Streamflow Data Research Articles (Page 1)

ABSTRACT Effective discharge plays an important role in the transport of suspended sediments in alluvial rivers. In the current study, the effective discharge of a large river is estimated using daily discharge and suspended sediment data for five gauging sites using streamflow and sediment data of 29 years. The empirical approach and analytical approach based on the Magnitude Frequency Approach (MFA) are used in the study to estimate the effective discharge on the regulated Upper Narmada River under pre- and post-dam scenarios and its inter-comparison was carried out. Different discharge class intervals were investigated in the first approach while in the second, log-normal probability distribution was used to estimate the effective discharge. The results depicted that empirical approach with the classes of equal arithmetic intervals and the classes in geometric progression showed similar trends of effective discharge for the various gauging sites for the pre- and post-dam scenarios. Regardless of the subdivision, the analytical approach significantly underestimated the effective discharge. There is a decrease in effective discharge in stations situated in the downstream of the dam in the post-dam scenario. The results of this investigation can be utilized for efficient planning and management of the Upper Narmada River.

This paper advances machine learning (ML)-based streamflow prediction by strategically selecting rainfall events, introducing a new loss function, and addressing rainfall forecast uncertainties. Focusing on the Iowa River Basin, we applied the stochastic storm transposition (SST) method to create realistic rainfall events, which were input into a hydrological model to generate corresponding streamflow data for training and testing deterministic and probabilistic ML models. Long short-term memory (LSTM) networks were employed to predict streamflow up to 12 h ahead. An active learning approach was used to identify the most informative rainfall events, reducing data generation effort. Additionally, we introduced a novel asymmetric peak loss function to improve peak streamflow prediction accuracy. Incorporating rainfall forecast uncertainties, our probabilistic LSTM model provided uncertainty quantification for streamflow predictions. Performance evaluation using different metrics improved the accuracy and reliability of our models. These contributions enhance flood forecasting and decision-making while significantly reducing computational time and costs.

Abstract Frequency analysis is crucial in low flow statistics, helping estimate the probability of water availability during low flow seasons and droughts. Low flow frequency analysis typically assumes stationarity, which has been challenged by climate change and variability. Therefore, non-stationary frequency analysis is essential when trends and non-stationarity exist in low streamflow data. This study developed a methodology that includes trend, change point, non-stationarity detection, and stationary and non-stationary low flow frequency analysis for annual minimum streamflow series of 7-day (Q7), 14-day (Q14), 30-day (Q30) and 90-day (Q90) periods, applied to selected river basins in Victoria, Australia. Significant decreasing trends were detected in several basins, with the strongest trends observed in the East Gippsland Basin, where the trend slopes were − 1.02, − 0.989, − 1.035 and − 1.534 for Q7, Q14, Q30, and Q90, respectively. Similarly, significant change points were found with year 2002 being the most common change point year, followed by year 1996, 2000 and 2001. Non-stationary frequency analysis proved superior in capturing the changing characteristics of low flow series. Moreover, the non-stationary models that included physical covariates outperformed those with only time covariates, highlighting the benefit of using covariates related to the physical mechanisms of low flow events. This study emphasizes the importance of non-stationary frequency analysis to prevent misleading conclusions in low flow-based water management, thereby enhancing the reliability and effectiveness of water management strategies.

The National Water Model (NWM)’s streamflow forecasts are widely used by stakeholders to make critical water management decisions. This study evaluates the performance of the NWM v2.1 in simulating streamflow across the Alabama Black Belt Region (ABBR), in the southeastern United States. Using retrospective NWM and USGS observed streamflow data, model performance was assessed across four-time scales—hourly, daily, weekly, and monthly—using three metrics: Nash-Sutcliffe Efficiency (NSE), Root Mean Square Error Ratio (RSR), and Percent Bias (PBIAS). The results demonstrate that the NWM accuracy improves significantly with longer-term forecasts. At the monthly scale, 89% of evaluated stations reached above “Good” classification based on NSE (>0.75), and 85% based on RSR (<0.5). However, consistent negative bias was observed across all time scales, particularly in the underestimating flows. The results highlight the influence of environmental factors, including land use, topography, and soil characteristics, on model performance, as well as potential sources of systematic bias within the model’s processes. Although the NWM does not incorporate regulated protocols, its ability to capture flow variability improves at aggregated scales, suggesting its suitability for long-term planning applications. These findings underscore the need for further model structure refinement and regional calibration to enhance predictive reliability.

SIREN is a citizen science project that involves lay people in the digitization of historical daily discharge measurements from Italian rivers. Such data, largely available only in printed yearbooks, hinders scientific progress in hydrological studies and water resource management. In this article, we examine the motivations behind citizen engagement in SIREN. Our multi-step approach combines quantitative analysis of online contributions, pilot interviews with selected volunteers, and a comprehensive questionnaire collecting basic demographic data and subjective impressions of the experience. Through these approaches, we identify three participant profiles: two driven primarily by the activity itself and one by the scientific content. The first profile values the straightforward nature of data entry, seeing it as an easy way to contribute with existing skills. The second profile treats participation as a leisure activity, readily fitting into brief intervals of free time. The third profile stems from deeper engagement, encompassing volunteers with professional or personal interests in hydrology, Italian geography, or both. The study also highlights the significant role of retired individuals (an underrepresented group in the citizen science literature) who often contribute using skills developed during their careers. This work highlights the importance of creating citizen science projects that are accessible, meaningful, and connected to volunteers’ lives and interests.

ABSTRACT Ethiopia's Awash River Basin (ARB) data scarcity and quality concerns limit effective planning and research. This study evaluated 15 streamflow gauging stations through a two-week field inspection following World Meteorological Organization (WMO) protocols, combined with observer feedback and four statistical homogeneity tests. This study also conducts analysis of streamflow trends using daily data from 15 gauging stations over the period of 1965–2015 using Mann-Kendall test and Sen's slope estimator. Field assessments revealed outdated equipment, inadequate site conditions, and low observer satisfaction, often leading to errors in water level measurement. Homogeneity analysis showed that approximately 25, 40, and 33% of the stations in the Upper, Middle, and Lower Awash Basins, respectively, exhibit inhomogeneous data records, undermining long-term hydrological analyses. The study found a statistically significant increasing trend in annual streamflow in most middle Awash basin, while upper and lower basins showed insignificant trends.These findings highlight significant spatial variability in data reliability across the basin. The study concludes that upgrading gauging networks with telemetry, improving rating curve updates, and enhancing observer support are urgent priorities. Strengthening institutional coordination and capacity building will be critical to ensure reliable streamflow records, thereby improving hydrological forecasting and sustainable basin-wide water resources management.

The research will focus on the assessment, feasibility, and hydropower potential estimation in the Gedeo zone Dilla Ethiopia with an analysis of the viability of the systems for rural community electrification. Waleme River Catchment is located in the Rift Valley basin, covering an area of around 80 km2 and It extends up to 30 km with a river basin This hydropower plant considers the reliability, sustainability, and environmental protections of supplying electricity to the village, particularly for remote communities where grid extension is not suitable. The hydropower renewable energy system will be the best solution for the off-grid areas. Due to international policy and the reduction of carbon dioxide emissions, the generation of electricity using renewable energy sources has become more significant. Currently, it is among the most intriguing and eco-friendly technology solutions. The hydropower potential of the site will be analyzed by measuring the gross head with the help of a Geographical Position System (GPS) and stream flow data analysis. The proposed research will be completed within two years with a total estimated budget of 363,075 ETB, by site surveying, data collection, and estimating the hydropower potential of Waleme River and finally the paper will be prepared for publication.

ABSTRACT Accurate prediction of streamflow using reliable and cost-effective techniques is paramount for watershed management. The current study aimed to predict streamflow of Katar River, Ethiopia using different standalone and hybrid models. The modeling was carried out using Random Forest(RF), gradient boost regression(GBR) and least square support vector machine (LSSVM) models. Subsequently, four novel hybrid models were developed by conjugating RF and GBR with Harris Hawks optimization(HHO) and Jaya Optimization Algorithms (i.e., RF-Jaya, RF-HHO, GBR-Jaya, and GBR-HHO). Ten years of daily streamflow and rainfall data were utilized for calibrating and validating the models. Several statistical metrics such as determination coefficient (R2), Nash–Sutcliffe efficiency(NSE), RMSE-observation standard deviation ratio (RSR), Root mean square error (RMSE), Percent bias(Pbias), and visually-based evaluation metrics were utilized to compare the efficiency of the models. The result revealed that RF surpassed LSSVM and GBR with the validation period NSE = 0.915, R2 = 0.916, RMSE = 5.943 m3/s, Pbias = −0.939% and RSR = 0.085. The result of the study also demonstrated that, in the validation period, the best streamflow prediction was obtained from hybrid RF-Jaya (RMSE = 4.976 m3/s, NSE = 0.94, R2 = 0.94, RSR = 0.059 and Pbias = 0.624%). Generally, the Jaya and HHO optimization methods demonstrated improved streamflow results compared to the standalone models in the study catchment.

ABSTRACTUnderstanding aquifer storage characteristics is critical for revealing river–aquifer interactions and is thus essential for effective water resource management. Hydrological studies often analyse watersheds as single units, potentially overlooking spatial variability in groundwater storage, especially in diverse hydrogeological settings. This study examines groundwater storage dynamics and river–aquifer interactions in the Chemoga watershed, Ethiopia, using a nested‐watershed approach. Groundwater level data from eight monitoring stations and streamflow data from five gauging stations were integrated to assess groundwater storage dynamics. The Wilcoxon test revealed significant spatial variations (p < 0.05) in median groundwater levels from 1.3 to 17 m, exhibiting temporal sensitivity with a coefficient of variation of 18%–55%. When analysed as a single unit, the watershed exhibited a mean annual storage of 191 mm year−1. However, the nested‐watershed approach uncovered a wide range of mean annual storage values, from −302 to +1777 mm year−1. Negative storage changes were observed in highland sub‐watersheds (GS1, GS2) and the Wuseta River sub‐watershed (GS4), whereas positive changes occurred in the midland floodplain (GS3) and lowland valley (GS5). These findings indicate that the Chemoga River acts as a gaining stream in the highlands but transitions to a losing stream in the midland floodplain and lowland valley. Additionally, a 1‐month lag in baseflow response and hydro–stratigraphic evidence suggest potential lateral flow from GS3 to GS4 sub‐watershed. This study highlights the limitations of treating watersheds as single units and advocates for spatially explicit approaches to better understand groundwater storage dynamics and river–aquifer interactions in complex hydrogeological environments.

ABSTRACT Tanzania faces pressing environmental challenges from land use and land cover changes (LULCC), which significantly impact hydrological responses. This study evaluates LULCC impacts on the Mkomazi River Catchment's streamflow in the Pangani River Basin, Tanzania, from 1995 to 2021. Utilizing satellite imagery, historical streamflow, and climate data, the soil and water assessment tool (SWAT) was applied to simulate LULCC effects on streamflow, emphasizing seasonal shifts and components such as surface runoff (SURQ), groundwater flow (GWQ), and lateral flow (LATQ). Findings showed that, built-up areas increased by 22.1%, while forest cover decreased by 16.4%. Cropland expanded by 10.3% by 2021, and barren land increased by 5.1% from 2008 to 2021. Wet season streamflow rose from 38.9 to 45.4 m3/s, while dry season flow dropped from 13.4 to 8.3 m3/s. Monthly SURQ increased from 9.6 to 22.2 mm, GWQ declined from 18.3 to 8.3 mm, and LATQ dropped from 0.9 to 0.2 mm, largely due to deforestation and encroachment. The results highlight the need for effective land use management strategies in the Mkomazi River Catchment to mitigate the negative effects on water resources and ensure sustainable hydrological conditions for the catchment's sustainable development.

ABSTRACT Study region: Densu River Basin (DRB) in Ghana. Study focus: Urbanization, agricultural expansion, and population growth are transforming land use across West Africa, impacting hydrological regimes and ecosystem resilience. This study assesses land use and land cover (LULC) changes in Ghana’s Densu River Basin (DRB) using the Soil and Water Assessment Tool (SWAT). The model, calibrated (2012–2019) and validated (1990–2011) with streamflow data, showed strong performance (NSE = 0.79; R² = 0.85). Three LULC scenarios – agriculture-, forest-, and urban-dominated – were simulated using satellite imagery from 1990 to 2019. The 2018 Landsat baseline map, validated with 91.5% accuracy, showed the urban scenario increased surface runoff (>50% of water yield) and sediment load (>83,000 metric tons/year), while the forest scenario enhanced percolation and groundwater recharge. Sedimentation in the Weija Reservoir, a key water source for Greater Accra, deposits ∼47,500 m³ annually, posing long-term risks to reservoir capacity and water security. These findings highlight tropical catchment's vulnerability to land use changes and support scenario-based hydrological modelling for river basin planning in rapidly urbanizing, data-scarce sub-Saharan Africa.

ABSTRACT In this paper, we explore the value of assimilating the AMSR-E product for hydrological simulation using Soil Water Assessment Tool (SWAT). Three scenarios are defined and evaluated: univariate assimilation of soil moisture data, univariate streamflow data assimilation and bivariate data assimilation of both soil moisture and streamflow data. For comparative analysis, a baseline scenario (BS) is considered using the SUFI-2 algorithm in the SWAT-CUP package. The proposed method is applied to a watershed located in the Mahabad River Basin, northwest of Iran. The results demonstrate that univariate assimilation of soil moisture data significantly improves soil moisture simulation, increasing the Nash–Sutcliffe efficiency (NSE) from –2.73 (in BS) to 0.34, but it leads to a decline in streamflow estimation accuracy. Conversely, univariate assimilation of streamflow observations improves streamflow estimation NSE from 0.73 to 0.77. This scenario severely reduces soil moisture estimation accuracy, with the NSE dropping from −2.73 to −5.48. In comparison, the multivariate assimilation scenario offers a more balanced outcome with improving soil moisture estimation accuracy to an NSE of 0.10 while maintaining streamflow accuracy at a level comparable to the BS.

Abstract This study examines the influence of the El Niño–Southern Oscillation on the statistical distribution of daily inflows to the 15 most important hydropower reservoirs across Colombia over the period 2000–2024. Twenty-five years of daily streamflow data were analyzed to determine whether El Niño–Southern Oscillation phases (La Niña, Normal, El Niño) produce statistically distinct hydrological signatures. Using the Oceanic Niño Index to classify each month into one of the three El Niño–Southern Oscillation phases, daily inflow records were grouped accordingly and applied the Anderson–Darling test to evaluate statistical differences among distributions. The analysis was performed independently for each river and calendar month, resulting in 540 pairwise comparisons. Six of eight possible outcome patterns were observed, with the most frequent pattern–rejection of the null hypothesis in all three comparisons–occurring in nearly 70% of the 180 river-month cases, indicating strong phase-dependent hydrological differentiation. The results exhibit clear spatial and temporal variability in El Niño–Southern Oscillation influence, with strongest signals during December–March and considerable complementarity among major reservoirs. By comparing monthly and daily streamflow statistics, the analysis demonstrates that daily resolution provides richer and more operationally relevant information, especially for reservoir management. These findings highlight the value of incorporating El Niño–Southern Oscillation-phase-resolved diagnostics into water resources planning and energy system operations in tropical regions influenced by climate variability.

ABSTRACT Accurate water balance estimation is essential for sustainable water resource management, especially in large-scale studies. This research investigates the dynamics of water availability and distribution to enhance agricultural productivity using the Soil and Water Assessment Tool (SWAT+) in Awash Basin, Ethiopia. We propose a multi-site calibration approach for large-scale streamflow simulation and mass balance estimation, integrating both observation and soft data. This approach aims to improve the representation of reservoir operations and irrigation practices within the model and to assess how calibration and management practices affect model performance. Streamflow simulated and mass balance components at the Awash-Awash station outlet were estimated after the model was calibrated and validated using monthly streamflow data from 1997 to 2019. Irrigation practice and the Koka Reservoir release rule were implemented using decision tables. Our findings indicate that the operations of the Koka Reservoir, combined with effective irrigation practices, significantly improved streamflow simulation and water balance estimation at the downstream gauging station. The SWAT+ model demonstrated satisfactory to very good performance in replicating both streamflow and water balance components in the study area. The methodological framework presents a valuable tool for sustainable water resource management that can be adopted in other river basins.

ABSTRACT Drought is a disastrous natural phenomenon that causes social, economic, environmental and political crisis to a given region due to water scarcity. This study presents the hydrological drought characteristics in the Ethiopian Rift Valley Lakes Basin by using streamflow drought index (SDI). The streamflow data were collected from 18 stations from Ministry of Water and Energy (MoWE) of Ethiopia. The SDI value was computed by employing the drought index calculator (DrinC) software and the trend of hydrological drought for three periods (SDI-3, SDI-6 and SDI-12) was detected by applying the non-parametric Mann-Kendall trend test and Sen's slope method. The findings of the study indicate that there was no significant trend on a seasonal basis, whereas there was significant increasing and decreasing trends on biannual and annual bases in Kulfo, Tikurwuha, Kola and Katar stations. The most common severe hydrological drought periods in the basin were 1984, 1985, 1999 and 2002 and the most common extreme drought registered years were 1984, 1986, 1991 and 2002. The probability of drought occurrence varies from catchment to catchment and timescales used. Hence, this study may provide basic information about the hydrological conditions that helps to develop better water adaptation and mitigation strategies in the basin.

Estimating missing daily streamflow data in a tropical basin with pronounced seasonal variability: A comparative case study from the Guayas River Basin, Ecuador

ABSTRACTIntermittent streams are prevalent worldwide, yet the understanding of drivers of their changing flow patterns remains incomplete. We examined hydrological changes spanning four decades (1982–2020) in Kings Creek, an intermittent grassland stream within the Konza Prairie Biological Station in Kansas, USA. We analysed streamflow data from a US Geological Survey gauge on Kings Creek and three upstream Long Term Ecological Reasearch (LTER) sub‐watersheds with annual, biennial or quadrennial burn frequencies and linked trajectories of woody encroachment to increased evapotranspiration and changes in streamflow. Riparian woody cover doubled in the annually and biannually burned sub‐watersheds and sevenfold in the quadrennially burned watersheds. We observed significant decreases (84%) in daily discharge and number of annual flow days (55%) at the downstream USGS Kings Creek gauge, with similar changes in the LTER sub‐watersheds. The changing riparian cover, propelled by the regional expansion of woody plants, contributed to decreased streamflow by amplifying actual evapotranspiration (ET). Seasonal assessments underscored the critical influence of late summer conditions (July–September), under which increases in ET were linked to rising temperatures and increased evapotranspiration by riparian cover. Our results highlight the significant hydrological impacts of woody encroachment in grasslands and emphasize the importance of long‐term ecohydrological monitoring in unravelling the interplay between climate and vegetation as controls on the hyper‐variable flow patterns in this intermittent stream. Predicting and managing hydrological impacts on the flow of intermittent grassland rivers and streams worldwide requires accounting for the effects of accelerating woody encroachment.

Water security in small island developing countries like Sri Lanka is increasingly threatened by climate change and anthropogenic pressures. Maha Oya River, located in the intermediate climatic zone of Sri Lanka, is a vital water source for over four million people who face increasing water demand and hydrological stress. Despite its socio-economic centrality, the basin’s hydrological sensitivity to climate change remains inadequately quantified by scientific research. Therefore, this study assessed the climate-driven dynamics of low-flows in the Maha Oya River basin by a hydrologic modelling approach coupled with future rainfall projections to evaluate the adequacy of low flows for planned potable water extractions. HEC-HMS hydrological model was calibrated and validated using observed streamflow data at Giriulla and Badalgama from 2009 to 2018, with goodness-of-fit values falling within acceptable ranges. The study utilized NASA NEX-GDDP CMIP6 dataset and HadGEM3-GC31-LL and CESM2 Global Climate Models (GCMs), under SSP245 and SSP585 scenarios, to project future rainfall, with bias correction conducted using the Quantile Delta Mapping (QDM) method. The calibrated HEC-HMS model was used to simulate the future streamflow across near-future (2019–2049), mid-future (2050–2074), and far-future (2075–2099) periods. Low-flow trends, defined by the 90-100% flow exceedance percentiles, were identified across all projected time periods and evaluated against anticipated potable water abstraction demands. Results revealed severe reductions in low-flow conditions, with decreases of 90–100%, anticipated by the end of the 21st century under both scenarios. Key intake locations, including Mawanella-Kappagoda and Hemmathagama, faced persistent near-zero flows, jeopardizing planned extractions (333,230 m³/day by 2040). Deficits were intensified under SSP585, with the CESM2 projecting 100% loss of low flows by mid-century. While upstream zones demonstrated transient recovery, downstream regions exhibited irreversible drying. The proposed expansions in potable water extractions at Rambukkana (22,000 m³/day) and Hemmathagama (24,200 m³/day) significantly exceeded projected availability, risking supply-demand imbalances during critical dry periods. This study underscores the urgency of adaptive strategies such as managed aquifer recharge and demand governance to mitigate water insecurity. Furthermore, the study contributes to the development of a methodology that integrates CMIP6 projections into basin-scale hydrologic modelling, providing broadly applicable insights for assessing climate-impacted water availability in tropical catchments.

A probabilistic approach to enhance drought modelling in alpine regions: Transition from meteorological to hydrological droughts.

Streamflow regime characterization in the changing boreal ecosystem: wildfire impacts from stream-to-regional scales.