Watershed System Research Articles

Spatiotemporal variations and the ecological risks of microplastics in the watersheds of China: Implying the impacts of the COVID-19 pandemic

China is not only the first reported place of the COVID-19 pandemic but also is the biggest microplastic emitter in the world. Nevertheless, the impact of the COVID-19 pandemic on microplastic pollution in the watersheds of China remains poorly understood. To address this, the present study conducted a data mining and multivariate statistical analysis based on 8898 microplastic samples from 23 Chinese watershed systems before and during the COVID-19 pandemic. The results showed that the COVID-19 pandemic extensively affected the abundance, colors, shapes, polymer types, and particle sizes of microplastic in Chinese watershed systems. Before and during the COVID-19 pandemic, 77.27 % of the Chinese watershed systems observed increased microplastic abundance. Moreover, the COVID-19 pandemic itself, natural conditions (such as altitude and weather), and anthropogenic factors (such as civil aviation throughput) are highly intertwined, jointly impacting the microplastic in the watersheds of China. From the perspective of ecological risks, the COVID-19 pandemic was more likely to aggravate the microplastic pollution in the middle and down reaches of the Yangtze River Watersheds. Overall, whether before or during the COVID-19 pandemic, the main watershed systems of China still stayed at a high pollution level, which rang the alarm bell that watershed systems of China had been at serious ecological risk accused of microplastic contamination.

Spatial Predictive Modeling of Liver Fluke Opisthorchis viverrine (OV) Infection under the Mathematical Models in Hexagonal Symmetrical Shapes Using Machine Learning-Based Forest Classification Regression

Infection with liver flukes (Opisthorchis viverrini) is partly due to their ability to thrive in habitats in sub-basin areas, causing the intermediate host to remain in the watershed system throughout the year. Spatial modeling is used to predict water source infections, which involves designing appropriate area units with hexagonal grids. This allows for the creation of a set of independent variables, which are then covered using machine learning techniques such as forest-based classification regression methods. The independent variable set was obtained from the local public health agency and used to establish a relationship with a mathematical model. The ordinary least (OLS) model approach was used to screen the variables, and the most consistent set was selected to create a new set of variables using the principal of component analysis (PCA) method. The results showed that the forest classification and regression (FCR) model was able to accurately predict the infection rates, with the PCA factor yielding a reliability value of 0.915. This was followed by values of 0.794, 0.741, and 0.632, respectively. This article provides detailed information on the factors related to water body infection, including the length and density of water flow lines in hexagonal form, and traces the depth of each process.

Modelling the impacts of future droughts and post-droughts on hydrology, crop yields, and their linkages through assessing virtual water trade in agricultural watersheds of high-latitude regions

This study elaborates on the effects of future drought and post-drought conditions on the reliability of global breadbaskets. The study simulates agro-hydrological processes in the Nelson River Basin, a large agricultural watershed in western Canada that supplies food for over 170 countries globally. In temperate zones of higher latitudes, future climate change scenarios suggest that global breadbaskets will likely experience increased precipitation and higher crop yields (Y). This could be perceived as an increased export potential of food from these regions. However, projected drought events in the future can affect agro-hydrological processes, Y, and, therefore, export potentials during and following the drought events. Using a process-based agro-hydrologic model, this research examines the potential impacts of future agricultural droughts and post-drought conditions on hydrological water yield (WYLD), Y, and their linkages through assessing the net virtual water export (NVWE), the water embodied in the production of crops that are destined for export. The results indicate that long-term average Y, NVWE, and WYLD are expected to improve in the future. However, droughts will become more extreme in the future, leading to considerable reductions in Y, NVWE, and WYLD. During the post-drought period, the recovery time for WYLD is considerably longer than Y and NVWE across regions. The slow recovery of WYLD, following an agricultural drought, is related to crop water uptake, which can be controlled by optimization of the cropping pattern. The continuous loss of WYLD during and after prolonged and more frequent droughts in the future can significantly affect not only the environment and several economic sectors but also the irrigated crop production and export potential from these regions. This finding highlights the connections between local hydrology and global trade systems in agricultural watersheds of higher-latitude regions. Future adaptation measures, such as changes in cropping patterns, can preserve WYLD during and after droughts, supporting water and food security.

Half of the soil erosion in the Alps during the Holocene is explained by transient erosion crises as a consequence of rapid human land clearing

Human land use changes have altered soil erosion for millennia with extensive consequences on terrestrial and aquatic ecosystems as well as on biogeochemical cycles along the land-ocean continuum. Despite their great importance, past erosion trends have high uncertainties limiting quantitative estimates of long-term erosion dynamics. Here, we applied a new approach combining well-dated paleo-records of soil erosion from lake sediments and a spatially distributed semi-empirical model to simulate annual soil erosion in six lake watershed systems in the Northwestern Alps during the Holocene. Progressive and abrupt changes in soil erosion are detected in the six watersheds. Progressive erosion explains most of the soil exports observed during the Early to Mid-Holocene period (from 11,700 to 3000 cal. yr. BP), while transient erosion crises (i.e., periods of abrupt increase in the erosion rates spanning approximately 1000 ± 500 years) led to massive soil losses during the Late-Holocene period (from 3000 to 1000 cal. yr. BP). Our coupled approach of proxy-model reconstruction shows that the transient erosion crises represent the half of the total soil erosion exports during the Holocene. These estimates defy current representations of large-scale soil erosion during the Holocene that do not consider transient erosion crises, hence potentially underestimating the anthropogenic perturbation of lateral fluxes and fate along the land-ocean continuum. Our results further suggest that erosion and/or land cover proxies need to be consistently integrated into model approaches when attempting to estimate past variations in mass exports from terrestrial to aquatic ecosystems over centennial to millennial timescales.

Coupling Reservoir Operation and Rainfall‐Runoff Processes for Streamflow Simulation in Watersheds

AbstractWe assess the overall watershed system representation via fully coupling a generic reservoir operation model with a conceptual rainfall‐runoff model. The performance of the coupled model is evaluated comprehensively by examining watershed outflow simulations, model parameter values, and a key internal flux of the watershed model (here reservoir inflow). Five published generic reservoir operation models are coupled with a watershed rainfall‐runoff model, and results are compared across the coupled models and one additional model called ResIgnore that ignores reservoir operation. Traditional loosely coupled watershed hydrologic models (where calibrated inflow is routed through reservoir operation models) are used as baselines to examine the differences in simulation performance and parameterization obtained from the fully coupled models. We find that fully coupling the Generic Data‐Driven Reservoir Operation Model (GDROM) and the Dynamically Zoned Target Release (DZTR) reservoir operation models with the rainfall‐runoff model obtains robust simulations of watershed outflow with realistic parameterization, suggesting that they can be reliably integrated into large‐scale hydrological models for simulating streamflow in heavily dammed watersheds. Our results also show that compared to ResIgnore, the fully coupled watershed models more accurately simulate the entire distribution of watershed outflow, obtain more realistic values of model parameters, and simulate reservoir inflow with higher accuracy. Finally, we note that the prediction intervals of watershed outflow obtained from the GDROM‐ and DZTR‐based fully coupled models consistently envelop observed watershed outflow across the study watersheds, indicating that GDROM and DZTR can be suitable reservoir components of large‐scale hydrology models.

A hybrid deep learning approach to predict hourly riverine nitrate concentrations using routine monitored data

With high-frequency data of nitrate (NO3–N) concentrations in waters becoming increasingly important for understanding of watershed system behaviors and ecosystem managements, the accurate and economic acquisition of high-frequency NO3–N concentration data has become a key point. This study attempted to use coupled deep learning neural networks and routine monitored data to predict hourly NO3–N concentrations in a river. The hourly NO3–N concentration at the outlet of the Oyster River watershed in New Hampshire, USA, was predicted through neural networks with a hybrid model architecture coupling the Convolutional Neural Networks and the Long Short-Term Memory model (CNN-LSTM). The routine monitored data (the river depth, water temperature, air temperature, precipitation, specific conductivity, pH and dissolved oxygen concentrations) for model training were collected from a nested high-frequency monitoring network, while the high-frequency NO3–N concentration data obtained at the outlet were not included as inputs. The whole dataset was separated into training, validation, and testing processes according to the ratio of 5:3:2, respectively. The hybrid CNN-LSTM model with different input lengths (1d, 3d, 7d, 15d, 30d) displayed comparable even better performance than other studies with lower frequencies, showing mean values of the Nash-Sutcliffe Efficiency 0.60–0.83. Models with shorter input lengths demonstrated both the higher modeling accuracy and stability. The water level, water temperature and pH values at monitoring sites were main controlling factors for forecasting performances. This study provided a new insight of using deep learning networks with a coupled architecture and routine monitored data for high-frequency riverine NO3–N concentration forecasting and suggestions about strategies about variable and input length selection during preprocessing of input data.

Coordination analysis of flood-sediment transportation, eco-environment, and socio-economy coupling in the governance of the Yellow River Basin system

The watershed system has a complex game relationship between the benign operation and coordinated development of various elements of flood-sediment transportation, eco-environment, and socio-economy (FES). With the increasing breadth, depth, and intensity of human activities in watersheds, it is urgent to coordinate the FES. The relationship of water–sediment in the Yellow River Basin (YRB) is complex, with a prominent contradiction in water supply and a fragile ecosystem. This research tries to build a comprehensive evaluation model for FES and explore the complex interaction between FES in the YRB from 2000 to 2020. The results demonstrated that (1) the comprehensive flood-sediment transportation index (CFTI) and comprehensive eco-environment index (CEI) presented fluctuating growth. In contrast, the comprehensive socio-economy index (CSI) revealed a linear growth trend. The CFTI of Sanmenxia, CEI of Toudaokuan, and CSI of Ningxia had the highest growth rates, with 36.03%, 6.48%, and 107.5%, respectively. (2) FES's positive and negative effects were alternating, with heterogeneity in both time and space. (3) The coupling coordination degree (CCD) in the YRB indicated an increasing trend, ranging from 0.53 to 0.87, from reluctantly coordinated development to good coordinated development. The lagging subsystem was CFTI (2000–2001 and 2008–2020) and CSI (2002–2007), and the CEI was not lagging. (4) Exploratory Spatial Data Analysis (ESDA) demonstrated significant differences in the CCD of the YRB, and areas with similar CCD within the basin tend to be centrally distributed in space. At the same time, there was negative spatial autocorrelation in coordination. The results provide a scientific theoretical and methodological framework for strategic research on the YRB system's governance, protection, and management.

Enhancing environmental data imputation: A physically-constrained machine learning framework

In water resources management, new computational capabilities have made it possible to develop integrated models to jointly analyze climatic conditions and water quantity/quality of the entire watershed system. Although the value of this integrated approach has been demonstrated so far, the limited availability of field data may hinder its applicability by causing high uncertainty in the model response. In this context, before collecting additional data, it is recommended first to recognize what improvement in model performance would occur if all available records could be well exploited. This work proposes a novel machine learning framework with physical constraints capable of successfully imputing a high percentage of missing data belonging to several environmental domains (meteorology, water quantity, water quality), yielding satisfactory results. In particular, the minimum NSE computed for meteorologic variables is 0.72. For hydrometric variables, NSE is always >0.97. More than 78 % of the physical-water-quality variables is characterized by NSE > 0.45, and >66 % of the chemical-water quality variables reaches NSE > 0.35. This work's results demonstrate the proposed framework's effectiveness as a data augmentation tool to improve the performance of integrated environmental modeling.

Flood peak estimation for the keureuto watershed using the hydrological modeling system HEC-HMS

The Keureuto watershed has been the water resource used mainly for irrigation and municipal use. Extreme rainfall along with changes in the system may trigger rainfall excess causing flood at downstream. The occurrences may impact the usability of the system that supports the water resources supply. Investigating the impact of the floods requires initial research on the rainfall-runoff process of the watershed system. Rainfall-runoff modeling is an approach widely used for estimating the discharge hydrograph of the river as the result of the processes. The HEC-HMS rainfall-runoff model tool is applied in this study to estimate the peak flood discharge for the Keureto watershed located in the North Aceh region. The data used are satellite Global Precipitation Measurement (GPM) data and Automatic Water Level Recorder (AWLR) data at the watershed outlet. The 302 km2 Keureuto Watershed is divided into 23 sub-basins for the HEC-HMS model. The SCS Curve Number is applied to the model. The simulated freturncompared to the observed flow using the Nash Sutcliffe Efficiency criteria which returns the NSE value of 0.38 for seven years simulation, based on data of year 2015 to 2021. At an event based on October 2020 to March 2021, the NSE value returns 0.52, satisfactory for the simulation data range. The highest peak of the event is captured by the model simulation in the timing and the magnitude which value lower 20% from the observed flow.

Seasonal prediction of end-of-dry-season watershed behavior in a highly interconnected alluvial watershed in northern California

Abstract. In undammed watersheds in Mediterranean climates, the timing and abruptness of the transition from the dry season to the wet season have major implications for aquatic ecosystems. Of particular concern in many coastal areas is whether this transition can provide sufficient flows at the right time to allow passage for spawning anadromous fish, which is determined by dry season baseflow rates and the timing of the onset of the rainy season. In (semi-) ephemeral watershed systems, these functional flows also dictate the timing of full reconnection of the stream system. In this study, we propose methods to predict, approximately 5 months in advance, two key hydrologic metrics in the undammed rural Scott River watershed in northern California. The two metrics are intended to characterize (1) the severity of a dry year and (2) the relative timing of the transition from the dry to the wet season. The ability to predict these metrics in advance could support seasonal adaptive management. The first metric is the minimum 30 d dry season baseflow volume, Vmin, which occurs at the end of the dry season (September–October) in this Mediterranean climate. The second metric is the cumulative precipitation, starting 1 September, necessary to bring the watershed to a “full” or “spilling” condition (i.e., initiate the onset of wet season storm- or baseflows) after the end of the dry season, referred to here as Pspill. As potential predictors of these two metrics, we assess maximum snowpack, cumulative precipitation, the timing of the snowpack and precipitation, spring groundwater levels, spring river flows, reference evapotranspiration, and a subset of these metrics from the previous water year. Though many of these predictors are correlated with the two metrics of interest, we find that the best prediction for both metrics is a linear combination of the maximum snowpack water content and total October–April precipitation. These two linear models could reproduce historical values of Vmin and Pspill with an average model error (RMSE) of 1.4 Mm3 per 30 d (19.4 cfs) and 25.4 mm (1 in.), corresponding to 49 % and 37 % of mean observed values, respectively. Although these predictive indices could be used by governance entities to support local water management, careful consideration of baseline conditions used as a basis for prediction is necessary.

Potential impacts of coal mining activities on nitrate sources and transport in a karst river basin in southwest China.

Typical sources of nitrate pollution in the fragile ecological environment of karst areas, such as agricultural production activities and domestic sewage, have long attracted serious concern. However, coal development can play an equally significant role in releasing the nitrogen fixed in coal into surface watersheds in the form of nitrate, nitrite, or ammonia, consequently threatening the water quality of surface water systems in mining areas. In this study, a typical karst surface watershed system affected by coal mining activities was selected for an in-depth investigation with the aim of realistically assessing the potential contribution of coal mining to nitrogen pollution. The results reveal increasingly concerning nitrate pollution from August 2020 to November 2021 in the Huatan River watershed under the influence of anthropogenic activities, especially mining development and agricultural production. Given that the nitrogen and oxygen isotope compositions of nitrate do not support the presence of denitrification, the variation in the NO3-/Cl- ratio and the relatively stable Cl- concentration may be a reflection of nitrification. Although the leaching of atmospheric precipitation on the strata in the basin promoted the release of nitrogen associated with coal mining, the higher rate of nitrogen cycling in the oligotrophic mine water environment limited the contribution of coal mining to nitrogen pollution in the surface watershed. Specifically, the contribution of coal mining activities to nitrogen pollution in surface karst river is mainly NH4+-N, which contributes 10% or less to the nitrate input to the waters of the Huatan River. The findings thus highlight the necessity of further uncovering the geochemical cycling process of nitrogen during the transport of mine water in the coal mining environment.

Spatial prediction of the probability of liver fluke infection in water resource within sub-basin using an optimized geographically-weighted regression model.

Infection with liver flukes (Opisthorchis viverrini) is partly attributed to their ability to thrive in sub-basin habitats, causing the intermediate host to remain within the watershed system throughout the year. It is crucial to conduct spatial monitoring of fluke infection at a small basin analysis scale as it helps in studying the spatial factors influencing these infections. The number of infected individuals was obtained from local authorities, converted into a percentage, and visually represented as raster data through a heat map. This approach generates continuous data with dependent variables. The independent set comprises nine variables, including both vector and raster data, that establish a connection between the location of an infected person and their village. Design spatial units optimized for geo-weighted modeling by utilizing a clustering and overlay approach, thereby facilitating the optimal prediction of alternative models for infection. The Model-3 demonstrated the strongest correlation between the variables X5 (stream) and X7 (ndmi), which are associated with the percentage of infected individuals. The statistical analysis showed t-statistics values of -2.045 and 0.784, with corresponding p-values of 0.016 and 0.085. The RMSE was determined to be 2.571%, and the AUC was 0.659, providing support for these findings. Several alternative models were tested, and a generalized mathematical model was developed to incorporate the independent variables. This new model improved the accuracy of the GWR model by 5.75% and increased the R 2 value from 0.754 to 0.800. Additionally, spatial autocorrelation confirmed the difference in predictions between the modeled and actual infection values. This study demonstrates that when using GWR to create spatial models at the sub-basin level, it is possible to identify variables that are associated with liver fluke infection.

How wide is the problem? Leveraging alternative data sources to enhance channel width representation in watershed modeling

Watershed models have been increasingly applied to investigate the impacts of environmental changes on water quantity and quality and to support decision-making. Thus, accurately capturing physical processes and the characteristics of the watershed system is vital to achieving reliable results. Channel geometry features such as cross-sectional shape, bankfull channel width, and depth affect hydraulic parameters like hydraulic radius, wetted perimeter, and cross-sectional area. These, in turn, play important roles in streamflow dynamics, flow velocity, erosion, nutrient transport, and stream ecology. However, the representation of bankfull channel width is often oversimplified in watershed models. Power-law regression equations relating geometric parameters and upstream drainage area are commonly used to calculate bankfull channel width in watershed models. This approach has limitations and may resonate in the misrepresentation of channel geometry with implications for water quantity and quality estimations. This study evaluates how bankfull channel width is represented in a popular watershed model and presents alternative data sources derived from aerial measurements, empirical models, LiDAR, and a global database to mitigate potential misrepresentations. To assess the impacts of bankfull channel width on water quantity and quality, we designed a series of modeling experiments through the Soil and Water Assessment Tool (SWAT) model. We test our methodology in the Alabama-Coosa-Tallapoosa river basin, a large watershed system draining to the Gulf of Mexico coast. Our findings indicate that, overall, the regression equation used in the ArcSWAT program (SWAT's GIS interface) can overestimate bankfull channel width by as much as three times in our study domain. In testing the effects of bankfull width on model simulations we found negligeable implication for water yield predictions. On the other hand, 1-day maximum flows were greatly increased (20%) by using channel width values from the alternative data sources. Simulated water quality was also affected, with stream water temperature showing better agreement with observations under the proposed scenarios. Sediment loadings increased by as much as 118% under the alternative data sources of channel width. Nitrate and phosphate loadings had opposite responses and showed decreases as high as 8.3 and 18.8%, respectively, relative to the default model. Our findings demonstrate that bankfull channel width is misrepresented in watershed models with underlying impacts on simulated water quantity, quality, and ecological flows. Our analysis found bankfull channel width as an important parameter affecting high flows and water quality, and the freely available data sources tested in this study showed good matches with field measurements, making our findings readily available and broadly useful to the modeling community.

Machine-Learning-Based Forest Classification and Regression (FCR) for Spatial Prediction of Liver Fluke Opisthorchis viverrini (OV) Infection in Small Sub-Watersheds

Infection of liver flukes (Opisthorchis viverrini) is partly due to their suitability for habitats in sub-basin areas, which causes the intermediate host to remain in the watershed system in all seasons. The spatial monitoring of fluke at the small basin scale is important because this can enable analysis at the level of the factors involved that influence infections. A spatial mathematical model was weighted by the nine spatial factors X1 (index of land-use types), X2 (index of soil drainage properties), X3 (distance index from the road network, X4 (distance index from surface water resources), X5 (distance index from the flow accumulation lines), X6 (index of average surface temperature), X7 (average surface moisture index), X8 (average normalized difference vegetation index), and X9 (average soil-adjusted vegetation index) by dividing the analysis into two steps: (1) the sub-basin boundary level was analyzed with an ordinary least square (OLS) model used to select the spatial criteria of liver flukes aimed at analyzing the factors related to human liver fluke infection according to sub-watersheds, and (2) we used the infection risk positional analysis level through machine-learning-based forest classification and regression (FCR) to display the predictive results of infection risk locations along stream lines. The analysis results show four prototype models that import different independent variable factors. The results show that Model 1 and Model 2 gave the most AUC (0.964), and the variables that influenced infection risk the most were the distance to stream lines and the distance to water bodies; the NDMI and NDVI factors rarely affected the accuracy. This FCR machine-learning application approach can be applied to the analysis of infection risk areas at the sub-basin level, but independent variables must be screened with a preliminary mathematical model weighted to the spatial units in order to obtain the most accurate predictions.

Identification of surface water - groundwater nitrate governing factors in Jianghuai hilly area based on coupled SWAT-MODFLOW-RT3D modeling approach

A comprehensive understanding of the key controlling factors on NO3-N spatiotemporal distribution in surface and groundwater is of great significance to nitrogen pollution control and water resources management in watershed. Hence, the coupled SWAT-MODFLOW-RT3D model was employed to simulate nitrate (NO3−) fate and transport in Huashan watershed system. The model was calibrated using a combination of stream discharge, groundwater levels, NO3-N in-stream loading and groundwater NO3-N concentrations. The simulation revealed the significant spatiotemporal variations in surface water-groundwater nitrate interactions. The annual average percolation of NO3− from rivers to groundwater was 171.5 kg/km2 and the annual average discharge NO3− content from groundwater into rivers was 451.9 kg/km2 over the simulation period. The highest percolation of NO3− from rivers to groundwater occurred in April and the highest discharge NO3− content from groundwater into rivers occurred in July. Grassland and agriculture land contributed more nitrate contents in river water and groundwater compared to bare land and forest in the study area and the water exchange was the primary driving force for nitrate interactions in the surface water-groundwater system. Sensitivity analysis indicated that river runoff and groundwater levels were most influenced by the SCS runoff curve number f (CN2) and aquifer hydraulic conductivity (K), which, in turn, significantly affected nitrate transport. Regarding water quality parameters, the denitrification exponential rate coefficient (CDN) had the most pronounced impact on NO3-N in-stream loading and groundwater NO3-N concentrations. This study underscores the central role of surface-groundwater (SW-GW) interactions in watershed-scale nitrate research and suggests that parameters with higher sensitivity should be prioritized in analogous watershed modeling.

Temporal and Spatial Propagation Characteristics of the Meteorological, Agricultural and Hydrological Drought System in Different Climatic Conditions within the Framework of the Watershed Water Cycle

Investigating systematic meteorological–agricultural–hydrological drought propagation within an integrated watershed framework is crucial yet challenging for advancing robust early warning systems and targeted resilience strategies. To address this gap, this study employs the standardized precipitation evapotranspiration index (SPEI), standardized soil moisture index (SSMI), and nonlinear joint hydrological drought index (NJHDI) to characterize meteorological, agricultural, and hydrological drought, respectively. Utilizing the strongest correlation method, variations in the propagation time along moisture movement pathways within the system are quantified using the Soil and Water Assessment Tool (SWAT) model. The Yellow River basin (YRB) is selected as the case study area. Key results reveal distinct seasonal patterns in meteorological–agricultural propagation across the basin, with escalated spring and summer timescales. However, weaker correlations and limited seasonality emerge for agricultural–hydrological linkages due to sustaining baseflow buffers. Specifically, the arid and semiarid region exhibited shorter propagation time with 1–5 months, while the semiarid and semihumid region displayed longer timescales with 7–12 months. These findings provide valuable scientific references for enhancing real-time early warning systems tailored to coupled watershed systems. The integrated methodology underscores the importance of unraveling fine-scale spatiotemporal propagation variability for localized drought resilience.

A Case Study on Simulation of Urban Inundation by Inland Flooding and River Flooding

Urban flooding can occur due to a combination of factors such as insufficient drainage capacity in stormwater systems, inland flooding from elevated river levels, and river flooding. This study proposes a method for analyzing urban inundation resulting from these complex phenomena using XP-SWMM. This is essentially a model capable of calculating the flow of drainage systems in urban watersheds and analyzing surface inundation. Moreover, it can accommodate irregular channel cross-sections and bridges, enabling river flooding modeling. However, it has limitations in terms of replicating the actual flooding patterns of rivers due to its representation of rivers through one-dimensional links and nodes. This study implemented virtual control structures to reflect the flooding effects caused by river flooding. This approach was applied to the Dongcheon watershed in Busan, where two instances of river and inland flooding coincided in July 2020. The results indicated the appropriateness of the approach.

Runoff hydrographs using Snyder and SCS synthetic unit hydrograph methods: A case study of ungauged watersheds of Imam Turki Bin Abdullah Royal Nature Reserve (ITBA) - Saudi Arabia

Background: The estimation of surface runoff and discharge peak using the Snyder’s model and SCS Dimensionless UH (SCS-DUH) method are one of the best alternatives solutions appropriate for analyzing the hydrologic systems in the ungauged watersheds. These two methods were applied to estimate the discharge peak and design their hydrographs in the drainage basins of Royal Nature Reserve (Imam Turki Bin Abdullah) in Saudi Arabia. - Aims: To estimate the discharge peak by applying Snyder’s model and SCS-DUH method using the morphometric parameters deriving from the SRTM DEM30m and three rainstorms of 40, 50 and 60mm. - Methods: The estimation of discharge peak (Qp) using Snyder’s model was depended on the SRTM DEM30m outputs and the hydrology parameters of the duration (Td) of Unit Hydrograph (UH), time to peak (Tp), time base of the UH (Tb), UH width at 50% and 75% of the peak discharge. However, the estimation of the discharge peak by The SCS-DUH method was computed using the drainage area (A), the direct runoff (Qd) and the time to peak (Tp). The direct runoff was determined by the runoff ratio 0.20 of the unimproved areas, proposed by the Soil Conservation Service (SCS). But, the time to peak was computed using the average of the concentration time obtained from Johnstone-Cross and Dooge models. The hydrographs design was done by the time ratio method recommended by the SCS. - Results: The estimated discharge peak computed by Snyder’s model varies with a maximum of 93.4 to 2092.9 m3/s, an average of 61.1 to 1368.4 m3/s and a minimum of 28.7 to 644.0 m3/s in Al Shuwaiki and Khtal watersheds, respectively. The estimated discharge peak computed by SCS-DUH method was ranged from 21.5 to 482.3 m3/s for the rainstorm of 40mm in Al Suwayki and Khtal, respectively, from 28.5 to 602.9 m3/s for the rainstorm of 50mm and from 34.1 to 723.5 m3/s for the rainstorm of 60mm, in Um Nqy and Khtal, respectively. - Conclusion: The hydrology study of ungauged basins in Royal Nature Reserve (ITBA) shows the great potential of the integrated emplyement of GIS technics and the hydrology models to estimate the discharge peak and design their hydrographs.

An integrated modeling approach to evaluate the impacts of nature-based solutions of flood mitigation across a small watershed in the southeast United States

Abstract. Floods are among the most destructive natural hazards in the world, posing numerous risks to societies and economies globally. Accurately understanding and modeling floods driven by extreme rainfall events has long been a challenging task in the domains of hydrologic science and engineering. Unusual catchment responses to flooding cause great difficulty in predicting the variability and magnitude of floods, as well as proposing solutions to manage large volumes of overland flow. The usage of nature-based solutions (NBSs) has proved to be effective in the mitigation of flood peak rate and volume in urban or coastal areas, yet it is still not widely implemented due to limited knowledge and testing compared to traditional engineering solutions. This research examined an integrated hydrological and hydraulic modeling system to understand the response of an at-risk watershed system to flooding and evaluate the efficacy of NBS measures. Using the Hydrologic Engineering Center Hydrologic Modeling System and River Analysis System (HEC-HMS and HEC-RAS) software, an integrated hydrologic–hydraulic model was developed for Hurricane Matthew- (2016) and Florence-driven (2018) floods across the Little Pee Dee–Lumber River watershed, North and South Carolina (the Carolinas), US. The focus was on Nichols, a small town that has disproportionately been impacted by flooding during these two hurricane events. The present article proposes a methodology for selecting, modeling, and evaluating the performance of NBS measures within a catchment, which can be extended to other case studies. Different NBS measures, including flood storage ponds, riparian reforestation, and afforestation in croplands, were designed, modeled, and evaluated. Hurricane Matthew's flooding event was used for evaluating the NBS scenarios given its high simulation accuracy in flood inundation compared to the less accurate results obtained for Hurricane Florence. The scenario comparison evidenced that large-scale natural interventions, such as afforestation in croplands, can reduce the inundated area in Nichols by 8 % to 18 %. On the contrary, the smaller-scale interventions such as riparian reforestation and flood storage ponds showed a negligible effect of only 1 % on flood mitigation.

A procedure for risk assessment of check dam systems: A case study of Wangmaogou watershed.

Flood-based hydrodynamic damage to check dam systems on the Loess Plateau of China occurs frequently, and there is a strong desire to carry out risk assessments of such check dam systems. This study proposes a weighting method that combines the analytic hierarchy process, entropy method, and TOPSIS to assess the risk of check dam systems. The combined weight-TOPSIS model avoids weight calculation only considers the influence of subjective or objective preference and the bias of the single weighting method. The proposed method is capable of multi-objective risk ranking. It is applied to the Wangmaogou check dam system located in a small watershed on the Loess Plateau. The result of risk ranking matches the reality of the situation. The gray correlation theory model is utilized to rank the risks in the same research area and compared with the results of the combined weight-TOPSIS model. The combined weight-TOPSIS model is more favorable to risk assessment than the gray correlation theory model. The resolution level and decisive judgment of the combined weight-TOPSIS model are more advantageous. These results are in line with the actual conditions. It proves that the combined weight-TOPSIS model can provide a technical reference for the risk assessment of check dam systems in small watersheds.