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Summary: The Danjiangkou reservoir lies in the upper Hanjiang basin and is the source of water for the middle route of the South-to-North Water Diversion Project (SNWDP) in China. Any significant change in the magnitude or timing of runoff from the Danjiangkou reservoir induced by changes in climatic variables would have significant implications for the economic prosperity of the area in the Hanjiang basin as well as for the South-to-North Water Diversion Project. In this paper the following issues are investigated: (1) Temporal trends of annual and seasonal precipitation and temperature from 1951 to 2003 in the Hanjiang basin are analyzed using the Mann–Kendall and the linear regression methods; spatial distributions of precipitation and temperature are interpolated by the inverse distance weighted interpolation method. (2) Temporal trends of runoff, precipitation and temperature from 1951 to 2003 in the Danjiangkou reservoir, an upper stream basin of the Hanjiang River, are further tested. (3) To assess the impact of climate change on water resources and predict the future runoff change in the Danjiangkou reservoir basin, a two-parameter water balance model is used to simulate the hydrological response for the climate change predicted by GCMs for the region for the period of 2021–2050. The results indicate that (1) at the α =0.05 significance level precipitation in the Hanjiang basin has no trend, but the temperature in the same region has significant upward trends in most parts of the Hanjiang basin. (2) The mean annual, spring, and winter runoffs in the Danjiangkou reservoir basin have decreasing trends. (3) The results simulated for the period 2021–2050 show that runoff of the Danjiangkou reservoir would increase in all the seasons, mainly in response to the predicted precipitation increase in the region. Sensitivity analysis shows that a 1°C and 2°C increase in temperature would reduce the mean annual runoff to about 3.5% and 7%, respectively. A decrease/increase of the mean monthly precipitation of 20% and 10% would decrease/increase the mean annual runoff to about 30% and 15%, respectively. The results of this study provide a scientific reference not only for assessing the impact of the climate change on water resources and the flood prevention in the Hanjiang basin, but also for dimensioning the middle route of the SNWDP in China. [Copyright &y& Elsevier]

Summary: Limitations in water quantity and quality are among the greatest social and economic problems facing mankind. However, difficulties in estimating the global long-term average runoff have led to differences of as much as 30% when integrated to the whole earth. Model estimates of runoff are especially uncertain for the 50% of the global land surface lacking consistent runoff data. In this study, we present the WASMOD-M global water-balance model, constructed to provide robust runoff estimates both for gauged and ungauged basins. WASMOD-M is a conceptual water-budgeting model with two state-variables and five tunable parameters. A simple parameter-value estimation procedure allowed “acceptable” parameter values to be identified both for the majority of gauged basins, and for most ungauged basins. Acceptable global simulations could be accomplished with continentally constant parameter values but at the cost of compensating errors on a basin scale. A “standard”, spatially-distributed parameter-value set was derived for a ”best” global simulation. Of the simulated 59132 0.5°×0.5° cells, 45% got “good” parameter values as a by-product of regionalisation, 41% from regionalisation, whereas 14% were given a default value set. This global set allowed simulation of the 1915–2000 world water balance. The simulation was in the same range as previously published model results and compilations of runoff measurements. Long-term average within-year runoff variations agreed well with previously published results for most of the studied runoff stations although WASMOD-M was only calibrated against long-term average runoff. Improvement of WASMOD-M and other global water-balance models should be simplified by a common definition of basin boundaries and areas, as well as runoff. Further, modelling progress will depend on improved global datasets of precipitation and runoff regulation. [Copyright &y& Elsevier]

Summary: Large differences in future climatic scenarios found when different global circulation models (GCMs) are employed have been extensively discussed in the scientific literature. However, differences in hydrological responses to the climatic scenarios resulting from the use of different hydrological models have received much less attention. Therefore, comparing and quantifying such differences are of particular importance for the water resources management of a catchment, a region, a continent, or even the globe. This study investigates potential impacts of human-induced climate change on the water availability in the Dongjiang basin, South China, using six monthly water balance models, namely the Thornthwaite–Mather (TM), Vrije Universitet Brussel (VUB), Xinanjiang (XAJ), Guo (GM), WatBal (WM), and Schaake (SM) models. The study utilizes 29-year long records of monthly streamflow and climate in the Dongjiang basin. The capability of the six models in simulating the present climate water balance components is first evaluated and the results of the models in simulating the impact of the postulated climate change are then analyzed and compared. The results of analysis reveal that (1) all six conceptual models have similar capabilities in reproducing historical water balance components; (2) greater differences in the model results occur when the models are used to simulate the hydrological impact of the postulated climate changes; and (3) a model without a threshold in soil moisture simulation results in greater changes in model-predicted soil moisture with respect to alternative climates than the models with a threshold soil moisture. The study provides insights into the plausible changes in basin hydrology due to climate change, that is, it shows that there can be significant implications for the investigation of response strategies for water supply and flood control due to climate change. [Copyright &y& Elsevier]

Abstract: Annual maximum streamflow and annual maximum water level and their variations exert most serious influences on human society. In this paper, temporal trends and frequency changes at three major stations of Yangtze River, i.e. Yichang, Hankou and Datong representing upper, middle and lower reaches, respectively, were detected with the help of parametric t-test, Mann–Kendall (MK) analysis and wavelet transform methods. The results show that: (1) there is a significant upward trend in streamflow at middle Yangtze River, indicating that flood hazard in the middle reach of the river, the flood rich region, will be more serious; (2) there is a consistent increase of water level from upper to lower reaches of the river which does not always coincide with the maximum streamflow variations; and (3) the periods of water level changes are decreasing over time, indicating the increasing occurrence frequency of annual maximum water level over time. This phenomenon is more obvious from upper Yangtze River to the lower Yangtze River. Human activities like destruction of vegetation, land reclamation and construction of levees reduced lake sizes and filled up the river bed, reducing the flood storage capacity of lakes and fluvial channel. These factors led to higher water level even some times the streamflow is small. Human should adjust his activity to enhance his adaptive capacity to flood hazard in the future. [Copyright &y& Elsevier]

Abstract: Most climate predictions show significant consequences globally and regionally, but many of its critical impacts will occur at sub-regional and local scales. Downscaling methods are, thus, needed to assess effects of large-scale atmospheric circulation on local parameters such as precipitation and runoff. This study aims at evaluating the analogue method (AM) as a benchmark method for precipitation downscaling in northern Europe. The predictors used in this study were daily and monthly gridded sea-level pressures from 1960 to 1997 in an area 45–75°N and 30°W–40°E with a resolution of 5×5° long-lat. Analogues for daily and monthly precipitation at seven precipitation stations in south-central Sweden were established with two techniques, principal-component analysis (PCA) and the Teweles-Wobus score (TWS). The results showed that AM downscaling on both daily and monthly basis was commonly generally much better than a random baseline but depended on the objective function used for assessment; PCA and TWS produced similar results in most cases but TWS was superior in simulating precipitation duration and intensity. Downscaling was improved when seasonality was included and when the SLP field was confined to those geographical areas that contributed most to precipitation in south-central Sweden. [Copyright &y& Elsevier]

• A robust input determination method was developed for monthly saltwater intrusion. • Combined use of MIC and r was applied to identify the relevant statistical inputs. • BMA provided more reliable forecast than individual member models. Mid- and long-term saltwater intrusion forecasts for estuaries are challenging due to a wide range of dynamic interactions and the limited amount of available data. This study proposes a tailor-made method for input determination for ensemble forecasts of monthly estuarine saltwater intrusion. The proposed method is based on determining the initial set of candidates by the combined use of Pearson's Coefficient (r) and Maximal Information Coefficient (MIC); and afterwards reducing the dimension of the input data sets by Principal Component Analysis (PCA). The current study uses Bayesian Model Averaging (BMA) method to combine the forecasting results of Random Forest (RF), Support Vector Machine (SVM) and Elman Neural Network (ENN) models to create an integrated forecast. The proposed modeling approach was tested and compared with seven alternative procedures to forecast the monthly saltwater intrusion at the Pearl River Delta (PRD). The results indicated that: (a) the monthly dynamics of saltwater intrusion are more sensitive to the long-term solar activities than the local wind force; (b) the valuable non-linear signals hidden in the related time series could be identified by the combined use of r and MIC; (c) dynamic statistics related to low runoff, high antecedent chlorinity, strong tidal force, and strong wind force are preferable over the monthly average values as model inputs; and (d) the proposed method achieved highest forecast accuracy with Nash-Sutcliffe coefficient (NSE) of 0.78. This study provides insights to the input determination for data-driven models of complex estuarine saltwater intrusion. [ABSTRACT FROM AUTHOR]

• Intelligent adaptation parameter method improves the hydrological model performance. • LightGBM builds flood characteristics-parameter intelligent adaptation relationship. • The XAJ-IAP model can provide accurate and reliable flood simulation. • LightGBM analysis reveals importance of flood characteristics and parameters. Traditional hydrological modeling methods use a set of parameters to simulate flood processes with complex causes and variable intensity, which can easily lead to parameter instability. To address the problem of parameter instability, this study proposes an approach integrating the hydrological model with Intelligent Adaptation Parameters (IAP), whose intelligent adaptation relationship is established by the light gradient-boosting machine (LightGBM) based on individual calibration parameters by each flood event and flood characteristics including flood-caused rainstorm information and initial soil moisture. A widely used hydrological model, Xin 'anjiang (XAJ) model, is chosen to be integrated with IAP (XAJ-IAP) in this study, which has a relatively complex structure and a total of 15 model parameters. The obtained findings demonstrate that: (1) recalibrating the sensitive runoff concentration and separation parameters with a single flood leads to a notable enhancement in simulation accuracy, while simultaneously considering the model's physical significance; (2) the XAJ overestimates large floods and underestimates small floods. Compared with the XAJ, the XAJ-IAP has a better rain-flood response relationship and simulation accuracy for floods of different magnitudes, solving the problem of parameter instability that exists in XAJ; and (3) evaluated in terms of information gain, sensitive parameters contribute the most to the establishment of the intelligent adaptation relationship in the LightGBM compared to flood-caused rainstorm information and initial soil moisture, indicating that sensitive parameters are the most important input features of the LightGBM. It can be concluded that the intelligent adaptation system can not only solve the problem of parameter instability that exists when traditional hydrological models simulate complex and changeable floods, but also further reveal the relationship between the model and floods. [ABSTRACT FROM AUTHOR]

• An evaluation method of model transferability is proposed. • The complex models (XAJ and HBV) have better spatial transferability than the simple model (GR4J). • The performance of models transferred from the wettest to the driest period is worse than that of models transferred from the driest to the wettest period. Changing conditions of the climate and underlying surface have altered the rainfall-runoff relationships in many basins, greatly increasing additional challenges in the applicability of hydrological models for studying the hydrological response to those potential changes. However, systematic and simultaneous testing and comparing of both temporal and spatial transferabilities of different hydrological models under changing conditions have not received enough attention. The present study investigates the potential differences between temporal and spatial transferabilities of different hydrological models under different climatic and underlying surface conditions, which are synthesized from two basins in Southern China with 50-year historical records (1966–2015). The transferability of five hydrological models, i.e., XAJ, HBV, SIMHYD, IHACRES and GR4J, is investigated under the synthesised changing conditions by using a new evaluation method, proposed in this study. The results show that: (1) the proposed evaluation method is proved to be effective in evaluating the transferability of the models; (2) for temporal transferability under stationary condition, the five models show similar performances, but for spatial transferability, the performances of complex models (XAJ and HBV) are better than that of the simple model (GR4J); (3) the difference in underlying surface conditions in the target basin affects spatial transferability of the models; (4) hydrological models have much better transferability from dry to wet period than otherwise. This study provides an insight to test temporal and spatial transferabilities of hydrological models in the context of changing climate and underlying surface conditions. [ABSTRACT FROM AUTHOR]

• A new framework to cope with data scarcity problem is developed for lager scale flash flood risk mapping. • Analytic hierarchy process is improved considering the objective variability of data. • Data mining technique overcomes the difficulty in defining risk ranks' threshold. Flash floods are one of the most severe natural disasters throughout the world, and are responsible for sizeable social and economic losses, as well as countless injuries and death. Risk assessment, which identifies areas susceptible to flooding, has been shown to be an effective tool for managing and mitigating flash floods. The study aims to introduce the methods to determine the weights of the risk indices, and identify the different risk clusters. In this regard, we proposed a methodology for comprehensively assessing flash flood risk in a GIS environment, by the improved analytic hierarchy process (IAHP) method, and an integration of iterative self-organizing data (ISODATA) analysis and maximum likelihood (ISO-Maximum) clustering algorithm. The weight for each risk index is determined by the IAHP, which integrates the subjective characteristics with objective attributes of the assessment data. Based on the data mining technology, the integration of ISO-Maximum clustering algorithm derives a more reasonable classification. The Guangdong Province of China was selected for testing the proposed method's applicability, and we used a receiver operating characteristics (ROC) curve approach to validate the modeling of the flash-flood risk distribution. The validation against the historical flash flood data indicates a high reliability of this method for comprehensive flash flood risk assessment. In order to verify the proposed method's superiority, in addition, the technique for order performance by similarity to ideal solution (TOPSIS) and the weights-of-evidence (WE) methods are used for comparison with the IAHP and ISO-Maximum clustering algorithm method. Moreover, we analyzed and compared the regularity of flash floods in the rural and urban areas. This study not only provides a new approach for large-scale flash flood comprehensive risk assessment, but also assists researchers and local decision-makers in designing flash flood mitigation strategies. [ABSTRACT FROM AUTHOR]

• A systematic scheme to detect and verify abrupt shift of runoff was developed. • A model was proposed to forecast natural streamflow in data-poor areas. • Mechanism of abrupt shift in minor periods in human-impacted runoff was explored. Understanding the long-term variability of streamflow and its response to human activities in water-limited areas is essential for socio-hydrologic models' development. In this study, a framework for the detection and attribution of abrupt shift of minor periods in human-impacted streamflow is proposed. First, the most significant abrupt shift in human-impacted streamflow is detected using the Pettitt test and verified based on different statistical characteristics of streamflow series (trend, periodicity, and different quantiles) and main physical causes (main reservoirs operations). According to the breakpoint, the study period was divided into approximately natural sub-period and human-impacted sub-period. Interestingly, we found the "missing" of minor (2–4-year timescale) periods of the runoff records after abrupt shift points in the study cases. To investigate its mechanisms, we proposed an Improved Multivariate Fuzzy Mean Generating Function (IMFMGF) model to simulate the natural runoff in the human-impacted period and decomposed the observed runoff into natural runoff component and human-impacted changing runoff component. Then, the periodicity of these two components was compared based on Morlet wavelet analysis and Ensemble Empirical Mode Decomposition (EEMD). Results showed that the minor periods' wave crests and troughs of the above two components had excellent negative correspondence. The candidate mechanism is that the offsetting effects (i.e., the regular anthropogenic withdrawal or intake of water.) resulted in the disappearance of minor periods of the human-impacted observed, which can give more certain inputs into the prediction of non-stationary streamflow series. [ABSTRACT FROM AUTHOR]

• Area and position indicators are created to assess extracted surface water path (SWP). • Theoretical SWP is deduced based on the formula of synthetic surfaces. • Artificial dispersion occurs in SWPs extracted by multiple flow direction algorithms. • Upscaling DEM resolution can decrease the value and position precisions. • Cross compensation decreases value precision but increases position precision. The surface water path (SWP) extracted from digital elevation model (DEM) by flow direction algorithms is widely employed to obtain a variety of topographic variables used in hydrological modeling. Accurate SWPs can facilitate understanding the underlying mechanisms of water movement on Earth's surface. However, the accuracy of extracted SWPs by different flow direction algorithms has not been systematically studied. In this work, two indicators are developed to measure the area and position errors of extracted SWPs relative to theoretical SWPs on four synthetic surfaces representing typical terrains of natural watersheds. Based on the formulas of the synthetic surfaces, theoretical true SWP can be derived for any grid cell on the DEM discretized from the synthetic surfaces. Several widely used flow direction algorithms including three single flow direction (SFD) algorithms (i.e. D8, Rho8 and D8-LTD approaches) and three multiple flow direction (MFD) algorithms (i.e. FDFM, MFD-md and D∞ approaches) are implemented to extract SWPs. Results suggest that significant distinctions can be detected in SWPs extracted by different flow direction algorithms. The SWPs extracted by SFD algorithms are always one-dimensional non-dispersive lines because SFD algorithms allow only one flow direction at each grid cell. In contrast, the SWPs extracted by MFD algorithms show excessive artificial dispersion. The average area error of extracted SWPs ranges from 16.3% to 75.2% on different synthetic surfaces and the minimum is obtained by FDFM approach for all synthetic surfaces. The average position error falls in the range of 46.0% to 161.4%. The maximum is gained by D8 or FDFM approach, and the minimum by D8-LTD or D∞ approach. The cross compensation of SWP area induced by artificial dispersion leads to relatively high area accuracy but relatively low position accuracy of MFD algorithms. In addition, increasing DEM resolution without capturing more topographic variability can decrease the area and position accuracy due to error accumulation from more steps of flow direction calculation. Our findings provide a beneficial insight into applying SWP-derived topographic variables to hydrological modeling. [ABSTRACT FROM AUTHOR]

The evolutionary algorithms can solve reservoir operation with a fast convergence rate whereas the major impediments in handling the joint operation of mega cascade reservoirs easily trigger the technical bottlenecks, i.e. trapping into a local optimum, instability and loss of good solutions. This study proposes a methodology that fuses three auxiliary strategies into the Kidney Algorithm (KA) to optimize the hydropower output for conquering the bottlenecks in the KA concerning the joint operation of six mega cascade reservoirs located in the Jin-Sha River basin in China. The proposed theme would contribute to the application of the state-of-the-art evolutionary algorithms in boosting the cleaner hydropower production of mega cascade reservoirs. The three auxiliary strategies are that: firstly, the exploration and exploitation strategy is employed to stimulate the movement of solutions to surmount technical drawback of trapping into a local optimum; secondly, the adaptive strategy is used to automatically adjust algorithm parameter values to overcome the instability problem; lastly, the elitism strategy is introduced to preserve the best solution at every epoch to avoid the loss of good solutions. Our methodology, without expanding or upgrading hydraulic infrastructures, can increase the hydropower production of the six mega cascade reservoirs by 7.8%, as compared with the standard operation policy. The hydropower production can reach 4.8 billion kW·h/year, which can decrease 3.77 billion kg/year in CO 2 emission, and bring 217.44 million USD/year in hydropower benefits. The improved KA can considerably increase the reliability and resilience of hydropower output as well as largely decrease the vulnerability of hydropower output. The results suggest that our methodology can stimulate hydropower output to yield more benefits regarding cleaner production, carbon emission reduction and sustainability. Image 1 • Hybrid of three auxiliary strategies conquers bottlenecks of Kidney Algorithm. • Improved Kidney Algorithm optimizes joint operation of mega cascade reservoirs. • Optimal reservoir operation benefits cleaner production and reduces CO2 emission. [ABSTRACT FROM AUTHOR]

• Differences in the length of the forecast periods for different reservoirs in the system are considered. • The proposed two-stage risk analysis method is verified using Monte Carlo simulations. • The proposed real-time flood-control operation model can achieve dynamic control of reservoir storages during flood season. The real-time operation of multi-reservoir systems is a vital issue in the field of reservoir management. The uncertainty caused by inflow forecasting means that risk analysis is necessary for such real-time operation. However, differences in the length of the forecast periods for different reservoirs in the system are seldom considered in the risk analysis of multi-reservoir systems. This paper presents a two-stage flood risk analysis method of multi-reservoir systems that takes the differences in the length of forecast lead-times into consideration. The aim of the proposed method is to evaluate the uncertainty of the flood forecasting by dividing the operation horizon into the forecast lead-time and the beyond-forecast time period. The risk within the forecast lead-time is estimated by counting the frequency of failure numbers among all scenarios with the help of the scenario-based forecasts. The risk beyond the forecast time period is determined using reservoir flood routing with the design flood hydrographs, which are selected according to the differences in the length of the forecast periods between any two reservoirs. The proposed two-stage risk analysis method is verified using stochastic simulations method based on Monte Carlo sampling. A real-time flood control operation model is established by taking the proposed two-stage risk analysis method as a constraint. China's Ankang-Danjiangkou Cascade Reservoirs are considered as a case study, and the results indicate that the proposed real-time operation model can increase hydropower generation by 0.22 billion kWh during the summer flood season without increasing the flood risk in the multi-reservoir system. The proposed method enhances our understanding of risk management for the real-time flood control operations in multi-reservoir systems. [ABSTRACT FROM AUTHOR]

Monitoring of droughts is the first step into human adaptation and related management of drought hazards. Therefore, drought index is critical in drought monitoring practice. However, the standing drought indices include no information about agricultural irrigation. In this case, based on the Palmer Drought Severity Index (PDSI) and the Self-Calibrating Palmer Drought Severity Index (sc-PDSI), here we proposed the Modified Palmer Drought Severity Index (MPDSI) by considering agricultural irrigation such as irrigation quotas and soil water deficits. We compared changes of droughts monitored by MPDSI and other drought indices considered in this study, and found that MPDSI can well monitor drought conditions in irrigated regions. In this sense, MPDSI can monitor the actual drought conditions under human influences such as irrigation. Besides, we also found that MPDSI can well lessen overestimation of drought conditions by PDSI in terms of drought duration and drought intensity. Therefore, we can conclude that MPDSI can be accepted in drought monitoring practice across China. It should be noted here that the idea behind development of MPDSI and also the MDPSI proposed in this study can be well referenced in drought monitoring in other regions of the globe. • We improved the standing PDSI. • We found less sensitivity of Modified PDSI to warming climate than PDSI. • We found improved drought monitoring performance of modified PDSI than PDSI. [ABSTRACT FROM AUTHOR]

[Display omitted] • A cascade model chain suitable for the risk of future DWAA events is proposed. • Evolution characteristics of DWAA in the future period of YRB are revealed. • Severity of DWAA events in far-future is greater than that in the near-future. • Extreme precipitation events have strong synchronization with DWAA events. At present, many studies have investigated the evolution characteristics, frequency estimation techniques, and prediction of droughts and floods, but a comprehensive understanding of the spatiotemporal evolution law and joint risk of these two composite extreme events is still lacking. The synergistic effect of dry and wet abrupt alternation (DWAA) events has more significant consequences than a single drought and flood event, and these events have a considerable impact on agriculture, ecology, and economy. Furthermore, owing to the intensification of global warming and climate change, the potential synchronisation between DWAA events and extreme precipitation in the future requires special attention. Therefore, it is necessary to establish a reliable framework to analyse the evolution characteristics and risks of composite extreme events (DWAA). In this study, a cascade modelling chain comprising climate model downscaling, non-uniform bias-correction technique, and model integration was developed to study the spatiotemporal evolution characteristics and combined risk impact of DWAA events. The proposed methodology was applied to the Yellow River Basin (YRB) in the historical period (1960–2014, Hist) and the future period (2021–2060, FUT1; and 2061–2100, FUT2) under the Shared Socioeconomic Pathway (SSP) 3–7.0 and 5–8.5 scenarios. The results show that: (1) The constructed cascade modelling chain has high simulation accuracy for precipitation, and can be used to analyse future DWAA events. (2) Considering the precipitation characteristics under SSP3-7.0 and SSP5-8.5 scenarios, the precipitation increases slightly in the near future FUT1 (about 5–10%), and increases significantly in the far future FUT2 (about 10–70%). (3) Following either scenario, the frequency of DWAA events is expected to decrease in the future compared to that in the Hist, but the average intensity is expected to increase compared to that in the Hist. (4) The joint risk of the impact area and intensity of future DWAA events of FUT2 is greater than that of FUT1. [ABSTRACT FROM AUTHOR]

• Two-component mixture distributions (TCMD) are used to model annual maximum floods. • Usage of flood timescale (FT) to classify distinct flood generating mechanisms. • Snowmelt-induced floods and rainfall-induced floods are identified in Norway. • The FT -based TCMD reduces uncertainty of design flood by 40% than traditional TCMD. The conventional flood frequency analysis typically assumes the annual maximum flood series (AMFS) result from a homogeneous flood population. However, actually AMFS are frequently generated by distinct flood generation mechanisms (FGMs), which are controlled by the interaction between different meteorological triggers (e.g., thunderstorms, typhoon, snowmelt) and properties of underlying surface (e.g., antecedent soil moisture and land-cover types). To consider the possibility of two FGMs in flood frequency analysis, researchers often use the two-component mixture distributions (TCMD) without explicitly linking each component distribution to a particular FGM. To improve the mixture distribution modeling in seasonally snow covered regions, an index called flood timescale (FT), defined as the ratio of the flood volume to peak value and chosen to reflect the relevant FGM, is employed to classify each flood into one of two types, i.e., the snowmelt-induced long-duration floods and the rainfall-induced short-duration floods, thus identifying the weighting coefficient of each component distribution beforehand. In applying the FT -based TCMD to model the AMFS of 34 watersheds in Norway, ten types of mixture distributions are considered. The design floods and associated confidence intervals are calculated using parametric bootstrap method. The results indicate that the FT -based TCMD model reduces the uncertainty in the estimation of design floods for high return periods by up to 40% with respect to the traditional TCMD. The improved predictive ability of the FT -based TCMD model is attributed to its explicit recognition of distinct generation mechanisms of floods, thereby being able to identify the weighting coefficient and FGM of each component distribution without optimization. [ABSTRACT FROM AUTHOR]

Abstract Wetlands are thought to be the most unique ecosystem in the world which plays an important role in water and material circulation. However, investigation of ecosystem dynamics in those lake floodplain wetlands that suffering rapid and significant short-term water level fluctuation is quite a challenge. In this study, the short- and long-term characteristics of vegetation NPP (net primary productivity) and their driving mechanism were investigated in the Poyang Lake floodplain wetland, an important international wetland that listed in the Global Eco-region by the World Wildlife Fund (WWF). Attempts were achieved through validating the Carnegie-Ames-Stanford Approach (CASA) model based on observed biomasses of different vegetation types and reconstructed continuous high spatiotemporal resolution (30 m and 16 days) of NDVI data during 2000–2015 according to the fused Landsat and MODIS data. Major result indicates that the intra-annual variation of NPP of most vegetation types shows two peaks in a year due to combined effects of vegetation growth rhythm and seasonal exposure of the lake floodplain. Annual NPP of the lake floodplain ranges in 360.09–735.94 gC/m2 and shows an increasing trend during the study period. The change of NPP in space indicates that the distribution elevation of the major vegetation types decreased and evoluted toward the center lake floodplain. Different from the terrestrial ecosystem, inundation duration is the dominant factor that controls NPP dynamics in the lake floodplain, while the influences of other meteorological factors are much weakened. Recent decline of lake water level was the major reason for the spatio-temporal evolution of annual and seasonal vegetation NPP in the lake floodplain. Graphical abstract Unlabelled Image Highlights • Spatio-temporal evolution of NPP was investigated in a typical lake floodplain. • The increase of NPP in the lake floodplain was significant in autumn. • The distribution elevation of major vegetation types decreased in the lake floodplain. • NPP evolution in the floodplain was dominated by the decline of lake water level. [ABSTRACT FROM AUTHOR]

Abstract The Himalayan-Tibetan Plateau (HTP), often known as the "Third Pole" and the "Asian Water Tower", is the source of water resources for many Asian rivers and in turn for hundreds of millions of people living downstream. The HTP has direct impacts on the establishment and maintenance of Asian monsoon, and therefore on the climate of its surrounding areas. Besides, soil moisture plays a critical role in the hydrological cycle and is a critical link between land surface and atmosphere. Hence, soil moisture was greatly emphasized by Global Climate Observing System Programme as an Essential Climate Variable. However, little is known about soil moisture changes on the HTP from a long-term perspective. By comparing remotely sensed and modelled soil moisture datasets against in-situ observations from 100 observation stations, here we find that Noah performed better than other soil moisture datasets. In past years, soil moisture first decreased and then increased obviously. In most regions on HTP, precipitation changes can be taken as the major cause behind soil moisture variations. In future, there is persistently decreasing soil moisture trend since ~2010 with a decreasing rate of −0.044 kg/m2/10a, −0.031 kg/m2/10a and −0.0p 88 kg/m2/10a under RCP2.6, RCP4.5 and RCP8.5 scenarios, respectively, in CMIP5 (Coupled Model Intercomparision Project Phase 5). Specifically, a sudden decrease of soil moisture with a rate of −0.372 kg/m2/10a can be expected after ~2080 under RCP8.5 scenario. Amplifying terrestrial aridity due to increasing precipitation but more significant increasing potential evapotranspiration potentially results in drying HTP. Potential water deficiency for Asian rivers due to drying HTP should arouse considerable concerns. Graphical abstract Unlabelled Image Highlights • New finding about impacts of soil moisture on near-surface air temperature via evaporation fraction. • Identification of different response regimes via relations between soil moisture and evaporation fraction. • Vegetation coverage is the major influencing factor between coupling between soil moisture and temperature. [ABSTRACT FROM AUTHOR]

Abstract Editors of several journals in the field of hydrology met during the General Assembly of the European Geosciences Union—EGU in Vienna in April 2017. This event was a follow-up of similar meetings held in 2013 and 2015. These meetings enable the group of editors to review the current status of the journals and the publication process, and to share thoughts on future strategies. Journals were represented at the 2017 meeting by their editors, as shown in the list of authors. The main points on invigorating hydrological research through journal publications are communicated in this joint editorial published in the above journals. [ABSTRACT FROM AUTHOR]

Highlights • A progressive segmented optimization algorithm (PSOA) for calibration was proposed. • The PSOA calibrated time-variant parameters of Snowmelt Runoff Model effectively. • The PSOA outperformed the segmented optimization algorithm (SOA) Abstract To capture the temporal variability of parameters of hydrological models, the segmented optimization algorithm (SOA) is usually used which subdivides the calibration period into a number of sub-periods and seeks optimal parameters for each sub-period by optimizing the objective function based on the measured and estimated data in the same sub-period. In this paper, we developed a new method that is called a progressive segmented optimization algorithm (PSOA), which seeks optimal parameters by optimizing the objective function based on both the current and all the prior sub-periods. We applied and compared the SOA and PSOA algorithms to the Snowmelt Runoff Model (SRM) in simulating snow-melt streamflow for the Manasi River basin, northwest of China, during snowmelt seasons of 2001–2012. The study showed: (1) PSOA can effectively calibrate the time-variant model parameters while avoiding too much computational time caused by a significant increase of parameter dimensionality. (2) PSOA outperforms SOA for both single-snowmelt-season and multi-snowmelt-season simulations. (3) For single-snowmelt-season simulation, the length of the sub-period has an apparent effect on model performance, the shorter the sub-period is, the better the model performance will be, when the model is calibrated using the PSOA method. (4) For multi-snowmelt-season simulation, an over-short sub-period may cause overfitting problems in some cases such as the situation of taking Nash-Sutcliffe efficiency (NSE) as the objective function. A compromised length of sub-period and objective function may have to be chosen as a trade-off among evaluation criteria and between the importance of calibration and validation. [ABSTRACT FROM AUTHOR]

Highlights • Propose successive approximation to solve curse of dimensionality raised in NSGA-II. • NSGA-II with successive approximation optimizes 21 mega-reservoirs’ joint operation. • Lift synergy of hydropower output, flood control, water supply and CO 2 mitigation. Abstract The high complexity of multi-objective joint reservoir operation imposes challenging barriers to the pursuit of optimal hydroelectricity output. Inasmuch as multi-objective evolution optimization algorithms, including the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II), are trapped in the curse of dimensionality which could not be effectively solved the multi-objective operation of more than ten reservoirs. This study proposes a methodology that integrate the NSGA-II with a successive approximation approach to optimize the hydropower output for conquering the curse of dimensionality under the joint operation of 21 mega cascade reservoirs located in the Upper Yangtze River Basin of China. The successive approximation approach could effectively decompose the mutually related M-dimensional problem into M individual one-dimensional problems, which ingeniously overcomes the curse of dimensionality. The proposed model is anchored with strategies of advancing impoundment timings and raising water levels of cascade reservoirs. We show that our methodology, without adding or upgrading hydraulic infrastructures, empowers the joint operation to reach 110.79 billion kW·h/year (9.8% improvement) in hydropower output, which could reduce 86.97 billion kg/year in CO 2 emission, and to provide 44.97 billion m3/year in water supply with flood risk less than 0.016. The results suggest that our methodology can spur hydroelectricity output to support China’s tactics in fulfilling the pledge of carbon emission reduction and non-fossil energy expansion to 20% by 2030. [ABSTRACT FROM AUTHOR]

• The path analysis method is proposed to explain the interplay of drivers in soil moisture drought. • Precipitation deficits dominates the interannual variation of soil moisture drought while potential evapotranspiration impacts are magnified by drought deterioration. • Atmospheric movement and temperate indirectly affects soil moisture drought through precipitation and potential evapotranspiration. • Proportion of drought explanation decreased with increasing soil depth. Soil moisture drought, as one of the most important drought categories, is determined by both water supply (e.g., precipitation) and demand (e.g., potential evapotranspiration). To shed light on the underlying mechanisms driving soil moisture drought, the statistical multiple linear regression, machine learning, and modeling experiments methods have been pervasively used in early studies. However, these methods neglect the collinearity and interactions of climate variables, and thus cannot reflect the direct and indirect interaction of factors leading to soil moisture drought. To reveal the synergistic effects of water supply and demand on soil moisture drought, this study quantified the contributions of key drivers to the change of soil moisture drought by a path analysis method to exhibit the relationships between atmospheric movement state and soil moisture drought. Prior to applying the systematic path analysis model, we identified the spatial patterns of soil moisture droughts at different depths by using a state-of-art three-dimensional drought recognition method in China. Our results showed that precipitation deficits dominated the interannual variation of soil moisture drought while increasing potential evapotranspiration only had marginal intensification in drought. The response of soil moisture drought to potential evapotranspiration was magnified by drought deterioration, especially in basically severe drought conditions. The total column water vapor and the horizontal divergence of the vapor flux, as well as temperature, directly affected precipitation and potential evapotranspiration and led to soil moisture drought through various direct and indirect processes. This study highlighted that the interactions among precipitation, potential evapotranspiration, and atmospheric vapor movement state in space and time were important for understanding the drought development mechanisms. [ABSTRACT FROM AUTHOR]

• Continuous high spatial–temporal resolution inundation dataset was reconstructed. • Inundation dynamics of the lake show obvious spatiotemporal heterogeneity. • The formation mechanism of two hysteretic functions was conceptually generalized. • Seasonal floodplain lakes have crucial impact on the stage-area hysteresis. • Magnitude and direction of stage–area hysteretic relationship changes with time. The hysteretic relationship between the water level and the inundated area is one of the basic non-linear characteristics of lake hydrology. However, it is difficult to obtain this relationship accurately, especially for large floodplain lakes that exhibit time-varying boundaries with rapid water-level fluctuations. Taking the largest lake-floodplain system of the Yangtze River basin – Poyang Lake and its extremely productive wetland – as an example, we investigated the spatial–temporal variation of the stage-area hysteretic relationship in large heterogeneous lake-floodplain systems by adopting the Enhanced Spatial and Temporal Adaptive Reflection Fusion Model (ESTARFM) based on the observed water levels and reconstructed high spatial–temporal resolution inundation datasets using multi-source remote sensing data. The major results indicate that the inundation dynamics in the regions of the main lake and seasonal floodplain lakes are remarkably inconsistent. Concerning the inundation behavior of the river and lake-floodplain, a conceptual model was established to explain the formation mechanism of the counter-clockwise and clockwise stage-area hysteretic relationships in the Poyang lake–floodplain system. Further investigation revealed that seasonal lakes exist widely in floodplain settings and have a crucial impact on increasing the hysteresis of upstream stations and decreasing that of downstream stations. The magnitude and direction of the stage-area hysteretic relationships varied with time in a changing environment. This study extends the understanding of the complexity of hydrological behavior in large heterogeneous lake-floodplain systems, which is of vital importance for lake water resources and ecological management. [ABSTRACT FROM AUTHOR]

Design flood values give estimates of flood magnitude within a given return period and are essential to making adaptive decisions around land use planning, infrastructure design, and disaster mitigation. Many hydrologic applications where flood retention is important, e.g. floodplain management and reservoir design, need design flood values for different durations. Flood–Duration–Frequency (QDF) models extend the standard statistical flood frequency analysis framework to multiple flood durations and are analogous to intensity–duration–frequency models for precipitation. Implementations of QDF models commonly assume simple scaling, where only the magnitude of the index flood is assumed to change with duration, despite empirical analyses showing a more complex dependence structure. We propose a multiscaling extension to existing QDF models where the magnitude of the index flood and the slope of the growth curve may scale independently with duration. In an application to 12 locations in Norway, we assess how three different QDF models capture relationships between floods of different duration. Incorporating duration dependency independently in both the index flood and the growth curve (extended QDF model) improves modeling of both short-duration events and events with long return periods. This model extension further expands the models' ability to simultaneously model a wide range of durations. As measured by the integrated quadratic distance, the extended QDF model performs better than the original QDF model in 83% of the out of sample subdaily durations studied. Additionally, we find that the choice of durations used to fit QDF models is a highly influential aspect of the modeling process. • Flood–Duration–Frequency (QDF) models produce flood statistics at multiple durations. • We extend QDF models so that different quantiles can have different slopes. • This improves estimates and allows for a wider range of durations to be modeled. • Additionally, we introduce a Bayesian framework for QDF estimation. [ABSTRACT FROM AUTHOR]

Hydrological impact assessments are increasingly performed at fine spatial and temporal resolutions in order to resolve local-scale changes under a future climate. Apart from the uncertainty represented by different climate models, emission scenarios and post-processing methods, the local-scale internal variability of the climate can be a major source of uncertainty for hydrological projections. To assess the latter at the catchment scale, this paper presents a methodology which is particularly suitable for spatially distributed hydrological models. An ensemble of daily precipitation and daily mean temperature realizations on a high-resolution grid is simulated from stochastic weather generators (WGs) trained on historical data and equipped with climate change information obtained from a regional climate model. Based on the resulting simulated daily runoff data, the significance of changes in the runoff regime is assessed using a statistical hypothesis test, and the variability contributed by the two input variables is quantified using the analysis of variance (ANOVA). As a proof of concept, simulations on a 1-km grid over a period of 19 years are carried out for nine catchments in central Norway. Significant changes in runoff regimes are found, indicating that the trends introduced in the WGs are not overwhelmed by the local-scale internal variability. Variability in the runoff simulations varies substantially throughout the year; it is highest in periods with potential snowmelt and lowest during winter. Temperature is the dominant source of variability in the colder months (November–March) due to its influence on rainfall and snowmelt. High variability in May–June is contributed comparably by both temperature and precipitation. In summer and early autumn the runoff variability is precipitation dominated. The results are in line with findings in the literature where the runoff variability is driven by the large-scale internal climate variability. This indicates that ignoring the local-scale internal variability may yield an underestimation of the overall variability in runoff projections and projected changes. • Gridded climate input data simulated using stochastic weather generators. • Significance of changes in runoff regime assessed using a statistical test. • Runoff variability due to input variables quantified using ANOVA. • Results in line with findings based on large-scale internal climate variability. • Runoff projections need to consider local-scale internal variability of climate. [ABSTRACT FROM AUTHOR]

Climate change imposes profound influence on regional hydrological cycle and water security in many alpine regions worldwide. Investigating regional climate impacts using watershed scale hydrological models requires a large number of input data such as topography, meteorological and hydrological data. However, data scarcity in alpine regions seriously restricts evaluation of climate change impacts on water cycle using conventional approaches based on global or regional climate models, statistical downscaling methods and hydrological models. Therefore, this study is dedicated to development of a probabilistic model to replace the conventional approaches for streamflow projection. The probabilistic model was built upon an advanced Bayesian Neural Network (BNN) approach directly fed by the large-scale climate predictor variables and tested in a typical data sparse alpine region, the Kaidu River basin in Central Asia. Results show that BNN model performs better than the general methods across a number of statistical measures. The BNN method with flexible model structures by active indicator functions, which reduce the dependence on the initial specification for the input variables and the number of hidden units, can work well in a data limited region. Moreover, it can provide more reliable streamflow projections with a robust generalization ability. Forced by the latest bias-corrected GCM scenarios, streamflow projections for the 21st century under three RCP emission pathways were constructed and analyzed. Briefly, the proposed probabilistic projection approach could improve runoff predictive ability over conventional methods and provide better support to water resources planning and management under data limited conditions as well as enable a facilitated climate change impact analysis on runoff and water resources in alpine regions worldwide. [ABSTRACT FROM AUTHOR]

Uncertainty is recognized as a critical consideration for accurately predicting stream water nitrogen (N) loading, but identifying the relative contribution of individual uncertainty sources within the total uncertainty remains unclear. In this study, a powerful method, referred to as the Bayesian inference combined with analysis of variance (BayeANOVA) was adopted to detect the timing and magnitude of multiple uncertainty sources and their relative contributions to total uncertainty in simulating daily loadings of three stream water N species (ammonium-N: NH 4 + -N, nitrate-N: NO 3 − -N and total N: TN) in a rice agricultural watershed (the Tuojia watershed) as influenced by non-point source N pollution. Five sources of uncertainty have been analyzed in this study, which arise from model structure, parameters, inputs, interaction effects between parameters and inputs, and internal variability (induced by random errors of model or environment). The results show that uncertainty in parameters relating to the processes of both N and hydrologic cycles contributed the largest fractions of total uncertainty in N loading simulations (58.83%, 63.48% and 61.64% for NH 4 + -N, NO 3 − -N and TN loading, respectively). Additionally, three of the largest uncertainties (i.e. parameters, inputs and interaction effects) in all three simulated N loadings were on average significantly greater in the rice-growing season relative to the fallow season, primarily due to the excess fertilization application during the rice-growing season. The predicted TN uncertainty was mainly attributed to the inaccuracy of NO 3 − -N simulation, which contributed to 75.48% of predicted TN uncertainty. It is concluded that reducing the parameter uncertainty of NO 3 − -N loading simulation during the rice-growing season is the key factor to improving stream water N modeling precision in rice agricultural watersheds. [ABSTRACT FROM AUTHOR]

Uncertainty estimation of climate change impacts on hydrology has received much attention in the research community. The choice of a global climate model (GCM) is usually considered as the largest contributor to the uncertainty of climate change impacts. The temporal variation of GCM uncertainty needs to be investigated for making long-term decisions to deal with climate change. Accordingly, this study investigated the temporal variation (mainly long-term) of uncertainty related to the choice of a GCM in predicting climate change impacts on hydrology by using multi-GCMs over multiple continuous future periods. Specifically, twenty CMIP5 GCMs under RCP4.5 and RCP8.5 emission scenarios were adapted to adequately represent this uncertainty envelope, fifty-one 30-year future periods moving from 2021 to 2100 with 1-year interval were produced to express the temporal variation. Future climatic and hydrological regimes over all future periods were compared to those in the reference period (1971–2000) using a set of metrics, including mean and extremes. The periodicity of climatic and hydrological changes and their uncertainty were analyzed using wavelet analysis, while the trend was analyzed using Mann-Kendall trend test and regression analysis. The results showed that both future climate change (precipitation and temperature) and hydrological response predicted by the twenty GCMs were highly uncertain, and the uncertainty increased significantly over time. For example, the change of mean annual precipitation increased from 1.4% in 2021–2050 to 6.5% in 2071–2100 for RCP4.5 in terms of the median value of multi-models, but the projected uncertainty reached 21.7% in 2021–2050 and 25.1% in 2071–2100 for RCP4.5. The uncertainty under a high emission scenario (RCP8.5) was much larger than that under a relatively low emission scenario (RCP4.5). Almost all climatic and hydrological regimes and their uncertainty did not show significant periodicity at the P = .05 significance level, but their temporal variation could be well modeled by using the fourth-order polynomial. Overall, this study further emphasized the importance of using multiple GCMs for studying climate change impacts on hydrology. Furthermore, the temporal variation of uncertainty sourced from GCMs should be given more attention. [ABSTRACT FROM AUTHOR]

The Indicators of Hydrologic Alteration (IHA) based on 33 parameters in five dimensions (flow magnitude, timing, duration, frequency and change rate) have been widely used in evaluation of hydrologic alteration in river systems. Yet, inter-correlation seriously exists amongst those parameters, therefore constantly underestimates or overestimates actual hydrological changes. Toward the end, a new method (Representative-IHA, RIHA) is developed by removing repetitions based on Criteria Importance Through Intercriteria Correlation (CRITIC) algorithm. RIHA is testified in evaluating effects of future climate change on hydro-ecology in the Niger River of Africa. Future flows are projected using three watershed hydrological models forced by five general circulation models (GCMs) under three Representative Concentration Pathways (RCPs) scenarios. Results show that: (1) RIHA is able to eliminate self-correlations amongst IHA indicators and identify the dominant characteristics of hydrological alteration in the Upper Niger River, (2) March streamflow, September streamflow, December streamflow, 30-day annual maximum, low pluses duration and fall rates tends to increase over the period 2010–2099, while July streamflow and 90-day annual minimum streamflow shows decrease, (3) the Niger River will undergo moderate flow alteration under RCP8.5 in 2050s and 2080s and low alteration other scenarios, (4) future flow alteration may induce increase water temperatures, reduction dissolved oxygen and food resources. Consequently, aquatic biodiversity and fish community of Upper Niger River would become more vulnerable in the future. The new method enables more scientific evaluation for multi-dimensional hydrologic alteration under the context of climate change. [ABSTRACT FROM AUTHOR]

The hydrologic design of nonstationary flood extremes is an emerging field that is essential for water resources management and hydrologic engineering design to cope with changing environment. This paper aims to investigate and compare the capability of four nonstationary hydrologic design strategies, including the expected number of exceedances (ENE), design life level (DLL), equivalent reliability (ER), and average design life level (ADLL), with the last three methods taking into consideration the design life of the project. The confidence intervals of the calculated design floods were also estimated using the nonstationary bootstrap approach. A comparison of these four methods was performed using the annual maximum flood series (AMFS) of the Weihe River basin, Jinghe River basin, and Assunpink Creek basin. The results indicated that ENE, ER and ADLL yielded the same or very similar design values and confidence intervals for both increasing and decreasing trends of AMFS considered. DLL also yields similar design values if the relationship between DLL and ER/ADLL return periods is considered. Both ER and ADLL are recommended for practical use as they have associated design floods with the design life period of projects and yield reasonable design quantiles and confidence intervals. Furthermore, by assuming that the design results using either a stationary or nonstationary hydrologic design strategy should have the same reliability, the ER method enables us to solve the nonstationary hydrologic design problems by adopting the stationary design reliability, thus bridging the gap between stationary and nonstationary design criteria. [ABSTRACT FROM AUTHOR]

• A method of regional non-stationary flood frequency analysis is proposed. • The index incorporates climatic and reservoir factors as covariates. • MRI is more suitable for non-stationary flood frequency analysis. • The prediction period of flood frequency analysis is extended. Global climate change and reservoir regulations can alter the natural flow of rivers. Influenced by these two drivers, flood sequences may no longer satisfy the assumption of stationary, thereby making it difficult to accurately analysis flood frequency and to design water conservancy projects. Therefore, it is of great significance to analyse the non-stationary frequency of flood sequences in a changing environment. In this study, we proposed a method for conducting nonstationary flood frequency analysis caused by cascade reservoirs as well as the low-frequency climate indices. The proposed non-stationary model 2, with the explanatory variables of climate indices and modified reservoir index (MRI), was compared with the traditional stationary model and the widely used non-stationary model 1 with time as the explanatory variable. The study was conducted at six hydrological stations in the main stream and tributaries of the upper reaches of the Yangtze River in China (considered as the Three Gorges Reservoir Area). The results of the generalized additive model for location, scale and shape (GAMLSS) showed that the Akaike information criterion and Bayesian information criterion values of the proposed non-stationary model method 2 are smaller than those of the two comparison models. When the low-frequency South Oscillation Index is high or the Arctic Oscillation and North Pacific Oscillation are low, the stationary model underestimates the design value of flood quantiles compared with the non-stationary model 2. Compared with the non-stationary model 1, the MRI and low-frequency climate indices as the explanatory variables in model 2 can better describe the non-stationary characteristics of flood frequency and amplitude. In addition, the non-stationary model considering external physical factors can provide better prediction of future design flood compared with two traditional models. [ABSTRACT FROM AUTHOR]

[Display omitted] • Parameter σ 2 in MCMC approach is interpreted and estimated through statistical inference and theoretical analysis. • A new label called Confidence Level of Model (CLM) is developed to guide the estimation of parameter σ 2. • The natural logarithm of posterior probability distribution for NSCE is a first-order linear equation associated with CLM. • The MCMC method based on CLM performs well in generating posterior distributions and confidence intervals. Markov Chain Monte Carlo (MCMC) method has been increasingly popular in uncertainty analysis of hydrological simulation. In MCMC approach, deviations between model outputs and observations are commonly assumed to follow Gaussian distribution with zero medium and constant standard deviation σ 2. However, the estimation of σ 2 is a difficulty in terms of that it was assigned subjectively in previous studies, hindering the improvement of performance for uncertainty assessment. This work systemically investigates the statistical meaning of parameter σ 2. σ could be expressed as the product of data length and two standard deviations, one of which is for observations (i.e. σ Obs) and the other for Nash-Sutcliffe Coefficient of Efficiency (NSCE) (i.e. σ s). A new label called Confidence Level of Model (CLM) is developed to interpret σ s. The natural logarithm of the posterior probability distribution for NSCE is a first-order linear equation associated with CLM. The CLM could be employed to guide the construction of σ s and then the estimation of σ 2. Uncertainty analysis of a flow duration curve (FDC) model is conducted using the MCMC method based on CLM , and the generalized likelihood uncertainty estimation (GLUE) method is employed for comparison. Results show that the CLM affects the MCMC results by three kinds of trade-offs, and the MCMC method based on CLM performs well in generating regular posterior distributions of model parameters and discharges. The MCMC method also yields narrow and symmetrical confidence intervals. Findings of this paper could interpret typical uncertainty behaviors commonly existing in hydrological modeling, and provide beneficial insights for the uncertainty analysis of other environmental modeling. [ABSTRACT FROM AUTHOR]

• A multi-objective cluster-decomposition framework optimizes flood control operation. • Pareto-front solutions improve flood control operation of cascade reservoirs. • The proposed framework simultaneously copes with three flood control objectives. • The proposed framework offers compromised decisions to boost flood control synergies. Multi-objective flood control operation of cascade reservoirs is a vital issue in river basin management. However, traditional multi-objective approaches commonly provide one operation scheme only and fail to offer decision-makers with more Pareto-front options. This study explores a multi-objective cluster-decomposition framework for optimizing the flood control operation rules of cascade reservoirs in a river basin. The proposed framework involves a multi-objective optimization module, a cluster-decomposition module, and an evaluation and sorting module. The multi-objective cluster-decomposition framework simultaneously deals with three objectives: to minimize the flood peaks of flood control points (O1); to minimize the reservoir capacity used for flood control (O2); and to minimize the flood diversion volume of the flood detention area (O3). The complex flood control system composed of two cascade reservoirs, four navigation-power junctions, one flood detection area, and three flood control points in the Ganjiang River basin of China constitutes the case study. The results demonstrate that the proposed framework can significantly improve the comprehensive benefits of the cascade reservoirs, where the maximum reduction in objectives O1–O3 is 2071 m3/s (the improvement rate is 2.64 %), 219 million m3 (the improvement rate is 44.60 %), and 167 million m3 (the improvement rate is 78.13 %), respectively. Furthermore, in contrast to the traditional multi-attribute evaluation method, the proposed framework can effectively identify compromised decisions through a cluster-decomposition module, which provides beneficial trade-off guidance in making a sound decision upon Pareto-front options. [ABSTRACT FROM AUTHOR]

• A multi-objective cluster-decomposition framework optimizes flood control operation. • Pareto-front solutions improve flood control operation of cascade reservoirs. • The proposed framework simultaneously copes with three flood control objectives. • The proposed framework offers compromised decisions to boost flood control synergies. Multi-objective flood control operation of cascade reservoirs is a vital issue in river basin management. However, traditional multi-objective approaches commonly provide one operation scheme only and fail to offer decision-makers with more Pareto-front options. This study explores a multi-objective cluster-decomposition framework for optimizing the flood control operation rules of cascade reservoirs in a river basin. The proposed framework involves a multi-objective optimization module, a cluster-decomposition module, and an evaluation and sorting module. The multi-objective cluster-decomposition framework simultaneously deals with three objectives: to minimize the flood peaks of flood control points (O1); to minimize the reservoir capacity used for flood control (O2); and to minimize the flood diversion volume of the flood detention area (O3). The complex flood control system composed of two cascade reservoirs, four navigation-power junctions, one flood detection area, and three flood control points in the Ganjiang River basin of China constitutes the case study. The results demonstrate that the proposed framework can significantly improve the comprehensive benefits of the cascade reservoirs, where the maximum reduction in objectives O1–O3 is 2071 m3/s (the improvement rate is 2.64 %), 219 million m3 (the improvement rate is 44.60 %), and 167 million m3 (the improvement rate is 78.13 %), respectively. Furthermore, in contrast to the traditional multi-attribute evaluation method, the proposed framework can effectively identify compromised decisions through a cluster-decomposition module, which provides beneficial trade-off guidance in making a sound decision upon Pareto-front options. [ABSTRACT FROM AUTHOR]

• The most suitable ecological streamflow was calculated via kernel density estimation method. • Ecological drought was identified using the variable threshold method based on ecological streamflow. • Impacts of climate variability and human activities on ecological drought were quantified. • Human activities are the dominant factor aggravating the ecological drought in the Weihe River Basin. Although various studies have investigated the impacts of climate variability and human activities on drought, researches specifically analysing the impact on ecological drought are still limited. A deep understanding of the climatic and anthropogenic effects on ecological drought processes is crucial for ecological regulation and management in the changing environments. In the present study, an integrated approach for comprehensive understanding and quantification of ecological drought in rivers was proposed which first applied the nonparametric kernel density estimation (KDE) method to calculate the most suitable ecological streamflow (MSES) for a river ecosystem. Then, the variable threshold level method based on the MSES for each month and the run theory method were applied to identify the ecological drought duration and deficit volumes. Finally, a quantification approach based on hydrological model simulation was proposed to attribute the impacts of climate variability and human activities on ecological drought. The proposed approach was applied on two catchments, Xianyang (XY) and Huaxian (HX) within the Weihe River Basin (WRB) in northern China. Comparison results obtained using the two empirical methods revealed that the MSES calculated using the KDE method was reasonable and can be used for ecological drought identification. The identification results showed that both the median and upper quartile values of the drought duration and deficit volumes during the disturbed period (1991–2017) were greater than those during the undisturbed period (1961–1990). Quantification results showed that human activities were the dominant factor aggravating ecological drought in the WRB after 1990. The contribution rates of climate variability and human activities toward ecological drought variations were 25.6% and 74.4%, respectively, for the XY station and 42.7% and 57.3%, respectively, for the HX station. Although the WRB was selected as a case study, the proposed approach can also be applied to other regions to provide scientific guidance for regional ecological management. [ABSTRACT FROM AUTHOR]

• All GCMs except MIROC-ESM simulate a higher mean temperature in Medieval Climate Anomaly (MCA) period than Little Ice Age (LIA) period, while simulated annual precipitation varies a lot in different GCMs and catchments. • No significant change of flood characteristics during the last millennium from ensemble-mean results. • In future, we will have an extraordinary shift in flood seasonality and generating processes, and flood frequency increase in most of the study catchments in Norway. • Climate projections represent the largest contribution to overall uncertainty in the projected changes in hydrological extremes for most of the catchments. With the recent warming trend over Europe and the Arctic, the Nordic regions have experienced more frequent and damaging extreme hydrological events which are anticipated to increase towards the end of the 21st century. Despite explicit trends, large variations have been observed across basins and regions when it comes to precipitation and floods hinting at a strong natural hydroclimatic variability that further complicates any assessment of potential future changes. In this study, we aim to better understand how climate variability links with the current extremes and future projections of floods in Norway in the context of the last millennium and the future. Specifically, we simulate over 1000 years (850–2099) daily discharge and floods at 34 catchments over five regions of Norway from the last millennium (including a warm period and a cold period; 850–1849) to the end of the 21st century by an ensemble model-chain method including four global climate models (GCMs), two bias-correction methods and two hydrological models. The modelling results show (i) all GCMs except MIROC-ESM simulate a higher mean temperature in Medieval Climate Anomaly (MCA) period than Little Ice Age (LIA) period, while simulated annual precipitation varies a lot in different GCMs and catchments, (ii) no significant change of flood characteristics during the last millennium from ensemble-mean results, (iii) in future, we will have an extraordinary shift in flood seasonality and generating processes, and flood frequency increase in most of the study catchments in Norway, and (iv) climate projections represent the largest contribution to overall uncertainty in the projected changes in hydrological extremes for most of the catchments. [ABSTRACT FROM AUTHOR]

• A longer calibration period is required for catchments with nonstationary rainfall-runoff relationships to achieve stable simulations. • With the increase in the length of the interval between the calibration and transfer periods, the transferability of the model decreases gradually. • When forecasting runoff under nonstationary rainfall-runoff relationships, the closeness of the total rainfall amount between calibration and transfer periods is more important than the similarity in rainfall processes. Changing climatic conditions have changed the stationary rainfall-runoff relationships in many basins. In this context, the value of the model parameters will depend more on the selection of the calibration period, which directly affects the accuracy of runoff forecasting. However, systematic exploration and testing of the impact of calibration conditions on the transferability of hydrological models under stationary and nonstationary climatic conditions require more effort. The present study investigates the impact of four calibration conditions on model transferability, including the length of the calibration period, the length of the interval between the calibration and transfer periods, the difference in climate conditions as measured by the total rainfall amount between the calibration and transfer periods, and the difference in the similarity of rainfall processes between the calibration and transfer periods. Two catchments with stationary and nonstationary climatic conditions, and five models, including XAJ, HBV, IHACRES, SIMHYD and GR4J, are used in this study. The results show that (1) a longer calibration period is required for catchments with nonstationary rainfall-runoff relationships to achieve stable simulations; (2) with the increase in the length of the interval between the calibration and transfer periods, the transferability of the model decreases gradually, and the degree of reduction is greater for catchments with nonstationary climatic conditions and rainfall-runoff relationships; and (3) when forecasting future runoff under nonstationary rainfall-runoff relationships, the closeness of the total rainfall amount between the calibration and transfer periods is more important than the similarity in rainfall series between the calibration and transfer periods. This study provides insight into the impact of calibration conditions on the transferability of hydrological models in the context of climate change. [ABSTRACT FROM AUTHOR]

• Causes of the annual evapotranspiration (ET) and water yield trends over vegetated mainland China were investigated. • Vegetation and precipitation dominated ET trends over 55% and 32% of study area, respectively. • Human-distributed vegetation and natural vegetation combined explained water yield trends over 30% areas. • Vegetation (particularly for human-distributed vegetation) greening was crucial for the hydrological cycle changes. Quantifying the contributions of climate and vegetation to the dynamics of evapotranspiration (ET) and water yield (i.e., precipitation minus ET) will help us better understand the changes in the water budget. In this study, we identified the contributions of climate variables (including precipitation, radiation, temperature, and relative humidity), human-disturbed vegetation, and natural vegetation to the trends in annual ET and water yield over vegetated mainland China during 2001–2020, using a process-based terrestrial ecosystem model and a joint-solution method with multiple sensitivity numerical experiments. Results showed that 46% of the study area experienced significant (p <0.05) increases in ET, with an overall increase of 2.32 mm y−1. Meanwhile, the overall trend in water yield was 2.56 mm y−1 but insignificant. Spatially, vegetation and precipitation are the dominant factors for ET trends over 55% and 32% of vegetated mainland China, respectively. Over the regions where vegetation dominates the ET trends, nearly half of these regions are covered by human-disturbed vegetation (e.g., cropland or regions with land cover changes), suggesting that anthropogenic activities play a crucial role in the hydrological cycle there. Concerning the trends in water yield, precipitation is the dominant factor over 64% areas. Human-disturbed vegetation and natural vegetation play similar roles and combined can explain the water yield trends over 30% areas. Our study highlights the spatial variations in the mechanisms behind changes in the water budget over mainland China, particularly in regions covered by human-disturbed vegetation. This finding should be considered in the existing and future national ecological recovery policies to maximize its eco-hydrological benefits. [ABSTRACT FROM AUTHOR]

• Nonstationary models that incorporate climatic variables and reservoir index are constructed. • The nonstationary models provide a better fitting of nonstationary flood and low flow series. • Nonstationary frequency analysis of flood and low flow in upper of Huaihe River Basin are conducted. • Climatic and reservoir impacts are related to nonstationarity of flood and low flow respectively. Conventional water infrastructure designs for flood and low flows are usually based on the assumption of stationarity of extreme events. However, recent evidence suggests that the influences of climate variability and human activities have made the hypothesis of stationarity questionable. In this study, we used the generalized additive models for location, scale, and shape (GAMLSS) to construct a nonstationary model in which the parameters of the selected distributions were modelled as a function of climatic variables (i.e., climate indices and precipitation) and/or the reservoir index (RI). The nonstationary models were then used to analyse annual flood and low flow frequency at four hydrological stations in the upper reaches of the Huaihe River Basin, including Dapoling (DPL), Changtaiguan (CTG), Zhuganpu (ZGP), and Xixian (XX) stations. Annual floods were represented by the maximum daily streamflow in each year, and low flows were represented by the 95th quantile of the daily streamflow (Q 95) in each year. The change point and trend analysis revealed that the flood series of the ZGP station and the low flow series of the DPL and XX stations exhibited significant downward and upward trends (p < 0.1)), respectively. The low flow series of the ZGP station showed a significant change point in 1980 (p < 0.1). GAMLSS modelling results showed that, in comparison with stationary models, nonstationary models that included precipitation and the Arctic Oscillation climate index as covariates for the gamma distribution location parameter provided a superior description of the flood series at the four stations. Nonstationary models that incorporated precipitation and/or RI as covariates for the Weibull distribution parameters fit the low flow series better than stationary models at all stations. Furthermore, we found that nonstationary models outperformed stationary models in terms of flood frequency analysis, covering all flood observation points and capturing the generally decreasing trend in flood series, as well as a decrease in the scatter of estimated flood value magnitudes. For the low flow frequency analysis, the comparison results showed that the nonstationary and stationary models performed identically for the DPL, CTG, and XX stations, where no significant change point was detected. However, for the ZGP station, where a significant change point was detected, the nonstationary models performed better than the stationary models and could accurately capture the changes in the magnitude of the estimated low flow values before and after the change point. Overall, the proposed nonstationary model can serve as a tool for nonstationary frequency analysis of flood and low flow series under the influence of climate variability and reservoir regulations, thus providing a reference for regional water infrastructure design. [ABSTRACT FROM AUTHOR]

• Nonstationary models that incorporate climatic variables and reservoir index are constructed. • The nonstationary models provide a better fitting of nonstationary flood and low flow series. • Nonstationary frequency analysis of flood and low flow in upper of Huaihe River Basin are conducted. • Climatic and reservoir impacts are related to nonstationarity of flood and low flow respectively. Conventional water infrastructure designs for flood and low flows are usually based on the assumption of stationarity of extreme events. However, recent evidence suggests that the influences of climate variability and human activities have made the hypothesis of stationarity questionable. In this study, we used the generalized additive models for location, scale, and shape (GAMLSS) to construct a nonstationary model in which the parameters of the selected distributions were modelled as a function of climatic variables (i.e., climate indices and precipitation) and/or the reservoir index (RI). The nonstationary models were then used to analyse annual flood and low flow frequency at four hydrological stations in the upper reaches of the Huaihe River Basin, including Dapoling (DPL), Changtaiguan (CTG), Zhuganpu (ZGP), and Xixian (XX) stations. Annual floods were represented by the maximum daily streamflow in each year, and low flows were represented by the 95th quantile of the daily streamflow (Q 95) in each year. The change point and trend analysis revealed that the flood series of the ZGP station and the low flow series of the DPL and XX stations exhibited significant downward and upward trends (p < 0.1)), respectively. The low flow series of the ZGP station showed a significant change point in 1980 (p < 0.1). GAMLSS modelling results showed that, in comparison with stationary models, nonstationary models that included precipitation and the Arctic Oscillation climate index as covariates for the gamma distribution location parameter provided a superior description of the flood series at the four stations. Nonstationary models that incorporated precipitation and/or RI as covariates for the Weibull distribution parameters fit the low flow series better than stationary models at all stations. Furthermore, we found that nonstationary models outperformed stationary models in terms of flood frequency analysis, covering all flood observation points and capturing the generally decreasing trend in flood series, as well as a decrease in the scatter of estimated flood value magnitudes. For the low flow frequency analysis, the comparison results showed that the nonstationary and stationary models performed identically for the DPL, CTG, and XX stations, where no significant change point was detected. However, for the ZGP station, where a significant change point was detected, the nonstationary models performed better than the stationary models and could accurately capture the changes in the magnitude of the estimated low flow values before and after the change point. Overall, the proposed nonstationary model can serve as a tool for nonstationary frequency analysis of flood and low flow series under the influence of climate variability and reservoir regulations, thus providing a reference for regional water infrastructure design. [ABSTRACT FROM AUTHOR]

• We developed a modified drought severity index (MDSI) with a constrained optimization technique. • MDSI improves drought monitoring for forests and farmland. • Ecological restoration and cultivated land reclamation can help mitigate droughts, while urbanization can potentially intensify droughts. With advancement of remote sensing techniques, remote-sensing drought indices have been widely used for drought monitoring. However, the monitoring accuracy of a specific drought index regionally varies. Considering the deficiency of existing drought indices in reflecting vegetation growth, here we propose a Modified Drought Severity Index (MDSI) with local optimization method constrained by the inclusion of vegetation greenness, crop water shortage, canopy temperature, vegetation structure, and physiological status. We evaluated drought monitoring performance of MDSI across China, and detected high correlations between MDSI and soil moisture (SM), Standardized Precipitation Index at a 3-month scale (SPI-3), actual drought-affected areas (ADA), evidencing higher drought performance of MDSI when compared to 8 widely-used drought indices. Besides, MDSI performed better in monitoring agricultural drought. We found amplifying short-term drought intensity in the future. Ecological restoration and cultivated land reclamation can alleviate drought effects. However, urbanization can potentially intensify droughts. How to adapt human behavior to droughts is a challenging task. [ABSTRACT FROM AUTHOR]

Summary Homogenization and similarities of precipitation and flow regimes across China are thoroughly investigated using Gini coefficient analysis method and the Analysis Of Similarity (ANOSIM) technique, respectively based on daily precipitation data from 554 meteorological stations and monthly streamflow data from 370 hydrological stations covering the period of 1960–2000. The results indicate that: (1) Homogenization of precipitation regimes is increasing from the northwest to the southeast China. However, different spatial patterns of homogenization of flow regions are identified. Spatially, lower homogenization of flow regimes is detected in the northeast China and higher homogenization of flow regimes in the central and southeast China. Temporally, flow regimes during 1961–2000 are characterized mainly by increasing homogenization, and it is particularly true after 1980; (2) precipitation regimes during 1961–2000 are characterized by decreasing dissimilarities. Larger areas of China are characterized by decreasing dissimilarities of precipitation regimes during 1980–2000 when compared to those during 1961–1980, which should be due to increasing precipitation concentration and intensifying precipitation regimes in recent years; (3) distinctly dissimilar precipitation and flow regimes can be identified between geographically separate river basins. Interregional similarities of flow regimes are enhancing after 1980 when compared to those before 1980 though interregional similarities of precipitation regimes are not changed much; and (4) spatial mismatch is evident in terms of spatial range and changing degree of flow and precipitation regimes. Roughly spatial match can be observed between changes of flow and precipitation indicates and it is particularly the case for precipitation and flow changes in dry season such as winter in China. However, influences of human activities and precipitation changes on streamflow are varying as for specific river basins, such as the Yangtze and the Yellow River basins. Damming-induced fragmentation of river basins is the major cause behind higher homogenization of flow regimes. Thus, human interferences in hydrological processes via damming and construction of reservoirs greatly alter streamflow vs. precipitation relations. The results of this study provide theoretical and practical grounds for management and planning of water resources at basin or interbasin scales under the influences of human activities and climate changes. [ABSTRACT FROM AUTHOR]

Summary Reservoirs perform both flood control and integrated water resources development, in which the flood limited water level (FLWL) is the most significant parameter of tradeoff between flood control and conservation. This study was aimed at developing the varied seasonal FLWL to obtain more economic benefits without decreasing the original flood prevention standards. The Copula function was used to build the joint distribution of seasonal floods, which clarified the relationship between the frequencies of the seasonal flood quantiles and those of the annual maximum. A constraint was then established to meet the requirement that the total flood risk of the seasonal FLWL should be less than that of the original FLWL. The seasonal FLWL can optimally be determined because numerous schemes of seasonal design floods are able to satisfy a given flood prevention standard. As a result, a simulation-based optimization model was proposed to maximize multiple benefits, such as flood control, hydropower generation and navigation. Using the case study of the China’s Three Gorges Reservoir (TGR), the proposed method was demonstrated to provide an effective design for the seasonal FLWL, which decreases a slight FLWL for the main flood season to largely increase the FLWL of the pre-flood and post-flood seasons. The optimal designed seasonal FLWL scheme involves tradeoffs among flood control, hydropower generation and navigation, and enhancement of the economic benefits without increasing the flood risk. [ABSTRACT FROM AUTHOR]

• We evaluated soil moisture droughts with duration, magnitude and extremum in monsoon and non-monsoon regions. • We identified more evident impacts of anthropogenic forcing on soil moisture drought in monsoon region than in non-monsoon region. • We found larger impacts of anthropogenic forcing on drought magnitude, relative to drought duration and drought extremum. The spatio-temporal patterns of drought changes and relevant forcings are still open for debate, especially under global warming, even though agricultural drought has long been receiving increasing concern for food security and sustainable development. In this study, we depicted global spatiotemporal patterns of agricultural drought using the Soil Water Deficit Index (SWDI) and reflected on the underlying forcings using the optimal fingerprint method. Three aspects of droughts were analyzed, i.e. drought duration (DD), drought magnitude (DM) and drought extremum (DE) over three regions, i.e. global, monsoon and non-monsoon regions. We found distinct spatial heterogeneity of DD, DM and DE. However, DM (DE) had mainly a decreasing (increasing) tendency. Anthropogenic forcing ([ANT] including greenhouse gas, anthropogenic aerosol, and ozone) and greenhouse gas forcing (GHG) played a prominent role in driving drought changes and were followed by the combination of anthropogenic and natural forcing (ALL). Soil moisture drought (DD, DM and DE) responses to external forcing of ANT and GHG were detected more easily in the monsoon region than in the non-monsoon region. Specifically, DM changes due to ANT (2.58 per century) contributed 39.88% of the DM changes by ALL (6.47 per century) in the monsoon regions, comparatively, the GHG and ANT induced changes of DM in the non-monsoon regions were quite slight. This study further clarified the impacts of anthropogenic warming on agricultural drought over the globe. [ABSTRACT FROM AUTHOR]

• Multi-reservoir system's flood damage assessment incorporating CVaR is established. • Proposed optimal model deduce the flood storage combination scheme's feasible area. • The sensitivity of each reservoir to the system's flood loss has been analyzed. • Optimal model helps to trade-off between flood control and power generation benefits. The joint design and operation of multi-reservoir systems is a vital issue for reservoir management. Existing studies mostly focus on determining the optimal scheme by establishing an optimization model and relying on intelligent algorithms. However, the research on the mutual feedback mechanism between the flood control capacity of each reservoir has not been adequately addressed. The overall aim of this paper is to propose a new joint optimization method for design and operation of multi-reservoir systems considering the conditional value-at-risk (CVa R α). In the proposed method, the flood damage assessment with CVa R α for a multi-reservoir system is constructed firstly, and then the feasible flood storage combination scheme (FSCS) of reservoirs in the system is deduced. Finally, the tradeoffs between the hydropower generation benefit and flood damage loss have been analyzed. Selecting China's Ankang-Danjiangkou Cascade Reservoirs as a case study, the results indicate that (1) the CVa R α value is more sensitive to changes in the flood storage of Danjiangkou reservoir (larger and downstream) than that of Ankang reservoir (smaller and upstream); (2) the feasible area of the FSCSs can be described as a triangle, and the boundary of this feasible interval is determined by the allowable minimum flood storage (AMFS) value of each reservoir; and (3) the relationship between the hydropower generation benefit and the flood damage assessment index CVa R α is not monotonic. These findings are helpful for understanding the relationship between flood storage values of each reservoir from the perspective of the entire multi-reservoir system. [ABSTRACT FROM AUTHOR]