Haoyong Tian

Attribution analysis of runoff variation holds significant importance for water resource conservation and management. This study established a methodological framework for runoff reconstruction, quantification, and analysis of contribution rates. The framework comprises four core components: (1) Aggregation of hydro-meteorological variables across multiple time scales, thereby identifying the most representative temporal scale for a given river basin, effectively overcoming the limitation of single-scale approaches common in previous research. (2) Optimization of explanatory variables specific to each temporal scale by integrating Spearman correlation and variance inflation factor (VIF) analysis, effectively addressing multicollinearity issues often overlooked in previous studies. (3) Reconstruction of runoff using Random Forest Regression Model (RFRM) and Soil and Water Assessment Tool (SWAT), with multi-metric validation metrics, to ensure transferability across diverse river basins. (4) Across multiple temporal scales, the integration of remote sensing and statistical data to identify anthropogenic drivers addresses the limitations of single-factor attribution analysis. Tested in the Lan River Basin, the framework identified precipitation as the dominant meteorological driver (Spearman’s p = 0.90 at the seasonal scale), determined monthly and bimonthly scales as optimal for modeling, and revealed the complementary strengths of RFRM and SWAT—the latter excelling particularly in simulating high-flow extremes. Integration of multi-source data further elucidated the dual role of human activities on runoff, underscoring the value of quantitative attribution. These findings offer methodological guidance for temporal scale and model selection while providing a transferable approach for runoff attribution.

Attribution analysis of runoff variation holds significant importance for water resource conservation and management. This study established a methodological framework for runoff reconstruction, quantification, and analysis of contribution rates. The framework comprises four core components: (1) Multi-temporal scale analysis: implementing explicit aggregation rules based on variable physical meanings to aggregate hydro-meteorological variables across daily, weekly, two-week, monthly, two-month, seasonal, and annual scales; (2) Variable screening mechanism: determining optimal explanatory variables by calculating Spearman correlations between meteorological factors and runoff at each time scale combined with variance inflation factor (VIF) analysis to address multicollinearity; (3) Multi-model comparison: reconstructing runoff using Random Forest Regression Model (RFRM) and Soil and Water Assessment Tool (SWAT), with validation through metrics including Coefficient of Determination (R ² ) and relative bias (RBIAS); (4) Integrated attribution approach: combining remote sensing and statistical data to identify driving factors of anthropogenic impacts. Tested on river time-series data from the Lan River Basin, this framework quantified contribution percentages and provided methodological references for temporal scale/model selection and driver analysis in watershed hydrological studies.

Introduction: Upstream damming significantly changes the flow and sediment conditions entering downstream reaches, resulting in remarkable channel adjustments. Several response models were applied to analyze the channel adjustments in response to flow and sediment conditions. However, the influence of water surface slope has been underexplored, and there is a lack of comparative analysis between various flow and sediment factors (FSF) and response models.Methods: To address these gaps, this study compares the effects of different FSFs (water surface slope included) on the dynamic adjustments of the Lower Jingjiang Reach (LJR) by applying two response models.Results: The results show that, for the LJR, the combinations of fluvial erosion intensity (the square of the discharge divided by sediment concentration) during the flood season and water surface slope agree well with measured data. Moreover, the factors incorporating the water surface slope consistently outperformed those without it.Discussion: This study provides valuable insights into selecting flow and sediment factors for response models, enhancing the simulation and prediction of channel adjustments.

The development of renewable energy is an effective avenue for achieving net zero goals. It requires many energy storage systems (ESSs) for adjusting the unstable power generated by renewable energy. To date, PSH is the most technically mature, economically reasonable, and reliable ESS. Currently, various countries have developed PSH. As of 2022, the global installed capacity of PSH has reached 175,060 MW, with an annual increase of 10,300 MW. This paper addresses several technical considerations in the preliminary design of PSH systems, drawing on extensive design experience. Key factors such as the selection of dam sites, installed capacity, and characteristic water levels are thoroughly discussed. These design choices are influenced by a range of factors, including geological and topographical conditions, hydrological parameters, environmental impacts, sedimentation, submersion areas, and resettlement issues. PSH is highly effective in meeting power demands, regulating frequency and phase, serving as an emergency power reserve, and improving the power factor of electrical networks. It enhances the quality of renewable energy sources such as wind, photovoltaic, and tidal power, which are characterized by intermittent supply. Beyond its technical advantages, PSH also contributes to local employment and tourism and supports pollutant reduction efforts. Compared to other energy storage systems, PSH has a more significant environmental impact and requires a longer construction period. Thus, exploring new forms of PSH is crucial. Innovative approaches such as utilizing constructed reservoirs, lakes, seas, and abandoned pits can reduce both investment and construction time while minimizing the environmental impact. This paper aims to provide some technical references and feasible plans to governments, owners, and engineers during the planning and preliminary design stages of a PSH project.

China’s coal-based energy structure and its large proportion of the manufacturing industry have resulted in China having the highest CO₂ emissions in the world, accounting for about one-third of the world’s total emissions. Achieving the carbon peak by 2030 and carbon neutrality by 2060, while maintaining economic development, presents a significant challenge. Industrial parks, as core units of industrial clusters, are major sites for energy consumption and CO₂ emissions. Thus, industry parks with great carbon reduction potential are crucial for China’s carbon reduction efforts. This review systematically examines the current state of the carbon emissions of industrial parks in China and analyzes the situation and challenges faced in the low-carbon transition of these parks. Then, the major pathways, technologies, and key policies for the low-carbon transition are summarized based on the current situation. This paper assists in the low-carbon development of industrial parks and promotes the proposal and implementation of carbon emission reduction strategies.

Debris-flow modeling is a great challenge due to its complex physical mechanism that remains poorly understood. The present research incorporates the effect of rheological features of the non-Newtonian fluid into the complete quasi-single-phase mixture model, which explicitly accommodates the interactions between flow, non-uniform sediment transport, and bed evolution. The effect of rheological features is estimated by Hersch–Bulkley–Papanastasiou model that can be simplified to Bingham or Newtonian models with specific coefficients. The governing equations are solved by a fully conservative numerical algorithm, using an explicit finite volume discretization with well-balanced slope-limited centered scheme combined with an implicit discretization method. One set of large-scaled U.S. Geological Survey debris-flow experiments is applied to investigate the influence of the non-Newtonian fluid on debris-flow modeling. It is found that the present model closed by Hersch–Bulkley–Papanastasiou model performs better than the models without considering effect of rheological features, which may facilitate the development of quasi-single-phase mixture modeling for debris flows.

The runoff process in the Dongting Lake has been influenced by climate change and human activities in recent decades. To manage the Dongting Lake efficiently and exploit water resources properly under the background of water shortage, it is desired to detect the factors of runoff change in the Dongting Lake. Hydro-meteorological data from 1961 to 2019 are analyzed to reveal the climate change and runoff alteration of the Dongting Lake comprehensively. Mutation test is used to detect the change points of runoff depth series, finding that 1984 and 2005 are change points and therefore 1961–1983, 1984–2004, and 2005–2019 are regarded as baseline period (BP), period 1 (P1), and period 2 (P2), respectively. Eight methods are used to quantitatively assess the relative contribution of human activities and climate change on runoff variation. It reveals that climate change especially precipitation change plays the dominant role (climate change makes runoff depth increase 70.14–121.51 mm, human activities make runoff depth decrease 51.98–103.35 mm) in runoff alteration in P1 while human activities play a prime role (account for 88.47–93.17%) in P2. Human activities such as reservoir construction, water consumption, and land-use (land-cover) change may be the main factors that influence the runoff in the Dongting Lake in P2. According to the sensitivity analysis, runoff in the Dongting Lake is more sensitive to climate change in P2 compared with that in P1, and no matter in P1 or P2, runoff is more sensitive to change in precipitation than the change in potential evapotranspiration. Combined with climate forecast, the results of sensitivity analysis can be used to estimate runoff change caused by climate change in the future.