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• Parameter uncertainty varies over time scales due to land use change (LUC). • The calibrated daily runoff model is deficient for the extreme flow changes. • Model structure impacts the extreme runoff direction change for moderate LUC. Hydrological modeling has provided key insights into the mechanisms of land-use change impacts on runoff. However, the uncertainty of this impact is poorly understood. This paper examines the uncertainty originated from hydrological models' parameters and structure in simulating hydrological responses to land use change on daily, monthly and annual time scales. Two hydrological models, SWAT and HSPF, were applied to simulate the runoff response in the Xitiaoxi basin of eastern China. The effects of three land use scenarios (in 1985, 2002 and 2014) were analyzed with respect to the expansion of urban areas and reduction of cropland and forests. The changes in streamflow with multi-timescales between two models were compared at the whole basin and the sub-basin scales. The parameter uncertainty was estimated based on a sequential uncertainty analysis method (SUFI-2). The results indicate that the two models (SWAT and HSPF) could reproduce the observed daily and seasonal flows well, but they cannot accurately reconstruct the extreme flows (such as annual 7-day minimum discharge, R_7dMIN). The effect of parameter uncertainty on streamflow varies over time scales. The simulated annual and monthly runoff change values show large discrepancies but with same trends. The simulated changes for annual maximum discharge (R_1dMAX) and R_7dMIN often show different signs. The average width of the predictive interval in the relative change of different flow characteristics exceeded 100% of the average relative changes for both models. HSPF tends to present larger relative changes for the annual and monthly runoffs compared to SWAT. Moreover, the model choice could affect the direction change in R_1dMAX and R_7dMIN. However, when land use change was significant to a certain degree, both models simulated increased R_1dMAX and decreased R_7dMIN, but with different significance levels. The study suggests that the model structure represents an additional uncertainty, which should be accounted for in the land-use change impact assessment. [ABSTRACT FROM AUTHOR]

As one of the fastest developing regions in China, the middle-lower Yangtze River (MLYR) is vulnerable to floods and droughts. With obtained time series of annual highest water level (HWL), annual lowest water level (LWL) and the corresponding river discharges from three gauging stations in MLYR that covering the period 1987-2011, the current study evaluated the change characteristics of annual extreme water levels and the correlation with river discharges by using the methods of trend test, Mann-Whitney-Pettitt (MWP) test and double mass analysis. Major result indicated a decreasing/increasing trend for annual HWL/LWL of all stations in MLYR during the study period. A change point in 1999 was identified for annual HWL at the Hankou and Datong stations. The year 2006 was found to be the critical year that the relationship between annual extreme water levels and river discharges changed in the MLYR. With contrast to annual LWL in MLYR, further investigation revealed that the change characteristics of annual HWL were highly consistent with regional precipitation in the Yangtze River Basin, while the linkage with Three Gorges Dam (TGD) operation is not strong. Our observation also pointed out that the effect of serious down cutting of the riverbed and the enlargement of the cross-section area during the initial period of TGD operation caused the downward trend of the relationship between annual LWL and river discharge. Whereas, the relatively raised river water level before the flood season due to TGD regulation since 2006 explained for the changing upward trend of the relationship between annual HWL and river discharge. [ABSTRACT FROM AUTHOR]

Different conclusions have been drawn in literature as using reanalysis rainfall products to drive hydrological models for the simulation of streamflow, which warrant a need of further investigation before a generalised conclusion can be drawn. This paper assesses the utility of two widely used reanalysis rainfall datasets (WFD (developed by the WATCH project) and APHRODITE (Asian Precipitation–Highly-Resolved Observational Data Integration Towards Evaluation of the water resources)) against the gauged rainfall in terms of flood simulation in lumped (Xinanjiang Model) and distributed (SWAT Model) hydrological models in a tributary basin of Yangtze River with 94,660 km 2 drainage area in humid region of southern China. Differences in terms of rainfall accumulation, number of rainy days, spatial patterns of the rainfall amount and frequency distribution of the rain rates are evaluated. The APHRODITE product shows high consistency with the gauged rainfall while WFD data gives large errors in various statistical indices in the study region. Simulated discharges from the gauged and reanalysis rainfall data, respectively, are analysed and compared with the observed discharge in basin outlet over the period 1991–2005. The APHRODITE data show relatively high ability in modelling hydrological responses while the WFD data based models give large error in simulating the discharge. For the assessment of the high flows, both datasets exhibit some skills in flood prediction, however, APHRODITE data perform better than the WFD data when forcing into the lumped Xinanjiang Model than into the distributed SWAT Model in terms of flood duration, Probability of Detection, False Alarm Rate, regressions of annual peaks and partial duration series. [ABSTRACT FROM AUTHOR]

Summary Rain gauge networks are used to provide estimates of area average, spatial variability and point rainfalls at catchment scale and provide the most important input for hydrological models. Therefore, it is desired to design the optimal rain gauge networks with a minimal number of rain gauges to provide reliable data with both areal mean values and spatial–temporal variability. Based on a dense rain gauge network of 185 rain gauges in Xiangjiang River Basin, southern China, this study used an entropy theory based multi-criteria method which simultaneously considers the information derived from rainfall series, minimize the bias of areal mean rainfall as well as minimize the information overlapped by different gauges to resample the rain gauge networks with different gauge densities. The optimal networks were examined using two hydrological models: The lumped Xinanjiang Model and the distributed SWAT Model. The results indicate that the performances of the lumped model using different optimal networks are stable while the performances of the distributed model keep on improving as the number of rain gauges increases. The results reveal that the entropy theory based multi-criteria strategy provides an optimal design of rain gauge network which is of vital importance in regional hydrological study and water resources management. [ABSTRACT FROM AUTHOR]

Highlights: [•] This paper examines the impacts of rain gauge densities on runoff simulations. [•] Variation of rain gauge densities impacts the areal mean rainfall considerably. [•] The locations of rain gauges impact the model performances considerably. [•] Great impacts on model performances occurring when rain gauges fall below 0.4 station per 1000km2. [Copyright &y& Elsevier]

Spatial and temporal characteristics of actual evapotranspiration over the Haihe River basin in China during 1960-2002 are estimated using the complementary relationship and the Thornthwaite water balance (WB) approaches. Firstly, the long-term water balance equation is used to validate and select the most suitable long-term average annual actual evapotranspiration equations for nine subbasins. Then, the most suitable method, the Pike equation, is used to calibrate parameters of the complementary relationship models and the WB model at each station. The results show that the advection aridity (AA) model more closely estimates actual evapotranspiration than does the Granger and Gray (GG) model especially considering the annual and summer evapotranspiration when compared with the WB model estimates. The results from the AA model and the WB model are then used to analyze spatial and temporal changing characteristics of the actual evapotranspiration over the basin. The analysis shows that the annual actual evapotranspirations during 1960-2002 exhibit similar decreasing trends in most parts of the Haihe River basin for the AA and WB models. Decreasing trends in annual precipitation and potential evapotranspiration, which directly affect water supply and the energy available for actual evapotranspiration respectively, jointly lead to the decrease in actual evapotranspiration in the basin. A weakening of the water cycle seems to have appeared, and as a consequence, the water supply capacity has been on the decrease, aggravating water shortage and restricting sustainable social and economic development in the region. [ABSTRACT FROM AUTHOR]

Summary: In this study a rigorous evaluation and comparison of the difference in water balance simulations resulted from using different downscaling techniques, GCMs and hydrological models is performed in upper Hanjiang basin in China. The study consists of the following steps: (1) the NCEP/NCAR reanalysis data for the period 1961–2000 are used to calibrate and validate the statistical downscaling techniques, i.e. SSVM (Smooth Support Vector Machine) and SDSM (Statistical Downscaling Model); (2) the A2 emission scenarios from CGCM3 and HadCM3 for the same period are used as input to the statistical downscaling models; and (3) the downscaled local scale climate scenarios are then used as the input to the Xin-anjiang and HBV hydrological models. The results show that: (1) for the same GCM, the simulated runoffs vary greatly when using rainfall provided by different statistical downscaling techniques as the input to the hydrological models; (2) although most widely used statistics in the literature for evaluation of statistical downscaling methods show SDSM has better performance than SSVM in downscaling rainfall except the Nash–Sutcliffe efficiency (NSC) and root mean square error-observations standard deviation ratio (RSR), the runoff simulation efficiency driven by SDSM rainfall is far lower than by SSVM; and (3) by comparing different statistics in rainfall and runoff simulation, it can be concluded that NSC and RSR between simulated and observed rainfall can be used as key statistics to evaluate the statistical downscaling models’ performance when downscaled precipitation scenarios are used as input for hydrological models. [Copyright &y& Elsevier]

The Pearl River Delta (PRD) is a complicated criss-cross river network. The booming economy and intensifying human activity have greatly altered the natural water levels, which threatens regional sustainable development. The Mann-Kendall trend test and the kriging interpolation method were used to detect the spatial and temporal patterns in the trends of extreme high/low water levels related to different magnitudes of streamflow, in order to explore the impacts of hydrological processes on the water-level changes throughout the PRD. The results indicate that: (a) streamflow changes at the Sanshui and Makou stations exhibit different characteristics. No significant trend can be identified in the streamflow changes at Makou station; however, the streamflow at Sanshui station shows a significant increasing trend, especially in low-flow periods. The decreasing Makou/Sanshui streamflow ratio exerts tremendous impacts on the water-level changes in the hinterland of the PRD region. (b) Extreme high/low water levels exhibit similar changing patterns. The extreme high/low water levels in the high/normal flow periods are decreasing in both the upper PRD and the hinterland of the PRD region. Increasing extreme high/low water levels in low-flow periods can be identified in the hinterland of the PRD region. The coastal regions are characterized by increasing extreme high/low water levels. (c) Extreme high/low water levels for high/normal flow periods in the hinterland of the PRD are heavily impacted by topographic changes due to in-channel dredging. Increasing extreme high/low water levels along the coastal regions are mainly backwater effects caused by serious siltation and rising sea level. This study has scientific and practical merits in regional fluvial management and mitigation of natural hazards. Citation Zhang, Q., Xu, C.-Y. & Chen, Y. D. (2010) Variability of water levels and impacts from streamflow changes and human activity within the Pearl River Delta, China. Hydrol. Sci. J. 55(4), 512-525. [ABSTRACT FROM AUTHOR]

Low-flow is widely regarded as the primary flow conditions for the anthropogenic and aquatic communities in most rivers, particularly in such an arid and semi-arid area as the Yellow River. This study presents a method integrating Mann–Kendall trend test, wavelet transform analysis and spatial mapping techniques to identify the temporal and spatial patterns of low-flow changes in the Yellow River (1955–2005). The results indicate that: (1) no trend can be identified in the major low-flow conditions in the upper Yellow River, but downward trends can be found in the middle and lower Yellow River; (2) similar periodic patterns are detected in the 7-day minima (AM7Q) in the upper and middle Yellow River, while different patterns are found in the lower Yellow River; (3) the increasing coefficients of variance in the primary low-flow conditions suggest that the variability of the low-flow is increasing from the upper to lower stream; (4) climate change and uneven temporal-spatial patterns of precipitation, jointly with highly intensified water resource utilization, are recognized as the major factors that led to the decrease of low-flow in the lower Yellow River in recent decades. The current investigation should be helpful for regional water resources management in the Yellow River basin, which is characterized by serious water shortage. [ABSTRACT FROM AUTHOR]

The Pearl River Delta (PRD) has one of the most complicated deltaic drainage systems with probably the highest density of crisscross-river network in the world. This article presents a regional flood frequency analysis and recognition of spatial patterns for flood-frequency variations in the PRD region using the well-known index flood L-moments approach together with some advanced statistical test and spatial analysis methods. Results indicate that: (1) the whole PRD region is definitely heterogeneous according to the heterogeneity test and can be divided into three homogeneous regions; (2) the spatial maps for annual maximum flood stage corresponding to different return periods in the PRD region suggest that the flood stage decreases gradually from the riverine system to the tide dominated costal areas; (3) from a regional perspective, the spatial patterns of flood-frequency variations demonstrate the most serious flood-risk in the coastal region because it is extremely prone to the emerging flood hazards, typhoons, storm surges and well-evidenced sea-level rising. Excessive rainfall in the upstream basins will lead to moderate floods in the upper and middle PRD region. The flood risks of rest parts are identified as the lowest in entire PRD. In order to obtain more reliable estimates, the stationarity and serial-independence are tested prior to frequency analysis. The characterization of the spatial patterns of flood-frequency variations is conducted to reveal the potential influences of climate change and intensified human activities. These findings will definitely contribute to formulating the regional development strategies for policymakers and stakeholders in water resource management against the menaces of frequently emerged floods and well-evidenced sea level rising. [ABSTRACT FROM AUTHOR]

Summary: In this study, we analyze the long streamflow series of three hydrological stations of the lower Pearl River basin and the streamflow ratio between Makou and Sanshui stations by using statistical techniques. Furthermore, we also attempt to address influences of precipitation and human activities (human-induced deepening of river channels) on streamflow ratio. The results indicate that: (1) the streamflow variations show remarkable relations with precipitation changes in West and East River basins, implying tremendous influences of climate changes on hydrological processes. Decreasing precipitation was observed in North River basin. However, the streamflow amount of the Sanshui station largely increased due to enlarged streamflow allocation from the West River to the North River; (2) increasing streamflow ratio of Sanshui/(Makou+Sanshui) is the result of morphological changes (downcut) of river channels in the upper Pearl River Delta. The fast downcut of river channels is mainly due to intensive sand mining. Larger magnitude of increase in streamflow ratio corresponds well to the higher intensity of in-channel sand dredging; (3) after late-1990s, decreasing precipitation of the Pearl River basin abates the streamflow amount and also the streamflow ratio. The influences of human activities and climate changes are varying in different time intervals and in different river basins. Due to tremendous impacts of increased streamflow ratio between Sanshui and Makou station, relations between streamflow and precipitation relations in the North River basin are not statistically good. This study helps to improve understandings of the causes underlying altered streamflow variations in the lower Pearl River basin and the hydrological alterations within the Pearl River Delta region. [Copyright &y& Elsevier]

Abstract: Predicting hydrological variables in ungauged catchments has been singled out as one of the major issues in the hydrological sciences. In this study, the conceptual rainfall-runoff model, HBV, was applied to Dongjiang basin and its 13 sub-basins for the purposes of examining the applicability of this well-known model in south China and exploring the possibility of transferring the calibrated parameter values to ungauged basins. For testing the applicability of the model in gauged basins, the model was calibrated for a period of 1978–1983 and validated for a period of 1984–1988. For testing the transferability of parameter values to ungauged basins, two parameter regionalization methods – proxy-basins and global mean – were investigated. The results showed that: 1) the HBV model worked well in the Dongjiang basin with the average indexes of agreement (D) and coefficient of efficiency (ME), respectively, equal to 0.79 and 0.82 in the calibration period, and 0.76 and 0.78 in the validation period; 2) transferring the parameter values from basins that passed the cross-basin test with higher ME values to the hypothetical ungauged catchments produced acceptable results with an average ME value equals to 0.72; 3) compared with the proxy-basin method of parameter estimation, the model produced equally good results for the global mean method with an average ME value equals to 0.74 when using simple arithmetic mean values. Neither the area weighted mean method nor the Thiessen polygon method produced regional parameter values could markedly improve the accuracy of modeling results. It was concluded that both regionalization methods could effectively estimate parameters for ungauged catchments in the Dongjiang basin, and similar model performances were obtained. [Copyright &y& Elsevier]

Summary: Long monthly streamflow and sediment load series observed at the Datong station located in the lower Yangtze River basin were analyzed using the scanning t-test, F-test and coherency analysis techniques. The results indicated that: (1) different changing properties of the first and the second moments of the hydrological series on different time scales were observed, reflecting different driving factors influencing the hydrological processes of the lower Yangtze River basin; (2) a generally decreasing trend can be identified after the mid-1980s. Significant abrupt changes in sediment load were analyzed in the sediment load series. However, more complicated changing patterns can be observed in the changes in streamflow. Generally decreasing sediment load and increasing streamflow gave rise to anti-phase relations between sediment load and the streamflow on longer time scales. In-phase relations between sediment load and streamflow on shorter time scales may imply a considerable influence of the hydrological dynamics on sediment transport; and (3) human activities, particularly the construction of water storage reservoirs, exerted a massive influence on sediment load variations. Construction of a large amount of water reservoirs on the tributaries of the Yangtze River and the Gezhouba Dam on the mainstem of the Yangtze River seem to be the main factors responsible for abrupt changes in the sediment load. Construction of the Three Gorges Dam causes a sharp decrease and unstable variability in sediment load variations, which may pose new challenges for the ecological environment conservation and the deltaic management of the Yangtze Delta region. [Copyright &y& Elsevier]

Abstract: Scaling behaviors of the long daily streamflow series of four hydrological stations (Longchuan (1952–2002), Heyuan (1951–2002), Lingxia (1953–2002) and Boluo (1953–2002)) in the mainstream East River, one of the tributaries of the Pearl River (Zhujiang River) basin, were analyzed using multifractal detrended fluctuation analysis (MF-DFA). The research results indicated that streamflow series of the East River basin are characterized by anti-persistence. MF-DFA technique showed similar scaling properties in the streamflow series of the East River basin on shorter time scales, indicating universal scaling properties over the East River basin. Different intercept values of the fitted lines of log–log curve of versus implied hydrological regulation of water reservoirs. Based on the numerical results, we suggested that regulation activities by water reservoirs could not impact the scaling properties of the streamflow series. The regulation activities by water reservoir only influenced the fluctuation magnitude. Therefore, we concluded that the streamflow variations were mainly the results of climate changes, and precipitation variations in particular. Strong dependence of generalized Hurst exponent on demonstrated multifractal behavior of streamflow series of the East River basin, showing ‘universal’ multifractal behavior of river runoffs. The results of this study may provide valuable information for prediction and assessment of water resources under impacts of climatic changes and human activities in the East River basin. [Copyright &y& Elsevier]

Summary: Annual runoff and annual suspended sediment loads of hydrological gauging stations along the mainstream of the Yangtze River basin (Pingshan station, Yichang station, Hankou station and Datong station) and main tributaries (Beipei station in Jialingjiang River, Wulong station in Wujiang River and Huangzhuang station in Hanjiang River) were analyzed with the help of Mann–Kendall trend analysis and linear regression analysis. Research results indicate that (1) changing patterns of runoff and sediment loads are different in different parts of the Yangtze River basin. No significant trend is detected for annual runoff at all stations at >95% confidence level. Changes of sediment loads, however, demonstrate different pictures in the Yangtze River basin. The sediment loads are in increasing trend in Pingshan station- the most upstream station on the Yangtze River basin (this increasing trend is significant at >95% confidence level after about 1990), but are in decreasing trend at other stations (including stations in the tributaries studied in this paper). This decreasing trend becomes more obvious from Yichang station to Datong station. (2) Water reservoirs exerted more influences on changes of sediment loads than on runoff, which is the main reason for the decreasing trend of sediment loads found in most stations. (3) Influences of water reservoirs on sediment loads are more obvious in the tributaries than in the mainstream of the Yangtze River basin, while in the mainstream the variation patterns of sediment loads are determined by multiple factors. [Copyright &y& Elsevier]

• A framework was provided for analyzing change causes of runoff and sediment load. • Runoff change attribution analysis was incorporated in quantifying sediment change. • Dam construction obviously disturbed the evolution characteristics of sediment load. Under the background of climate variability and anthropogenic stresses, changes of the runoff and sediment processes have drawn increasing concerns during the past decades. Analyzing the evolution characteristics and causes of runoff and sediment load is critical for the sustainable development of water resource and terrestrial ecosystems of river basins. This study presents an integrated approach for analyzing the evolution characteristics of the annual runoff and sediment load, including the trend, periodicity, change point and their correlations in the Wujiang River basin (WRB) –a typical mountainous river system in southwest China, based on the observed data during the period 1970–2016. On this basis, the contributions of climate variability and human activities on runoff change were first distinguished by using a widely applied Budyko-based hydrological sensitivity method, and then the quantified result was further applied in the attribution analysis of river sediment load change. Research results revealed that the change patterns of runoff and sediment processes, such as the trend, periodicity and potential change point, as well as the relationships of precipitation-runoff and runoff-sediment load are quite different in the WRB. Quantitative assessment revealed that climate variability contributes to 71.5%–85.6% changes of mean annual runoff in 1985–1994, 1995–2004 and 2005–2016 with reference to the baseline period of 1970–1984. In contrary, human activities dominate the reduction of sediment load in the river, and the contribution rate ranged in 67.2%–107.5%. Variations of annual runoff in the WRB was the direct result of climate variability, while land use change played a secondary role. The construction of cascade hydropower reservoirs, particularly in the upper and middle reaches of the Wujiang River is fundamentally responsible for the significant decreasing and the weakened periodicity of sediment load as well as the changed runoff-sediment load correlation at Wulong station in recent years. However, this influence was not big enough to modify the regime of annual runoff. [ABSTRACT FROM AUTHOR]

The Lowest navigable water level (LNWL) is an important indicator used for navigation design to balance the relationship between navigation safety and economic benefits of a waterway. However, it is a challenge of accurately estimating LNWLs due to the nonstationary characteristics of observed water level data series. In this study, a comprehensive framework was developed for handling this issue. In this framework, inter-annual variabilities in both the mean and variance of water level series were described by decomposing original series and were eliminated by composing new series. Intra-annual variability was determined by detecting indicators describing intra-annual water level distributions. Considerations of inter- and intra-annual variabilities were combined by designing annual water level processes for the past and current environments. Shipping risks during both annual and multi-annual periods were considered in the framework as well. The framework was demonstrated in estimating LNWLs at the Gaodao and Shijiao stations in the North River basin, southern China. The recommended LNWLs at the Gaodao station were 22.32 m for 95% guaranteed rate and 21.84 m for 98% guaranteed rate; LNWLs at the Shijiao station were 0.27 m for 95% guaranteed rate and 0.15 m for 98% guaranteed rate. The impact of variance variability on estimations of LNWLs was also evaluated. Results indicated that the recommended LNWLs would have errors of 0.11 ~ 0.48 m at the Gaodao station and 0.03 ~ 0.04 m at the Shijiao station if the variance variability was not considered. The proposed framework was then compared with Nonstationary synthetic duration curve (NSDC) method, and results illustrated that the duration curves plotted by NSDC method were unreasonable, leading to inaccurate design values. Overall, the developed framework is more reasonable and suitable for designing LNWLs of waterways where the variabilities of the water levels at different time scales are different or where the historical water level data contain various variations. [ABSTRACT FROM AUTHOR]

The rise of extreme precipitation events has attracted wide attention throughout the world. However, the analysis of precipitation extremes is not sufficient in the short period of instrumental observation and it is complicated by their non‐stationary behaviours. As a period dominated by natural forcing, the last millennium is helpful for understanding long‐term changes in precipitation extremes. Therefore, this study aimed to (1) investigate changes in daily and subdaily precipitation extremes in China from the last millennium to the end of the twenty‐first century, (2) detect the non‐stationarity of precipitation extremes and (3) compare design storms by using stationary and non‐stationary distributions. The annual maximum 1‐day precipitation (RX1day) and annual maximum 3‐hour precipitation (RX3hour) are used to quantify variations of precipitation extremes. The results show that the trend of RX1day was insignificant for the Medieval Climate Anomaly (MCA, 950–1250) and the Little Ice Age (LIA, 1500–1800). For the historical period (1850–2014), the trend of RX1day and RX3hour was insignificant in most subregions. In addition, the differences for RX1day between the historical period and the MCA/LIA were mostly in the range of −5% to 5%. For the future period (2015–2100), RX1day and RX3hour are projected to increase in almost all land grids compared with the historical period, and they show greater increases in western China than eastern China. The non‐stationarity of RX1day and RX3hour is mainly found under the high‐emission scenario. Moreover, stationary generalized extreme value (GEV) distribution underestimates 50‐year return levels for RX1day and RX3hour in most areas compared with non‐stationary GEV distribution under the high‐emission scenario. Overall, this study suggests that daily and subdaily extreme precipitation will intensify over China in the future. For design storms, non‐stationary methods need to be used for risk management and engineering design under the high‐emission scenario. [ABSTRACT FROM AUTHOR]

Quantifying hydrologic uncertainty associated with the nonlinear and heteroscedastic dependence structure of multi-step-ahead deterministic flood forecasts is fundamentally challenging. A hydrologic uncertainty processor (HUP) can effectively measure hydrologic uncertainty raised in deterministic forecast models under the hypothesis that there is no input uncertainty. This study explored a HUP embedded with a copula function for configuring the probabilistic flood forecast model. The outputs of the HUP formed a posterior distribution of the process, conditional upon the deterministic forecasts. The main merit of the copula-based HUP lies in capturing the features of the nonlinear and heteroscedastic dependence structure among hydrologic variables as well as in alleviating the uncertainty encountered in operational flood forecasts. To demonstrate reliability and effectiveness of the proposed method, the copula-based HUP was implemented to generate multi-step-ahead forecasts from horizons t+1 up to t+4 at a time scale of 6-hours for a flood-induced inflow series of the Three Gorges Reservoir (TGR) in China. The back propagation neural network and the nonlinear autoregressive with exogenous inputs neural network were investigated to offer a better deterministic forecast result for further probabilistic forecasting. For comparison purpose, the meta-Gaussian HUP was also conducted. The results indicated that the proposed method not only outperformed the comparative HUP but also significantly enhanced the accuracy of probabilistic flood forecasts for the TGR inflows, where effective quantification of hydrologic uncertainty was achieved. We demonstrated that the copula-based HUP could suitably extract the complex nonlinear and heteroscedastic dependence structure relating to the outputs of the deterministic forecast model. Consequently, hydrologic uncertainty could be alleviated and model reliability as well as forecast accuracy for future horizons could be significantly improved.Acknowledgements: This work is supported by the Research Council of Norway (FRINATEK Project 274310). [ABSTRACT FROM AUTHOR]

Climate model response (M) and greenhouse gas emissions (S) uncertainties are consistently estimated as spreads of multi‐model and multi‐scenario climate change projections. There has been less agreement in estimating internal climate variability (V). In recent years, an initial condition ensemble (ICE) of a climate model has been developed to study V. ICE is simulated by running a climate model using an identical climate forcing but different initial conditions. Inter‐member differences of an ICE manifestly represent V. However, ICE has been barely used to investigate relative importance of climate change uncertainties. Accordingly, this study proposes a method of using ICEs, without assuming V as constant, for investigating the relative importance of climate change uncertainties and its temporal–spatial variation. Prior to investigating temporal–spatial variation in China, V estimated using ICE was compared to that using multi‐model individual time series at national scale. Results show that V using ICE is qualitatively similar to that using multi‐model individual time series for temperature. However, V is not constant for average and extreme precipitations. V and M dominate before 2050s especially for precipitation. S is dominant in the late 21st century especially for temperature. Mean temperature change is projected to be 30–70% greater than its uncertainty until 2050s, while uncertainty becomes 10–40% greater than the change in the late 21st century. Precipitation change uncertainty overwhelms its change by 70–150% throughout 21st century. Cold regions (e.g., northern China and Qinghai‐Tibetan Plateau) tend to have greater temperature change uncertainties. In dry regions (e.g., northwest China), all three uncertainties tend to be great for changes in average and extreme precipitations. This study emphasizes the importance of considering climate change uncertainty in impact studies, especially taking into account that V is irreducible in the future. Using ICEs without assuming V as constant is an appropriate approach to study climate change uncertainty. Using initial condition ensembles without assuming internal variability as constant is an appropriate approach to study climate change uncertainties. Changes in internal variabilities of average and extreme precipitations are significant. Temperature change is greater than its uncertainty first, then the other way round. Precipitation change is less than its uncertainty in this century. Cold regions have great temperature change uncertainties, while dry regions have great precipitation change uncertainties. [ABSTRACT FROM AUTHOR]

Lakes and vegetation are important factors of the Earth's hydrological cycle and can be called an “indicator” of climate change. In this study, long-term changes of lakes' area and vegetation coverage in the Qinghai-Tibetan Plateau (QTP) and their relations to the climate change were analyzed by using Mann-Kendall method during the past 30 years. Results showed that: 1) the lakes' area of the QTP increased significantly during the past 30 years as a whole, and the increasing rates have been dramatically sped up since the year of 2000. Among them, the area of Ayakekumu Lake has the fastest growing rate of 51.35%, which increased from 618 km 2 in the 1980s to 983 km 2 in the 2010s; 2) overall, the Normalized Difference Vegetation Index (NDVI) increased in the QTP during the past 30 years. Above 79% of the area in the QTP showed increasing trend of NDVI before the year of 2000; 3) the air temperature increased significantly, the precipitation increased slightly, and the pan evaporation decreased significantly during the past 30 years. The lake area and vegetation coverage changes might be related to the climate change. The shifts in the temporal climate trend occurred around the year 2000 had led the lake area and vegetation coverage increasing. This study is of importance in further understanding the environmental changes under global warming over the QTP. [ABSTRACT FROM AUTHOR]

To improve the accuracy and reliability of precipitation estimation, numerous models based on machine learning technology have been developed for integrating data from multiple sources. However, little attention has been paid to extracting the spatiotemporal correlation patterns between satellite products and rain gauge observations during the merging process. This paper focuses on this issue by proposing an integrated framework to generate an accurate and reliable spatiotemporal estimation of precipitation. The proposed framework integrates Funk-Singular Value Decomposition (F-SVD) in the recommender system to achieve the accurate spatial distribution of precipitation based on the spatiotemporal interpolation of rain gauge observations and Convolutional Long Short-Term Memory (ConvLSTM) to merge precipitation data from interpolation results and satellite observation through exploiting the spatiotemporal correlation pattern between them. The framework (FS-ConvLSTM) is utilized to obtain hourly precipitation merging data with a resolution of 0.1° in Jianxi Basin, southeast of China, from both rain gauge data and Global Precipitation Measurement (GPM) from 2006 to 2018. The LSTM and Inverse Distance Weighting (IDW) are constructed for comparison purposes. The results demonstrate that the framework could not only provide more accurate precipitation distribution but also achieve better stability and reliability. Compared with other models, it performs better in variation process description and rainfall capture capability, and the root mean square error (RSME) and probability of detection (POD) are improved by 63.6% and 22.9% from the original GPM, respectively. In addition, the merged precipitation combines the strength of different data while mitigating their weaknesses and has good agreement with observed precipitation in terms of magnitude and spatial distribution. Consequently, the proposed framework provides a valuable tool to improve the accuracy of precipitation estimation, which can have important implications for water resource management and natural disaster preparedness. [ABSTRACT FROM AUTHOR]

Whether or not large-scale vegetation restoration will lead to a decrease in regional terrestrial water storage is a controversial topic. This study employed the Geodetector model, in conjunction with observed and satellite hydro-meteorological data, to detect the changes in terrestrial water storage anomaly (TWSA) and to identify the contributions of climate change and vegetation greening across China during the years 1982–2019. The results revealed that: (1) during the period of 1982–2019, TWSA showed a downward trend in about two thirds of the country, with significant declines in North China, southeast Tibet, and northwest Xinjiang, and an upward trend in the remaining third of the country, with significant increases mainly in the Qaidam Basin, the Yangtze River, and the Songhua River; (2) the positive correlation between normalized vegetation index (NDVI) and TWSA accounts for 48.64% of the total vegetation area across China. In addition, the response of vegetation greenness lags behind the TWSA and precipitation, and the lag time was shorter in arid and semi-arid regions dominated by grasslands, and longer in relatively humid regions dominated by forests and savannas; (3) furthermore, TWSAs decreased with the increase in NDVI and evapotranspiration (ET) in arid and semi-arid areas, and increased with the rise in NDVI and ET in the humid regions. The Geodetector model was used to detect the effects of climate, vegetation, and human factors on TWSA. It is worth mentioning that NDVI, precipitation, and ET were some of the main factors affecting TWSA. Therefore, it is essential to implement rational ecological engineering to mitigate climate change's negative effects and maintain water resources' sustainability in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

Understanding the evolution of river morphology is crucial for comprehending changes in water resources and implementing development projects along rivers. This study proposes an integrated approach utilizing remote sensing image data combined with deep learning and visual interpretation algorithms to analyze continuous-type changes in river morphology. This research focuses on the lower reaches of the Minjiang River in China and comprehensively analyzes the river's morphological evolution from 1986 to 2021. The results show that the proposed method of river water identification in this study demonstrates high accuracy and effectiveness, with an F1 score and Kappa coefficient greater than 0.96 and 0.91, respectively. The morphology of the river channel remains stable in the upstream and estuarine sections of the study region while undergoing substantial alterations in the middle section. Additionally, this study also identifies several factors that significantly impact the evolution of river morphology, including reservoir construction, river sediment mining, river training measures, geological conditions, and large flood events. The findings of this study can provide some insights into the management and conservation of water resources. [ABSTRACT FROM AUTHOR]

Identifying the links between variations in large-scale climate patterns and precipitation is of tremendous assistance in characterizing surplus or deficit of precipitation, which is especially important for evaluation of local water resources and ecosystems in semi-humid and semi-arid regions. Restricted by current limited knowledge on underlying mechanisms, statistical correlation methods are often used rather than physical based model to characterize the connections. Nevertheless, available correlation methods are generally unable to reveal the interactions among a wide range of climate oscillations and associated effects on precipitation, especially on extreme precipitation. In this work, a probabilistic analysis approach by means of a state-of-the-art Copula-based joint probability distribution is developed to characterize the aggregated behaviors for large-scale climate patterns and their connections to precipitation. This method is employed to identify the complex connections between climate patterns (Atlantic Multidecadal Oscillation (AMO), El Niño-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO)) and seasonal precipitation over a typical semi-humid and semi-arid region, the Haihe River Basin in China. Results show that the interactions among multiple climate oscillations are non-uniform in most seasons and phases. Certain joint extreme phases can significantly trigger extreme precipitation (flood and drought) owing to the amplification effect among climate oscillations. [ABSTRACT FROM AUTHOR]

Rubber (Hevea brasiliensis) plantations have high water consumption through evapotranspiration, which can contribute to water scarcity. In addition, there is a lack of spatial observation data and estimation methods for evapotranspiration (ET) for rubber plantations. To alleviate the water stress of expanding rubber plantations caused by seasonal drought in Xishuangbanna, Southwest China, an up to 7 days crop evapotranspiration (ETc) forecast method, "Kc-ET0" for rubber plantations with limited meteorological data, was proposed by using rubber crop coefficient Kc and public weather forecasts. The results showed that the average absolute error (MAE) of forecasted ETc was 0.68 mm d−1, the root mean square error (RMSE) was 0.85 mm d−1, and the average correlation coefficient (R) was 0.69 during the rainy season, while during the dry season these metrics were 0.52 mm d−1, 0.68 mm d−1, and 0.85, respectively. The accuracy of ETc forecast in the dry season was higher. Additionally, Kc was the main factor influencing the accuracy of rubber plantations ETc forecast, while the accuracy of the temperature forecast and the chosen Hargreaves-Samani (HS) model were also considerable. Our results suggested that the "Kc-ET0" short-term rubber plantation ETc forecasting method shows good performance and acceptable accuracy, especially in the dry season. The study provides an important basis for creating ET-based irrigation scheduling for improving regional-scale water management in high water consumption rubber plantations. [ABSTRACT FROM AUTHOR]

• 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]

• 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]

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]

Summary: Spatial rainfall is a key input to distributed hydrological models, and its precisions heavily affect the accuracy of stream flow predictions from a hydrological model. Traditional interpolation techniques which obtain the spatial rainfall distribution from rain gauge data have some limitations caused by data scarcity and bad quality, especially in developing countries or remote locations. Satellite-based precipitation products are expected to offer an alternative to ground-based rainfall estimates in the present and the foreseeable future. For this purpose, the quality and usefulness of satellite-based precipitation products need to be evaluated. The present study compares the difference of Tropical Rainfall Measuring Mission (TRMM) rainfall with rain gauges data at different time scales and evaluates the usefulness of the TRMM rainfall for hydrological processes simulation and water balance analysis at the Xinjiang catchment, located in the lower reaches of the Yangtze River in China. The results show at daily time step TRMM rainfall data are better at determining rain occurrence and mean values than at determining the rainfall extremes, and larger difference exists for the maximal daily and maximal 5-day rainfalls. At monthly time scale, good linear relationships between TRMM rainfall and rain gauges rainfall data are received with the determination coefficients (R 2) varying between 0.81 and 0.89 for the individual stations and 0.88 for areal average rainfall data, respectively. But the slope of regression line ranges between 0.74 for Yingtan and 0.94 for Yushan, indicating that the TRMM satellite is inclined to underestimate the monthly rainfall in this area. The simulation of daily hydrological processes shows that the Water Flow Model for Lake Catchment (WATLAC) model using conventional rain gauge data produces an overall good fit, but the simulation results using TRMM rainfall data are discontented. The evaluation results imply that the TRMM rainfall data are unsuited for daily stream flow simulation in this study area with desired precisions. However, good performance can be received using TRMM rainfall data for monthly stream flow simulations. The comparison of the simulated annual water balance components shows that the different rainfall data sources can change the volume value and proportion of water balance components to some extent, but it generally meets the need of practical use. [Copyright &y& Elsevier]

Many impact studies require climate change information at a finer resolution than that provided by general circulation models (GCMs). Therefore the outputs from GCMs have to be downscaled to obtain the finer resolution climate change scenarios. In this study, an automated statistical downscaling (ASD) regression-based approach is proposed for predicting the daily precipitation of 138 main meteorological stations in the Yangtze River basin for 2010-2099 by statistical downscaling of the outputs of general circulation model (HadCM3) under A2 and B2 scenarios. After that, the spatial-temporal changes of the amount and the extremes of predicted precipitation in the Yangtze River basin are investigated by Mann-Kendall trend test and spatial interpolation. The results showed that: (1) the amount and the change pattern of precipitation could be reasonably simulated by ASD; (2) the predicted annual precipitation will decrease in all sub-catchments during 2020s, while increase in all sub-catchments of the Yangtze River Basin during 2050s and during 2080s, respectively, under A2 scenario. However, they have mix-trend in each sub-catchment of Yangtze River basin during 2020s, but increase in all sub-catchments during 2050s and 2080s, except for Hanjiang River region during 2080s, as far as B2 scenario is concerned; and (3) the significant increasing trend of the precipitation intensity and maximum precipitation are mainly occurred in the northwest upper part and the middle part of the Yangtze River basin for the whole year and summer under both climate change scenarios and the middle of 2040-2060 can be regarded as the starting point for pattern change of precipitation maxima. [ABSTRACT FROM AUTHOR]

Summary: In this paper, the trends of the annual streamflow and precipitation and cross correlations between them were analyzed in nine large river basins of China during 1956–2005. The results indicate that: (1) the annual mean streamflow decreases in arid and semi-arid regions of north China; however, increasing trends occur in south and Southwest China; (2) the annual streamflow and precipitation exhibit reasonable correlation in nine large river basins except those located in inland areas. The annual streamflow over most areas of China is fed by precipitation; however, the decline in streamflow is faster than the decreases of precipitation since 1970s in the arid and semi-arid regions of north China. The relationship between the annual precipitation and streamflow presents a non-stationary state in north China. This non-stationary relationship is strongly influenced by both human activities and precipitation changes; (3) a significant increase of water use might be the major factor responsible for the steeper decline in streamflow than in precipitation in Haihe River, Yellow River and Songliao River basins in north China. In inland river areas, increase of water use and actual evapotranspiration might result in decline in streamflow although precipitation has an increase tendency. This paper sheds light on the non-stationary relationship between annual precipitation and streamflow and possible underlying causes, which will be helpful for a better understanding of the changes of precipitation and streamflow in China at large scale and in other regions of the world. [Copyright &y& Elsevier]

Abstract: Long monthly streamflow series of three control hydrological stations of the Pearl River basin were analyzed by using the scanning t-test and the scanning F-test. Possible implications of the changing properties of streamflow variations for the water resource management of the Pearl River Delta are also discussed. The results indicated that: 1) more complicated changes were observed in terms of the second center moment when compared to the first original moment. More significant abrupt changes of the second center moment imply more sensitive response of streamflow stability to climate changes and human activities; 2) abrupt behaviors of the first (second) center moment of the streamflow variations tend to be more sensitive to climate changes and/or human activities in the larger river basin when compared to those in the smaller river basin. These phenomena are attributed to buffering functions of more storage space of longer river channel, and more complicated and longer runoff yield and concentration processes in the river basin of larger drainage area; 3) annual minimum streamflow of the Pearl River basin tends to be increasing. This will be helpful for better human mitigation of the salinity intrusion in dry seasons across the Pearl River Delta. Annual maximum streamflow, when compared to annual minimum streamflow, shows larger-magnitude variability reflected by larger standard deviation, implying unfavorable conditions for flood mitigation in the Pearl River Delta. The results of this paper are of scientific and practical merits for water resource management and sound human mitigation to water hazards across the Pearl River Delta, and also are a good case study for similar researches in other river deltas in the world under the changing environment. [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]

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

Given the threats from rainstorm events that have been intensified due to climate change, the investigation of heavy/extreme precipitation is attracting increasing attention. The investigation of extreme precipitation, with its restricted tools and data, is quite different from that of nonextreme precipitation. Satellite precipitation estimates (SPEs) are promising precipitation measurements due to fine spatial resolution. However, the spatiotemporal patterns of SPE extremes remain unclear. Therefore, this study attempted to systematically analyse the spatiotemporal patterns of extreme SPEs provided by the Tropical Rainfall Measurement Mission (TRMM) research product from statistical characterization to stochastic behaviour modelling. The statistical characterization was depicted using comprehensive extreme precipitation indices (EPIs), while the stochastic behaviour was modelled using the generalized Pareto distribution (GPD) to fit the 'peak over threshold' samples and employing the max‐stable process to fit the 'block maximum' samples (Max: annual 1‐day maximum, RX1day: monthly 1‐day maximum, and RX5day: monthly 5‐day maximum) of the TRMM data. The TRMM extremes were analysed across the Xijiang River Basin, China. The results of the statistical characterization revealed that compared with gauge precipitation, most TRMM EPIs were discovered to have higher temporal dynamic levels. Moreover, the spatial median of the return level for each return period derived from temporal TRMM extreme modelling with the GPD was lower than that from gauge extremes. The spatial dependence of Max in TRMM was weaker than that of either RX1day or RX5day, primarily due to different precipitation‐generating processes existing between two extreme events at annual scale compared with monthly scale. Each of the Max, RX1day, and RX5day in TRMM exhibited stronger spatial dependence than that in gauge extreme precipitation, maybe related to the area‐average‐based representation of the SPEs. These findings can enhance our insight into the spatiotemporal patterns of extreme satellite precipitation, which is beneficial for operational flood risk management. [ABSTRACT FROM AUTHOR]

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

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

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

As a large open lake that connects to the lower Yangtze River, the Poyang Lake, China, has persisted dramatic water level decline since 2003. For better management of lake water resources and ecosystem, this study quantitatively examined the contributions and spatiotemporal differences of compounding influencing factors to this hydrological change. Attempts were achieved through the reconstruction of lake water level series by two combined neural network models. The results indicate that with reference to the period 1980–1999, the average contributions to lake decline during 2003–2014 of lake-bottom topography change, the operation of the Three Gorges Dam (TGD), and climate change and other human activities over the Yangtze River basin were 50%, 18%, and 32%, respectively. The response of the lake water level to the three factors shows obvious spatiotemporal differences due to different influencing mechanisms. It is worth noting that the effect of lake-bottom topography change is still increasing across the lake. In addition, the reduction of precipitation in the Yangtze River basin should be highly correlated to the water level decline of the lake. From the perspective of maintaining a certain water level of the lake and preventing continuous decline and shrinkage, constructing the proposed water control structure would be an effective solution. [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]

General circulation models (GCMs) are important tools for the study of climate change, but their resolutions are too coarse for station-scale impact assessments. Statistical and dynamical downscaling methods are widely used to translate the predictions of GCMs to the finer spatial scale and it is important to understand the difference between statistical and dynamical downscaling methods in different climatic zones and time periods. Moreover, statistical downscaling can be used on both GCM and regional climate model (RCM) outputs. In this study, two sets of GCM precipitations were dynamically and statistically downscaled and their performances were evaluated against the observed precipitation from 308 stations distributed throughout the Yellow, Yangtze, and Pearl River basins. These stations have distinct climatic characteristics from the historical period (1961–2000) and future period (2031–2050). Results suggest dynamically downscaled GCM precipitation does not present lower biases when comparing observed site-specific precipitation to GCM outputs, and biases of the initial dynamically downscaled GCM outputs decreased in areas with higher humidity. This demonstrates that statistical downscaling can improve GCM and RCM outputs, and the statistical downscaling method can reproduce local-scale precipitation satisfactorily without dynamical downscaling. However, statistical downscaling reduced spatial regularity of the biases that exist in GCM and RCM outputs between the observations and simulation. Additionally, the spatial discrepancy between statistically downscaled GCM and RCM precipitations was very small. In the future period, discrepancies between statistically downscaled RCM and GCM precipitations in the two climate scenarios were larger than the historical period for all climate zones. [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]

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 statistical post-processing methods have been widely used to correct the biases of ensemble weather forecasts. However, most of the statistical post-processing methods are applicable to single weather variable at a single location, thus neglecting the spatiotemporal dependency structures of forecasting variables. This study extends the traditional single-site and single-variate (SSSV) method to multi-site and multivariate (MSMV) version by inducing the MSMV dependences based on the rank shuffle and Copula theory. Therefore, the study uses one SSSV method, Generator-based Post-Processing method (GPP), and three dependence reconstruction methods, i.e., Rank shuffle (RS), Gaussian Copula Coupling (GCC) and Empirical Copula Coupling (ECC). The dependence reconstructing methods are used to generate the spatially correlated random field for GPP to produce the MSMV ensembles (The pre-shuffling procedure) or to shuffle the generated GPP ensemble (The post-shuffling procedure). Therefore, a total of 6 MSMV methods (Pre-RS, Pre-GCC, Pre-ECC, Post-RS, Post-GCC, and Post-ECC) were evaluated in producing the skillful and correlated ensemble precipitation and temperature forecasts for the Xiangjiang Basin in China based on the 11-ensemble forecasts from the Global Ensemble Forecasting System (GEFS) reforecasts. The results showed that the uncorrected GEFS forecasts tend to be biased, under-dispersive, and over-estimated the inter-site and inter-variable correlation for precipitation and temperature. GPP is skillful to remove bias in the ensemble mean and improve the skill of the ensemble spread but at the expense of losing the inter-site and inter-variable dependences. All the 6 MSMV methods tend to perform well in removing the bias in the inter-site dependencies for one fixed variable, but the post-shuffling methods (Post-RS, Post-GCC, and Post-ECC) are more effective in reconstructing the inter-site and inter-variate dependencies while preserving the univariate ensemble forecasts performance. [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]

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

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

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