Watershed Model Research Articles

Utilizing Watershed-Based Planning as a Tool to Protect Source Water: A Case Study from the Lower Caw Caw Swamp

A watershed-based plan (WBP) is an example of a management tool that addresses nonpoint sources of pollution that affect surface water quality. We apply a simple spreadsheet tool to the Lower Caw Caw Swamp watershed in Orangeburg, South Carolina, to quantify the impact of modest management practices on water quality, and to examine how climate and land-use change could affect water quality in future decades. Three separate climate and land use scenarios are used to measure annual pollutant loads in the basin. Our analysis shows that E. coli loads in current conditions are largely attributed to anthropogenic sources of bacteria and have the potential to be reduced by about 15% with the implementation of structural stormwater best management practices (such as stormwater ponds and bioretention) and non-structural strategies (programmatic changes such as rooftop disconnection in residential areas and improving on-site sewage disposal systems). Modeled E. coli loads in future scenarios were almost twice the current loads as a result of a warmer climate and greater volumes of stormwater runoff generated by additional impervious surfaces and increased annual precipitation. These findings are consistent with others investigating water quality using future climate and land-use scenarios. We demonstrate that a simple watershed model can provide the basis for a watershed-based plan (WBP) to address nonpoint sources of pollution that affect surface water quality. This specific WBP analysis will be used to make recommendations to protect the City of Orangeburg’s source of drinking water along the North Fork of the Edisto River.

Hydrometeorological Trends in a Low-Gradient Forested Watershed on the Southeastern Atlantic Coastal Plain in the USA

Hydrology and meteorological data from relatively undisturbed watersheds aid in identifying effects on ecosystem services, tracking hydroclimatic trends, and reducing model uncertainties. Sustainable forest, water, and infrastructure management depends on assessing the impacts of extreme events and land use change on flooding, droughts, and biogeochemical processes. For example, global climate models predict more frequent high-intensity storms and longer dry periods for the southeastern USA. We summarized 17 years (2005–2021) of hydrometeorological data recorded in the 52 km2, third-order Turkey Creek watershed at the Santee Experimental Forest (SEF), Southeastern Coastal Plain, USA. This is a non-tidal headwater system of the Charleston Harbor estuary. The study period included a wide range of weather conditions; annual precipitation (P) and potential evapotranspiration (PET) ranged from 994 mm and 1212 mm in 2007 to 2243 mm and 1063 in 2015, respectively. The annual runoff coefficient (ROC) varied from 0.09 in 2007 (with water table (WT) as deep as 2.4 m below surface) to 0.52 in 2015 (with frequently ponded WT conditions), with an average of 0.22. Although the average P (1470 mm) was 11% higher than the historic 1964–1976 average (1320 mm), no significant (α= 0.05) trend was found in the annual P (p = 0.11), ROC (p = 0.17) or runoff (p = 0.27). Runoff occurred on 76.4% of all days in the study period, exceeding 20 mm/day for 1.25% of all days, mostly due to intense storms in the summer and lower ET demand in the winter. No-flow conditions were common during most of the summer growing season. WT recharge occurred during water-surplus conditions, and storm-event base flow contributed 23–47% of the total runoff as estimated using a hydrograph separation method. Storm-event peak discharge in the Turkey Creek was dominated by shallow subsurface runoff and was correlated with 48 h precipitation totals. Estimated precipitation intensity–duration–frequency and flood frequency relationships were found to be larger than those found by NOAA for the 1893–2002 period (for durations ≥ 3 h), and by USGS regional frequencies (for ≥10-year return intervals), respectively, for the same location. We recommend an integrated analysis of these data together with available water quality data to (1) assess the impacts of rising tides on the hydroperiod and biogeochemical processes in riparian forests of the estuary headwaters, (2) validate rainfall–runoff models including watershed scale models to assess land use and climate change on hydrology and water quality, and (3) inform watershed restoration goals, strategies, and infrastructure design in coastal watersheds.

Assessing ammonium pollution and mitigation measures through a modified watershed non-point source model

Watershed water quality modeling is a valuable tool for managing ammonium (NH4+) pollution. However, simulating NH4+ pollution presents unique challenges due to the inherent instability of NH4+ in natural environment. This study modified the widely-used Soil and Water Assessment Tool (SWAT) model to simulate non-point source (NPS) NH4+ processes, specifically incorporating the simulation of land-to-water NH4+ delivery. The Jiulong River Watershed (JRW) is the study area, a coastal watershed in Southeast China with substantial sewage discharge, livestock farming, and fertilizer application. The results demonstrate that the modified model can effectively simulate the NPS NH4+ processes. It is recommended to use multiple sets of observations to calibrate NH4+ simulation to enhance model reliability. Despite constituting a minor proportion (5.6 %), point source inputs significantly contribute to NH4+ load at watershed outlet (32.4∼51.9 %), while NPS inputs contribute 15.3∼17.3 % of NH4+ loads. NH4+ primarily enters water through surface runoff and lateral flow, with negligible leaching. Average NH4+ land-to-water delivery rate is about 2.35 to 2.90 kg N/ha/a. High delivery rates mainly occur at agricultural areas. Notably, proposed NH4+ mitigation measures, including urban sewage treatment enhancement, livestock manure management improvement, and fertilizer application reduction, demonstrate potential to collectively reduce the NH4+ load at watershed outlet by 1/4 to 1/3 and significantly enhance water quality standard compliance frequency. Insights gained from modeling experience in the JRW offer valuable implications for NH4+ modeling and management in regions with similar climates and significant anthropogenic nitrogen inputs.

Assessment of current and future trends in water resources in the Gambia River Basin in a context of climate change

Accurate assessment of water resources at the watershed level is crucial for effective integrated watershed management. While semi-distributed/distributed models require complex structures and large amounts of input data, conceptual models have gained attention as an alternative to watershed modeling. In this paper, the performance of the GR4J conceptual model for runoff simulation in the Gambia watershed at Simenti station is analyzed over the calibration (1981–1990) and validation period (1991–2000 and 2001–2010). The main inputs to conceptual models like GR4J are daily precipitation data and potential evapotranspiration (PET) measured from the same catchment or a nearby location. Calibration of these models is typically performed using the Nash–Sutcliffe daily efficiency with a bias penalty as the objective function. In this case, the GR4J model is calibrated using four optimization parameters. To evaluate the effectiveness of the model's runoff predictions, various statistical measures such as Nash–Sutcliffe efficiency, coefficient of determination, bias, and linear correlation coefficient are calculated. The results obtained in the Gambia watershed at Simenti station indicate satisfactory performance of the GR4J model in terms of forecast accuracy and computational efficiency. The Nash–Sutcliffe (Q) values are 0.623 and 0.711 during the calibration period (1981–1990) and the validation period (1991–2000), respectively. The average annual flow observed during the calibration period is 0.385 mm while it increases with a value of 0.603 mm during the validation period. As for the average flow simulated by the model, it is 0.142 mm during the calibration period (i.e., a delay of 0.142 mm compared to the observed flow), 0.626 mm in the validation period (i.e., an excess of 0.023 mm compared to the observed flow). However, this study is significant because it shows significant changes in all metrics in the watershed sample under different scenarios, especially the SSP245 and SSP585 scenarios over the period 2021–2100. These changes suggest a downward trend in flows, which would pose significant challenges for water management. Therefore, it is clear that sustainable water management would require substantial adaptation measures to cope with these changes.

Enhanced watershed model evaluation incorporating hydrologic signatures and consistency within efficient surrogate multi-objective optimization

This paper presents a new framework for calibrating computationally expensive watershed models with multi-objective optimization methods and hydrological consistency analysis. The analysis evaluates different algorithms' efficiencies for finding watershed model calibration solutions within a limited budget. Two surrogate multi-objective algorithms GOMORS and ParEGO are compared to five evolutionary algorithms without surrogates on two watershed models. We test the algorithms’ performance with two multi-objective formulations (i.e., threshold-based flow separation and decomposition of the Nash-Sutcliffe Efficiency (NSE)). Results indicate that the surrogate-based GOMORS is the most computationally efficient overall. We also propose a framework to select among the calibration solutions obtained from multi-objective optimization using different hydrologic signatures. GOMORS is assessed for its ability to identify hydrologically acceptable calibrations. The decomposition of NSE is the most effective calibration formulation in terms of hydraulic consistency analysis. In addition, hydrologic signatures could be used effectively to filter non-dominated solutions obtained from multi-objective optimization.

Geo-spatial assessment of pluvial floods in city district Lahore, Pakistan.

This study is an effort of geo-spatial assessment of pluvial floods in District Lahore, Pakistan, caused by urban expansion and the growing frequency and intensity of high-intensity rainfall events. The use of geospatial techniques such as watershed modeling, maximum likelihood image classification, and weighted overlay analysis based on secondary data has enabled the researchers to assess the extent and severity of pluvial floods in the study area. The study's findings highlight the high risk of pluvial floods in the central part of the study area, which is dominated by built-up land and concrete roads. The increase in the area of built-up land from 34.913 km2 in 2018 to 37.442 km2 in 2022 has further intensified the risk of pluvial floods. The findings of this study can assist policymakers in developing effective strategies to reduce the risks associated with pluvial floods. Alongside, it also highlights the importance of geospatial techniques to better understand and address the complex challenges of urbanization and climate change. Flood risk zone-specific strategies are recommended to reduce the risk of pluvial floods.

Rainfall distributional properties control hydrologic model parameter importance.

Study regionSemi-arid region of the Western United States of America in 16.6 km2 WS10 watershed using data from the highly instrumented Walnut Gulch Experimental Watershed managed by the USDA-Agricultural Research Services. Study focusHydrologic model parameters are generally designed to represent watershed physiographic properties. This study investigates the possible impact of climate forcing, particularly precipitation across a watershed, on model parameter identification – a topic that has historically received minimal attention. To address this gap, we conducted time-varying and time-aggregate sensitivity analysis of a physically based distributed hydrologic model on a heavily instrumented watershed under various observed and synthetic storm events to assess how the distributional properties of rainfall in space and time influence the importance model parameters. Particularly, we focused on a semi-arid watershed with an area larger than the typical convective storms in the region. New hydrologic insightResults show that there is a significant variation in parameter importance following rainfall spatial and temporal properties and that characterizing the relationship between parameter importance and rainfall properties is essential for identifying parameter values that adequately capture the watershed behaviors. We further showed that a power-law function linking parameter importance to precipitation properties can extrapolate results to other climate regimes. More analyses across different watersheds, climate forcing, and models are required to improve our understanding and strategies for parametrization and calibration of watershed models.

Quantifying climate change impacts on future water resources and salinity transport in a high semi-arid watershed

High salinity mobilization and movement from salt-laden deposits in semi-arid landscapes impair soils and water resources worldwide. Semi-arid regions worldwide are expected to experience rising temperatures and lower precipitation, impacting water supply and spatio-temporal patterns of salinity loads and affecting downstream water quality. This study quantifies the impact of future climate on hydrologic fluxes and salt loads in the Gunnison River Watershed (GRW) (14,608 km2), Colorado, using the APEX-MODFLOW-Salt hydro-chemical watershed model and three different CMIP5 climate models projection downscaled by Multivariate Adaptive Constructed Analogs (MACA) for the period 2020–2099. The APEX-MODFLOW-Salt model accounts for the reactive transport of major salt ions (SO42−, Cl−, CO32−, HCO3−, Ca2+, Na+, Mg2+, and K+) to streams via surface runoff, rainfall erosional runoff, soil lateral flow, quick return flow and groundwater-stream exchange. Model results are analyzed for spatial and temporal trends in water yield and salt loading pathways. Although streamflow is primarily derived from surface runoff (65%), the predominant source of salt loads is the aquifer (73%) due to elevated concentrations of groundwater salt. Annual salt loading from the watershed is 582 Mkg, approximately 10% of the salt load in the Colorado River measured at Lee's Ferry, AZ. For future climate scenarios, annual salt loads from the watershed increased between 4.1% and 9.6% from the historical period due to increased salt loading from groundwater and quick return flow. From the results, applying the APEX-MODFLOW-Salt model with downscaled future climate forcings can be a helpful modeling framework for investigating hydrology and salt mobilization, transport, and export in historical and predictive settings for salt-affected watersheds.

How to account for irrigation withdrawals in a watershed model

Abstract. In agricultural areas, the downstream flow can be highly influenced by human activities during low-flow periods, especially during dam releases and irrigation withdrawals. Irrigation is indeed the major use of freshwater in the world. This study aims at precisely taking these factors into account in a watershed model. The Soil and Water Assessment Tool (SWAT+) agro-hydrological model was chosen for its capacity to model crop dynamics and management. Two different crop models were compared in terms of their ability to estimate water needs and actual irrigation. The first crop model is based on air temperature as the main determining factor for growth, whereas the second relies on high-resolution data from the Sentinel-2 satellite to monitor plant growth. Both are applied at the plot scale in a watershed of 800 km2 that is characterized by irrigation withdrawals. Results show that including remote sensing data leads to more realistic modeled emergence dates for summer crops. However, both approaches have proven to be able to reproduce the evolution of daily irrigation withdrawals throughout the year. As a result, both approaches allowed us to simulate the downstream flow with a good daily accuracy, especially during low-flow periods.

A watershed sustainability model based on the integration of livestock, conservation agriculture, and local wisdom in Indonesia

Watersheds play an important role in maintaining the balance of the ecosystem and protecting water resources. However, there are currently a number of environmental problems which threaten the sustainability of this ecosystem. The goal of this research is to describe a watershed sustainability model to deal with these problems. The research was carried out in the Keduang Watershed in Wonogiri Regency, Central Java Province. A qualitative research method was used to gain a deep and comprehensive understanding and to adapt current methods to the developing situation through the integration of livestock, conservation agriculture, and local wisdom. The secondary data collected included the agriculture, livestock farming, and conservation conditions. The primary data were obtained through participative observation and in-depth interviews with farmers, livestock breeders, and stakeholders, to understand the practices used, the challenges faced, and the benefits felt. The data analysis used a process of coding, thematization, and grouping to identify the patterns, themes, and perspectives that emerged. The interpretation of results included an evaluation of the success, challenges, and benefits of the model applied. The research results show that the watershed sustainability model based on the integration of livestock, conservation agriculture, and local wisdom has a positive impact on economic, socio-cultural, and environmental aspects. This model can serve as a strong foundation for implementing sustainable agriculture and livestock farming practices, as well as contributing to the conservation of natural resources, and preserving local wisdom.

Taking full advantage of ‘low-quality’ datasets in watershed modeling and management: From a perspective of parameter calibration

The quality of calibration datasets is critical for establishing well-calibrated models for reliable decision-making support. However, the analysis of the influence of calibration dataset quality and the discussion on how to use flawed and/or incomplete datasets are still far from sufficient. An evaluation framework for the impact of model calibration data on parameter identifiability, sensitivity, and uncertainty (ISU) was established. Three quantitative and normalized indicators were designed to describe the magnitude of ISU. With the case study of the upper Daqing River watershed, China and the model SWAT (Soil and Water Assessment Tool), one ideal dataset without quality flaws and 79 datasets with different types of flaws including observation error, low monitoring frequency, short data duration and low data resolution were evaluated. The result showed that 4 of 13 parameters that control canopy, groundwater and channel processes have higher ISU values, indicating the high identifiability, high sensitivity, and low uncertainty. The largest gap of parameter ISU between dataset with quality flaw and ideal dataset was 0.61 due to short data duration, while the smallest gap was −0.28 due to low monitoring data frequency. Although some defective datasets caused unacceptable calibration results and model output, some defective datasets can still be valuable for model calibration which depends on the hydrological processes of interest when applying the model. Equivalent calibration results were yielded by the datasets with similar statistical properties. When using datasets with traditional defective issues for calibration, a new step checking the consistency among decision goal, representative system process, determinative parameters and calibration datasets is suggested. Practices including process-related data selection, dataset regrouping and risk self-reporting when using low-quality datasets are encouraged to increase the reliability of model-based watershed management.

Effectiveness and resilience of BMPs to watershed climate adaptation considering the uncertainty of hydrological model and GCMs

Climate change significantly impacts catchment hydrologic and water quality processes. Best management practices (BMPs) can serve as adaptation strategies to negate the impact of climate change on sediment and total nitrogen (TN) loads. One major controversial issue in climate change adaptation studies is the highly uncertain nature of such changes. Previous studies have rarely focused on the combined impact of the uncertainty in climate change and watershed model parameters, which could be the main sources of uncertainty in climate change adaptation research. In this study, the effectiveness and resilience (defined as continued effectiveness under a changing climate) of BMPs in reducing sediment and TN loads were explored under future climate change in the Shanmei Reservoir watershed (SMW) of Southeast China. Climate change projections provided by 10 general circulation models (GCMs) under the SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios from 2031 to 2060 were applied in the Annualized Agricultural Nonpoint Source (AnnAGNPS) model to evaluate the effectiveness and resilience of 4 BMPs (riparian buffers (RB), no-tillage (NT), fertilization reduction (FR), and parallel terraces (PT)). The ensemble average of 10 GCMs and 10 behavior parameter groups were adopted to reduce the uncertainty resulting from the hydrological model parameters and GCMs. The results indicated that the average annual temperature and precipitation in the SMW will increase in the future. On a seasonal scale, the average temperature during all seasons will increase, and precipitation will decrease in summer and autumn but increase in spring and winter. The annual sediment and TN loads will decrease, but the loads in spring and winter will increase. BMPs could be effective as climate adaptation strategies for reducing sediment and TN loads under future climate conditions, with PT as the most effective option. Structural BMPs were more effective in reducing sediment and TN loads in spring and winter, whereas nonstructural BMPs were more effective in reducing loads in summer and autumn. BMPs were more resilient when future watershed runoff changes were slight or climate sensitivity was reduced, with higher BMP resilience in spring and winter than in summer and autumn. This study aimed to provide systematic references for watersheds through the implementation of BMPs for mitigating the effects of climate change and extending the boundaries of the AnnAGNPS model application.

PAVLIB4SWAT: a Python analysis and visualization tool and library based on Kepler.gl for SWAT models

Abstract The Soil and Water Assessment Tool (SWAT) has been widely applied to simulate the hydrological cycle, investigate cause-and-effect relationships, and aid decision-making for better watershed management. However, the software tools for model dataset analysis and visualization to support informed decision-making in a web environment are not considered fully fledged and are technically intensive to implement. This study focuses on addressing these issues by establishing a tool and library (named PAVLIB4SWAT) that can largely reduce technical expertise requirements for developers to adopt and customize this work to their own demands. Specifically, we created PAVLIB4SWAT based on a Kepler.gl widget to visualize SWAT model data, including shapefiles from the watershed delineation process, model inputs, and simulated results via dynamic and interactive maps. We evaluated PAVLIB4SWAT through a Jinjiang watershed SWAT model use case to demonstrate its utility and ease of adoption. The case study shows that PAVLIB4SWAT can provide various geospatial analysis and mapping functionalities for SWAT models and can flexibly distribute visualized results as standalone offline web pages and web servers. In addition, PAVLIB4SWAT was designed as an open-source project and implemented purely in the Python programming language; thus, developers can easily adapt and customize it to suit their demands.

Estimation of changes in runoff and its sources in response to future climate change in a critical zone of the Karakoram mountainous region, Pakistan in the near and far future

The inconsistent pattern of precipitation, a shift in the seasonality of river flows, and the early onset of snow and glacier melt in recent decades across river basins of High Mountain Asia (HMA) has compelled us to further investigate future variations in sources of runoff under projected climate change scenarios. This will help in determining the timing and magnitude of runoff components and this will help in management of future water resources. The current study employed the University of British Columbia Watershed Model (UBC WM) to estimate the spatiotemporal variations in simulated runoff components (i.e. snowmelt, glacier melt, rainfall-runoff, and baseflow) and their relative contribution to total runoff of Gilgit River regarding the baseline period (1981–2010) in near (2021–2050) and far future (2071–2100) under low (SSP1), medium (SSP2) and high (SSP5) emission scenarios. A significant increase in the magnitude of mean annual temperature and precipitation is expected in the near future (2021–2050) than far future (2071–2100) under most SSPs. Moreover, high-altitude stations of the Gilgit River basin are expected to experience more warming in the near and far future than low altitudes under all SSPs. On average, regarding the baseline period, the simulated runoff is projected to increase in the near (27%, 30%, and 33%) and far future (30%, 53%, and 91%) under SSP1, SSP2, and SSP5, respectively. Moreover, an early onset of snow/glacier melting is predicted in the far future due to an increase in summer air temperature and a decline in winter (DJF) precipitation. Besides, the rise in high altitude temperature is expected to cause the melting of snow/glaciers even above 6000 m elevation in the far future.

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.

Sediment-Nitrogen (N) connectivity: suspended sediments in streams as N exporters and reactors for denitrification and assimilatory N uptake during storms

Nitrogen (N) pollution in riverine ecosystems has substantial environmental, economic, and policy consequences. Various riverine N removal processes include permanent dissimilatory sinks such as denitrification (Uden) and temporary assimilatory sink such as microbial N uptake (Uassim). Both processes have been extensively evaluated in benthic sediments but only sparsely in the water column, particularly for storm flows producing high suspended sediment (SS) concentrations. Stormflows also increase the sediment bound N (Sed-N) export, and in turn, the overall N exports from watersheds. The balance between N removal by Uden and Uassim vs. Sed-N export has not been studied and is a key knowledge gap. We assessed the magnitude of Uden and Uassim against stormflow Sed-N exports for multiple storm events of varying magnitude and across two drainage areas (750 ha and 15,330 ha) in a mixed landuse mid-Atlantic US watershed. We asked: How do the Uden and Uassim sinks compare with Sed-N exports and how do these N fluxes vary across the drainage areas for sampled storms on the rising and falling limbs of the discharge hydrograph? Mean Uden and Uassim as % of the Sed-N exports ranged between 0.1–40% and 0.6–22%, respectively. Storm event Uassim fluxes were generally slightly lower than the corresponding Uden fluxes. Similarly, comparable but slightly higher Uden fluxes were observed for the second order vs. the fourth order stream, while Uassim fluxes were slightly higher in the fourth-order stream. Both of these N sinks were higher on the falling vs. rising limbs of the hydrograph. This suggests that while the N sinks are not trivial, sediment bound N exports during large stormflows will likely overshadow any gains in N removal by SS associated denitrification. Understanding these N source-sink dynamics for storm events is critical for accurate watershed nutrient modeling and for better pollution mitigation strategies for downstream aquatic ecosystems. These results are especially important within the context of climate change as extreme hydrological events including storms are becoming more and more frequent.

The importance of explicitly representing the streambed in watershed models

AbstractThe streambed is the critical interface between the aquatic and terrestrial systems and hosts important biogeochemical hot spots within river corridors. Although the streambed characteristics are significantly different from those of its surrounding soil, the streambed itself has not been explicitly represented in watershed models. Here, we explicitly incorporated a streambed layer into an integrated hydrologic model through model parameterization and discretization. We examined the hydrological effects of streambed characteristics, including hydraulic conductivity (K), layer thickness, and resolution, on the exchange fluxes across the streambed as well as the streamflow at the watershed outlet. The numerical experiments were performed in the American River Watershed, a headwater, mountainous watershed within the Yakima River Basin in central Washington. Despite having a negligible effect on the watershed streamflow, an explicit representation of the streambed with distinctive properties dramatically changed the magnitude and variability of the exchange flux. In general, a larger streambed K along with a thicker streambed layer induced larger exchange fluxes. The exchange flux was most sensitive to the streambed resolution. A finer streambed resolution increased exchange fluxes per unit area while reducing the overall exchange volumes across the entire streambed. The amount of baseflow decreased by 6% as the streambed resolution increased from 250 to 50 m. This finding is important because these hydrological changes may, in turn, affect the exchange of nutrients and contaminants between surface water and groundwater and the associated biogeochemical processes. Our work demonstrated the importance of representing streambeds in fully distributed, process‐based watershed models to better capture the exchange flow dynamics in river corridors.

Measuring environmental and cost benefits of riparian buffers for drinking water production in a Midwest watershed

AbstractThis study focuses on the economic value and carbon benefits of riparian buffers in urban drinking water production. The impact of riparian buffers on the waterworks operation in the Raccoon River watershed was quantified using the following metrics: nitrate concentration, days of nitrate removal operation, material and energy cost (based on 17 years of historical records with a watershed model), regression, and cost analysis. The findings indicate that the presence of riparian buffers in agricultural land can substantially decrease nitrate concentration in the water intake of the waterworks during the crop‐growing season: 19% in April, 9% in May, and 11% in June. These reductions mean less nitrate treatment of the plant intake flow: 23% in April, 12% in May, and 3% in June. These changes lead to significant resource savings: 425 metric tons of sodium chloride (NaCl), 147 810 kWh of electricity, 253 metric tons of powdered activated carbon, and 20.8 million liters of fresh water. The buffers would also reduce greenhouse gas emissions by 86.9 metric tons (CO2 equivalent) in 17 years. The total cost saving was estimated at $327 326, with the highest potential savings in May ($215 100), followed by April ($65 465), and June ($46 761). When factoring in buffer installation, cropland loss, nitrate removal, and cost associated with buffer harvest for biofuel in an established biomass market, the benefit to the entire watershed community would be $2.63 million annually. The results underscore the significant cost benefits and environmental benefits associated with cropland riparian buffers in a watershed community. The approach employed in this study holds promise for assessing riparian buffer benefits in other watersheds, contributes to an understanding of sustainable water management practices, and provides a basis for decision making in a wide range of agricultural regions.

A Laboratory Watershed Model to Study the Effect of Rainfall Intensity and Soil Surface Slope on Surface Runoff Rate of Karbala Desert Soil

Precipitation generating surface runoff is very important in various water resources development and management activities such as flood control and management, irrigation scheduling, irrigation design, and drainage networks. The aim of this study is to indicate the effect of rainfall intensity and soil surface slope on the surface runoff rate of Karbala desert soil. This study was conducted on the soil of the Karbala desert in Iraq, located between the provinces of Karbala and Najaf, extended from the longitude 32o44'186'' E and latitude 44o100'960''N. In this study, a laboratory watershed model was designed and constructed to be used to examine surface runoff of the Karbala desert soil and other hydrological properties, under the influence of different precipitation intensities (1.83 cm/min, 1.67 cm/min, 0.9 cm/min, and 0.64 cm/min) and different slopes of the soil surface (0.0 %, 2.0 %, 3.3 %, and 6.7 %). A Gene Expression Programming model was used to develop an equation of the surface runoff rate for the Karbala desert soil. The results showed that the rate of surface runoff increased with increasing rainfall intensity. The highest surface runoff rate of 101.53 cm/hr was obtained when the rainfall intensity was 1.83 cm/min, while at the rainfall intensity of 0.64 cm/min, the highest value of surface runoff rate of 31.17 cm/hr was obtained. The results also showed that the slope of the soil surface has a clear effect on the surface runoff rates. The highest value of surface runoff rate of 96.82 cm/hr was obtained when the slope of the soil surface was 0.0%, while at the slope of the soil surface of 6.7%, the highest value of surface runoff rate of 101.53 cm/hr was obtained. According to the simulation results, the surface runoff rate equation created using the Gene Expression Programming model did a great job of predicting the Karbala desert soil surface runoff, with a coefficient of determination R2 equal to 0.8812 for training and 0.8155 for testing.

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.