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• A statistical model of flood elasticity under changing conditions was derived. • The model was applied in a typical tropical monsoon basin of Thailand. • The flood peaks are expected to increase due to deforestation and climate change. • The absolute value of storm-flood elasticity is larger for shorter return periods. • Climate change tends to dominate future flood changes over deforestation. Storms and the resultant floods have caused considerable fatalities and have raised increasing global concerns in the context of climate change and socioeconomic development. The relationships between storms and floods have been explored as storm-flood elasticity, but the shifts of the storm and flood peak distributions under changing conditions have not been sufficiently considered. In this study, a novel statistical model that reflects the relationship between storm distribution and flood distribution under changing conditions has been developed. Due to its concise structure, the statistical model can be easily used to explicitly derive the storm-flood elasticity and quantify the future flood responses to changing climatic and land surface conditions. The statistical model is then applied in a typical tropical monsoon basin in Southeast Asia, the Upper Chao Phraya River Basin (UCPRB), which underwent significant climate change and deforestation. The shifts of the storm and flood frequency curves are modeled via GAMLSS with the aid of wet season dew point and forest cover ratio, and the curves derived from statistical models prove to be consistent with the results from the more computationally intensive process-based model. By examining flood elasticity including storm-flood elasticity and forest-flood elasticity under changing conditions, the response of flood peak to storm peak and forest cover change is expected to weaken as the return period increases. Future flood peaks are expected to change substantially under most emission scenarios, and the changes can be mostly attributed to climate change rather than deforestation. The dominance of climate change over deforestation strengthens in the long-term future and is more evident for shorter return periods, highlighting the indispensable role of climate change mitigation in reducing future flood risks in the UCPRB. The analytical framework developed by this study can also be applied in other climate regions besides tropical regions, which could help to provide insights for future changes in floods and their relationship with climatic and land surface characteristics under changing conditions. [ABSTRACT FROM AUTHOR]

[Display omitted] • CO 2 fluxes were measured in three subtropical mangroves for 16 site years. • VPD stress inhibits mangrove productivity during both exposed and inundated periods. • Meteorological factors and VPD have interactive effects on mangrove productivity. • Tides have independent effects on mangrove productivity through salinity changes. • The threshold effect range of VPD stress on mangrove productivity is 2.50–2.95 kPa. Subtropical mangroves have great potential to sequester atmospheric carbon dioxide (CO 2) and thus significantly contribute to climate change mitigation. Meanwhile, the carbon cycling processes in subtropical mangroves are vulnerable to the changing climatic conditions, such as the warming-induced vapor pressure deficit (VPD) increases. However, the impacts of VPD on net ecosystem production (NEP) in subtropical mangroves remain poorly understood due to a lack of detailed assessment of NEP responses to VPD among different mangrove forests over a long-term observational period. In this study, we deployed eddy-covariance systems to measure net ecosystem CO 2 exchange at three subtropical mangrove forests for 16 site-years, comprising two natural and one artificial forests. We employed a state-of-the-art data-driven modeling approach (i.e., Shapley additive explanations (SHAP) framework based on extreme gradient boosting model), which enabled us to explore the interactive effects of meteorological and tidal factors (e.g. salinity) with VPD on the mangrove NEP. We revealed that air temperature, global solar radiation (GR) and wind speed have significant interactions on the response of NEP to VPD stress in subtropical mangroves. For instance, when GR was high, the SHAP interaction values of VPD and GR on NEP decreased with increasing VPD, but when GR was low, the trends were the opposite. However, instead of identifying interactive effects between tidal salinity and VPD on mangrove NEP, we came across potential independent influence of salinity on the same. SHAP analysis was also able to disentangle the impact of VPD from other abiotic drivers. Thus, we evaluated the threshold effect of VPD stress on NEP loss in subtropical mangroves and observed a range of 2.50–2.95 kPa. Above this range, VPD stress leveled off. The subtropical mangrove responses to VPD should be therefore considered in the dynamic global vegetation models to increase the accuracy in carbon cycle simulations in the future. [ABSTRACT FROM AUTHOR]

Highlights • We assessed the impact of climate change on lake/reservoir nutrient levels. • Temperature increases could enhance Chl a levels without added nutrient inputs. • Stricter nutrient criteria should be considered as global warming progresses. • Dual-nutrient control could have a greater impact than single-nutrient control. Abstract Climate change has the potential to exacerbate eutrophication and the risk of harmful algal blooms in lakes. Periodic adjustment of nutrient criteria, as the cornerstone of eutrophication control and water quality management, is expected to help mitigate the impact of climate change on harmful cyanobacterial blooms. The nutrient criteria that do not threaten the function of the water body determined with generalized additive models (GAMs) were 0.88 mg/L for total nitrogen (TN) and 0.021 mg/L for total phosphorus (TP) during the period of 1996–2005 based on 26 lakes and reservoirs in China. Climate change-induced temperature increases will substantially reduce the required nutrient criteria by 46.17% for TN and 15.24% for TP in lakes and reservoirs by the end of the century under the "business-as-usual" scenario. The results indicate that some lakes that currently meet the nutrient criteria could become dominated by cyanobacteria and experience blooms in a warmer future, even without increases in nutrient inputs. Therefore, as climate change persists, nutrient loads should be further reduced to achieve the desired water quality without the threat of eutrophication. Strict control of TP would be particularly advantageous to suppress algal blooms. It is imperative that water quality management strategies account for the impact of projected future climate changes on nutrient criteria and that nutrient criteria are periodically modified. The corresponding nutrient risk thresholds should be determined to ensure that lakes and reservoirs escape the threat of harmful algal blooms under the impact of extreme weather events. [ABSTRACT FROM AUTHOR]

Highlights • Processing mechanisms and modeling of GI to treat stormwater were provided. • Applications and Hydrological benefits of GIs around the different nations were reviewed. • Barriers and Strategies for promoting worldwide utilization of GIs were collected. Abstract Green Infrastructure (GI) is considered to be an innovative stormwater management approach that offers numerous other environmental benefits including reduction in air pollution and climate change mitigation compared with conventional gray infrastructures, and it has been gradually accepted and used worldwide. This review mainly focuses on the hydrological aspect of GI and provides a summary of the knowledge about GI as a stormwater management alternative. In this review, we discuss the operating mechanisms of a few widely-used GIs in the aspect of stormwater management. We critically examine policies for promoting GI implementation in some countries, including the sponge city in China and water sensitive urban design (WSUD) in Australia, and review the effectiveness of GI on treating stormwater quantity in real world applications. In addition, we also used Bibliometrics to analyze the GI research trends and found GI has been popular in an increasing number of countries and regions, which China has the greatest future growth potential in GIs research. Moreover, many barriers impeding the further development of GI and strategies to overcome these barriers are also summarized. This review gathers knowledge from many sources to provide an overview of GI to better understand its mechanisms and applications, and to highlight the areas that require future study. [ABSTRACT FROM AUTHOR]

Highlights • An inter catchment wastewater transfer method for sewer system was investigated. • The technology behind Google Translation was adapted to solve hydrology problem. • The inter catchment wastewater transfer could significantly reduce sewer overflow. • The long short-term memory (LSTM) could be of great use in predicting sewer flow. Abstract Sewer overflow is a priority concern for many cities. Owing to the high density of buildings and populations, constructing new storage tanks in urban areas is getting more and more difficult. Therefore, this paper proposes a novel Inter Catchment Wastewater Transfer (ICWT) method for sewer overflow mitigation. The ICWT method aims at redistributing the spatial mismatched sewer flow according to the treatment capacity of Wastewater Treatment Plant (WWTP). Three tasks are involved in the development of ICWT. First, test the effectiveness of ICWT. Second, study how to properly redistribute inflow to the WWTP. Third, the operation of ICWT highly depends on inflow to the WWTP, to support the management of ICWT, it is imperative to construct a flow rate prediction model. A hydraulic model is used for task 1 and 2, individual and combined effects of the storage tank and ICWT on sewer overflow are investigated. The simulation results show that less overflow is obtained. In considering the deficiencies of hydraulic model, one of the most promising deep learning techniques, Long Short-Term Memory (LSTM), is employed to undertake task 3. Experiments demonstrated that LSTM could be of great use in predicting sewer flow. [ABSTRACT FROM AUTHOR]

[Display omitted] • All year climate change impact in an urban catchment is evaluated using SWMM. • Average annual runoff and seasonal extreme flows are expected to increase. • An overall increase in the number of flow events is modelled, notably in winter. • A holistic stormwater management is needed to mitigate and adapt to climate change. • The benefit of using convection-permitting climate models is evident. Urbanization coupled with climate change is expected to put pressure on the urban stormwater network. To predict and mitigate the effects of these trends, accurate modeling of urban stormwater changes is required at scales and resolutions meaningful to stormwater management. Although numerous studies have analyzed the effect of climate change on urban flooding risk using the event-based approach, none have incorporated the continuous modelling approach to investigate the whole spectrum of changes in an urban catchment. This study analyzes seasonal changes in future urban hydrological behavior using a mini-ensemble of six state-of-the-art climate model projections and a calibrated hydrological-hydraulic Storm Water Management Model (SWMM). The modelling results show future changes in seasonal and monthly hydrological behavior. The notable winter warming is the major driver in the future snow processes, resulting in considerably less snow days and an increase in the flow events frequency during the winter months. The modelling results also suggest an increase in the annual maximum hourly flow in all seasons, with the clearest trend modelled in winter. Monthly average runoff during the cold period is modelled to increase, while no clear trends are detected for the rest of the year. There is a clear added benefit in using convection-permitting regional climate models throughout the year. Overall, the climate change mitigation and adaption strategies in urban catchments should focus more on the whole spectrum of changes rather than only on urban pluvial flooding risk. These findings call for a transition from traditional to a more advanced stormwater management. [ABSTRACT FROM AUTHOR]

Highlights • A rapid urban flood inundation and damage assessment model (RUFIDAM) is developed. • Different model configurations are evaluated through a sensitivity analysis. • Rapid flood models are suitable for predicting flood extent and depths. • RUFIDAM is most suitable to compare mitigation solutions for damage reduction. Abstract Urban pluvial flooding is a global challenge that is frequently caused by the lack of available infiltration, retention and drainage capacity in cities. This paper presents RUFIDAM, an urban pluvial flood model, developed using GIS technology with the intention of rapidly estimating flood extent, depth and its associated damage. RUFIDAM integrates a 1D hydraulic drainage network model (SWMM or MOUSE) with an adapted version of rapid flood inundation models. One-metre resolution topographic data was used to identify depressions in an urban catchment. Volume-elevation relationships and minimum elevation between adjacent depressions were determined. Mass balance considerations were then used to simulate movement of water between depressions. Surcharge volumes from the 1D drainage network model were fed statically into the rapid inundation model. The model was tested on three urban catchments located in southeast Melbourne. Results of flood depth, extent and damage costs were compared to those produced using MIKE FLOOD; a well-known 1D-2D hydrodynamic model. Results showed that RUFIDAM can predict flood extent and accumulated damage cost with acceptable accuracy. Although some variations in the simulated location of flooding were observed, simulation time was reduced by two orders of magnitude compared to MIKE FLOOD. As such, RUFIDAM is suitable for large-scale flood studies and risk-based approaches that rely on a large number of simulations. [ABSTRACT FROM AUTHOR]

Highlights • A bidirectional stage routing model (BSR) is proposed for tidal rivers. • The model combines flood wave movement and tidal wave propagation. • The parameter variation and lateral inflow effect are considered. • Results show good performance in river stage simulation. • The BSR can be applied in tidal rivers when cross-section data are unavailable. Abstract Water stage variations are complicated in tidal rivers due to the combination of upstream flood and downstream tide. This study proposed a bidirectional stage routing model (BSR) for tidal rivers by considering the stage hydrograph as the overlay of bidirectional propagation of upstream flood wave and downstream tidal wave. The basic stage routing equations derived from the storage equation and water balance equation are used to calculate the upstream flood wave routing and the downstream tidal wave propagation, respectively. The lateral inflow can be directly specified as an additive term in the basic equation of BSR model or indirectly estimated by a lateral flow rate reflecting the proportion of lateral inflow to the upstream water. Parts of the model parameters are time-variable to account for the effect of water surface slope on water stage. The model inputs include stage hydrographs at the upstream and downstream boundaries. The channel geometric characteristics data are not required when the average channel width is estimated as a parameter. The case study results in the tidal reaches of the Caoe River and Qiantang River in China demonstrate that the BSR model performs well in water stage simulation. With relatively simple structure, modest data requirement and stable performance, the physically-based BSR model has the potential for water stage routing in tidal rivers when channel geometry data are not available. [ABSTRACT FROM AUTHOR]

Climate change is affecting hydrological variables and consequently is impacting water resources management. Historical strategies are no longer applicable under climate change. Therefore, adaptive management, especially adaptive operating rules for reservoirs, has been developed to mitigate the possible adverse effects of climate change. However, to date, adaptive operating rules are generally based on future projections involving uncertainties under climate change, yet ignoring historical information. To address this, we propose an approach for deriving adaptive operating rules considering both historical information and future projections, namely historical and future operating rules (HAFOR). A robustness index was developed by comparing benefits from HAFOR with benefits from conventional operating rules (COR). For both historical and future streamflow series, maximizations of both average benefits and the robustness index were employed as objectives, and four trade-offs were implemented to solve the multi-objective problem. Based on the integrated objective, the simulation-based optimization method was used to optimize the parameters of HAFOR. Using the Dongwushi Reservoir in China as a case study, HAFOR was demonstrated to be an effective and robust method for developing adaptive operating rules under the uncertain changing environment. Compared with historical or projected future operating rules (HOR or FPOR), HAFOR can reduce the uncertainty and increase the robustness for future projections, especially regarding results of reservoir releases and volumes. HAFOR, therefore, facilitates adaptive management in the context that climate change is difficult to predict accurately. [ABSTRACT FROM AUTHOR]

• Precipitation, NDVI, and LUCC dominated recipitation the spatial heterogeneity of SM. • Precipitation changes caused up to 40% of the drying up of SM. • SM anomalies at 28–289 cm mainly caused by one-month-old precipitation. • The influences of climate and vegetation were observed to decrease with soil depth. As an essential state variable of the earth's terrestrial system, soil moisture (SM) is highly significant regarding hydrological processes, agricultural production, land management in response to issues like soil erosion etc. However, the net effects of different environmental factors on the heterogeneity and variations of SM remain unclear, due to the complex interactions between variables. Indeed, investigating such effects in the highly climate-vulnerable Mongolian Plateau is imperative for water resource management and climate change adaptation. In this study, we analyzed the contributions of different environmental factors on the spatial heterogeneity and variations of SM in the Mongolian Plateau between 1982 and 2020, using a geographic detector model (GDM) and a novel nonlinear Granger causality model. The results of the GDM analysis demonstrated that precipitation and vegetation were the main controls relevant to SM heterogeneity, with their explanatory powers (Q statistics of GDM) found to be higher than 0.67. In two breakpoints—one in the early 1990′s and another in 2007—SM demonstrated a pattern of increase, then a decrease, subsequently followed by an increase (insignificant decrease in SM at 100–289 cm depth only). Precipitation was identified as the Granger causal of SM variations over 33.39–97.65% of the plateau's vegetated area, leading to the drying up of 40% of its SM. The greatest contribution to SM in 28–289 cm of the plateau was observed as coming from one-month-old precipitation, due to the lag in the manifestation of effects. Further, rising temperatures were found to have an immediate influence (2.73–5.23%) on SM in 14.61–54.90% of the whole plateau's vegetated area, an impact which was relatively high (>14%) in its northeastern wetting zone. Vegetation change posed a relatively weak effect (<2%) on SM over a limited area of the plateau (27.75–35.42%). Although lesser than the impacts of general climate changes, the contributions of climatic extremes could not be neglected, ultimately accounting for up to 10% of SM changes. In summary, this study provides new insights into the individual contributions of various environmental factors on the spatial heterogeneity and variations of SM in the Mongolian Plateau, offering vital information for policymaking regarding climate change mitigation and adaptation, sustainable use of water resources, and ecological restoration for arid and semi-arid region. [ABSTRACT FROM AUTHOR]

• Estimates of the time of emergence of climate change (ToE) for Australian rivers. • Stochastic data generation helps to better define baseline variability. • Uncertainty in ToE represented by 37 CMIP6 projections and 3 emissions scenarios. • ToE in river flows can precede ToE in precipitation by up to 50 years. • ToE substantially delayed or avoided in a low emissions pathway (SSP1-2.6). Climate change impacts on river flows are leading to substantial changes in water availability for human and natural systems. The significance of these impacts largely depends on the degree of change relative to natural variability. The 'Time of Emergence' (ToE) is the point in time (past or future) when the signal of climate change emerges from the noise of background variability. The ToE has been assessed for some climate variables but rarely streamflows. Here we investigate ToE of changes in streamflow across Australia for various characteristic periods relevant to different water management objectives. We assess the uncertainty in ToE estimates from different emissions pathways and a large ensemble of CMIP6 climate model projections. ToE is likely to occur earlier in hydrologic systems subject to low natural variability than those subject to high variability, and to systems that are vulnerable to failure over longer rather than shorter periods. There are clear regional differences in the patterns of ToE, with changes first emerging in south-west regions of Australia, followed by the south-east, then east and north. We estimate that ToE has already occurred in over 10% of sampled rivers for longer characteristic periods, primarily in south-west Australia. Importantly for planning, the results show that ToE in flows precedes ToE in precipitation by up to 50 years due to compounding effects of changes in annual precipitation, precipitation seasonality, and increasing temperatures. The number of catchments projected to have reached ToE by 2080 is heavily influenced by the trajectory of future emissions. A low emissions pathway generally consistent with aspirational goals of the Paris Climate Agreement (SSP1-2.6) substantially delays the emergence of significant hydrological change by up to 50 years, or negates emergence entirely by 2080, depending on location. Our methods can be applied to catchments globally, and our findings have implications for the prioritisation of climate adaptation efforts across different river systems and the urgency of continued action on climate change mitigation. [ABSTRACT FROM AUTHOR]

• Amplification effects of higher air temperature on the range of changes in flood frequency and magnitude are projected. • Southeast Eurasia, Africa, and South America are hotspots with higher flood defense pressures. • Most watersheds worldwide would face increasing pressures from current flood defense standards in warming climates. Floods usually threaten human lives and cause serious economic losses, which can be more severe with global warming. Therefore, it is a salient challenge to find out how global flood characteristic changes and whether current flood protection standards will face more pressures. This study aims to characterize changes in global floods and explicit flood defense pressures in warming climates of 1.5–3.0 °C above pre-industrial levels by running four well-calibrated lumped hydrological models using bias-corrected Global Climate Model (GCM) simulations for 9045 watersheds worldwide. The results show that global warming from 1.5 to 3.0 °C has increasingly dominated all continents, with amplification effects on changes of flood frequency and magnitude. Southeast Eurasia, Africa, and South America are hotspots of changes for significant proportions of watersheds with larger flood patterns and greater changing extents than others. For example, for the 3.0 °C warming period under the combination of shared socioeconomic pathway 2 and representative concentration pathway 4.5 (SSP245) scenario, the regionally averaged 50-year flood magnitude will increase by 25.6 %, 30.6 %, and 16.4 % for these regions, respectively. The increases in occurrence and magnitude indicate that current flood protection standards will face increasing pressures in future warming climates. The design-level flood frequency is projected to increase for about 47 %, 55 %, 70 %, and 74 % of watersheds in 1.5, 2.0, 2.5, and 3.0 °C warming periods under the SSP245 scenario. However, large uncertainty are observed for the change of flood characteristics dominated by GCMs and their interactions with SSP scenarios and hydrological models. This study implies that the current flood defense standards should be enhanced and climate adaptation and mitigation strategies should be proposed to cope the change of future flood. Floods usually threaten human lives and cause serious economic losses, which can be more severe in the context of global warming. It is a salient challenge to find out how global flood risk changes and whether current flood protection standards will face more pressures. This study aims to characterize changes in global floods and explicit flood defense pressures in warming climates of 1.5, 2.0, 2.5, and 3.0 °C above pre-industrial levels. Here we show that amplification effects of higher air temperature on the range of changes in flood frequency and magnitude are projected. Southeast Eurasia, Africa, and South America are hotspots of changes for significant proportions of watersheds with larger flood patterns and greater changing extents than others. Most watersheds worldwide is likely to face increasing flood defense pressures in warming climates. Our findings could improve the understanding of future flood conditions under the warming climates and provide information to mitigation and adaptation policymaking. [ABSTRACT FROM AUTHOR]

[Display omitted] • A framework for quantifying the effects of wetlands on hydrological droughts is proposed. • Wetland effects on drought characteristics and propagation of meteorological to hydrological droughts were determined using a hydrological modeling platform. • Wetlands can mitigate the development process, accelerate the recovery, shorten the duration and alleviate the severity of hydrological droughts. • Wetlands can extend the propagation time and weaken the transition from meteorological to hydrological drought. Wetlands have been singled out as a potential nature-based solution for improving the resilience and reducing the risks of hydrometeorological extremes. However, whether and to what extent wetlands can affect hydrological droughts is not well understood. To fill this gap, we proposed a general framework to discern the effect of wetlands on: (i) the characteristics (duration, severity, development and recovery processes) of hydrological droughts, and (ii) the propagation of meteorological droughts to hydrological droughts. First, hydrological modeling was conducted with a spatially-explicit model integrated with wetland modules. Then, the run theory and pooling method were selected to recognize hydrological drought events and identify their characteristics. Further, the Pearson correlation coefficient, temporal shift method and cross wavelet transform were used to explore the propagation processes. Finally, the characteristics and propagation processes were compared to quantify wetland mitigation services on hydrological droughts. To validate the proposed framework, two river basins from China and Canada (the Gan River Bain and the Nelson River Bain), with distinct land cover, were chosen to perform hydrological modeling and quantify wetland effects. The results indicate that wetlands mainly contribute to alleviating hydrological droughts by decelerating the development process, accelerating the recovery, shortening the duration, and reducing the severity of the hydrological drought events. However, the effects are variable as they may have weak impacts and even worsen drought conditions. Wetlands can extend drought propagation time and weaken the transition of meteorological to hydrological droughts. The likelihood of hydrological drought formation due to meteorological decreased by 19% and 18% respectively, for the Gan River Bain and Nelson River Bain, thanks to the mitigation services of wetlands. These findings highlight the drought-mitigation roles of wetlands and the proposed modelling framework has the potential to be useful in beneficial in assisting basin management on drought risks in the context of climate change mitigation. [ABSTRACT FROM AUTHOR]

• Innovate a BaPMI method for macro-scale distributed hydrologic modeling. • Apply it to a typical large cold-region watershed, Athabasca River Basin in Canada. • Verify feasibility & advantages in characterizing macro-scale cold-region hydrology. • Quantify parametric & hyperparametric sensitivities for facilitating its applications. • Reveal variabilities of climate-driven cross-scale uppermost hydro-variational effects. At macroscales (e.g., large river basins, nations, continents, or the globe), hydro-hazard mitigation under climate change requires accurate, applicable hydrologic models, especially over cold regions of complicated hydroclimatic relations. As an effort to address such a challenge, we innovate a macroscale distributed hydro-modeling method, Bayesian principal-monotonicity inference (BaPMI), based on climate classification, representative-grids selection, statistical hydrology, surrogates-based Bayesian optimization, and variance-based sensitivity analyses. The method is applied to a typical large cold-region watershed, i.e., Athabasca River Basin in Canada, and reveals a series of findings such as the following representatives. BaPMI shows promising skills in quantitatively characterizing macroscale cold-region hydrology under overall impacts of heterogeneous climates. Bayesian optimization of which hyperparameters are insensitive decreases hydro-modeling computational time by up to 96.3% and, together with climate classification, largely enhances BaPMI applicability while ensuring modeling accuracies. Individual BaPMI hydro-model parameters can explain over ¾ of the variation of hydro-modeling accuracies and, due to their interaction, joint calibration is required for avoiding underestimation of the impacts. Climatic conditions dominating cross-scale uppermost hydrologic variations consist of night temperature (40%), day temperature (38%), and precipitation (22%) for all catchments; all river flows along the mainstem are associated with upstream climatic events such as glacier melts. Climatic impacts decline from upstream to downstream and from summer, spring, winter to autumn; this may relate to tempo-spatial heterogeneity of soil, vegetation, geology, human interference and other non-climatic factors. Without this study, macroscale distributed hydrologic modeling would lack one advanced method (i.e., BaPMI) that can immunize against effects of data uncertainty, subjective judgement and climatic collinearity, adapt to complicated hydroclimatic relations and diverse hydro-variable(s) distributions, reveal cross-scale uppermost hydrologic variations and, through inexpensive computations, enhance hydro-modeling accuracies to avoid climatic-impact underestimation. The findings of this study could facilitate BaPMI applications and advance macroscale (cold-region) hydrology. [ABSTRACT FROM AUTHOR]

Summary We tested whether the current understanding of insignificant effect of drainage on evapotranspiration (ET) in the temperate region wetlands applies to those in the subtropics. Hydro-climatic drivers causing the changes in drained wetlands were identified and used to develop a generic model to predict wetland ET. Eddy covariance (EC)-based ET measurements were made for two years at two differently drained but close by wetlands, a heavily drained wetland (SW) (97% reduced surface storage) and a more functional wetland (DW) (42% reduced storage). Annual ET for more intensively drained SW was 836 mm, 34% less than DW (1271 mm) and the difference was significant ( p = 0.001). This difference was mainly due to drainage driven differences in inundation and associated effects on net radiation ( R n ) and local relative humidity. Two generic daily ET models, a regression model (MSE = 0.44 mm 2 , R 2 = 0.80) and a machine learning-based Relevance Vector Machine (RVM) model (MSE = 0.36 mm 2 , R 2 = 0.84), were developed with the latter being more robust. The RVM model can predict changes in ET for different restoration scenarios; a 1.1 m rise in drainage level showed 7% increase ET (18 mm) at SW while the increase at DW was negligible. The additional ET, 28% of surface flow, can enhance water storage, flood protection, and climate mitigation services at SW compared to DW. More intensely drained wetlands at higher elevation should be targeted for restoration for enhanced storage through increased ET. The models developed can predict changes in ET for improved evaluation of basin-scale effects of restoration programs and climate change scenarios. [ABSTRACT FROM AUTHOR]

Highlights: [•] Build a systematic water allocation scheme to mitigate droughts. [•] Derive evaluation diagrams from exceedence probabilities of hydrologic conditions. [•] Identify drought thresholds based on the derived evaluation diagrams. [•] Learn the neuro-fuzzy mechanism from derived diagrams to produce referential impacts. [•] Decide a proper discount rate on irrigation water based on referential impacts. [ABSTRACT FROM AUTHOR]

Summary: This article aims to assess the value of adaptive reservoir management versus traditional operation practices in the context of climatic change for Northern California. The assessment uses adaptive decision model being developed for planning and operational management of the Northern California (central valley) water resources system (), coupled with a dynamic downscaling and hydrologic modeling system described in . The assessment process compares the water system response in four simulated scenarios, pertaining to two management policies (traditional and adaptive) and two hydrologic data sets (one for the historical and a second for a future scenario). The assessments show that the current policy, which is tuned to the historical hydrologic regime, is unable to cope effectively with the more variable future climate. As a result, the water supply, energy, and environmental water uses cannot be effectively satisfied during future droughts, exposing the system to higher vulnerabilities and risks. By contrast, the adaptive policy maintains similar performance under both hydrologic scenarios, suggesting that adaptive management constitutes an effective mitigation measure to climate change. [ABSTRACT FROM AUTHOR]

Summary: Organic carbon solution and transport processes which occur during periods of heavy rainfall and periods or little or no rainfall, can exert a significant control over a systems’ annual organic carbon budget. In addition, either or both extremes can be key contributors to contaminant release, water discolouration, flood risk or vegetation growth. Although there is an increasing body of work studying hydrological responses to peatland restoration, there are very little available data on the performance of restored peatlands during these key periods. This study builds on previous work from an upland peatland in Wales that has been restored through drain-blocking, and presents evidence from a landscape scale experimental study at the site. A comparison of sampling scales within the study demonstrates the necessity of larger spatial scales, in combination with high resolution datasets, in assessing catchment level responses. Our results suggest that drain blocking leads to higher and more stable water tables that are able to better resist drought periods, and thus lead to more stable discharge from the system. The shallower water tables and pooling in drains also appear to reduce the production and transport of fluvial organic carbon, and thus less organic material is available to be released as during peak flow or dry periods. Despite restoration apparently reducing the available water storage within the peat, the increase in overland flow and in pooling within blocked drains appears to have led to a less flashy system. Peak flow responses in both drains and upland streams are less severe, with more rainfall being retained within the bog. We suggest that restoration leads to a more buffered system, with more moderate responses to extreme events, and reduced release of both dissolved and particulate organic carbon. We discuss the implications of this for fluxes of fluvial organic carbon and sediment loss. [ABSTRACT FROM AUTHOR]

[Display omitted] • We propose a novel framework for urban drainage adaptation to climate change. • We assess climate change impacts on rainfall extremes, IDF curves, drainage systems. • We introduce an index for assessing overall reliability of urban drainage networks. • We introduce an index for assessing adaptive capacity of urban drainage networks. • We incorporate benefits offered by ecosystem services in economic terms. A novel methodological framework for urban drainage adaptation to climate change is introduced and tested in a small urban basin. The impact of climate change on Intensity-Duration-Frequency curves and on urban drainage is reported. The flood mitigation measures comprised both green and grey alternatives, i.e., permeable surfaces and sewer enlargement. The Storm Water Management Model (SWMM5) was calibrated and employed. Prior to calibration, sensitivity analysis of SWMM hydrologic-hydraulic and permeable surfaces input parameters was conducted using a global sensitivity analysis method. Two novel indices, the Index for Reliability of Urban Drainage Adaptation (IRUDA) and the Climate Variability Adaptation Index (CVAI), were also introduced. IRUDA assesses the total reliability of an urban drainage network in hydraulic, temporal and volumetric terms. Sizing of flood mitigation measures was undertaken employing construction cost along with IRUDA. CVAI was used for alternative selection among different flood mitigation scenarios. CVAI assesses the total adaptive capacity of the drainage network based on hydrological, hydraulic and economic aspects of the proposed adaptation plan. A forward uncertainty propagation scheme was employed on the selected alternative in order to examine input data and parametric uncertainty with respect to the peak discharge at the outlet of the basin. Finally, monetization of ecosystem services offered by green measures is presented. The simulation results for the study area illustrated that under the non-stationarity assumption future rainfall intensities and urban flooding risk will increase, and therefore, further flood mitigation measures will be required. Furthermore, the majority of the adaptation strategies examined were found to improve the capacity and reliability of the drainage network. Moreover, the results revealed that permeable pavements perform better for storm events with return period of up to five years. Overall, it can be concluded that design criteria need to be reviewed and also green measures, such as permeable surfaces, can be introduced in the design procedure. [ABSTRACT FROM AUTHOR]

[Display omitted] • Reservoir sedimentation is unsustainably diminishing storage capacity and benefits. • Sedimentation leads to costly dam decommissioning and intergenerational inequity. • A new sustainable management paradigm is needed for all reservoirs. • Monitoring is needed to measure the extent and rate of sedimentation. • Sustainable management strategies are sediment yield reduction, bypass, or removal. Although the hydrologic cycle is a continuously renewable resource, the natural rate of water delivery is highly variable. Water is made available to our society on a consistent and reliable basis largely due to flow regulation by storage reservoirs. However, under current management, the reservoir storage capacity needed for flow regulation is a non-renewable resource because this capacity is steadily being lost to sedimentation. Today's reservoirs occupy unique sites and may be considered largely irreplaceable, making the nation dependent on a non-sustainable resource. Sedimentation is steadily depleting storage capacity and progressively degrading the ability of reservoirs to fulfill their designated purposes. Sedimentation is also causing environmental impacts upstream and downstream of reservoirs. In the United States, the combined impacts of sedimentation and population growth have resulted in an estimated 35% decline in storage capacity per capita since this value peaked around 1970. In absolute terms, the estimated total reservoir storage capacity in the U.S. has dropped from a peak of 850 Gm3 in the late 1980s to 810 Gm3 today. Yet, sustaining the nation's long-term reservoir storage capacity has not been a priority for many public or private dam owners, especially when they lack a reservoir sedimentation monitoring policy. In many regions, future reservoir storage will have to serve an important role in the mitigation of climate change to help ensure water, food, and energy, and the reduction of flood risk. There is an imperative need to preserve existing reservoir storage capacity due to rising demands associated with population growth, and increasing hydrologic variability associated with climate change, and the challenges and costs associated with either expanding existing capacity or decommissioning and developing new storage capacity. The trapping of sediment behind dams has also contributed to the decline of freshwater and coastal environments downstream of dams. Reversing these dangerous trends in storage capacity and environmental integrity will require increased monitoring of reservoirs, application of both established and emerging sediment management technologies, and a new paradigm for sustainable reservoir design and management. It requires moving from the traditional design life (reservoir life expectancy) approach to the adoption of sustainable use as the appropriate criteria for reservoir design and operation, achieving a sediment balance across reservoirs to permit the indefinite operation of this critical infrastructure. [ABSTRACT FROM AUTHOR]

• Streamflow components change divergently across various permafrost rivers. • Generally, higher percentile flows have increased more than lower percentile flows. • Low flows in most rivers increase while high flows exhibit both increase and decrease. • Low flows are more sensitive to warming and less sensitive to rainfall than high flows. • The results suggest increased deep groundwater contributions to river discharge. Cryohydrological processes in the Eurasian Arctic and Qinghai-Tibet Plateau (QTP) permafrost regions have been changing rapidly, yet the details of the river runoff changes are still obscure. Here we use multidecadal natural daily discharge data to compute annual flow duration curves (FDCs) and percentile flows for 20 Eurasian Arctic and QTP river basins with permafrost underlain. We provide detailed trends of various percentile flows that almost all runoff components change variably, either with the distinct magnitude or opposite direction or both. We demonstrate that the lower flows increase monotonically faster than higher flows. Most low flows increase while the high flows show spatial heterogeneity with both increased and decreased trends observed. The portion of deep and total groundwater flow in total discharge increases, with the deep groundwater flow increases faster. Interestingly, the river runoff components respond differently to climatic change factors. The low flows, mainly sustained by groundwater, are more sensitive to warming than high flows, while the high flows are more sensitive to precipitation than low flows. Our results may suggest an increased subsurface hydrological connectivity under the warming climate and thawing permafrost. We show that FDCs have the potential to reveal the long-term runoff components change. Future climatic change may alter the shapes and distributions of FDCs since the asynchronous changes of runoff components, which have important implications for cold region river management and mitigation under climatic change. [ABSTRACT FROM AUTHOR]

[Display omitted] • A human-machine interactive method is proposed for real-time reservoir flood control operation. • Modeling, observation, and human experiences are integrated for more effective decision support. • Optimization, simulation, and observation are combined for flood control via data assimilation. Real world reservoir operations are usually not fully automatic based on computer models; instead, reservoir operators conduct the operations based on their experiences, professional justification, as well as modeling support. In this paper, we propose a human-machine interactive method, namely Real-time Optimization Model Enhanced by Data Assimilation (ROMEDA) tested with simple but reasonable rules for direct interaction between operators and a computer model for reservoirs which have complex storage and stage relations (e.g. long and narrow reservoirs). ROMEDA couples 1) an optimization model to search for optimal releases, 2) a reservoir storage-stage simulation and data assimilation schedule to update the storage based on real-time reservoir stage observations, and 3) reservoir operators' choices based on the optimization model solutions, as well as their experiences, knowledge, and behaviors. For every time period and based on the updated storage, ROMEDA provides optimal releases as recommendations and adopts actual releases made by operators. ROMEDA does not assume that operators strictly accept the recommendations, and the storage will be updated based on the actual release at each time period via a data assimilation procedure, which plays a key linkage between human and machine in ROMEDA. Via a case study on-channel reservoir, it is found that for both small and large flood events, ROMEDA, which integrates the advantages of both machine and human, shows better performance on flood risk mitigation and water use (hydropower) benefit than the case with historical operation records (HOR) or optimization with single/multi-objective. ROMEDA is one of the first attempts of a human-machine interactive method for online use of an optimization model for real-time reservoir operation based on integrated modeling, observation, and operators' choice. [ABSTRACT FROM AUTHOR]

• Modification of drought index allows easy comparison of future droughts. • Scenario means predict 14.2% to 25.1% decrease in flow depending on the spring. • After 2059, multi-year droughts are forecast for all springs under all scenarios. • Drought duration & monthly water deficit increase (36.8%–533% and 27.3%–83.6%) • Drought duration and intensity increase as spring water retention time increases. Karst springs are the exclusive source of water during dry months for many Mediterranean regions. The impact of climate change on the hydrology of Mediterranean karst springs has not received proper attention in the scientific literature. Specifically, the effect of climate change on the drought characteristics of karst springs has not been studied. Here, the response to climate change of three Mediterranean karst springs with different retention times is examined, focusing on the frequency, duration and intensity of their drought events. The Karst-SWAT model is used to quantify the karst springs flow. A set of representative climate change scenarios are used to assess the climate change impact on the springs and surface flow for the period 2019–2098. A non-parametric drought index is modified to estimate the future meteorological and hydrological drought characteristics in comparison to the reference period. The most adverse effects of climate change on the three karst springs are expected to prevail after 2059. Depending on the spring, the mean annual karst flow decreases from 14.2% to 25.1%, the mean number of drought events ranges from a decrease of 8.1% to an increase of 77.5%, the duration of drought events increases from 36.8% to 533%, and the mean monthly water deficit increases from 27.3% to 83.6%, when comparing the period 2059–2098 to the reference period (1979–2018). As the water retention time of the spring increases, the duration and intensity of the droughts are likely to increase more significantly. After 2059, multi-year droughts are forecast for all springs under all scenarios. Both low and high flows will increase, with the former occupying even wet months. The results of this study can be used as a guide for local and regional authorities and water management organizations, to adapt their practices for the mitigation of the impact of climate change on the water resources of Mediterranean karst springs. [ABSTRACT FROM AUTHOR]

• A time-varying modeling approach for extended short-term streamflow forecasting is proposed. • The concept of dummy variable optimization is utilized in the superposing problem. • Behaviour recurrence prompts the utilization of structural parametrization. • The proposed approach is discussed within an Ensemble Learning architecture. • The results from the study show the improved performance of the proposed model. Extended streamflow forecasting is nowadays important for early flood warning and risk mitigation under a changing climate. However, the absence of reliable estimates of the usual streamflow descriptors, at the considered time horizons, renders common input–output modeling approaches inapt for extended forecasting. For such a problem, system recurrence information is vital to produce relatively improved forecasts. Like any time series generated by complex systems, river flow can be represented by a time-varying parameter (TVP) model. TVP modeling frameworks often assume that the system evolution exhibits superstatistical random walks, and a number of statistical assumptions is followed upon, to infer better forecasts from the system. Also, the TVPs should hone a model's ability to capture system recurrence, which in this case translates to a number of state-based model structures. In this work, we develop an ensemble-based computationally efficient method that extracts useful cyclic-type TVPs which are strongly coupled over extended, but more continuous and less aggregated, time horizons. A multi-level modeling framework is proposed to facilitate the creation and direct use of updated parameter states to produce more reliable forecasts on the considered temporal resolutions. A dummy-variable optimization technique is also proposed within the state-updating procedure to allow smooth state transition. We show that the modeling framework produces stable results of parameter variations at different time horizons when applied to estimate the streamflow of Clearwater River. The proposed framework can incorporate exogenous descriptors at different levels without loss of generality and can be used for various forecasting applications. [ABSTRACT FROM AUTHOR]