Peak Discharge Research Articles

Investigation the Impact of the Climate Change on Intensity Duration Frequency (IDF) Curve Development: Case Study at Hulu Terengganu, Malaysia

The intensity-duration-frequency (IDF) curves are the most common form of design rainfall data used for peak discharge estimation. Thus, the IDF curve needs to be improved with the expectation that rainfall intensity and frequency have increased as a result of climate change. The main purpose of this study was to investigate the changes of IDF curves considering the climate change impacts on Hulu Terengganu. The climate projection from MRI-ESM2-0, CMCC-CM2-SR5, and GFDL-ESM4 under 3 different scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) were used to provide the climate changes pattern in the future year (∆2050). In order to downscale the climate projection, the statistical downscaling method (SD-LS) was employed to correct the biases of these three GCMs. The IDF curves for the return periods of 2, 5, 10, 20, 50, 100, and 200 year were then developed based on the maximum rainfall intensity that were projected by the SD-LS model. The results clearly indicated that there are possibilities for increasing patterns in the projected annual and monthly rainfall for both time periods compared to historical data. Thus, the future extreme rainfall events for various durations with different return periods are all likely to increase over time. The largest potential increase is predicted at Sg. Gawi (+2.0% to +86.0%) based on the different return periods and rainfall durations. It could change the pattern of IDF curve that been developed based on projected rainfall by various SSPs. The developed IDF curves shows higher rainfall intensities in a shorter duration under the same return periods. Therefore, comprehensive action must be taken immediately to regulate and manage the effects of climate change.

Continental Scale Regional Flood Frequency Analysis: Combining Enhanced Datasets and a Bayesian Framework

Flood frequency analysis at large scales, essential for the development of flood risk maps, is hindered by the scarcity of gauge flow data. Suitable methods are thus required to predict flooding in ungauged basins, a notoriously complex problem in hydrology. We develop a Bayesian hierarchical model (BHM) based on the generalized extreme value (GEV) and the generalized Pareto distribution for regional flood frequency analysis at high resolution across a large part of North America. Our model leverages annual maximum flow data from ≈20,000 gauged stations and a dataset of 130 static catchment-specific covariates to predict extreme flows at all catchments over the continent as well as their associated statistical uncertainty. Additionally, a modification is made to the data layer of the BHM to include peaks over threshold flow data when available, which improves the precision of the discharge level estimates. We validated the model using a hold-out approach and found that its predictive power is very good for the GEV distribution location and scale parameters and improvable for the shape parameter, which is notoriously hard to estimate. The resulting discharge return levels yield a satisfying agreement when compared with the available design peak discharge from various government sources. The assessment of the covariates’ contributions to the model is also informative with regard to the most relevant underlying factors influencing flood-inducing peak flows. According to the developed aggregate importance score, the key covariates in our model are temperature-related bioindicators, the catchment drainage area and the geographical location.

Riverine 129I dynamics during high-flow events on the Abukuma River in Fukushima

This study presents the riverine 129I concentrations and fluxes during high-flow events. The river water samples obtained by a previous study were subjected to 129I analyses. River water samples were collected at the midstream of the Abukuma River for five and seven times during high-flow events in July 2018 and October 2018, respectively. Suspended solids and filtered water samples were measured for 129I/127I ratios using an accelerator mass spectrometer system and for 127I concentrations using ICP-MS. Aggregated mean values of dissolved 129I concentration and 129I concentrations in suspended solids were 0.11 μBq/L (n = 12) and 0.60 mBq/kg (n = 12), respectively. Corresponding values of 129I/127I ratios were in 2.2 × 10−9 (n = 12) and 3.7 × 10−9 (n = 12), respectively. These concentrations and ratios were comparable to those at the background level before the Fukushima Dai-ichi nuclear power plant accident. Positive correlations with dissolved 137Cs and Cl− concentrations suggested that the dissolved 129I concentration decreased due to dilution of the river water during the events. A positive correlation with total carbon content suggested that organic matter contents determine 129I concentrations in suspended solids. The total 129I fluxes during JUL18 and OCT18 were 9.9 × 103 and 2.2 × 104 Bq, respectively. Although dissolved 129I was predominant under low-flow conditions, the particulate 129I flux increased by one or two orders of magnitude during the peak water discharge phases. These results underline the importance of particulate 129I dynamics during high-flow events for quantitative evaluation of the 129I cycle.

Prediction of flash flood peak discharge in hilly areas with ungauged basins based on machine learning

ABSTRACT Peak discharge is an essential element of hydrological forecasting. Due to rapid outbreaks of flash floods in hilly areas and the lack of measured data, the fast and accurate estimation of peak discharge is crucial for flash flood hazard management. Three machine learning algorithms were applied to estimate peak discharge; this estimation was compared with the results of hydrological–hydraulic models, and the results were verified with measured watershed data. In this paper, 10 hydrological and geomorphological parameters were selected to predict the flood peak discharge in 103 watersheds in Taiyi Mountain North District. The results show that the particle swarm optimization backpropagation (PSO-BP) neural network model outperforms the BP neural network and random forest regression in prediction performance. PSO-BP has a lower mean absolute error (2.51%), root mean square error (3.74%), and mean absolute percentage error (2.74%) than the other models, which indicates that PSO-BP has high prediction accuracy. Importance analysis revealed that rainfall, early impact rainfall, catchment area, and rain intensity are the key input parameters of PSO-BP. The proposed method was confirmed to be a fast and relatively accurate algorithm for estimating the peak discharge of flash floods in ungauged basins.

Water Tales from Turkistan: Challenges and Opportunities for the Badam-Sayram Water System under a Changing Climate

The Badam River, a tributary to the Arys River located in the Syr Darya basin, is a crucial natural resource for ecological, social, and economic activities in the semi-arid region of southern Kazakhstan. The river basin is heavily influenced by manmade water infrastructure and faces water scarcity, particularly during summer, highlighting the importance of understanding its hydrological processes for effective water resource management. In this study, a semi-distributed conceptual hydrological model of the Badam River was implemented using the RS MINERVE hydrological software to evaluate the impacts of climate change on hydrology and to test the resilience of the water system. Connected HBV models were implemented for each of the hydrological response units that were defined as altitudinal zones. The hydrological model was calibrated using daily time steps between 1979 and 2011, and the resulting flow exceedance curves and hydrographs were used to assess the potential impacts of climate change on the basin, using CMIP6 precipitation and temperature scenarios. Future climate scenarios for the 2054 – 2064 period demonstrate that the peak discharge will be shifted to spring/late spring compared to the current early summer with no significant decrease in average discharge per day of the year. The insights gained from this hydrological-hydraulic model can be used to effectively manage the water system and inform future hydropower design decisions and serve as a blueprint for similar studies in the region and elsewhere.

A Robust Quantitative Method to Distinguish Runoff‐Generated Debris Flows From Floods

AbstractDebris flows and floods generated by rainfall runoff occur in rocky mountainous landscapes and burned steeplands. Flow type is commonly identified post‐event through interpretation of depositional structures, but these may be poorly preserved or misinterpreted. Prior research indicates that discharge magnitude is commonly amplified in debris flows relative to floods due to volumetric bulking and increased frictional resistance. Here, we use this flow amplification to develop a metric (Q*) to separate debris flows from floods based on the ratio of observed peak discharge to the theoretical maximum water discharge from rainfall runoff. We compile 642 observations of floods and debris flows and demonstrate that Q* distinguishes flow type to ∼92% accuracy. Q* allows for accurate identification of debris flows through simple channel cross‐section surveys rather than through qualitative interpretation of deposits, and therefore should increase the performance of models and engineered structures that require accurate flow‐type observations.

Prediction of runoff characteristics in permeable pavements using experimental data and intelligent models.

Considering the growing use of permeable pavements, the prediction of runoff passing through this pavement model is of considerable importance. The prediction of rainfall-runoff relationships can be a challenge because of several factors including data uncertainty, non-linear relationships, and high temporal and spatial variability. To deal with these challenges, intelligent algorithms are often used to predict such complex phenomena. In this research, runoff control parameters were investigated in two types of permeable pavements (permeable interlocking concrete pavement (PICP) and high strength clogging resistant permeable pavement (CRP)) using support vector machine (SVM), support vector machine-bat (SVM-BA) and support vector machine-grasshopper (SVM-GOA). Variables used in the models included percentage of coverage by permeable pavement (A), rainfall intensity (I), slope (S), and pavement type coefficient (K) as input data, and runoff coefficient (C), time to runoff (Tr), and peak discharge (Qp) as output data. In this research, from the total of 108 data extracted from the experimental results, 86 data were used in the training period, and 22 data were used in the test period. The results of the test period show that the SVM-BA model has the best performance with values of MAE = 0.010 in predicting C, MAE = 1.330 min in predicting Tr, and MAE = 0.029 lit/min in predicting Qp. The SVM-GOA model is ranked second with values of MAE = 0.051 in predicting C, MAE = 3.285 min in predicting Tr, and MAE = 0.097 lit/min in predicting Qp. Also, the SVM model is ranked third with values of MAE = 0.063 in predicting C, MAE = 4.470 min in predicting Tr, and MAE = 0.121 lit/min in predicting Qp. In summary, the SVM-BA algorithm showed the best performance and the SVM algorithm showed the weakest performance in predicting runoff characteristics in permeable pavements.

Landscape Planning for the Preservation of Cisadane Sub-Watershed in Situgede Area with Bioregion-based

Abstract Situ has an important role in controlling the water system by restraining the inflow hydrograph and reducing the peak discharge of the outflow. Situgede watershed, part of the Cisadane Sub-watershed consists of several Sub Catchment, namely Situ Gede, Situ Panjang, and Situ Burung, that can threaten the water system’s sustainability. Water preservation in the Situgede Sub-watershed is needed as an effort to maintain the existence and sustainability in terms of the balance and function of water resources. The availability of water resources in sufficient quantity and quality for human needs, both now and in the future must be guaranteed. This study aims to develop a landscape plan of the Situgede watershed based on Bioregion. Landscape planning is developed for each bioregion unit, especially place units, based on the direction of the small watershed zoning concept. Furthermore, based on small watershed spatial plans (zoning), landscape plans can be produced and derivative a hardscape plan, green plan, infrastructure, and utility plans. Based on bioregion-based zonation (place units) can be developed landscape planning into three zone (water management, buffer and development). Landscape plan can be prepared for the preservation of the Cisadane Sub-watershed in the Situgede Area which consists of a water body of 9.05 ha (3.92%), WWTP of 0.2 ha (0.09% ), green space in the form of research forests and greenbelts of 39.95 ha (17.29%), residential areas and public facilities of 34.48 ha (14.93%), agriculture of 131.9 ha (57.1%), agricultural buffer area of 131.9 ha (57.1%), water harvesting ponds of 4.32 ha (1.87%) in units 5-8 and 14-17, bioswale and other open space of 4 ha (1.73%). Moreover, based on the landscape plan, thematic plans can be derived, namely: hardscape plan, green plan, infrastructure, and utility plans.

Impact of climate change on major floods flowing into the Georges River estuary, Australia

Coastal flooding induced by storm surges and heavy rainfall is one of the most frequent climate-related natural hazards along the southeast Australian coast, home to more than 55% of the Australian population. Flooding in this densely populated region is a threat to public safety, coastal infrastructure, ecological systems and the economy. Although climate change is expected to cause an increase in major floods, few studies have quantified the potential changes in flood severity. This study quantifies the changes in flood peak discharge flowing to the Georges River estuary in Australia due to climate-change. An event-based hydrological model, Watershed Bounded Network Model (WBNM), was used to predict flood discharge. This hydrological model was forced by rainfall data obtained from the New South Wales and Australian Capital Territory Regional Climate Modelling Project version 1.5 (NARCliM1.5) for both historical and the Representative Concentration Pathway 8.5 (RCP8.5) conditions. Model calibration for the floods of March 1978 and March 2022 achieved a general agreement between the predicted and observed hydrographs, with an overall average 14% error in the peak values, further demonstrating that the modelling approach is generally reliable in projections of flood severity. Using high resolution climate model projections, the present study observed an increase of 22% in the model ensemble average from historical conditions to the RCP8.5 scenario for the 20-year average recurrence interval (ARI) 24 h extreme rainfall. This heightened extreme rainfall consequently resulted in the changes in flood discharge with an average rise of 55%. This study provides specific assessment of climate-generated risks for densely-populated regions, especially those on Australian east coast. Global studies have suggested that extreme precipitation events will increase under climate change. This study supports and enhances these assertions by using high resolution downscaling to quantify the specific changes within a large catchment.

Testing the Feasibility of an Agent-Based Model for Hydrologic Flow Simulation

Modeling streamflow is essential for understanding flow inundation. Traditionally, this involves hydrologic and numerical models. This research introduces a framework using agent-based modeling (ABM) combined with data-driven modeling (DDM) and Artificial Intelligence (AI). An agent-driven model simulates streamflow and its interactions with river courses and surroundings, considering hydrologic phenomena related to precipitation, water level, and discharge as well as channel and basin characteristics causing increased water levels in the Medio River. A five-year dataset of hourly precipitation, water level, and discharge measurements was used to simulate streamflow. The model’s accuracy was evaluated using statistical metrics like correlation coefficient (r), coefficient of determination (R2), root mean squared error (RMSE), and percentage error in peak discharge (Qpk). The ABM’s simulated peak discharge (Qpk) was compared with the measured peak discharge across four experimental scenarios. The best simulations occurred in scenario 3, using only rainfall and streamflow data. Data management and visualization facilitated input, output, and analysis. This study’s ABM combined with DDM and AI offers a novel approach for simulating streamflow and predicting floods. Future studies could extend this framework to other river basins and incorporate advanced sensor data to enhance the accuracy and responsiveness of flood forecasting.

Modeling peak discharge on the Siret River (Romania)

Modeling peak discharge on the Siret River (Romania)

Restored vegetation dominates the decrease in surface and subsurface runoff on the Loess Plateau

Understanding the runoff generation response to environmental changes at the basin scale can provide insights into the evolution of hydrological processes. The objective of this study is to detect and attribute the changes in surface and subsurface runoff in the Chinese Loess Plateau, a region with significant environmental and hydrological changes in the past several decades, to understand how and to what extent the runoff generation can be changed. Taking 13 basins (comprising 56 hydrological stations) on the Chinese Loess Plateau as the study area, we identified rainfall-runoff events and established indicators (event surface runoff coefficient, event timescale, normalized event peak discharge, baseflow, and baseflow index) to characterize the runoff generation, and further attributed the changes in runoff generation characteristics before (1961–2000) and after (2001–2019) the revegetation project. In particular, we inversely estimated the parameters of Soil Conservation Service Curve Number (SCS-CN) to analyze the initial abstraction and potential maximum retention under changing environment. Surface and subsurface runoff significantly decreased, with the surface runoff coefficient and baseflow decreasing by 34 % ± 24 % and 26 % ± 23 %, respectively. The surface runoff hydrograph became flatter with a 75 % decrease in the normalized event peak discharge and 82 % increase in the timescale of surface runoff, respectively. Normalized Difference Vegetation Index (32 %) and population (34 %) dominated the increase in the potential maximum retention, with 84 % of stations experiencing growth exceeding 100 %. The reduction in surface runoff can be primarily attributed to the enhanced soil infiltration due to revegetation and other human activities. Higher evapotranspiration resulting from revegetation was likely the dominant factor leading to the decrease in subsurface runoff. This study detailed the impacts of revegetation on surface and subsurface runoff generation, which can enhance our understanding of hydrological changes.

The role of estuarine convergence on the salinity distribution and the estuary response to short river discharge pulses

Estuarine behaviour is primarily controlled by the interaction of river discharge and tidal mixing. Transient increases in river discharge, which occur naturally during the wet season or can be managed in regulated basins, alter estuarine dynamics and reduce salt intrusion. Two characteristic times have been identified in the response to these river pulses: the adjustment time (the time required to adapt to the peak discharge) and the recovery time (the time required to return to steady state conditions). Previous work has analysed the effect of peak discharge and pulse duration in the adjustment process, focusing on long discharges (i.e. longer than the intrinsic estuarine timescale) and straight channels. This paper presents an idealised 3-dimensional model (Delft3D) to assess the effect of estuarine convergence on the response to short river pulses in tide dominated, well-mixed estuaries. The similarity between the tidal period, the estuarine timescale and the duration of the pulse led to the introduction of the time between tidal and river conditions as an additional parameter in the analysis. The results show that salt intrusion in convergent estuaries have a lower sensitivity to variations in river discharge compared to the behaviour of prismatic estuaries. Conversely, the influence of convergence on changes in salt intrusion due to the increased river discharge gains relevance in convergent estuaries. Seaward transport of salt is significantly enhanced by freshwater releases that occur between early and mid ebb, especially during spring tides. Increasing convergence reduces the asymmetry of the estuarine response, shortens the recovery time, and shifts the onset of maximum salt displacement and the minimum adjustment time. The results are particularly relevant for water management and salt intrusion control in regulated basins with limited resources, as the volume of fresh water used to reduce salt intrusion can be optimised considering the timing of the pulse release, which depends on the tidal conditions at the estuary.

Evaluation and Comparison of Breach Parametric Model for Embankment Dams

Reliable approximations for breach parameters using a parametric model due to embankment dam failure are very significant factors in dam safety and mitigation measures. Therefore, utilizing information on historically unsuccessful embankment dams, several empirical models have been formulated by different researchers. There is still no universal method for calculating the occurrence of dam breaches. The main objective of this research is to evaluate and compare the selected parametric dam breach models that are available in the literature. Four models (Xu et al., Vescher et al., Froehlich, and USB Reclamation) were chosen to forecast the breach event's parameters and peak discharges. Historically, 59 failed embankment dams’ data in various countries around the world were used. In order to assess and compare the breach models and recommend the best-performing model for predicting the peak discharge, breach width, and failure time, ten (10) statistical quantitative indicators were used. The breach model developed by Xu et al. has good performance. Finally, to show the impacts of erodibility, validation, and sensitivity of the selected model, it was checked using eight dams’ data. During the validation, the calculated and observed results were in agreement. Sensitivity revealed that large fluctuations in the breach parameter and peak discharge were seen when the erodibility coefficient increased or decreased.

Flood Potential Portal: A web tool for understanding flood variability and predicting peak discharges

AbstractThe Flood Potential Portal (https://floodpotential.erams.com/) has been developed for the contiguous United States, as a practitioner‐focused tool that uses observational data (streamgages) to enhance understanding of how floods vary in space and time, and assist users in making more informed peak discharge predictions for infrastructure design and floodplain management. This capability is presented through several modules. The Mapping module provides tools to explore variability using multiple indices, and provides detailed information, figures, and algorithms describing and comparing flooding characteristics. The Cross‐Section Analysis module allows users to cut regional‐scale sections to interpret the role of topography in driving flood variability. The Watershed Analysis module provides multiple methods for quantifying expected peak discharge magnitudes and flood frequency relationships at user‐selected locations, including the integration of observed trends in flood magnitudes due to climate change and other sources of nonstationarity into decision making. The Streamgage Analysis module performs streamgage flood‐frequency analyses. These modules are based in part on the flood potential method, through the use of 207 zones of similar flood response defined using more than 8200 streamgages with watershed areas <10,000 km2. Regression models that define each zone had high explained variance (average R2 = 0.93). An example is provided to illustrate use of the Flood Potential Portal for the design of a hypothetical bridge replacement.

Modelación de mapa de inundaciones durante el Huracán Alex: simulación usando precipitación multisensorial en la ciudad de Monterrey, México

Alex Hurricane was one of the most intense tropical cyclones in the North Atlantic, that caused fatalities and loses in the Northeast of Mexico due to the flash floods. Flood hazard mapping is a vital tool to assess inundation areas, which can be simulated using hydraulic and hydrologic models. This study describes the modelling of a flood event during Alex Hurricane in the Santa Catarina River Watershed, Northeast of Mexico, applying HEC-HMS and two dimensional (2D) HEC-RAS models forced with Multi Radar Multi Sensor - Quantitative Precipitation Estimation (MRMS-QPE). A HEC-HMS model was developed forced by (MRMS-QPE) as input to simulate discharges along the Santa Catarina River. The simulated discharges were introduced as border conditions along the mainstream of the Santa Catarina River inside a HEC-RAS 2D model to simulate a flood map along the mainstream of the Santa Catrina River. The observed against the simulated peak discharges achieved a R-squared of 0.97 and a Nash - Sutcliffe coefficient of 0.97. The observed against the simulated accumulated discharges achieved a R-squared of 0.99 and a Nash - Sutcliffe coefficient of 1.0. The observed against the simulated stages achieved a R-squared of 0.74 and, a Nash - Sutcliffe coefficient of 0.68. The use of HEC-HMS and HEC-RAS 2D models coupled with MRMS-QPE. shows that these models are user friendly to setup, the model has stability and the capacity to simulate flood maps along the whole mainstream of the Santa Catarina River with good results.

Spring resting egg production of the calanoid copepod, Eurytemora affinis, in a freshet-dominated estuary.

Seasonal peaks in river discharge, such as snowmelt-dominated freshets, are predictable events that can have a large effect on flushing rates and salinity in estuaries. Resting eggs, which many coastal and estuarine copepods produce for overwintering or aestivation, could also serve to bridge predictable peaks in river discharge. We assessed the timing of resting egg production of the egg-carrying estuarine copepod, Eurytemora affinis (Poppe), in relation to river discharge in the Fraser River Estuary, Canada. Approximately 30 field-collected females were individually incubated on 12 occasions over the period February 2015-May 2016. Eurytemora affinis abundance and population structure were investigated from vertical net tow samples collected twice monthly to monthly. Resting eggs occurred primarily in May 2015 and May 2016 (6.5 and 9.2 eggs day-1, respectively), a month prior to peak flows, and the proportion of offspring that were resting eggs increased with river discharge. Eurytemora affinis reached a minimum abundance in July 2015, when the population was dominated by adults (86%). Resting egg production in E. affinis is typically considered an overwintering mechanism but we suggest that the ultimate driver of resting egg production in this population is avoidance of flushing and/or low salinities.

Experimental Investigation on the Effect of Sequences of Unsteady Flows on Bedload Sediment Transport

Flash floods in ephemeral streams are rare, short and difficult to forecast and thus to monitor. During these events, bedload transport reaches very high rates and most sediment transport occurs within a limited number of hours during the course of a year. Because monitoring of bedload in ephemeral rivers is challenging, here we present the results of a series of flume experiments designed to simulate short, flashy floods. Since most flume experiments usually involve single events, here we add to existing evidence by testing the effects of sequences of multiple floods in rapid succession. The flume is 10 m long, 0.3 m wide and 0.5 m deep. Two bed sediment mixtures (well sorted and poorly sorted) with similar median grain size but a different standard deviation were used. Bedload was monitored continuously during each hydrograph, but no sediment was fed. The flume experiments used six triangular hydrographs with peak flows ranging from 0.0147 to 0.02 m3s−1 and durations ranging from 150 to 400 s. Results indicate that the sediment transport rate decreases progressively from the first to the third hydrograph, and that this pattern is consistent for all permutations of peak discharge and flood duration. In all of the runs, the sediment transport rate at a specified flow was higher during the rising limb than the falling limb of the hydrograph, indicating clockwise hysteresis. Furthermore, in the subsequent repetitions of the same hydrograph, the degree of hysteresis generally diminishes in magnitude from the first to the last repetition for all the experiments, irrespective of their magnitude and duration.

Associating reservoir operations with 2D inundation risk and climate uncertainty

ABSTRACT Bridging the research gap between reservoir operations and inundation risks under the future climate, this study integrates a hydrologic reservoir management model with a 2D hydrodynamic model, comparing the conventional regulations and the optimized reservoir operations based on the particle swarm optimization (PSO) algorithm. Results reveal that optimized operations using the PSO algorithm consistently outperform conventional strategies by better-managing peak discharges and controlling downstream inundation. The study further differentiates between PSO-optimized plans: PSO1, which focuses on minimizing inundation areas, and PSO2, which prioritizes peak reduction at the flood control point. Interestingly, PSO2 proves superior for single-point peak reduction, typically the primary objective in current practices, whereas PSO1, despite lesser peak reduction, achieves a smaller inundation area, enhancing basin-scale flood resilience. This discrepancy reveals the need to consider downstream inundation risks as critical evaluation metrics in reservoir optimization, a factor often overlooked in existing studies. The research underscores the importance of updating operational frameworks to incorporate 2D inundation risks and adapt to increased flood risks under changing climate conditions. Despite optimization, future climate scenarios predict increased flood exposure, indicating that the current safety discharge rates and flow regulations at control points are outdated and require revision.

Analysis of hydrological responses using semi-distributed conceptual models in a mountainous catchment in the Hindu Kush Himalayan region

ABSTRACT The Hindu Kush Karakoram Himalaya region, an important global water unit, is the origin of 10 major river basins. The Upper Indus basin, a part of this region, is a primary water source for the downstream population and is primarily fed by snow and glacier melt. Four semi-distributed conceptual hydrological models were used to understand this basin’s hydrological responses. As the observed data was unavailable, global gridded precipitation and discharge data from the Global Flood Awareness System (GloFAS) were used to set up the model at the catchment outlet. Degree-day factor (DDF)-based snow and glacier modules are incorporated into the model structure to measure melt contribution. The results showed very good performance of all four models with Nash-Sutcliffe efficiency > 0.75, with the GR4J model exhibiting superior performance. The dominance of snow and glacier melt during summer monsoon affects the peak discharge and offers insights for investigations at the sub-basin scale.