Sensitivity of Runoff to Climatic Factors and the Attribution of Runoff Variation in the Upper Shule River, North-West China

Accurate and reliable mid-to-long-term runoff prediction (MLTRP) is of great importance in water resource management. However, the MLTRP is not suitable in each basin, and how to evaluate the applicability of MLTRP is still a question. Therefore, the total mutual information (TMI) index is developed in this study based on the predictor selection method using mutual information (MI) and partial MI (PMI). The relationship between the TMI and the predictive performance of five AI models is analyzed by applying five models to 222 forecasting scenarios in Australia. This results in over 222 forecasting scenarios which demonstrate that, compared with the MI, the developed TMI index can better represent the available information in the predictors and has a more significant negative correlation with the RRMSE, with a correlation coefficient between −0.62 and −0.85. This means that the model’s predictive performance will become better along with the increase in TMI, and therefore, the developed TMI index can be used to evaluate the applicability of MLTRP. When the TMI is more than 0.1, the available information in the predictors can support the construction of MLTRP models. In addition, the TMI can be used to partly explain the differences in predictive performance among five models. In general, the complex models, which can better utilize the contained information, are more sensitive to the TMI and have more significant improvement in terms of predictive performance along with the increase in TMI.

An Index Used to Evaluate the Applicability of Mid-to-Long-Term Runoff Prediction in a Basin Based on Mutual Information

Xie,

Xiang,

Wang

et al. 2024

Study on the Driving Mechanism of Ecohydrological Regime in the Wandering Section of the Lower Yellow River

Xie,

Yin,

Jiang

et al. 2024

Climate change and human activities exert significant impacts on runoff generation and convergence mechanisms. Understanding the evolution mechanisms and driving forces of runoff is crucial for the sustainable development of water resources. This study is based on the runoff data of the Huayuankou (HYK), Jiahetan (JHT), and Gaocun (GC) stations in the lower reaches of the Yellow River from 1951 to 2019. The objectives are to identify and quantify the hydrological pattern and its driving mechanism of the three stations by the Mann–Kendall test, cumulative deviation method, wavelet analysis, the IHA-RVA method, SCRCO method, and the Budyko method. Our study revealed that the runoff disturbance points at all three stations occurred in 1985. During the two periods before and after the disturbance, the trends and periodicities within the year exhibited high consistency, showing an overall downward trend, with runoff increasing in October and decreasing in June and the primary cycles being 5 and 7 years. All three stations experienced high-degree changes in their hydrological situations, with the high-degree change occupying the largest proportion. At the HYK, JHT, and GC stations, human activities accounted for 66.05%, 71.94%, and 74.38% of runoff changes, respectively. Furthermore, we verified the attribution conclusion of runoff at HYK using the Budyko model, confirming that human activities are the primary factor influencing runoff. Finally, we explored the interactive relationships along the spatial trajectory of runoff at the three stations, analyzed 32 hydrological indicators, and detailed the land use changes in the Yellow River Basin. Our research findings complement the understanding of hydrological change mechanisms in the lower reaches of the Yellow River Basin and provide a scientific basis for future water resource management and flood prevention measures.

Current Status and Future Research of Groundwater Under Climate Change: A Bibliometric Analysis

Xue,

Niu,

Zhang

et al. 2024

Groundwater research on climate change is one of the significant topics in the field of natural sciences that is receiving increasing attention. However, no bibliometric research papers have been published that have analyzed the field for relevant knowledge graphs and relational networks. R-Bibliometrix and VOSviewer software are used to quantitatively analyze and visually represent the literature on the research of groundwater under climate change from 2010 to 2024 based on the Web of Science Core Collection. The findings demonstrated that a total of 4748 articles have been published on this topic, with publications increasing yearly. The most influential journal was the Journal of Hydrology, and the ten most influential papers were published from 2013 to 2017. The United States and China published significantly more research than any other country and had the closest cooperation, while Europe is the continent with the most publications. The Chinese Academy of Sciences was the leading institution in terms of the number of publications, and the Delft University of Technology was the institution with the highest average number of citations. British scholar Chris Soulsby was the author with the highest number of articles. The top three keywords were ‘climate change’, ‘groundwater’, and ‘model’. Machine-learning methods and remote-sensing techniques were emerging research hotspots that guided future research directions.