Hydrological forecasting plays a crucial role in mitigating flood risks and managing water resources. Data-driven hydrological models demonstrate exceptional fitting capabilities and adaptability. Recognizing the limitations of single-model forecasting, this study introduces an innovative approach known as the Improved K-Nearest Neighbor Multi-Model Ensemble (IKNN-MME) method to enhance the runoff prediction. IKNN-MME dynamically adjusts model weights based on the similarity of historical data, acknowledging the influence of different training data features on localized predictions. By combining an enhanced K-Nearest Neighbor (KNN) algorithm with adaptive weighting, it offers a more powerful and flexible ensemble. This study evaluates the performance of the IKNN-MME method across four basins in the United States and compares it to other multi-model ensemble methods and benchmark models. The results underscore its outstanding performance and adaptability, offering a promising avenue for improving runoff forecasting. 

Application of the Improved K-Nearest Neighbor-Based Multi-Model Ensemble Method for Runoff Prediction

Study Region: The Fenhe River Basin (FRB) is an important tributary of the Yellow River Basin and locate in a semi-arid and semi-humid region in northwest China. Over the past four decades, significant anthropogenic activities have resulted in significant land use and land cover change (LUCC) that have markedly impacted hydrological processes in FRB. Study Focus: To analyze land use changes under multiple historical land use scenarios from LU1980 to LU2015, and to establish the Water and Energy transfer Processes in Large basins (WEP-L) model to quantify the influences of LUCC on hydrological processes, particularly on baseflow. New hydrological insights for the region: LUCC from LU1980 to LU2015 resulted in a 14.5 % increase in water yield (WY) and a 2.7 % and 1.4 % decrease in baseflow (BF) and evapotranspiration, respectively. Urbanization led to a significant increase in WY and a reduction in BF, exhibiting strong spatial gradients between urban centers and their peripheries. Increases in forestland decreased WY, while ET showed no significant change due to large absolute values and moisture limitations. Furthermore, the response of BF to forest cover exhibits spatial variation. A negative correlation between BF and forest cover was evident in upstream, whereas no significant correlation was observed in downstream. The findings of this study offer valuable insights for land use planning and sustainable watershed management in semi-arid semi-humid region. 

Influence of human-induced land use change on hydrological processes in semi-humid and semi-arid region: A case in the Fenhe River Basin

Accurate forecasts of water demand are a crucial factor in the strategic planning and judicious use of finite water resources within a region, underpinning sustainable socio-economic development. This study aims to compare the applicability of various artificial intelligence models for long-term water demand forecasting across different water use sectors. We utilized the Tuojiang River basin in Sichuan Province as our case study, comparing the performance of five artificial intelligence models: Genetic Algorithm optimized Back Propagation Neural Network (GA-BP), Extreme Learning Machine (ELM), Gaussian Process Regression (GPR), Support Vector Regression (SVR), and Random Forest (RF). These models were employed to predict water demand in the agricultural, industrial, domestic, and ecological sectors using actual water demand data and relevant influential factors from 2005 to 2020. Model performance was evaluated based on the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), with the most effective model used for 2025 water demand projections for each sector within the study area. Our findings reveal that the GPR model demonstrated superior results in predicting water demand for the agricultural, domestic, and ecological sectors, attaining R2 values of 0.9811, 0.9338, and 0.9142 for the respective test sets. Also, the GA-BP model performed optimally in predicting industrial water demand, with an R2 of 0.8580. The identified optimal prediction model provides a useful tool for future long-term water demand forecasting, promoting sustainable water resource management.

Long-term water demand forecasting using artificial intelligence models in the Tuojiang River basin, China

The distributed unit hydrograph (DUH) method has been widely used for flow routing in a watershed, because it adequately characterizes the underlying surface characteristics and varying rainfall intensity. Fundamental to the calculation of DUH is flow velocity. However, the currently used velocity formula assumes a global equilibrium of the watershed and ignores the impact of time-varying soil moisture content on flow velocity, which thus leads to a larger flow velocity. The objective of this study is to identify a soil moisture content factor, which, based on the tension water storage capacity curve, was derived to investigate the response of DUH to soil moisture content in unsaturated areas. Thus, an improved distributed unit hydrograph, based on time-varying soil moisture content, was developed. The proposed DUH considered the impact of both the time-varying rainfall intensity and soil moisture content on flow velocity, assuming the watershed to be not in equilibrium but varying with soil moisture. The Qin River basin and Longhu River basin were selected as two case studies, and the synthetic unit hydrograph (SUH), time-varying distributed unit hydrograph (TDUH) and the current DUH methods were compared with the proposed method. The influence of time-varying soil moisture content on the flow velocity and flow routing was evaluated. Results showed that the proposed method performed the best among the four methods. The shape and duration of the unit hydrograph can be mainly related to the soil moisture content at the initial stage of a rainstorm. When the watershed is approximately saturated, the grid flow velocity is mainly dominated by excess rainfall. 

Reply on RC1

Abstract. The distributed unit hydrograph (DUH) method has been widely used for flow routing in a watershed because it adequately characterizes the underlying surface characteristics and varying rainfall intensity. Fundamental to the calculation of DUH is flow velocity. However, the currently used velocity formula assumes a global equilibrium of the watershed and ignores the impact of time-varying soil moisture content on flow velocity, which thus leads to a larger flow velocity. The objective of this study was to identify a soil moisture content factor, which, based on the tension water storage capacity curve, was derived to investigate the response of DUH to soil moisture content in unsaturated areas. Thus, an improved distributed unit hydrograph, based on time-varying soil moisture content, was obtained. The proposed DUH considered the impact of both time-varying rainfall intensity and soil moisture content on flow velocity, assuming the watershed to be not in equilibrium but varying with soil moisture. The Qin River basin and Longhu River basin were selected as two case studies, and the synthetic unit hydrograph (SUH), the time-varying distributed unit hydrograph (TDUH) and the current DUH methods were compared with the proposed method. Then, the influence of time-varying soil moisture content on flow velocity and flow routing was evaluated, and results showed that the proposed method performed the best among the four methods. The shape and duration of the unit hydrograph (UH) were mainly related to the soil moisture content at the initial stage of a rainstorm, and when the watershed was approximately saturated, the grid flow velocity was mainly dominated by excess rainfall. The proposed method can be used for the watersheds with sparse gauging stations and limited observed rainfall and runoff data.

Reply on RC2

The saturation-excess mechanism, infiltration-excess mechanism and subsurface stormflow mechanism are the fundamental of the hybrid runoff generation models. Existing hybrid runoff generation methods pay much attention to the surface runoff and subsurface runoff, but less to the characterization of subsurface stormflow. Additionally, the subsurface stormflow was mostly separated from the saturated-excess. This kind of separation ignores subsurface stormflow in unsaturated areas, and therefore may result in significant errors sometimes. Therefore, a novel conceptual method for subsurface stormflow in unsaturated areas over the interface was proposed through a combination of the Green-Ampt equation with the parabolic infiltration capacity distribution curve. Then, the subsurface stormflow was considered as the sum of the infiltration excess over the unsaturated areas and the saturation excess over the saturated areas. On this basis, an improved flexible runoff generation strategy was developed to incorporate the spatial heterogeneity of underlying surface conditions and rainfall conditions based on the three single runoff generation mechanisms. Finally, the Xun River basin was selected as a case study. Performances of different runoff generation strategies were compared and discussed based on flood events and continues flow. And the sensitivity analysis of the parameters was carried out. Results show that the proposed strategy performed the best among the five strategies, and the accuracy of the simulation results was affected significantly by the infiltration-excess subsurface stormflow over the unsaturated areas. The proposed method can be used for the watersheds where there may be abundant subsurface stormflow. 

Hydrological modelling with an improved flexible hybrid runoff generation strategy


 Systematic domestic water demand prediction is of great importance for social and economic sustainable development. To explain the system of domestic water demand prediction more completely, a system dynamic model based on the social-hydrology method was proposed in this paper. The key natural and socio-economic factors that affect the domestic water demand most were identified and investigated. The prediction equations of water price considering the feedback between humans and nature were deduced. The expressions of water-saving consciousness were proposed for the first time. Furthermore, the differential and difference equations for domestic water demand prediction were established to realize the future domestic water demand forecasts considering the natural and social driving forces. Finally, three scenarios were designed to investigate the impact of climate change and economic development on the domestic water demand. The upper and middle Pearl River Basin was selected as a case study. The predicted domestic water demand is consistent with the historical results, which demonstrates that the proposed model can be well used for the prediction of the domestic water demand. Under the three scenarios, the domestic water demand increased by 33, 27 and 18% in 2050 compared with that in 2017, respectively.

Domestic water demand prediction based on system dynamics combined with social-hydrology methods

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Hydrological modelling with an improved flexible hybrid runoff generation strategy

Domestic water demand prediction based on system dynamics combined with social-hydrology methods