The systemic biases of Regional Climate Models (RCMs) impede their application in regional hydrological climate-change effects analysis and lead to errors. As a consequence, bias correction has become a necessary prerequisite for the study of climate change. This paper compares the performance of available bias correction methods that focus on the performance of precipitation and temperature projections. The hydrological effects of these correction methods are evaluated by the modelled discharges of the Kaidu River Basin. The results show that all used methods improve the performance of the original RCM precipitation and temperature simulations across a number of levels. The corrected results obtained by precipitation correction methods demonstrate larger diversities than those produced by the temperature correction methods. The performance of hydrological modelling is highly influenced by the choice of precipitation correction methods. Furthermore, no substantial differences can be identified from the results of the temperature-corrected methods. The biases from input data are often greater from the works of hydrological modelling. The suitability of these approaches depends upon the regional context and the RCM model, while their application procedure and a number of results can be adapted from region to region.

Flooding, one of the most serious natural disasters, poses a significant threat to people’s lives and property. At present, the forecasting method uses simple snowmelt accumulation and has certain regional restrictions that limit the accuracy and timeliness of flood simulation and prediction. In this paper, the influence of accumulated temperature (AT) and maximum temperature (MT) on snow melting was considered in order to (1) reclassify the precipitation categories of the watershed using a separation algorithm of rain and snow that incorporates AT and MT, and (2) develop a new snow-melting process utilizing the algorithm in the Soil and Water Assessment Tool Model (SWAT) by considering the effects of AT and MT. The SWAT model was used to simulate snowmelt and flooding in the Tizinafu River Basin (TRB). We found that the modified SWAT model increased the value of the average flood peak flow by 43%, the snowmelt amounts increased by 45%, and the contribution of snowmelt to runoff increased from 44.7% to 54.07%. In comparison, we concluded the snowmelt contribution to runoff, flood peak performance, flood process simulation, model accuracy, and time accuracy. The new method provides a more accurate simulation technique for snowmelt floods and flood simulation.

The effects of global climate change threaten the availability of water resources worldwide and modify their tempo-spatial pattern. Properly quantifying the possible effects of climate change on water resources under different hydrological models is a great challenge in ungauged alpine regions. By using remote sensing data to support established models, this study aimed to reveal the effects of climate change using two models of hydrological processes including total water resources, peak flows, evapotranspiration, snowmelt and snow accumulation in the ungauged Hotan River Basin under future representative concentration pathway (RCP) scenarios. The results revealed that stream flow was much more sensitive to temperature variation than precipitation change and increased by 0.9–10.0% according to MIKE SHE or 6.5–10.5% according to SWAT. Increased evapotranspiration was similar for both models with a range of 7.6–31.3%. The snow-covered area shrank from 32.5% to 11.9% between the elevations of 4200–6400 m, respectively, and snow accumulation increased when the elevation exceeded 6400 m above sea level (asl). The results also suggested that the fully distributed and semi-distributed structures of these two models strongly influenced the responses to climate change. The study proposes a practical approach to assess the climate change effect in ungauged regions.

In the 21st century, heavier rainfall events and warmer temperatures in mountainous regions have significant impacts on hydrological processes and the occurrence of flood/drought extremes. Long-term modeling and peak flow detection of streamflow series are crucial in understanding the behavior of flood and drought. This study was conducted to analyze the impacts of future climate change on extreme flows in the Kaidu River Basin, northwestern China. The soil water assessment tool (SWAT) was used for hydrological modeling. The projected future precipitation and temperature under Intergovernmental Panel on Climate Change (IPCC) representative concentration pathway (RCP) scenarios were downscaled and used to drive the validated SWAT model. A generalized extreme value (GEV) distribution was employed to assess the probability distribution of flood events. The modeling results showed that the simulated discharge well matched the observed ones both in the calibration and validation periods. Comparing with the historical period, the ensemble with 15 general circulation models (GCMs) showed that the annual precipitation will increase by 7.9–16.1% in the future, and extreme precipitation events will increase in winter months. Future temperature will increase from 0.42 °C/10 a to 0.70 °C/10 a. However, with respect to the hydrological response to climate change, annual mean runoff will decrease by 21.5–40.0% under the mean conditions of the four RCP scenarios. A reduction in streamflow will occur in winter, while significantly increased discharge will occur from April to May. In addition, designed floods for return periods of five, 10 and 20 years in the future, as predicted by the GEV distribution, will decrease by 3–20% over the entire Kaidu watershed compared to those in the historical period. The results will be used to help local water resource management with hazard warning and flood control.

Since the flow variations of Aksu River are strongly influenced by climate change and human activities which threat the local ecosystem and sustainable development, it is necessary to quantify the impact degree of the driving factors. Therefore, this study aims to quantify the impacts of climate change and human activities on the variability of runoff in the Aksu River Basin. The Mann-Kendall trend test and accumulative anomaly method were used to detect the break points of the flow difference value (FDV) between the upstream and downstream flume stations. The improved slope change ratio of cumulative quantity (SCRCQ) method and the Soil and Water Assessment Tool (SWAT) model were applied to decouple the contribution of each driving factor to the FDV variations. Furthermore, a Pearson Correlation Analysis was performed to show the relationships among the driving factors and the FDV. The time series prior to the year (1988) of break point was considered as the baseline period. Based on the annual precipitation and the potential evapotranspiration (PET), the relative impacts of precipitation, PET and human activities on FDV variations as determined by the SCRCQ method were 77.35%, −0.98% and 23.63%, respectively. In addition, the SWAT model indicated that climate factors and human activities were responsible for 92.28% and 7.72% of the variability, respectively. Thus, climate change and human activities showed a similar scale of impact on FDV changes.

Since the concept of hydrological response units (HRUs) is used widely in hydrological modeling, the land use change scenarios analysis based on HRU may have direct influence on hydrological processes due to its simplified flow routing and HRU spatial distribution. This paper intends to overcome this issue based on a new analysis approach to explain what impacts for the impact of land use/cover change on hydrological processes (LUCCIHP), and compare whether differences exist between the conventional approach and the improved approach. Therefore, we proposed a sub-basin segmentation approach to obtain more reasonable impact assessment of LUCC scenario by re-discretizing the HRUs and prolonging the flow path in which the LUCC occurs. As a scenario study, the SWAT model is used in the Aksu River Basin, China, to simulate the response of hydrological processes to LUCC over ten years. Moreover, the impacts of LUCC on hydrological processes before and after model modification are compared and analyzed at three levels (catchment scale, sub-basin scale and HRU scale). Comparative analysis of Nash–Sutcliffe coefficient (NSE), RSR and Pbias, model simulations before and after model improvement shows that NSE increased by up to 2%, RSR decreased from 0.73 to 0.72, and Pbias decreased from 0.13 to 0.05. The major LUCCs affecting hydrological elements in this basin are related to the degradation of grassland and snow/ice and expansion of farmland and bare land. Model simulations before and after model improvement show that the average variation of flow components in typical sub-basins (surface runoff, lateral flow and groundwater flow) are changed by +11.09%, −4.51%, and −6.58%, and +10.53%, −1.55%, and −8.98% from the base period model scenario, respectively. Moreover, the spatial response of surface runoff at the HRU level reveals clear spatial differences between before and after model improvement. This alternative approach illustrates the potential bias caused by the conventional configuration and offers the possible application.