision Scaling approach, an ex-post scenario analysis method, with our integrated model to study the bidirectional feedback of the CNHS under future changing climate conditions. With the Columbia River Basin (CRB) selected as the case study area, the calibration results show that the integrated model can simultaneously capture the historical irrigated water consumption and streamflow dynamics. Modeling results show that the trade-off between irrigated water consumption, hydropower generation, and streamflow will become more pronounced under hotter and wetter climate conditions at both the entire basin and regional (states and provinces) levels. Special attention should be given to "temperature thresholds" of different regions when the trade-off pattern started. The trade-off results can potentially inform the Columbia River Treaty renegotiation and provide insights for long-term water management policies. [ABSTRACT FROM AUTHOR]

• We propose a new method to quantify Climate Change Effects at hydrological scales. • The method allows to assimilate climatic projections in CDFs at rain gauge scale. • The initial (i.e., before any change scenario begins) skewness plays a crucial role. • A strong increase of outliers frequency is expected for series EV1 alike skewed. Quantifying the potential effects of Climate Change (CC) on hydrological scales is a topic with a more and more increasing interest for the scientific community, given the CC impact on agriculture, industry, economy, human health, ecosystems, among the others. In this context, the paper focuses on the sub-daily Annual Maxima (AM) of rainfall height time series, and specifically on the crucial role played by initial skewness, i.e. the skewness of the observed rainfall series used to evaluate potential parametric trends. Here a quick and user-friendly methodology is proposed, that is aimed at quantifying plausible future changes in terms of probability distributions assumed at rain gauge scale, from projections of any climatic model. In detail, the Generalized Extreme Value (GEV) and the Two-Component Extreme Value (TCEV) distributions are adopted as probability functions suitable for modelling observed rainfall AM series, which could increase their frequency and magnitude into future horizons under CC. EURO-CORDEX projections for Europe are considered, under the hypothesis that the values of the change factor, i.e. the ratio between the values of a specific quantile at two specific time horizons, are invariant when moving from an areally-averaged scale (typical for any climate model) to a point rain gauge scale, which induces that future changes are provided (in terms of frequency and magnitude of extreme events) without the need of any spatial downscaling from the assumed projections. The proposed methodology can contribute to hazard quantification associated to potential climate changes, and thus it can play a crucial role in the assessment of hydraulic structures resilience; the obtained results, specific for the study area of Italy, but easily extendable on a global scale, showed that larger increases in frequency of future heavy events are expected for time series with "EV1 alike" values of initial skewness. [ABSTRACT FROM AUTHOR]

• Exploring the urban inundation response law based on designed rainstorms by coupled hydrodynamic and rainfall-tracking model. • Source tracking analysis method is novelty proposed to excavate the response law of the connectivity among source areas to characteristics of rainstorms. • The analysis of source areas contribution further illustrates the reason why the study area would undergo inundation damage when the precipitation exceeds 100 mm. • The study is helpful to further understand the response law of inundation and relationship among source areas to characteristics of rainstorms, provide novel insights for constructing integrated flooding intervention practices. Extreme stormwater and high-impervious area, caused by climate change and high-urbanization, are widely considered as the essential factors for causing urban flood. Understanding the response law of urban inundation to characteristics of rainstorms is helpful to implement integrated flood mitigation measures. Therefore, this paper is focused on exploring the urban inundation response law based on designed rainstorms by coupled hydrodynamic and rainfall-tracking model. Pilot area of Guyuan sponge city, which is divided into 5 source areas, is adopted to conduct 36 types of rainfall scenario simulations with the combination of different rainfall amounts and durations as well as peak coefficients. Inundation analysis methods is used to obtain the relationship between maximum inundation maps and characteristics of rainstorms, and source tracking analysis method is novelty proposed to excavate the response law of the connectivity among source areas to characteristics of rainstorms. First, Results of inundation analysis reveal that rainfall amount, duration and peak value are the first, second and third critical rainstorm characteristics that determine the severity of disasters, the variation of inundation volume are 140 %, 30 % and 4.2 % when precipitation and duration as well as peak value double. Second, the inundation area that water depth more than 0.5 m increases quickly is the major reason that inundation volume varies with 2 times as the increasing of precipitation; the transferring runoff volume between source areas and land use reaches 0–6 % and 0–10 % of inundation volume. Third, SA 1 and 2 are the critical sub-areas to manage the runoff, when precipitation equals to 200 mm, SA 1 contributes 19.5–33.7 % of inundation volume in SA 2, and SA 2 contributes 28–30 % of inundation volume in SA 3. The study is helpful to further understand the response law of inundation and relationship among source areas to characteristics of rainstorms, provide novel insights for constructing integrated flooding intervention practices. [ABSTRACT FROM AUTHOR]

[Display omitted] • Ten multi-model combination methods (MMCMs) are created from 29 models in 50 basins. • MMCMs improve some performance indicators, especially the Granger-Ramanathan method. • MMCMs do not improve performance in reproducing distributions of hydrological signatures. • Application with series of targeted signatures does not improve MMCM performance. • MMCM performance is improved by selecting more robust candidate models for ensembles. Multi-model combination (averaging) methods (MMCMs) are used to improve the accuracy of hydrological (precipitation-runoff) outputs in simulation or forecasting/prediction modes. In this paper, we examined if the application of MMCMs can improve model performance in reproducing distributions of hydrological signatures, such as annual maxima or minima of varying durations. To this end, ten MMCMs were applied to 29 bucket-type models to simulate runoff in 50 high-latitude catchments. The MMCMs were evaluated by comparing the resulting simulated flows to the reference (i.e., best-performing) individual model, considering various commonly used performance indicators, as well as model performance in reproducing the distributions of signatures. Additionally, we analysed whether (1) the selection of the candidate models, or (2) targeting specific signatures, such as annual maxima or minima, can improve performance of the model combinations. The results suggest that the application of MMCMs can improve accuracy of runoff simulations in terms of traditional performance indicators, but fails to improve performance in reproducing the distributions of signatures. Neither excluding poor-performing models nor applying the MMCMs with the targeted signatures, improves this aspect of model performance. These findings clearly reveal the need for further research aiming at enhancing model performance in reproducing the distributions of hydrological signatures, which is essential for climate-change impact studies. [ABSTRACT FROM AUTHOR]

• Salinity intrusion (SI) is intensified in the Vietnamese Mekong Delta (VMD). • Freshwater exploitability is investigated to cope with SI driven by climate change. • SI in the VMD has progressively increased and started earlier in the dry season. • Early and the late of the dry season are suitable time for freshwater exploitation. • Storing freshwater in the river is a promising strategy. Climate change-driven sea level rise has intensified salinity intrusion (SI) in deltas worldwide, posing significant threats to the exploitation of freshwater resources. In the Vietnamese Mekong Delta (VMD), the third largest delta globally, SI is a recurring challenge along the coastline, degrading freshwater resources for agricultural and domestic use and affecting socio-economic development. In this paper, we investigate the spatiotemporal extent of salinity intrusion in the Ben Tre Province, the hotspot of salinity disaster within the VMD. Long-term salinity monitoring data (25 years from 1996 to 2020) has been analyzed, and a 1D (Mike 11) coupled with 3D hydrodynamic model (Mike 3) was developed. Three scenarios were used to investigate the freshwater resources exploitation: (i) the year of investigation (2021), (ii) 2021 to 2030 climate change impacts, considering different annual exceedance probability of the upstream Mekong discharge (i.e., average flow, relatively low, low and very low), and (iii) extreme salinity intrusion (i.e., the 2016 condition). Our results indicated that salinity patterns are well-stratified at the beginning and end of the dry season but well-mixed during the middle period. Furthermore, over the last 25 years, SI has progressively increased and started earlier in the dry season. The modeling scenarios for SI have also revealed a growing complexity in the exploitation of freshwater resources, highlighting challenges related to timing, depth, and geographical location. The exceedance probability scenarios disclosed higher and deeper salinity intrusion along the channel in VMD, ranging from 50 % to 95 %. This poses significant limitations on the feasibility of freshwater exploitation throughout the Ben Tre Province. Under the current trajectory of climate change, the 2030 scenario anticipates salinity intrusion reaching further inland from the 2021 scenario. This is likely to exacerbate the existing challenges in freshwater resource exploitation, even with comprehensive water infrastructure. We, therefore, propose several management strategies to adapt to salinity intrusion: storing freshwater in main rivers, maintaining consistent operation of water infrastructure systems, and encouraging water-saving distribution and exploitation methods. Moreover, we also recommend supporting the development of new drought-tolerant crop patterns. [ABSTRACT FROM AUTHOR]

• Developed a hybrid statistical-numerical model which predicts that without intervention the compound flood risk will increase in Haikou City under the influence of future climate change. • Under the influence of climate change, both rainfall and sea water level will increase. • Climate change will increase the probability and the failure probability of compound flood, and will intensify inundation (increase flood depths, extend the flood extent and longer flood durations). • The combined impact of future changes in rainfall and water level on the magnitude of flooding is greater than the sum of their individual impacts. Compound flooding caused by a combination of rainfall and water level may occur in coastal cities. Under the influence of climate change, compound flooding is likely to be intensified. In this paper, using the main urban area of Haikou City as an example, we developed an event-based statistical-numerical hybrid framework to investigate the effects of sea level rise (SLR) and future rainfall changes on compound flood risk. Future rainfall was predicted by using five global climate models (GCMs) in CMIP5. A combination of GCMs and IPCC AR5 was used to predict the future SLR. The copula was then used to calculate the probabilistic risk of compound flooding (OR joint probability and failure probability) for historical and future scenarios, and the numerical model was utilized to evaluate the risk of inundation under historical and future scenarios. The probabilistic risk results show that both the probability and the failure probability of compound flooding will increase in the future. Output from GCMs indicate that when the OR event of the 5-year event (the compound flooding when at least one of the 5-year rainfall and the 5-year water level happens) occurs, the joint probability of future compound flooding will likely increase to 37.5%. The numerical model results indicate that the flood extent, flood depth and flood duration will increase under the influence of future rainfall and SLR. Under the impact of climate change, the flood depth in the FS 5 case will increase by 22.85% and the flood extent in the FS 5 case will increase by 41.42% compared to HS 5. By controlling for variables, we also find that the combined impact of future changes in rainfall and water level on the magnitude of flooding is greater than the sum of their individual impacts. Finally, we find that water storage facilities and tide gates could be set up to mitigate compound flooding under climate change. [ABSTRACT FROM AUTHOR]

• Understanding the riparian zone from the watershed river-groundwater transformation. • Revealing the contributions of groundwater to landscape ecological patterns. • Proposing pathways to maintain a healthy landscape ecological pattern. In arid and semiarid areas with scarce precipitation, strong evaporation, and fragile ecosystems, rivers and groundwater interact frequently. Dynamic hydrological changes and solute transport jointly determine the evolution of the landscape in riparian zones, resulting in the formation of a landscape ecological pattern in arid areas that is unique and includes a system of rivers and lakes. In recent decades, unsustainable and mono-purpose grey infrastructure has been implemented in many arid areas, and the effect of river-groundwater interactions on the landscape ecological pattern in riparian zones has often been neglected. These processes have triggered variations in or even the reversal of landscape ecological patterns in these arid areas. This study selected the arid and semiarid basin in northwestern China as the study area. Multiple methods were used to study the evolution of the landscape ecological pattern in the riparian zones driven by typical hydrological processes from the perspective of river-groundwater transformation. Three results were obtained from the analysis: (1) The driving force of the evolution was identified, and the spatial geological heterogeneity, meteorological and hydrological conditions, and river-groundwater interactions played dominant roles in the evolution of the landscape ecological pattern in the riparian zones. (2) Human activities and climate change controlled the direction and intensity of landscape evolution. (3) Pathways for maintaining a healthy landscape ecological pattern in riparian zones and suggestions for identifying the key issues that should be studied in the future were proposed. This paper primarily contributes to a better understanding of landscape ecological patterns in the riparian zone in arid areas from the watershed river-groundwater transformation perspective, thereby promoting integrated watershed management. [ABSTRACT FROM AUTHOR]

• An accurate SWAT model is constructed by considering the influences of various human activities. • The influence of GCM uncertainty on climate change prediction is reduced through multi-model integration and bias correction. • Combined with SDSM model and SWAT model, it is concluded that the future temperature, precipitation and runoff of Yongding watershed will increase. Climate change has affected hydrological processes at global scale, and redistributed water resources in space and time. Anthropogenic activities even exacerbated the water resources problems. In this paper, Statistical DownScaling Model (SDSM) was used to downscale the predicted precipitation and temperature in the mountainous area of Yongding watershed under 4 SSP-RCP climate scenarios in 4 General Circulation Models (GCMs). Bayesian model averaging (BMA) method was employed for multi-model integration to reduce the impact of GCM uncertainty on climate change prediction. Then, the multi-model ensemble data were corrected by the Quantile mapping (QM) method. Soil and Water Assessment Tool (SWAT) was established in the study area considering the intensive human activities, including the water use in irrigation areas and regulation of the 8 large and medium-sized reservoirs. The SWAT model was applied to simulate the runoff response under the future climate change. The results showed that the maximum temperature (T max), minimum temperature (T min), precipitation (P) and runoff all show an upward trend under the four future climate scenarios. SSP585 scenario always showed the highest increase in temperature and precipitation. The simulated future annual runoff increases significantly compared with the reference period, indicating that the water shortage of the watershed may be alleviated in the future. The research results can provide a scientific basis for the decision-making of water resources management and protection in the watershed. [ABSTRACT FROM AUTHOR]

• Three models were used to cover model structure uncertainty for each catchment. • AET is larger in the karst-dominated than the adjacent nonkarst-dominated catchment. • AET of karst-dominated catchment is more sensitive to the change in climate. • The karst-dominated catchment will experience much drier hydrological projections. Karst systems are well-known for their special features, such as epikarst zone and quick flow, compared to other hydrological systems. However, few studies explored the difference in hydrological responses to climate change between karst and nonkarst catchments. In this paper, two adjacent catchments dominated by karst and nonkarst in Southwest China (subtropical monsoon climate) were selected to investigate their different hydrological behaviors under future climates. To account for potential model structure uncertainties, three different karst hydrological models and three traditional catchment models were used to simulate these two catchments. The results demonstrate that different models yield comparable streamflow simulations within each catchment, allowing for the focus to be on the impact of climate change and catchment hydrological characteristics. The karst-dominated catchment is modeled to have larger actual evapotranspiration (AET) than the nonkarst-dominated catchment under similar climate conditions, with the AET of the former being more sensitive to changes in precipitation and potential evapotranspiration. These differences are primarily due to the presence of the epikarst in the unsaturated zone of the karst system, which can store more infiltration rainwater for evapotranspiration. Both catchments are expected to experience a wetter projection in the wet season and a drier projection in the dry season in the future. However, the karst-dominated catchment will face a drier climate with a significant increase in precipitation in just two months (July and August). Additionally, because of a higher sensitivity of monthly AET to climate change, the karst-dominated catchment will experience a greater monthly discharge reduction in the first two wet months (April and May), a relatively lower discharge increase in other wet months, and a lower monthly discharge in dry months, while the monthly discharge of the nonkarst-dominated catchment increases in the wet season and decrease in the dry season. Furthermore, the karst-dominated catchment will have a much lower annual discharge than the nonkarst-dominated catchment, particularly during extremely dry years in the future period due to its larger AET consumption. These all suggest that the karst-dominated catchment will be more vulnerable to droughts. Overall, due to the drier climate and the different hydrological backgrounds of the karst-dominated catchment, it is expected to have much drier hydrological conditions in the future compared to the nonkarst-dominated catchment. [ABSTRACT FROM AUTHOR]

[Display omitted] • Bias adjustment improves accuracy and consistency of simulated hydrological signatures. • Not a single bias adjustment method enhances performance in all analyzed signatures. • Univariate distribution scaling (DS) performs well, especially in rainfall-driven catchments. • Multivariate methods perform better for low-flow signatures in snowmelt-driven catchments. Hydrological climate-change-impact studies depend on climatic variables simulated by climate models. Due to parametrization and numerous simplifications, however, climate-model outputs come with systematic biases compared to the observations. In the past decade, several methods of different complexity and dimensionality for adjustment of such biases were introduced, but their benefits for impact studies and accurate streamflow projections are still debated. In this paper, we evaluated the ability of two state-of-the-art, advanced multivariate bias-adjustment methods to accurately reproduce 16 hydrological signatures, and compared their performance against two parsimonious univariate bias-adjustment methods based on a multi-criteria performance evaluation. The results indicated that all bias-adjustment methods considerably reduced biases and increased the consistency of simulated hydrological signatures. The added value of multivariate methods in maintaining dependence structures between precipitation and temperature was not systematically reflected in the resulting hydrological signatures, as they were generally outperformed by univariate methods. The benefits of multivariate methods only emerged for low-flow signatures in snowmelt-driven catchments. Based on these findings, we identified the most suitable bias-adjustment methods for water-resources management in Nordic regions under a changing climate, and provided practical guidelines for the selection of bias-adjustment methods given specific research targets and hydroclimatic regimes. [ABSTRACT FROM AUTHOR]

• A comprehensive review of remote sensing applications in water resources monitoring and management in Africa is provided. • Evolution of remote sensing techniques, particularly in hydrological modeling is summarized. • Role of sensors, observations, and analytical models in monitoring Africa's water resources at different scales are highlighted. • In-situ hydrological monitoring networks and limited long-term rainfall and climate data remains a challenge in Africa. • The adoption of multi-source data integration, and big data analytics provides new opportunities to improve water resource management in Africa. This paper provides an overview of the progress made in remote sensing of water resources in Africa, focusing on various applications such as precipitation estimation, land surface temperature analysis, soil moisture assessment, surface water extent measurement, surface runoff and streamflow analysis, water quality evaluation, land cover/land use mapping, and groundwater analysis. Specifically, the study sheds light on the remarkable progress made in remote sensing applications, showcasing scientific advancements and highlighting the challenges encountered. Moreover, it identifies crucial knowledge gaps that need to be addressed in order to further advance this field. The review's key findings indicate that the availability of sensors and observations, along with analytical models, has contributed to monitoring Africa's water resources at various scales. The availability and accessibility of hydrological data for monitoring and assessing water resources in Africa have been partially improved through the adoption of satellite data and processing technologies. Additionally, the emergence of various international remote sensing initiatives, North-South research collaborations, and projects has contributed to the research progress. Prominent satellite data series such as Landsat, MODIS, and GRACE have played significant roles in African hydrological research. However, the limited and malfunctioning in-situ hydrological monitoring networks in Africa have affected the accurate calibration and validation of remotely sensed hydrological models. Insufficient long-term rainfall and climate data pose challenges to long-term earth observation research on African water systems. The lack of high-resolution spatial and temporal data hampered accurate monitoring of hydrological processes at smaller scales. Despite the widespread use of rainfall satellite products, validation attempts over Africa, particularly in western and southern regions, have been limited. Furthermore, the reliability of hydrological satellite datasets is affected by factors such as the number and coverage of surface stations, retrieval algorithms, data integration techniques, and cloud cover. Overall, this work demonstrates the importance of earth observation in understanding Africa's hydrology, previously hindered by the lack of in-situ data. Nevertheless, efforts are therefore needed to enhance the adoption and application of remote sensing, particularly in groundwater and surface water estimation at smaller scales. Future research should focus on multi-source data integration, assimilation, and big data analytics using cloud computing and machine learning to address complex hydrological research questions at various scales. [ABSTRACT FROM AUTHOR]

• Review of hydro-morphological responses to LULC and climate changes. • The first review paper of this type focused on tropical regions. • A comprehensive review of 60 case studies. • Meta-analysis of the findings of all case studies. • Significant correlations are revealed between LULCC and hydrological processes. Tropical regions have experienced the fastest Land Use Land Cover Change (LULCC) in the last decades, coupled with climate change (CC) this has affected the hydrological and geomorphological processes of river systems. With the increased demand for land, the general trend has been the loss of forest land to agriculture and settlements. These changes have altered the water balance components through enhanced or reduced evaporation, peak flow, flooding, and river morphology. The aim of this review paper is to provide a meta -analysis on the effects of spatiotemporal changes in climate and LULC on river hydro-morphology in the tropics. Following a systematic search, 60 case studies were identified, of which the majority (68%) experienced forest loss due to agricultural and urban expansion, resulting in increased streamflow, surface flow, and total water yield and decreased ET and groundwater recharge. 12% of the case studies showed the impacts of LULCC on channel morphology features through sediment transport and riverbank erosion. Results from this study show limited correlation between LULCC and hydrological variables, indicating that there are likely other factors controlling hydrological processes. Catchment heterogeneity including soil and topography play an important role. Based on studies that project these changes into the future, similar trends are expected over the next decades, with differences based on LU and climate scenarios. There are still limited studies on river hydro-morphology responses to LULCC and CC in the tropics despite the major changes taking place there. In light of future changes, more studies are needed to improve our understanding. [ABSTRACT FROM AUTHOR]

[Display omitted] • China's water footprint (WF) under the SSP-RCP scenario was predicted dynamically. • A new attempt to combine the system dynamic (SD) model with SSP-RCP was made. • The grey WF decreases significantly under all scenarios during the forecast year. • The agricultural WF is the main contributor to China's water footprint. • SSP1-2.6 is the best scenario to alleviate water shortages and water pollution. Under the dual influence of socioeconomic development and climate change, water resources in China are under increasing stress. It is of great significance to comprehensively explore the changing trend of China's water footprint (WF) in the future, clarify the water resource challenges that China will face, and alleviate water shortage and water pollution problems. This paper uses System Dynamics (SD) to build a simulation model of China's WF, calculate China's WF from 2000 to 2019, and for the first time, simulate and optimize China's WF from 2020 to 2050 under the SSP-RCP scenario matrix composed of Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs). The results are as follows. (1) From 2000 to 2019, China's WF increased to 2009 and began to decline. The main contributors are grey WF and agricultural WF. (2) During 2020–2050, China's total WF under the SSP1-2.6 scenario will always be at the lowest; compared with other scenarios, the simulation result of China's total WF under the SSP2-4.5 scenario is the closest to the actual situation in China in 2020, 2021 and 2022. China's total WF under the SSP4-6.0 scenario is close to that under the SSP2-4.5 scenario, but the numerical variation characteristics of the WF composition are different. China's total WF under the SSP3-7.0 and SSP5-8.5 scenarios is high. (3) Based on the changes in WF and water resources supply/demand ratio under different scenarios, the SSP1-2.6 scenario is the best scenario to mitigate a future water shortage and water pollution in China. The research results can help decision-makers formulate relevant management policies and socioeconomic development models for water resource utilization and provide decision support for alleviating water shortages and water pollution in China. [ABSTRACT FROM AUTHOR]

• Drought propagation time increased with decreasing precipitation. • Climate change is the main driver of agricultural drought. • Precipitation was the main driver of human activities. • Prolonged drought propagation time reduced winter wheat NPP. • The sensitivity of winter wheat NPP to agricultural drought is higher in humid area. Drought is a major natural disaster worldwide. Understanding the correlation between meteorological drought (MD) and agricultural drought (AD) is essential for relevant policymaking. In this paper, standardized precipitation evapotranspiration index and standardized soil moisture index were used to estimate the MD and AD in the North China Plain (NCP) to identify the correlation between MD and AD during the growth period of winter wheat. In addition, we investigated the contributions of climate change (CC) and human activity (HA) to AD and the factors influencing the loss of winter wheat net primary production (NPP). Drought propagation time (PT) increased spatially from the southern to northern NCP (from 3 to 11 months). PT first increased and then decreased during the phenological period of winter wheat, and the decreasing trend was delayed with an increasing latitude. In general, the relative contribution of CC to AD was higher than that of HA; the correlation between MD and AD exhibited a weakening trend, particularly during the middle and late phenological stages of winter wheat. Precipitation was the main driver of the effects of HA on AD; the effects were stronger in areas with less precipitation. However, because of the improved irrigation conditions and scarce rainfall during the growth period of winter wheat in the study area, the effects of precipitation on AD were nonsignificant. Instead, temperature, wind, and total solar radiation, which are highly correlated with evapotranspiration, were identified as the primary drivers of AD; spatiotemporal variations were noted in these correlations. Prolonged drought PT reduced NPP; the sensitivity of winter wheat NPP to AD was higher in humid areas than in semiarid or semihumid areas. NPP loss occurred primarily due to HA. Our findings revealed a correlation between MD and AD in agroecosystems and may facilitate policymaking related to drought mitigation and food security. [ABSTRACT FROM AUTHOR]

• New physics-informed machine learning model architecture 'PT-LSTM'. • Mass conservation of water and pollutants is enforced in the model structure. • Demonstrated for studying salt water intrusion in shipping canals. • A higher accuracy is achieved when compared with reference RNN and LSTM models. A reduced fresh water supply, e.g. as a consequence of climate change, can cause an increased salinization of (coastal) surface water systems and accurate, robust and computationally efficient models are needed to study these phenomena. While machine learning (ML) techniques are rapidly gaining popularity, their application is not straightforward and their 'black-box' structure is typically not interpretable, making the models unreliable for predictions under boundary conditions that differ from the training dataset. This paper presents a new ML model architecture, based on the LSTM-cell. The principle of mass conservation is enforced in the model's architecture to introduce scientific knowledge with the goal of increasing the technique's robustness. The model is compared with established reference neural networks for predicting salinization of navigable waterways in Belgium. Training data are generated with a detailed, physics-based solute transport model. The proposed model shows higher accuracy under new conditions when compared to the reference models. [ABSTRACT FROM AUTHOR]

• Quantile-quantile rainfall mapping methods are extended to improve streamflow simulations. • Extensions correct rainfall bias at multiple time scales and model rainfall autocorrelation. • Methods reduce bias in rainfall sequencing metrics for downscaled GCM simulations. • Methods reduce bias in streamflow simulations forced by downscaled GCM simulations. Global circulation models (GCMs) provide important insights into future climate change. Bias-correction of downscaled GCM output is integral to any hydrological investigations of climate change due to discrepancies between the statistics of downscaled GCM simulations and observations. Many bias-correction techniques have been developed to support hydrological applications. However, there have been few comparisons of the sensitivity of hydrological simulations to different bias-correction assumptions. This paper investigates the importance of two common assumptions: (i) simultaneously correcting bias at multiple time scales, and (ii) explicitly handling rainfall autocorrelation; in quantile mapping of downscaled GCM rainfall data for hydrological simulation. Four quantile mapping methods are applied to correct bias in dynamically downscaled reanalysis and historical GCM simulations of rainfall for 11 catchments in south-eastern Australia and the performance of bias-corrected rainfall and streamflow simulations is evaluated. All quantile mapping methods investigated can effectively eliminate bias in monthly and annual rainfall totals. Quantile mapping methods that consider differences in the temporal dependence (autocorrelation) structure of downscaled GCM and observed rainfall are most effective in reducing bias in rainfall sequencing statistics, such as probability of consecutive wet or dry days. Streamflow simulations of mean and high streamflow percentiles are underestimated when generated using rainfall that is corrected using quantile mapping methods that do not consider differences in the temporal dependence structure of downscaled GCM and observed rainfall. Future hydrological investigations of climate change should therefore adopt methods that explicitly consider the temporal dependence structures of downscaled GCM and observed rainfall. [ABSTRACT FROM AUTHOR]

[Display omitted] • Improve the accuracy of LID parameters through physical experiments. • Offer a new perspective on studying the urban hydrological effects in LID regions. • Even a tiny percentage of LID practices can be effective in runoff control. • LID removal or density reduction leads to an increased possibility of flood risk. Global climate change and rapid urbanization led to frequent extreme rainstorms and waterlogging. Low impact development (LID) as an essential measure of urban stormwater control, it is necessary to explore the urban hydrological response under low impact development combined with physical experiment and numerical simulation. This paper takes the Beiyangyuan campus of Tianjin University, where numerous LID practices have been implemented, as the study area. The prototype permeable pavement in the campus was scaled down to a laboratory-scale for infiltration experiments. The results show that the Horton infiltration model could describe the infiltration process of permeable concrete and grass-planting brick. The initial infiltration rate of the permeable concrete is large but decays faster. In contrast, the infiltration process of the grass-planting brick decays slower, and the stable infiltration rate and infiltration capacity are higher than that of the permeable concrete. The mean infiltration rates measured for the permeable concrete and the grass-planting brick were 95 mm/hr and 190 mm/hr, respectively. A SWMM-MIKE21 coupled model was constructed based on the LID experimental results to explore the effects of the removal or density reduction of the LID practices on surface hydrological processes. The results show that permeable pavement performs best in the reduction of runoff volume, inundation area, and inundation depth, while the bio-retention cell takes the second place, and the sunken greenbelt has limited effect. Frequent light rainfall is sensitive to the density of LID practices, and the implementation of even a tiny percentage (4% of the catchment area) of LID practices can be effective in controlling runoff. The removal or density reduction of LID practices has little effect on the inundation region but leads to an increased possibility of flood risk due to increasing inundation depth. [ABSTRACT FROM AUTHOR]

• Future rainfall and sea level rise are predicted. • Impact of sea level rise and future rainfall change on compound flooding is assessed. • Changes in depth, extent, and duration of floods under climate change are analyzed. Flood disasters in coastal cities are generally caused by the combined effect of typhoon rainfall, storm surge and other factors. Coastal cities will face more severe compound flooding under climate change. Although some studies have analyzed compound flooding under climate change, there is a lack of research considering the combined effect of sea level rise (SLR) and future rainfall changes, especially lack of the jacking effect of SLR on the volume and duration of compound flooding. This paper investigates the impact of future rainfall changes and SLR on compound flood risk, via a case study of a coastal city, Haikou, China. Future rainfall in Haikou is obtained by bias correction of rainfall from five global climate models (GCMs) in CMIP5. The SLR in 2050 and 2090 is predicted according to IPCC. Then, a 1D/2D hydrological-hydrodynamic model for dynamic simulation of surface runoff is applied to simulate compound flooding. Results show that both future rainfall changes and SLR will lead to the increase of flood extent, flood depth and flood duration. But future rainfall changes will have a greater impact on flood, and the combined effect of future rainfall changes and SLR on flood is greater than the sum of their individual effects. The individual effects of SLR and future rainfall changes would increase flood depth in 10.6 % and 32.1 % of the study area, respectively, while their combined effect would increase this proportion to 34.9 %. Also, the jacking effect of SLR on compound flooding will lead to an increase in flood volume, but it presents a nonlinear relationship that gradually slows down with the increase of SLR. A SLR of 0.3 m and 0.63 m will lead to a 2.34 % and 3.22 % increase in flood volume, respectively. And the jacking effect of SLR is more obvious when future rainfall changes are considered, which will lead to a sharp increase in the duration of compound flooding. Moreover, water storage facilities and tidal gates are effective ways to reduce the impact of climate change on compound flooding. [ABSTRACT FROM AUTHOR]

• We modify a method to assess yield of carryover reservoir with environmental flow. • The method can be applied at large scales, under historic and future climates. • We quantify yield declines due to climate change and environmental flow provision. Protecting freshwater systems to ensure sustainability requires diagnosing threats over a broad range of scales, from local to global. There is growing acceptance that environmental flows must be protected to sustain river channels and floodplain ecosystems downstream of reservoirs. Meeting environmental water requirements has proven difficult because they reduce water yield available for human use – a particular challenge in regions already experiencing declining water availability with climate change. A further challenge is the difficulty of assessing the impact of environmental water requirements on water availability for other purposes. This paper provides a modification of the Gould-Dincer method to assess the yield of carry-over reservoirs subject to environmental water requirements. The method characterizes reservoir inflows using readily available flow characteristics (annual mean and C v). We develop and test the method using ten examples with well-understood environmental water requirements and a range of annual flow C v s. We then demonstrate this method at continental scale, to investigate the impact on yield of providing high-reliability supply including environmental water requirements, under a changing climate. According to our results, considering climate change and environmental flow provision simultaneously, the decline in water yield is projected to be about 27% and 18% respectively for Australia and US (based on hypothetical reservoirs capacity of one times mean annual inflow). Adapting environmental flow demands to future hydrological conditions results in a 2% and 3% increase in Australia and US-wide yield for human use respectively. While further testing is needed, the method provides a simple and rapid way to estimate the impact of providing environmental flow on yield, applicable at global scale. [ABSTRACT FROM AUTHOR]

• Removal of GEDI outliers and water level evaluation. • Bias adjustment between mission ICESat-2 and GEDI. • Water level dynamics from integrating ICESat-2 and GEDI. • Water levels cross validation with DAHITI and Hydroweb datasets. • Factors that affecting the accuracy of water level monitoring. The water levels of inland lakes on the Tibetan Plateau are extremely sensitive to global climate change and can objectively reflect the temporal and spatial changes in local water resources. However, monthly and seasonal variations in lake water levels are difficult to monitor due to the lack of sufficient in situ gauges across mountainous areas. Moreover, GEDI products exhibit large uncertainties in mountainous surroundings. In this paper, taking Qinghai Lake as an example, we first refined the raw data of the GEDI and ICESat-2 missions by implementing a quality control procedure involving outlier removal tailored to the characteristics of each mission; then, we analyzed the accuracy of each mission, especially targeting factors that affect the water level retrievals in the GEDI products. Third, the bias between the two missions was adjusted by selecting the overlapping or adjacent observation dates. Finally, we constructed dense temporal water-level data by integrating the refined ICESat-2 and GEDI data. Data from water level stations and the DAHITI and Hydroweb datasets were also utilized for validation. The results show that (1) very accurate results can be obtained from the ICESat-2 ATL13 product, and the standard deviations of most observed days are under 0.05 m; (2) the GEDI products derived from algorithm 2 can offer more effective footprints than those from algorithm 1, with an improvement of approximately 9.78 %. Moreover, large differences exist among the different GEDI beams, and beams 1 and 2 are recommended for further analysis. Overall, most beams overestimated the lake levels with a bias of 0.264 ± 0.357 m; (3) the long-time-series water levels showed a mean increasing trend of 0.243 m/yr from 2018 to 2021. The relatively high-water-level periods were distributed mostly in August and September, while the low-water-level periods were distributed mostly in February and March. The combined water levels were very correlated with the DAHITI and Hydroweb datasets, with R values larger than 0.8, and highly consistent with the observations from hydrological stations (the inter-year change range spanned from 0.015 m to 0.327 m, and the intra-year difference range varied from 0.03 m to 0.16 m); and (4) Integrating the GEDI and ICESat-2 missions allowed us to capture the monthly, seasonal and annual dynamics of the lake water level, and the results indicate that the combined dataset presents a valuable resource for hydrological and climatic change studies. [ABSTRACT FROM AUTHOR]

• Developed a Decision Support Framework to classify uncertainties and management options using participatory scoping for scenarios analysis. • DSS includes: reservoirs water balance, statistical downscaling, hydrological modelling, sediment assessment, water management and decision support for water management plans. • DSS framework was applied to Tandula reservoir Complex, India and performance was evaluated using multi-criteria decision support in the three-layer hierarchal process. • DSS framework provides a solution for strategic water resources planning and management in a basin using the participation of farmers, technocrats, and water resource managers. The climate uncertainties, data scarcity, and socio-techno challenges pose significant threats and options respectively to the future planning of complex water projects in developing countries. This paper attempts to develop a decision support system (DSS) framework to classify uncertainties and management options using participatory scoping for scenarios analysis with the aid of six interrelated modules namely (i) water balance of reservoirs for computation of runoff (ii) statistical downscaling for climate projection (iii) hydrological modelling (iv) sediment assessment (v) water management and (vi) decision support for the assessment of the performance of multiple management plans designed for three future assessment periods of 2020-35, 2046-64 and 2081-99. The proposed framework was applied to the Tandula complex system of Chhattisgarh state of India consists of three reservoirs namely Tandula, Kharkhara, and Gondli having water transfer in their commands and for industrial use. The developed DSS framework provides the opportunity to analyze the management plans with climate uncertainties for the complex water resources projects having data scarcity. Here, in the present paper, the developed DSS framework incorporates eighty-four diverse management plans devised from constant interaction with stakeholders and technocrats resulting to deal with climate uncertainties using possible improvement in efficiencies and consumptive use for three future assessment periods. Furthermore, the performance of the developed plans was evaluated using multi-criteria decision support in the three-layer hierarchal process. The decision support analysis suggested that the second assessment period (FP-2: 2046-64) will be more crucial from the management point of view where 54 out of 81 plans could not perform as par with the present performance of the system. The developed DSS framework provides a solution for strategic water resources planning and management under projected climate scenarios in a basin having problems of scarcity, surplus, and non-uniform distribution of surface water using the participation of farmers, technocrats, and water resource managers. [ABSTRACT FROM AUTHOR]

• Dissolved C fluxes from 843 points on worldwide rivers are analysed. • Mean of DIC fluxes are 92% of the total (7.7 GT C yr−1), while 8% is for DOC fluxes. • Asia exports the highest dissolved C (5.8 GT C yr−1), Africa exports the lowest (0.1 GT C yr−1). • Dissolved C fluxes are higher in tropics due to high interflow contribution to river flow. • Dissolved C fluxes are low in temperate and cold climates due to high groundwater contribution to river flow. Despite dissolved carbon (C) exports from continents being crucial in the connection between terrestrial, atmospheric and oceanic C pools, the relative contribution and process controls on dissolved organic (DOC) and inorganic (DIC) fluxes within river basins are not fully understood. This paper presents an analysis of data from 843 sites worldwide from 45 Web of Science SCI-Expanded (ex ISI) indexed papers that report both the DOC and DIC contents and fluxes in conjunction with chemical elements, such as Si, Na and 13C abundance, to investigate the likely origin, pathways and fate of the dissolved C. The reviewed papers also report on selected environmental factors (e.g., MAP and MAT: mean annual precipitation and temperature respectively; Climate, LU: land use, A: basin surface area). Regarding dissolved C fluxes rivers transported on average, 8.4 GT C yr−1 of dissolved C with 7.7 GT C yr−1 (92%) being DIC and remaining (8%) DOC. Asia has the highest within-river dissolved C transport (5.8 GT C yr−1) followed by Europe (2.0 GT C yr−1), America (0.4 GT C yr−1), and Africa (0.1 GT C yr−1). The dissolved C fluxes tend to increase with river water fluxes and contribution of interflow as seen in the tropical climate, which results in the export of relatively fresh DOC with low aromaticity. In contrast, the lower river water fluxes in sub-tropical and temperate climates corresponds to high concentration of comparatively more decomposed DOC, which pointed to a high proportion of groundwater contribution to the water fluxes. The results also imply that surface area and morphology of the river basins, as well as land use, has insignificant impact on dissolved C dynamics. Such quantitative results, including on the important role of climate on the dynamics of dissolved C in river basins, are helpful in improving predictions on the impact of climate change on the global C cycle. New and innovative multidisciplinary research on the subject is called for. [ABSTRACT FROM AUTHOR]

• Adverse impacts of sea-level rises are not limited to a landward shift in shoreline. • Shallow groundwater can impact inland infrastructure kilometers from the coastline. • About 2/3 of the stormdrain length is subject to seawater & groundwater intrusion. • Coastal stressors can increase flood magnitude, frequency, and duration. • Compound flooding volume increases up to 6 times by a 2 m sea-level rise. Low-lying coastal areas are susceptible to multiple types of flooding from marine, subsurface, and surface sources. The co-occurrence of a rainfall event with high sea level, which raises coastal groundwater tables conjointly, may result in a compound flooding event, which could be much more widespread and dangerous than that of an individual flooding source. Focusing on Imperial Beach as a low-elevation coastal community in California, this paper provides a generalized methodology to determine the vulnerability of coastal stormdrain systems to compound seawater, groundwater, and stormwater flooding under a changing climate. Although marine inundation by itself is expected to flood only 7% of the study area by 2100 under the most pessimistic scenario (i.e. 2 m rise), our results show that more than 20% of the study area's subterranean stormdrain system is already at the risk of subsurface flooding at current sea levels. While marine inundation is a concern near the coastline as sea level rises, the results show that seawater intrusion into the stormdrain system can impact areas kilometers away from the coastline. The consequences of sea-level rise (SLR) can be exacerbated by an under-resourced stormdrain system, caused by groundwater infiltration through system defects. As such, by a 2 m rise in current sea-level, the flooding volume may double in an ideal system (i.e., no defects) while this ratio can increase six fold in a slightly defective stormdrain system (with 0.25 % porosity systemwide). The continuous simulations of the stormdrain system performance indicate that flood events will be more destructive and frequent under these circumstances. [ABSTRACT FROM AUTHOR]

[Display omitted] • The fuzzy C-means algorithm was used to cluster basins based on their differences in hydrological characteristics. • Budyko equations considering spatial correlations of multiple factors were constructed for runoff decomposition. • Precipitation, temperature, vegetation coverage and irrigation water consumption decreased with elevation in the YZRB. • The impact of climate change on runoff changes exceeds human activities except for downstream YZRB. On large scales, runoff changes are complex due to the regional differences in basin characteristics and interactions among multiple influencing factors. Accurately and quantitatively decomposing the impacts of climate change and human activities on runoff changes in large basins with significant spatial heterogeneity remains a challenge. In this paper, the fuzzy C-means (FCM) algorithm was used to identify the regional differences in hydrological processes of the Yangtze River Basin (YZRB) based on multiple factors. Then, regional Budyko equations with synthetic parameters were constructed for runoff decomposition using the hierarchical Bayesian model, considering the spatial correlations of multiple factors among basins. Results showed that 44 subbasins in the YZRB can be clustered into five clusters based on 10 factors including precipitation, temperature, precipitation seasonality, vegetation coverage, saturated hydraulic conductivity, elevation, slope, topographic wetness index, irrigation water consumption, and proportion of built-up area per unit area. From Cluster 1 to 5, P , T , IRR , and B decreased from about 1400 mm, 17 °C, 57.9 mm, and 1.59 % to about 870 mm, 12 °C, 15.6 mm, and 0.40 %, respectively, while ELEV and SLP increased from about 300 m and 3° to about 3500 m and 10°, respectively. Hierarchical Bayesian models can improve the performance of Budyko equations by better-simulating n , compared with linear regression models. Their R 2, RB , and RMSE for the whole YZRB were as follows: 0.9104 versus 0.7591, 4.33 % versus 8.21 %, and 0.06 versus 0.11, respectively. Runoff decomposition from 1982 to 2018 showed that precipitation led to more than 34 % change in runoff in Cluster 2, while the influence of potential evapotranspiration on runoff changes was less than 3.00 % in each cluster. Rising temperature caused runoff to decrease by more than 7.90 % in Cluster 1, 3, and 4. In addition, precipitation seasonality, vegetation, and irrigation water consumption had different influences in different clusters. This study improves the ability of the Budyko equation to quantitatively decompose the impacts of multiple factors on regional runoff changes and provides more reasonable suggestions for water resource management. [ABSTRACT FROM AUTHOR]

• The novel target flow control (TFC) approach for the real-time control (RTC) of stormwater storages is proposed. • The TFC approach does not require calibration. • The approach achieves target flows for a wide range of rainfall events with minor errors. • The approach is practical as it only requires real-time storage level information. Stormwater systems will likely require major upgrades due to increases in peak flows caused by the combined effects of urbanization, densification (urban infill) and climate change. Recently, the real-time control (RTC) of storages has been considered as a means to reduce peak flows and potentially avoid major infrastructure upgrades. This paper introduces a RTC approach, the Target Flow Control (TFC) approach, which is able to maintain system outflows at or below specified target flows (e.g., existing system capacity). The key features of the approach are that it does not require calibration to catchment specific data, as is the case for existing approaches and is only based on storage level information measured in real-time during rainfall events. This makes the approach generally applicable to different catchments and able to respond to future changes in rainfall due to land use and/or climate change. The TFC approach is tested on a simple two-storage system for 750 design rainfall events from a range of climates, event durations, rainfall intensities and temporal patterns using a practically achievable control time step of 30 s. Results show that the TFC approach can achieve the desired target flows effectively, with 95% of the experiments having less than 10% errors in target flows. This is an exciting achievement, given the TFC approach does not require calibration and only requires measurable storage level information. The outcomes highlight the potential of the TFC approach as a practical RTC approach that can maximize the effectiveness of existing stormwater conveyance systems by maintaining stormwater system outflows at desired target flows, thereby potentially avoiding stormwater infrastructure upgrades due to land use and climate change. [ABSTRACT FROM AUTHOR]

• The novel target flow control (TFC) approach for the real-time control (RTC) of stormwater storages is proposed. • The TFC approach does not require calibration. • The approach achieves target flows for a wide range of rainfall events with minor errors. • The approach is practical as it only requires real-time storage level information. Stormwater systems will likely require major upgrades due to increases in peak flows caused by the combined effects of urbanization, densification (urban infill) and climate change. Recently, the real-time control (RTC) of storages has been considered as a means to reduce peak flows and potentially avoid major infrastructure upgrades. This paper introduces a RTC approach, the Target Flow Control (TFC) approach, which is able to maintain system outflows at or below specified target flows (e.g., existing system capacity). The key features of the approach are that it does not require calibration to catchment specific data, as is the case for existing approaches and is only based on storage level information measured in real-time during rainfall events. This makes the approach generally applicable to different catchments and able to respond to future changes in rainfall due to land use and/or climate change. The TFC approach is tested on a simple two-storage system for 750 design rainfall events from a range of climates, event durations, rainfall intensities and temporal patterns using a practically achievable control time step of 30 s. Results show that the TFC approach can achieve the desired target flows effectively, with 95% of the experiments having less than 10% errors in target flows. This is an exciting achievement, given the TFC approach does not require calibration and only requires measurable storage level information. The outcomes highlight the potential of the TFC approach as a practical RTC approach that can maximize the effectiveness of existing stormwater conveyance systems by maintaining stormwater system outflows at desired target flows, thereby potentially avoiding stormwater infrastructure upgrades due to land use and climate change. [ABSTRACT FROM AUTHOR]

[Display omitted] • River thermal seasons (RTS) are good indicators of climate warming. • The temporal distribution and duration of RTS varies depending on the river size. • Elements of the environment modify properties of RTS. • The method of designation of RTS is universal. Temperature is one of the most important properties of rivers. Interest in river temperature (RT) has been rapidly increasing in the current period of climate change. It is related to among others the application of models for the development of scenarios of RT increase. The article presents an attempt to identify one of the most important elements of fluvial regime: river thermal seasons (RTS). No attempts have been undertaken to date to determine such seasons, although they are among the most important elements of fluvial regime. This paper proposes another approach to the issue of changes in RT. It involves the designation of river thermal seasons RTS based on unique daily data from a period 1961–2020. The thermal season was defined as a sequence of days with intrinsic similarity resulting from mean multiannual water temperature, its variability in the multiannual period, and autocorrelation. A total of 18 types of RTS were designated in 5 groups of RTS: very cold (0.0–1.0 °C), cold (1.1-4.0 °C), moderate (4.1–10.0 °C), warm (10.1–15.0 °C), and very warm (15.1–20.0 °C). Moroever, two sub-periods were designated: namely 1961–1992 and 1993–2020, permitting the identification of the character of RTS in both sub-periods, and the assessment of changes in their distribution. The applied method allowed for conducting comparative analyses between rivers of various sizes. The occurrence of different thermal seasons was evidenced, as well as various directions and effects of changes resulting from climate warming. The greatest contribution in a year was reached by the group of very warm seasons (34.9%). In comparison with sub-period 1961–1992, it increased by 5.1%. A smaller share was determined for the group of cold seasons (29.0%) which also increased (by 10%) in comparison to the earlier sub-season. An increase in the contribution of cold seasons resulted from a considerable reduction of the duration of seasons from the moderate (from 27.5% to 16.5%) and very cold group (from 9.1% to 1.7%). The proposed study method is universal. It can be applied to analyses of river temperature in other climate zones. [ABSTRACT FROM AUTHOR]

• A novel method for assessing prediction of extremal hydrological events. • Bayesian causal modelling tool for predictive assessment. • Hybridization of Classical and new Hydrology based on Artificial Intelligence. • HydroPredicT_Extreme as a new tool for the prediction of potential flooding events. • Applicability to different time scales of extreme and sudden events. Disastrous losses related to high-flow events have increased dramatically over the past decades largely due to an increase in flood-prone regions settlements and shift in hydrological trends largely due to Climate Change. To mitigate the societal impact of hydrological and hydraulic extremes, knowledge of the processes leading to these extreme events is vital. Hydrological modelling is one of the main tools in this quest for knowledge but comes with uncertainties. For that it is necessary to deeply study the impact of hydrological models' structure on the magnitude and timing of extreme rainfall-runoff events. This paper is mainly aimed to show the development of a method called "HydroPredicT_Extreme" based on Bayesian Causal Modelling (BCM), a technique within Artificial Intelligence (AI). This method may enhance predictive capacity of extreme rainfall-runoff events. "HydroPredicT_Extreme" follows an iterative methodology that comprise 2 main stages. First one comprises a mixed graphical/analytical method from Hydrograph. This stage is conditioned by two initial constraints which are, a) pluviometry station is representative of hydrograph downstream flow behaviour; b) there must be independence of events. This first stage comprises sub-phases such as: 1.1. Calculation of Response Time (RT) through a mixed graphical/analytical approach, 1.2 Subtraction of RT from the flow series to remove the Rainfall-Flow delay; 1.3 Calculation base flow rate; 1.4 Subtraction base-flow from flow series to work on absolute inputs. Second man stage is called Bayesian Causal Modelling Translation (BCMT) that comprises the 2.1 Learning, 2.2 Training, 2.3 Simulation through BCM modelling, 2.4 Sensitivity Analysis-Validation. This whole methodology will become a digital application and software that could be extrapolated to several similar case studies. This may be coupled with posterior devices for the prevention of catastrophic flood consequences in the form of MultiHazard-Early Warning System (MH-EWS) or others. [ABSTRACT FROM AUTHOR]

[Display omitted] • Wildfires are becoming more severe and frequent and their effects increasingly expose water resources to stress. • Climate, wildfire, streamflow records (1975 to 2018), topography, and landcover data from 92 catchments were combined to quantify the contribution of wildfire to streamflow variability across Australian temperate forests. • On average wildfire regimes relatively results in 8.8% increases in streamflow across Australian temperate forests. • Wildfire effects on regional streamflow varied spatially depending on hydroclimate factors, particularly precipitation. As wildfires become more frequent and severe, there are concerns regarding their impacts on water yield from forested catchments. While there are many studies in Australia about the effects of individual wildfires on streamflow at fine scales (<1 km2) in specific geographic settings, the effects of wildfire regimes on streamflow at broad spatial scales across temperate forests in Australia are not well understood. In this paper, we combined climate, wildfire, streamflow records (1975 to 2018), topographic, and landcover data from 92 catchments (74 – 4740 km2) in the Australian temperate zone to quantify the contribution of wildfire regimes over time to streamflow variability in different hydroclimatic settings (humid, dry sub-humid, and semi-arid) and geographic regions (Southeast Australia (SEA), Southwest Western Australia (SWWA), and Tasmania (TAS)). Wildfire regimes were represented by two metrics: the burnt area to drainage area (BDA) ratio for wildfire events in each year and a spatially averaged metric of Time Since Fire (TSF), which is a spatial average of time since the last wildfire in the catchment. By comparing prefire and postfire runoff ratios our study found that on average there was a short-term increase in runoff ratio (∼3% in year 1 and ∼6% in year 2 post-fire) after wildfires with BDA > 25%. No influence of fire was found in the long-term (15–20 years after the wildfire) runoff ratio. We found that wildfire regime, measured by TSF, explained ∼8.8% of the variation in annual streamflow across the Australian temperate zone, and that with decreasing TSF (i.e., increased wildfire impact), the average streamflow increased. Streamflow variation explained by wildfire regimes varied with hydroclimate. The explained variance of streamflow by wildfire regimes in semi-arid catchments (23%) and dry sub-humid catchments (13%) were higher than humid catchments (5%). Our results provide a broad-scale understanding of how wildfire regimes influence streamflow variability at broad temporal and spatial scales, and provided important context and baseline information for determining the implications of changes in climate and fire regimes for regional water availability. [ABSTRACT FROM AUTHOR]

• Comparison of Budyko-based methods to separate catchment and climate effects on flow. • Probabilistic nature of catchment parameter and its uncertainty considered. • New, improved Two-Stage Decomposition method proposed for separation. • Proposed method considers uncertainty in evaporative index using bootstrap method. • Proposed method more robust than using any single earlier-practiced method. Streamflow variability is affected by both climate change and direct human impacts within the catchment. Quantification of the role of these two factors have significant implications for water resources. In recent years, Budyko-based analytical methods have been widely used for such quantification due to their simplicity and efficacy. This paper presents a comparison of such methods to attribute changes in streamflow in four Model Parameter Estimation Experiment (MOPEX) catchments in the contiguous United States. Further, a new, improved Two-stage Decomposition method to separate the role of climate and catchment changes on streamflow is proposed, considering the uncertainty in the catchment parameter that further leads to a range of evaporative index values within a catchment. We find that climate and catchment effects are sensitive to the choice of Budyko equation, disaggregation algorithm and approximation technique. Further, the catchment parameter can be expressed probabilistically, to account for the uncertainty because of limited hydro-meteorological records. The proposed approach employs a bootstrap analysis to obtain the stochastic range of the catchment parameter, considering different choices of Budyko equations. We argue that rather than relying on a single Budyko-based approach for separation of climate and catchment effects on streamflow, the proposed method offers a more robust way by considering the entire range of uncertainty of the catchment parameter. [ABSTRACT FROM AUTHOR]

• A hydrological modeling approach was proposed to distinguish the relative contribution of climate changes and human activities on Daihai Lake's recession. • In the absence of natural runoff and human-induced water withdrawal observations, the natural state's lake water storage changes were reconstructed. • Without considering the high water years, the anthropogenic factor took the responsibility for the lake shrinkage about 58%, while the climatic factor took roughly about 42% in period 1974–2018. • This investigation serves as a useful example of applying a distributed model to the assessment of lake hydrology, especially for the lake catchments with limited observations. In recent decades, water levels in many closed inland lakes over north-western China experienced severe declines, among which the Daihai Lake in Inner Mongolia, China was particularly significant. Remotely sensed investigations indicated that the Daihai Lake had shrunk approximately 64% in an area with the dramatic decline of water level approximately 9 m from the 1980 s to 2010 s, which has caused serious concern on the sustainable development of the surrounding region. Consequently, several previous studies have concentrated on the possible causes of lake shrinkage. However, predicting climate variability and anthropogenic activities' relative contributions to lake water decline is challenging due to the lack of direct water consumption upstream and watershed-scale natural runoff observations. In this paper, a hydrological modeling approach was presented to quantitatively evaluate and separate the relative contributions of climate variations and intensifying anthropogenic activities. The simulation accuracy was evaluated through comparisons with observed data and cross-validation with previous studies. Based on a baseline period, the natural streamflow draining into the lake was reconstructed using the model. The results revealed that the water loss of the Daihai Lake was caused predominately by human activities rather than climate variability. Specifically, for wet conditions, the climate factors posed a negative effect on the water level decline of the lake and delayed the shrinking process of the lake area. The anthropogenic factors took a dominant role (roughly 58%) of lake shrinkage at the annual average scale for normal and dry conditions, while the climate factors accounted for approximately 42%. Hence, the rational policy for water resource management, ecological restoration, and dam and reservoir constructions/operations should be the essential precautions for sustainable development of the Daihai Lake and its surroundings. Additionally, the hydrological modeling approach adopted in this investigation serves as a useful example of the quantitative assessment of climate variations and human activities, especially for the ungauged catchments. [ABSTRACT FROM AUTHOR]

• The mean groundwater table depth in China dropped by approximately 0.2 m (P < 0.01) during 1979–2018. • The Noah-MP land model forced with reanalysis climate data can reasonably capture terrestrial water and radiation processes across China. • Incorporating the groundwater lateral flow process enhances the seasonality of modeled terrestrial water and radiation processes on a regional scale. Faced with intensified climate change and ever-increasing water demand, the groundwater table depth is vital to sustaining terrestrial environments in a country such as China; however, on a regional scale, insufficient research has been conducted on its variability thus far. Using a land model forced with reanalysis climate data, this paper investigates groundwater table depth changes across China for the 1979–2018 period. The results show a significant overall reduction in the groundwater table depth in China during the past 40 years without explicit consideration of direct human influence. There is an approximately 0.2 m decrease on average, but it is approximately 0.5 m in southern China and 0.8 m to the north of 35°N despite a 1.2 m rise in part of this region due to an increasingly wet climate and a topographic water convergence. The findings provide a first-order pattern of historical groundwater table responses to climate change, highlighting inherent links among subsurface-surface-atmosphere water cycles and worsened terrestrial water availability in China. A model evaluation compared with in situ observations has demonstrated the model's ability to realistically portray terrestrial water and energy balances on regional scales, as well as the enhancement of their seasonality with incorporated lateral groundwater flow processes. This ability gives us reasonable confidence in the simulated trends and spatial variability. Nonetheless, the mean groundwater table still shows large biases relative to observations, and the problems of human influence, two-way interactions between groundwater and surface water, and local topographical complexity have yet to be properly addressed in the present study and deserve further research. [ABSTRACT FROM AUTHOR]

• Simple approach to evaluate the impact of climate change on groundwater. • Correlation between meteorological and groundwater indices. • Regional climate model outputs to estimate future groundwater levels. This paper investigates the impacts of climate change on groundwater droughts making use of regional projections and standardized indices: the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Groundwater Index (SGI). The method adopted, using historical precipitation and temperature data and water levels collected in monitoring wells, first investigates the possible correlations between meteorological and groundwater indices at each well. Then, if there is a correlation, a linear regression analysis is used to model the relationships between SGIs and SPIs, and SGIs and SPEIs. The same relationships are used to infer future SGIs from SPI and SPEI projections obtained by means of an ensemble of Regional Climate Models (RCMs), under different climate scenarios (RCP 4.5 and RCP 8.5). This methodology has been applied to data collected in northern Tuscany (Italy) in an area served by a water company, where historical series of daily climate variables (since 1934) and daily records for 16 wells, covering the period 2005–2020, are available. The impacts on groundwater have been computed in the short- (2006–2035), medium- (2036–2065) and long-term (2066–2095). The analysis indicates that, in the historical period and for most of the monitoring wells, there is a good correlation between SGIs and SPIs or SPEIs. The results point out that making use of the SGI-SPI relationships, slight variations in the availability of groundwater are expected in the future. However, in a global warming scenario, the influence of temperature on evapotranspiration phenomena cannot be overlooked and, for this reason, the SGI-SPEI relationships seem more suitable to forecast groundwater droughts. According to these relationships, negative effects on groundwater levels in almost all wells are estimated for the future. For the RCP 4.5 scenario, the largest decline in groundwater level is expected in the medium-term, while for the RCP 8.5 scenario future SGIs will significantly decrease over the long-term. Due to the type of data required and its simplicity, this methodology can be applied to different areas of interest for a quick estimate of groundwater availability under climate change scenarios. [ABSTRACT FROM AUTHOR]

• DRASTIC-AgLU-CC is applied to examine future groundwater contamination risk. • Climate and land use factors are treated as dynamic model inputs. • The influence of land use scheme complexity on model output is assessed. • Detailed agricultural land use representation may improve model predictions. • Predicted contamination risk varies depending on land use representation. Rural regions, which are commonly reliant on groundwater for water supply, often face groundwater quality challenges related to agricultural activity. To sustainably manage and protect groundwater resources, it is necessary to have a clear understanding of inherent groundwater vulnerability and the contamination risk posed by pollutant loading. Index-based methods typically assess groundwater contamination risk in a static manner and commonly represent agricultural land as a single land use class despite the complexity of land management practices that may be present. This paper illustrates modification of the conventional DRASTIC-LU methodology, through a newly developed DRASTIC-AgLU-CC approach, to better understand how groundwater contamination risk may change by mid-century while simultaneously examining the potential influence of land use scheme complexity on model performance and prediction. Recharge, depth to water table, and land use are treated as dynamic factors through the integration of downscaled global climate model data, empirically estimated water table fluctuation and recharge, and trend-based land change modelling. The Upper Parkhill watershed in southwestern Ontario, Canada serves as a case study for method application. Study results indicate that more detailed agricultural land use representation, specifically the inclusion of crop rotation and tile drainage data, has the potential to improve model predictive ability; furthermore, predicted future changes in groundwater contamination risk may vary depending on the manner in which land use is represented. Within the Upper Parkhill watershed, mid-century land use and climate conditions may increase groundwater contamination risk, although minimal change is anticipated. The presented approach provides a valuable screening tool to better understand the potential for changing groundwater contamination risk in rural regions and can be used to guide groundwater monitoring programs and water supply development. [ABSTRACT FROM AUTHOR]

• Proposes an analytic framework to identify when a adaptation operating rule is needed. • Introduces the probability change-point analysis technique into failure warning time. • Evaluates the proposed framework based on a real-world case study in China. This paper presents a methodology for the identification of the time tipping point where historical operating rules of a reservoir system should be replaced by new alternatives due to climate change. The time tipping point is a time indicator of the failure probability change point of the historical operating rules for the reservoir system under future climate change, called the failure warning time. For this study, the primary purpose of the reservoir system is hydropower generation. We first estimate the probability density function of the annual benefit of hydropower generation. Then, based on the estimated probability density function and the given risk tolerance level of the reservoir system, we determine the risk-based warning threshold, and further identify the failure warning time of the historical operating rules via the probability change-point analysis technique. We apply the proposed methodology to the Xiluodu-Xiangjiaba-Three Gorges cascade reservoir system on the Yangtze River in China. The results indicate that the failure warning time for the cascade reservoir system will occur in at least another 10 years in the future under climate change. We also find that the failure response in the cascade reservoir system is determined by both the reservoir that has the first failure occurrence and its storage capacity. The developed methodology can be used to determine the time tipping point to launch adaptation strategies for the river basin management, and further can help policymakers cope with climate change. [ABSTRACT FROM AUTHOR]

• IBIS may be more impacted by climate change in UIB than local hydrology. • Sedimentation combined with climate change exacerbates dry season water shortages. • Water availability reductions in the dry season could impact Pakistan's food security. Pakistan relies greatly on the water of the Upper Indus basin (UIB) for its vast agriculture in the Indus Basin Irrigation System (IBIS). There are already concerns about the extent of unsustainable water use in the IBIS, particularly of groundwater, and these concerns are likely to increase in the future due to population increase and associated increased demand for food. In addition, climate change, continuing reservoir sedimentation in the major dams, and other impacts on water supply and demand are likely to have an impact on the practical implementation of the landmark inter-provincial Water Apportionment Accord (the Accord), which governs surface water allocation in the IBIS. There is, therefore, a need to assess the sustainability of irrigation in light of the potential variations of water availability in the basin under a series of plausible climate change and reservoir sedimentation scenarios. In this paper, we examine the impact of continuing reservoir sedimentation and climate change by 2050 on the water balance of canal commands in the IBIS. Firstly, we used a river system model, the first to include water management rules as in the Accord, to propagate climate induced changes on flows in the UIB and their impact on IBIS flow regulation and canal deliveries (I cw), with and without reservoir sedimentation. Secondly, we used a selection of General Circulation Models (GCMs) that showed skill at simulating the local climate of the IBIS to assess the projected changes to rainfall (P n) and evapotranspiration (ET a). The analysis revealed that water balances in IBIS canal command areas are more significantly affected by changed inflows from the UIB than by localised climate induced changes (i.e. P n and ET a). Continuing reservoir sedimentation in the dams to 2050 will diminish the capacity of the system to store flows during the wet summer Kharif season for its later release in the dry winter Rabi season. This will decrease the supply of I cw in the Rabi season to the downstream IBIS areas in Sindh and Balochistan provinces, and potentially increase supplies in the Kharif season. The impact of reservoir sedimentation on the Rabi water supply (33 mm (13%) decrease in median I cw , equivalent to 1.89 km3), is greater than the impact of projected localised climate change within the IBIS (2 mm (12%) decrease in median P n , equivalent to 0.13 km3). Sedimentation combined with climate change decreases the median I cw by 51 mm (20%, equivalent to 2.91 km3) in Rabi in the low flow scenario and increases it by 48 mm (8%, equivalent to 2.72 km3) in Kharif in the high flow scenario. In the upstream Punjab province, however, reservoir sedimentation will have limited impact by 2050 due to lower ET a in Rabi and the availability of groundwater. Continued reservoir sedimentation beyond 2050 will also affect Punjab, again diminishing the supply of I cw in Rabi and increasing it in Kharif. Concurrently, declining I cw could lead to a substantial reduction in crop areas in the Rabi season. To avoid this undesirable scenario, Pakistan needs to explore both demand and supply management options to achieve food and economic security, as well as controlling sedimentation rates in reservoirs. [ABSTRACT FROM AUTHOR]

• An observation-based natural and human-impacted catchment comparison method is proposed. • Human influence are quantified based on gridded socio-economic and land use data. • Climatic and anthropogenic influences on drought propagation are investigated. • Climate change accelerates the response of hydrological drought to meteorological drought. • Human activities delay the propagation from meteorological to hydrological drought. It is crucial to investigate how a precipitation deficit is transformed into hydrological drought and how climate change and human activities affect this transformation process, which is helpful to gain a deep understanding of drought propagation process in this changing environment. This study proposed an observation-based natural and human-impacted catchment comparison method to assess the impacts of climate change and human activities on propagation from meteorological drought to hydrological drought. The method mainly consists of the following three steps: (1) selection of natural catchments through analysis of trends and change points of hydro-meteorological data, as well as statistics analysis of human influence based on land use and socio-economic indicators data sets; (2) calculation of drought propagation characteristics (e.g., drought severity, duration, and propagation time) based on run theory and the Pearson correlation coefficient; and (3) comparison of drought propagation characteristics of natural catchments between undisturbed and disturbed periods to identify the impacts of climate change on drought propagation, and comparison of the propagation characteristics between natural and human-impacted catchments during the disturbed period to investigate human influence on drought propagation. The Laohahe basin (with eleven sub-catchments), located in northern China, was evaluated via the proposed procedure, and standardized precipitation index (SPI) and standardized runoff index (SRI) were used to characterize meteorological and hydrological droughts, respectively. The results demonstrate that the proposed method is suitable tool for distinguishing natural and human-impacted catchments, and separating the impacts of climate change and human activities on drought propagation. Furthermore, the comparison results of different schemes show that climate change accelerates the propagation from meteorological drought to hydrological drought in the Laohahe basin, shortening it by approximately 3 months. Human activities, however, disturb and then delay the natural propagation from meteorological drought to hydrological drought, retarding it by 11–12 months. Although the Laohahe basin was selected as a case study in this paper, the proposed method can be applied in other regions as well to improve drought prediction and water resources management. [ABSTRACT FROM AUTHOR]

• Use of distributed storage with optimised layout and control reduces peak flows. • Two-step optimisation identifies contribution of layout/RTC to peak flow reduction. • Peak flow reductions due to distributed storage range from 5% to 40% • RTC achieves higher relative peak flow reduction for a smaller storage volume. • Flood hydrograph analysis provides insights on peak flow reduction mechanisms. The impact of urban flooding is increasing due to the effects of increasing urbanisation and climate change. The use of storages is a relatively well-established approach to reduce peak flows and therefore reduce the need for costly upgrades to stormwater conveyance infrastructure. Recently, real-time control (RTC) has been considered as a means of increasing the performance of these storages. However, previous RTC studies have generally focussed on evaluating RTC with pre-determined storage layouts, i.e., storage locations and volumes, and have ignored the impact of optimising the storage layout. Hence, the objectives of this paper are (i) to introduce a two-step approach to minimising peak flows at the catchment scale by firstly optimising the layout of distributed storages and then optimising their RTC strategies, and (ii) to test the effectiveness of this approach on a real catchment in Adelaide, South Australia. Results show that distributed storage with optimised layouts can achieve significantly higher peak flow reductions than more commonly used end-of-system storage. These range from 5% for 100 m3 of system storage up to 40% for 700 m3 of system storage – whereas end-of-system storage achieved no peak flow reduction for up to 700 m3 of storage. The addition of optimised RTC to distributed storages is able to achieve an additional 10% in peak flow reduction. This increase in performance due to RTC is more significant for smaller system storage volumes. Analysis of the flood hydrographs shows the system peak flow reductions are achieved by attenuating hydrographs at individual storages, as well as delaying hydrographs to reduce their coincidence at the confluence of sub-catchments. These results highlight the potential for using optimised distributed storage layout and RTC as an alternative approach to end-of-system storage to reduce flood peaks. [ABSTRACT FROM AUTHOR]

• Depending on stream temperature model, different warming of streamwater is projected. • Projection from particular model occasionally differ from the majority of models. • A number of stream temperature models should be used together for future climates. • Stream temperature in analyzed streams is to be warmed by 2–3.5 °C by 2100 (RCP 8.5). • In specific streams marginal cooling in projected in some months until 2050. In the majority of studies aiming at stream temperature warming due to climate change just a single water temperature model is used. Choosing a single model may highly impact the conclusions from the study. In this paper four relatively different empirical or semi-empirical models: perceptron neural networks, product unit networks, extended logistic regression and air2stream were applied to project the impact of climate change on water temperature in rivers located in temperate climatic zones of the USA and Poland. The models were driven by daily air temperature and streamflow projected by the rainfall-runoff model. In the first step, the models were calibrated and validated. Then the projections of water temperature were derived for the historical periods and two future periods taking into account: (a) climate simulations from the CORDEX initiative (NA-CORDEX and EURO-CORDEX), (b) the GR4J rainfall-runoff model and (c) different water temperature models. The obtained results indicate that due to global warming, the stream temperatures are expected to increase by about 1–2 °C for 2021–2050 and by 2–3 °C for 2071–2100 periods. These changes are not uniformly distributed throughout the year. The largest warming in the USA is found in the summertime, in Poland – in spring and autumn. For some months the discrepancies in the projected stream temperature between various stream temperature models are large. Product unit neural network, logistic regression-based model or air2stream occasionally led to projections that differ from those obtained by the majority of models even by 2 °C. We strongly recommend using at least a few stream temperature models for analysing the impact of climate change on water temperatures or the fate of the aquatic ecosystem. [ABSTRACT FROM AUTHOR]

• Stormwater management Low-impact-development (LID) optimization and resilience. • Bibliometric analysis and systematic literature review. • Analysis of citation networks of articles, authors and countries. • Identification of research clusters and gaps-LID optimization and resilience. The implication of optimal low impact development (LID) implementations has been attracting researchers' attention, aiming to alleviate the detrimental impacts of urbanization and climate change and enhance resilience. The rapidly increasing number of publications on LID optimization over recent years makes it one of the leading-edge research areas in the field of urban stormwater management. This study aims to conduct a systematic bibliometric review of the optimization and resilience within LID stormwater management practices. LID related publications of 17 years (2004–2020, August) were retrieved from the Web of Science database and thoroughly analyzed. This review looks into the progression of current research themes, previous work outcomes, and key research gaps. Using a clustering tool, four main research clusters have been identified. Employing text mining, each cluster reflecting a research theme is identified based on the analysis of the top fifteen papers. The clusters themes are outlined as (1) optimizing LID type and size, (2) spatial layout optimization with parameter uncertainty and climate and land-use change impacts, (3) hydraulic LID parameter optimization and adoption of multi-criteria analysis and (4) experimental studies on bioretention for quantity and quality assessment. Subsequently, the cross-cutting research gaps are identified considering all articles. Climate change and resilience are identified as key hot topics from authors' keyword analysis, highlighting current research frontiers and laying out the directions for future research thrust in this critically important emerging research field. [ABSTRACT FROM AUTHOR]

• Evaporation trends of tropical reservoirs in Brazil were investigated. • Both positive and negative trends in evaporation were observed. • In the industrial region, the negative trend was potentially caused by the impact of pollutant released to the atmosphere. • In the coastal region, the positive trend was related to increasing air temperature. This paper aims to analyze the trends (Mann-Kendall test) of evaporation and climatological variables, including air temperature, relative humidity and wind speed in four reservoirs located in the Brazilian Northeast. The evaporation was estimated through remote sensing using the Surface Energy Balance System for Water (AquaSEBS) model from 1985 to 2018. In addition, it was verified which meteorological parameter has the highest correlation (R2) with the evaporation in order to assess why this process is changing throughout the years in each reservoir. The model performed well, with root mean square error (RMSE) ≤ 1.25 mm/day, percent bias (PBIAS) ≤ 13.70% and coefficient of determination (R2) ≥ 0.51. One reservoir located closer to a coastal zone presented a positive trend (+0.24 mm/34 years), corroborating the measurements of a reference class A pan, and Penman-Monteith equation, while the others, located closer to industrial areas, displayed negative trends (−0.26 to −0.080 mm/34 years). This reduced evaporation was attributed to the impact of industrial pollutant release to the atmosphere, while the increasing effect in the other reservoir was due to its proximity to the coast, which implies an air renewal as the sea breeze contributes for an improvement in the air quality. The meteorological data also presented positive and negative correlations: Temperature exhibited a positive trend (+0.57 °C/34 years), whereas humidity (−4.83%) and wind speed (−0.29 m/s), a negative trend. Evaporation displayed moderate coefficient of determination (R2 > 0.31) with wind speed and humidity. Yet, the reduction of evaporation may be associated with a possible counterbalancing effect of wind speed over humidity. Overall, the results contribute to a better understanding of evaporation trends in a water-scarce region and can be used in a water resources management context as the increasing/reduction of evaporation might significantly impact water availability in this area. [ABSTRACT FROM AUTHOR]

• Hydrological responses to land use and climate change are simulated using integrated modelling. • Scoring method is applied to select representative climate models. • Mean temperature is likely to increase by 1.79 °C (3.57 °C) under mid (high) emission scenarios. • Annual and monsoon flow is likely to increase during the 2050s and 2080s. • Hydrological sensitivity of the basin is analysed against changes in climatic variables. Accurate spatial and temporal measurement of water budget play a crucial role in achieving the sustainable planning and management of water resources. This paper presents a comprehensive integrated approach to simulate hydrological responses under dynamics of land use land cover (LULC) and climate change scenarios over Upper Narmada Basin, India. In order to select the representative climate models, scoring method was applied at regional scale. Best five performing climate models (MIROC5, CNRM-CM5, MPI-ESM-LR, GFDL-ESM2G and IPSL-CM5A-MR) were coupled with semi-distributed hydrological model to simulate the hydrological responses. Considering the heterogeneity of the large basin, multi-site calibration and parameters sensitivity analysis were performed using Sequential Uncertainty Fitting (SUFI-2) algorithm. To compute the hydrological sensitivity against land use change, calibrated model was coupled with historical and futuristic land use scenarios (1990, 2000, 2010 and 2030). The results indicate intensified precipitation towards the late 21st century, whereas annual mean temperature could be raised by 1.79 °C and 3.57 °C under mid and high emission scenarios respectively, at the end of century. Annual and monsoon flow in the basin likely to increase during the 2050 s (2041–2070) and 2080 s (2071–2100). Moreover, the relation between climate variables and water budget components were identified to analyse the hydrological sensitivity of the basin under changing climate. [ABSTRACT FROM AUTHOR]

• Food security in Jiangsu can be ensured by improving crop water and nutrient use efficiency. • Water and nutrient use efficiency can be improved by adapting human agency. • Conditions with more water and land, and less population, labor more important than machinery. • Under unfavorable conditions, machinery more important for higher crop production. Food security is important for human well-being worldwide. However, changing climate, population growth and shrinking land resources are threatening food security in many regions of the world. Jiangsu Province, China, is one such region. It is a major food-producing region of the country but is witnessing rapid population growth and urbanization that is putting pressure on agricultural water and land resources and threatening food security of the region. This paper interprets the nexus between regional water availability and food security in Jiangsu Province under different climate change and socio-economic scenarios of population growth and land resource availability. Climate change scenarios are generated based on historical data and Global Climate Model (GCM) products. Socio-economic scenarios are generated based on population growth and crop planted area projections. The uptake of water and nutrients are considered as two dominant biophysical processes of crop growth and food production. Complementing it is human agency, including human labor, irrigation and land-preparation machinery, which are the factors behind water and nutrient use efficiencies of crops grown. Two dominant crops are considered, rice and wheat, that contribute to 61.4% of total crops produced in the province. Results show that adaptation by human agency is necessary to ensure that food supply meets at least the demand of the province under all climate change and socio-economic scenarios. Under relatively favorable scenarios, labor could replace land-preparing machinery since the level of food production can be easily maintained with abundant water and land availability. Mechanization in agricultural production significantly increases food production under unfavorable conditions, since it improves water and nutrient use efficiencies and leads to higher crop yields. This demonstrates that human agency plays an important role in securing food under stressful scenarios of drier climate, population growth, and contraction of agricultural lands. [ABSTRACT FROM AUTHOR]