Search

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]

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]