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• We proposed a new drought monitoring index; • We corroborated the applicability of this newly-proposed drought index; • We characterized spatial and temporal patterns of droughts across China. In this study, we proposed a new integrated remote sensing drought monitoring indices, i.e. Multiple Remote Sensing Drought Index integrated by Principal Component Analysis (PSDI), Multiple Remote Sensing Drought Index integrated by multiple linear regression (MRSDI) and Multiple Remote Sensing drought index integrated by gradient boosting method (GBMDI), based on the Precipitation Condition Index (PCI), Temperature Condition Index (TCI), Vegetation Condition Index (VCI), and Soil Moisture Condition Index (SMCI). The monitoring performance of PSDI, MRSDI and GBMDI was compared and verified based on the real-world observed droughts during 2002 to 2016. We also evidenced drought monitoring performance of the PSDI MRSDI and GBMDI by comparison between PSDI, MRSDI, GBMDI and SPEI, SPI and PDSI based on the in situ observed meteorological data. We found that the spatiotemporal characteristics of droughts monitored by the PSDI, MRSDI and GBMDI were generally in good agreement with those by the SPI and SPEI. The GBMDI performs better than PSDI and MRSDI in describing drought processes and spatial patterns of droughts of different drought intensities. Comparison between the real-world observed drought-affected croplands and those monitored by PSDI, MRSDI and GBMDI indicated better drought monitoring performance of GBMDI than PSDI and MRSDI in monitoring droughts across widespread drought-affected regions. Besides, the trend of GBMDI is in good agreement with standardized crop yield. Therefore GBMDI should be the first choice in drought monitoring practice. The GBMDI developed in this study can help to provide an alternative drought monitoring index for large-scale drought monitoring across China and also in other regions of the globe. [ABSTRACT FROM AUTHOR]

• Soil moisture dynamics were compared between four typical types of land use and land cover in a humid mountainous watershed. • The bamboo forest had the largest variability in soil moisture content. • The cumulative infiltration increased but the infiltration rate decreased with the rainfall grade. • The runoff coefficient during representative rainfall events varied with the LULC. Identifying soil moisture dynamics is critical for understanding watershed hydrological processes. Soil moisture responses to rainfall vary with land use and land covers (LULCs) and thus influence rainfall-runoff processes, however, the knowledge about how different LULCs affect such processes was less revealed, especially in humid areas. In this study, we investigated the characteristics of soil moisture content (SMC) and rainfall-related soil moisture responses under four typical LULCs, i.e., waxberry forest, farmland, sloping farmland, and bamboo forest, based on in-situ observations in a humid mountainous watershed. We then used the HYDRUS-1D model to analyze the patterns of the rainfall-infiltration processes and quantified corresponding runoff generation to reveal the partitioning between infiltration and runoff at the event scale. We found significant differences of the average SMC at multiple depths for four LULCs. The deep soil (80 cm) had the largest SMC for the waxberry forest, farmland, and sloping farmland, while the surface soil (10 cm) was the wettest soil layer for the bamboo forest. During the representative rainstorms, although soil moisture responses exhibited depth gradients, the earlier responses at some deep layers indicated the occurrence of preferential flow. Generally, the cumulative infiltration increased while the infiltration rate decreased with the rainfall grade. Farmland and bamboo forest showed the most and the least infiltration, respectively. These results suggested that both the rainfall properties and LULCs had a strong impact on the soil moisture responses. Meanwhile, the estimated average runoff coefficients of four typical LULCs increased with rainfall grade, and the sloping farmland generated the most runoff during the representative rainfall events. Our findings provide new insights into soil moisture dynamics and infiltration regimes under different LULCs in humid areas, which can contribute to hydrological modeling at the field-scale and regional water resource management. [ABSTRACT FROM AUTHOR]

• Four hydrological models calibrated with four objective functions are compared. • The Granger Ramanathan average variant C (GRC) method performs the best. • Using more than nine ensemble members does not further improve performance. • Combinations of models and objective functions are better than the single model and objective. • Averaging outperforms the ensemble members except in low-flow simulations. The multi-model combination is a technique to improve the performances of hydrological streamflow simulations. An area that has not been investigated much is the performance dependence of combination techniques on the hydrological model calibration strategy and ensemble size. This study aims at investigating the joint effect of the hydrological models, calibration strategies and ensemble sizes on combination abilities for selecting the most appropriate multi-model combination method. The ensemble members were constructed by applying four hydrological models and four objective functions over 383 catchments in China. The ensemble members were combined by using nine commonly used methods, which are Equal Weights (EWA), Akaike Information Criterion (AICA), Bayes Information Criterion (BICA), Bates and Granger (BGA), Granger Ramanathan A, B, and C (GRA, GRB, and GRC), Bayesian Model Averaging (BMA) and Multi-model Super Ensemble (MMSE). The GRC is found as the best multi-model combination method for hydrological simulations. Adding ensemble members by either multiple hydrological models or calibration strategies could help to improve the simulation abilities. Specifically, the increase of ensemble members can obviously enhance the performance of multi-model combinations when the ensemble size is less than six, while only limited improvement is achieved when the ensemble size is more than nine. The combination of ensemble members with various calibration strategies is hard to compensate for the weakness of hydrological model structures. As well, the application of a single calibration strategy in ensemble members only emphasizes single discharge periods and neglects other important discharge periods. This study found that various models with different objective functions are more robust and efficient. The combination performs better than any individual model in terms of Nash–Sutcliffe efficiency (NSE) for approximately 70% catchments, but the multi-model combination is less efficient in terms of low-flow simulations. [ABSTRACT FROM AUTHOR]

Drought is a natural disaster and its occurrence and development can be attributed to a range of driving factors, such as deficient precipitation, wind, temperature, land use/land cover changes, and so on. Therefore, attribution and monitoring of droughts are complicated and challenging. Standardized Precipitation Evapotranspiration Index (SPEI) has been widely used in meteorological drought monitoring. Nonstationarity has not been considered in SPEI-based drought monitoring. Here we propose a modified SPEI considering the nonstationarity of hydrometeorological processes, i.e. NSPEI hereafter. We observe nonstationarity SPEI in the Northeast, the Huang-Huai-Hai Plain, the Yangtze River Delta, and the Qinghai-Tibet Plateau. The goodness-of-fit can be determined for 76% of the total stations. Under the assumption of nonstationarity, the drought monitoring performance of the NSPEI is more robust than the traditional SPEI. Besides, we evaluate drought conditions across China under four shared socioeconomic pathways (SSPs), i.e. SSP126, SSP245, SSP370 and SSP585. We identify intensifying droughts in southwest China but a wetting tendency in northern China in winter under the SSP126 scenario; but a significant wetting tendency in southwest China and a drying tendency in northeast China under SSP245, SSP370 and SSP585 scenarios. This study provides a novel meteorological drought index and highlights a fresh picture of drought conditions across China under different SSPs. • A non-stationary SPEI index is developed. • The drought monitoring performance of the NSPEI is robust than the traditional SPEI. • We reevaluated drought conditions across China in future. [ABSTRACT FROM AUTHOR]

Flow direction algorithms have important application in attracting geomorphic features and topographic attributes, which serve as inputs for some hydrological and topographical models. Evaluating flow direction algorithms is of great significance and often conducted on synthetic surfaces instead of real digital elevation models for free of approximation errors. However, most widely-used synthetic surfaces are too simplified to represent complex topographical relief of real-world terrains. For this, this work applies a complex synthetic surface of modified Himmelblau's function (HF) to simulate sophisticated terrains encountered in real landscapes. HF surface is spatially smooth and continuous with four hilltops and one valley, where plan curvatures are clustered mainly from −0.1 to 0.1. In addition, a slope line-based discretization numerical (SLDN) approach is designed for obtaining numerical solution to theoretical total contributing area (TCA) on synthetic surfaces of non-integrable slope lines (e.g. HF surface). TCAs estimated by several flow direction algorithms are compared with SLDN-derived TCA quantitatively. Results indicate that the largest and smallest mean size errors are obtained by Random eight-node (Rho8) (i.e. 82.6 %) and Freeman multiple flow direction (FMFD) (i.e. 17.9 %), while the largest and smallest mean extent errors by Eight drainage directions (D8) (i.e. 128.2 %) and Eight drainage directions, least transversal deviation (D8-LTD) (i.e.55.4 %). Most mean errors are larger than 20.0 %, which may not be satisfactory in practice. This work can provide a reference for flow direction algorithms application in digital terrain analysis, and therefore improve accuracy of hydrological, geological and geomorphological models. [Display omitted] • Characteristics of the complex HF surface are comprehensively investigated. • An SLDN approach is used to obtain theoretical TCAs on complex synthetic surfaces. • D8-LTD and FMFD are respectively the best SFD and MFD algorithms on HF surface. [ABSTRACT FROM AUTHOR]

Understanding changes in human water utilization under the policy environment of the national top-down water unified allocation and regulation (TWAR) is crucial for promoting river health, sustainable resource utilization, and high-quality socioeconomic development along the whole Yellow River, a congenitally water-deficient river in China. This study assessed the TWAR policy effects on the total/sectoral/per capita water uses using the canonical difference-in-difference (DID) model, synthetic control method (SCM), and generalized synthetic control method (GSCM). The net effects of TWAR were assumed to be spatially heterogeneous and associated with variations in water-conserving efficiency, described by two indicators: water-conserving irrigation (WCI) and industrial recycled water (IRW). The suitable threshold intervals of WCI and IRW for benefiting decelerating water utilization were analyzed using a linear mediating effect (ME) model and panel smoothing transformation regression (PSTR) model, to explain the rebound effect in water use under the impact of the prevailing TWAR. The results show that TWAR initially had sustained positive effects on curbing water use growth, with the highest contribution to per capita agricultural water use in the lower section and high-quota region of the Yellow River Basin, but these effects decreased after a decade. Only when the WCI shifted within the threshold interval [0.283, 0.771] and the IRW did not exceed 0.962 could TWAR restrain water use. Emphasizing the conservation-oriented improvement of water use efficiency may necessitate extra attention to regions where water-conserving practices already reach high levels beyond the maximum WCI or IRW thresholds. These regions might be inclined to overutilize saved water to boost economic benefits and thereby potentially exacerbate the undesirable rebound effect from new uncontrolled water extraction. • TWAR policy effects on total/sectoral/per capita water uses were assessed. • Three quasi-natural experimental methods were employed: DID, SCM, and GSCM. • Water use initially slowed by TWAR may rebound after decadal periods. • TWAR's causality showed heterogeneity due to sectoral water-conserving efficiency. • Inhibiting role on water use was functional within suitable efficiency thresholds. [ABSTRACT FROM AUTHOR]

In this study, a spatiotemporal estimation method based on Funk singular value decomposition (F-SVD) that considers the spatiotemporal correlation of rainfall is proposed to improve estimations from gauge observations. Hourly rainfall data of several flood events are selected to verify the proposed method by comparing with Inverse Distance Weighting (IDW) and Ordinary Kriging (OK) in Hanjiang basin, China. The results show that (1) F-SVD has the best performance in rainfall estimation, the larger the amount of rainfall event, the greater the improvement of F-SVD method as compared to OK and IDW; (2) through the combination/integration with F-SVD, the accuracy of IDW and OK can be greatly improved. Therefore, F-SVD can be employed as a practical method to estimate rainfall spatial distribution, which is essential data for regional hydrological modelling and water resource analysis. • A spatiotemporal estimation method based on F-SVD is proposed to estimate rainfall using gauge observation. • F-SVD is utilized to decompose the spatiotemporal matrix consisted of rainfall data. • F-SVD has higher accuracy and lower uncertainty compared to OK and IDW. • Through combination with F-SVD, the accuracy of IDW and OK can be greatly improved. • It is a practical method to process data for regional hydrological modelling. [ABSTRACT FROM AUTHOR]

• 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]

[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]

• 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]

• 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]

• 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]

• A novel Encoder-Decoder with an Exogenous input (EDE) structure is proposed. • Four models are evaluated and compared from different perspectives. • The EDE structure is more suitable for long lead-time flood forecasting. • The LSTM-EDE model improves the multi-step-ahead flood forecasting accuracy. Accurate and reliable multi-step-ahead flood forecasting is beneficial for reservoir operation and water resources management. The Encoder-Decoder (ED) that can tackle sequence-to-sequence problems is suitable for multi-step-ahead flood forecasting. This study proposes a novel ED with an exogenous input (EDE) structure for multi-step-ahead flood forecasting. The exogenous input can be the outputs of process-based hydrological models. This study constructs four multi-step-ahead flood forecasting approaches, including the Xinanjiang (XAJ) hydrological model, the single-output long short-term memory (LSTM) neural network with recursive strategies, the recursive ED combined with the LSTM neural network (LSTM-RED), and the LSTM-EDE models. The performance of these four models is evaluated and compared by the long-term 3 h hydrologic data series of the Lushui and Jianxi basins in China. The results show that the LSTM-RED model that integrates recursive strategies into the training process of neural networks is more advantageous than the LSTM model. The proposed LSTM-EDE model can overcome the exposure bias problem, simplify its model structure, increase the computational efficiency in the validation process, and improve the multi-step-ahead flood forecasting accuracy, as compared to the LSTM-RED model. [ABSTRACT FROM AUTHOR]

• A photogrammetry-based method for river surface velocity estimation is proposed. • The general variational formulation is derived for the proposed method. • The proposed method has good performance in various surface velocity estimations. The ease of access to media resources and computational power has recently generated interest in using vision-based approaches for hydraulic monitoring. A key challenge for non-intrusive, image-based hydrology measurement methods is incorporating different hydraulic variables as prior knowledge with image information. We propose a photogrammetry-based method called L 1-Diffusion derived from the convection–diffusion equation commonly used in hydrodynamics with an additional regularization term to estimate the fluid motion field in the image plane, from which the free surface velocity can be further obtained using the photogrammetric projection relationship between the image plane and world coordinates. The inverse problem is used to discuss the relationship between the widely used space–time image velocimetry (STIV) and the proposed L 1-Diffusion. To validate the proposed method, unmanned aerial vehicle (UAV) images as well as in-situ acoustic Doppler current profiler (ADCP) experiments were carried out. Based on comparison results with the ADCP measurement and vision-based flow field estimation, the newly proposed L 1-Diffusion algorithm can accurately and efficiently estimate the free surface velocity of a river from the image sequences in a variety of scenarios. [ABSTRACT FROM AUTHOR]

• We propose a dynamic assessment of ecological vulnerabilityfrom three dimensions. • We compare ecological vulnerability across different ecosystems&different dimensions. • We explore the ecological response of the QTP through influencing factors analysis. At present, climate change has brought huge challenges to vegetation and ecosystems. As the Qinghai-Tibet Plateau (QTP) is a sensitive area of global climate change, the dynamic assessment of its ecological vulnerability is very important. In order to better quantify the relative size of the ecological vulnerability of the QTP, this study starts from the background characteristics and dynamic change process of the ecosystem, by fitting the vegetation index net primary productivity (NPP) and the temperature, precipitation and meteorological elements. The coefficients of autocorrelation multiple linear regression are used to construct an ecological vulnerability model from the three dimensions of "exposure-sensitivity-elasticity" to conduct a dynamic assessment of ecological vulnerability. Based on the evaluation results, from 2000 to 2015, the ecologically fragile areas were mainly distributed in the eastern and central areas of the QTP. The ecological fragility of the western region showed obvious discontinuities, with high and low vulnerabilities staggered. The three ecosystems of forest, grassland, and bare land have significant differences in their ecological vulnerability to climate change, showing a clear positive correlation in the three dimensions of exposure, sensitivity, and resilience, but in terms of the contribution rates of the three dimensions. The performance is relatively similar, and the relative relationship between the three dimensions is relatively balanced. The longitude and precipitation of the sample points have a greater impact on ecological vulnerability and its three dimensions, and the impact of precipitation on ecological vulnerability is more significant than that of temperature. This research provides theoretical support for plateau ecological conservation and ecological security under the influence of global climate. [ABSTRACT FROM AUTHOR]

• Machine learning assists hybrid model to promote flood forecasting and early warning. • Hybridizing MCQRNN with XAJ model for flood probability density forecasting. • XAJ-MCQRNN conquers overfitting and biased-prediction bottlenecks. • XAJ-MCQRNN improves accuracy and reliability of flood probability density forecasts. Making accurate and reliable probability density forecasts of flood processes is fundamentally challenging for machine learning techniques, especially when prediction targets are outside the range of training data. Conceptual hydrological models can reduce rainfall-runoff modelling errors with efficient quasi-physical mechanisms. The Monotone Composite Quantile Regression Neural Network (MCQRNN) is used for the first time to make probability density forecasts of flood processes and serves as a benchmark model, whereas it confronts the drawbacks of overfitting and biased-prediction. Here we propose an integrated model (i.e. XAJ-MCQRNN) that incorporates Xinanjiang conceptual model (XAJ) and MCQRNN to overcome the phenomena of error propagation and accumulation encountered in multi-step-ahead flood probability density forecasts. We consider flood forecasts as a function of rainfall factors and runoff data. The models are evaluated by long-term (2009–2015) 3-hour streamflow series of the Jianxi River catchment in China and rainfall products of the European Centre for Medium-Range Weather Forecasts. Results demonstrated that the proposed XAJ-MCQRNN model can not only outperform the MCQRNN model but also prominently enhance the accuracy and reliability of multi-step-ahead probability density forecasts of flood process. Regarding short-term forecasts in testing stages at four horizons, the XAJ-MCQRNN model achieved higher Nash-Sutcliffe Efficiency but lower Root Mean Square Error values, while improving Coverage Ratio and Relative Bandwidth values in comparison to the MCQRNN model. Consequently, the improvement can benefit the mitigation of the impacts associated with uncertainties of extreme flood and rainfall events as well as promote the accuracy and reliability of flood forecasting and early warning. [ABSTRACT FROM AUTHOR]

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

• Evaluated 14 regionalization methods with four hydrological models in 3444 catchments. • Keeping poorly calibrated gauged catchments is preferable in poorly gauged regions. • The rank of different regionalization methods is similar among different climatic regions and hydrological models. Over recent decades, hydrologists have proposed a variety of methods to predict discharge in ungauged catchments, and significant progress has been made in the field of hydrological model parameter regionalization. However, uncertainties from both hydrological models and regionalization methods make it a challenge to draw clear conclusions for some questions in regionalization (e.g., the best performing regionalization method, the optimal number of donor catchments, and the optimal efficiency threshold of donor catchments). In this study, for the first time, we made an attempt to address such questions in one paper through a comprehensive evaluation of model performance by using five regionalization methods with two weighting schemes, two averaging options, five efficiency thresholds, and four lumped hydrological models over a broad set of 3444 catchments under varying hydroclimatic conditions in North America. The results show that: (1) Spatial Proximity with the Inverse Distance Weighting method and the output average option (SPI-out) generally performs better than or comparable to other regionalization methods for different climate regions and hydrological models, while the global mean method performs the worst. (2) The rank of different regionalization methods is similar among different climatic regions and hydrological models. Compared to catchments with other climates, regionalization methods perform worst in the arid regions. (3) The selection of five donors is relatively efficient for distance/attributes-based regionalization approaches with the output averaging option disregarding the efficiency threshold. (4) The differences of median Kling-Gupta efficiency values among thresholds of "all", 0.6 and 0.7 are no more than 0.05 for each regionalization method. However, the regionalization performance significantly deteriorates from using efficiency thresholds of 0.7 to 0.9 due to the significant reduction of available donor catchments. Thus, the poorly calibrated catchments may need to be included in the regionalization process, especially when the number of catchments is insufficient. [ABSTRACT FROM AUTHOR]

• An improved Budyko framework was proposed to evaluate basin hydrologic resilience to adverse climate change; • All the16 basins except two on the Tibetan plateau have low hydrologic resilience; • Vegetation structure plays an important role in regulating the hydrologic resilience. Climate change has a significant impact on the runoff of basins in cold, dry areas. The quantification of regional ecohydrological responses to climate change such as warming and drought is essential for establishing proper water resource management schemes. We propose a simple and novel method based on the Budyko framework to evaluate the hydrologic resilience of 16 basins that conform the Asian Water Tower in the Tibetan Plateau (TP). Our method defines two metrics within the Budyko domain – tolerance (ψ) and plasticity (φ) – that characterize the hydrologic resilience of a basin. Based on an ecohydrological point of view, a basin is considered hydrologically resilient if ψ and φ are both greater than 1 or its φ is negative and ψ is greater than 1. Our results show that ψ varies between 0.27 and 0.74, with an average value of 0.45 and φ varies between 2 and 16.33, with an average value of 6.90, for 14 out of the 16 basins. Only two basins – Taohe and Datonghe – had negative φ (-11.67 and -8.11, respectively) and ψ greater than 1 (2.26 and 19.58, respectively), suggesting that these two are the only basins with a hydrologic resilience to climatic warming/drying in the TP. Within the non-resilient basins, we found vegetation to play a key role in the level of tolerance and plasticity indicating that basins with a larger vegetation cover display a lower capability to adapt to adverse climatic changes. Following these results, we call for afforestation efforts to be carefully considered in cold, dry areas. The proposed method and conclusions drawn by this study may help predict the hydrologic responses to future adverse climatic conditions. [ABSTRACT FROM AUTHOR]

• A comprehensive framework for quantifying the climatic and anthropogenic influences on river hydrological health is proposed. • Human activities significantly aggravate the degradation of river hydrological health for the studied catchment. • Widespread artificial water withdrawal and reservoir operation are the two crucial human activities. • Adequate rainfall maintains the river hydrological health at a good status in the 1990 s. Climate change and human activities have together altered the river hydrological regime and consequently threatened the health of river ecosystems. Quantifying the impact of climate change and human activities on river hydrological health regimes is essential for water resource management and river ecology protection. Although previous studies have analysed the hydrologic alterations using some indicators, separating effects of climate change and human activities on river hydrological health is needed for developing adaptive measures to protect the ecosystem of river basins. In this study, a comprehensive assessment framework for quantifying climatic and anthropogenic influences on river hydrological health variation was proposed. The framework consists of the following steps: (1) the reconstruction of natural river streamflow using the variable infiltration capacity (VIC) hydrological model, (2) calculation of river hydrological health through the ecological flow threshold method, and (3) quantification of the impacts of climate change and human activities on river hydrological health using the 'observed–simulated' comparison approach. The semi-arid Laohahe Basin in northern China, which consists of three human-influenced catchments (Taipingzhuang, Chifeng, and Xinglongpo) and one natural catchment (Xiquan), was selected as the case study area. The case study demonstrated that the proposed procedure is efficient in quantifying climatic and anthropogenic influences on river hydrological health. The results revealed that the hydrological health level has significantly declined in the three human-influenced catchments for the human-influenced period (1980–2016), particularly in the 2000 s and 2010 s, where it degraded much more severely. Whereas, the relatively adequate rainfall in the 1990 s maintained the river hydrological health at a good status. The quantitative evaluation showed that human activities were the main driving factors for the hydrological health degradation during the whole human-influenced period, with contributions of 80.8%, 91.9%, and 86.0% for the Taipingzhuang, Chifeng, and Xinglongpo catchments, respectively. Widespread artificial water withdrawal and reservoir operation were the two crucial human activities that caused the degradation of river hydrological health for the studied catchment. The proposed procedure and findings of this study not only help in deeper understanding of the evolutionary characteristics and driving mechanisms of river hydrological health in a changing environment in general, but also provide scientific basis for local water resources management and river ecosystems protection. [ABSTRACT FROM AUTHOR]