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The hydrological response to the potential future climate change in Yangtze River Basin (YRB), China, was assessed by using an ensemble of 54 climate change simulations. The Coupled Model Intercomparison Project 5 simulations under two new Representative Concentration Pathways (RCP) 4.5 and 8.5 emission scenarios were downscaled and used to drive the Variable Infiltration Capacity hydrological model. This study found that the range of temperature changes is homogeneous for almost the entire region, with an average annual increase of more than 2 °C under RCP4.5 and even more than 4 °C under RCP8.5 in the end of the twenty first century. The warmest period (June-July-August) of the year would experience lower changes than the colder ones (December-January-February). Overall, mean precipitation was projected to increase slightly in YRB, with large dispersion among different global climate models, especially during the dry season months. These phenomena lead to changes in future streamflow for three mainstream hydrological stations (Cuntan, Yichang, and Datong), with slightly increasing annual average streamflows, especially at the end of twenty first century. Compared with the percentage change of mean flow, the high flow shows (90th percentile on the probability of no exceedance) a higher increasing trend and the low flow (10th percentile) shows a decreasing trend or lower increasing trend. The maximum daily discharges with 5, 10, 15, and 30-year return periods show an increasing trend in most sub-basins in the future. Therefore, extreme hydrological events (e.g., floods and droughts) will increase significantly, although the annual mean streamflow shows insignificant change. The findings of this study would provide scientific supports to implement the integrated adaptive water resource management for climate change at regional scales in the YRB. [ABSTRACT FROM AUTHOR]

With a booming development characterized by new urbanization in current China, urban water consumption attracts growing concerns. An efficient and probabilistic prediction of urban water consumption plays a vital role for urban planning toward sustainable development, especially in megacities limited by water resources. However, the data insufficiency issue commonly exists nowadays and seriously restricts further development of urban water simulation. In this article, we proposed a consolidated framework for probabilistic prediction of water consumption under an incompletely informational circumstance to deal with the challenge. The model was constructed based on a state-of-the-art Bayesian neural networks (BNNs) technique. Three dominated influencing factors were identified and included into the BNN model. Future impact factors were generated by using a variety of methods including a quadratic polynomial model, a regression and auto-regressive moving average combination model and a Grey Verhulst model. Thereafter, water consumption projection (2013-2020) and uncertainty estimates was done. Results showed that the model matched well with observations. Through reducing the dependence on large amount of information and constructing a probabilistic means incorporating uncertainty estimation, the new approach can work better than conventional means in support of water resources planning and management under an incompletely informational circumstance. [ABSTRACT FROM AUTHOR]

Accurate soil moisture information is useful in agricultural practice, weather forecasting, and various hydrological applications. Although land surface modeling provides a viable approach to simulating soil moisture, many factors such as errors in the precipitation can affect the accuracy of soil moisture simulations. This paper examined how precipitation rate and evapotranspiration rate affect the accuracy of soil moisture simulation using simple biosphere model with and without data assimilation through ensemble Kalman filter (EnKF). For each of the two variables, seven levels of relative errors (−20, −10, −5, 0, 5, 10 and 20 %) were introduced independently, thus a total of 49 combined cases were investigated. Observations from Wudaogou Hydrology Experimental site in the Huaihe River basin, China, were used to drive and verify the simulations. Results indicate that when the error of precipitation rate is within 10 % of the observations, the resulting error in soil moisture simulations is less significant and manageable, thus the simulated precipitation can be used to drive hydrological models in poorly gauged catchments when observations are not available. When the error of evapotranspiration rate is within 20 % of the observations, which is partly caused by model structural and parameterization errors, its impact on soil moisture simulation is less significant and can be acceptable. This study also demonstrated that the EnKF can perform consistently well to improve soil moisture simulation with less sensitivity to precipitation errors. [ABSTRACT FROM AUTHOR]

We used the Decision Support System for Agro-technology Transfer-Cropping System Model (DSSAT) and data assimilation scheme (DSSAT-DA) to estimate maize (i.e., corn) yield and to evaluate the sensitivity of maize yield to hydroclimatic variables (i.e., precipitation, air temperatures, solar radiation, soil water). The remotely sensed soil moisture products, which includes Advanced Microwave Scanning Radiometer and the Soil Moisture and Ocean Salinity, were assimilated to DSSAT model by using the Ensemble Kalman Filtering approach. It was observed that both DSSAT and DSSAT-DA models can able to capture the annual trend of maize yield, although they overestimate the observed maize yield. The DSSAT-DA scheme assimilated with remotely sensed products slightly improves the model performance. The antecedent hydroclimatic information can influence the subsequent maize yield. The maize yield is sensitive to the soil water availability and precipitation amount, especially at the antecedent 1 month time to sowing and the subsequent second and third month's growing period. [ABSTRACT FROM AUTHOR]

In recent decades, saltwater intrusion over some low-lying coastal regions was deteriorated by rising sea-level and decreasing streamflow in the context of climate change. Though physically-based hydrodynamic models are the most detailed means to simulate salinity processes, they are commonly restricted by data insufficiency issues both in spatial resolution and temporal lasting. This motivates us to build a statistical model enable simulation and scenario analysis for coastal salinity change with limited observations. A Bayesian neural network (BNN) model is built hereby to simulate salinity. It offers more precise estimation compared with the conventional artificial neural network. Meanwhile, the model gives the uncertainty behaviors of the final salinity simulation which is not available for other methods. Future scenarios of salinity change are constructed and analyzed in different time periods on the basis of the validated BNN model. Results indicate that the water quality over lower Pearl River is degrading along with more significant uncertainties. Further analysis suggests that streamflow alteration has a more direct impact on salinity variations than the sea-level change does. The method allows a profound analysis of the potential influence on water quality degradation in coastal and low-lying regions in support of water management and adaptation toward global climate change. [ABSTRACT FROM AUTHOR]

A growing number of studies on streamflow projection in the context of global climate change have been widely reported in past years. However, current knowledge on the role of different hydrological models to estimate future climate impact on flood in the Tibet Plateau is still limited so far. In this work, a group of hydrological models in conjunction with statistical downscaling outputs (SDSM and ANN) from the HadCM3 GCM model are used to evaluate the impacts of climate change on floods in the 21st century over the headwater catchment of Yellow River, the Tibet Plateau. The influence of different hydrological models on flood projection and quantile estimation are addressed. The results show that: (1) three hydrological models generate acceptable results of flood magnitude and frequency at Tangnaihai station during the past 50 years; (2) quite similar projections for future floods are obtained by means of different hydrological models, decreasing flood magnitude corresponding to the 2-, 5-, 10- and 50-year return periods is found under most scenarios in the 21st century. Meanwhile, flood frequency is likely to reduce in response to climate change; (3) the uncertainty in projected flood quantile by three different hydrological models increases with recurrence interval in term of relative length of confidence interval (RL). Besides, RL for flood quantile in future climate scenarios is likely to become larger than the baseline period. The results are valuable to improve our current knowledge of climate impact research in the alpine regions. [ABSTRACT FROM AUTHOR]

Six global climate models (GCMs) from Coupled Model Intercomparison Project Phase 5 under three Respectively Concentration Pathways (RCP2.6, RCP4.5 and RCP8.5) were used to assess the impact of climate change on streamflow for the Huangnizhuang catchment (HNZ) in China. Change factor method was used for bias correction between GCM outputs and observations and the SWAT model was used to simulate the hydrological processes. The results indicated that the SWAT model performed well in the study catchment with a monthly Nash-Sutcliffe efficiency (NS) of 0.93 and 0.91 and daily NS of 0.63 and 0.68 for calibration and validation periods respectively. Their corresponding relative errors were −2.2 and 8.9, and −2.6 and 8.5 % respectively. The ensemble of multi-GCMs projected an increase of precipitation in the middle and end of twenty-first century over the HNZ, ranging from −2.4 to 9 %. However, streamflow is likely to decline in the future, ranging −6.9 to 0.8 %, mainly due to an increase of evapotranspiration in a warming world, as air temperature shows steadily increases for all the GCMs and RCPs. Average monthly streamflow from six GCMs are likely to increase in August and September but decline from October to June. The associated uncertainties of the reported results were also discussed. It includes, but is not limit to, different GCMs, emissions scenarios, downscaling techniques as well as hydrological simulations. The results of this study can inform planning of long-term basin water management strategies taking into account global change scenarios. [ABSTRACT FROM AUTHOR]

An ensemble artificial neural network (ENN) based hybrid function approximator (named PEK), integrating the partial mutual information (PMI) based separate input variable selection (IVS) scheme, ENN-based output estimation, and K-nearest neighbor regression based output error estimation, has been proposed to improve event-based rainfall-runoff (RR) simulation. A hybrid data-driven RR model, named non-updating PEK (NU-PEK), is also developed on the basis of the PEK approximator. The rainfall and simulated antecedent discharges input variables for the NU-PEK model are selected separately by using a PMI-based IVS algorithm. A newly proposed candidate rainfall input set, sliding window cumulative rainfall is also proposed. These two methods are integrated to make a good compromise between the adequacy and parsimony of the input information and make contribution to the understandings of the hydrologic responses to the regional precipitation. The number of component networks and the topology and parameter settings of each component network are optimized simultaneously by using the multi-objective NSGA-II optimization algorithm and the early stopping Levenberg-Marquardt algorithm. The optimal combination weights of the ENN are obtained according to the Akaike information criterions of component networks. By combining all these methods, the simulation accuracy and generalization property of the PEK approximator are much better than traditional artificial neural network. The NU-PEK model is constructed by combining the PEK approximator with a newly proposed non-updating modeling approach to improve event-based RR simulation. The NU-PEK model was applied to three Chinese catchments for RR simulation and compared with two popular RR models, including the conceptual Xinanjiang model and the conceptual-data-driven IHACRES model. The results of simulation and sensitivity analysis indicate that the developed model generally outperforms the other two models. The NU-PEK model is capable of producing high accuracy non-updating RR simulation without the use of the real-time information, e.g. the observed discharges at previous time steps. [ABSTRACT FROM AUTHOR]

This study aims to identify the parameters that are most important in controlling the Noah land surface model (LSM), the analysis of parameter interactions, and the evaluation of the performance of parameter optimization using the parameter estimation software PEST. We found it necessary to analyze parameter sensitivity in order to properly simulate hydrological variables such as latent heat flux in the Huaihe River Basin, China. The parameters under study in the Noah LSM link thermodynamic and hydrological parts into a complete model. To our knowledge, this parameter interaction in the Noah LSM has never been studied before. There are, however, several studies concerning the influence of vegetation types and climate conditions on parameter sensitivity of the Noah LSM. Three sensitivity analysis methods, the including local sensitivity analysis method SENSAN, regional sensitivity analysis, and Sobol's method, were tested. Five experimental sites in the Huaihe River Basin were chosen to perform the simulations. The results show that the Noah LSM parameter sensitivities were impacted by the choice of the analysis method. The local method SENSAN often produced significant differences in results compared to the two global methods. The parameter interactions investigated made a significant contribution towards elucidating how one process influences another in the Noah LSM. The results show that parameters were not transferable solely based on vegetation types but also rely on climate conditions. According to the sensitivity analysis results, four sensitive parameters were chosen to be optimized using the PEST method. PEST is a widely used method for estimating parameters in models. Root-mean-square error was used to evaluate the effect of the optimization. Generally in all sites, the optimized parameters values perform better than the original parameter values. [ABSTRACT FROM AUTHOR]

Climate change in North China would result in significant changes in temperature, precipitation and their spatial/temporal distributions. Consequently, these induced changes will have profound effects on the hydrological cycle and water resources in both agricultural and natural ecosystems. Panjiakou reservoir in the middle Luanhe River basin-a tributary of the Haihe River basin-is one of the important sources of water for industrial and agricultural development in Beijing, Tianjin and Hebei province, China. Any significant change in the magnitude and/or timing of runoff from the reservoir induced by changes in climatic variables would have significant implication for the economic prosperity in North China. This paper investigates the impacts of climate change on hydrological processes in the Luanhe River basin as follows. Firstly, spatial and temporal patterns of precipitation, temperature and runoff at both annual and seasonal scales from 1957 to 2000 in the Luanhe River basin are analyzed using Mann-Kendall trend analysis, linear regression methods and inverse distance weighted interpolation. For the impact study, four Global Climate Models (GCMs) (named CSIRO, HadCM3, CNRM and GFDL) were used to produce precipitation and temperature data under A2 scenario by mean of a widely used quantile-quantile transformation. Projected meteorological variables were used to force a two-parameter hydrologic model to simulate the hydrological response to climate change in the future (2021-2050). Moreover, a sensitivity analysis is conducted to assess how precipitation and temperature affect the runoff. Results suggested that most part of the Luanhe River basin was dominated by significant increasing trends of temperature and no significant trends of precipitation in annual and seasonal scale during the past decades. Annual, spring and autumn runoffs present significant decreasing trends in the Panjiakou reservoir basin. Meanwhile, runoff is more strongly related to precipitation than to temperature. All GCMs projected precipitation and temperature series after bias correction indicated increasing temperature and increasing precipitation trends for the period 2021-2050 except that CNRM showed a slight decreasing trend in precipitation. Great enhancements can be found in projected runoff except CNRM by driving the two-parameter water balance model. The study provides valuable information on the assessment of the impact of the climate change on water resources in the Luanhe River basin as well for allocating and designing water resources projects. [ABSTRACT FROM AUTHOR]

The recent (1970-1999) and future (2070-2099) climates under the SRES A1B scenario, simulated by the regional climate model RegCM4.0 driven with lateral boundary conditions from the ECHAM5 general circulation model, are utilized to force a large-scale hydrological model for assessing the hydrological response to climate changes in the Yangtze River Basin, China. The variable infiltration capacity model (VIC) is utilized to simulate various hydrological components for examining the changes in streamflow at various locations throughout the Yangtze River Basin. In the end of the twenty-first century, most of the Yangtze River Basin stands out as 'hotspots' of climate change in China, with an annual temperature increase of approximately 3.5 °C, an increase of annual precipitation in North and a decrease in South. Runoff in the upper reach of Yangtze River is projected to increase throughout the year in the future, especially in spring when the increase will be approximately 30 %. Runoff from the catchments in the northern part of Yangtze River will increase by approximately 10 %, whereas that in the southern part will decrease, especially in the dry season, following precipitation changes. The frequency of extreme floods at three mainstream stations (Cuntan, Yichang, and Datong) is projected to increase significantly. The original extreme floods with return periods of 50, 20, and 10 years will change into floods with return periods of no more than 20, 10, and 5 years. The projected increase in extreme floods will have significant impacts on water resources management and flood control systems in the Yangtze River Basin. [ABSTRACT FROM AUTHOR]

Five downscaling techniques, namely the statistical downscaling model, the automated statistical downscaling method, the change factor (CF) method, the advanced CF method, the Weather generator (LarsWG5) method, are applied to the upstream basin of the Huaihe River. Changes in regional climate scenarios and hydrology variables are compared in future periods to investigate the uncertainty associated with the downscaling techniques. Paired-sample T test is applied to evaluation the significant of the difference of the means between the observed data and the downscaled data in the future. The Xinanjiang rainfall-runoff model is employed to simulate the rainfall-runoff relation. The results demonstrate that the downscaling techniques utilized herein predict an increased tendency in the future. The increases range of maximum temperature (T) is between 3.7 and 4.7 °C until the time period of 2070-2099 (2080s). While, the increases range of minimum temperature (T) is between 2.8 and 4.9 °C until 2080s. The research presented herein determined that there is an increase predicted for the peaks over threshold (discussed in the paper) and a decrease predicted for the peaks below the threshold (discussed in the paper) in the future, which illustrates that the temperature would rise gradually in the future. Precipitation changes are not as obvious as temperatures changes and tend to be influence by the season. Most downscaling techniques predict increases, and others indict decreases. The annual mean precipitation range changes between 3.2 and 53.3 %, and moreover, these changes vary from season to season. [ABSTRACT FROM AUTHOR]

With a booming expansion of urbanization, urban water consumption (WC) attracts increasing concerns in developing countries worldwide, particularly for megacities. In this study, an urban WC model for Shenzhen, a rapidly developing flagship megacity in South China from a small agrarian fishery village since 1979, was built up to simulate the WC changes (1994-2009) with aim to formulate local water resources management strategies. Basically, the model was constructed using a variety of methods including a back-propagation artificial neuron network (BP-ANN), a quadratic polynomial model, a regression and auto-regressive moving average combination model, and a Grey Verhulst model. Simulation of the WC was conducted using a multiple regression forecasting model and a BP-ANN model. The results from these two models showed that the BP-ANN model is outperformed. Subsequently, a series of social-economic and demographic scenarios were formulated to project WC (2011-2020) with uncertainty analysis. The results suggest that the total WC will increase slower and slower over the decade. It might approach a saturated threshold soon after 2020. Scenarios of WC incorporating uncertainty analysis aiming to provide reliable prediction results constitute the highlight of this study. This study will be beneficial to formulate appropriate sustainable development strategies of water resources for similar megacities in South China. [ABSTRACT FROM AUTHOR]

Spatiotemporal changes in climatic extremes in the Yellow River Basin from 1959 to 2008 were investigated on the basis of a suite of 27 climatic indices derived from daily temperature and precipitation data from 75 meteorological stations with the help of the Mann-Kendall test, linear regression method and GIS technique. Furthermore, the changes in the probability distribution of the extreme indices were examined. The results indicate: (1) The whole basin is dominated by significant increase in the frequency of warm days and warm nights, and dominated by significant decrease in the frequency of cold days and cold nights. Although trends in absolute temperature indices show less spatial coherence compared with that in the percentile-based temperature indices, overall increasing trends can be found in Max Tmax (TXx), Min Tmax (TXn), Max Tmin (TNx) and Min Tmin (TNn). (2) Although the spatial patterns and the number of stations with significant changes for threshold and duration temperature indices are also not identical, general positive trends in warm indices (i.e., summer days (SU25), tropical nights (TR20), warm spell duration indicator and growing season length) and negative trends in cold indices (i.e., frost days, ice days and cold spell duration indicator) can be found in the basin. Annual nighttime temperature has increased at a faster rate than that in daytime temperature, leading to obvious decrease in diurnal temperature range. (3) The changes in precipitation indices are much weaker and less spatially coherent compared with these of temperature indices. For all precipitation indices, only few stations are characterized by significantly change in extreme precipitation, and their spatial patterns are always characterized by irregular and insignificant positive and negative changes. However, generally, changes in precipitation extremes present drying trends, although most of the changes are insignificant. (4) Results at seasonal scale show that warming trends occur for all seasons, particularly in winter. Different from that in other three seasons, general positive trends in max 1-day precipitation (Rx1DAY) and max 5-day precipitation (Rx5DAY) are found in winter. Analysis of changes in probability distributions of indices for 1959-1983 and 1984-2008 indicate a remarkable shift toward warmer condition and a less pronounced tendency toward drier condition during the past decades. The results can provide beneficial reference to water resource and eco-environment management strategies in the Yellow River Basin for associated policymakers and stakeholders. [ABSTRACT FROM AUTHOR]

For sake of improving our current understanding on soil erosion processes in the hilly-gully loess regions of the middle Yellow River basin in China, a digital elevation model (DEM)-based runoff and sediment processes simulating model was developed. Infiltration excess runoff theory was used to describe the runoff generation process while a kinematic wave equation was solved using the finite-difference technique to simulate concentration processes on hillslopes. The soil erosion processes were modelled using the particular characteristics of loess slope, gully slope, and groove to characterize the unique features of steep hillslopes and a large variety of gullies based on a number of experiments. The constructed model was calibrated and verified in the Chabagou catchment, located in the middle Yellow River of China and dominated by an extreme soil-erosion rate. Moreover, spatio-temporal characterization of the soil erosion processes in small catchments and in-depth analysis between discharge and sediment concentration for the hyper-concentrated flows were addressed in detail. Thereafter, the calibrated model was applied to the Xingzihe catchment, which is dominated by similar soil erosion processes in the Yellow River basin. Results indicate that the model is capable of simulating runoff and soil erosion processes in such hilly-gully loess regions. The developed model are expected to contribute to further understanding of runoff generation and soil erosion processes in small catchments characterized by steep hillslopes, a large variety of gullies, and hyper-concentrated flow, and will be beneficial to water and soil conservation planning and management for catchments dealing with serious water and soil loss in the Loess Plateau. [ABSTRACT FROM AUTHOR]

Reliable projections of extremes at finer spatial scales are important in assessing the potential impacts of climate change on societal and natural systems, particularly for elevated and cold regions in the Tibetan Plateau. This paper presents future projections of extremes of daily precipitation and temperature, under different future scenarios in the headwater catchment of Yellow River basin over the 21st century, using the statistical downscaling model (SDSM). The results indicate that: (1) although the mean temperature was simulated perfectly, followed by monthly pan evaporation, the skill scores in simulating extreme indices of precipitation are inadequate; (2) The inter-annual variabilities for most extreme indices were underestimated, although the model could reproduce the extreme temperatures well. In fact, the simulation of extreme indices for precipitation and evaporation were not satisfactory in many cases. (3) In future period from 2011 to 2100, increases in the temperature and evaporation indices are projected under a range of climate scenarios, although decreasing mean and maximum precipitation are found in summer during 2020s. The findings of this work will contribute toward a better understanding of future climate changes for this unique region. [ABSTRACT FROM AUTHOR]