Search

Understanding the responses of water yield (WY) to climate change and vegetation greening is crucial for water resources management. However, quantifying the feedback relationships among climate, vegetation, and WY remains challenging. In this study, we developed a system feedback assessment framework based on Integrated Valuation of Ecosystem Services and Tradeoff (InVEST), gravity center model, and structural equation model to reveal these relationships. The framework involves identifying land cover transfer projects, assessing spatio-temporal variations in climate, vegetation, and WY, and quantifying their system feedback relationships, which was applied in the Yellow River Basin (YRB) with significant climate change and vegetation greening. The results demonstrated that precipitation, Normalized Difference Vegetation Index (NDVI), and WY showed significant increasing trends (p < 0.05) at rates of 2.92 mm/a, 0.004 /a and 0.71 mm/a, respectively, during 1991–2020. The areas of significant increase in precipitation, NDVI, and WY were primarily concentrated in the source and midstream of the YRB. The gravity centre of WY moved southwest from 1991 to 2000 and then northeast from 2000 to 2020 in the YRB. Precipitation changes and ecological restoration were identified as key driving factors affecting the variation of WY. Our findings highlight the possible negative effects of ecological restoration on WY variation and provide important information for developing adaptation strategies for land use management in the YRB or other regions. [ABSTRACT FROM AUTHOR]

The changing frequency of flooding in global watersheds, driven by various human and natural factors like land use/cover changes and global warming, necessitates innovative approaches in flood frequency analysis and risk assessment. Nonetheless, the reliability of nonstationary frequency analysis models remains a concern given challenges in accurately measuring the uncertainty introduced by these methods and the impact on design flood values. In this study, deviation-based differential sensitivity indices, including single-parameter (SDDSI) and entire-parameter (EDDSI) measures were developed to assess the influence of parameter uncertainty in nonstationary models using Bayesian statistics and "equivalent reliability" nonstationary design. The Weihe River, the largest tributary of the Yellow River which is experiencing both climate change and heavy impact of human activities, is chosen to be the study area to investigate the impact of precipitation change and land use change on nonstationary flood frequency. Results show that in the One-At-A-Time (OAT) sensitivity analysis under a small uncertainty scenario (SUS) for parameter inputs, the shape parameter stands out as the most influential factor (SDDSI_SUS = 0.347) affecting the 100-year design flood in the Stationary Generalized Extreme Value (SGEV) model. For the Non-Stationary GEV (NGEV) models, the influence of this parameter is less pronounced, with SDDSI_SUS values of 0.095 and 0.093 for the SSP126 and SSP585 scenarios, respectively. Instead, attention turns to the regression coefficient of the grassland area, associated with the GEV scale parameter. In global sensitivity analysis under the posterior uncertainty scenario (PUS) for parameter inputs, the EDDSI_PUS values for SGEV, NGEV_SSP126, and NGEV_SSP585 models were 0.52, 1.41, and 1.30, respectively, inferring heightened sensitivity of NGEV models to perturbations from entire parameters. It is anticipated that incorporating additional evidence, such as historical flood data, is essential for accurate nonstationary hydrological design to mitigating the influence of parameter uncertainty. The sensitivity indices in this study provide significant insights for assessing the robustness of nonstationary hydrological design in flood risk management and applications. [ABSTRACT FROM AUTHOR]

Intensifying droughts under climatic warming are of widespread concern owing to their devastating impacts on water resources, societies and ecosystems. However, the effects of exogeneous drivers on regional droughts remain poorly understood. Using the Lagrangian method, atmospheric reanalysis data and climate projections from the Coupled Model Inter-comparison Project phase 6 (CMIP6), we show how exogenous precipitation minus evaporation (PME) deficit drives droughts across China. More specifically, we demonstrate that four distinct trajectories of such exogenous PME deficit fuel regional droughts. Three of these trajectories relate to oceanic PME deficit originating from the North Atlantic, eastern Bering Sea and Indian Ocean, and one trajectory characterizes exogenous terrestrial PME deficit from the Siberian Plateau. We show that during 1980–2020, droughts induced by exogenous PME deficit account for 45% of all droughts that occurred in China's coastal region, and for 7% of all droughts in the northwestern regions. Under climate scenario SSP245 (SSP585), limiting warming to 1.5 °C compared to 2 °C above pre-industrial levels could avoid 60% (84%) of exogenous drought exposure. This would in turn reduce population exposure by 40% (49%), and economic exposure by 73% (66%). Our study unravels how exogenous PME deficit drives droughts in China, underscoring the role that external drivers have on regional droughts and associated future prediction. [ABSTRACT FROM AUTHOR]

Quantifying the contributions of climate change (CC) and human activities (HA) to streamflow alteration is significant for effective water resources management. However, numerous studies fail to differentiate the individual impacts of various HA on streamflow. In this study, a comprehensive streamflow attribution framework that incorporates climate, vegetation, and water withdrawal (WW) was proposed. In this framework, traditional streamflow attribution methods such as statistical analysis (Double Mass Curve and Slope Change Ratio of Accumulative Quantity), elasticity (Budyko), and modeling simulation (Variable Infiltration Capacity and Long Short-term Memory) are employed to separate the influence of meteorological factors (MF) on streamflow. Subsequently, the impacts of WW on streamflow are assessed using global WW data. The Residual Analysis method is utilized to quantify the effects of vegetation alteration caused by both CC (Lcc) and HA (Lha) on streamflow alteration. To demonstrate the applicability of our proposed framework, two stations, Xianyang and Huaxian, located within the Weihe River Basin in Northwest China were selected as the case study area. The results demonstrated that compared to the baseline period (1961–1990), the average contributions of MF, Lcc, Lha, and WW to streamflow reduction during the variation periods (1991–2019) were as follows: for the Xianyang station, 26.0%, 13.5%, 30.9%, and 29.6% respectively; and for the Huaxian station, 28.9%, 5.5%, 17.7%, and 47.9% respectively. Additionally, during the variation periods, the contributions of CC and HA to vegetation variation were 30.5% and 69.5% respectively in Xianyang, and 23.7% and 76.3% respectively in Huaxian. The framework developed herein also provides a solution for quantifying the indirect effects of CC on streamflow through vegetation. [ABSTRACT FROM AUTHOR]

Understanding hydrological responses to rising levels of greenhouse gases are essential for climate and impact research. It is, however, often limited by a lack of long record of observational data to provide a basis for understanding the long-term behavior of the climate system. Integrating reconstructed data and (global climate and hydrological) model simulations will help us to better understand the variability of climate and hydrology over timescales ranging from decades to centuries. In this study, we proposed an integrated approach to study flood variability in the upper reach of the Yangtze River over the last millennium to the end of the 21st century. To accomplish this, we first drove hydrological models using the precipitation and temperature from four Global Climate Models (GCM), BCC-CSM1.1, MIROC, MRI-CGCM3, and CCSM4, to simulate daily discharge for the upper reach of the Yangtze River during the period of the last millennium (850–1849), historical period (1850–2005), and a future period (2006–2099). Then, we evaluated whether the modeled precipitation, temperature, and extreme discharge had statistical properties similar to those shown in the documented dry-wet periods, temperature anomalies, and paleoflood records. Finally, we explored the extreme discharge variability using model simulations. The results indicate that: (1) The MIROC-ESM model, differing from the other three GCM models, revealed positive temperature changes from the warm period (Medieval Climate Anomaly; MCA) to the cold period (Little Ice Age; LIA), while the temperature variability of the other models was similar to the records. (2) The BCC-CSM1.1 model performed better than the others regarding correlations between modeled precipitation and documented dry-wet periods. (3) Over most of the subbasins in the upper Yangtze River, the magnitude of extreme discharge in the BCC-CSM1.1 model results showed that there was a decrease from the MCA to the LIA period and an increase in the historical period relative to the cold period, while a future increase was projected by the four GCMs under the influence of climate change. [ABSTRACT FROM AUTHOR]

The performance of urban drainage system (UDS) and green infrastructures (GI) significantly depends on the accurate determination of the rainfall characteristics of an urban area, especially because these rainfall characteristic variables are closely related. A bivariate copula method is used to evaluate and compare the joint impact of rainfall depth and duration on the hydrological performance of UDS and GI by considering the dependent structure of flood drivers. The peak flow, peak flow occurrence time, outflow, runoff coefficient, overflow, and resilience serve as the performance indicators. The estimated joint probability based on the optimal copula is used to define the boundary conditions for the calibrated Storm Water Management Model to simulate the hydrological process and the magnitude of floods caused by various combination of rainfall depth and durations. Results demonstrate that considering the dependence structure of rainfall characteristics, especially for the peak flow, runoff coefficient, and overflow, is critical for the comprehensive evaluation of the performance of the UDS. Neglecting the interaction between flood drivers can lead to overestimating the performance of UDS and GI in mitigating urban flood risk. The GI can effectively reduce urban flood and improve resilience, which varies with rainfall characteristics. Our results suggest that the utilization of accurate data on rainfall characteristics enhances the performance of the GI in flood mitigation and resilience improvement. [ABSTRACT FROM AUTHOR]

Water resources sustainability in High Mountain Asia (HMA) surrounding the Tibetan Plateau (TP)—known as Asia’s water tower—has triggered widespread concerns because HMA protects millions of people against water stress1,2. However, the mechanisms behind the heterogeneous trends observed in terrestrial water storage (TWS) over the TP remain poorly understood. Here we use a Lagrangian particle dispersion model and satellite observations to attribute about 1 Gt of monthly TWS decline in the southern TP during 2003–2016 to westerlies-carried deficit in precipitation minus evaporation (PME) from the southeast North Atlantic. We further show that HMA blocks the propagation of PME deficit into the central TP, causing a monthly TWS increase by about 0.5 Gt. Furthermore, warming-induced snow and glacial melt as well as drying-induced TWS depletion in HMA weaken the blocking of HMA’s mountains, causing persistent northward expansion of the TP’s TWS deficit since 2009. Future projections under two emissions scenarios verified by satellite observations during 2020–2021 indicate that, by the end of the twenty-first century, up to 84% (for scenario SSP245) and 97% (for scenario SSP585) of the TP could be afflicted by TWS deficits. Our findings indicate a trajectory towards unsustainable water systems in HMA that could exacerbate downstream water stress.Weakening blocking effect of the High Mountain Asia on the westerlies-carried deficit in precipitation minus evaporation from the southeast North Atlantic is demonstrated, leading to persistent northward expansion of terrestrial water storage deficit in the Tibet Plateau. [ABSTRACT FROM AUTHOR]

Energy efficiency is crucial to greenhouse gas (GHG) emission pathways reported by the Intergovernmental Panel on Climate Change. Electrical overload frequently occurs and causes unwanted outages in distribution networks, which reduces energy utilization efficiency and raises environmental risks endangering public safety. Electrical load, however, has a dynamically fluctuating behavior with notoriously nonlinear hourly, daily, and seasonal patterns. Accurate and reliable load forecasting plays an important role in scheduling power generation processes and preventing electrical systems from overloading; nevertheless, such forecasting is fundamentally challenging, especially under highly variable power load and climate conditions. This study proposed a deep learning–based monotone composite quantile regression neural network (D-MCQRNN) model to extract the multiple non-crossing and nonlinear quantile functions while conquering the drawbacks of error propagation and accumulation encountered in multi-step-ahead probability density forecasting. The constructed models were assessed by an hourly power load series collected at the electric grid center of Henan Province in China in two recent years, along with the corresponding meteorological data collected at 16 monitoring stations. The results demonstrated that the proposed D-MCQRNN model could significantly alleviate the time-lag and biased-prediction phenomena and noticeably improve the accuracy and reliability of multi-step-ahead probability density forecasts on power load. Consequently, the proposed model can significantly reduce the risk and impact of overload faults and effectively promote energy utilization efficiency, thereby mitigating GHG emissions and moving toward cleaner energy production. [ABSTRACT FROM AUTHOR]

Most researches on regional flood frequency analysis (RFFA) have proved that the incorporation of hydrologic information (e.g., catchment attributes and flood records) from different sites in a region can provide more accurate flood estimation than using only the observed flood series at the site of concern. One kind of RFFA is based on the Bayesian method with prior information inferred from regional regression by using the generalized least squares (GLS) model, which is more flexible than other RFFA methods. However, the GLS model for regional regression is a stationary method and not suitable for coping with nonstationary prior information. In this study, in nonstationary condition, the Bayesian RFFA with the prior information inferred from regional regression by using the linear mixed effect (LME) model (i.e. a model that adds random effects to the GLS model) is investigated. Both the GLS-based and LME-based Bayesian RFFA methods have been applied to four hydrological stations within the Dongting Lake basin for comparison, and the results show that the performance of nonstationary LME-based Bayesian RFFA method is better than that of stationary GLS-based Bayesian RFFA method according to the deviance information criterion (DIC). Compared with the stationary GLS-based Bayesian RFFA method, changes in uncertainty of regression coefficients estimation of at-site flood distribution parameters are different from site to site by using the nonstationary LME-based Bayesian RFFA method. The use of nonstationary LME-based Bayesian RFFA method reduces design flood uncertainty, especially for the very small exceedance probability at the tail. This study extends the application of the Bayesian RFFA method to the nonstationary condition, which is helpful for nonstationary flood frequency analysis of ungauged sites. [ABSTRACT FROM AUTHOR]

Intelligent scheduling of urban drainage systems is generally regarded as a potentially sustainable strategy for urban flood management. To investigate the effectiveness of the intelligent scheduling strategy in mitigating urban flooding, a new intelligent scheduling model (ISM) that couples the Storm Water Management Model (SWMM) and a multiobjective particle swarm optimization algorithm is proposed for a simulation–optimization framework. The objectives of the ISM are to minimize the flooding volume, front-pool water level fluctuation, and operational cost. Synthetic rainfall events with different durations and return periods based on the Gumbel distribution and observed rainfall events are utilized to comprehensively assess the designed model's performance in the Dealim3 catchment, South Korea. The selected ISM-based scheduling strategies are assessed in accordance with climate change mitigation (i.e., reducing greenhouse gas emissions) and local adaptation strategies (i.e., improving drainage systems). The results indicate that these strategies generated by ISM lead to reductions in flooding, water level fluctuation, and operational costs. The maximum daily rainfall with a 100-year return period increased by 2.1% and 6.8% during 2025–2064 under SSP1-2.6 and SSP5-8.5, respectively, compared with the historical period (1975–2014), thereby increasing the magnitude of urban flooding. The ISM may also significantly lower the flooding process at specific nodes. The ISM-based strategy outperforms climate change mitigation and other adaptation strategies. This study shows that the ISM-based strategy are very useful to deal with the impact of climate change on urban flooding. [ABSTRACT FROM AUTHOR]

Increasing frequency and intensity of heatwaves (HWs) in a warming climate exert catastrophic impacts on human society and natural environment. However, spatiotemporal variations of HW and their driving factors still remain obscure, especially for HW changes over Eurasia, the region with the largest population of the world. Here we provide a systematic investigation of the HW changes over Eurasia and quantify the contributions of different natural and anthropogenic factors to these changes. Increasing frequency, duration and intensity of HW are observed in most parts of Eurasia, and the occurrence of the first HW event tends to be earlier as well, especially in Europe, East Asia, Central Asia, Southwest Asia, and the Mediterranean region. These intensified HW activities are particularly stronger and more widespread after 1990 s. The spatial pattern of the increasing HW trend is closely tied to the interdecadal changes of sea surface temperature in the North Pacific. More intense hot airmass convection, atmospheric circulation obstruction over the Mediterranean region and the enhanced Mongolian high hinders the southward movement of cold air and cold and wet airmass exchange. Further analyses suggest that the intensifying Eurasian HW tendency is a combined result of both climate change and human activities. Overall, the fractional contributions of climate warming, urbanization, standardized precipitation evaporation index, and Atlantic Multi-decadal Oscillation to the frequency of Eurasian HWs are 30%, 25%, 21% and 24%, respectively. It is also suggested that the relative influential rate of different driving factors for HW varies over time and differs in different areas. [ABSTRACT FROM AUTHOR]

Generalized likelihood uncertainty estimate (GLUE) approach is affected heavily by the choice of cut-off threshold (Tr) and likelihood measure. This work presents a research on investigating potential mechanisms behind the impacts of Tr and likelihood measure on 95% confidence interval estimated by GLUE (95 CI). Several typical likelihood measures are classified into five groups according to error models. Theoretical analysis reveals strict mathematical relationships between likelihood measures based on a same error model. Besides, a multiple attribute decision making (MADM) framework is proposed to assess overall quality of various geometrical features for 95 CI by integrating a range of interval indicators into a comprehensive score. Case studies indicate that (1) As Tr increases, 95 CI widens in low-level flow section, moves upward in recession phase of medium-level flow section and narrows in high-level flow section. The variations of interval indicators with Tr can be attributed to trade-off mechanism amongst the variations of geometrical features caused by widening, moving and narrowing trends of 95 CIs. (2) Much higher similarity in interval indicators and 95 CIs occurs for likelihood measures of a same group than those of different groups. (3) Formula of likelihood measure controls individuality of 95 CIs, error model of likelihood measure controls commonality of 95 CIs, and Tr acts as a regulator between individuality and commonality. (4) 95 CI shows similar responses to the choice of Tr and likelihood measure regardless of time span and climate variability. To summarize, this work offers further understanding and guidance in chose of Tr and likelihood measure for GLUE applications. [ABSTRACT FROM AUTHOR]

Eurasia, home to ~70% of global population, is characterized by (semi-)arid climate. Water scarcity in the mid-latitude Eurasia (MLE) has been exacerbated by a consistent decline in terrestrial water storage (TWS), attributed primarily to human activities. However, the atmospheric mechanisms behind such TWS decline remain unclear. Here, we investigate teleconnections between drying in low-latitude North Atlantic Ocean (LNATO) and TWS depletions across MLE. We elucidate mechanistic linkages and detecte high correlations between decreased TWS in MLE and the decreased precipitation-minus-evapotranspiration (PME) in LNATO. TWS in MLE declines by ~257% during 2003-2017 due to northeastward propagation of PME deficit following two distinct seasonal landfalling routes during January-May and June-January. The same mechanism reduces TWS during 2031-2050 by ~107% and ~447% under scenarios SSP245 and SSP585, respectively. Our findings highlight the risk of increased future water scarcity across MLE caused by large-scale climatic drivers, compounding the impacts of human activities. Total water storage in Eurasia can be driven by both climate variability and human activities, with the latter suggested as the key factor for water loss. However, here the authors show that drying in the low-latitude Atlantic Ocean is the dominant force in storage depeletion during 2003-2017. [ABSTRACT FROM AUTHOR]

Under current global change, the driving force of evolution of drought has gradually transitioned from a single natural factor to a combination of natural and anthropogenic factors. Therefore, widely used standardised drought indices based on assumption of stationarity are challenged and may not accurately assess characteristics of drought processes. In this study, a nonstationary standardised streamflow index (NSSI) that incorporates climate and reservoir indices as external covariates was developed to access nonstationary hydrological drought. The first step of the proposed approach is to apply methods of trend and change point analysis to assess the nonstationarity of streamflow series to determine type of streamflow regime, that is, the natural and altered regime. Then, different nonstationary models were constructed to calculate the NSSI by selecting climate indices as covariates for streamflow series with natural regime, and climate and reservoir indices as covariate for streamflow series with altered regime. Four stations in the upper reaches of the Huaihe River basin, China, were selected to examine the performance of the proposed NSSI. The results indicated that Dapoling (DPL), Changtaiguan (CTG), and Xixian (XX) stations had natural streamflow regimes, while the Nanwan (NW) station had an altered regime. The global deviances of the optimal nonstationary models were 17 (2.2%), 18 (2.9%), 26 (4.0%), and 22 (3.5%) less than those of stationary models for DPL, CTG, NW, and XX stations, respectively. Especially, for the NW station influenced by reservoir regulations, the frequency of slight drought and moderate drought of NSSI was 12.8% lower than and 13.1% greater than those of SSI, respectively. Overall, the NSSI that incorporates the influence of climate variability and reservoir regulations provided more reliable assessment of hydrological drought than the traditional SSI. [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]

Flood wave superposition (FWS) of upstream and tributary rivers, as a typical compound hydrological event, may lead to considerable downstream flood hazards. In spite of this, the quantitative identification of FWS classification so far remains elusive. In this study, we quantitatively examined the typical matching patterns of FWS based on flood peak magnitude and timing between the upstream and tributary discharge, to evaluate the flood severity for both present-day conditions and future climate projections. The future FWS projection was realized using hydrological modeling coupled with multiple outputs of global climate models (GCMs) under the Representative Concentration Pathway (RCP) 2.6 and 8.5 emission scenarios. A triple point of discharge stations, including upstream, downstream and tributary stations, located at a river confluence of the Poyang Lake Basin, China, was selected as the study area. The results revealed that the frequency of perfect temporal matching (0-day time lag) of projected peaks between upstream and tributary floods increased from 61% in the reference period to >68% and > 66% in the future under RCPs 2.6 and 8.5, respectively. Furthermore, both the future numbers and frequencies of the projected peaks between upstream and tributary floods with both perfect temporal and magnitude matching (the classification associated with the most damage in FWS) would substantially increase across all GCMs under RCPs 2.6 and 8.5. These findings indicate that future FWS is expected to experience increasing severity due to the changing climate under no matter RCP 2.6 or 8.5 emission scenarios. Overall, it is suggested that effective adaptation strategies be developed in order to stagger the timing of upstream and tributary floods in the future. [ABSTRACT FROM AUTHOR]

Glaciers are important freshwater storage systems in the Tianshan Mountains. Under the context of climate change, quantifying changes in glacier mass balance, the melt-season (June–September) runoff and its key runoff component (glacier runoff) is of importance for understanding the discharge composition and ensuring adequate management of water resources. In this study, the modified HBV-D (Hydrologiska Byrans Vattenbalansavdelning-D) hydrological model was used to simulate hydrological processes for a data-sparse glacierized watershed, the headwater catchment of Manas River basin (MRB) in the Tianshan Mountains. Meanwhile, the roles of three modified elements of HBV-D in simulating glacier dynamics are identified. Sequently, the glacier mass balance and runoff during 1984–2006 are reconstructed and their responses to climate change are investigated. The analysis showed (1) the snow/glacier melt method makes more contribution to improving the performance of HBV-D model in simulating historical change of glacier volume, followed by the glacier dynamic method. (2) The reconstructed mass balance follows a decreased trend in the MRB. The maximum accumulation of glacier mass balance occurs in June. Snowmelt over the surface of glacier and glacier melt reach peak in June and August, respectively. Furthermore, sensitivity experiments showed increased mass balance induced by a 10% increase in precipitation cannot compensate for the decreased mass balance due to a 1 °C temperature rise. (3) Significant contribution (about 41.5%) of runoff in glacierized area to the melt-season total runoff of the catchment is identified. Both the glacier runoff and its contribution to melt-season total runoff show increased trends during the simulation period. Compared with the melt-season mean temperature and annual precipitation over glaciers, the melt-season positive accumulated temperature over glaciers played the most important role in influencing changes in glacier runoff in MRB. The findings in this study are beneficial for implementing adaptive countermeasures for water resources management in the data-scarce glaciated high-mountainous region. [ABSTRACT FROM AUTHOR]

The accurate simulation and prediction of flood response in urbanized basins remains a great challenge due to the spatial and temporal heterogeneities in land surface properties. We hereby propose an integrated modelling approach that consists of a semi-distributed conceptual hydrological model and a novel parameterization strategy. The modelling approach integrates the Xinanjiang (XAJ) model, Taihu Basin (TB) model, and Nash instantaneous unit hydrograph (IUH) into a framework. Model parameters are calibrated by optimizing their relationships with corresponding physical factors. The proposed modelling approach is applied in the Qinhuai River basin (QRB), China. The modelling approach shows satisfactory performance in flood simulation both for calibration and validation of flood events in the QRB. The approach has temporal and spatial prediction capability by using the established relationships between parameter values and physical factors. Robustness analysis reveals that the different sets of flood events used for parameter relationship calibration led to similar model performance. Numerical experiments show that impervious coverage poses strong influences on the model performance and needs to be considered in flood routing simulations for small- or medium-intensity flood events. [ABSTRACT FROM AUTHOR]

Understanding of the vegetation dynamics is essential for addressing the potential threats of terrestrial ecosystem. In recent years, the vegetation coverage of the Yangtze River Basin (YRB) has increased significantly, yet the spatio-temporal variations and potential driving meteorological factors of carbon use efficiency (CUE) under the context of global warming are still not clear. In this study, MODIS-based public-domain data during 2000–2015 was used to analyze these aspects in the YRB, a large river basin with powerful ecological functions in China. Spatio-temporal variations of CUE in different sub-basins and land cover types were investigated and the correlations with potential driving meteorological factors were examined. Results revealed that CUE in the YRB had strong spatiotemporal variability and varied remarkably in different land cover types. For the whole YRB, the average CUE of vegetated land was 0.519, while the long-term change trend of CUE was obscure. Along the rising altitude, CUE generally showed an increasing trend until the altitude of 3900 m and then followed by a decreasing trend. CUE of grasslands was generally higher than that of croplands, and then forest lands. The inter-annual variation of CUE in the YRB is likely to be driven by precipitation as a strong positive partial correlation between the inter-annual variability of CUE and precipitation was observed in most of sub-basins and land cover types in the YRB. The influence of temperature and relative humidity is also outstanding in certain regions and land cover types. Our findings are useful from the view point of carbon cycle and reasonable land cover management under the context of global warming. [ABSTRACT FROM AUTHOR]

Practical application of hydrological models requires parameter transfer, both temporally and spatially, to compensate for the lack of data. In this study, the transferability of parameters is evaluated using a lumped hydrological model called the Xinanjiang model to simulate runoff at different spatiotemporal scales in the Jianxi basin in south-east China and its four sub-basins. The functional relationships are built based on the posterior distribution derived by the Differential Evolution Adaptive Metropolis (DREAM) algorithm. The results show that (1) the sensitivity of parameters KE, SM, KI and KG shows obvious temporal characteristics, and the sensitivity of NK and CG shows strong spatial characteristics; (2) most relationships between sensitive parameters and scales are remarkable with goodness-of-fit coefficient higher than 0.9, which has been verified to achieve good performance in the temporal and spatial transfer; (3) the spatial transferability of the model is greatly influenced by the difference between the basin sizes; and (4) those parameters with strong spatial characteristics, such as NK and CG, show obvious impacts on the performance and uncertainty of the model transferred from the larger/smaller to smaller/larger basins. [ABSTRACT FROM AUTHOR]

Given a changing environment, estimating a flood magnitude corresponding to a desired return period considering nonstationarity is crucial for hydrological engineering designs. Four nonstationary design methods, namely expected waiting time (EWT), expected number of exceedances (ENE), equivalent reliability (ER), and average design life level (ADLL) have already been proposed in recent years. Among them, the EWT method needs to estimate design flood magnitudes by solving numerically. In addition, EWT requires estimating design quantiles for infinite lifespan, or extrapolation time (textra), to guarantee the convergence of the EWT solution under certain conditions. However, few studies have systematically evaluated pros and cons of the EWT method as to how to determine the textra and what kinds of misunderstandings on the applicability of the EWT method exist. In this study, we aim to provide the first investigation of various factors that influence the value of textra in the EWT method, and provide comprehensive comparison of the four methods from the perspectives of textra, design values and associated uncertainties. The annual maximum flood series (AMFS) of 25 hydrological stations, with increasing and decreasing trends, in Pearl River and Weihe River were chosen for illustrations. The results indicate that: (1) the textra of EWT is considerably affected by the trend of AMFS and the choice of extreme distributions. In other words, the textra of stations with increasing trends was significantly smaller than that of stations with decreasing trends, and the textra was also larger for distributions with heavier tail; (2) EWT produced larger design values than ENE for increasing trends, and both EWT and ENE yielded larger design values than ER and ADLL for higher return periods, while complete opposite results were obtained for decreasing trends. [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]

Meteorological Ensemble Streamflow Prediction (ESP), which uses Ensemble Weather forecasts (EWFs) to drive hydrological models, is a useful methodology for extending forecast periods and to provide valuable uncertainty information to improve the operation of future water resources. However, raw EWFs are usually biased and under-dispersive and so cannot be directly used in ESP, leading to the development of several post-processing methods. The performance of these methods needs to be evaluated/compared in building ESP based on deterministic and probabilistic criteria. In addition, likely influencing factors also need to be identified. This study evaluated the performance of four state-of-the-art methods: the Generator-based Post-Processing (GPP) method, Extended Logistic Regression (ExLR), Bayesian Model Averaging (BMA) and Affine Kernel Dressing (AKD), using a simple bias correction (BC) method as a benchmark. The evaluation was carried out over four watersheds with different basin areas in the humid region of central-south China based on the weather reforecasts from the Global Ensemble Forecasting System (GEFS). The results show that the performance of the post-processing methods varies with the forecast variable (precipitation, or air temperature or streamflow), but all of them outperform the BC and GEFS. For the four post-processing methods, the advantage of the generator-based methods (GPP and ExLR) lies in their probabilistic performance, which outperforms the distribution-based methods (BMA and AKD) by about 10% in precipitation forecasts and about 20% in streamflow forecasts, while the distribution-based methods (BMA and AKD) are better at their deterministic performance for precipitation forecasts, with a benefit of about 15%. Meanwhile, the post-processing methods generally perform better for precipitation and streamflow forecasts, but worse for air temperature forecasts for a bigger basin compared to the distribution-based methods. The results of this study emphasize the importance of considering the uncertainty of post-processing methods in ESP. [ABSTRACT FROM AUTHOR]

Statistical methods have been widely used to build different streamflow prediction models; however, lacking of physical mechanism prevents precise streamflow prediction in alpine regions dominated by rainfall, snow and glacier. To improve precision, a new hybrid model (HBNN) integrating HBV hydrological model, Bayesian neural network (BNN) and uncertainty analysis is proposed. In this approach, the HBV is mainly used to generate initial snow-melt and glacier-melt runoffs that are regarded as new inputs of BNN for precision improvement. To examine model reliability, a hybrid deterministic model called HLSSVM incorporating the HBV model and least-square support vector machine is also developed and compared with HBNN in a typical region, the Yarkant River basin in Central Asia. The findings suggest that the HBNN model is a robust streamflow prediction model for alpine regions and capable of combining strengths of both the BNN statistical model and the HBV hydrological model, providing not only more precise streamflow prediction but also more reasonable uncertainty intervals than competitors particularly at high flows. It can be used in predicting streamflow for similar regions worldwide. [ABSTRACT FROM AUTHOR]

The source region of Yellow river is an alpine river sensitive to climate changes, but the potential effects of climate change on hydrological regime characteristics and ecological implications are less understood. This study aims to assess the response of the alterations in the flow regimes over the source region of Yellow river to climate change using Soil and Water Integrated Model driven by different Global Circulation Models (GFDL-ESM2M, IPSL-CM5A-LR and MIROC-ESM-CHEM) under three Representative Concentration Pathway emission scenarios (RCP2.6, RCP4.5 and RCP8.5). Indicators of hydrological alteration and River impact index are employed to evaluate streamflow regime alterations at multiple temporal scales. Results show that the magnitude of monthly and annual streamflow except May, the magnitude and duration of the annual extreme, and the number of reversals are projected to increase in the near future period (2020-2049) and far future period (2070-2099) compared to the baseline period (1971-2000). The timing of annual maximum flows is expected to shift backwards. The source region of Yellow river is expected to undergo low change degree as per the scenarios RCP2.6 for both two future periods and under the scenarios RCP4.5 for the near future period, whereas high change degree under RCP4.5 and RCP8.5 in the far period on the daily scale. On the monthly scale, climate changes mainly have effects on river flow magnitude and timing. The basin would suffer an incipient impact alteration in the far period under RCP4.5 and RCP8.5, while low impact in other scenarios. These changes in flow regimes could have several positive impacts on aquatic ecosystems in the near period but more detrimental effects in the far period. [ABSTRACT FROM AUTHOR]

The occurrence of flood and drought frequency is highly correlated with the temporal fluctuations of streamflow series; understanding of these fluctuations is essential for the improved modeling and statistical prediction of extreme changes in river basins. In this study, the complexity of daily streamflow fluctuations was investigated by using multifractal detrended fluctuation analysis (MF-DFA) in a large heterogeneous lake basin, the Poyang Lake basin in China, and the potential impacts of human activities were also explored. Major results indicate that the multifractality of streamflow fluctuations shows significant regional characteristics. In the study catchment, all the daily streamflow series present a strong long-range correlation with Hurst exponents bigger than 0.8. The q-order Hurst exponent h(q) of all the hydrostations can be characterized well by only two parameters: a (0.354 ≤ a ≤ 0.384) and b (0.627 ≤ b ≤ 0.677), with no pronounced differences. Singularity spectrum analysis pointed out that small fluctuations play a dominant role in all daily streamflow series. Our research also revealed that both the correlation properties and the broad probability density function (PDF) of hydrological series can be responsible for the multifractality of streamflow series that depends on watershed areas. In addition, we emphasized the relationship between watershed area and the estimated multifractal parameters, such as the Hurst exponent and fitted parameters a and b from the q-order Hurst exponent h(q). However, the relationship between the width of the singularity spectrum (Δα) and watershed area is not clear. Further investigation revealed that increasing forest coverage and reservoir storage can effectively enhance the persistence of daily streamflow, decrease the hydrological complexity of large fluctuations, and increase the small fluctuations. [ABSTRACT FROM AUTHOR]

Currently used goodness-of-fit (GOF) indicators (i.e. efficiency criteria) are largely empirical and different GOF indicators emphasize different aspects of model performance; a thorough assessment of model skill may require the use of robust skill matrices. In this study, based on the maximum likelihood method, a statistical measure termed BC-GED error model is proposed, which firstly uses the Box-Cox (BC) transformation method to remove the heteroscedasticity of model residuals, and then employs the generalized error distribution (GED) with zero-mean to fit the distribution of model residuals after BC transformation. Various distance-based GOF indicators can be explicitly expressed by the BC-GED error model for different values of the BC transformation parameter λ and GED kurtosis coefficient β. Our study proves that (1) the shape of error distribution implied in the GOF indicators affects the model performance on high or low flow discharges because large error-power (β) value can cause low probability of large residuals and small β value will lead to high probability of zero value; (2) the mean absolute error could balance consideration of low and high flow value as its assumed error distribution (i.e. Laplace distribution, where β = 1) is the turning point of GED derivative at zero value. The results of a study performed in the Baocun watershed via comparison of the SWAT model-calibration results using six distance-based GOF indicators show that even though the formal BC-GED is theoretically reasonable, the calibrated model parameters do not always correspond to high performance of model-simulation results because of imperfection of the hydrologic model. However, the derived distance-based GOF indicators using the maximum likelihood method offer an easy way of choosing GOF indicators for different study purposes and developing multi-objective calibration strategies. [ABSTRACT FROM AUTHOR]

In recent years, dramatic decline in China’s largest freshwater lake, Poyang Lake, has raised wide concerns about water supply and ecological crises in the middle–lower Yangtze River reaches. To assist in resolving the debates regarding the low water regime of the lake, the current study quantitatively assessed the enhanced water level decline from the changing underlying surface in the Poyang Lake region. It is the first time that the magnitude, temporal–spatial difference, trend development and background mechanism of lake water level variation and its causes are studied comprehensively. The results revealed that the changing underlying surface in the lake region has caused an average decline of annual water level of 0.26 m ~ 0.75 m across the lake during 2000–2014, which shows great seasonal and spatial differences. The enlarged outflow cross–section due to extensive sand mining was the major reason for the effect on water level decline in the northern lake. While, increased water surface gradient should be attributed to water level decline in the southern lake. The long–term increasing trend of annual lake water level decline reflects the cumulative effects of lake bottom topography change caused by the continuous south movement of sand mining activities. [ABSTRACT FROM AUTHOR]

The temporal resolution of observed data is a critical element in determining the parameters, prediction performance, and applicability of hydrological models. In this study, runoff simulations were performed at different temporal resolutions using the Xinanjiang model to evaluate the influence of temporal resolution on the model parameters and performance. Based on the sensitivity analysis of the model parameters, the posterior distribution of the sensitive parameters was derived using the Bayesian method and Differential Evolution Adaptive Metropolis (DREAM) algorithm at different temporal resolutions. The transformation functions of the model parameters were put forward to transform the parameters according to the regulatory between the parameters and time-steps on the basis of the parameters posterior distribution. The model performance and uncertainty for runoff simulation were compared and discussed at each temporal resolution. The results show that (1) the parameters related with the process of the water balance and runoff routing are identified as sensitive to the temporal resolutions, and there exist linear or power function relationships between parameter values at different temporal resolutions; and (2) the quantitative relationship equations have been verified to have good capacity for model simulation when the model parameters are transformed from other temporal resolution. [ABSTRACT FROM AUTHOR]

Calibration is an important step in most hydrological modeling processes because it helps produce reasonable results. This study aims to investigate the seasonal sensitivity of streamflow parameters and to evaluate the ability of a season-based multi-objective approach to calibrate the Soil and Water Assessment Tool (SWAT) model. The primary goal was achieved through an integrated approach. A variance-based global sensitivity technique, Sobol’ method, was used to evaluate the seasonal sensitivity of streamflow parameters. For the multi-objective approach, three objective functions were considered: the Nash–Sutcliffe efficiency, Nash–Sutcliffe efficiency of logarithmic transformed discharge, and relative bias. The model performances of the season-based multi-objective approach MOO(II), based on these functions and flow duration curves during wet and dry seasons, were compared to three other methods: SOO(I), a conventional single-objective approach to the entire series; SOO(II), a season-based single-objective approach; and MOO(I), a multi-objective approach for the entire series. The four methods were assessed using the SWAT model to predict daily discharge in the Jinjiang basin in southeastern China. The results showed that sensitivity of model parameters varied between the wet and dry seasons. The seasonal calibration approaches, MOO(II) and SOO(II), showed significantly better simulation performances during the dry season while the multi-objective approaches produced more accurate simulations of different aspects of the hydrograph, including peak and low flows and overall water balance, compared to the single-objective methods. MOO(II) captured the seasonal variation of hydrological processes best, compared to the other methods, and the parameter values it identified demonstrated significant seasonal variations. [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]

The regionalization methods, which 'trade space for time' by pooling information from different locations in the frequency analysis, are efficient tools to enhance the reliability of extreme quantile estimates. This paper aims at improving the understanding of the regional frequency of extreme precipitation by using regionalization methods, and providing scientific background and practical assistance in formulating the regional development strategies for water resources management in one of the most developed and flood-prone regions in China, the Yangtze River Delta (YRD) region. To achieve the main goals, L-moment-based index-flood (LMIF) method, one of the most popular regionalization methods, is used in the regional frequency analysis of extreme precipitation with special attention paid to inter-site dependence and its influence on the accuracy of quantile estimates, which has not been considered by most of the studies using LMIF method. Extensive data screening of stationarity, serial dependence, and inter-site dependence was carried out first. The entire YRD region was then categorized into four homogeneous regions through cluster analysis and homogenous analysis. Based on goodness-of-fit statistic and L-moment ratio diagrams, generalized extreme-value (GEV) and generalized normal (GNO) distributions were identified as the best fitted distributions for most of the sub-regions, and estimated quantiles for each region were obtained. Monte Carlo simulation was used to evaluate the accuracy of the quantile estimates taking inter-site dependence into consideration. The results showed that the root-mean-square errors (RMSEs) were bigger and the 90 % error bounds were wider with inter-site dependence than those without inter-site dependence for both the regional growth curve and quantile curve. The spatial patterns of extreme precipitation with a return period of 100 years were finally obtained which indicated that there are two regions with highest precipitation extremes and a large region with low precipitation extremes. However, the regions with low precipitation extremes are the most developed and densely populated regions of the country, and floods will cause great loss of human life and property damage due to the high vulnerability. The study methods and procedure demonstrated in this paper will provide useful reference for frequency analysis of precipitation extremes in large regions, and the findings of the paper will be beneficial in flood control and management in the study area. [ABSTRACT FROM AUTHOR]

Climate change has significant implications for glaciers and water resources in the Himalayan region. There is an urgent need to improve our current knowledge and methods in quantifying changes in water resources in this region. This study uses an integrated approach that couples a hydrological model and a glacier retreat model to assess the future water resources for two Himalayan basins. They are the Chamkhar Chhu basin in Bhutan (Eastern Himalayas) and the Beas basin in India (Western Himalayas). The future climate is simulated by two Regional Climate Models (RCMs) for south Asia under three Representative Concentration Pathways (Rcp2.6, Rcp4.5 and Rcp8.5). The six climate projections for the period 2010-2100 indicate significant warming effects; however, projected changes in precipitation are not consistent. Discrepancies in precipitation are noteworthy between the RCMs and greenhouse gases emissions scenarios. The glaciers in the Chamkhar Chhu basin are predicted to disappear or reduce to a small size before the 2050s, whereas the glaciers in the Beas basin are expected to lose mass before the 2060s, and afterwards to gain mass under Rcp2.6 and Rcp4.5, or to melt at a high rate under Rcp8.5. The available water resources per capita of two basins are projected to decrease in the period 2010-2050. The decreasing water resources are jointly induced by climate change and population growth. The latter is responsible for roughly 40 % of the water declines. Both basins are facing water shortages at present and the water shortages will intensify in the future. [ABSTRACT FROM AUTHOR]

This paper compares the original and bias corrected global gridded precipitation datasets, tropical rainfall measuring mission (TRMM) and water and global change forcing data (WFD) with gauged precipitation and evaluates the usefulness of gridded precipitation datasets for hydrological simulations using the distributed soil and water assessment tool (SWAT) and lumped Xinanjiang model in Xiangjiang River basin, Southern China. The results show that the differences in areal mean rainfalls of original TRMM and WFD datasets and gauged dataset are in acceptable limits of less than 10 %, while larger differences exist in maximal 5-day rainfalls, dry spells and Fréchet distance. The bias correction methods are able to significantly improve the biases in the mean values of TRMM and WFD datasets. The nonlinear bias correction method gives good results in correcting the standard deviations of TRMM/WFD data. The hydrological modelling results show that the WFD datasets perform relatively better than TRMM datasets even though the results are poor as compared with using gauged rainfall as input in daily step hydrological models. At monthly time step, both TRMM and WFD data produce acceptable model simulation results in terms of Nash-Sutcliffe efficiency ( E > 0.7 for original TRMM/WFD data and E > 0.8 for linearly corrected TRMM/WFD data) and relative error (| R| < 10 %). [ABSTRACT FROM AUTHOR]

Flood frequency analysis is concerned with fitting a probability distribution to observed data to make predictions about the occurrence of floods in the future. Under conditions of climate change, or other changes to the water cycle that impact flood runoff, the flood series is likely to exhibit non-stationarity, in which case the return period of a flood event of a certain magnitude would change over time. In non-stationary flood frequency analysis, it is customary to examine only the non-stationarity of annual maximum flood data. We developed a way of considering the effect of non-stationarity in the annual daily flow series on the non-stationarity in the annual maximum flood series, which we termed the norming constants method (NCM) of non-stationary flood frequency analysis (FFA). After developing and explaining a framework for application of the method, we tested it using data from the Wei River, China. After detecting significant non-stationarity in both the annual maximum daily flood series and the annual daily flow series, application of the method revealed superior model performance compared to modelling the annual maximum daily flood series under the assumption of stationarity, and the result was further improved if explanatory climatic variables were considered. We conclude that the NCM of non-stationary FFA has potential for widespread application due to the now generally accepted weakness of the assumption of stationarity of flood time series. [ABSTRACT FROM AUTHOR]

In this study, we thoroughly analyzed spatial and temporal distributions of runoff and their relation with precipitation changes based on monthly runoff dataset at 25 hydrological stations and monthly precipitation at 127 stations in Guangdong Province, south China. Trends of the runoff and precipitation are detected using Mann-Kendall trend test technique. Correlations between runoff and precipitation are tested using Spearman's and Pearson's correlation coefficients. The results indicate that: (1) annual maximum monthly runoff is mainly in decreasing tendency and significant increasing annual minimum monthly runoff is observed in the northern and eastern Guangdong Province. In addition, annual mean runoff is observed to be increasing at the stations located in the West and North Rivers and the coastal region; (2) analysis of seasonal runoff variations indicates increasing runoff in spring, autumn and winter. Wherein, significant increase of runoff is found at 8 stations and only 3 stations are dominated by decreasing runoff in winter; (3) runoff changes of the Guangdong Province are mainly the results of precipitation changes. The Guangdong Province is wetter in winter, spring and autumn. Summer is coming to be drier as reflected by decreasing runoff in the season; (4) both precipitation change and water reservoirs also play important roles in the increasing of annual minimum monthly streamflow. Seasonal shifts of runoff variations may pose new challenges for the water resources management under the influences of climate changes and intensifying human activities. [ABSTRACT FROM AUTHOR]

Poyang Lake, directly connected with the Yangtze River, is one of the most frequently flooded areas in China. The frequent large floods have caused huge damages to the environment and economy and threatened the life of approximately 10 million people. Understanding the changing characteristics of floods as well as the affecting factors is an important prerequisite of flood disaster prevention and mitigation. In this study, the characteristics of historical floods in Poyang Lake were identified and examined based on several widely used indices and Mann-Kendall test. The study also analyzed the related driving forces and discussed their relationships with Poyang Lake floods. The results show that the floods in Poyang Lake mainly occurred in mid- and late July. The inter-annual variation of highest flood stages and duration showed a long-term increasing linear trend. Also, a slightly increasing linear trend in the timing of highest stages indicated the floods have occurred later and later during the last 60 years. At the decadal scale, the flood situations were most severe in 1990s while gentle in 2000s in terms of the occurrence frequency and average duration. The climate change was the primary influence factor for changing of flood characteristics in Poyang Lake; i.e., the abnormally large rainfall during the flood season and subsequent large discharges of Yangtze River and runoff inflow from the basin were mainly responsible for the severe floods in 1990s. Also, the smallest storage capacity of Poyang Lake in 1990s due to the intensive human activities such as the great floodplain occupancy and levee construction further increased the severity of floods. While the rare floods in 2000s can be attributed to, on the one hand, the decrease in rainfall over the middle reaches of Yangtze River which caused the low streamflows of Yangtze River and runoff inflow from the Poyang Lake basin. On the other hand, the 'return land to lake' policy, intensive sand mining in the lake and the flood control of Three Gorges Dam also played an important role in mitigation of flood frequency and severity. [ABSTRACT FROM AUTHOR]

The input uncertainty is as significant as model error, which affects the parameter estimation, yields bias and misleading results. This study performed a comprehensive comparison and evaluation of uncertainty estimates according to the impact of precipitation errors by GLUE and Bayesian methods using the Metropolis Hasting algorithm in a validated conceptual hydrological model (WASMOD). It aims to explain the sensitivity and differences between the GLUE and Bayesian method applied to hydrological model under precipitation errors with constant multiplier parameter and random multiplier parameter. The 95 % confidence interval of monthly discharge in low flow, medium flow and high flow were selected for comparison. Four indices, i.e. the average relative interval length, the percentage of observations bracketed by the confidence interval, the percentage of observations bracketed by the unit confidence interval and the continuous rank probability score ( CRPS) were used in this study for sensitivity analysis under model input error via GLUE and Bayesian methods. It was found that (1) the posterior distributions derived by the Bayesian method are narrower and sharper than those obtained by the GLUE under precipitation errors, but the differences are quite small; (2) Bayesian method performs more sensitive in uncertainty estimates of discharge than GLUE according to the impact of precipitation errors; (3) GLUE and Bayesian methods are more sensitive in uncertainty estimate of high flow than the other flows by the impact of precipitation errors; and (4) under the impact of precipitation, the results of CRPS for low and medium flows are quite stable from both GLUE and Bayesian method while it is sensitive for high flow by Bayesian method. [ABSTRACT FROM AUTHOR]

During the past two decades, numerous datasets have been developed for global/regional hydrological assessment and modeling, but these datasets often show differences in their spatial and temporal distributions of precipitation, which is one of the most critical input variables in global/regional hydrological modeling. This paper is aimed to explore the precipitation characteristics of the Water and Global Change (WATCH) forcing data (WFD) and compare these with the corresponding characteristics derived from satellite-gauge data (TRMM 3B42 and GPCP 1DD) and rain gauge data. It compared the consistency and difference between the WFD and satellite-gauge data in India and examined whether the pattern of seasonal (winter, pre-monsoon, monsoon and post-monsoon) precipitation over six regions [e.g. North Mountainous India (NMI), Northwest India (NWI), North Central India (NCI), West Peninsular India (WPI), East Peninsular India (EPI) and South Peninsular India (SPI)] of India agrees well for the gridded data to be useful in precipitation variability analyses. The multi-time scale of precipitation in India was analysed by wavelet transformation method using gauged and WFD precipitation data. In general, precipitation from WFD is larger than that from satellite-gauge data in NMI and Western Ghats region whereas it is smaller in the dry region of NWI. Both WFD and satellite-gauge datasets underestimate precipitation compared to the measured data but the precipitation from WFD is better estimated than that from satellite-gauge data. It was found that the wavelet power spectrum of precipitation based on WFD is reasonably close to that of measured precipitation in NWI and NCI, while slightly different in NMI. It is felt that the WFD data can be used as a potential dataset for hydrological study in India. [ABSTRACT FROM AUTHOR]

Satellite-based precipitation products (SPPs) have greatly improved their applicability and are expected to offer an alternative to ground-based precipitation estimates in the present and the foreseeable future. There is a strong need for a quantitative evaluation of the usefulness and limitations of SPPs in operational meteorology and hydrology. This study compared two widely used high-resolution SPPs, the Tropical Rainfall Measuring Mission (TRMM) and Precipitation Estimation from Remote Sensing Information using Artificial Neural Network (PERSIANN) in Poyang Lake basin which is located in the middle reach of the Yangtze River in China. The bias of rainfall amount and occurrence frequency under different rainfall intensities and the dependence of SPPs performance on elevation and slope were investigated using different statistical indices. The results revealed that (1) TRMM 3B42 usually underestimates the rainy days and overestimates the average rainfall as well as annual rainfall, while the PERSIANN data were markedly lower than rain gauge data; (2) the rainfall contribution rates were underestimated by TRMM 3B42 in the middle rainfall class but overestimated in the heavy rainfall class, while the opposite trend was observed for PERSIANN; (3) although the temporal distribution characteristics of monthly rainfall were correctly described by both SPPs, PERSIANN tended to suffer a systematic underestimation of rainfall in every month; and (4) the performances of both SPPs had clear dependence on elevation and slope, and their relationships can be fitted using quadratic equations. [ABSTRACT FROM AUTHOR]

Daily precipitation data during the period of 1960 to 2005 from 147 rain gauging stations over the Yangtze River Basin are analyzed to investigate precipitation variations based on precipitation indices and also consecutive rainfall regimes in both space and time. Results indicate decreasing annual/monthly mean precipitation. Distinct decreases in rainfall days are observed over most parts of the Yangtze River Basin, but precipitation intensity is increasing over most parts of the Yangtze River Basin, particularly the lower Yangtze River Basin. Besides, durations of precipitation regimes are shortening; however, the fractional contribution of short-lasting precipitation regimes to the total precipitation amount is increasing. In this sense, the precipitation processes in the Yangtze River Basin are dominated by precipitation regimes of shorter durations. These results indicate intensified hydrological cycle reflected by shortening precipitation regimes. This finding is different from that in Europe where the intensifying precipitation changes are reflected mainly by lengthening precipitation regimes, implying different regional responses of hydrological cycle to climate changes. The results of this study will be of considerable relevance in basin-scale water resources management, human mitigation of natural hazards, and in understanding regional hydrological responses to changing climate at regional scales. [ABSTRACT FROM AUTHOR]

The projected impacts of climate change and variability on floods in the southern Africa has not been well studied despite the threat they pose to human life and property. In this study, the potential impacts of climate change on floods in the upper Kafue River basin, a major tributary of the Zambezi River in southern Africa, were investigated. Catchment hydrography was delineated using the Hydro1k at a spatial resolution of 1 km. The daily global hydrological model WASMOD-D model was calibrated and validated during 1971-1986 and 1987-2001 with the simple-split sample test and during 1971-1980 and 1981-1990 with the differential split sample test, against observed discharge at Machiya gauging station. Predicted discharge for 2021-2050 and 2071-2100 were obtained by forcing the calibrated WASMOD-D with outputs from three GCMs (ECHAM, CMCC3 and IPSL) under the IPCC's SRES A2 and B1 scenarios. The three GCMs derived daily discharges were combined by assigning a weight to each of them according to their skills to reproduce the daily discharge. The two calibration and validation tests suggested that model performance based on evaluation criteria including the Nash-Sutcliffe coefficient, Pearson's correlation coefficient (r), Percent Bias and R was satisfactory. Flood frequency analysis for the reference period (1960-1990) and two future time slices and climate change scenarios was performed using the peak over threshold analysis. The magnitude of flood peaks was shown to follow generalised Pareto distribution. The simulated floods in the scenario periods showed considerable departures from the reference period. In general, flood events increased during both scenario periods with 2021-2050 showing larger change. The approach in our study has a strong potential for similar assessments in other data scarce regions. [ABSTRACT FROM AUTHOR]

Global warming has brought great pressure on the environment and livelihood conditions in Sudan and South Sudan. It is desirable to analyze and predict the change of critical climatic variables, such as temperature and precipitation, which will provide valuable reference results for future water resources planning and management in the region. The aims of this study are to test the applicability of the Long Ashton Research Station Weather Generator (LARS-WG) model in downscaling daily precipitation and daily maximum (Tmax) and daily minimum (Tmin) temperatures in Sudan and South Sudan and use it to predict future changes of precipitation; Tmin and Tmax for nine stations in Sudan and South Sudan are based on the SRA2 scenario of seven General Circulation Models (GCMs) outputs for the periods of 2011-2030, 2046-2065, and 2080-2099. The results showed that (1) the LARS-WG model produces good performance in downscaling daily precipitation and excellent performance in downscaling Tmax and Tmin in the study region; (2) downscaled precipitation from the prediction of seven GCMs showed great inconsistency in these two regions, which illustrates the great uncertainty in GCMs' results in the regions; (3) predicted precipitation in rainy season JJA (June, July, and August) based on the ensemble mean of seven GCMs showed a decreasing trend in the periods of 2011-2030, 2046-2065, and 2080-2099 in Sudan; however, an increasing trend can be found in SON (September, October, and November) in the future; (4) precipitation in South Sudan has an increasing trend in most seasons in the future except in MAM (March, April, and May) season in 2011-2030; and (5) predictions from seven GCMs showed a similar and continuous increasing trend for Tmax and Tmin in all three future periods, which will bring severe negative influence on improving livelihoods and reducing poverty in Sudan and South Sudan. [ABSTRACT FROM AUTHOR]

Actual evapotranspiration is a key process of hydrological cycle and a sole term that links land surface water balance and land surface energy balance. Evapotranspiration plays a key role in simulating hydrological effect of climate change, and a review of evapotranspiration estimation methods in hydrological models is of vital importance. This paper firstly summarizes the evapotranspiration estimation methods applied in hydrological models and then classifies them into the integrated converting methods and the classification gathering methods by their mechanism. Integrated converting methods are usually used in hydrological models and two differences exist among them: one is in the potential evaporation estimation methods, while the other in the function for defining relationship between potential evaporation and actual evapotranspiration. Due to the higher information requirements of the Penman-Monteith method and the existing data uncertainty, simplified empirical methods for calculating potential and actual evapotranspiration are widely used in hydrological models. Different evapotranspiration calculation methods are used depending on the complexity of the hydrological model, and importance and difficulty in the selection of the most suitable evapotranspiration methods is discussed. Finally, this paper points out the prospective development trends of the evapotranspiration estimating methods in hydrological modeling. [ABSTRACT FROM AUTHOR]

A growing number of investigations on uncertainty quantification for hydrological models have been widely reported in past years. However, limited studies are found on uncertainty assessment in simulating streamflow extremes so far. This article presents an intercomparison of uncertainty assessment of three different well-known hydrological models in simulating extreme streamflows using the approach of generalized likelihood uncertainty estimation (GLUE). Results indicate that: (1) The three modified hydrological models can reproduce daily streamflow series with acceptable accuracy. When the threshold value used to select behavioral parameter sets is 0.7, XAJ model generates the best GLUE estimates in simulating daily flows. However, the percentage of observations contained in the calculated 95 % confidence intervals (P-95CI) is low (<50 %) when simulating the high-flow index (Q10). (2) Decreasing average relative length (ARIL), P-95CI and increasing average asymmetry degree (AAD) are found, when the threshold value increases for both daily-flows and high-flows. However, there is a significant inconsistence between sensitivity of daily-flows and high-flows to various threshold values of the likelihood function. Uncertainty sources from parameter sets, model structure and inputs collectively accounts for above sensitivity. (3) The best hydrological model in simulating daily-flows is not identical under different threshold values. High P-95CIs of GLUE estimate for high-flows (Q10 and Q25) indicate that TOPMODEL generally performs best under different threshold values, while XAJ model produces the smallest ARIL under different threshold values. The results are expected to contribute toward knowledge improvement on uncertainty behaviors in simulating streamflow extremes by a variety of hydrological models. [ABSTRACT FROM AUTHOR]

Water resources and soil erosion are the most important environmental concerns in the Yangtze River basin, where soil erosion and sediment yield are closely related to rainfall erosivity. The present study explores the spatial and temporal changing patterns of the rainfall erosivity in the Yangtze River basin of China during 1960-2005 at annual, seasonal and monthly scales. The Mann-Kendall test is employed to detect the trends during 1960-2005, and the T test is applied to investigate possible changes between 1991-2005 and 1960-1990. Meanwhile the Rescaled Range Analysis is used for exploring future trend of rainfall erosivity. Moreover the continuous wavelet transform technique is using studying the periodicity of the rainfall erosivity. The results show that: (1) The Yangtze River basin is an area characterized by uneven spatial distribution of rainfall erosivity in China, with the annual average rainfall erosivity range from 131.21 to 16842 MJ mm ha h. (2) Although the directions of trends in annual rainfall erosivity at most stations are upward, only 22 stations have significant trends at the 90 % confidence level, and these stations are mainly located in the Jinshajiang River basin and Boyang Lake basin. Winter and summer are the seasons showing strong upward trends. For the monthly series, significant increasing trends are mainly found during January, June and July. (3) Generally speaking, the results detected by the T test are quite consistent with those detected by the Mann-Kendall test. (4) The rainfall erosivity of Yangtze River basin during winter and summer will maintain a detected significant increasing trend in the near future, which may bring greater risks to soil erosion. (5) The annual and seasonal erosivity of Yangtze River basin all have one significant periodicity of 2-4 years. [ABSTRACT FROM AUTHOR]

Using daily precipitation data spanning 1960-2005 from 51 meteorological stations in Xinjiang province, China, spatial and temporal changes in consecutive maximum wet days in the year, summer, and winter were investigated. Fifteen precipitation extreme indices, which reflect the attributes of consecutive maximum wet days, were defined, and the modified Mann-Kendall test was applied to detect the tendencies, and changes in the indices were evaluated through linear regression with the F test. Results showed that: (1) two consecutive wet days occurred most frequently in the year and summer, and the fractional contributions and precipitation intensities decreased as the duration increased; in winter, one wet day had the maximum possibility, fractional contributions decreased and intensities increased as the duration increased. (2) The possibility of consecutive wet days which had short durations reduced, while those of long durations increased; annual fractional contributions of short durations decreased, while those of long durations increased; summer and winter fractional contribution of all durations decreased first and then increased; the intensities of all durations increased. (3) The wet tendency was identified in Xinjiang; the wet trend in Southern Xinjiang was more significant than Northern Xinjiang in summer, while in winter the wet tendency in Northern Xinjiang was more pronounced. [ABSTRACT FROM AUTHOR]

Qingjiang River, the second largest tributary of the Yangtze River in Hubei Province, has taken on the important tasks for power generation and flood control in Hubei Province. The Qingjiang River watershed has a subtropical monsoon climate and, as a result, has dramatic diversity in its water resources. Recently, global warming and climate change have seriously affected the Qingjiang watershed's integrated water resources management. In this article, general circulation model (GCM) and watershed hydrological models were applied to analyze the impacts of climate change on future runoff of Qingjiang Watershed. To couple the scale difference between GCM and watershed hydrological models, a statistical downscaling method based on the smooth support vector machine was used to downscale the GCM's large-scale output. With the downscaled precipitation and evaporation, the Xin-anjiang hydrological model and HBV model were applied to predict the future runoff of Qingjiang Watershed under A2 and B2 scenarios. The preformance of the one-way coupling approach in simulating the hydrological impact of climate change in the Qingjiang watershed is evaluated, and the change trend of the future runoff of Qingjiang Watershed under the impacts of climate change is presented and discussed. [ABSTRACT FROM AUTHOR]