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While reliable drought prediction is fundamental for drought mitigation and water resources management, it is still a challenge to develop robust drought prediction models due to complex local hydro‐climatic conditions and various predictors. Sea surface temperature (SST) is considered as the fundamental predictor to develop drought prediction models. However, traditional models usually extract SST signals from one or several specific sea zones within a given time span, which limits full use of SST signals for drought prediction. Here, we introduce a new meteorological drought prediction approach by using the antecedent SST fluctuation pattern (ASFP) and machine learning techniques (e.g., support vector regression (SVR), random forest (RF), and extreme learning machine (ELM)). Three models (i.e., ASFP‐SVR, ASFP‐ELM, and ASFP‐RF) are developed for ensemble, probability, and deterministic drought predictions. The Colorado, Danube, Orange, and Pearl River basins with frequent droughts over different continents are selected, as the cases, where standardized precipitation evapotranspiration index (SPEI) are predicted at the 1° × 1° resolution with 1‐ and 3‐month lead times. Results show that the ASFP‐ELM model can effectively predict space‐time evolutions of drought events with satisfactory skills, outperforming the ASFP‐SVR and ASFP‐RF models. Our study has potential to provide a reliable tool for drought prediction, which further supports the development of drought early warning systems. Key Points: Antecedent sea surface temperature fluctuations are introduced as predictors for meteorological drought predictionExtreme learning machine predicts droughts more effectively than support vector regression and random forestThe new approach allows for ensemble, probability, and deterministic drought predictions [ABSTRACT FROM AUTHOR]

Previous studies have shown that the seasonal dynamics of model parameters can compensate for structural defects of hydrological models and improve the accuracy and robustness of the streamflow forecast to some extent. However, some fundamental issues for improving model performance with seasonal dynamic parameters still need to be addressed. In this regard, this study is dedicated to (1) proposing a novel framework for seasonal variations of hydrological model parameters to improve model performance and (2) expanding the discussion on model results and the response of seasonal dynamic parameters to dynamic characteristics of catchments. The procedure of the framework is developed with (1) extraction of the dynamic catchment characteristics using current data-mining techniques, (2) subperiod calibration operations for seasonal dynamic parameters, considering the effects of the significant correlation between the parameters, the number of multiplying parameters, and the temporal memory in the model states in two adjacent subperiods on calibration operations, and (3) multi-metric assessment of model performance designed for various flow phases. The main finding is that (1) the proposed framework significantly improved the accuracy and robustness of the model; (2) however, there was a generally poor response of the seasonal dynamic parameter set to catchment dynamics. Namely, the dynamic changes in parameters did not follow the dynamics of catchment characteristics. Hence, we deepen the discussion on the poor response in terms of (1) the evolutionary processes of seasonal dynamic parameters optimized by global optimization, considering that the possible failure in finding the global optimum might lead to unreasonable seasonal dynamic parameter values. Moreover, a practical tool for visualizing the evolutionary processes of seasonal dynamic parameters was designed using geometry visualization techniques. (2) We also discuss the strong correlation between parameters considering that dynamic changes in one parameter might be interfered with by other parameters due to their interdependence. Consequently, the poor response of the seasonal dynamic parameter set to dynamic catchment characteristics may be attributed in part to the possible failure in finding the global optimum and strong correlation between parameters. Further analysis also revealed that even though individual parameters cannot respond well to dynamic catchment characteristics, a dynamic parameter set could carry the information extracted from dynamic catchment characteristics and improve the model performance. [ABSTRACT FROM AUTHOR]

Understanding the propagation of prolonged meteorological drought helps solve the problem of intensified water scarcity around the world. Most of the existing literature studied the propagation of drought from one type to another (e.g., from meteorological to hydrological drought) with statistical approaches; there remains difficulty in revealing the causality between meteorological drought and potential changes in the catchment water storage capacity (CWSC). This study aims to identify the response of the CWSC to the meteorological drought by examining the changes of hydrological-model parameters after drought events. Firstly, the temporal variation of a model parameter that denotes that the CWSC is estimated to reflect the potential changes in the real CWSC. Next, the change points of the CWSC parameter were determined based on the Bayesian change point analysis. Finally, the possible association and linkage between the shift in the CWSC and the time lag of the catchment (i.e., time lag between the onset of the drought and the change point) with multiple catchment properties and climate characteristics were identified. A total of 83 catchments from southeastern Australia were selected as the study areas. Results indicated that (1) significant shifts in the CWSC can be observed in 62.7 % of the catchments, which can be divided into two subgroups with the opposite response, i.e., 48.2 % of catchments had lower runoff generation rates, while 14.5 % of catchments had higher runoff generation rate; (2) the increase in the CWSC during a chronic drought can be observed in smaller catchments with lower elevation, slope and forest coverage of evergreen broadleaf forest, while the decrease in the CWSC can be observed in larger catchments with higher elevation and larger coverage of evergreen broadleaf forest; (3) catchments with a lower proportion of evergreen broadleaf forest usually have a longer time lag and are more resilient. This study improves our understanding of possible changes in the CWSC induced by a prolonged meteorological drought, which will help improve our ability to simulate the hydrological system under climate change. [ABSTRACT FROM AUTHOR]

Recent events across many regions around the world have shown that short-term droughts (i.e., daily or weekly) with sudden occurrence can lead to huge losses to a wide array of environmental and societal sectors. However, the most commonly used drought indices can only identify drought at the monthly scale. Here, we introduced a daily scale drought index, that is, the standardized antecedent precipitation evapotranspiration index (SAPEI) that utilizes precipitation and potential evapotranspiration and also considers the effect of early water balance on dry/wet conditions on the current day. The robustness of SAPEI is first assessed through comparison with two typical monthly indices [Palmer drought severity index (PDSI) and standardized precipitation evapotranspiration index (SPEI)] and soil moisture, and then applied to tracking short-term droughts during 1961–2015 for the Pearl River basin in south China. It is demonstrated that SAPEI performs as well as SPEI/self-calibrating PDSI at the monthly scale but outperforms SPEI at the weekly scale. Moreover, SAPEI is capable of revealing daily drought conditions, fairly consistent with soil moisture changes. Results also show that many of the historical short-term droughts over the Pearl River basin have multiple peaks in terms of severity, affected area, and intensity. The daily scale SAPEI provides an effective way of exploring drought initiation, development, and decay, which could be conducive for decision-makers and stakeholders to make early and timely warnings. [ABSTRACT FROM AUTHOR]

The Himalayan Mountains are the source region of one of the world's largest supplies of freshwater. The changes in glacier melt may lead to droughts as well as floods in the Himalayan basins, which are vulnerable to hydrological changes. This study used an integrated glacio-hydrological model, the Glacier and Snow Melt – WASMOD model (GSM-WASMOD), for hydrological projections under 21st century climate change by two ensembles of four global climate models (GCMs) under two Representative Concentration Pathways (RCP4.5 and RCP8.5) and two bias-correction methods (i.e., the daily bias correction (DBC) and the local intensity scaling (LOCI)) in order to assess the future hydrological changes in the Himalayan Beas basin up to Pandoh Dam (upper Beas basin). Besides, the glacier extent loss during the 21st century was also investigated as part of the glacio-hydrological modeling as an ensemble simulation. In addition, a high-resolution WRF precipitation dataset suggested much heavier winter precipitation over the high-altitude ungauged area, which was used for precipitation correction in the study. The glacio-hydrological modeling shows that the glacier ablation accounted for about 5 % of the annual total runoff during 1986–2004 in this area. Under climate change, the temperature will increase by 1.8–2.8 ∘ C at the middle of the century (2046–2065), and by 2.3–5.4 ∘ C until the end of the century (2080–2099). It is very likely that the upper Beas basin will get warmer and wetter compared to the historical period. In this study, the glacier extent in the upper Beas basin is projected to decrease over the range of 63 %–87 % by the middle of the century and 89 %–100 % at the end of the century compared to the glacier extent in 2005. This loss in glacier area will in general result in a reduction in glacier discharge in the future, while the future streamflow is most likely to have a slight increase because of the increase in both precipitation and temperature under all the scenarios. However, there is widespread uncertainty regarding the changes in total discharge in the future, including the seasonality and magnitude. In general, the largest increase in river total discharge also has the largest spread. The uncertainty in future hydrological change is not only from GCMs, but also from the bias-correction methods and hydrological modeling. A decrease in discharge is found in July from DBC, while it is opposite for LOCI. Besides, there is a decrease in evaporation in September from DBC, which cannot be seen from LOCI. The study helps to understand the hydrological impacts of climate change in northern India and contributes to stakeholder and policymaker engagement in the management of future water resources in northern India. [ABSTRACT FROM AUTHOR]

Considering rapid increases in the number of climate model simulations being produced by modelling centres, it is often infeasible to use all of them in climate change impact studies. In order to thoughtfully select subsets of climate simulations from a large ensemble, several envelope-based methods have been proposed. The subsets are expected to cover a similar uncertainty envelope to the full ensemble in terms of climate variables. However, it is not a given that the uncertainty in hydrological impacts will be similarly well represented. Therefore, this study investigates the transferability of climate uncertainty related to the choice of climate simulations to hydrological impacts. Two envelope-based selection methods, K means clustering and the Katsavounidis--Kuo--Zhang (KKZ) method, are used to select subsets from an ensemble of 50 climate simulations over two watersheds with very different climates using 31 precipitation and temperature variables. Transferability is evaluated by comparing uncertainty coverage between climate variables and 17 hydrological variables simulated by a hydrological model. The importance of choosing climate variables properly when selecting subsets is investigated by including and excluding temperature variables. Results show that KKZ performs better than K means at selecting subsets of climate simulations for hydrological impacts, and the uncertainty coverage of climate variables is similar to that of hydrological variables. The subset of the first 10 simulations covers over 85% of total uncertainty. As expected, temperature variables are important for the snow-related watershed, but less important for the rainfall-driven watershed. Overall, envelope-based selection of around 10 climate simulations, based on climate variables that characterize the physical processes controlling the hydrology of the watershed, is recommended for hydrological impact studies. [ABSTRACT FROM AUTHOR]

Copyright of Hydrological Sciences Journal/Journal des Sciences Hydrologiques is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Hydrological Sciences Journal/Journal des Sciences Hydrologiques is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Hydrological Sciences Journal/Journal des Sciences Hydrologiques is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Levee breaches are some of the most common hazards in the world and cause the loss of lives, livelihoods, and property destruction. During the 2010 flood in Pakistan, the most devastating breach occurred at Tori Levee on the right bank of the Indus River, downstream of the Guddu Barrage, which caused residual floods in northern Sindh and the adjoining regions of the Balochistan province. In this study, 2D unsteady flow modeling performed for Tori Levee breach computed residual flood inundation by coupling a HEC-RAS (Hydrological Engineering Centre—River Analysis System) 2D hydraulic model with remote sensing and Geographic Information System techniques. The model performance was judged by comparing the observed and simulated water levels (stage) during peak flow at seven different gauging stations located within the Indus River reach and daily flood extents and multi-day composites. The quantitative values for the calibration and validation of the HEC-RAS model showed good performance with a range of difference from 0.13 to −0.54 m between the simulated and observed water levels (stage), 84% match for the maximum flood inundation area, and 73.2% for the measure of fit. The overall averages of these values for the daily flood comparison were 57.12 and 75%, respectively. Furthermore, the simulated maximum flow passed through the Tori Levee breach, which was found to be 4994.47 cumecs (about 15% of peak flow) with a head water stage of 71.56 m. By using the simulated flows through the Tori Levee breach, the flood risk maps for the 2010 flood identified hazard zones according to the flood characteristics (depth, velocity, depth times velocity, arrival time, and duration). All the flood risk maps concluded the fact that the active flood plain was uninhabitable under flood conditions. [ABSTRACT FROM AUTHOR]

Knowledge of vegetation dynamics in relation to climatic changes and human activities is essential for addressing the terrestrial carbon cycle in the context of global warming. Scientific detection and quantitative attribution of vegetation dynamic changes in different climatic zones and human activities are the focus and challenge of the relevant research. Taking the Poyang Lake basin as the research area, this study aimed to reveal how climate and land use drive changes in net primary productivity (NPP) in the subtropical humid basin. Change patterns of vegetation NPP and their relationships with meteorological factors across the basin were first investigated based on the estimation of 18 year (2000–2017 year) NPP by using a typical light energy utilization model, the Carnegie-Ames-Stanford Approach (CASA) model. Quantitative analysis was then conducted to explicitly distinguish the driving effects of climate change and land-use change on NPP dynamics in two different periods. Results show that annual NPP and total production (TP) of the Poyang Lake basin increased significantly from 2000 to 2017. During this period, land-use change in the basin was driven by the process of urbanization expansion and the efforts of ecological protection. Climatically, the temperature is the major influencing climatic factor in determining vegetation productivity in the subtropical humid basin, followed by precipitation and solar radiation. In addition, our investigation also revealed that with comparison to the period of 2000s, the increased TP of the Poyang Lake basin due to climate change in 2010s was much bigger than the decreased TP due to land-use change. However, in the areas where the land-use change occurred, the decreased TP was mainly attributed to the impact of land-use change, even though climate change showed a positive effect of increasing productivity. [ABSTRACT FROM AUTHOR]

Characterized by increasing surface air temperature, global warming has altered the hydrological cycle at global and regional scales. In order to adapt water resources management under the context of global warming, attribution analysis on regional differentiation of water resources in the Yangtze River basin (YRB) was conducted in this study. Meteoro-hydrological variations across the basin were examined for the period 1960–2013, and then a statistic-based method was applied in quantifying the contributions of climate variability and human activities on annual runoff variations in different tributary sub-basins in the YRB. Our observation indicates that both the annual increasing rate and the inter-annual fluctuations of temperature in China and in the YRB were higher than that of the global average since the turn of the century. Climate change analysis indicates that the YRB experienced a hot-wet period during 1994–2002 and a hot-dry period during 2003–2013, since the step change of temperature in 1993. Contributions of climate change and human activities on runoff variations varied spatially in the basin. With reference to the baseline period of 1960–1993, the contribution of climate change played a dominant role in most regions of the basin, especially in those upstream sub-basins. The effect of human activities in the basin was quite complicated, not only its regional differentiation, but also its contribution were opposite during the hot-wet period and the hot-dry period in some sub-basins. The result of this study is helpful in understanding the impacts of climate change and human activities on water resources variation in both temporal and spatial scales. [ABSTRACT FROM AUTHOR]

The impacts of climate change on water resources in snow- and glacier-dominated basins are of great importance for water resource management. The Snowmelt Runoff Model (SRM) was developed to simulate and predict daily streamflow for high mountain basins where snowmelt runoff is a major contributor. However, there are many sources of uncertainty when using an SRM for hydrological simulations, such as low-quality input data, imperfect model structure and model parameters, and uncertainty from climate scenarios. Among these, the identification of model parameters is considered to be one of the major sources of uncertainty. This study evaluates the parameter uncertainty for SRM simulation based on different calibration strategies, as well as its impact on future hydrological projections in a data-scarce deglaciating river basin. The generalized likelihood uncertainty estimation (GLUE) method implemented by Monte Carlo sampling was used to estimate the model uncertainty arising from parameters calibrated by means of different strategies. Future snowmelt runoff projections under climate change impacts in the middle of the century and their uncertainty were assessed using average annual hydrographs, annual discharge and flow duration curves as the evaluation criteria. The results show that: (1) the strategy with a division of one or two sub-period(s) in a hydrological year is more appropriate for SRM calibration, and is also more rational for hydrological climate change impact assessment; (2) the multi-year calibration strategy is also more stable; and (3) the future runoff projection contains a large amount of uncertainty, among which parameter uncertainty plays a significant role. The projections also indicate that the onset of snowmelt runoff is likely to shift earlier in the year, and the discharge over the snowmelt season is projected to increase. Overall, this study emphasizes the importance of considering the parameter uncertainty of time-varying hydrological processes in hydrological modelling and climate change impact assessment. [ABSTRACT FROM AUTHOR]

Net Primary Productivity (NPP) is a basis of material and energy flows in terrestrial ecosystems, and it is also an important component in the research on carbon cycle and carbon budget. This paper evaluated the spatial distribution pattern and temporal change trends for forest NPP simulated by the LPJ (Lund-Potsdam-Jena) model and NDVI (normalized difference vegetation index) in the Yangtze River basin from 1982 to 2013. The results revealed that: (1) the spatial distribution of the forest NPP and NDVI in the Yangtze River basin has gradually decreased from the southeast coast to the northwest. The forest NPP and NDVI in the mid-lower Yangtze were higher than that of the upper Yangtze; (2) the forest NPP and NDVI in most areas of the Yangtze River basin were positively correlated with the temperature and precipitation. Moreover, the correlations among the temperature with the forest NPP and NDVI were stronger than that of correlations among precipitation with forest NPP and NDVI. Moreover, the extreme drought event in the year of 2004–2005 led the NPP to decrease in the middle and lower Yangtze River basin; (3) human activity such as major ecological projects would have a certain impact on the NPP and NDVI. The increase in forest areas from 2000 to 2010 was larger than that from 1990 to 2000. Moreover, the increasing rate for the NDVI was higher than that of NPP, especially after the year 2000, which indicates that the major ecological projects might have great impacts on the vegetation dynamics. Moreover, more attention should be paid on the joint impacts of human activity and climate change on terrestrial NPP and NDVI. [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]

Non-stationary hydrological sequences and rainfall-runoff relationship in a changingenvironment have brought new challenges to the application of hydrological models. Robusthydrological models are more reliable to evaluate the impact of changing environment onwater resources at the catchment scale. However, robustness differences in hydrologicalresponses to changing environment resulting from the use of different hydrological modelshave received much less attention. Therefore, this study compares the robustness of sixconceptual hydrological models (Xinanjiang, HBV, SIMHYD, IHACRES, GR4J andWASMOD) in different test scenarios in the Yellow River Basin and the Yangtze River Basinof China. The hierarchical scheme for systematic testing of hydrological models according tothe modelling tasks proposed by Klemes (1986) and 48-year long records of daily runoff andclimate are used in this study. Firstly, as the least challenging test in hierarchical scheme, thesimple split-sample test is performed for testing models’ ability to reproduce historicalrunoff under stationary conditions. Secondly, the proxy-basin test is carried out as abasic test for geographical transferability. In order to avoid the influence causedby the climate change, the single years of a basin are calibrated and the doubleyears of other basins are validated. Thirdly, the climate comparison periods (thedriest and wettest five years in a row) are selected as calibration and validationperiods. The differential split-sample test and proxy-basin differential split-sampletest are executed when calibration and validation occur in the same basin and indifferent basins respectively. By comparing the performance of different models in fourtests, more stable models can be selected and used in future studies. This study canprovide useful information for model selection and increase the credibility of modelsabout the research of hydrological impacts produced by changing environment. [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]