River Watershed Research Articles

A novel approach to a multi-model ensemble for climate change models: Perspectives on the representation of natural variability and historical and future climate

This study developed a novel approach that integrated climate model selection and multi-model ensemble (MME) construction to effectively represent model uncertainties and, consequently, improve consistency in the evaluation of changes to extreme rainfall in different scenarios. Our focus was to combine 10 regional climate model (RCM) simulations, forced by two global climate models (GCM), especially for estimation of design rainfall in a changing climate. We hypothesized that the natural variability and statistically higher moment attributes in the extreme rainfall simulations from RCMs were not fully preserved. Therefore, the MME approach could be more effective in climate change studies, largely due to the use of multiple climate models. First, an experimental study was proposed to validate the efficacy of the proposed modeling framework approach adopting L-moments to quantify relative importance among climate models and their use for representing natural variability in the MME construction. The proposed approach was then applied to climate change scenarios collected from multiple RCMs for the Han-River watershed during both historical (1981–2005) and future (2006–2 100) periods. The results showed that the climate model selection informed by natural variability demonstrated better performance, representing nearly identical distribution to the observed annual maximum rainfall (AMR) in the Han River watershed. The range of the selected scenario was relatively narrower than that of all the scenarios, and the change rate was more consistent with the limited zero crossing, reflecting improvement in both model performance and consistency over historical and future periods, respectively. The change rate of the MME under RCP8.5 appeared to be an approximately 20% increase for the near (2011–2040) and far (2071–2 100) future, and the degree of increase in the rate for the mid-future (2041–2070) was slightly lower than that for the other periods, with increase of approximately 10%.

A review of the satellite remote sensing techniques for assessment of runoff and sediment in soil erosion

Abstract Soil erosion monitoring is essential for the ecological evaluation and dynamic monitoring of land resources via remote sensing technology. In this paper, we provide new insights into the existing problems and development directions of traditional models, which are supported by new technologies. An important role is played by remote sensing information acquisition technology in the qualitative and quantitative evaluation of soil erosion, and the data and technical support provided are systematically reviewed. We provide a detailed overview of the research progress associated with empirical statistical models and physically driven process models of soil erosion, and the limitations of their application are also summarized. The preliminary integration of remote sensing data sources with high spatial and temporal resolution and new technologies for soil erosion monitoring enables the high-precision quantitative estimation of sediment transport trajectories, the watershed river network density, and the terrain slope, enhancing the accuracy of erosion factor identification, such as spectral feature recognition from erosion information, gully erosion feature extraction, and vegetation coverage estimation. However, the current erosion models, driven by algorithms and models, are not comprehensive enough, particularly in terms of the spatial feature extraction of erosion information, and there are limitations in the applicability and accurate estimation of such models.

Surface water quality prediction in the lower Thoubal river watershed, India: A hyper-tuned machine learning approach and DNN-based sensitivity analysis

The accurate and efficient monitoring, assessment, and prediction of water resources is crucial for the sustenance of life and health of the environment. Traditional methods for assessing water quality can be laborious and time-consuming, but the use of machine learning algorithms can improve the speed and precision of these predictions. The study aimed to evaluate the quality of surface water in the lower Thoubal river using a comprehensive approach that analyzed 12 physico-chemical properties of the water samples collected from 16 different sites. Four machine learning algorithms namely, deep neural network (DNN), gradient boost model (GBM), generalized linear model (GLM), and random forest (RF) were applied and compared for the prediction efficiency of a water quality index (WQI). Sensitivity and uncertainty analysis of the parameters were carried out using a DNN-based model. The study incorporated model evaluation using learning curve along with five key performance assessors. The findings revealed marked variation in the WQI in the watershed. Of the samples tested, about 70 % were found to have good water quality, 18 % had excellent water quality, 11 % had poor water quality, 1 % had very poor water quality, and less than 1 % were deemed unsuitable. Turbidity, BOD, and COD were found to be the most influential parameter towards WQI prediction. The effectiveness of the predictive models ranked as GLM>DNN>RF>GBM, GLM>GBM>DNN>RF, and GLM>DNN>RF>GBM; based on testing, training and 5-fold cross-validation. The GLM model consistently showed superior performance with the lowest RMSEs of 0.14783 in training, 0.15936 in validation, and 0.26115 in 5-fold cross-validation. Conversely, during the training, RF displayed the worst RMSEs of 5.38354, 4.79754 by GBM in validation, and 7.04887 by GBM in 5-fold cross-validation. The analysis using the Taylor diagram and learning curve further supported that GLM was the most effective model for predicting surface water quality. Biological oxygen demand, chemical oxygen demand, electrical conductivity, turbidity and total hardness were found to be the key parameters influencing the water quality. Unregulated and illegal sand mining from the riverbed, garbage dumping into the water bodies, runoffs from agricultural fields have significantly deteriorated the water quality in the study area. The scalable approach of the study and its results can benefit the local water managers and water research community.

Landscape changes and watershed erosion in Prince George's County, Maryland

AbstractThis study evaluated erosion rates and sediment production in streams, and factors potentially influencing them throughout the Anacostia, Patuxent, and Potomac (non‐Anacostia) River watersheds within Prince George's County, Maryland, US. As part of the County's watershed‐scale biological monitoring program, from approx. 1999 to 2008, permanent monuments were established to allow measurement of stream channel cross‐sectional (XS) area. The intent of this study was to characterize the intensity and spatial distribution of fluvial geomorphic instability across the county and use the results to target and plan stormwater management and stream restoration actions. For this study, 78 stream locations were re‐surveyed in 2020, representing a time lapse of from 12 to 21 years. Data collected included XS dimensions, modified Wolman 100‐particle pebble counts, and reach‐specific soil bulk density. Land use/land cover data were compiled from the National Land Cover Dataset (NLCD), precipitation from the National Weather Service Center for Environmental Information (NCEI), and soils from the Natural Resources Conservation Service Web Soil Survey (NRCS/WSS). We calculated percent change in XS area, rates of erosion, sediment yield, and assigned geomorphic classifications, and interpreted them in the context of spatial positions relative to changes in land cover characteristics. Sediment yields among the 78 reaches exhibited a combination of those undergoing enlargement/erosion (67.9%), reduction/deposition (25.6%), and the remaining 6.4% with essentially no change over the period of record. Of the top 20 most geomorphically active reaches surveyed in the County, 12 are in the Anacostia River basin, with the other scattered among the Patuxent River and Potomac River basins.

Insights to the water balance of a Boreal watershed using a SWAT model

The hydrological characteristics of a watershed play a crucial role in shaping ecosystems within the Boreal zone and have a significant impact on regional environments. Knowing these characteristics, such as the distinctive topography, vegetation, soil composition, and climatic conditions in the Canadian Boreal ecozone, is essential for implementing sustainable water management. This study focuses on assessing the hydrological dynamics of the Upper Humber River Watershed (UHRW) in western Newfoundland, Canada, using the Soil and Water Assessment Tool (SWAT) model. The UHRW includes sub-basins and hydrological response units (HRUs), with diverse land uses, soil types, and slope characteristics. Key parameters influencing streamflow simulation were identified through sensitivity analysis, including the runoff curve number, the effective hydraulic conductivity, the temperature lapse rate, the soil evaporation compensation factor, and the available water capacity of the soil layer. The SWAT model, using data from the Reidville hydrometric station, shows favorable performance metrics, with R2 values of 0.79 and 0.83 during the calibration and evaluation periods, respectively. The model effectively captures seasonal and monthly flow patterns, displaying right-skewed distributions and seasonal variations. The analyzed hydrological parameters, such as precipitation, evaporation, transpiration, surface runoff, and groundwater flow, reveal their significant contributions to the water balance. The flow duration curve analysis indicates the model’s capability to estimate peak and low flows, with slight under-prediction during the recession phase. Seasonal analysis further supports the model’s performance, with positive Nash-Sutcliffe Efficiency (NSE) values ranging from 0.65 to 0.91. The study concludes that the SWAT model is suitable for simulating the hydrological processes in the studied watershed providing valuable insights for sustainable water resource management and decision-making in the UHRW. The results can be useful for other Boreal ecozone watersheds.

A changing river: Long‐term changes of sinuosity and land cover in the Navasota River Watershed, Texas

AbstractThe Navasota River Basin, itself a tributary of the Brazos River in Texas, is a dynamic watershed undergoing many natural and anthropogenic changes. Local stakeholder involvement in this watershed is quite high, and many landowners in the southern portion of the watershed have concerns regarding the increasing frequency and duration of flooding on private property adjacent to the river, often attributing these impacts to the construction of the Lake Limestone dam. In this study, we examine historical flow data, channel morphology, land use/land cover, and precipitation. Our findings indicate that while there appears to be increasing flow in the northern portion of the watershed, temporal data gaps near the watershed outfall prevent the indication of such a trend in the southern portion of the watershed. Nevertheless, other natural and anthropogenic factors are evident in the watershed that may have a significant influence on downstream flooding. Overall river sinuosity (meandering) declined over the study period, with some river segments encountering significant straightening. Total river length declined by 4.3 km from 1972 to 2020. The number and length of offtake channels also decreased substantially during this period. Land use/land cover use shifted dramatically, with a 39.2% increase in impervious cover and a 12.5% decrease in herbaceous cover since 1972. Finally, yearly precipitation increased, with the change point occurring in 1972. Our findings suggest that the shortening and straightening of the river has reduced its volumetric capacity over time. Coupled with increasing impervious surface cover and precipitation, more water is being delivered downstream at a rate exceeding the watershed's ability to discharge it, thereby contributing to flooding issues expressed by stakeholders. We recommend that bathymetric data and supplemental flow monitoring and modeling within the watershed is needed to fully understand how anthropogenic and natural forces may further affect streamflow in the future.

An updated survey of freshwater fishes within Letchworth State Park and surrounding area's of the Genesee River.

The goal of this study was to gather information about freshwater fishes in Letchworth State Park (42.615275° N, -77.992825° W), a portion of New York State-owned land located in the Genesee River Watershed that lacks known data about its fish diversity. Fish collection took place between 2017 and 2019 in the Genesse River upstream and downstream of the falls using electrofishing, gill, hoop, and seine netting. This was the first attempt at a comprehensive survey of this portion of the river, which allowed for a baseline to be established regarding fish biodiversity in the region. The updated total number of species found in this portion of the Genesee River was 25, 22 of which were newly identified downstream of the falls in Letchworth State Park. We encourage further collection and continuation of this survey with consistent sampling techniques to raise awareness about the importance of freshwater fish diversity in stream ecosystems across the globe.

Hydrological Model of the Diyala River Watershed in Iraq Using Soil Water Assessment Tool

Hydrological Model of the Diyala River Watershed in Iraq Using Soil Water Assessment Tool

Evaluation of the Best Management Practices for Reducing Phosphorus Load in a Watershed in Terms of Cost and Greenhouse Gas Emissions

Effective management of water quality in watersheds is crucial because it is directly linked to the sustainability of aquatic ecosystems. In conventional watershed management, best management practices (BMPs) have been instrumental in addressing deteriorating water quality issues caused by non-point source pollution. Greenhouse gas (GHG) emissions have emerged as a global concern, necessitating immediate and diverse environmental actions to mitigate their impacts. This study aims to explore BMPs that maximize total phosphorus (TP) load removal efficiencies, while minimizing costs and GHG emissions within watersheds, using the Soil and Water Assessment Tool (SWAT) and non-dominated sorting genetic algorithm III (NSGA-III). The Yeongsan River Watershed between 2012 and 2021 was selected as the study area. Hydrological and BMP data were analyzed. Applying identical BMPs to the watershed showed that the BMPs with high TP removal efficiency may not be effective in terms of cost and GHG emissions. Therefore, the optimal combination of BMPs for the Yeongsan River Watershed was determined using NSGA-III considering TP removal efficiency, cost, and GHG emissions. This study is the first to consider GHG emissions at the watershed level when applying BMPs and is expected to contribute to the development of BMP implementation incorporating GHG emissions.

Riparian Forests as Nature-Based Solutions within the Mediterranean Context: A Biophysical and Economic Assessment for the Koiliaris River Watershed (Crete, Greece)

The Mediterranean Basin is severely impacted by anthropogenic changes affecting both natural ecosystems and human livelihoods. The region is highly vulnerable to natural hazards, with floods being considered the most important, due both to their frequency and impacts. Koiliaris watershed (northwest of Crete Island, Greece) represents a relevant case study as past land-use changes via deforestation and intense cultivation practices induce soil organic matter losses, making soils susceptible to water erosion and desertification. The restoration of native riparian forests has been identified as the most effective nature-based solution (NBS) for the area. Through modeling, our study assessed the effectiveness of this NBS in addressing flood risk and erosion while providing additional ecosystem services (carbon sequestration and biodiversity conservation). A cost–benefit analysis has been then implemented to also investigate the sustainability of the investment from an economic point of view. Our results show the NBS would be successful in ensuring a better flow of targeted ecosystem services compared to the business-as-usual conditions. The associated investment would result in economic sustainability and associated costs would be paid back in five years. Though site-specific, our study provides lessons learned for dealing with future land-restoration challenges in the Mediterranean to cope with climate change-related challenges.

Using an interpretable deep learning model for the prediction of riverine suspended sediment load.

The prediction of suspended sediment load (SSL) within riverine systems is critical to understanding the watershed's hydrology. Therefore, the novelty of our research is developing an interpretable (explainable) model based on deep learning (DL) and Shapley Additive ExPlanations (SHAP) interpretation technique for prediction of SSL in the riverine systems. This paper investigates the abilities of four DL models, including dense deep neural networks (DDNN), long short-term memory (LSTM), gated recurrent unit (GRU), and simple recurrent neural network (RNN) models for the prediction of daily SSL using river discharge and rainfall data at a daily time scale in the Taleghan River watershed, northwestern Tehran, Iran. The performance of models was evaluated by using several quantitative and graphical criteria. The effect of parameter settings on the performance of deep models on SSL prediction was also investigated. The optimal optimization algorithms, maximum iteration (MI), and batch size (BC) were obtained for modeling daily SSL, and structure of the model impact on prediction remarkably. The comparison of prediction accuracy of the models illustrated that DDNN (with R2 = 0.96, RMSE = 333.46) outperformed LSTM (R2 = 0.75, RMSE = 786.20), GRU (R2 = 0.73, RMSE = 825.67), and simple RNN (R2 = 0.78, RMSE = 741.45). Furthermore, the Taylor diagram confirmed that DDNN has the highest performance among other models. Interpretation techniques can address the black-box nature of models, and here, SHAP was applied to develop an interpretable DL model to interpret of DL model's output. The results of SHAP showed that river discharge has the strongest impact on the model's output in estimating SSL. Overall, we conclude that DL models have great potential in watersheds to predict SSL. Therefore, different interpretation techniques as tools to interpret DL model's output (DL model is as black-box model) are recommended in future research.

Mercury Bioconcentration and Translocation in Rooted Macrophytes (Paspalum repens Berg.) from Floodplain Lakes in the Araguaia River Watershed, Brazilian Savanna

Macrophytes are fundamental photosynthetic organisms for functioning freshwater ecosystems, identified as potential bioindicators of mercury (Hg) in the environment. We quantified the concentrations of total Hg (THg) in water and macrophytes (Paspalum repens Berg.) from 17 lakes on the Araguaia River floodplain, aiming to compare the bioconcentration factor (BCF) in the aerial tissues and roots; evaluate the translocation factor (TF) between plant tissues; and assess the influence of environmental factors and land use on THg concentrations in water and macrophytes. The BCF was significantly higher in roots (1.29 ± 0.32) than in aerial tissues (0.41 ± 0.34), with low TF between plant tissues (0.14 ± 0.06). The highest concentrations of THg in water were determined in lakes with higher land use intensity and a pH close to neutral, indicating the transport of particulate-bound Hg and the immobilization in the water column. In contrast, wetlands were priority areas for the bioconcentration of THg in macrophytes, associated with sulfate, dissolved oxygen, and oxidation–reduction potential in the water. Thus, although P. repens is not a suitable bioindicator of Hg mobilization by anthropogenic land use in our study area, our results suggest the potential of macrophytes as bioindicators of sites that are favorable to Hg methylation.

Evaluating Best Management Practice Efficacy Based on Seasonal Variability and Spatial Scales

Implementing best management practices (BMPs) has proven to be an efficient method for reducing non-point source (NPS) pollutants. Agricultural NPS pollution is considered to be a major contributor to water quality impairment. This study aims to assess the variation in hydrologic and water quality outputs at field and watershed scales when BMPs are implemented using modeling approaches. The Yazoo River Watershed (YRW) is the largest watershed basin in the state of Mississippi with approximately 50% agricultural land. Runoff generated from agricultural areas carries sediments and nutrients. The Merigold watershed (MW) is a sub-basin of the YRW and a field-scale watershed with most of the land use being agriculture. It is essential to quantify the streamflow, sediment, total nitrogen (TN), and total phosphorus (TP) when BMPs are implemented. BMPs such as vegetative filter strips (VFS) and cover crops (CC) were tested in this study. The Soil and Water Assessment Tool (SWAT) model was applied to quantify the watershed’s hydrologic and water quality outputs. SWAT model accuracy assessment was performed by calibration and validation process using the Nash and Sutcliffe Efficiency Index (NSE). Model performance was satisfactory for monthly streamflow, with NSE values in the range of 0.62 to 0.81, and for daily sediments, TN, and TP load estimation, with NSE values of 0.21, 0.20, and 0.47, respectively. CC was planted after harvesting the main crop. Therefore, it is essential to quantify the seasonal reduction in pollutants. Water quality was improved after BMP implementation, and an overall decrease in streamflow, sediment, TN, and TP loads was observed for both MW and YRW during dry and wet seasons. Previous studies regarding seasonal assessments with CC implementation in the MW and YRW were limited. Therefore, the results from this study could be a unique addition to the scientific literature.

Simulating block-scale flood inundation and streamflow using the WRF-Hydro model in the New York City metropolitan area

This study assesses the performance of the Weather Research and Forecasting-Hydrological modeling system (WRF-Hydro) in the simulation of street-scale flood inundation. The case study is the Hackensack River Watershed in New Jersey, US, which is part of the operational Stevens Flood Advisory System (SFAS), a one-way coupled hydrodynamic-hydrologic system that currently uses the Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS) to simulate streamflow. The performance of the 50-m gridded WRF-Hydro model was assessed for potential integration into the operational SFAS system. The model was calibrated with the dynamically dimensioned search algorithm using streamflow observations. The model performance was assessed using (i) streamflow observations, (ii) USGS HWMs, and (iii) crowdsourced data on street inundation. Results show that WRF-Hydro outperformed the HEC-HMS model. WRF-Hydro over and underestimated flood inundation extent due to the inaccuracy of the synthetic rating curves and the modeling structure errors. An agreement was noticed between WRF-Hydro and crowdsourced data on flood extent.

Evaluate the groundwater quality and human health risks for sustainable drinking and irrigation purposes in mountainous region of Chongqing, Southwest China

Groundwater is crucial for agriculture and domestic consumption. This research investigated the hydrogeochemical properties and contaminant sources of groundwater within the mountainous terrain of northern Chongqing, with the objective of evaluating its appropriateness for irrigation and potable use. The hydrochemical type of the groundwater was HCO3 − Ca, dominated by silicate and calcite dissolutions. High NO3− (29.03% exceeds 10 mg/L) were attributed to the overuse of agricultural fertilizers. A comprehensive evaluation was conducted to determine the groundwater suitability for agricultural and potable uses. The results showed that groundwater in the southwestern region, particularly within the Yangtze River mainstem watershed, exhibited less suitability for irrigation owing to its lower mineralization, in contrast to the northeastern region near the Daning River watershed. But this trend is reversed for drinking purposes. Overall, the groundwater was appropriate for both drinking (93.55% were classified as excellent) and irrigation (70.98% were classified as low restriction) purposes in the study area. Deterministic and probabilistic noncarcinogenic health risk analyses centered on nitrate exposure revealed that infants (with 13.79% of samples >1) were at greater risk than children (8.58%), adult males (6.98%), and adult females (5.24%). This underscores the urgency to reduce nitrogen fertilizer usage and improve water management in the region. This research will provide guidance for the sustainable groundwater management in mountainous regions.

Modeling the impacts of oil palm plantations on water quantity and quality in the Kais River Watershed of Indonesia

Oil palm plantations can impact hydrological processes in many tropical watersheds. The rapid conversion of tropical rainforests for commercial operations in recent decades has been associated with water scarcity, flooding, and polluted rivers. However, this widespread and emerging issue is less studied and underreported due to limited data availability, modeling complexity, and the remote nature of these landscapes. Ecohydrologic modeling enables us to investigate changes in watershed conditions caused by large-scale land cover changes from plantations. This study examines the impact of oil plantations on water quantity and quality using the SWAT+ model in the Kais River Watershed, West Papua, Indonesia. The objective is to assess the hydrological changes concerning land cover conversion to oil palm plantations. Results show that establishing oil palm plantations increased surface runoff by 21 %, and sediment yields rose by 16.9 % compared to the baseline. There was also a significant increase of 78 % in mean annual total nitrogen and 144 % in total phosphorous after the plantations' establishment. The results show that forest conversion to oil palm plantations in the Kais River watershed is a primary driver of change in hydrological regimes, resulting in the deterioration of water quality. There is a need for conservation strategies to mitigate the impacts of significant landscape changes in watershed ecosystems.

Support vector machine (SVM) model development for prediction of fecal coliform of Upper Green River Watershed, Kentucky, USA

Support vector machine (SVM) model development for prediction of fecal coliform of Upper Green River Watershed, Kentucky, USA

Latest Pleistocene glacial chronology and paleoclimate reconstruction for the East River watershed, Colorado, USA

AbstractReconstructing Pleistocene glaciation timing and extent is vital for understanding paleoclimate. Whereas late Pleistocene glaciation has been studied extensively in western North American mountain ranges, the glacial history of the western Elk Range in Colorado remains understudied, particularly in the East River watershed, a site of intense scientific focus. Here we use cosmogenic nuclide exposure and depth–profile dating methods to determine the timing of glaciation in the East River watershed. We use glacier modeling to reconstruct paleoglacier extents and quantify past climate conditions. Our findings indicate that the East River glacier retreated from its maximum position approximately 17–18 ka, moving to recessional positions between 13 and 15 ka, before experiencing more substantial retreat to high-elevation cirques around 13 ka. Glacier modeling suggests that the maximum ice extents at 17–18 ka could have been sustained by temperature depressions of approximately −6.5°C compared to modern conditions, assuming consistent precipitation. Additionally, the ice position at 13–15 ka could have been supported by temperature depressions of around −4.0°C. These results offer insights into the deglaciation timeline in the East River watershed and broader western Elk Range as well as paleoclimate conditions during the late Pleistocene, which may aid future research on critical zone evolution in the East River watershed.

Development of an Enrichment Book on the Dynamics of Life in River Watersheds from an Islamic Prophetic Paradigm Perspective

The decline in watershed quality requires a handling role from the community. As part of society, students need to broaden their horizons, starting by studying watershed dynamics so they can carry out actual practice. Using the Islamic Prophetic Paradigm as a new perspective in learning can increase students' interest in general knowledge, religious fields, and practical examples of natural movements in everyday life. This research aims to develop an enriching book on the dynamics of river-watershed life from an Islamic prophetic paradigm perspective, intending to enhance students' learning and deepen their understanding of watershed dynamics by integrating scientific insights with Islamic teachings. The research and development of the ADDIE model uses questionnaire techniques in the form of pre-research questionnaires and validator questionnaires. The product's validity and feasibility level are tested using validity tests and field trials. The research data analysis technique uses descriptive qualitative analysis techniques and descriptive statistics. The research results show that the product follows learning outcomes, can increase students' interest in studying the Quran, and makes studying watershed dynamics easier. This research shows that product development is better than previous research and development. So, students can take advantage of this product.

Responses of streamflow to forest expansion in a typical subhumid watershed under future climate conditions

Water availability in the subhumid region is highly vulnerable to frequent droughts. Water scarcity in this region has become a limiting factor for ecosystem health, human livelihood, and regional economic development. A notable pattern of land cover change in the subhumid region of the United States is the increasing forest area due to afforestation/reforestation and woody plant encroachment (WPE). Given the distinct hydrological processes and runoff generation between forests and grasslands, it is important to evaluate the impacts of forest expansion on water resources, especially under future climate conditions. In this study, we focused on a typical subhumid watershed in the United States – the Little River Watershed (LRW). Utilizing SWAT + simulations, we projected streamflow dynamics at the end of the 21st century in two climate scenarios (RCP45 and RCP85) and eleven forest expansion scenarios. In comparison to the period of 2000–2019, future climate change during 2080–2099 will increase streamflow in the Little River by 5.1% in the RCP45 but reduce streamflow significantly by 30.1% in the RCP85. Additionally, our simulations revealed a linear decline in streamflow with increasing forest coverage. If all grasslands in LRW were converted into forests, it would lead to an additional 41% reduction in streamflow. Of significant concern is Lake Thunderbird, the primary reservoir supplying drinking water to the Oklahoma City metropolitan area. Our simulation showed that if all grasslands were replaced by forests, Lake Thunderbird during 2080–2099 would experience an average of 8.6 years in the RCP45 and 9.4 years in the RCP85 with water inflow amount lower than that during the extreme drought event in 2011/2012. These findings hold crucial implications for the formulation of policies related to afforestation/reforestation and WPE management in subhumid regions, which is essential to ensuring the sustainability of water resources.