Runoff Processes Research Articles

Regulatory Threshold of Soil and Water Conservation Measures on Runoff and Sediment Processes in the Sanchuan River Basin

Research on the runoff and sediment reduction effects of soil and water conservation measures has always been a topic of interest, which is of great significance for carrying out sustainable strategies for soil and water conservation in the Yellow River Basin. This study aims to find the threshold years of soil and water conservation measures for reductions in runoff and sediment. Through the analysis of various soil and water conservation measures, runoff, sediment, and rainfall data in the Sanchuan River Basin from 1960 to 2019, we determined the threshold years of soil and water conservation measures on runoff and sediment processes using the Hydrology and Lagrange Multiplier method. The results are as follows: The trend in flood season rainfall and annual rainfall in the Sanchuan River Basin is consistent. The 1990s was a turning period in the annual rainfall and flood season rainfall of the Sanchuan River Basin. The 2000s was a turning period of the runoff in the Sanchuan River Basin, while the sediment entered a stable period after 2000. The best periods for reducing runoff and sediment were the initial treatment period (1967–1979) and the centralized treatment period (1980–1996). The runoff and sediment reduction effects of each soil and water conservation measure during the initial treatment period (1967–1979) were terrace (32.8%) > dam (30.1%) > grass (18.6%) > forest (18.5%), while their effects during the centralized treatment period (1980–1996) were grass (53.7%) > terrace (20.7%) > dam (14.6%) > forest (11.0%). The runoff and sediment reduction effects of various soil and water conservation measures during different treatment periods indicate that the runoff reduction effect reached its peak in 2003–2005, while the sediment reduction benefit reached its peak in 2013–2015. Based on the comprehensive benefits of runoff and sediment regulation, 2013–2015 are considered to be the threshold years for various soil and water conservation measures, with the measures covering respective average areas of 4.85 × 104, 17.80 × 104, 1.15 × 104, and 0.82 × 104 hm2. These research results will have a certain significance for the reasonable allocation of soil and water conservation measures and sustainable development in the Yellow River Basin.

The Effect of the Construction of a Tillage Layer on the Infiltration of Snowmelt Water into Freeze–Thaw Soil in Cold Regions

The snow melting and runoff process in the black soil area of Northeast China has led to soil quality degradation in farmland, posing a threat to sustainable agricultural development. To investigate the regulatory effect of tillage layer construction on the infiltration characteristics of snowmelt water, a typical black soil in Northeast China was selected as the research object. Based on field experiments, four protective tillage treatments (CK: control treatment; SB: sub-soiling treatment; BC: biochar regulation treatment; SB + BC: sub-soiling tillage and biochar composite treatment) were set up, and the evolution of soil physical structure, soil thawing rate, snow melting infiltration characteristics, and the feedback effect of frozen layer evolution on snowmelt infiltration were analyzed. The research results indicate that sub-soiling and the application of biochar effectively regulate soil aggregate particle size and increase soil total porosity. Among them, at the 0–10 cm soil layer, the soil mean weight diameter (MWD) values under SB, BC, and SB + BC treatment conditions increased by 6.25%, 16.67%, and 19.35%, respectively, compared to the CK treatment. Sub-soiling increases the frequency of energy exchange between the soil and the environment, while biochar enhances soil heat storage performance and accelerates the melting rate of frozen soil layers. Therefore, under the SB + BC treatment conditions, the maximum soil freezing rate increased by 21.92%, 5.67%, and 25.12% compared to the CK, SB, and BC treatments, respectively. In addition, sub-soiling and biochar treatment effectively improved the penetration performance of snowmelt water into frozen soil layers, significantly enhancing the soil’s ability to store snowmelt water. Overall, it can be concluded that biochar regulation has a good improvement effect on the infiltration capacity of surface soil snowmelt water. Sub-soiling can enhance the overall snowmelt water holding capacity, and the synergistic effect of biochar and deep tillage is the best. These research results have important guiding significance for the rational construction of a protective tillage system model and the improvement of the utilization efficiency of snowmelt water resources in black soil areas.

The Role of Bedrock Topography in the Runoff Process and Soil Erosion on Karst Steep Slopes

The Role of Bedrock Topography in the Runoff Process and Soil Erosion on Karst Steep Slopes

Assessment and Application of Multi-Source Precipitation Products in Cold Regions Based on the Improved SWAT Model

In hydrological modeling, the accuracy of precipitation data and the reflection of the model’s physical mechanisms are crucial for accurately describing hydrological processes. Identifying reliable data sources and exploring reasonable hydrological evolution mechanisms for hydrology and water resources research in high-altitude mountainous regions with sparse stations and limited data constitute a significant challenge and focus in the field of hydrology. This study focuses on the Yarkant River Basin in Xinjiang, which originates from glaciers and contains a substantial amount of meltwater runoff. A dynamic glacier melt module considering the synergistic effects of multiple meteorological factors was developed and integrated into the original Soil and Water Assessment Tool (SWAT) model. Four precipitation datasets (ERA5-land, MSWEP, CMA V2.0, and CHM-PRE) were selected to train the model, including remote sensing precipitation products and station-interpolated precipitation data. The applicability of the improved SWAT model and precipitation datasets in the source region of the Yarkant River was evaluated and analyzed using statistical indicators, hydrological characteristic values, and watershed runoff simulation effectiveness. The optimal dataset was further used to analyze glacier evolution characteristics in the basin. The results revealed the following: (1) The improved model fills the gap in glacier runoff simulation with respect to the original SWAT model, with the simulation results more closely aligning with the actual runoff variation patterns in the study area, better describing the meltwater runoff process. (2) CMA V2.0 precipitation data has the best applicability in the study area. This is specifically reflected in the rationality of the spatial and temporal distribution patterns of the inverted precipitation, the accuracy observed in capturing precipitation events and actual precipitation characteristics, the goodness of fit in driving hydrological models, and the observed precision in reflecting the composition of watershed runoff, all of which are superior to those pertaining to other precipitation products. (3) The glacier melt calculated using the improved SWAT model informed by CMA V2.0 shows that during the study period, the basin formed a pattern with a positive–negative glacier balance demarcation at 36.5° N, featuring melting at higher latitudes and accumulation at lower latitudes. The results of this study are of significant importance for hydrometeorological applications and hydrological and water resources research in this region.

Soil surface roughness impacts erosion behavior through selective regulation of flow properties in rainfall-seepage scenarios

Soil surface roughness (SSR) impacts runoff dynamics of surface-subsurface and the magnitude of soil erosion, limited attention has been paid to how SSR governs runoff hydrodynamics to affect erosion behavior and the effectiveness of erosion reduction under rainfall-seepage scenarios on low-permeability purple soil slopes. Herein the seepage rates of 2, 4, and 8 L min⁻¹ were sequentially simulated under a rainfall intensity of 1.0 mm min⁻¹ among different microrelief treatments (CT: conventional tillage; AD: artificial digging; RT: ridge tillage) on purple soil slopes with gradients of 5°, 10°, and 15°. These simulations aimed to investigate the mechanisms underlying the erosion reduction benefits associated with flow properties due to microrelief. The results showed that increased SSR altered erosion kinetic energy under rainfall-seepage conditions. The benefits of rough slopes to control erosion decreased as rainfall-seepage intensity and slope gradient increased. During rainfall-seepage events, the variation in runoff behavior was regulated positively by the effect of SSR on unit stream power. However, the increasing net output power of runoff due to flow turbulence altered sediment output, thereby affecting sediment control benefits. Overall, the impact of rainfall-seepage intensity on surface roughness became more significant with increasing slope gradient. Our findings suggest the capable of integrating for interrelated microrelief and runoff processes in factors analysis of driving soil erosion at rainfall-seepage hydrologic states to elucidate erosion effect.

Modeling the rainfall–runoff process in the Bird Creek River Basin: a comparative analysis of conceptual and artificial neural network techniques

ABSTRACT The modeling of the rainfall–runoff (RR) process is a key component for water resources projects, planning, and management for which conceptual and data-driven modeling techniques are utilized. However, these techniques in the modeling of the RR process have their own benefits, and their performance need to be explored in real basins. In this paper, five conceptual models (namely, AWBM, Sacramento, SimHyd, SMAR, and TANK) and an artificial neural network (ANN) model have been developed and their performances have been assessed using the rainfall, runoff, and other climatic data derived from the Bird Creek Basin, USA. The results obtained from the study suggest that the SimHyd performed the best among all the conceptual models during testing. The ANN model performance in simulating the RR process was found to be the best among all the models developed in this study during testing with the highest values of R = 0.941, E = 0.994 and all threshold statistics and least values of AARE = 38.9 and NRMSE = 0.031. Overall, it can be concluded that although the conceptual models are highly comprehensible, the ANN models are able to simulate the flow more accurately than any of the conceptual models developed in this study.

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.

IRainSnowHydro v1.0: A distributed integrated rainfall-runoff and snowmelt-runoff simulation model for alpine watersheds

Snowmelt runoff is an essential runoff component in alpine watersheds. On the Tibetan Plateau, the complex hydrometeorological and underlying surface conditions make a single runoff generation mode (either snowmelt-runoff or rainfall-runoff) cannot accurately simulate the runoff process. In this study, we developed a new method that combines the curve number, topographic index, and fractional snow cover to identify the sub-basin seasonal dominant runoff generation mode within the Jinsha River Basin. By constructing a surface ‘snow reservoir’ to depict snow melting impact on runoff generation, and quantitatively classifying the precipitation composition, an innovative integrated hydrological model named the distributed integrated Rainfall-runoff and Snowmelt-runoff simulation Hydrological model (iRainSnowHydro) is developed. With model, a method for identifying the seasonal varying dominant runoff generation mode is proposed. The results show that most sub-basins experience both snowmelt and rainfall driven runoff generation in spring, with snowmelt occurring earlier in regions of lower latitude and elevation. Besides, iRainSnowHydro performs well in daily runoff simulations at Zhimenda and Shigu stations with Nash coefficients of 0.81 and 0.85 in the calibration period, and 0.72 and 0.81 in the validation period. The correlation coefficient ranges from 0.92 to 0.96. Additionally, calculation through iRainSnowHydro indicates a noteworthy percentage of spring snowmelt water. Notably, the Zhimenda watershed, characterized by higher latitudes and elevations, displays an escalating trend from 56.6 % to 78.9 % of total precipitation for spring snowmelt water between 2014 and 2020, while the Shigu watershed maintains stable within 27 % ± 6 %. The methodologies outlined bear significance for simulating and predicting runoff in alpine watersheds and offers valuable insights into how snow cover responds to climate change on the Tibetan Plateau.

Modeling the effect of land-use change on runoff water quality in the urban watershed: a case study of Settat city, Morocco

ABSTRACT The purpose of this study is to create a model of the quantity and quality of runoff from the urban watershed. The modeling was performed using a personal computer stormwater management model (PCSWMM) tool and was based on washoff and buildup for total suspended solids (TSS), total nitrogen (TN), and total phosphorus (TP) measured in the field. In addition, low impact development (LID) was used under four different scenarios to improve runoff control of the urban hydrologic cycle and reduce pollutant concentrations. Model performance was analyzed using the R2 and NSE goodness-of-fit indices. Thus, three typical rainfall events, as well as field measurement parameters (TSS, TN, and TP), were used for the model's calibration and validation. The base scenario quantified the runoff volume and pollutant load without any intervention in the runoff process control. Scenarios S2 (BR: bioretention) and S3 (PP: permeable pavement) contributed, separately, in reducing runoff and pollutant load but with relatively small percentages. In addition, the results show that scenario S4 (BR and PP) has the most significant impact on flow reduction, by about 10% compared with the base scenario, as well as on the pollutant load reduction, by about 31, 29, and 40% for the parameters TSS, TN, and TP, respectively.

Hybrid hydrological modeling for large alpine basins: a semi-distributed approach

Abstract. Alpine basins are important water sources for human life, and reliable hydrological modeling can enhance the water resource management in alpine basins. Recently, hybrid hydrological models, coupling process-based models and deep learning (DL), have exhibited considerable promise in hydrological simulations. However, a notable limitation of existing hybrid models lies in their failure to incorporate spatial information within the basin and describe alpine hydrological processes, which restricts their applicability in hydrological modeling in large alpine basins. To address this issue, we develop a set of hybrid semi-distributed hydrological models by employing a process-based model as the backbone and utilizing embedded neural networks (ENNs) to parameterize and replace different internal modules. The proposed models are tested on three large alpine basins on the Tibetan Plateau. A climate perturbation method is further used to test the applicability of the hybrid models to analyze the hydrological sensitivities to climate change in large alpine basins. Results indicate that proposed hybrid hydrological models can perform well in predicting runoff processes and simulating runoff component contributions in large alpine basins. The optimal hybrid model with Nash–Sutcliffe efficiencies (NSEs) higher than 0.87 shows comparable performance to state-of-the-art DL models. The hybrid model also exhibits remarkable capability in simulating hydrological processes at ungauged sites within the basin, markedly surpassing traditional distributed models. In addition, the results also show reasonable patterns in the analysis of the hydrological sensitivities to climate change. Overall, this study provides a high-performance tool enriched with explicit hydrological knowledge for hydrological prediction and improves our understanding about the hydrological sensitivities to climate change in large alpine basins.

Research on a cloud model intelligent computing platform for water resource management

ABSTRACT As the demand for water management information systems continues to increase, addressing issues such as poor generalizability, low reusability, and difficulties in updating and maintaining water resource planning cloud model service platforms becomes crucial. To achieve goals like business-oriented functionality, high availability, and reliability, this study proposes constructing a cloud model service platform for basin water resource planning based on cloud computing technology and business workflows. This study couples water cycle models with multi-objective optimization models for water resource allocation, using digital topological water networks to achieve dynamic regional water resource allocation. The cloud service platform adopts a business-oriented modeling method based on B/S development architecture. This paper takes the Weihe River Basin as an example to simulate and analyze the evolution of the water cycle pattern and optimize the annual water resources allocation plan. Results show that: (1) the water cycle model of the cloud model service platform can better describe the runoff change process in the verification period; (2) through the cloud platform service model, the water shortage rate of the Weihe River Basin in 2025 is 7.95%. The research findings provide technical references and insights for intelligent water management and refined allocation of water resources in the Weihe River Basin.

Influence of Rainfall Patterns on Rainfall–Runoff Processes: Indices for the Quantification of Temporal Distribution of Rainfall

To understand the influence of rainfall patterns on rainfall–runoff processes, we propose two indices: skewnessPEAK (skewp), representing the relative timing of peak rainfall, and the normalized root-mean-square error peak (NRMSEp), which quantifies the concentration of rainfall near the peak. By analyzing approximately 25,000 rainfall scenarios, we examined the relationship between these indices and peak flood discharge in the rainfall–runoff process. The analysis revealed that peak flood discharge positively correlates with the NRMSEp, indicating that concentrated rainfall near the peak substantially increases discharge. Conversely, a negative correlation with skewp suggests that earlier peak rainfall reduces discharge. These insights were synthesized into a three-dimensional solution space providing a comprehensive framework for predicting how variations in rainfall distribution affect flood discharge. The findings underscore the importance of incorporating these indices into real-time flood forecasting models and urban flood risk management strategies.

Development of a distributed hydrological model combined with sediment transport calculations and its application to a medium-scale basin in northeast China

Abstract An accurate assessment of sediment transport can assist land and resource managers in any region. The primary objective of this study was to develop a distributed hydrological model for calculating rainfall-runoff coupled with sediment transport to provide a practical method for estimating sediment transport in western Heilongjiang Province, China. The Alun River Basin, which has typical meteorological and hydrological characteristics of western Heilongjiang Province, was chosen as the study area. A sediment transport calculation module was developed based on physically based formulas for the suspended load and bedload, which was coupled with a numerical model of the physical distributed rainfall-runoff process. The model’s ability was validated by comparing the sediment sampling survey results of the particle size group distribution (PSGD) in the flow and the corresponding value of the model calculation results for a control case. The root mean square error (RMSE) and the mean absolute error (MAE) were mostly <10%. The model was used to calculate the rainfall-runoff combined with sediment transport in 2016, and the results indicate that the primary form of sediment transport was as suspended load. The suspended load accounted for over 90% of the total sediment transport caused by the maximum flood peak, and the suspended load accounted for over 80% during the period of normal water level across the selected sections. The sediment transport process was dominated by the runoff process. The contribution of the monthly sediment transport in winter to the annual total value was mostly <1%; while in the rainy season, the contribution of the monthly sediment transport to the annual total value was >15%. The results provide a basis for the assessment of sediment transport and the development of a numerical platform for modeling hydrological processes coupled with land surface processes in western Heilongjiang Province, China.

Assessment of the water quality in the coastal Yaqui valley (Mexico): implications for human health and ecological risks.

This study examines the water quality in the Yaqui Valley in Mexico, a semi-arid region impacted by mining, agriculture, and aquaculture. Contamination sources, health risks and ecological impacts are investigated. Freshwater was found to be contaminated by dissolved As, presumed to result from mining activities in the mountains. Drainage water revealed an overall contamination by dissolved As and by suspended particles enriched with Al, Fe and Mn, associated with runoff processes. Intermittent contamination of drainage water by Cu, K+, NO3- and PO43- is attributed to the use of fertilizers or pesticides. In the coastal area, drainage water contains high concentrations of Na, Ca, SO42- and Cl, related to salinization processes, as well as higher concentrations of dissolved As, related to solid/liquid interactions that are enhanced by salinization. This drainage water discharges into the bay, degrading the seawater quality and increasing ecological risks. Outputs of this study can serve as a reference for the protection of this economically important coastal ecosystem. Concerning health risks, this study demonstrates that groundwater is also contaminated by dissolved As, believed to be associated with transfers from the geological basement to the dissolved phase of water, and other major chemicals related to salinization processes. The findings indicate that ingesting the groundwater poses a significant risk to human health with a primary exposure risk associated with dissolved As, particularly among children. This study presents crucial data for the Yaqui population, water managers and researchers, and provides novel insights into the management and mitigation of the identified risks.

Seasonal Variability in Baffin Bay

AbstractThree dominant characteristics and underlying dynamics of the seasonal cycle in Baffin Bay are discussed. The study is based on a regional, high‐resolution coupled sea ice‐ocean numerical model that complements our understanding drawn from observations. Subject to forcing from the atmosphere, sea ice, Greenland, and other ocean basins, the ocean circulation exhibits complex seasonal variations that influence Arctic freshwater storage and export. The basin‐scale barotropic circulation is generally stronger (weaker) in summer (winter). The interior recirculation (∼2 Sv) is primarily driven by oscillating along‐topography surface stress. The volume transport along the Baffin Island coast is also influenced by Arctic inflows (∼0.6 Sv) via Smith Sound and Lancaster Sound with maximum (minimum) in June‐August (October‐December). In addition to the barotropic variation, the Baffin Island Current also has changing vertical structure with the upper‐ocean baroclinicity weakened in winter‐spring. It is due to a cross‐shelf circulation associated with spatially variable ice‐ocean stresses that flattens isopycnals. Greenland runoff and sea ice processes dominate buoyancy forcing to Baffin Bay. Opposite to the runoff that freshens the west Greenland shelf, stronger salinification by ice formation compared to freshening by ice melt enables a net densification in the interior of Baffin Bay. Net sea ice formation in the past 30 years contributes to ∼25% of sea ice export via Davis Strait. The seasonal variability in baroclinicity and water mass transformation changes in recent decades based on the simulation.

Influence of Rivers, Tides, and Tidal Wetlands on Estuarine Carbonate System Dynamics

Variations in estuarine carbonate chemistry can have critical impacts on marine calcifying organisms, yet the drivers of this variability are difficult to quantify from observations alone, due to the strong spatiotemporal variability of these systems. Terrestrial runoff and wetland processes vary year to year based on local precipitation, and estuarine processes are often strongly modulated by tides. In this study, a 3D-coupled hydrodynamic-biogeochemical model is used to quantify the controls on the carbonate system of a coastal plain estuary, specifically the York River estuary. Experiments were conducted both with and without tidal wetlands. Results show that on average, wetlands account for 20–30% of total alkalinity (TA) and dissolved inorganic carbon (DIC) fluxes into the estuary, and double-estuarine CO2 outgassing. Strong quasi-monthly variability is driven by the tides and causes fluctuations between net heterotrophy and net autotrophy. On longer time scales, model results show that in wetter years, lower light availability decreases primary production relative to biological respiration (i.e., greater net heterotrophy) resulting in substantial increases in CO2 outgassing. Additionally, in wetter years, advective exports of DIC and TA to the Chesapeake Bay increase by a factor of three to four, resulting in lower concentrations of DIC and TA within the estuary. Quantifying the impacts of these complex drivers is not only essential for a better understanding of coastal carbon and alkalinity cycling, but also leads to an improved assessment of the health and functioning of coastal ecosystems both in the present day and under future climate change.

Using surface runoff to reveal the mechanisms of landscape patterns driving on various forms of nitrogen in non-point source pollution

Non-point source (NPS) pollution directly threatens river water quality, constrains sustainable economic development, and poses hazards to human health. Comprehension of the impact factors on NPS pollution is essential for scientific river water quality management. Despite the landscape pattern being considered to have a significant impact on NPS pollution, the driving mechanism of landscape patterns on NPS pollution remains unclear. Therefore, this study coupled multi-models including the Soil and Water Assessment Tool (SWAT), Random Forest, and Partial Least Squares Structural Equation Modeling (PLS-SEM) to construct the connection between landscape patterns, NPS pollution, and surface runoff. The results suggested that increased runoff during the wet season enhances the link between landscape patterns and NPS pollution, and the explained NPS pollution variation by landscape pattern increased from 59.6 % (dry season) to 84.9 % (wet season). Furthermore, from the impact pathways, we find that the sink landscape pattern can significantly and indirectly influence NPS pollution by regulating surface runoff during the wet season (0.301*). Meanwhile, the sink and source landscape patterns significantly and directly impact NPS pollution during different seasons. Moreover, we further find that the percentage of paddy land use (Pad_PLAND) and grassland patch density (Gra_PD) metrics can significantly predict the dissolved total nitrogen (DTN) and nitrate nitrogen (NO3−-N) variation. Thus, controlling the runoff migration process by guiding the rational evolution of watershed landscape patterns is an important development direction for watershed NPS pollution management.

Where has hydrogeologic science been, and where is it going? Research trends in hydrogeology publishing over the past 60 years

AbstractQuantifying historical research trends in the field of hydrogeology is not only generally informative for hydrogeologists but is essential for fostering interdisciplinary collaboration and assessing the relationship between academic study and societal interests in hydrogeologic issues. To address this, a topic model was applied to over 37,000 academic abstracts published in over 20 journals between 1963–2022 in the field of hydrogeology to study the evolution of topic trends through time. Model results were fed into the popular large-language model ChatGPT to assign topic names, representing an unsupervised method. The results indicate that, historically, popular topics related to methodological development and analytical and numerical models analytical and numerical methods in groundwater flow modeling and well hydraulics have given way to topics related to more increasingly complex models (groundwater monitoring and uncertainty estimation and groundwater modeling calibration and simulation) as data and computational capability becomes increasingly available. An insight into the period of boom-and-bust in contaminant hydrogeology is reflected by a shift in focus from topics related to assessment and characterization of contaminant sources toward topics related to degradation and remediation methods. Topics of emerging prevalence (sustainable groundwater resource management, catchment hydrology and runoff processes) in the current period reflect an increasing focus on treating the surface-water/groundwater system as a single system. In addition, results suggest that topic distribution within the field of hydrogeology has become more varied as time has progressed.

Study on long short-term memory based on vector direction of flood process for flood forecasting

Accurate flood forecasting is crucial for flood prevention and mitigation, safeguarding the lives and properties of residents, as well as the rational use of water resources. The study proposes a model of long and short-term memory (LSTM) combined with the vector direction (VD) of the flood process. The Jingle and Lushi basins were selected as the research objects, and the model was trained and validated using 50 and 49 measured flood rainfall-runoff data in a 7:3 division ratio, respectively. The results indicate that the VD-LSTM model has more advantages than the LSTM model, with increased NSE, and reduced RMSE and bias to varying degrees. The flow simulation results of VD-LSTM better match the observed flow hydrographs, improving the underestimation of peak flows and the lag issue of the model. Under the same task and dataset, with the same hyperparameter settings, VD-LSTM can more quickly reduce the loss function value and achieve a better fit compared to LSTM. The proposed VD-LSTM model couples the vectorization process of flood runoff with the LSTM neural network, which contributes to the model better exploring the change characteristics of rising and receding water in flood runoff processes, reducing the training gradient error of input–output data for the LSTM model, and more effectively simulating flood process.

The Causes of Waterlogging in Chinese Southern Hilly Cities Based on Hydrological Processes

The various hydrological processes that cause waterlogging exhibit regional differences. Studies on the causes of waterlogging in Chinese southern hilly cities from the perspective of urban regional hydrological processes are needed. This article examines Changsha Central City to study the hydrological processes of Chinese southern hilly cities based on waterlogging point data from 2015 to 2017, analyzing the relationships between the degree of waterlogging and changes in the material elements of runoff sources, runoff convergence terminals, and runoff convergence processes using correlation analysis, principal component analysis and comparative analysis. These results show that the urban waterlogging in Chinese southern hilly cities is caused by the hardening of convergence spaces, concentrated water distribution, a decrease in the connectivity of rainwater corridors, complex topographic slopes, and a lag in the construction of drainage facilities. The expansion of impervious surfaces, particularly in areas intended for convergence terminals, has significantly reduced the number of these critical structures. Additionally, disordered changes in topographic slopes, the division caused by roads, and the hardening of underlying surfaces in rainwater corridors have collectively diminished the structural and ecological connectivity of the rainwater corridor system. This obstruction of surface runoff into concentrated water bodies has rendered the runoff regulation function of these water bodies ineffective, making their size a leading cause of urban waterlogging. To mitigate the risk of waterlogging, Chinese southern hilly cities should prevent urban development from encroaching on natural runoff areas. They should also implement dispersed water body layouts, enhance both the structural and ecological connectivity of rainwater corridors and their underlying surfaces, and improve the design standards of drainage facilities.