Variability and availability of water resources under changing environment in a regional scale have been hot topics in recent years, due to the vulnerability of water resources associated with social and economic development. In this paper, four subbasins in the Dongjiang basin with a significant land use change were selected as case study. Runoffs of the four subbasins were simulated using the SCS monthly model to identify the quantitative impacts of land use and climate change. The results showed that (1), in the Dongjiang basin, temperature increased significantly, evaporation and sunlight decreased strongly, while precipitation showed a nonsignificant increase; (2) since the 1980s, land uses in the Dongjiang basin have experienced a significant change with a prominent increase in urban areas, a moderate increase in farmlands, and a great decrease in forest areas; (3) the SCS monthly model performed well in the four subbasins giving that the more significant land use change in each subbasin, the more runoff change correspondingly; (4) overall, runoff change was contributed half and half by climate change and human activities, respectively, in all the subbasins, in which about 20%~30% change was contributed by land use change.

Water quality evaluation is an essential measure to analyze water quality. However, excessive randomness and fuzziness affect the process of evaluation, thus reducing the accuracy of evaluation. Therefore, this study proposed a cloud model for evaluating the water quality to alleviate this problem. Analytic hierarchy process and entropy theory were used to calculate the subjective weight and objective weight, respectively, and then they were coupled as a combination weight (CW) via game theory. The proposed game theory-based cloud model (GCM) was then applied to the Qixinggang section of the Beijiang River. The results show that the CW ranks fecal coliform as the most important factor, followed by total nitrogen and total phosphorus, while biochemical oxygen demand and fluoride were considered least important. There were 19 months (31.67%) at grade I, 39 months (65.00%) at grade II, and one month at grade IV and grade V during 2010–2014. A total of 52 months (86.6%) of GCM were identical to the comprehensive evaluation result (CER). The obtained water quality grades of GCM are close to the grades of the analytic hierarchy process weight (AHPW) due to the weight coefficient of AHPW set to 0.7487. Generally, one or two grade gaps exist among the results of the three groups of weights, suggesting that the index weight is not particularly sensitive to the cloud model. The evaluated accuracy of water quality can be improved by modifying the quantitative boundaries. This study could provide a reference for water quality evaluation, prevention, and improvement of water quality assessment and other applications.

Sediment load and its response to the variations of the hydrological elements are important for the healthy utilization of a river. In this study, the response of sediment load to hydrological change was explored in the upstream part of the Lancang-Mekong River, a major transboundary river originating from the Tibetan Plateau and running through China, over the past 50 years. A sediment rating curve for the Jiuzhou Station was developed based on the available SSC-Q (suspended sediment concentration (SSC) and flow) data and trends in annual precipitation, runoff, peak flow (PF), low flow (LF), maximum water level (MWL), and sediment load were analyzed from 1957 to 2006. The correlation analysis method and Random Forest (RF) were adopted to qualitatively and quantitatively quantify the contribution of each hydrological element to the sediment load change. Results indicated that both the runoff and sediment load showed a significantly upward trend, especially after 1979, at the 95% confidence level. The sediment load had significantly positive correlations with runoff, PF, and MWL at the 99% confidence level, respectively. In particular, the sediment load had the largest significant positive correlation with runoff since 1980. Runoff had the largest variable importance to the sediment load change, followed by PF, MWL, precipitation, and LF. The increasing trend in the sediment load was mainly attributed to the increase of runoff in the upstream part of the Lancang-Mekong River since the mid-1980s.

Reliability and accuracy of soil moisture datasets are essential for understanding changes in regional climate such as precipitation and temperature. Soil moisture datasets from the Essential Climate Variable (ECV), the Coupled Model Intercomparison Project Phase 5 (CMIP5), the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), the Global Land Data Assimilation System (GLDAS), and reanalysis products are widely used. These datasets generated by different techniques are compared in a common framework over China in this study. The comparison focuses on four aspects: spatial pattern, temporal correlation, long-term trend, and the relationships with precipitation and the Normalized Difference Vegetation Index (NDVI). The results indicate that all soil moisture datasets reach a good agreement on the spatial patterns of wet and dry soil. These patterns are also consistent with that of precipitation. However, there are considerable discrepancies in the absolute values of soil moisture among these datasets. In terms of unbiased Root-Mean-Square Difference (unRMSE, i.e., removing the differences in absolute values), all modeled datasets obtain performances comparable with ECV observations. Our results also suggest that a multi-model ensemble of soil moisture datasets can improve the representation of soil moisture conditions. The optimal dataset from which the wetting/drying trends in soil moisture have the highest consistency in terms of changes in precipitation and NDVI varies by season. Specifically, in spring, CMIP5 in northwest China shows that the trends in soil moisture are consistent with the changes in precipitation and NDVI. In summer, ECV presents the most identical performance compared to the changes in precipitation and NDVI. In autumn, GLDAS and Reanalysis have better performance in south China and parts of north China. In winter, GLDAS performs the best in the east of south China, followed by the Reanalysis dataset. These discrepancies among the datasets present various changes in different regions, which should be well noted and discussed before use.

Based on the idea of inputting more available useful information for evaluation to gain less uncertainty, this study focuses on how well the uncertainty can be reduced by considering the baseflow estimation information obtained from the smoothed minima method (SMM). The Xinanjiang model and the generalized likelihood uncertainty estimation (GLUE) method with the shuffled complex evolution Metropolis (SCEM‐UA) sampling algorithm were used for hydrological modeling and uncertainty analysis, respectively. The Jiangkou basin, located in the upper of the Hanjiang River, was selected as case study. It was found that the number and standard deviation of behavioral parameter sets both decreased when the threshold value for the baseflow efficiency index increased, and the high Nash‐Sutcliffe efficiency coefficients correspond well with the high baseflow efficiency coefficients. The results also showed that uncertainty interval width decreased significantly, while containing ratio did not decrease by much and the simulated runoff with the behavioral parameter sets can fit better to the observed runoff, when threshold for the baseflow efficiency index was taken into consideration. These implied that using the baseflow estimation information can reduce the uncertainty in hydrological modeling to some degree and gain more reasonable prediction bounds.