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Non-point source (NPS) pollution has always been the focus of research worldwide, and understanding the migration process is the basis for effective control of NPS pollution. In this study, the SWAT model and digital filtering algorithm were combined to explore the contribution of NPS pollution that migrated with underground runoff (UR) process to the Xiangxi River watershed. The results showed that the surface runoff (SR) was the main migration process of NPS pollution, while the contribution of NPS pollution that migrated with the UR process only accounted for 30.9%. With the decrease in annual precipitation among the three selected hydrological years, the proportion of NPS pollution that migrated with the UR process for TN decreased, whereas the proportion for TP increased. The contribution of NPS pollution migrated with UR process varied remarkably during different months. Although the maximum total load and the load of NPS pollution that migrated with the UR process for TN and TP all appeared in the wet season, due to the hysteresis effect, the load of NPS pollution that migrated with the UR process for TP appeared 1 month later than the total load of NPS pollution. With an increase in precipitation from the dry season to the wet season, the proportion of NPS pollution that migrated with the UR process for TN and TP decreased gradually, and the degree of decrease in NPS pollution that migrated with the UR process for TP was more evident than that for TN. Besides, being affected by topography, land use, and other factors, the proportion of NPS pollution that migrated with the UR process for TN decreased from 80% in upstream areas to 9% in downstream areas, while that for TP reached a maximum of 20% in downstream areas. Based on the research results, the contribution of soil and groundwater cumulative nitrogen and phosphorus should be considered, and different managements and control measures for different migration routes should be adopted in controlling pollution.

The rainfall–runoff (RR) process in a catchment is non-uniform, complex, dynamic, and non-linear in nature. Although a number of advanced conceptual and data-driven techniques have been proposed in the past, the accurate estimation of daily runoff still remains a challenging task. A majority of conceptual models proposed so far suffer from the assumptions of linearity during their modeling. In this paper, novel hybrid approaches are proposed that are capable of exploiting the strength of both conceptual and data-driven techniques in RR modeling. A conceptual technique is first used to generate sub-basins’ runoff hydrographs in upstream reaches and then data-driven techniques are employed for routing them to the outlet of the catchment. The hybrid models’ performances are compared with standalone conceptual and data-driven models by employing the daily rainfall, runoff, and temperature data derived from the Kentucky River basin, USA. The results show that the proposed hybrid models, which do not assume the RR process to be a linear process to simulate the flow, outperform their individual counterparts. It is concluded that in order to achieve improved accuracy in RR modeling, the real-life process needs to be represented as accurately as possible in the modeling effort rather than making simplified assumptions.

Managing overland flow (OF) is essential in steep high-rainfall regions. A key strategy is to increase ground cover either naturally or through management. In Japanese cypress plantations, low ground cover increases OF and flood risks during intense rainfall. We analyzed OF and soil water content (SWC) in three plots of a Japanese cypress plantation under clear-cutting, 40% thinning, and control conditions over one year (2022–2023). The SWC remained consistently higher in the clear-cut plot than in the thinned and control plots. In contrast, the OF rate was greatest in the control plot (1.97%), intermediate in the thinned plot (1.03%), and lowest in the clear-cut plot (0.58%) with 5, 5, and 35% ground cover, respectively. Event-based analyses showed that in the clear-cut plot, OF was correlated with total rainfall (r = 0.597, p = 0.003), suggesting a tendency toward subsurface flow. Conversely, in the control plot, OF was correlated with 60 min of maximum rainfall (r = 0.90, p < 0.001), indicating Hortonian flow. No significant relationships were observed in the thinned plot, likely because of response variability. Our findings imply that ground cover dynamics following management regulate OF, emphasizing the importance of continued monitoring.

The inherent nonlinear characteristics of the watershed runoff process related to storm magnitude and watershed size are discussed in detail in this study. The first type of nonlinearity is referred to rainfall-runoff dynamic process and the second type is with respect to a Power-law relation between peak discharge and upstream drainage area. The dynamic nonlinearity induced by storm magnitude was first demonstrated by inspecting rainfall-runoff records at three watersheds in Taiwan. Then the derivation of the watershed unit hydrograph (UH) using two linear hydrological models shows that the peak discharge and time to peak discharge that characterize the shape of UH vary event-to-event. Hence, the intention of deriving a unique and universal UH for all rainfall-runoff simulation cases is questionable. In contrast, the UHs by the other two adopted nonlinear hydrological models were responsive to rainfall intensity without relying on linear proportion principle, and are excellent in presenting dynamic nonlinearity. Based on the two-segment regression, the scaling nonlinearity between peak discharge and drainage area was investigated by analyzing the variation of Power-law exponent. The results demonstrate that the scaling nonlinearity is particularly significant for a watershed having larger area and subjecting to a small-size of storm. For three study watersheds, a large tributary that contributes relatively great drainage area or inflow is found to cause a transition break in scaling relationship and convert the scaling relationship from linearity to nonlinearity.

Runoff plays an essential part in the hydrological cycle, as it regulates the quantity of water which flows into streams and returns surplus water into the oceans. Runoff modelling may assist in understanding, controlling, and monitoring the quality and amount of water resources. The aim of this article is to discuss various categories of rainfall–runoff models, recent developments, and challenges of rainfall–runoff models in flood prediction in the modern era. Rainfall–runoff models are classified into conceptual, empirical, and physical process-based models depending upon the framework and spatial processing of their algorithms. Well-known runoff models which belong to these categories include the Soil Conservation Service Curve Number (SCS-CN) model, Storm Water Management model (SWMM), Hydrologiska Byråns Vattenbalansavdelning (HBV) model, Soil and Water Assessment Tool (SWAT) model, and the Variable Infiltration Capacity (VIC) model, etc. In addition, the data-driven models such as Adaptive Neuro Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN), Deep Neural Network (DNN), and Support Vector Machine (SVM) have proven to be better performance solutions in runoff modelling and flood prediction in recent decades. The data-driven models detect the best relationship based on the input data series and the output in order to model the runoff process. Finally, the strengths and downsides of the outlined models in terms of understanding variation in runoff modelling and flood prediction were discussed. The findings of this comprehensive study suggested that hybrid models for runoff modeling and flood prediction should be developed by combining the strengths of traditional models and machine learning methods. This article suggests future research initiatives that could help with filling existing gaps in rainfall–runoff research and will also assist hydrological scientists in selecting appropriate rainfall–runoff models for flood prediction and mitigation based on their benefits and drawbacks.

Storm runoff in basins is comprised of various runoff processes with widely disparate infiltration and storage capacities, such as Hortonian overland flow (HOF), saturated overland flow (SOF), sub-surface flow (SSF), and deep percolation (DP). Areas may be classified according to these various runoff processes based on the soil characteristics, geology, topography, and land-use. This study analyzes changes in runoff components in the Jialu River basin and the Fen River (Jingle sub-basin) during runoff generation from 1980 to 2013 using the runoff segmentation method. Based on the decision scheme, the dominant runoff process (DRP) in the basins was distinguished using geographic information system (GIS) tools. The impact of different runoff process distributions on the changes in the runoff for the basin was determined. The results show that the floods in the Jialu River basin and Jingle sub-basin were dominated by overland flow components. Compared with 1980–1999, the proportion of overland flow components for 2000–2013 in two basins showed a decreasing trend by 8.3% and 7.1%, respectively, while the interflow and underground runoff components increased. In addition, HOF was the DRP in the Jialu River basin and Jingle sub-basin from 2000 to 2013. The area of the rapid runoff processes (HOF, SOF1, and SSF1) in the Jialu River basin and Jingle sub-basin accounted for 89% and 78% of the entire basin, respectively. In contrast, the slow runoff processes (SOF2, SSF2, and DP) accounted for 11% and 22% of the entire basin, respectively. The runoff of the Jingle sub-basin was substantially lower than that of the Jialu River basin under the same rainfall conditions, because of the influence of the distribution of different runoff processes. Compared with the Jialu River Basin, the peak discharge and runoff of Jingle sub-basin were 190.4 m3/s and 2.85 mm lower on average, respectively. The results of this study provide useful information to understand land-use changes and formulate management practices to reduce flooding in the Yellow River.

In water-scarce arid areas, the water cycle is affected by urban development and natural river changes, and urbanization has a profound impact on the hydrological system of the basin. Through an ecohydrological observation system established in the Shiyang River basin in the inland arid zone, we studied the impact of urbanization on the water cycle of the basin using isotope methods. The results showed that urbanization significantly changed the water cycle process in the basin and accelerated the rainfall-runoff process due to the increase in urban land area, and the mean residence time (MRT) of river water showed a fluctuating downward trend from upstream to downstream and was shortest in the urban area in the middle reaches, and the MRT was mainly controlled by the landscape characteristics of the basin. In addition, our study showed that river water and groundwater isotope data were progressively enriched from upstream to downstream due to the construction of metropolitan landscape dams, which exacerbated evaporative losses of river water and also strengthened the hydraulic connection between groundwater and river water around the city. Our findings have important implications for local water resource management and urban planning and provide important insights into the hydrologic dynamics of urban areas.

Rainfall–runoff modeling is a complex nonlinear time-series problem in the field of hydrology. Various methods, such as physical-driven and data-driven models, have been developed to study the highly random rainfall–runoff process. In the past 2 years, with the advancement of computing hardware resources and algorithms, deep-learning methods, such as temporal convolutional network (TCN), have been shown to be good prospects in time-series prediction tasks. The aim of this study is to develop a prediction model based on TCN structure to simulate the hourly rainfall–runoff relationship. We use two datasets in the Jingle and Kuye watersheds to test the model under different network structures and compare with the other four models. The results show that the TCN model outperforms the Excess Infiltration and Excess Storage Model (EIESM), artificial neural network, and long short-term memory and improves the flood forecasting accuracy at different foreseeable periods. It is shown that the TCN has a faster convergence rate and is an effective method for hydrological forecasting.

Although the Volterra models are non-parsimonious ones, they are being used because they can mimic dynamics of complex systems. However, applying and identification of the Volterra models using data may result in overfitting problem and uncertainty. In this investigation we evaluate capability of different wavelet forms for decomposing and compressing the Volterra kernels in order to overcome this problem by reducing the number of the model coefficients to be estimated and generating smooth kernels. A simulation study on a rainfall−runoff process over the Cache River watershed showed that the method performance is successful due to multi-resolution capacity of the wavelet analysis and high capability of the Volterra model. The results also revealed that db2 and sym2 wavelets have the same high potential in improving the linear Volterra model performance. However, QS wavelet was more successful in yielding smooth kernels. Moreover, the probability of overfitting while identifying the nonlinear Volterra model may be less than the linear model.

The growing concerns surrounding water supply, driven by factors such as population growth and industrialization, have highlighted the need for accurate estimation of streamflow at the river basin level. To achieve this, rainfall-runoff models are widely employed as valuable tools in watershed management. For this specific study, two modelling approaches were employed: the Soil and Water Assessment Tool (SWAT) model and a set of eight artificial intelligence (AI) models. The AI models consisted of seven data-driven approaches, namely k -nearest neighbour regression, support vector regression, linear regression, artificial neural networks, random forest regression, XGBoost, and Histogram-based Gradient Boost regression. Additionally, a deep learning model known as Long Short-Term Memory (LSTM) was also utilized. The study focused on monthly streamflow modelling in the Murredu River basin, with a calibration period from 1999 to 2003 and a validation period from 2004 to 2005, spanning a total of 7 years from 1999 to 2005. The results indicated that all nine models were generally suitable for simulating the rainfall-runoff process, with the LSTM model demonstrating exceptional performance in both the calibration ( R 2 is 0.97 and NSE is 0.96) and validation ( R 2 is 0.97 and NSE is 0.92) periods. Its high coefficient of determination ( R 2 ) and Nash–Sutcliffe efficiency (NSE) values indicated its superior ability to accurately model the rainfall-runoff relationship. While the other models also produced satisfactory results, the findings suggest that selecting the most efficient model, such as the LSTM model, could significantly contribute to the effective management and planning of sustainable water resources in the Murredu watershed.