Streamflow Simulation Research Articles

A hybrid technique to enhance the rainfall-runoff prediction of physical and data-driven model: a case study of Upper Narmada River Sub-basin, India

Accurate streamflow prediction is crucial for effective water resource management and planning. This study aims to enhance streamflow simulation accuracy in the data-scarce Upper Narmada River Basin (UNB) by proposing a novel hybrid approach, ANNHybrid, which combines a physically-based model (WEAP) with a data-driven model (ANN). The WEAP model was calibrated and validated using observed streamflow data, while the ANN model was trained and tested using meteorological variables and simulated streamflow. The ANNHybrid model integrates simulated flow from both WEAP and ANN to improve prediction accuracy. The results demonstrate that the ANNHybrid model outperforms the standalone WEAP and ANN models, with higher NSE values of 95.5% and 92.3% during training and testing periods, respectively, along with an impressive R2 value of 0.96. The improved streamflow predictions can support better decision-making related to water allocation, reservoir operations, and flood and drought risk assessment. The novelty of this research lies in the development of the ANNHybrid model, which leverages the strengths of both physically-based and data-driven approaches to enhance streamflow simulation accuracy in data-limited regions. The proposed methodology offers a promising tool for sustainable water management strategies in the UNB and other similar catchments.

Comparison of Calibration Strategies for Daily Streamflow Simulations in Semi-Arid Basins

Comparison of Calibration Strategies for Daily Streamflow Simulations in Semi-Arid Basins

Detection of Hydrological Alteration and soil erosion in a conserved tropical sub-humid ecosystem of Ethiopia

Soil erosion poses a significant challenge in the sub-humid Ethiopian highlands, yet research on the long-term effectiveness of soil and water conservation (SWC) practices in this region using pre- and post-conservation approaches remains limited. This study addresses this knowledge gap by evaluating the impact of SWC practices on water balance and soil erosion in the Debre Mawi watershed. The study covers two-period analyses: pre-conservation (2010–2014) and post-conservation (2015–2022) using the Soil and Water Assessment Tool (SWAT) to simulate hydrological water balance. Hydrological changes were assessed with the Indicators of Hydrological Alteration (IHA) software. Spatial and weekly sediment distribution were also computed. Results showed the SWAT effectively simulated stream flow, though sediment yield estimation was less accurate. The data demonstrated a reduction in surface runoff by 18% and a decrease in sediment yield by 75%. Conversely, evapotranspiration and groundwater storage experienced increases of 13% and 34%, respectively. The decrease in runoff and sediment can be attributed to the implementation of SWC structures with infiltration furrows, which are presently filled with sediment. Moreover, the expansion of eucalyptus tree acreage may deplete soil water during dry periods, thereby prolonging the time needed for the soil to become saturated and produce runoff, but the impact has yet to be quantified. The IHA analysis confirmed a decrease in mean annual flow from 0.06 m3/s to 0.02 m3/s, and sediment concentration decreased from 831.2 mg/l to 285 mg/l between the pre-and post-conservation periods. The study detected that soil erosion is higher than the allowable limits recommended for Ethiopia even after implementing SWCPs. Additionally, sediment transport reduced after the first three weeks due to improved ground cover and soil stability, although significant amounts were recorded until the end of the rainy season, primarily from gullies. The study found significant hydrological alterations in flow and sediment dynamics following the implementation of SWC practices, particularly pronounced in the early years post-conservation (2015–2018). However, the effectiveness of SWC practices diminished over time, with conditions beginning to revert to pre-conservation levels after 10 years. This suggests that these techniques (infiltration furrows) may be unsuitable for sub-humid watersheds, or that they require improved design and major maintenance beyond the third year. This study offers valuable insights into the dynamics of SWC interventions, underscoring the importance of integrating agronomic practices with SWC efforts to sustain long-term soil and water conservation in Ethiopia's sub-humid highlands. Future research should explore the hydrological effects of eucalyptus expansion and refine SWC practices suited to these unique conditions.

Assimilation of Sentinel‐Based Leaf Area Index for Modeling Surface‐Ground Water Interactions in Irrigation Districts

AbstractVegetation‐related processes, such as evapotranspiration (ET), irrigation water withdrawal, and groundwater recharge, are influencing surface water (SW)—groundwater (GW) interaction in irrigation districts. Meanwhile, conventional numerical models of SW‐GW interaction are not developed based on satellite‐based observations of vegetation indices. In this paper, we propose a novel methodology for multivariate assimilation of Sentinel‐based leaf area index (LAI) as well as in‐situ records of streamflow. Moreover, the GW model is initially calibrated based on water table observations. These observations are assimilated into the SWAT‐MODFLOW model to accurately analyze the advantage of considering high‐resolution LAI data for SW‐GW modeling. We develop a data assimilation (DA) framework for SWAT‐MODFLOW model using the particle filter based on the sampling importance resampling (PF‐SIR). Parameters of MODFLOW are calibrated using the parameter estimation (PEST) algorithm and based on in‐situ observation of the GW table. The methodology is implemented over the Mahabad Irrigation Plain, located in the Urmia Lake Basin in Iran. Some DA scenarios are closely examined, including univariate LAI assimilation (L‐DA), univariate streamflow assimilation (S‐DA), and multivariate streamflow‐LAI assimilation (SL‐DA). Results show that the SL‐DA scenario results in the best estimations of streamflow, LAI, and GW level, compared to other DA scenarios. The streamflow DA does not improve the accuracy of LAI estimation, while the LAI assimilation scenario results in significant improvements in streamflow simulation, where, compared to the open loop run, the (absolute) bias decreases from 75% to 6%. Moreover, S‐DA, compared to L‐DA, underestimates irrigation water use and demand as well as potential and actual crop yield.

Benefits and Pitfalls of GRACE and Streamflow Assimilation for Improving the Streamflow Simulations of the WaterGAP Global Hydrology Model

AbstractDistribution and change of freshwater resources is often simulated with global hydrological models. However, owing to process representation limitations and forcing data uncertainties, these model simulations have shortcomings. Combining them with observations via data assimilation, for example, with data from the Gravity Recovery and Climate Experiment (GRACE) mission or streamflow measured at in situ stations is considered to improve the realism of the simulations. We assimilate gridded total water storage anomaly (TWSA) from GRACE into the WaterGAP Global Hydrology Model (WGHM) over the Mississippi River basin via an Ensemble Kalman Filter. Our results agree with previous studies where assimilating GRACE observations nudges TWSA simulations closer to the observations, reducing the root mean square error (RMSE) by 21% compared to an uncalibrated model. However, simulations of streamflow show degeneration at more than 90% of all gauge stations for metrics such as RMSE and correlations; only the annual phase of simulated streamflow improves at half the stations. Therefore, for the first time, we instead assimilated streamflow observations into the WGHM, which improved simulated streamflow at up to nearly 80% of the stations, with normalized RMSE showing improvements of up to 0.1, while TWSA was well‐simulated in all metrics. Combining both approaches, that is, jointly assimilating GRACE‐derived TWSA and streamflow observations, leads to a trade‐off between a good fit of both variables albeit skewed to the GRACE observations. Overall, we speculate that our findings point to limitations of process representation in WGHM hindering consistent flux simulation from the storage history, especially in dry regions.

Advancing streamflow prediction in data-scarce regions through vegetation-constrained distributed hybrid ecohydrological models

Hybrid models that combine deep learning with physical principles have recently shown significant promise in improving streamflow prediction in data-scarce regions, achieving generally greater accuracy and reliability than either method alone. However, existing hybrid hydrological models underutilize the extensive and readily available remote sensing data on vegetation, which plays a critical role in hydrologic processes. Incorporating vegetation information can better constrain model processes by accounting for the interactions and feedbacks between vegetation and hydrology, thereby enabling more robust predictions. In this context, we present a novel distributed hybrid ecohydrological model that integrates dynamic vegetation processes into a distributed hybrid hydrological model, capable of simultaneously simulating leaf area index (LAI) and streamflow through multi-task learning. Our results indicate that in scenarios with limited available streamflow data, the combined constraint of LAI spatiotemporal patterns and streamflow significantly enhances the model’s ability to generalize streamflow simulation across space and time compared to models trained solely with either LAI or streamflow. Even with the increased availability of streamflow data, models utilizing both LAI and streamflow continue to exhibit superior robustness in streamflow prediction. The distributed ecohydrological model, which employs multi-process coupling and multi-source data constraints, accurately simulates the target variables (i.e., streamflow and LAI) while reliably capturing dynamic changes in other ecohydrological processes, such as evapotranspiration. This capability highlights its potential for multi-process diagnostics. Overall, our approach offers a new perspective for streamflow prediction in data-scarce regions and represents an important step towards integrating observational constraints from multiple subsystems into hybrid Earth system modeling

Comparison and integration of physical and interpretable AI-driven models for rainfall-runoff simulation

Precise streamflow forecasting in river systems is crucial for water resources management and flood risk assessment. The Tagus Headwaters River Basin (THRB) in Spain is a key hydrological hub, providing regulated flow for agricultural, urban, and energy sectors, and facilitating water transfer to the Segura River Basin, a key semi-arid region. Given the basin's near-total allocation of water resources, accurate streamflow simulations are essential to optimize the socio-economic distribution and ensure sustainable management across both interconnected basins. This study conducts a comprehensive evaluation of the Soil and Water Assessment Tool (SWAT+), support vector regression (SVR), feed forward neural network (FFNN), and long short-term memory (LSTM) models in simulating the rainfall-runoff process at four gauging stations within the THRB. An ensemble machine learning technique is then applied to assess improvements in streamflow estimation. Results revealed that AI-based models significantly surpassed the SWAT+ model in performance. Furthermore, the application of an ensemble technique enhanced the precision of rainfall-runoff modeling by 18 to 26% during the calibration period and 4.1 to 9.2% during the validation period, compared to individual AI-based models. Additionally, the SWAT+ model's precision improved by 44 to 74% and 40 to 55% for the respective periods. The use of Shapley Additive Explanations (SHAP) methodology allowed the results of the ensemble with machine learning to be more interpretable by explaining how each model contributes to the prediction. This research offers significant contributions to hydrological modeling, highlighting the importance of ensemble techniques in elevating predictive accuracy for various river basins.

New objective functions for streamflow calibrations to specific model applications

Most hydrological models require a calibration process, performed usually using objective functions that measure the differences between observed and simulated data. Objective functions are typically based on statistical metrics, such as the mean squared error (MSE), which penalizes extreme values in statistics, or mean absolute error (MAE), which assigns equal weight to each measure. Although the MSE is effective in capturing model prediction bias or variability, the calibration results with MSE-based objective functions tend to focus on high flow events at the expense of low flow errors. As different objective functions may emphasize different model response aspects, an appropriate objective function is critical to successful model calibration for model application suitability to a given task. The primary aim of this study is not to directly compare the MSE and MAE to determine which is more relevant to model performance. Instead, as an extended formulation for the Kling–Gupta efficiency (KGE), the new objective functions were designed to incorporate performance metrics based on both the MSE and MAE to evaluate model performance more comprehensively across different flow regimes. The case study has investigated the influence of the proposed objective functions on long-term daily streamflow simulations from a land surface model (LSM) over four study watersheds in the Republic of Korea. The proposed objective functions can help simulate streamflow for the specific purpose of model applications through trade-offs between the MSE- and MAE-based variability metrics in the constituent components. The proposed calibration method allows for a more balanced consideration of various flow regime aspects, compared with the results obtained using traditional objective functions.

Application of a Workflow to Determine the Feasibility of Using Simulated Streamflow for Estimation of Streamflow Frequency Statistics

Application of a Workflow to Determine the Feasibility of Using Simulated Streamflow for Estimation of Streamflow Frequency Statistics

Hydrological Evaluation of CRA40 and ERA5 Reanalysis Precipitation Products over Ganjiang River Basin in Humid Southeastern China

This study evaluates two reanalysis precipitation products (CRA40 and ERA5) over the Ganjiang River Basin with precipitation data from 37 ground rainfall gauges and surface-observed stream flow data from January 1998 to December 2008. Direct comparison with rain gauge observations shows that both CRA40 and ERA5 can capture the spatial and temporal characteristics of precipitation at the basin scale of the Ganjiang River and reflect most of the precipitation events, but there are pronounced differences in the quality of precipitation between them. ERA5 performs better on the daily scale, capturing precipitation changes more accurately over short periods of time, while CRA40 performs better on the monthly scale, providing more stable and long-term precipitation trends. The results of stream flow simulations using two reanalysis precipitation products driving the VIC hydrological model show that (1) CRA40 outperforms ERA5 with a better Nash–Sutcliffe Efficiency (NSE, 0.65 and 0.6) and higher CC (0.96 and 0.91) in daily and monthly scale stream flow simulations, and ERA5 has a good CC (0.86 and 0.93, respectively), but its NSE is poor (0.29 and 0.30, respectively); (2) both CRA40 and ERA5 generally overestimate basin stream flows, especially during the flood season (April–September), with ERA5’s overestimation being more pronounced. This study is expected to provide a basis for the selection of reliable reanalysis products for Ganjiang River Basin precipitation and hydrological simulation.

Estimating impacts of land cover change on erosion in a data-scarce catchment: Bot River, South Africa

ABSTRACT Rainfall intensity and land use changes and from natural vegetation to cultivated fields both impact on river streamflows and sediment dynamics. Assessing the magnitude of these flow and sediment changes is important for water management. However, many rivers are still under-studied/monitored and data sparse. Fully distributed rainfall–runoff models, such as the Jena Adaptable Modelling System (JAMS/J2000), allow for simulation of streamflow, its flow components and potential sediment yield given land use and hydrological changes. This study demonstrates the use of this method in the Bot River, a data-scarce catchment in South Africa. A hydrological model was implemented, and sediment yield was estimated under three land use and two hydrological flow component scenarios. The model reported Nash-Sutcliffe efficiencies (E2) of 0.64 and 0.32 (for streamflow) for model calibration and validation periods, respectively. Modelled mean annual sediment yield increased from 423 t/km2/year in 1990 to 490 t/km2/year in 2018, attributable to land use changes.

Enhancing ecohydrological simulation with improved dynamic vegetation growth module in SWAT

The Soil and Water Assessment Tool (SWAT) model has been widely used for simulating ecohydrological processes such as streamflow and evapotranspiration (ET) under different land use and climate conditions. The growth of vegetation is a crucial link in the ecohydrological process. However, the model uses a simplified Environmental Policy Impact Climate (EPIC) model, which has limitations in simulation of dynamic vegetation growth. Therefore, this study aims to enhance SWAT by incorporating the forest aging data and optimizing the dormancy and forest vegetation growth modules. The application of this modified model with a case study shows that the accuracy of leaf area index (LAI) simulation has significantly improved (coefficient of determination (R2) increased by 0.16, the Nash-Sutcliffe efficiency (NSE) increased from −0.18 to 0.87, and the absolute value of percent bias (PBIAS) decreased by 50.99%), and with moderate improvement in streamflow simulation (NSE increased by 0.03, PBIAS decreased by 4.60%). Furthermore, optimizing the dynamic growth model of vegetation led to significant increases in LAI (151.58%) and ET (5.25%) values in the forest area, while streamflow and water yield decreased by 4.28% and 4.16%, respectively, across the watershed. The results indicate that dynamic vegetation growth led to increased ET, resulting in a decrease in streamflow and water yield. Overall, our results suggest that the modified SWAT model effectively simulated the process of forest growth from young forest to mature stages, significantly improving the accuracy of simulated hydrological processes. This methodology can be applied to other watersheds to simulate ecohydrological processes and can be valuable in management of ecosystems and water resources at watershed scale.

Streamflow Simulation for Nam Xong River Basin in Vang Vieng District, Vientiane Province

Nam Xong River is one of the tributaries of Nam Lik River, which is considered an important river that creates natural tourism value for the people in Vang Vieng District. Nam Xong Dam was another dam built to generate electricity and divert water to Nam Ngum 1 reservoir to increase hydropower generation capacity. Recently, Nam Xong River Basin (NXRB) has been affected by flooding almost every year due to changes in the amount and pattern of rainfall which is highly variable. Another issue is aggravated by the problem of ungauged basins where the number of hydrological and meteorological stations are limited and unequally distributed over the basins. This obstructs the reliability of streamflow prediction and water resources management. This study therefore aims to address the problems of ungauged basins by performing tasks that serve a main objective: application of HEC-HMS for simulating the streamflow for NXRB. The hydrological and meteorological data used in this study are daily streamflow 2 stations and rainfall 4 stations located in and around the basin. The simulation period is considered based on random flood events from 2005 to 2021. Out of these, 5 events (2005-2015) are selected for model calibration and another event of 2021 are considered for model validation. The results found that the HEC-HMS model can estimate the flow rate in accordance with the observed data, especially during the low flow period. Throughout the considered period, the average flow and total flow volume showed a downward trend. For the result of estimating the maximum flow from the model, the streamflow is lower than the observed data, and the time to peak from the model comes about 1 day later than the observed one in 2014, probably due to the distribution of rainfall in the study area with high variability. Therefore, the HEC-HMS model could be used to simulate daily streamflow in limited data areas. This study could provide recommendations for hydrological modeling for other basins that have similar characteristics to the NXRB.

Enhancing streamflow simulations with gated recurrent units deep learning models in the flood prone region with low-convergence streamflow data

Highly accurate streamflow simulations are essential for water resources and river management. However, there are longstanding challenges for streamflow modeling in the hydrology field. As a result, deep learning methods have been introduced as novel tools and are being used in simulation. This paper proposes a novel approach that utilizes unconventional data preprocessing techniques to improve the accuracy of daily streamflow predictions in flood-prone regions. We applied this approach to a flood-prone area in western Iran and simulated daily streamflow using a deep learning Gated Recurrent Unit (GRU) method under various data selection scenarios. These scenarios focused on identifying the optimal combination of input variables, time steps, and outlier removal techniques. The outlier removal methods investigated in this study include Mahalanobis distance, critical section removal, Z-score, and no removal. The average rainfall of the area, data driven precipitation, Normalized Difference Vegetation Index (NDVI), surface soil moisture, groundwater baseflows, and streamflow in the hydrometric station were evaluated using correlation control method, and inputs with the lowest correlation were removed. Based on the results obtained from the deep learning models produced in the research, it was found that the GRU model, with various modified inputs using Z-score removal, had the best performance. The model had an average of Root Mean Squared Error (RMSE) at 5.24 mm and R2 (Coefficient of Determination) at 0.91 during training, while during validation, it had an RMSE of 7.74 mm and R2 of 0.83. Considering using relatively low convergence data in streamflow simulation in this study, it can be said that the listed scenarios showed an appropriate result in dealing with the data and recognizing the complex pattern of the daily streamflow, and the future studies will show the improvement of the GRU models if higher convergence data will be used.

Coupling SWAT and LSTM for Improving Daily Streamflow Simulation in a Humid and Semi-humid River Basin

Coupling SWAT and LSTM for Improving Daily Streamflow Simulation in a Humid and Semi-humid River Basin

Evaluation of hydrological models for streamflow prediction: a case study of the Mille River, Lower Awash Basin, Ethiopia

ABSTRACT Accurate prediction of stream flow is essential for effective water resource management, particularly in regions with complex hydrological dynamics. The Mille River, which flows through this basin and has a watershed area of 4,862.3 km², plays a crucial role in the region's water resources. However, selecting the most suitable hydrological model for this task remains a challenge due to varying model performances. This study addresses the problem of identifying the most effective hydrological model for predicting stream flow in the Mille River by evaluating three models: Soil and Water Assessment Tool (SWAT), Hydrologiska Byråns Vattenbalansavdelning (HBV-IHMS), and Artificial Neural Network (ANN). The objectives are to compare these models’ abilities to simulate daily stream flow and to determine their accuracy. The significance of this evaluation lies to guide water management decisions by identifying which model provides the most reliable predictions. The methodology involves using performance metrics, Nash-Sutcliffe Efficiency (NSE) values, to assess the accuracy of each model. The results show that SWAT achieved the highest NSE values of 0.81 and 0.82, indicating the best performance in simulating stream flow. ANN followed with NSE values of 0.79 and 0.81, while HBV-IHMS had lower NSE values of 0.73 and 0.74. Although all models demonstrated potential, SWAT and ANN outperformed HBV-IHMS in capturing the complex hydrological processes of the Mille River. The findings from this study indicate that the choice of model significantly impacts the accuracy and reliability of streamflow predictions. The SWAT and ANN models show strong potential for use in operational streamflow forecasting in the Mille River and similar basins. In conclusion, further research is recommended to refine model calibration and validation processes. Incorporating additional data sources, such as remote sensing and climate projections, could further enhance model accuracy and provide a more comprehensive understanding of the region's hydrological dynamics.

Comparative analysis of HEC-HMS and machine learning models for rainfall-runoff prediction in the upper Baro watershed, Ethiopia

ABSTRACT Accurate streamflow simulation is crucial for effective hydrological management, especially in regions like the upper Baro watershed, Ethiopia, where data scarcity challenges conventional modeling approaches. This study evaluates the efficacy of three hydrological models: the Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS), artificial neural network (ANN), and support vector regression (SVR) in predicting runoff. Using data from 2000 to 2016, the analysis focused on various performance metrics such as the Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), and coefficient of determination (R2). The results indicated that the ANN model significantly outperformed the others, achieving an NSE of 0.98, RMSE of 24 m3/s, and R2 of 0.99. In comparison, the HEC-HMS model yielded an NSE of 0.85, RMSE of 113.4 m3/s, and R2 of 0.89, while the SVR model displayed an NSE of 0.97, RMSE of 27 m3/s, and R2 of 0.99. These findings highlight the superior performance of ANN in regions with limited hydrological data, suggesting its potential as a reliable alternative to traditional physical models. By demonstrating the efficacy of machine learning models, this research facilitates the way for innovative approaches to water resource management, offering valuable insights for policymakers and practitioners.

Superiority of artificial neural networks over conventional hydrological models in simulating urban catchment runoff

ABSTRACT The synergistic impacts of climate change and urbanisation have amplified the recurrence and austerity of intense rainfall events, exacerbating persistent flooding risk in urban environments. The intricate topography and inherent non-linearity of urban hydrological processes limit the predictive accuracy of conventional models, leading to significant discrepancies in flow estimation. Recent advancements in artificial neural network (ANNs) have demonstrated remarkable progress in mitigating most limitations, specifically in simulating complex, non-linear relationships, without an intricate comprehension of the underlying physical processes. This paper proposes a deep learning ANN-based flow estimation model for enhanced precision simulation of streamflow in urban catchments, with the research's distinctive contribution involving rigorous comparative evaluation of the developed model against the established Australian hydrological model, RORB. Gardiners Creek catchment, an urban catchment situated in East Melbourne was designated as the study area, with the model being calibrated upon historical storm incidences. The findings reveal that the ANN model substantially outperforms RORB, as evidenced by superior correlation, prediction efficiency, and lower generalisation error. This underscores the ANN's adeptness in accurately replicating non-linear-catchment responses to storm events, marking a substantial advancement over conventional modelling practices and indicating its transformative potential for enhancing flood prediction precision and revolutionising current estimation practices.

A multi-variable calibration framework at the grid scale for integrating streamflow with evapotranspiration data to improve the simulation of distributed hydrological model

Study regionThe Ganjiang River Basin, China Study focusParameter calibration is crucial for the accurate and reliable operation of hydrological models. Traditional methods face challenges in calibrating spatially heterogeneous parameters of distributed hydrological models, and existing multi-variable calibration strategies often fall short in comprehensively improving model performance, particularly in streamflow simulations. To address these challenges, this study proposes a multi-objective calibration framework that integrates observed streamflow data and satellite-based evapotranspiration (ET) data. The spatiotemporal information of the merged ET is utilized to calibrate six hydrological parameters of the Variable Infiltration Capacity (VIC) model at the grid scale, enhancing hydrological simulations for the Ganjiang River basin. New hydrological insightsCompared to the benchmark scheme based solely on streamflow, the proposed calibration framework improves simulations of area-average ET at the sub-basin scale and soil moisture content in the Ganjiang River basin, without compromising the accuracy of daily streamflow simulations. Additionally, notable enhancements are observed in monthly streamflow simulations. This study provides a promising and comprehensive calibration framework using satellite-based data to constrain parameters and enhance the performance of distributed hydrological models.

Estimating exploitable groundwater for agricultural use under environmental flow constraints using an integrated SWAT-MODFLOW model

Traditionally, the impact of groundwater abstraction on streamflow and aquifers has been assessed primarily in terms of water availability and depletion, often neglecting critical environmental flow (Eflow) requirements. To address this gap, our study employed an Eflow perspective to evaluate exploitable groundwater for agricultural use at a watershed scale using an integrated modeling approach. We used the SWAT-MODFLOW model, integrating the Soil and Water Assessment Tool (SWAT) and the Modular Finite-Difference Flow Model (MODFLOW), to analyze water balance segments and stream-aquifer interactions in the study region. Eflow for different management classes was determined using the flow duration curve (FDC) approach. The FDC method classifies Environmental Flow Management Classes (EMC) from “A” (natural or minor alteration) to “F” (critically modified). The relationship between the selected EMC and simulated streamflow, under the pressure of agricultural groundwater pumping, served as a constraint in evaluating exploitable groundwater alongside groundwater level fluctuations. As the traditional approach depends on average recharge for determining exploitable groundwater, this study also assessed the spatiotemporal distribution of groundwater recharge. Simulation periods were aligned with agricultural pumping schedules. Results indicated that groundwater pumping significantly impacts streamflow at the start of the irrigation period. Under moderately affected EMC and with an average groundwater level fluctuation of one meter, the exploitable groundwater during the irrigation period can reach up to 2.96 Mm3/day. With this amount of pumping, the average groundwater discharge to streams declined by around 13 %. While groundwater level fluctuations near the watershed outlet were minimal, in the upstream area, levels could decrease by as much as 2.5 m. The groundwater level fluctuation underscores the necessity of accounting for both spatial and temporal factors, particularly in highly affected regions.