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The study investigates flow regime in the Omo-Gibe River Basin to address hydrological complexity caused by precipitation and catchment features. Despite employing various methodologies, daily flow data highlight the need for a more comprehensive understanding of flow variability. The study aims to scrutinize flow regime classification, emphasizing the challenges posed by the basin's unique hydrological dynamics, with the ultimate goal of improving water management practices in the region. Using XLSTAT (Excel statistics software), the average base flow index (60.66%), zero flow index (0.25%), coefficient of variation (1.56%), and flashiness index (0.276%) were determined to be the primary hydrological indices that contributed to streamflow characterization. Finally, flow regime classification was described as non-perennial (13%) or perennial (87%) using the shape of the flow duration curve and this hydrological index. However, the magnitude of extreme flow events was judged depending on flow duration curve and calibrated by the flashiness index computed in the study. The study's findings serve as an input for streamflow regionalization and the foundation for future research on the ecology and hydrology of Ethiopia's river basins as well as the management of the water resources throughout the Omo-Gibe River Basin.

Mountains are an essential source of the terrestrial component of the hydrological cycle, supplying high‐quality water to river networks and floodplain aquifers, especially during droughts. Traditionally, mountain hydrology has focused on shallow processes, overlooking the significance of deep‐seated rock formations due to characterization challenges. Recent field studies have revealed that fractured rock formations can host rich aquifers despite their low permeability. Nonetheless, it is unclear how deep flows interact with the overall hydrological functioning of mountain areas, how they contribute to the long‐term water budget, and how climate, morphology, and geology jointly control them. Through numerical simulations, we have gained new insights into mountain aquifers, addressing (a) the proportion of groundwater base flow and its age distribution, (b) water storage and its sensitivity to groundwater recharge, (c) the impact of long term mean recharge on the extent of the groundwater‐fed surface drainage network under various morphological and geological settings. We showed that subsurface travel times follow a Gamma distribution, whose parameters are modulated by recharge, hydraulic conductivity, and topography. High recharge and strong decay with depth of the hydraulic conductivity in a hilly topography lead to a shallow water table mimicking the surface topography and spatially distributed low‐intensity outflows that feed a dense drainage network. In rugged catchments, the groundwater contribution intensifies and concentrates in the downstream portion of the river network as recharge declines. These findings can help assess how a changing climate might impact hydrological regimes under various geomorphological conditions and identify sustainable water uses in mountain environments. Plain Language Summary Mountainous regions cover a large portion of the planet. These areas are at the origin of the terrestrial component of the water cycle, supplying high‐quality water to river networks and downstream floodplains where most of the human population dwells. Mountains feed the hydrological cycle with a large amount of water compared to surrounding floodplains, and their large storage capacity is crucial for mitigating climatic variability and sustaining downstream habitats, particularly during droughts. Traditionally, deep water flows in mountain formations have been overlooked because they are considered less important than near‐surface flows. However, recent surveys have revealed that fractured rock formations can host a significant amount of flowing water, raising questions about the contribution of these flows to long‐term water budgets and their susceptibility to environmental factors. With this in mind, we investigate the impact of climate, geology, and topography on subsurface flows and assess the vulnerability of mountain aquifers to climate change. Our findings indicate that in mountain regions, a reduction in groundwater recharge, which may be caused by climate change, leads to a decrease in the extent of the river network with permanent flow. These results provide valuable insights into the potential effects of climate change on water resources. Key Points Catchment scale groundwater modeling highlights base flow characteristics of river networks River base flow bears the signature of aquifer recharge, catchment morphology, and hydrogeological properties Changes in groundwater recharge modify the portion of the river network that receives the contribution of groundwater

Springs represent critical lifelines for mountain water security; however, discharge reductions and seasonal drying increasingly threaten 1.3 billion people dependent on Himalayan water systems amid accelerating climate change. This study presents a systematic application of classical hydrological methods across eight representative springs in the Tawi catchment, J&K UT, India, providing a comprehensive and unprecedented hydrometric assessment framework for data-scarce regions of Indian Himalayas. Three complimentary hydrological approaches were adopted: (1) flow regime analysis using hydrograph assessment and base flow separation, (2) statistical assessment using correlation and persistence functions, and (3) flow reliability evaluation using duration curves and variability indices. Results revealed distinct hydrological signatures: three springs exhibited perennial behavior with high base flow indices (0.67–0.88), strong groundwater contribution (> 800,000 L total volume), and low variability (CV < 0.3), while five springs demonstrated seasonal-flashy responses with discharge variability ratios exceeding 2.5. Cross-correlation analysis identified response lag times ranging from immediate (1 day) for shallow systems to 49 days for deeper groundwater-fed springs. Auto-correlation functions revealed memory of 14–49 days, indicating varying aquifer storage capacities. The evidence-based classification supports development of targeted community interventions: stable springs require recharge zone protection and forest conservation, whereas vulnerable springs necessitate artificial recharge structures, check dams, and community-based storage systems. This framework directly supports UN Sustainable Development Goal 6 (Clean Water and Sanitation) and Goal 13 (Climate Action), demonstrating that systematic, scientifically robust classical methods offer cost-effective alternatives for springshed management, thereby supporting mountain water security globally.

For various reasons, it is not always possible to obtain adequate and reliable long-term streamflow records in a river basin. It is known that streamflow records are even shorter when the stations located on tributary channels are of the interest. Hence, it is necessary to develop dependable streamflow estimation models for the tributary streams that play a key role in the micro-hydrology of the basin. In this study, rainfall-runoff models are developed to estimate the daily streamflow in ungauged tributary streams. Precipitation and streamflow in the most similar gauging station on the main channel and lagged values up to three days before on the same tributary station are used as the input variables of the allocated models. To select the most similar gauging station, a similarity index criterion is developed and used in the analysis. Then, two scenarios based on the streamflow or the corresponding set of direct runoff and base-flow in the same station are used. By applying multivariate adaptive regression spline (MARS) and random forest (RF) methods, several rainfall-runoff models are developed and evaluated based on determination coefficient, mean absolute percentage error, root mean square error, relative peak flow, scatter plot and time series plot. Alternatively, the MARS and RF models are combined with a drainage area ratio (DAR) model to produce the DAR-MARS and DAR-RF models. It is concluded that the direct runoff in the mainstream is more effective on the streamflow of the tributary station, while the integration of models with DAR enhanced the capabilities of the models in estimation of extreme values in the streamflow time series.

Mineral water from the Changbai Mountain basalt area is China's most important source of drinking water. Mineral water with abundant output and enriched trace elements has driven the rapid development of the local economy. However, the extensive exploitation of mineral water and the neglect of ecological base flows threaten interdependent river ecosystems. In order to ensure the sustainable use of mineral water, it’s necessary to calculate the exploitation and utilization of mineral water according to the ecological base flow. In this study, four hydrological methods were used to analyze the intra-annual and inter-annual ecological base flows, namely the Tennant method, the base flow ratio method, the driest monthly average flow method and the Texas method. The results show that the ecological base flow during the flood season is about 3–4 times that during the non-flood season. Affected by rainfall and runoff, the inter-annual ecological base flow also fluctuated within a small range. This study divides the mineral water exploitation coefficient into five categories and proposes an assessment of the mineral water exploitation potential based on the ecological base flow. It shows that the flow of Baijiang River spring group is the largest, but it’s exploitation potential is normal. Huangni River spring group at weak level for exploitation because of its small flow rate. It’s obvious that river runoff is not the only factor that determines the exploitation coefficient of the spring group. The proportion of ecological base flow and the proportion of river base flow are also important factors.

PurposeThe purpose of the present study is to develop a new numerical framework that can predict the supersonic base flow more accurately, including the development of axisymmetrically separated shear layer and recompression shock. To this end, two aspects are improved and combined, i.e. a newly self-adaptive turbulence eddy simulation (SATES) turbulence modeling method and a high-order discretization numerical scheme. Furthermore, the performance of the new numerical framework within a general-purpose PHengLEI software is assessed in detail.Design/methodology/approachSatisfactory prediction of the supersonic separated shear layer with unsteady wake flow is quite challenging. By using a unified turbulence model called SATES combining high-order accurate discretization numerical schemes, the present study first assesses the performance of newly developed SATES for supersonic axisymmetric separation flows. A high-order finite differencing-based compressible computational fluid dynamics (CFD) code called PHengLEI is developed and several different numerical schemes are used to investigate the effects on shock-turbulence interactions, which include the monotonic upstream-centered scheme for conservation laws (MUSCL), weighted compact nonlinear scheme (WCNS) and hybrid cell-edge and cell-node dissipative compact scheme (HDCS).FindingsCompared with the available experimental data and the numerical predictions, the results of SATES by using high-order accurate WCNS or HDCS schemes agree better with the experiments than the results by using the MUSCL scheme. The WCNS and HDCS can also significantly improve the prediction of flow physics in terms of the instability of the annular shear layer and the evolution of the turbulent wake.Research limitations/implicationsThe small deviations in the recirculation region can be found between the present numerical results and experimental data, which could be caused by the inaccurate incoming boundary layer condition and compressible effects. Therefore, a proper incoming boundary layer condition with turbulent fluctuations and compressibility effects need to be considered to further improve the accuracy of simulations.Practical implicationsThe present study evaluates a high-order discretization-based SATES turbulence model for supersonic separation flows, which is quite valuable for improving the calculation accuracy of aeronautics applications, especially in supersonic conditions.Originality/valueFor the first time, the newly developed SATES turbulence modeling method combining the high-order accurate WCNS or HDCS numerical schemes is implemented on the PHengLEI software and successfully applied for the simulations of supersonic separation flows, and satisfactory results are obtained. The unsteady evolutions of the supersonic annular shear layer are analyzed, and the hairpin vortex structures are found in the simulation.

The baseflow index (BFI) is the ratio of long-term mean baseflow to total streamflow, where baseflow is the portion of streamflow sustained between precipitation events. Understanding spatial and temporal variation in the BFI is of key importance in river ecology and water resource planning and management. Here a benchmark suite of 92 river flow monitoring sites with long records in natural or near-natural basins was compiled. Spatial patterns of mean annual BFI values, ranging from 0.27 to 0.86 with an average of 0.57, were identified and are strongly influenced by geological conditions. Annual mean streamflow, quickflow, baseflow and BFI results for each site were examined visually and statistically for temporal trends and shifts for the first time nationwide. No significant trends in BFI and other flow values were detected except at sites, omitted from the final suite, where activities such as streamflow abstraction and land use change have impacted streamflow. No significant shifts in BFI values were found but were detected in streamflow, quickflow and baseflow at many sites in the South Island. Shifts, up and down, match changes in phase of the Interdecadal Pacific Oscillation and also match shifts in relevant streamflow- generating annual mean rainfall values. The major findings of this study are of key importance in understanding future flow regimes and in planning and management of water resources in New Zealand. This applies particularly to streamflow abstraction, the impacts of agricultural activity on surface and groundwater water quality and the preservation of aquatic ecosystems.

We investigated the seasonal dynamics of in-stream metabolism at the reach scale (∼ 150 m) of headwaters across contrasting geological sub-catchments: clay, Greensand, and Chalk of the upper River Avon (UK). Benthic metabolic activity was quantified by aquatic eddy co-variance while water column activity was assessed by bottle incubations. Seasonal dynamics across reaches were specific for the three types of geologies. During the spring, all reaches were net autotrophic, with rates of up to 290 mmol C m−2 d−1 in the clay reach. During the remaining seasons, the clay and Greensand reaches were net heterotrophic, with peak oxygen consumption of 206 mmol m−2 d−1 during the autumn, while the Chalk reach was net heterotrophic only in winter. Overall, the water column alone still contributed to ∼ 25% of the annual respiration and primary production in all reaches. Net ecosystem metabolism (NEM) across seasons and reaches followed a general linear relationship with increasing stream light availability. Sub-catchment specific NEM proved to be linearly related to the local hydrological connectivity, quantified as the ratio between base flow and stream discharge, and expressed on a timescale of 9 d on average. This timescale apparently represents the average period of hydrological imprint for carbon turnover within the reaches. Combining a general light response and sub-catchment specific base flow ratio provided a robust functional relationship for predicting NEM at the reach scale. The novel approach proposed in this study can help facilitate spatial and temporal upscaling of riverine metabolism that may be applicable to a broader spectrum of catchments.

Streamflow simulation in a snow dominated basin is complex due to the presence of a high number of interrelated hydrological processes. This complexity is affected by the delayed responses of the catchment to snow accumulation and snow melting processes. In this study, long short-term memory (LSTM) and artificial neural network (ANN) models were utilized for rainfall–runoff simulation in a snow dominated basin, the Carson River basin in the United States (US). The input structure of the models was determined using the simulated annealing algorithm with a naïve Bayes model from a high dimensional feature space to represent the long-term impacts of historical events (i.e. the hysteresis effect) on current observations. Further, to represent the different responses of the catchment in the model structure, a base flow separation method was included in the simulation framework. The obtained performance indices, root mean square error, percentage bias, Nash–Sutcliffe and Kling–Gupta efficiencies are 0.331 m3 s−1, 13.00%, 0.848, and 0.852 for the ANN model and 0.235 m3 s−1, − 0.80%, 0.923, and 0.934 for the LSTM model, respectively. The proposed methodology was found to be promising for improving the streamflow simulation capability of LSTM and ANN models by only considering precipitation, temperature, and potential evapotranspiration as input variables. Analysing the flow duration curves indicated that the LSTM model is more efficient in representing different flow dynamics within the basin due to embedded cell states. Further, the uncertainty and reliability analyses were conducted by using expanded uncertainty (U95), reliability, and resilience indices. The obtained U95, reliability and resilience indices are 1.78–1.72 m3 s−1, 31.28–66.67% and 11.58–38.27% for the ANN and LSTM models, respectively, showed that the LSTM model produced less uncertainty and is more reliable. However, while lacking a memory component, the proposed methodology significantly contributes to the simulation capability of the ANN model in rainfall–runoff modelling. The results of this study indicated that the proposed methodology could enhance the learning capabilities of machine learning models in rainfall–runoff simulation.

Chemical base flow separation is a widely applied technique in which contributions of groundwater and surface runoff to streamflow are estimated based on the chemical composition of stream water and the two end‐members. This method relies on the assumption that the groundwater end‐member can be accurately defined and remains constant. We simulate solute transport within the aquifer during and after single and multiple river flow events, to show that (1) water adjacent to the river will have a concentration intermediate between that of the river and that of regional groundwater and (2) the concentration of groundwater discharge will approach that of regional groundwater after a flow event but may take many months or years before it reaches it. In applying chemical base flow separation, if the concentration in the river prior to a flow event is used to represent the pre‐event or groundwater end‐member, then the groundwater contribution to streamflow will be overestimated. Alternatively, if the concentration of regional groundwater a sufficient distance from the river is used, then the pre‐event contribution to streamflow will be underestimated. Changes in concentration of groundwater discharge following changes in river stage predicted by a simple model of stream‐aquifer flows show remarkable similarity to changes in river chemistry measured over a 9 month period in the Cockburn River, southeast Australia. If the regional groundwater value was used as the groundwater end‐member, chemical base flow separation techniques would attribute 8% of streamflow to groundwater, as opposed to 25% if the maximum stream flow value was used.