Conceptual hydrological models are preferable for real-time flood forecasting, among which the Xin'anjiang (XAJ) model has been widely applied in humid and semi-humid regions of China. Although the relatively simple mass balance scheme ensures a good performance of runoff simulation during flood events, the model still has some defects. Previous studies have confirmed the importance of evapotranspiration (ET) and soil moisture content (SMC) in runoff simulation. In order to add more constraints to the original XAJ model, an energy balance scheme suitable for the XAJ model was developed and coupled with the original mass balance scheme of the XAJ model. The detailed parameterizations of the improved model, XAJ-EB, are presented in the first part of this paper. XAJ-EB employs various meteorological forcing and remote sensing data as input, simulating ET and runoff yield using a more physically based mass–energy balance scheme. In particular, the energy balance is solved by determining the representative equilibrium temperature (RET), which is comparable to land surface temperature (LST). The XAJ-EB was evaluated in the Lushui catchment situated in the middle reach of the Yangtze River basin for the period between 2004 and 2007. Validation using ground-measured runoff data proves that the XAJ-EB is capable of reproducing runoff comparable to the original XAJ model. Additionally, RET simulated by XAJ-EB agreed well with moderate resolution imaging spectroradiometer (MODIS)-retrieved LST, which further confirms that the model is able to simulate the mass–energy balance since LST reflects the interactions among various processes. The validation results prove that the XAJ-EB model has superior performance compared with the XAJ model and also extends its applicability.

The impacts of human-induced land-use and climate changes on hydrologic processes have become a great challenge and attracted widespread attention of many researchers. Dramatic changes in land use and climate have occurred in the mountainous Qingyi River watershed in southwest China in the last three decades. Variable infiltration capacity (VIC), a large-scale hydrologic model, was used in this study to assess the impacts of land-use and climate changes on surface runoff, base flow, streamflow, and evapotranspiration (ET) of this watershed. The analysis for this study includes (1) investigation of change in historical land-use patterns, (2) detection of climate change (precipitation and mean temperature), and (3) simulation and assessment of hydrologic responses to these changes. The Mann-Kendall test was used to identify the long-term monotonic trends in precipitation and temperature for the period of 1980–2005. The results suggest no significant change in annual precipitation and a significant i...

Flood risk assessment and mapping are considered essential tools for the improvement of flood management. This research aims to construct a more comprehensive flood assessment framework by emphasizing factors related to human resilience and integrating them with meteorological and geographical factors. Moreover, two ensemble learning models, namely voting and stacking, which utilize heterogeneous learners, were employed in this study, and their prediction performance was compared with that of traditional machine learning models, including support vector machine, random forest, multilayer perceptron, and gradient boosting decision tree. The six models were trained and tested using a sample database constructed from historical flood events in Hefei, China. The results demonstrated the following findings: (1) the RF model exhibited the highest accuracy, while the SVR model underestimated the extent of extremely high-risk areas. The stacking model underestimated the extent of very-high-risk areas. It should be noted that the prediction results of ensemble learning methods may not be superior to those of the base models upon which they are built. (2) The predicted high-risk and very-high-risk areas within the study area are predominantly clustered in low-lying regions along the rivers, aligning with the distribution of hazardous areas observed in historical inundation events. (3) It is worth noting that the factor of distance to pumping stations has the second most significant driving influence after the DEM (Digital Elevation Model). This underscores the importance of considering human resilience factors. This study expands the empirical evidence for the ability of machine learning methods to be employed in flood risk assessment and deepens our understanding of the potential mechanisms of human resilience in influencing urban flood risk.

Statistical post-processing for multi-model grand ensemble (GE) hydrologic predictions is necessary, in order to achieve more accurate and reliable probabilistic forecasts. This paper presents a case study which applies Bayesian model averaging (BMA) to statistically post-process raw GE runoff forecasts in the Fu River basin in China, at lead times ranging from 6 to 120 h. The raw forecasts were generated by running the Xinanjiang hydrologic model with ensemble forecasts (164 forecast members), using seven different “THORPEX Interactive Grand Global Ensemble” (TIGGE) weather centres as forcing inputs. Some measures, such as data transformation and high-dimensional optimization, were included in the experiment after considering the practical water regime and data conditions. The results indicate that the BMA post-processing method is capable of improving the performance of raw GE runoff forecasts, yielding more calibrated and sharp predictive probability density functions (PDFs), over a range of lead times from 24 to 120 h. The analysis of percentile forecasts in two different flood events illustrates the great potential and prospects of BMA GE probabilistic river discharge forecasts, for taking precautions against severe flooding events.

Drought is a complex natural disaster phenomenon. It is of great significance to analyze the occurrence and development of drought events for drought prevention. In this study, two drought characteristic variables (the drought duration and severity) were extracted by using the Theory of Runs based on four drought indexes (i.e., the percentage of precipitation anomaly, the standardized precipitation index, the standardized precipitation evapotranspiration index and the improved comprehensive meteorological drought index). The joint distribution model of drought characteristic variables was built based on four types of Archimedean copulas. The joint cumulative probability and the joint return period of drought events were analyzed and the relationship between the drought characteristics and the actual crop drought reduction area was also studied. The results showed that: (1) The area of the slight drought and the extreme drought were both the zonal increasing distribution from northeast to southwest in Yunnan Province from 1960 to 2015. The area of the high frequency middle drought was mainly distributed in Huize and Zhanyi in Northeast Yunnan, Kunming in Central Yunnan and some areas of Southwest Yunnan, whereas the severe drought was mainly occurred in Deqin, Gongshan and Zhongdian in Northwest Yunnan; (2) The drought duration and severity were fitted the Weibull and Gamma distribution, respectively and the Frank copula function was the optimal joint distribution function. The Drought events were mostly short duration and high severity, long duration and low severity and short duration and low severity. The joint cumulative probability and joint return period were increased with the increase of drought duration and severity; (3) The error range between the theoretical return period and the actual was 0.1–0.4 a. The year of the agricultural disaster can be accurately reflected by the combined return period in Yunnan Province. The research can provide guidelines for the agricultural management in the drought area.

Land use changes are important aspects of global change and affect regional water cycles, environmental quality, biodiversity and terrestrial ecosystems. To understand the temporal and spatial land use change in the Jialing River Basin and its impacts on the hydrological cycle, land use change models and the variable infiltration capacity (VIC) model were applied separately to the Jialing River Basin. Real change and final change were analyzed to determine the consequences of land use changes and their hydrological consequences. Real change is defined as the total variation during a fixed period, including increases and decreases. Thus, real change is the sum of the absolute values of the decrease and the increase. Final change is defined as the difference between the beginning and end of a given period for a specific factor. Overall, the amounts of settlement and shrub land area changed significantly in the entire Jialing River (with final change rates of 20.77% and −16.07%, respectively, and real change rates of 34.2% and 30.1%, respectively, from 1985 to 1995, as well as final and real change rates of 29.37%, 12.40%, 39.9% and 32.8%, respectively, from 1995 to 2000). Compared with the final change, the real change highlighted the rate of change and the change in woodland area. The land use changes in the Lueyang (LY), Shehong (SH) and Fengtan (FT) subcatchments were more dynamic than in the other subcatchments. The economy, population and macro-policy were the main factors responsible for driving the land use changes. The decrease in woodland area in the LY subcatchment corresponded with an increase in evapotranspiration (ET) and with decreases in the other hydrological elements. Overall, the final changes in the hydrological elements in the LY, SH and FT subcatchments were not significant due to the average and compensation effects. The LY subcatchment was mainly affected by the average effect, whereas the SH and FT subcatchments were affected by the average and compensation effects. The use of real change can increase the detectability of hydrological elements changes caused by land use change in SH and FT. The results of this study provide new insights regarding the examination of the effects of land use changes on hydrological regimes. These results are useful for land use planners and water resource managers.