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Determining the respective attribution proportions of climate change and land use change to streamflow variations in river systems is of increasing interest to researchers and practitioners tasked with managing river basins. This paper proposes an extension to established techniques of attributing the relative proportions of climate change (CC) and land use change (LUC) drivers to streamflow change by instead considering these proportions as distributed through a probability density function (pdf), rather than as a point value. The novel method is demonstrated for the River Tweed in the UK. Results are determined by the flow, temperature and precipitation data, and upon the algorithms used to identify change points in these vectors. The ratio of the LUC and CC attribution proportions (Land Use and Climate Change Attribution Proportions, LCAP) is more appropriately expressed as a vector of values, each associated with its own probability value within a probability density function. The paper demonstrates that the LCAP ratio pdf can vary considerably over time and that it is possible to track physical changes in the catchment in the evolution of the probability density function. Results show that the land-use change/climate change attribution proportion (LCAP) ratio varies over time and can be expressed as probabilistic estimate. It can be concluded, with a high degree of confidence, that for the Tweed catchment the LUC is a significant driver of streamflow change. Hence this finding may have implications for future catchment flood management utilising nature based solutions (NbS) to reverse landscape degradation and mitigate effects of climate change, provided that the economic and social costs are outweighed by the benefits. [ABSTRACT FROM AUTHOR]

• The stomatal conductance (g s) decreased at a diminishing rate as the CO 2 increased. • The modified hyperbolic model (MH) provided the best estimate of g s. • The MH gave the best ET estimate when applied into P-M model under increased CO 2. • Changes of the empirical parameter b in MH had a minimal effect on predicted ET. An increase in atmospheric CO 2 concentration (CO 2) affects stomatal conductance (g s). Since g s is an important parameter in the Penman–Monteith model (P-M), an accurate estimate of g s can improve the accuracy of the estimation of evapotranspiration (ET) by P-M when CO 2 increases. This paper identifies three models of the relationship between g s and CO 2 (the linear model , L, the hyperbolic model , H, and the modified hyperbolic model , MH), and compares their estimations of ET when they are incorporated into P-M. A two-year experiment which included four levels (400, 550, 700, and 900 μmol mol−1) and three levels (400, 550, and 700 μmol mol−1) of CO 2 in 2015 and 2016, respectively, was conducted in order to obtain validation data. Fifty papers that had studied the effect of increased CO 2 on g s , over a 45-year period, were analyzed to obtain calibration data. After being calibrated and validated, each of the three models was in turn included as a component of P-M to estimate ET under increased CO 2 , followed by the comparison between estimated and observed ET. The sensitivity of P-M to the parameters in three models was also analyzed. Our results show that g s decreased and the reduction rate gradually lessened as CO 2 increased. Of the three models, MH gave the best estimate of g s , having the largest values of the coefficient of determination (R 2), 0.97, the Nash–Sutcliffe efficiency coefficient (NSE), 0.96, and the modified affinity index (d), 0.93, and the smallest values of the root mean square error (RMSE) , 0.03, and the Akaike information criterion (AIC) , −47.48. Each of the three models gave different ET results for different levels of CO 2 when used as a component of P-M. The MH had better ET estimations than L and H when compared to observed ET across all CO 2 concentration levels, with the highest R2 , NSE , and d , and the lowest RMSE and AIC. The sensitivity of P-M to the empirical parameters varied among the three models, for instance, ±40 and ±20% changes of parameter p in L, g s max p and C s 0 in H, and b in MH resulted in −15 to 4%, −13 to 7%, −9 to 4%, and −3 to 3% changes in predicted ET , respectively. Thus, changes of parameter b in MH had minimum effect on predicted ET , indicating greater stability of P-M in ET estimation by incorporating MH. Therefore, using MH to model the response of g s to increased CO 2 when incorporated into P-M to estimate ET not only represents a more appropriate stomata physiological reaction to higher CO 2 , but also results in a more accurate estimate of ET , and making the model more broadly applicable. [ABSTRACT FROM AUTHOR]

Highlights • Innovative trend analysis (ITA) and Mann-Kendall (MK) determine average trend. • Double-ITA, Triple-ITA, and partial MK are proposed for stability test of trends. • ITA should be used together D-ITA or T-ITA for better trend analysis. • MK should be used together partial MK for better trend analysis. • Comparisons of ITA, D-ITA, T-ITA, classical MK, and partial MK are presented. Abstract The climate change is an important event that affects hydrological, agricultural and water resources planning variables, and therefore, the hydrologists and meteorologists frequently try to identify trend possibilities especially in rainfall, runoff and temperature time series. For this purpose, the classical Mann-Kendall (MK), Spearman's rho (SR), Sen's slope, and linear regression approaches are applied frequently in the literature. Recently, innovative trend analysis (ITA) provides visual inspection and identification of categorical trends, which is one of the main concerns in this paper. On the basis of ITA methodology, several improvements namely double-ITA (D-ITA) and triple-ITA (T-ITA) procedures are suggested using with simple ITA together. These methods are attractive for the trend stability assessment by comparing partial trend components during different sub-periods of a given record series. Furthermore, partial MK test approach is proposed in this paper for the same purpose. These procedures and approach are applied to a set of annual rainfall records at many stations in different regions of Turkey. As a result, the comparison of the suggested methods based on partial sub-series of the same time series helps to improve trend detection with stability identification. [ABSTRACT FROM AUTHOR]

• 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]

Water resources in Mediterranean regions are under increasing pressure due to climate change, economic development, and population growth. Many Mediterranean rivers have their headwaters in mountainous regions where hydrological processes are driven by snowpack dynamics and the specific variability of the Mediterranean climate. A good knowledge of the snow processes in the Mediterranean mountains is therefore a key element of water management strategies in such regions. The objective of this paper is to review the literature on snow hydrology in Mediterranean mountains to identify the existing knowledge, key research questions, and promising technologies. We collected 620 peer-reviewed papers, published between 1913 and 2016, that deal with the Mediterranean-like mountain regions in the western United States, the central Chilean Andes, and the Mediterranean basin. A large amount of studies in the western United States form a strong scientific basis for other Mediterranean mountain regions. We found that: (1) the persistence of snow cover is highly variable in space and time but mainly controlled by elevation and precipitation; (2) the snowmelt is driven by radiative fluxes, but the contribution of heat fluxes is stronger at the end of the snow season and during heat waves and rain-on-snow events; (3) the snow densification rates are higher in these regions when compared to other climate regions; and (4) the snow sublimation is an important component of snow ablation, especially in high-elevation regions. Among the pressing issues is the lack of continuous ground observation in high-elevation regions. However, a few years of snow depth (HS) and snow water equivalent (SWE) data can provide realistic information on snowpack variability. A better spatial characterization of snow cover can be achieved by combining ground observations with remotely sensed snow data. SWE reconstruction using satellite snow cover area and a melt model provides reasonable information that is suitable for hydrological applications. Further advances in our understanding of the snow processes in Mediterranean snow-dominated basins will be achieved by finer and more accurate representation of the climate forcing. While the theory on the snowpack energy and mass balance is now well established, the connections between the snow cover and the water resources involve complex interactions with the sub-surface processes, which demand future investigation. [ABSTRACT FROM AUTHOR]

Coastal aquifers provide a source of water for more than one billion people, with island freshwater lenses being some of the most vulnerable coastal groundwater systems due to their susceptibility to saltwater intrusion. Basic hydrogeological and hydrochemical knowledge regarding the recharge and salinisation processes of freshwater lenses is important to ensure sustainable utilisation, especially considering possible climate change effects. This paper makes an assessment of the fate of a freshwater lens in a drying climate through a comparison of current and historic hydrochemical data, which to the author’s knowledge is unique to this study. Fresh groundwater stable isotope signatures (δ 18 O, δ 2 H) reflect local amount weighted rainfall signatures (δ 18 O: −3.8‰; δ 2 H: −15.1‰), and confirm rainfall as the origin of fresh groundwater (δ 18 O: −4.47 to −3.82‰; δ 2 H: −20.0 to −16.6‰). Mixing with seawater was identified through enriched groundwater δ 18 O and δ 2 H signatures (maximum values of −0.36‰ and −1.4‰ respectively) compared to local rainfall and higher salinity (maximum 29,267 mg/L Total Dissolved Solids (TDS)) in a number of monitoring wells around the freshwater lens. Enhanced seawater intrusion detected in the northern section of the lens area was identified through significantly increased TDS values over the last 20–40 years, with increases of up to 3000% observed between 1990 and 2014. A reduction in the extent of freshwater by approximately 1 km 2 was identified since 1977, which was found to be primarily caused by a reduction in recharge to the freshwater lens due to a ∼20% decline in winter rainfall in the south-west Western Australian region since the mid 1960s. Groundwater abstraction was found to equate to between 5% and 9% of the estimated recharge for the island, and is not a significant factor in the reduction of the lens extent compared to the observed decline in rainfall recharge. Interestingly, seawater intrusion into the fresh water lens was found to occur by older seawater (0.03–0.09 TU) in regions of the lens that were previously fresh or slightly brackish, while one sample (0.67 TU) suggests either modern seawater intrusion or mixing of older saline groundwaters (>60 years) with rainfall recharge. The use of tritium dating in this island aquifer was essential in identifying ‘older’ seawater that was previously unidentified until now. The isotopic and hydrochemical tools used in this paper quantify the effects of groundwater abstraction and climate variability on the freshwater lens and have implications for the sustainable management of the groundwater resource on Rottnest Island, and elsewhere. [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]

• 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]

Summary The aim of this paper is to analyse the potential relationship between flash flood events and convective precipitation in Catalonia, as well as any related trends. The paper starts with an overview of flash floods and their trends in the Mediterranean region, along with their associated factors, followed by the definition of, identification of, and trends in convective precipitation. After this introduction the paper focuses on the north-eastern Iberian Peninsula, for which there is a long-term precipitation series (since 1928) of 1-min precipitation from the Fabra Observatory, as well as a shorter (1996–2011) but more extensive precipitation series (43 rain gauges) of 5-min precipitation. Both series have been used to characterise the degree of convective contribution to rainfall, introducing the β parameter as the ratio between convective precipitation versus total precipitation in any period. Information about flood events was obtained from the INUNGAMA database (a flood database created by the GAMA team), with the aim of finding any potential links to convective precipitation. These flood data were gathered using information on damage where flood is treated as a multifactorial risk, and where any trend or anomaly might have been caused by one or more factors affecting hazard, vulnerability or exposure. Trend analysis has shown an increase in flash flood events. The fact that no trends were detected in terms of extreme values of precipitation on a daily scale, nor on the associated ETCCDI (Expert Team on Climate Change Detection and Indices) extreme index, could point to an increase in vulnerability, an increase in exposure, or changes in land use. However, the summer increase in convective precipitation was concentrated in less torrential events, which could partially explain this positive trend in flash flood events. The β parameter has been also used to characterise the type of flood event according to the features of the precipitation. The highest values correspond to short and local events, usually with daily β values above 0.5, while the minimum threshold of daily β for catastrophic flash floods is 0.31. [ABSTRACT FROM AUTHOR]

Summary Growing water extractions combined with emerging demands for environment protection increase competition for scarce water resources worldwide, especially in arid and semiarid regions. In those regions, climate change is projected to exacerbate water scarcity and increase the recurrence and intensity of droughts. These circumstances call for methodologies that can support the design of sustainable water management. This paper presents a hydro-economic model that links a reduced form hydrological component, with economic and environmental components. The model is applied to an arid and semiarid basin in Southeastern Spain to analyze the effects of droughts and to assess alternative adaptation policies. Results indicate that drought events have large impacts on social welfare, with the main adjustments sustained by irrigation and the environment. The water market policy seems to be a suitable option to overcome the negative economic effects of droughts, although the environmental effects may weaken its advantages for society. The environmental water market policy, where water is acquired for the environment, is an appealing policy to reap the private benefits of markets while protecting ecosystems. The current water management approach in Spain, based on stakeholders’ cooperation, achieves almost the same economic outcomes and better environmental outcomes compared to a pure water market. These findings call for a reconsideration of the current management in arid and semiarid basins around the world. The paper illustrates the potential of hydro-economic modeling for integrating the multiple dimensions of water resources, becoming a valuable tool in the advancement of sustainable water management policies. [ABSTRACT FROM AUTHOR]

Summary The effects of climate change on water resources have been studied extensively throughout the world through the use of hydrologic models coupled with General Circulation Model (GCM) output or climate sensitivity scenarios. This paper examines the effects of hydrologic model parameterization uncertainty or equifinality, where multiple unique hydrologic model parameter sets can result in adequate calibration metrics, on hydrologic projections from downscaled GCMs for three snowmelt-dependent watersheds (upper reaches of the Clearwater, Gunnison, and Sacramento River watersheds) in the western United States. The hydrologic model used in this study is the Soil and Water Assessment Tool (SWAT) and is calibrated for discharge at the watershed outlet in each watershed. Despite achieving similar calibration metrics, a majority of hydrologic projections of average annual streamflow during the 2080s were statistically different, with differences in magnitude and direction (increase or decrease) compared to historical annual streamflows. At the average monthly time-scale, a majority of the hydrologic projections varied in peak streamflow timing, peak streamflow magnitude, summer streamflows, as well as overall increases or decreases compared to the historical monthly streamflows. Snowmelt projections from the SWAT model also widely varied, both in depth and snowmelt peak timing, for all watersheds. Since a large portion of the runoff-producing regions in the western United States is snowmelt-dependent, this has large implications for the prediction of the amount and timing of streamflow in the coming century. This paper shows that hydrologic model parameterizations that give similar adequate calibration metrics can lead to statistically significant differences in hydrologic projections under climate change. Therefore, researchers and water resource managers should account for this uncertainty by assembling ensemble projections from both multiple parameter sets and GCMs. [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]

Summary The global water cycle is a fundamental component of our climate and Earth system. Many, if not the majority, of the impacts of climate change are water related. We have an imperfect description and understanding of components of the water cycle. This arises from an incomplete observation of some of the stores and fluxes in the water cycle (in particular: precipitation, evaporation, soil moisture and groundwater), problems with the simulation of precipitation by global climate models and the wide diversity of global hydrological models currently in use. This paper discusses these sources of errors and, in particular, explores the errors and advantages of bias correcting climate model outputs for hydrological models using a single large catchment as an example (the Rhine). One conclusion from this analysis is that bias correction is necessary and has an impact on the mean flows and their seasonal cycle. However choice of hydrological model has an equal, if not larger effect on the quality of the simulation. The paper highlights the importance of improving hydrological models, which run at a continental and global scale, and the importance of quantifying uncertainties in impact studies. [ABSTRACT FROM AUTHOR]

• A review of challenges and advances in flood design under non-stationarity. • Sources of non-stationarity in hydrologic extremes are described. • Methods for design flood estimates assuming stationarity are discussed. • Methods assuming climate change induced non-stationarity are discussed. • Strategies to manage uncertainties and their consequences are discussed. Conventional methods for designing infrastructure that is subject to flood risk, such as dams and levees, assume a stationary design flood. However, observed and potential non-stationarity in floods can result in costly over-design or dangerous under-design. Despite substantial attention, evidence from the literature makes clear there is no consensus methodology for estimating design variables under climate change. Practical guidance remains elusive. This paper presents a review of the challenges and advances in design of infrastructure for floods under non-stationarity. First, potential sources of non-stationarity in time series of floods are described to provide context and motivation. Second, methods for estimating design floods that rely on the stationary assumption are presented and their limitations are discussed. Third, methods for estimating design floods that assume non-stationarity resulting from climate change are summarized. Finally, the inadequacies of current design methodologies in view of the pervasive uncertainties are assessed and strategies to manage the consequences of those uncertainties are presented. [ABSTRACT FROM AUTHOR]

• Relative impacts of human activities and climate change on river flow are assessed. • The proposed methodology is a stochastic version of the fixing-changing method. • The uncertainties of the models' parameters are analyzed using the DREAM algorithm. • To evaluate the methodology, it was applied to the Zayandehrud basin in Iran. • Results show the applicability and efficiency of the proposed methodology. Climate change and human activities are two important factors affecting surface runoff. In water resource planning and management, it is usually important to separate the contribution of the mentioned factors to the runoff change. In this paper, a new methodology is proposed to obtain the relative impact of human activities and climate change on streamflow under uncertainty. In this methodology, the breakpoint of the annual time series of the observed runoff is estimated and the time series is divided into two study periods; before and after the breakpoint, namely the "natural" and "impacted" periods. The Jazim monthly water balance model is used for monthly runoff simulation. To incorporate the effect of snow in simulating runoff, two Jazim-based monthly water balance models with Rao and Al Wagdany (1995) and McCabe and Markstrom (2007) snow modules are used. Then, the monthly water balance models are calibrated and verified for the natural and impacted periods. The main parameters of the water balance models are optimized using the Genetic Algorithms. The uncertainty of the parameters of the models is analyzed using differential evolution adaptive Metropolis (DREAM) algorithm, which is based on the Markov Chain Monte Carlo (MCMC) algorithm. This algorithm provides the posterior probability distribution functions (PDFs) of parameters of the runoff simulation models. Next, the PDFs of the contribution rate (CR) of the climate change and human activities to runoff are determined using the fixing-changing method by taking into account the PDFs of the models' parameters. To show the applicability and efficiency of the proposed methodology, it is applied to the Zayandehrud sub-basin in Iran. Using the Standard Normal Homogeneity Test (SNHT), Pettitt and Buishand range tests, one breakpoint at 2006 is detected in runoff time series. Through the monthly rainfall-runoff simulations and the fixing-changing method, it is estimated that decrease in the runoff after the breakpoint can be attributed to climate change by 57.8% and 45%, using the Jazim-Rao & Al Wagdany model and the Jazim-McCabe & Markstrom model, respectively. The results of the uncertainty analysis of the parameters of the water balance models show that the parameters of LSMX (maximum moisture capacity of the lower soil layer) and K 3 (deep percolation coefficient) are the most significant parameters in the uncertainty of runoff simulated by both the models. According to the PDFs of contribution rate (CR) of climate change and human activities which are obtained using each rainfall-runoff model, it can be inferred that the Jazim-McCabe & Markstrom model significantly decreased the uncertainty of estimations. [ABSTRACT FROM AUTHOR]

Highlights • SWMM green roof module was applied to predict runoff from several different roofs. • Runoff hydrographs were reproduced well, while runoff volumes were modelled less well. • Model parameter transferability between sites was investigated. • Transferability was only partly successful. • This questions the generality of the SWMM green roof LID module as a design tool. Abstract While extensive green roofs are popular measures for reducing and delaying stormwater runoff, design tools are needed to better predict roof performance based on material properties, geometry and climate. This paper investigates the EPA's Storm Water Management Model's (SWMM) green roof module for this purpose based on observed runoff from several roofs with different build-ups, geometry, and climates. First, the general model performance was investigated and secondly transferability of model parameters for similar roofs but different geometries and climates was tested. Individual models reproduced runoff hydrographs well (NSE 0.56–0.96), while the long-term modelling showed relatively large volume errors most likely due to insufficient representation of evapotranspiration in the model. Model parameters obtained at one site were only partly transferable to similar roof build-up at other sites. Transferability was better from models calibrated with wetter climates and higher intensity events to drier climates, than the opposite way. Multi-site calibration resulted in model parameters performing well for most sites, giving model parameters that could be used for the design of similar roof build-ups in comparable climates. However, large variability in obtained model parameters, large volume errors and the fact that the calibrated model parameters did not directly correspond to measured material properties, place concerns on the generality of the SWMM green roof module as a design tool. [ABSTRACT FROM AUTHOR]

Highlights • Comparison of two hydrological modeling studies at the local scale in the Everest region. • At high altitudes, snow-pack is the main driver of the discrepancy between the two models. • The time step impacts the snow-pack simulation, in particular during the summer season. • Estimations of soil processes are associated with high uncertainties due to models structure. Abstract This paper compares the hydrological responses at the local scale of two models using different degrees of refinement to represent physical processes in sparsely instrumented mountainous Himalayan catchments. This work presents the novelty of applying, at a small spatio-temporal scale and under the same forcing conditions, a fully distributed surface scheme based on mass and energy balance equations (ISBA surface scheme), and a semi-distributed calibrated model (J2000 hydrological model). A new conceptual module coupled to the ISBA surface scheme for flow routing is presented. Two small catchments located in mid- and high- mountain environments were chosen to represent the very different climatic and physiographic characteristics of the Central Himalayas in the Everest region of eastern Nepal. The results show that both models globally represent the dynamic of the processes for evaporation, quick runoff and discharge in a similar way. The differences in the model structures and results mainly concern the snow processes and the soil processes. In particular for the high-mountain catchment, the snow-pack simulation is shown to be the main driver of the discrepancy between the two models. The sub-daily variations of snow processes are shown to significantly influence the estimation of the snow-melt contribution to discharge. [ABSTRACT FROM AUTHOR]

Highlights • Water loss of the Chari River discharge is evaluated using a statistical approach. • The contribution of climate variability and human activities is distinguished. • Previously the contribution of human activities has been underestimated seriously. Abstract Lake Chad was once the sixth largest lake in the world, with a surface area of about 25,000 km2 in the early 1960s. However, it has shrunk to almost only one-twentieth of its maximum area by now. As the biggest river of the Lake Chad basin, the Chari/Logone River contributes approximately 90% of Lake Chad's water input. Consequently, several previous studies have focused on the Chari/Logone River system to investigate the causes of Lake Chad's shrinkage. However, due to the lack of authoritative irrigation and natural runoff observations, it is usually difficult to explain the relative contribution of human activities and climate variability. In this paper, unlike the hydrological modeling approaches used in previous studies, a conceptual and statistical approach was presented to investigate discharge variations and the causes leading to such variations. Specifically, the runoff change caused by climate variability (Δ Q climate ) was estimated using Budyko analysis, while that caused by human activities (Δ Q human ) was estimated using regression analysis. The accuracy of estimation was evaluated through cross validation and comparison with previous studies. The comparison suggests that due to the lack of natural runoff and irrigation withdrawal observations, the runoff simulation produced by previous hydrological models suffers from large errors. The water loss of the Chari/Logone River discharge is dominated by human activities rather than climate variability. On annual average scale, the total water loss caused by climate variability and anthropogenic activities is 16.76 km3. The relative contribution of Δ Q climate and Δ Q human is 26.83% and 73.17%, respectively. The role of human activities is particularly obvious in recent ten years from 2003 to 2013. Due to the increase of rainfall and irrigation withdrawals, the relative contribution of human activities accounts for more than 80% of the total water loss. [ABSTRACT FROM AUTHOR]

Highlights • MIKE SHE effectively simulates the hydrological regime of an ephemeral catchment. • Point estimates of ET components may help to reduce equifinality. • The discretization limit in MIKE SHE affects solute transport simulation efficiency. • Drop in salt output rate with increasing severity of climate change is predicted. • The catchment soil retains high salinity under future climate projections. Abstract This paper presents a model of flow and solute transport of intermittent catchments in arid and semi-arid regions, highlighting challenges caused by the complex interactions between subsurface and surface flows. A case study is used for the application of the integrated MIKE SHE model to investigate temporal and spatial dynamics of water and salinity in a small ephemeral catchment in southwestern Victoria, Australia. MIKE SHE was successfully calibrated and validated against experimental data of streamflow, groundwater levels, and salt discharged from the catchment. Projections of climatic conditions, adopting Representative Concentration Pathways (RCPs) 4.5, 6.0, and 8.5, were used to infer how salinity discharge and concentrations might be affected across a range of greenhouse gas emission scenarios with different severity. The calibration of the water fluxes was solely based on parameters associated with the model of evapotranspiration, with other parameters selected from field observations and literature. The calibrated parameters for the salt transport were the longitudinal and lateral dispersivities. The calibration and validation of streamflow series led to Index of Agreement (IoA) of 0.92 and 0.8 respectively, while the simulation of groundwater levels was more challenging (IoA ranging from 0.22 to 0.89). Parameter equifinality was observed, with different parameter sets achieving a good fit of streamflow and groundwater levels, although leading to differing spatial patterns of evapotranspiration rates. Calibration and validation of the discharge concentrations appeared more difficult (IoA of 0.35 for calibration and 0.44 for validation) as they are affected by errors in the simulated flow. Climate projections RCP 8.5 suggested a reduction of salt discharge with consequent increased concentrations within the catchment. The study showed the ability of MIKE SHE to be successfully applied to intermittent dry catchments to possibly support water and land management. [ABSTRACT FROM AUTHOR]

Highlights • Main controlling factors for groundwater recharge among 88 metrics were identified. • Same annual rainfall with a seasonal shift could result in a decline of recharge. • Lower groundwater depth could lead to a positive feedback of declining recharge. • Spatially the most significant factors for recharge were soil and land attributes. • Findings could serves as a reference for climate change impacts on groundwater. Abstract This paper demonstrated the benefits of statistical methods when investigating the climatic and non-climatic drivers responsible for variations in groundwater recharge with a series of up to 43 years of annual recharge for 426 bores in South-East South Australia. We identified the factors influencing groundwater recharge based on 71 climatic metrics and 17 non-climatic metrics (including groundwater abstraction). The results showed: (1) Rainfall during April to October was the most important variable influencing recharge temporal variation, with its decline identified as the most significant factor related to recharge reduction; (2) In contrast, a negative correlation between rainfall during December to February (DJF) and annual groundwater recharge was found. This suggests that a seasonal shift in rainfall (such as decreasing rainfall during April to October and an increase during DJF) can result in a decline in recharge even when the annual rainfall remains unchanged; (3) The length of wet spells (consecutive rain days) and increasing potential evapotranspiration (PET) were additional significant predictors for recharge temporal variation. It demonstrated that a simple empirical relationship (such as recharge as a fixed percentage of rainfall) is not a reliable estimation of renewable groundwater resources under changing climatic conditions; (4) There is a statistically significant correlation between mean groundwater depth and recharge, which implies that if groundwater level fall due to rainfall declines then a positive feedback loop can lead to further recharge declines; (5) Spatially the most statistically significant factors influencing groundwater recharge were soil types and land attributes. The findings of this study can identify which stressors should be included when investigating the impact of climate change on groundwater recharge. [ABSTRACT FROM AUTHOR]

Highlights • High resolution catchment scale rainfall predictions were generated from 8 GCMs. • Multiple GCMs is preferable over a best model to account for the future variability. • Simulated climate change has limited impact on pollutant treatment performance. • Larger WSUD systems are recommended to account for the future variability. • Bigger rainwater tank is suggested for same harvesting reliability in the future. Abstract Water Sensitive Urban Design (WSUD) stormwater systems, also known as Low Impact Development (LID) systems or Nature Based Solutions (NBS), are currently implemented based on the underlying assumption of statistical stationarity of rainfall, which threatens to become outdated under climatic uncertainty. This paper applies a new downscaling method to examine the implications of climate change on future rainfall and evaluate the reliability of WSUD stormwater infrastructure in pollution reduction, flow frequency mitigation and reliability as an alternative water supply. A variety of future atmospheric scenarios are considered as part of this comprehensive assessment by analysing an ensemble of eight different downscaled General Circulation Models (GCMs). High resolution catchment-scale rainfall projections for Melbourne, Australia were generated using a scheme called High-resolution Downscaling of Rainfall Using STEPS (HiDRUS) at a fine 1 km and 6-min scale for more precise analysis with uncertainty estimates. Statistical analyses show that, in general, the climate models predict a drier future with fewer rainfall events and longer dry periods when comparing the simulated near future (2040–2049) periods against the base-line period (1995–2004). The difference simulated between historical and future rainfall projections show minimum difference of WSUD performance in pollution removal and flow frequency reduction, with slightly lower harvesting reliability (<3%) observed under future climate; high variabilities, however, were observed across GCM simulations, indicating big uncertainties of system reliability under various conditions, e.g. design wetland sizes may vary from 2.5% to 4.0% of the impervious catchment area according to different future projects across GCMs. Larger WSUD systems are recommended to ensure reliable performance of pollution removal, as well as harvesting reliability under simulated future conditions. The significance of considering an ensemble of different GCMs as opposed to many scenarios generated by a single 'best' climate model was also demonstrated for the robust estimation of uncertainty in future WSUD reliability. This work highlights important considerations for the future design, management and quantitative evaluation of WSUD reliability. [ABSTRACT FROM AUTHOR]

Highlights • A framework to estimate landslide return period under climate change is proposed. • Method is based on the combined use of RCMs and Monte Carlo simulations. • A sequence of future landslide probabilities is estimated. • The sequence allows estimation of return period according to nonstationary formulas. • Application to the Peloritani Mts (Italy) with MED-Cordex projections is performed. Abstract Quantifying the potential influence of climate change on future landslide hazard requires methodologies that allow to properly take into account nonstationarities in the hydro-meteorological causes. In this paper we provide a methodology for estimating return period of landslide triggering under climate change. The methodology capitalizes on the combined use of a stochastic rainfall generator and a hydrological and slope stability model. The stochastic rainfall generator takes into account the statistical dependency between rainfall event duration and intensity through copulas. The hydrological model is based on an analytical solution of a simplified version of the Richards vertical infiltration equation and slope stability is assessed by the infinite slope model. The combined model enables to estimate landslide probability through Monte Carlo simulations. Climate change is then introduced by perturbing the parameters of the rainfall stochastic generator based on factors of change derived from the comparison of future scenarios and the baseline climate as simulated by Regional climate models (RCMs). The Monte Carlo simulations are conducted sequentially on a future moving time window, to derive a yearly series of future landslide triggering probability. This series is then used to compute landslide return period by formulas suitable under nonstationary conditions. An application to the landslide prone region of the Peloritani Mountains, Southern Italy, is carried out to demonstrate the proposed approach. For the application, climate change projections of three RCMs of the MED-CORDEX initiative are used, and a preliminary assessment of the impacts of intermediate- and high-emission Representative concentration pathways (RCPs) 4.5 and 8.5 is carried out. [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]

• 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]

Highlights • The predictive drought capacity of a seasonal forecast system was assessed using contingency tables. • An aggregate index for assessing predictive drought capacity was proposed. • The proposed methodology is useful for assessing the skill of climate models to predict droughts. • The dynamical System4 coupled model can be used for forecasting drought events. Abstract Droughts cause significant socio-economic and environmental impacts, so it has become an extremely important element in decision-making within water resource systems. For this reason, the research in this field has increased considerably over the last few decades. In order to be capable of making early decisions and reducing drought impacts, it is necessary to predict the occurrence of such events months or even years in advance. In this sense, various methods have been used to predict the occurrence of droughts. At present, seasonal forecast data can be used to forecast meteorological, hydrological, agricultural and operational droughts. However, the seasonal forecast data of these dynamical ocean-atmosphere coupled models must be analyzed in an exhaustive way, since it is known that these models may not adequately represent the climatic variability at river basin scale. Hence, this paper presents a new methodology for assessing the skill of a climate forecasting system in order to predict the occurrence of droughts by using contingency tables. The indices obtained from the contingency tables are necessary to perform the analysis of the predictive ability of the model in a semi-distributed way. All this taking into account the intensity of droughts using different scenarios based on the threshold below which it is considered to be in drought. Finally, a single value is obtained to determine the predictive ability of the forecasting model for the entire basin. The proposed methodology is applied to the Júcar river basin in Spain. It has been found that the analyzed forecast model shows better results than those obtained using an autoregressive model. Further work is needed to enhance climate forecasting from the perspective of water resources management, however, it should be mentioned that this type of data could be used for drought forecasting, allowing possible mitigation measures. [ABSTRACT FROM AUTHOR]

Highlights • Groundwater discharge to a lowland alluvial stream was estimated. • The estimates were obtained using methods at point-, reach-, and catchment-scale. • Groundwater discharge partitioned into streambed seepage and bank seepage. • Part of the baseflow actually came from tile drainage. • The multi-method approach enabled a reliable estimation of groundwater discharge. Abstract The groundwater contribution to streamflow along a lowland alluvial stream in Denmark was estimated using a variety of methods and at different spatial scales. At the point-scale (less than a few metres), groundwater discharge to the stream was measured using seepage meters. At the reach-scale (0.5–1.5 km), groundwater discharge to the stream was estimated using differential streamflow gauging. And at the catchment-scale (>1.5 km), groundwater discharge to the stream was estimated using hydrograph separation. The estimates of groundwater discharge obtained using point-scale measurements range from 12 to 41 cm/d which is lower than fluxes estimated at the reach-scale which range from 18 to 333 cm/d. This discrepancy was attributed to the partitioning of groundwater discharge into streambed seepage and bank seepage. On the other hand, the groundwater discharge estimates obtained using hydrograph separation were generally the highest and ranged from 194 to 289 cm/d. For this study, this discrepancy from the reach-scale estimates was attributed to the assumption that baseflow obtained using hydrograph separation represents groundwater discharge to the stream when part of the baseflow actually comes from artificial drainage systems. Anyhow, seepage meter measurements, differential streamflow gauging, and hydrograph separation showed similar trends. The increase in streamflow is mainly due to either groundwater discharge through the streambed and the banks in the lower part, or tile drainage discharge in the upper part of the catchment. Furthermore, estimation of uncertainty for the various groundwater discharge estimates showed that some of the flux estimates were insignificant compared with their propagated uncertainties. To this end, a novel method was developed to estimate the uncertainty of groundwater discharge estimates obtained by hydrograph separation. Overall, this paper shows that more than one method should be used to obtain a reliable estimate of groundwater discharge to a lowland alluvial stream from other discharge contributions such as tile drainage. [ABSTRACT FROM AUTHOR]

Highlights • Identify homogeneous regions in China based on the maximum daily air temperature. • Reveal the intra-annual changing patterns of regionalization and trends. • Explore linkages of changing patterns of regionalization to climatic conditions. • Propose an objective hybrid clustering analysis for a better regionalization. Abstract Regionalization plays an important role in climatology and hydrology research, as it can be used to understand the changing pattern of a hydroclimate variable better and explore its underlying drivers effectively by identifying homogeneous regions which are determined by topography and climatic conditions. However, as climatic drivers change over time, homogeneous regions should vary when the intra-annual patterns are of interest. In this paper we investigate the regionalization and changing patterns of maximum daily temperature in China based on the maximum air temperature (Tmax) derived from the CN05.1 daily surface air temperature gridded data from 1981 to 2010. We also propose an objective variable-based regionalization approach by combining the rotated empirical orthogonal function (REOF) method and K-means clustering analysis, to determine homogeneous regions effectively for such a large volume of daily grid data. The REOF approach is not able to identify discrete regional boundaries easily, but it is efficient at determining the number of regions and their rough spatial location. Furthermore, while clustering analysis suffers from convergence when the number of observations is large, it is able to provide clean boundaries between regions. The hybrid method tends to overcome their individual disadvantages while maintaining their individual advantages. The results show that the patterns of homogeneous regions have distinct features in both summer and winter seasons. The reasonableness of regionalization is then examined by the first principal component within regions, which shows the homogeneity within regions, and the scatterplots of Tmax time series between regions, which show the heterogeneity between regions. Furthermore, the boxplots of normalized Tmax within regions for both summer and winter seasons also clearly show the different characteristics of inter-annual variability in each region. In order to reveal detailed process of the change in the number and pattern of regions, we further conducted the regionalization for individual months and explored the potential linkages between regionalization and climatic conditions over time. This research is noteworthy in promoting research on regionalization with consideration of seasonal and intra-annual variation as well as extreme temperature events. The proposed hybrid regionalization method is broadly applicable to other regionalization studies. [ABSTRACT FROM AUTHOR]

Highlights • We propose a Lattice Boltzmann model to solve 2D fractional advection-dispersion equation. • The model is verified against the analytical solution of a benchmark problem. • We apply the model to simulate Cl− movement in a tracer experiment. • We discuss the pros and cons of the model in comparison with other numerical models. Abstract Natural soils and aquifers are inherently heterogenous and chemical transport through them is anomalous characterized by an early arrival followed by a persistent tail. How to describe such anomalous phenomena has been an interest and, as a result, a number of approaches have been proposed over the past few decades. Among others, the fractional advection-dispersion equation (FADE) is a model able to describe anomalous transport when the dispersion is regional rather than local as presumed in the classical advection-dispersion equation. Practical application of FADE needs numerical solutions, which is challenging because its spatial discretization gives rise to a full coefficient matrix. In this paper, we propose a Lattice Boltzmann model to solve the two-dimensional FADE. Given that the anomalous dispersion in soils and aquifers is hydrodynamic and caused by spatial variation in water velocity across the pore space and that the chemical plume spreads preferentially along the mean water-flow direction, the dispersion coefficient and the order of the fractional derivative should be both anisotropic. The anisotropies are solved by the two-relaxation time Lattice Boltzmann model using different relaxation parameters in different directions. Compared with existing numerical methods for the FADE, the proposed model has advantages that it is explicit, second-order accurate in both time and space, mass-conservative, and free of numerical dispersion; its stability is independent of dispersion coefficient. We verify the model against the analytical solution of a benchmark problem and then apply it to simulate Cl− movement in a tracer experiment conducted on an up-catchment hillslope. The results show that the FADE with backward skewness can reproduce the breakthrough curves measured from the experiment. [ABSTRACT FROM AUTHOR]

Anthropogenic warming may change precipitation patterns, impacting infrastructure performance and reliability. Future precipitation statistics generated using General Circulation Models (GCM) are, however, often biased and not easily applied to problems such as runoff estimation. Stochastic weather generation is hence used as an alternative to GCMs in hydrology and hydraulic modelling. This paper explores the dependence of fine temporal precipitation characteristics on air pressure and air temperature using historic observations. The goal is to develop, based on the key causes of precipitation, a climatological basis for a stochastic precipitation generator for non-stationary precipitation under climate change conditions. The analysis focuses on precipitation in the urban Northeast United States and utilizes pooled observations from meteorological stations in New York City, Philadelphia, and Boston over 60 years. A negative correlation between hourly Probability of Precipitation (POP) and air pressure is observed. When the historical records are discretized using air Pressure Change Events (PCE), Decreasing Pressure Change Events (DePCEs) had a higher POP and a higher Precipitation Depth (PD) than Increasing Pressure Change Events (InPCEs). Temperature was more strongly associated with PD during DePCEs than InPCEs; this association was more pronounced during high magnitude PCEs and extreme events. The potential for simulating future hourly precipitation by associating historic hourly precipitation patterns with PCE’s and monthly temperature is assessed. [ABSTRACT FROM AUTHOR]

In this paper we explored how landscape characteristics such as topography, geology, soils and land cover influence the way catchments respond to changing climate conditions. Based on an ensemble of 15 regional climate models bias-corrected with a distribution-mapping approach, present and future streamflow in 14 neighboring and rather similar catchments in Northern Sweden was simulated with the HBV model. We established functional relationships between a range of landscape characteristics and projected changes in streamflow signatures. These were then used to analyze hydrological consequences of physical perturbations in a hypothetically ungauged basin in a climate change context. Our analysis showed a strong connection between the forest cover extent and the sensitivity of different components of a catchment’s hydrological regime to changing climate conditions. This emphasizes the need to redefine forestry goals and practices in advance of climate change-related risks and uncertainties. [ABSTRACT FROM AUTHOR]

Snow is an important component of the hydrological cycle. As a major part of the cryosphere, snow cover also represents a valuable terrestrial water resource. In the context of climate change, the dynamics of snow cover play a crucial role in rebalancing the global energy and water budgets. Remote sensing, hydrological modeling and in situ observations are three techniques frequently utilized for snow cover investigations. However, the uncertainties caused by systematic errors, scale gaps, and complicated snow physics, among other factors, limit the usability of these three approaches in snow studies. In this paper, an overview of the advantages, limitations and recent progress of the three methods is presented, and more effective ways to estimate snow cover properties are evaluated. The possibility of improving remotely sensed snow information using ground-based observations is discussed. As a rapidly growing source of volunteered geographic information (VGI), web-based geotagged photos have great potential to provide ground truth data for remotely sensed products and hydrological models and thus contribute to procedures for cloud removal, correction, validation, forcing and assimilation. Finally, this review proposes a synergistic framework for the future of snow cover research. This framework highlights the cross-scale integration of in situ and remotely sensed snow measurements and the assimilation of improved remote sensing data into hydrological models. [ABSTRACT FROM AUTHOR]

Climate change will affect precipitation and flood regimes. It is anticipated that the Probable Maximum Precipitation (PMP) and Probable Maximum Flood (PMF) will be modified in a changing climate. This paper aims to quantify and analyze climate change influences on PMP and PMF in three watersheds with different climatic conditions across the province of Québec, Canada. Output data from the Canadian Regional Climate Model (CRCM) was used to estimate PMP and Probable Maximum Snow Accumulation (PMSA) in future climate projections, which was then used to force the SWAT hydrological model to estimate PMF. PMP and PMF values were estimated for two time horizons each spanning 30 years: 1961–1990 (recent past) and 2041–2070 (future). PMP and PMF were separately analyzed for two seasons: summer-fall and spring. Results show that PMF in the watershed located in southern Québec would remain unchanged in the future horizon, but the trend for the watersheds located in the northeastern and northern areas of the province is an increase of up to 11%. [ABSTRACT FROM AUTHOR]

In data scarce basins, such as the Nile River Basin (NRB) in Africa, constraints related to data availability, quality, and access often complicate attempts to estimate runoff sensitivity using conventional methods. In this paper, we show that by integrating the concept of the aridity index (AI) (derived from the Budyko curve) and climate elasticity, we can obtain the first order response of the runoff sensitivity using minimal data input and modeling expertise or experience. The concept of runoff elasticity relies on the fact that the energy available for evapotranspiration plays a major role in determining whether the precipitation received within a drainage basin generates runoff. The approach does not account for human impacts on runoff modification and or diversions. By making use of freely available gauge-corrected satellite data for precipitation, temperature, runoff, and potential evapotranspiration, we derived the sensitivity indicator ( β ) to determine the runoff response to changes in precipitation and temperature for four climatic zones in the NRB, namely, tropical, subtropical, semiarid and arid zones. The proposed sensitivity indicator can be partitioned into different elasticity components i.e: precipitation ( ε p ), potential evapotranspiration ( ε ET p ), temperature ( ε T ) and the total elasticity ( ε tot ) . These elasticities allow robust quantification of the runoff response to the potential changes in precipitation and temperature with a high degree of accuracy. Results indicate that the tropical zone is energy-constrained with low sensitivity, ( β < 1.0 ) , implying that input precipitation exceeds the amounts that can be evaporated given the available energy. The subtropical zone is subdivided into two distinct regions, the lowland (Machar and Sudd marshes), and the highland area (Blue Nile Basin), where each area has a unique sensitivity. The lowland area has high sensitivity, ( β > 1.0 ) . The subtropical-highland zone moves between energy-limited to water-limited conditions during periods of wet and dry spells with varying sensitivity. The semiarid and arid zones are water limited, with high sensitivity, ( β > 1.0 ) . The calculated runoff elasticities show that a 10% decrease in precipitation leads to a decrease in runoff of between 19% in the tropical zone and 30% in the arid zones. On the other hand, a 10% precipitation increase leads to a runoff increase of 14% in the tropical zone and 22% in the arid zone. The estimated runoff changes are consistent with the result obtained using other methods. Thus, the elasticity approach combines data parsimony and analytical simplicity to produce results that are practically useful for most purposes while facilitating communication with stakeholders with different levels of scientific knowledge. More research is needed to extend the application of the method to incorporate the effects of human activities, and land use change. [ABSTRACT FROM AUTHOR]

Vietnam is a country with diverse terrain and climatic conditions and a dependency on hydropower for a significant proportion of its power needs and as such, is particularly vulnerable to changes in climate. In this paper we apply SWAT (Soil and Water Assessment Tool) derived discharge simulation results coupled with regression analysis to estimate the performance of hydropower plants for Vietnam between 1995 and mid-2014 when both power supply and demand increased rapidly. Our approach is to examine the watershed formed from three large inter-boundary basins: The Red River, the Vietnam Coast and the Lower Mekong River, which have a total area of 977,964 km 2 . We then divide this area into 7,887 sub-basins with an average area of 131.6 km 2 (based on level 12 of HydroSHEDS/HydroBASINS datasets) and 53,024 Hydrological Response Units (HRUs). Next we simulate river flow for the 40 largest hydropower plants across Vietnam. Our validation process demonstrates that the simulated flows are significantly correlated with the gauged inflows into these dams and are able to serve as a good proxy for the inflows into hydropower dams in our baseline energy regression, which captures 87.7% of the variation in monthly power generation. In other results we estimate that large dams sacrifice on average around 18.2% of their contemporaneous production for the purpose of flood control. When we assess Vietnam’s current alignment of dams we find that the current cascades of large hydropower dams appear to be reasonably efficient: each MWh/day increase in upstream generation adds 0.146 MWh/day to downstream generation. The study provides evidence for the multiple benefits of a national system of large hydropower dams using a cascade design. Such a system may help overcome future adverse impacts from changes in climate conditions. However, our results show that there is still room for improvement in the harmonization of cascades in some basins. Finally, possible adverse hydro-ecological impacts due to the proliferation of large upstream dams, including those located beyond Vietnam’s border, need to be carefully considered. [ABSTRACT FROM AUTHOR]

The assessment for impacts of climate variability and human activities on suspended sediment yield (SSY) change has long been a question of great interest. However, the sediment generation processes are sophisticated with high nonlinearity and great uncertainty, which give rise to extreme complexity for SSY change assessment in Newtonian approach. Consequently, few approaches can be simply but widely applied to decompose impacts of climatic variability and human activities on SSY change. Thus, it is an urgent need to develop advanced methods that are simple and robust. Since that the Newtonian approach is hardly achievable due to limitation of either observations or knowledge of mechanisms, there have been repeated calls to capture the hydrologic system in Darwinian approach for hydrological change prediction or explanation. As streamflow is the carrier of suspended sediment, SSY change are thus documented in changes of sediment concentrated flow and suspended sediment concentration – water discharge ( C - Q ) relationships. By deduced corollaries, a differential equation of sediment discharge change was derived to explicitly decompose impacts of climate variability and human activities in Darwinian hydrology. Besides, a new form of sediment rating curves was proposed and curved as C - Q relationships and probability distribution of sediment concentrated flow. River sediment flux can be revealed by this representation, which simply elucidates mechanism of SSY generation covering a range of time scales from finer than rainfall-event to long term. By the new sediment rating curves, the differential equation was partly solved using a segmentation algorithm proposed and validated in this paper, and then was submitted to water balance framework expressed by Budyko-type equation. Thus, for catchment management, hydrologists can obtain explicit explanation of how climate variation and human activities propagate through landscape and result in sediment discharge change. The differential equation is simple and robust for widely application in sediment discharge change assessment, as only discrete data of precipitation, potential evaporation and C - Q observed at gauging stations are required. [ABSTRACT FROM AUTHOR]

This paper examines the hydrological impact of the seasonality of precipitation and maximum evaporation: seasonality is, after aridity, a second-order determinant of catchment water yield. Based on a data set of 171 French catchments (where aridity ranged between 0.2 and 1.2), we present a parameterization of three commonly-used water balance formulas (namely, Turc-Mezentsev, Tixeront-Fu and Oldekop formulas) to account for seasonality effects. We quantify the improvement of seasonality-based parameterization in terms of the reconstitution of both catchment streamflow and water yield. The significant improvement obtained (reduction of RMSE between 9 and 14% depending on the formula) demonstrates the importance of climate seasonality in the determination of long-term catchment water balance. [ABSTRACT FROM AUTHOR]

Climate change impact assessment on meteorological variables involves large uncertainties as a result of incomplete knowledge on the future greenhouse gas concentrations and climate model physics, next to the inherent internal variability of the climate system. Given that the alteration in greenhouse gas concentrations is the driver for the change, one expects the impacts to be highly dependent on the considered greenhouse gas scenario (GHS). In this study, we denote this behavior as GHS sensitivity. Due to the climate model related uncertainties, this sensitivity is, at local scale, not always that strong as expected. This paper aims to study the GHS sensitivity and its contributing role to climate scenarios for a case study in Belgium. An ensemble of 160 CMIP5 climate model runs is considered and climate change signals are studied for precipitation accumulation, daily precipitation intensities and wet day frequencies. This was done for the different seasons of the year and the scenario periods 2011–2040, 2031–2060, 2051–2081 and 2071–2100. By means of variance decomposition, the total variance in the climate change signals was separated in the contribution of the differences in GHSs and the other model-related uncertainty sources. These contributions were found dependent on the variable and season. Following the time of emergence concept, the GHS uncertainty contribution is found dependent on the time horizon and increases over time. For the most distinct time horizon (2071–2100), the climate model uncertainty accounts for the largest uncertainty contribution. The GHS differences explain up to 18% of the total variance in the climate change signals. The results point further at the importance of the climate model ensemble design, specifically the ensemble size and the combination of climate models, whereupon climate scenarios are based. The numerical noise, introduced at scales smaller than the skillful scale, e.g. at local scale, was not considered in this study. [ABSTRACT FROM AUTHOR]