Hydrological Model Research Articles

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

Setting expectations for hydrologic model performance with an ensemble of simple benchmarks

Setting expectations for hydrologic model performance with an ensemble of simple benchmarks

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.

Performance of Loss Models for Predicting Flood Hydrographs in a Semiarid Watershed with Limited Observations Using Deterministic and Probabilistic Hydrologic Models

Performance of Loss Models for Predicting Flood Hydrographs in a Semiarid Watershed with Limited Observations Using Deterministic and Probabilistic Hydrologic Models

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

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

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.

Stormwater retention capacity of blue-green roofs with various configurations: Observational data and modelling

Although dynamic changes in stormwater retention capacity (SRC) determine overall stormwater retention performance of blue-green roofs, few previous studies have investigated the dynamic changes of SRC of blue-green roofs with various configurations. In this research, an experiment was designed to observe hydrological processes of blue-green roofs in Beijing. Based on the water balance theory, a conceptual hydrological model was developed by integrating a modified FAO-56 Penman-Monteith model, an AET formula with a water stress coefficient and Dalton’s equation. Then the proposed model was validated using experimental data. Results show that the model can simulate the SRC dynamics well, with the Nash-Sutcliffe and determination coefficients both exceeding 0.6. The consumption and recovery of the SRC in blue-green roofs are mainly driven by rainfall and actual evapotranspiration, respectively. As actual evapotranspiration decreases with seasonal changes, the fluctuation amplitude of SRC in blue-green roofs diminishes, ranging from 32.53 mm to 11.34 mm. Although increasing the depth of substrate and storage layers is able to enhance the SRC of blue-green roofs, there are optimal upper limits for these depth because of regional climate conditions. In Beijing, the optimal upper limits for substrate and storage layer depths are 300 and 30 mm, respectively. This information can assist designers in evaluating the cost-effectiveness of design choices for blue-green roofs. The developed model serves as an effective tool for simulating SRC and is expected to aid in the design of blue-green roofs.

Geostatistical Simulation of Daily Rainfall Fields—Performance Assessment for Extremes in West Africa

Abstract The spatial description of high-resolution extreme daily rainfall fields is challenging because of the high spatial and temporal variability of rainfall, particularly in tropical regions due to the stochastic nature of convective rainfall. Geostatistical simulations offer a solution to this problem. In this study, a stochastic geostatistical simulation technique based on the spectral turning bands method is presented for modeling daily rainfall extremes in the data-scarce tropical Ouémé River basin (Benin). This technique uses meta-Gaussian frameworks built on Gaussian random fields, which are transformed into realistic rainfall fields using statistical transfer functions. The simulation framework can be conditioned on point observations and is computationally efficient in generating multiple ensembles of extreme rainfall fields. The results of tests and evaluations for multiple extremes demonstrate the effectiveness of the simulation framework in modeling more realistic rainfall fields and capturing their variability. It successfully reproduces the empirical cumulative distribution function of the observation samples and outperforms classical interpolation techniques like ordinary kriging in terms of spatial continuity and rainfall variability. The study also addresses the challenge of dealing with uncertainty in data-poor areas and proposes a novel approach for determining the spatial correlation structure even with low station density, resulting in a performance boost of 9.5% compared to traditional techniques. Additionally, we present a low-skill reference simulation method to facilitate a comprehensive comparison of the geostatistical simulation approaches. The simulations generated have the potential to provide valuable inputs for hydrological modeling.

Evapotranspiration and Rainfall Effects on Post‐Storm Salinization of Coastal Forests: Soil Characteristics as Important Factor for Salt‐Intolerant Tree Survival

AbstractFlooding and salinization triggered by storm surges threaten the survival of coastal forests. After a storm surge event, soil salinity can increase by evapotranspiration or decrease by rainfall dilution. Here we used a 1D hydrological model to study the combined effect of evapotranspiration and rainfall on coastal vegetated areas. Our results shed light on tree root uptake and salinity infiltration feedback as a function of soil characteristics. As evaporation increases from 0 to 2.5 mm/day, soil salinity reaches 80 ppt in both sandy and clay loam soils in the first 5 cm of soil depth. Transpiration instead involves the root zone located in the first 40 cm of depth, affecting salinization in a complex way. In sandy loam soils, storm surge events homogeneously salinize the root zone, while in clay loam soils salinization is stratified, partially affecting tree roots. Soil salinity stratification combined with low permeability maintain root uptakes in clay loam soils 4/5‐time higher with respect to sandy loam ones. When cumulative rainfall is larger than potential evapotranspiration ETp (ETp/Rainfall ratios lower than 1), dilution promotes fast recovery to pre‐storm soil salinity conditions, especially in sandy loam soils. Field data collected after two storm surge events support the results obtained. Electrical conductivity (a proxy for salinity) increases when the ratio ETp/Rainfall is around 1.76, while recovery occurs when the ratio is around 0.92. In future climate change scenarios with higher temperatures and storm‐surge frequency, coastal vegetation will be compromised, because of soil salinity values much higher than tolerable thresholds.

Impacts of LULC changes on runoff from rivers through a coupled SWAT and BiLSTM model: A case study in Zhanghe River Basin, China

Changes in river runoff have a significant impact on the sustainable use of water resources in a watershed, and these changes are closely linked to variations in land use/land cover (LULC). This research explores an innovative approach in the Zhang River Basin (ZRB), China, by coupling a concept-based hydrological model, the Soil and Water Assessment Tool (SWAT), with a deep-learning model, the Bidirectional Long Short-Term Memory Network (Bi-LSTM), to improve the accuracy of river runoff simulations. By analyzing LULC changes in 2002, 2012, and 2022, this study developed three SWAT models and three coupled SWAT-BiLSTM models to quantitatively assess the impacts of these changes on river runoff through eight LULC scenarios. The findings revealed significant LULC changes from 2002 to 2022, with cropland and grassland areas decreasing while forest and urban land areas increased. The total area of grassland, forest, and cropland made up over 93 % of the basin, indicating active land type conversions. Calibration and validation results demonstrated that the SWAT-BiLSTM model outperformed the conventional SWAT model, yielding higher accuracy in runoff simulations. Specifically, the SWAT-BiLSTM model achieved R2 values of 0.89 and 0.90 during calibration and validation, compared to the SWAT model's R2 values of 0.76 and 0.79. Scenario analyses indicated that expansions in farmland, grassland, and urban areas were correlated with increased river runoff, while an expansion in forested areas led to reduced runoff. Notably, urban land changes had the most pronounced impact on runoff, emphasizing the need for careful runoff management and flood risk mitigation in urban planning. By combining SWAT and Bi-LSTM models, this study provides an innovative assessment of the impact of LULC changes on water resources in the ZRB. The results offer valuable insights for water resource management, LULC optimization, and flood risk management, highlighting the potential application of deep learning techniques in hydrological simulation. This research serves as a scientific basis for policy-making and sustainable land use planning in the ZRB and similar regions.

Assessing Future hydrological response of an urban watershed using machine learning based LULC forecasting models

Urbanization in terms of land-use/cover (LULC) change has a long-term significant impact on the hydrological cycle as the LULC is one of the most important influencing parameters to produce curve number (CN). The drastic change in LULC changes the CN. This change directly affects surface water including peak flows. This study aims to assess the change in surface runoff due to changes in LULC. Hydrological modeling is done for the consistent long-term behavioral study of surface runoff. The area focused on the study is the Kharun river, a tributary of the Mahanadi River. For the assessment of the impact of LULC change on the catchment discharge, a daily-step conceptual model soil and water assessment tool (SWAT) was applied. Landuse maps were prepared with the Landsat Thematic Mapper satellite images. The future land use was forecasted with the technique of spatial statistical modeling. The machine learning (ML) tool is used for quantifying the influences on the LULC change dynamics and producing the LULC map for 2024 and 2030. Remote sensing (RS) and geographic information system (GIS) analysis were coupled with hydrological SWAT modeling to investigate the connection between the LULC change and hydrologic regime. The SWAT Model’s calibration efficiency is verified by comparing the simulated and observed discharge time series at the Patharidih gauge and discharge station. The monthly and daily calibrations were quite satisfactory, with Nash-Sutcliffe an efficiency coefficient of 0.86 and 0.67. This modeling provides reliable information for sustainable management of available water resources of the catchment.

Downscaling, bias correction, and spatial adjustment of extreme tropical cyclone rainfall in ERA5 using deep learning

Hydrological models that are used to analyse flood risk induced by tropical cyclones often input ERA5 reanalysis data. However, ERA5 precipitation has large systematic biases, especially over heavy precipitation events like Tropical Cyclones, compromising its usefulness in such scenarios. Few studies to date have performed bias correction of ERA5 precipitation and none of them for extreme rainfall induced by tropical cyclones. Additionally, most existing works on bias adjustment focus on adjusting pixel-wise metrics of bias, such as the Mean Squared Error (MSE). However, it is equally important to ensure that the rainfall peaks are correctly located within the rainfall maps, especially if these maps are then used as input to hydrological models. In this paper, we describe a novel machine learning model that addresses both gaps, RA-Ucmpd, based on the popular U-Net model. The key novelty of RA-Ucmpd is its loss function, the compound loss, which optimizes both a pixel-wise bias metric (the MSE) and a spatial verification metric (a modified version of the Fractions Skill Score). Our results show how RA-Ucmpd improves ERA5 in almost all metrics by 3-28%—more than the other models we used for comparison which actually worsen the total rainfall bias of ERA5—at the cost of a slightly increased (3%) error on the magnitude of the peak. We analyse the behaviour of RA-Ucmpd by visualizing accumulated maps of four particularly wet tropical cyclones and by dividing our data according to the Saffir-Simpson scale and to whether they made landfall, and we perform an error analysis to understand under what conditions our model performs best.

Daily river flow simulation using ensemble disjoint aggregating M5-Prime model

Accurate prediction of daily river flow (Qt) remains a challenging yet essential task in hydrological modeling, particularly crucial for flood mitigation and water resource management. This study introduces an advanced M5 Prime (M5P) predictive model designed to estimate Qt as well as one- and two-day-ahead river flow forecasts (i.e. Qt+1 and Qt+2). The predictive performance of M5P ensembles incorporating Bootstrap Aggregation (BA), Disjoint Aggregating (DA), Additive Regression (AR), Vote (V), Iterative classifier optimizer (ICO), Random Subspace (RS), and Rotation Forest (ROF) were comprehensively evaluated. The proposed models were applied to a case study data in Tuolumne County, US, using a dataset comprising measured precipitation (Pt), evaporation (Et), and Qt. A wide range of input scenarios were explored for predicting Qt, Qt+1, and Qt+2. Results indicate that Pt and Qt significantly influence prediction accuracy. Notably, relying solely on the most correlated variable (e.g., Qt-1) does not guarantee robust prediction of Qt. However, extending the forecast horizon mitigates the influence of low-correlation input variables on model accuracy. Performance metrics indicate that the DA-M5P model achieves superior results, with Nash-Sutcliff Efficiency of 0.916 and root mean square error of 23 m3/s, followed by ROF-M5P, BA-M5P, AR-M5P, AR-M5P, RS-M5P, V-M5P, ICO-M5P, and the standalone M5P model. The ensemble M5P modeling framework enhanced the predictive capability of the stand-alone M5P algorithm by 1.2 %–22.6 %, underscoring its efficacy and potential for advancing hydrological forecasting.

Selecting Suitable Sites for Rainwater Harvesting using GIS & RS Technology in Port Sudan City, Sudan Through Hydrological Modeling

Selecting Suitable Sites for Rainwater Harvesting using GIS & RS Technology in Port Sudan City, Sudan Through Hydrological Modeling

ВЛИЯНИЕ СОВРЕМЕННОГО ИЗМЕНЕНИЯ КЛИМАТА НА ИСПАРЕНИЕ С ВОДНОЙ ПОВЕРХНОСТИ В ИЛЕ-БАЛКАШСКОМ БАССЕЙНЕ

Using the Ile-Balkash basin as an example, changes in evaporation from the water surface over the last 40 years, as well as changes in the main meteorological elements that affect evaporation, are considered. Within the framework of this work, the changes in evaporation from the water surface for 1996-2020 in relation to the previous period of 1980-1995 were assessed. It was established that over the last 20 years, there was a slight increase in evaporation at many lowland stations of the Ile-Balkash basin and it is about 1-10 %. In mountainous areas and near large water areas, such as Balkash and Ulken Almaty Lakes, there is a decrease in evaporation by about 2-9 %. The main factors influencing the change in evaporation values from the water surface were analyzed. It was concluded that the increase in evaporation from the water surface at the lowland stations in the Ile-Balkash basin is mainly due to the increase in air temperature and some increase in precipitation, and the decrease in evaporation in mountainous areas and near a water area is caused by the decrease of average wind speed and an insignificant increase in air humidity. This study can help support decision making in the agriculture and water resources sector, since evaporation from the water surface is involved in many hydrological, climatic and hydrodynamic models. Therefore, the obtained results can be used in many scientific calculations.

Climate change impacts on the hydrological behaviour of watershed in northeastern Tunisia (Oued El Abid watershed)

Climate change significantly impacts watershed hydrology, altering water supplies, natural disasters, and eco-hydrologic processes. These effects vary geographically, with some regions facing severe droughts while others experience increased precipitation and flooding. Climate change influences atmospheric evaporation demand, precipitation patterns, vegetation composition, streamflow characteristics, and groundwater dynamics. The temperature increases and rainfall changes in some watersheds can lead to increased potential evapotranspiration and decreased streamflow and soil water. Flood frequency analyses indicate an overall increasing trend in the latter half of the 21st century. The Oued El Abid catchment area, as like all catchments in northern Tunisia, is likely to be subject to major climatic and hydrological variations as a result of climate change. Advanced hydrological modelling using the HBV-Light model was set up to simulate current conditions and future scenarios based on Afric-Cordex climate models. The model demonstrates robust performance, achieving Nash-Sutcliffe Efficiency (NSE) values of 0.71 in calibration (1972-1992), and 0.66 in validation from 1993-2001. This climate change has a direct effect on the hydrological projections, which reveal significant changes, with annual flow reductions ranging from 35% to 43%, with more severe impacts under the RCP8.5 scenario for 2069-2099. Spring and summer seasons are particularly vulnerable, showing flow decreases of up to 68% and over 40%, respectively, extreme monthly variability is projected, reaching 88% reduction in July under RCP8.5. This research provides important results for water resource managers, highlighting the necessity of long-term planning and sustainable water management practices in the face of climate change.

Landscape metrics as predictors of water-related ecosystem services: Insights from hydrological modeling and data-based approaches applied on the Arno River Basin, Italy

The study addresses the challenge of integrating complex landscape-hydrological interactions into predictive models for improved water resource management. The aim is to investigate the effectiveness of landscape metrics—quantitative indices measuring landscape composition and configuration—as predictors of WES in the Arno River Basin, Italy. Utilizing two hydrological models alongside a random forest algorithm, we assessed spatial and temporal variations in water yield, runoff, and groundwater recharge. The findings indicate that landscape metrics derived from high-resolution land use data significantly impact WES outcomes. Specifically, the models demonstrated average landscape metric importances of 16.8 % for spatial and 17.8 % for temporal predictions concerning runoff. For water yield, these averages were 32.9 % spatially and 43.5 % temporally, while groundwater modeling showed importances of 14.09 % spatially and 33.8 % temporally. Key landscape metrics identified include the core area index for broad-leaved forests and the perimeter-to-area ratio for non-irrigated agricultural areas as critical spatial and temporal predictors of water yield and groundwater recharge. Thresholds were observed, indicating landscape configurations that minimize hydrological variability. For instance, runoff variation is minimal when the landscape exhibits high forest fragmentation (over 1000 coniferous patches), low aggregation (aggregation index <75), and reduced connectivity (cohesion index under 80). Similarly, groundwater variation is minimized with decreased boundary length of vegetation patches (perimeter-to-area ratio <0.8), agricultural lands (perimeter-to-area ratio under 1), and the presence of low core agricultural areas (core area index above 8). The identified thresholds could inform land-use policies, such as targeted afforestation or crop diversification strategies, to optimize WES provision.

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.

Using national hydrologic models to obtain regional climate change impacts on streamflow basins with unrepresented processes

Climate change is increasingly impacting water availability. National-scale hydrologic models simulate streamflow resulting from many important processes, but often without processes such as human water use and management activities. This work explores and tests methods to account for such omitted processes using one national-scale hydrologic model. Two bias correction methods, Flow Duration Curve (FDC) and Auto-Regressive Integrated Moving Average (ARIMA), are tested on streamflow simulated by the US Geological Survey National Hydrologic Model (NHM-PRMS), which omits irrigation pumping. A semi-arid agricultural case study is used. FDC and ARIMA perform better for correcting low and high flows, respectively. A hybrid method performs well at both low and high flows; typical Nash-Sutcliffe values increased from <-1.00 to about 0.75. Results suggest methods with which national-scale hydrologic models can be bias-corrected for omitted processes to improve regional streamflow estimates. Utility of these correction methods in simulation of future projections is discussed.

Evaluating land use and climate change impacts on Ravi river flows using GIS and hydrological modeling approach

The study investigates the interplay of land use dynamics and climate change on the hydrological regime of the Ravi River using a comprehensive approach integrating Geographic Information System (GIS), remote sensing, and hydrological modeling at the catchment scale. Employing the Soil and Water Assessment Tool (SWAT) model, simulations were conducted to evaluate the hydrological response of the Ravi River to both current conditions and projected future scenarios of land use and climate change. This study differs from previous ones by simulating future land use and climate scenarios, offering a solid framework for understanding their impact on river flow dynamics. Model calibration and validation were performed for distinct periods (1999–2002 and 2003–2005), yielding satisfactory performance indicators (NSE, R2, PBIAS = 0.85, 0.83, and 10.01 in calibration and 0.87, 0.89, and 7.2 in validation). Through supervised classification techniques on Landsat imagery and TerrSet modeling, current and future land use maps were generated, revealing a notable increase in built-up areas from 1990 to 2020 and projections indicating further expansion by 31.7% from 2020 to 2100. Climate change projections under different socioeconomic pathways (SSP2 and SSP5) were derived for precipitation and temperature, with statistical downscaling applied using the CMhyd model. Results suggest substantial increases in precipitation (10.9 − 14.9%) and temperature (12.2 − 15.9%) across the SSP scenarios by the end of the century. Two scenarios, considering future climate conditions with current and future land use patterns, were analyzed to understand their combined impact on hydrological responses. In both scenarios, inflows to the Ravi River are projected to rise significantly (19.4 − 28.4%) from 2016 to 2100, indicating a considerable alteration in seasonal flow patterns. Additionally, historical data indicate a concerning trend of annual groundwater depth decline (0.8 m/year) from 1996 to 2020, attributed to land use and climate changes. The findings underscore the urgency for planners and managers to incorporate climate and land cover considerations into their strategies, given the potential implications for water resource management and environmental sustainability.