Runoff Evolution Research Articles

A novel daily runoff forecasting model based on global features and enhanced local feature interpretation

The development of artificial intelligence has introduced new perspectives to the field of hydrological forecasting. However, there is still a lack of research on efficiently identifying the physical characteristics of runoff sequences and developing prediction models that consider global and local sequence features. This study proposes a parallel computing prediction model called IMCAEN (Integrated Multi-Feature Causal Dilated Convolutional Attention Encoder Network) to address these issues. Unlike existing models, this model can monitor fluctuations and anomalies in time series. Incorporating the CDC-AA (Causal Dilated Convolutional Network with Aggregation Attention) and encoder structure captures both local sequence variations and global abrupt anomalies, allowing for comprehensive attention to sequence features. When predicting runoff data from three different hydrological conditions, the IMCAEN model achieved NSEC (Nash-Sutcliffe Efficiency Coefficient) values of 0.98, 0.97, and 0.88, respectively, and outperformed benchmark models in other evaluation indicators as well. Given the opacity of the feature distribution process in AI models, SHAP (SHapleyAdditive exPlanations) analysis and spatial expression of feature distribution are used to assess the contribution of each feature variable to the long-term trend of runoff and to verify the distribution of features trained in each module. The proposed IMCAEN model efficiently captures local and global information in the runoff evolution process through parallel computing and shared features, enabling accurate runoff forecasting and providing critical references for timely warnings and predictions.

Evolution characteristics and attribution analysis of runoff in the coal mining area in northwest China: A case study of the Kuye River Basin

ABSTRACT The Kuye River Basin is the main coal-producing area in northwest China, and it is also a typical arid and semi-arid area. Under the combined influence of climate change and human activities, the runoff of the basin decreases noticeably. The Mann–Kendall trend test, the Hurst index, cumulative anomaly, and wavelet analysis were used to analyze the runoff evolution characteristics of the Kuye River, and the contributions of climate change and human activities to the runoff reduction were quantitatively separated by the slope-changing ratio of the cumulative quantity. The results showed that the runoff decreased significantly from 1970 to 2020, and there was an obvious abrupt change in 1996, while the change trends in precipitation and evaporation were not significant. The contributions of climate change and human activities to runoff reduction were 22.71 and 77.29%, respectively, and human activities have become the dominant factor in runoff variations. Coal mining and the implementation of soil and water conservation measures are considered to be the main human activities contributing to the reduction in runoff. In particular, mining activities reduce groundwater recharge to rivers, which provides a profound understanding of the effect of human activities in mining areas on runoff variation.

Response of Surface Runoff Evolution to Landscape Patterns in Karst Areas: A Case Study of Yun–Gui Plateau

To control and improve the phenomena of rocky desertification and soil erosion in karst landform areas, which are caused by a series of human factors that include social and economic development and human activities, China has successively introduced many policies, resulting in spatial and temporal changes in the landscape pattern of the southern karst area. In this study, land use transfer intensity maps, the grid method, the sample line method, the semivariogram method, and the Spearman analysis method are used to explore the spatial and temporal evolutions in surface runoff as responses to landscape pattern and policy factors in karst landform area. Therefore, this study provides theoretical and policy support for improving the regional landscape structure, optimizing the landscape layout, introducing regional policies, reducing surface runoff, and alleviating soil erosion. The results show that the best scale for the study of landscape patterns in the southern karst area is 3000 m. Forests are the land type that make up the highest proportion in the southern karst area, and they have the strongest interception capacity for surface runoff. The spatial and temporal distributions of the surface runoff are significantly different, and urban expansion has led to an increase in impervious runoff year over year. Runoff is positively correlated with the Shannon diversity index (SHDI), patch density (PD), and landscape shape index (LSI). The stronger the landscape heterogeneity, the more runoff. DIVISION is positively correlated with forest runoff and negatively correlated with other land types. The higher is the degree of aggregation of impervious patches, the higher the regional runoff rate. The more dispersed the forest patches are, the smaller the area proportion, and the greater the runoff. In addition, policy factors have a significant impact on surface runoff.

Comparison of Process-Driven SWAT Model and Data-Driven Machine Learning Techniques in Simulating Streamflow: A Case Study in the Fenhe River Basin

Hydrological modeling is a crucial tool in hydrology and water resource management for analyzing runoff evolution patterns. In this study, the process-driven soil and water assessment tool (SWAT) model and data-driven machine learning techniques (XGBoost, random forest, LSTM, BILSTM, and GRU) were employed to simulate runoff at monthly and daily intervals in the Fenhe River basin, situated in the middle reaches of the Yellow River, respectively. The SWAT model demonstrated effective performance in simulating runoff at various scales, with the coefficient of determination (R2) exceeding 0.80 and the Nash–Sutcliffe efficiency (NSE) surpassing 0.79. Sensitivity analysis reveals varying degrees of sensitivity among the model parameters. Furthermore, the deep learning techniques (LSTM, BILSTM, and GRU) exhibited superior simulation generalization capabilities compared to the SWAT model across various scales. Additionally, the generalization abilities of traditional machine learning techniques (XGBoost and random forest) were comparable to the SWAT model. This indicates that deep learning techniques demonstrate remarkable stability and generalization capabilities across various scales. This analysis was motivated by the use of external continuous time series data as input and the application of deep learning techniques to internal mechanisms. Moreover, an integrated modeling approach was used to enhance simulation accuracy by combining the SWAT model with machine learning techniques. The results indicate that the integrated modeling approach improves simulation performance across various scales compared to the single-model approach. This research is significant for improving the efficiency of water resource utilization and management in the Fenhe River basin.

Impact of climate change on water quality evolution in cold regions using a water-heat-nitrogen processes coupled model.

Cold regions are particularly vulnerable to climate change. Thus, evaluating the response of water quality evolution to climate change in cold regions is vital for formulating adaptive countermeasures for pollution control under changing climatic conditions. Taking the Songhua River Basin (SRB) in Northeast China as the target area, we designed a water-heat-nitrogen coupled model based on the principle of water and energy transfer and nitrogen cycle processes model (WEP-N) in cold regions. The impact of climate change on pollution load and water quality was analyzed during the freezing, thawing, and non-freeze-thaw periods by taking the sudden change point (1998) of precipitation and runoff evolution in the SRB as the cut-off. The ammonia nitrogen load at Jiamusi station, the outlet control station in the SRB, was decreased by 1502.9 t in the change period (1999-2018) over the base period (1956-1998), with a - 9.2% decrease due to climate change. Compared to the ammonia nitrogen load during the base period, the ammonia nitrogen load decreased by - 171.3, - 506.9, and - 824.8 t during the freezing, thawing, and non-freeze-thaw periods, respectively, while the coefficient of variation showed an increasing trend during three periods, especially during the freezing and thawing periods. However, the water quality changes differed among periods owing to varying runoff during the year. Meanwhile, increasing runoff and decreasing ammonia nitrogen load improved water quality at Jiamusi station during the freezing period. During the thawing and non-freeze-thaw period, the water quality deteriorated due to the decrease in runoff more than the decrease in ammonia nitrogen load. Hence, the impact of climate change on water quality during thawing and non-freeze-thaw periods should be monitored to potentially offset the human influence on pollution control. The difference in the rate of change of the proportion of Class IV water between the two models with or without the soil freeze-thaw mechanism was 15.9%. The result shows that the application of a model that does not consider the freeze-thaw mechanism might slightly exaggerate the impact of climate change on water quality.

Contamination characteristics in runoff fractions from a nuclear facility in São Paulo, Brazil.

The stormwater runoff may act as a nonpoint pollutant source and contributes to aquatic ecosystem quality decay in urban environments. The aim of this work was to evaluate the runoff characteristics on the transport of total solids and total metals, as well as pH and conductivity responses during the rainfall evolution. During 2017 and 2018, 12 rain events were monitored in 4 sampling stations at a car parking lot located at Nuclear and Energy Research Institute (IPEN/CNEN) in São Paulo/Brazil. A 4-chamber integrated collector allowed the sequential/temporal runoff evolution assessment. The runoff composition, in decreasing order of quantities, was Ca > K > Mg > Si > Al > Fe > Na > Zn > Mn > Sr > Ti > Mo > V > Cu > B > Pb > Ni > Ce > Sb > Cr > La > U > Th > Cd. The amount of total solids, Al, and Fe exceeded the Brazilian water quality standards. Principal component analysis (PCA) identified the elemental clusters linked to the facility activity, soil, and traffic/atmospheric-related deposition. The results show that the runoff characteristics could be differentiated by pollutant source. Factors such as seasonal variation, rain event intensity, air mass from oceanic or continental origin, spatial distribution inside the monitoring area, and the intensity of the first flush must be considered in order to disentangle the elemental clusters and pollution source contributions. In winter, continental air masses were associated with higher concentrations of heavy metals in the surface runoff. Spatial changes with no seasonal variation were observed for U, Th, La, and Ce.

Bayesian parameter estimation in glacier mass-balance modelling using observations with distinct temporal resolutions and uncertainties

Abstract Empirical glacier mass-balance models are commonly used in assessments of glacier and runoff evolution. Recent satellite-borne geodetic mass-balance observations of global coverage facilitate large-scale model calibration that previously relied on sparse in situ observations of glacier mass change. Geodetic observations constitute temporally aggregated mass-balance signals with significant uncertainty, raising questions about the role of observations with different temporal resolutions and uncertainties in constraining model parameters. We employ a Bayesian approach and demonstrate the sensitivity of parameter values to commonly used mass-balance observations of seasonal, annual and decadal resolution with uncertainties characteristic to in situ and satellite-borne observations. For glaciers along a continentality gradient in Norway, the use of annual mass balances results in around 20% lower magnitude of modelled ablation and accumulation (1960–2020), compared to employing seasonal balances. Decadal mass balance also underestimates magnitudes of ablation and accumulation, but parameter values are strongly influenced by the prior distribution. The datasets yield similar estimates of annual mass balance with different margins of uncertainty. Decadal observations are afflicted with considerable uncertainty in mass-balance sensitivity due to high parameter uncertainty. Our results highlight the importance of seasonal observations when model applications require accurate magnitudes of ablation, e.g. to estimate meltwater runoff.

Runoff time series prediction using LSTM dynamic neural network optimized by logistic chaotic mapping chicken swarm algorithm

Abstract An algorithm, named long short-term memory (LSTM)-logistic chaos mapping chicken swarm algorithm (LCCSA), is proposed for initializing the weights and thresholds of LSTM neural networks using the Logistic chaotic mapping chicken swarm algorithm (CSA). This algorithm aims to improve mid- to long-term runoff sequence prediction in river basins. In this model, the logistic chaotic mapping method is used to initialize the chicken swarm, and LCCSA is employed to pre-train the weights and thresholds of each layer of LSTM 50 times, using the training results' initial weights of LSTM to enhance convergence accuracy and speed. Taking the Manas river and Kuitun river, two typical basins in northern Xinjiang, China, as the research objects, LSTM-LCCSA was used to forecast the mean monthly runoff in the mid- to long-term under different lag time series by using the runoff evolution data within a certain period. The example using the basin located in northern Xinjiang demonstrates the effectiveness, stability, and generality of the LSTM-LCCSA method in mid- to long-term prediction of average monthly runoff, and the prediction accuracy and universality of LSTM-LCCSA are better than other data-driven models.

Spatiotemporal differences in dominant drivers of streamflow evolution on the Loess Plateau

Previous studies have found that climate change and underlying surface change are the most direct and important drivers of the streamflow change in the Yellow River Basin. However, there are significant spatial differences in climate and underlying surface in the Yellow River Basin, especially in the Loess Plateau, attribution analysis of runoff evolution in the Loess Plateau from the perspective of spatial heterogeneity was rarely reported. Therefore, in order to further reveal the causes of runoff evolution in the Loess Plateau, this study have quantitatively revealed the spatial differences of climate and underlying surface changes driving streamflow changes in 34 main Loess Plateau basins from 1956 to 2020 basing on the Budyko equation with fixed parameters. Results show that the streamflow on the Loess Plateau decreased significantly from 1956 to 2020, and streamflow decreased by 17.19 mm (26.95%) after abrupt change in 1990. The sensitivity coefficients of streamflow change on the Loess Plateau to precipitation, potential evapotranspiration and underlying surface parameters are 0.30, −0.08 and −69.26 respectively. The contributions of precipitation, potential evapotranspiration and underlying surface change to streamflow change were 26.24%, 2.15% and 73.01%, respectively, and showed significant spatiotemporal differences. The spatial variability of streamflow is mainly controlled by underlying surface. For 29 of the whole 34 tributaries, underlying surface change was the dominant driver of streamflow change with the contribution of 52.50%–98.22%. The temporal variability of streamflow on the Loess Plateau is mainly controlled by precipitation. The wetter the climate is, the more sensitive of the streamflow changes are to climate change. And the smaller the underlying surface parameters are, the more sensitive the streamflow changes are to the underlying surface change. The results further deepen our understanding for the response of streamflow to climate and underlying surface changes in the Loess Plateau and can provide theoretical support for systematic regulation of basin water resources in the future.

Attribution of runoff changes in the Danjiang River Basin in the Qinba Mountains, China

IntroductionThe Danjiang River Basin has experienced significant alterations to its runoff patterns in recent years, primarily as a result of climate change and anthropogenic activities.MethodsThis study aims to investigate these factors’ influence on the basin’s runoff between 1960 and 2016 using the Mann–Kendall (M-K) test and Accumulative Anomaly Curve (AAC) to assess the abrupt change characteristics of runoff. Furthermore, the study evaluates the effects of climate and anthropogenic factors on runoff evolution using eight independent Budyko hypotheses.ResultsThe findings indicate that annual precipitation in the basin remained stable (P > 0.1), while annual runoff decreases significantly to −4.08 mm/a (P < 0.05) and the annual potential evapotranspiration slightly decreases to −1.76 mm/a (P < 0.05). The study also reveales that the reduction of runoff is primarily attributable to anthropogenic activities, contributing between 133.66 and 147.50%, while climate change contributes between −33.66 and −47.50%.DiscussionThe increased vegetation cover in the basin is identified as the primary cause of the reduction in runoff production and increase in evapotranspiration. Additionally, the construction of hydraulic engineering facilities in the basin is found to have had some impact on reducing runoff. These findings have significant implications for water resource management and soil and water conservation in the Danjiang River Basin.

Intra-annual variation in the attribution of runoff evolution in the Yellow River source area

Accurately understanding the intra-annual variation in runoff evolution attribution is essential for basin-scale water resources management. In this study, the runoff in the Yellow River source area was divided into two intra-annual time scales: flood season, non-flood season and spring, summer, autumn and winter, and the sensitivity and attribution differences of runoff changes in the Yellow River source area at different intra-annual time scales were quantitatively assessed based on the time-varying Budyko framework. Results show that the runoff in the Yellow River source area decreases during the flood season and spring, summer and autumn, and increases during the non-flood season and winter from 1960 to 2020, with an insignificant decreasing trend in annual runoff. Flood season and autumn runoff changes are the main reasons for the reduction in annual runoff. Runoff in the Yellow River source area is most sensitive to the underlying surface such as vegetation and soil freeze–thaw change, and in terms of climate change, non-flood season and autumn runoff is more sensitive to changes in precipitation, while flood season and summer runoff is more sensitive to changes in potential evaporation. The underlying surface change is the dominant factor for annual runoff change. And for the intra-annual runoff change, the non-flood season, spring and winter runoff in the Yellow River source area is dominated by the underlying surface change, and flood season, summer and autumn runoff is dominated by the climate change. These findings can provide theoretical support for the scientific response to environmental change and the enhancement of water conservation capacity in the Yellow River source area.

Evolutionary Characteristics of Runoff in a Changing Environment: A Case Study of Dawen River, China

Watershed water cycles undergo profound changes under changing environments. Analyses of runoff evolution characteristics are fundamental to our understanding of the evolution of water cycles under changing environments. In this study, linear regression, moving average, Mann–Kendall, Pettitt, accumulative anomaly, STARS, wavelet analysis, and CEEMDAN methods were used to analyze the trends, mutations, and periodic and intrinsic dynamic patterns of runoff evolution using long-term historical data. The intra-annual distribution of runoff in the Dawen River Basin was uneven, with an overall decreasing trend and mutations in 1975–1976. The main periods of runoff were 1.9 and 2.2 years, and the strongest oscillations in the study period occurred in 1978–1983. The runoff decomposition high-frequency term (intra-annual fluctuation term) had a stronger fluctuation frequency, with a period of 0.51–0.55 years, while the low-frequency term (interannual fluctuation term) had a period of 1.55–2.26 years. The trend term for the runoff decomposition tended to decrease throughout the monitoring period and gradually stabilized at the end of the monitoring period, while the period gradually decreased from upstream to downstream. In summary, we used multiple methods to analyze the evolutionary characteristics of runoff, which are of great relevance to the adaptive management of water resources under changing environments.

Analysis of Runoff Variation and Future Trends in a Changing Environment: Case Study for Shiyanghe River Basin, Northwest China

Changes in the hydrological cycle and water resources are inevitable consequences of environmental change, and runoff is an important element of the hydrological cycle. Therefore, the assessment of runoff changes is crucial for water resources management and socio-economic development. As an inland river basin in the arid zone of northwest China, the Shiyang River Basin is very vulnerable to environmental changes. Consequently, this study evaluated the past runoff evolution of the Shiyang River basin using a variety of statistical tools. In addition, the improved Soil and Water Assessment Tool (SWAT) was used to predict runoff trends from 2019 to 2050 under potential future climate change and land use projection scenarios in the future for the Shiyang River Basin. In the inland river basins, water resources mainly come from headwaters of the rivers in the upper mountainous regions, where they are more sensitive. Therefore, this study not only examined the mainstream of the Shiyang River, but also the six tributaries in the upper stream. The results indicate that the mainstream of the Shiyang River Basin and its six upstream tributaries all showed declining trends from the 1950s to 2019, and most of the rivers will continue to insignificantly decrease until 2050. Furthermore, there are two main timescales for runoff in the past as well as future: one is around 40 years and another is 20–30 years. In the meantime, the Shiyang River and its tributaries have relatively consistent change characteristics. The results of this study will provide assistance to basin management agencies in developing more appropriate water resource management plans.

Attribution analysis of runoff evolution in Kuye River Basin based on the time-varying budyko framework

The underlying surface parameters in the Budyko framework (such as parameter n in the Choudhury–Yang equation) are crucial for studying the relationship between precipitation, evapotranspiration, and runoff. It is important to accurately quantify the influence of climate and human activities on the evolution of underlying surface characteristic parameters. However, due to the spatiotemporal heterogeneity of underlying surface parameters, it is often difficult to accurately quantify these relationships. In this study, taking the Kuye River Basin located in the northern Loess Plateau as the research object, we first used trend analysis and non-linear regression methods to estimate the evolution characteristics of runoff and underlying surface parameter n. We then determined the contribution of runoff changes by using the elasticity coefficient method under the 9-year moving average window. The results showed that: 1) the Kuye River Basin runoff underwent a sudden change in 1997, and the complex human activities are the main reasons for the sharp runoff decrease. 2) In addition to precipitation and potential evapotranspiration, temperature changes will alter the basin’s underlying surface parameters, ultimately changing the runoff. Moreover, climate change first inhibited and then promoted the runoff reduction trend. 3) Human activities, represented by changes in vegetation coverage and coal mining, considerably influenced runoff evolution in Kuye River Basin. More importantly, the change of runoff in the Kuye River Basin caused by coal mining is approximately four times that of the normalized vegetation index. This study can improve the applicability of the Budyko framework in the Loess Plateau sub-basin and provide scientific guidance for water resource management.

Elaborate simulations of floods in a karst trough valley basin with the simplified Karst-Liuxihe model

The karst trough valley basin in Zhongliang of Chongqing is one of the most developed karst areas in Southwest China, with unique topographic features such as karst troughs and valleys that are prone to flash floods. Accurate simulation of flood processes in these areas using hydrological models can provide references for predicting future local runoff evolution trends. The main challenge for hydrological modelling in karst areas lies in building a model with limited hydrogeological data. In this study, the Karst-Liuxihe (KL) model was simplified and improved to simulate karst flood processes in the Zhongliang karst trough valley. The KL model consists of multiple, complex structures; a large amount of data is needed to model karst areas. To overcome the modelling data requirements, we modified the model structure and its parameters to propose a simplified KL (SKL) model. Additionally, we improved the runoff generation and confluence algorithms. Compared with the simulation results for 22 flood processes obtained by using the KL model, the results for this SKL model were better with less modelling data. For example, the mean value of the Akaike information criterion (AIC) index decreasing rate was 35%, and the process relative error and the peak flow relative error decreased by 10%, indicating that the technique of simplifying and improving the KL model is effective and that the simplified SKL model can quantitatively depict karst floods in karst trough valley areas.

Spatial difference analysis of the runoff evolution attribution in the Yellow River Basin

Attribution analysis of runoff evolution under changing environmental conditions is currently a hot yet challenging topic in hydrology research. In this study, the Budyko method was used to quantitatively identify the effects of climate conditions and the underlying surface such as topography, land use and vegetation cover on the runoff changes in the Yellow River Basin during 1961–2018, and to clarify the spatial differences in the sensitivity and attribution of the runoff evolution by dividing the entire basin into sub-basins. The results show that the runoff was more sensitive to the underlying surface and precipitation changes than to the potential evaporation. The sensitivity coefficients decreased from upstream to downstream and the aridity index increased. The contribution rates of the climatic and the underlying surface changes to the runoff changes were 10.3–36.8% and 62.9–88.7%, respectively, and the underlying surface changes were the dominant factor causing the decrease in the runoff. Climate change led to increased runoff in the upper reaches and decreased runoff in the middle and lower reaches; while the underlying surface changes led to decreased runoff throughout the entire basin, especially in the middle reaches of the Yellow River. The quantified spatial differences in the runoff evolution attribution can help improve regional water resource management and provide theoretical support for studying the water cycle evolution mechanism in the Yellow River Basin.

Response of runoff and suspended load to climate change and reservoir construction in the Lancang River

Abstract With the operation of six cascade reservoirs, the flow regime and sediment discharge of the Lancang River have changed greatly. The changes of runoff and suspended load have attracted extensive attention. The hydrological data of Gajiu and Yunjinghong stations in Lancang River from 1964 to 2019 were analyzed by using wavelet analysis, double mass curve and abrupt change analysis. The temporal trends in runoff and suspended load were evaluated. Results revealed that the reduction of suspended load was much more profound than the change of runoff. There was a slight downward trend in annual runoff due to climate change. After the completion of Xiaowan and Nuozhadu reservoirs, the proportion of runoff in flood season decreased by 22.64 and 30.75%, respectively. Wavelet analysis was used to reveal the characteristics of runoff evolution. With the operation of reservoirs, suspended load showed abrupt changes in 1993 and 2008. The amount of suspended load during 2009–2019 decreased by 95.47–98.78% compared with that before the reservoir construction. This paper presents the latest quantitative study on the temporal variation of runoff and suspended load since the completion of Xiaowan and Nuozhadu reservoirs, which is of great importance for guiding the operation of reservoirs and maximizing the value of the whole Lancang-Mekong River basin.

Runoff evolution and wetland ecological response in Lake Baiyangdian Basin in recent 60 years

白洋淀是雄安新区的核心生态功能区，近几十年来面临水源不足、湿地萎缩等生态环境问题.选取白洋淀流域上游王快、西大洋及拒马河3个子流域开展河川径流演变研究，结合1969年以来18期白洋淀湿地遥感影像，揭示白洋淀流域河川径流驱动湿地演变的过程机制.结果表明：近60年来白洋淀流域山区径流量呈持续衰减趋势，从P1阶段（1961—1979年）至P3阶段（1997—2019年），典型子流域年径流系数均值由0.29降至0.12，山区年径流总量由30.84×10⁸ m³/a降至11.37×10⁸ m³/a，降幅达63.1%，梯田修建、地下水开采等人类活动是导致径流衰减的主要原因；不同子流域产流和基流减少对径流衰减的贡献率存在差异，以变质岩为主的王快子流域径流衰减的主要原因是地表产流减少，碳酸盐岩分布较广的拒马河子流域径流衰减的主要原因是基流减少；白洋淀湿地面积变化受控于地表水位波动，近60年来白洋淀湿地退化的直接原因是入淀流量减少，根本原因是人类活动影响导致的山区径流衰减.;Baiyangdian Wetland is the main ecological functional area of Xiong'an New Area. In recent decades, Baiyangdian Wetland has been faced with ecological and environmental problems, such as water shortage and wetland degradation. Three typical sub-basins (Wangkuai, Xidayang and Juma River) in the upper reaches of Lake Baiyangdian Basin were selected to study the runoff evolution in the past 60 years. Based on 18 remote sensing images of Lake Baiyangdian Wetland since 1969, the evolution process of Lake Baiyangdian Wetland was analyzed. The results showed that the mountain runoff in Lake Baiyangdian Basin decreased continuously in the past 60 years. From P1 (1961-1979) to P3(1997-2019), the average annual runoff coefficient in typical basins decreased from 0.29 to 0.12, and the total annual runoff in mountainous areas decreased from 30.84×10⁸ m³/a to 11.37×10⁸ m³/a, decreasing by 63.1%. Human activities such as terrace construction and groundwater exploitation are the main causes of runoff attenuation. There are differences in runoff and baseflow reduction in different sub-basins. The lithology of Wangkuai sub-basins is mainly metamorphic rock. The main reason for runoff attenuation is the decrease in surface runoff. The karst strata in the Juma River basin are widely distributed, and the main reason for runoff attenuation is the decrease in base flow. The direct factor affecting the area of Lake Baiyangdian Wetland is the fluctuation of surface water level. The decrease of water inflow is the direct cause of the degradation of Lake Baiyangdian Wetland in recent 60 years, and the fundamental reason is the attenuation of mountain runoff caused by human activities.

Quantitative impacts of climate change and human activities on the runoff evolution process in the Yanhe River Basin

Since 1999, the surface vegetation coverage in the Yanhe River Basin has undergone constant change. The ecological environment in the basin has improved partially; however, water scarcity remains a critical issue for the Loess Plateau. The current quantitative analysis of runoff evolution affected by climatic and anthropogenic elements in the study area is insufficient. To evaluate the influence of anthropogenic elements and climatic variation on runoff evolution, we used the SWAT hydrological model to study key hydrological processes under different simulation scenarios along the river basin in this study. Overall, the runoff volume of the Yanhe River Basin showed a significant downward trend, although different trends were observed over different periods. Runoff declined from 1981 to 1998 and increased after 1998. A comparative analysis of three different scenarios shows that climate change contributes approximately twice as much to runoff evolution as land use changes. Climate change causes runoff to decrease by 65.64%, whereas land use change causes runoff to increase by 34.36%. The decrease in runoff in small watersheds is primarily due to climatic factors, whereas vegetation restoration is beneficial to the increase in runoff in small watersheds. The research results will be valuable in comprehending the quantitative influence of climatic and anthropogenic factors on the hydrological evolution of the Yellow River Valley.

An Analysis of Driving Factors on Water and Sediment Evolution

The underlying surface has an influence on the characteristics of runoff and sediment characteristics in river basins. And the features of the different scales underlying surfaces have various effects on the runoff and sediment discharge in Kuye River. By analysing the concerned data from 1980 to 2013, this paper took 3 different scales watersheds in Kuye River Basin as the objects to study the influence of the underlying surface change on the runoff and sediment evolution in different scales and calculate the correlation degree of the driving factors by using grey relational analysis.