Streamflow Measurements Research Articles (Page 1)

Increasing air temperatures are expected to change streamflow patterns in Arctic watersheds. However, the hydro-meteorological datasets necessary to evaluate these changes remain limited in the Arctic. We revised and updated a unique hydrologic dataset from Arctic Alaska with field measurements of continuous streamflow, precipitation, and evapotranspiration to identify changes in the hydrologic cycle. We analyzed water balance components and peak streamflow in Imnavait Creek, Upper Kuparuk River, and Kuparuk River during snowmelt and summer periods. Field measurements show that summer streamflow (mid-June to late-September) has increased in volume and magnitude. While snowmelt runoff still constitutes a sizable portion of annual runoff (28–58%), the relative proportion of summer runoff is notably increasing. High interannual and spatial variability is observed for both rainfall and summer runoff, whereas comparatively lower interannual variability is observed for snowmelt runoff. This paper compares streamflow changes occurring at three watersheds of varying sizes and identifies reasons for observed changes using snow, rainfall, and evapotranspiration measurements. We also provide hydrologic analysis for design considerations at Arctic Alaska communities, the Dalton Highway, and the oil pipeline; as this infrastructure may be at risk of increased erosional damage due to more frequent and higher magnitude summer flow events.

Abstract The predictability of hydrological variables (specifically, soil moisture and streamflow) at subseasonal timescales is examined with an offline (land-only) modeling system. Meteorological forcing for the offline system is derived from state-of-the-art subseasonal-to-seasonal (S2S) forecast products, ensuring that the offline simulations performed constitute fair forecasts in their own right – no observations from the forecast period are employed in the simulations. Validation data for the offline forecasts consist of in-situ measurements of soil moisture and streamflow. The offline system’s hydrological forecasts are found to be at least as skillful as those generated by the fully coupled S2S system. The subseasonal streamflow forecasts are less skillful than the soil moisture forecasts due to the poor quality of the subseasonal precipitation predictions; we can, however, increase the streamflow forecast skill considerably by using climatological precipitation forcing during part or all of the forecast period, thereby increasing the relative contribution of soil moisture initialization to the forecasts. Skill for both soil moisture and streamflow is found to be sensitive to the depth of the modeled soil column, with deeper depths, associated longer soil moisture memory, becoming more advantageous at longer leads. Overall, our results illustrate the potential of using the offline system for extracting additional hydrological prediction skill from standard S2S forecasts and, more generally, for examining the nature of hydrological predictability and the physical factors that control it.

ABSTRACTRenewable energy sources present significant temporal variability, which imposes difficulties for their integration into national power grids. This integration could be facilitated by an optimised distribution of renewable capacity that exploits their complementarity. This study brings a comprehensive analysis of the complementarity of wind, solar and hydroelectric resources on the interannual scale over Argentina using 38 years of reanalysis‐based data and streamflow measurements. The impact of low‐frequency ocean–atmosphere variations on these resources is also assessed in this study. We found little potential for complementarity between solar, wind and hydro resources for the current spatial distribution of renewable capacity in Argentina. This result highlights the need to study different options for coping with the intermittency of these resources, including different spatial schemes for future capacity additions. Climatic drivers exert significant control over wind, solar and hydro resources. The Antarctic Oscillation (AAO) index shows anticorrelations with wind speeds. Streamflows over northern Patagonia show also negative relationships with the AAO, and a positive relationship with the El Niño/Southern Oscillation. Streamflows over southern Patagonia show positive relationships with Southern Blob (SB) and AAO indices driven by variations in temperature and precipitation. The solar resource shows strong links with all ocean‐rooted climate indices during winter. These results provide an insight into the potential predictability of renewable energy resources on the interannual scale.

The study of interannual variability of snow accumulation in mountainous environments allows for an understanding of the hydric dynamics in basins that depend almost exclusively on meltwater. This paper presents an analysis of the hydro-snow dynamics of the upper Colorado River basin (Argentina), in the context of the vulnerability of Andean basins to the effects of climate change. A multitemporal series of MOD10A1 and MOD10A2 satellite products was used to estimate total snow cover during the period 2000-2022, as well as streamflow measurements from the Buta Ranquil station (Nqn). The results show a negative trend in maximum toal snow cover during the study period. In recent years, there has been a delay in the occurrence of snow precipitation, greater variability, an advancement in the annual cycle peak, and a negative trend in maximum snow-covered area, particularly in areas above 2,500 meters above sea level. The seasonal trend analisys indicated significant negative trends in the amplitude of the annual snow cover cycle and a negative trend in the phase for 66.9% of the analyzed area, indicating that snowfalls occurred around mid-winter in the later years of the analyzed period.

Abstract How mountain headwaters and their groundwater‐dependent ecosystems will respond to future climate change remains largely unknown. These challenges stem from the difficulty of gathering relevant hydrological observations and implementing modeling strategies suited to data‐scarce systems. To address this, we present a hydrological modeling framework to quantify and predict changes in groundwater discharge to headwater springs and streams. We applied a process‐based 3D groundwater flow model to a 4 km2 crystalline alpine catchment in the Saint‐Barthélemy Massif, French Pyrenees, with steep elevation gradients (1,120–2,350 m). In the absence of wells, subsurface hydraulic properties were calibrated using surface data, including stream networks and streamflow measurements, estimating hydraulic conductivity, specific yield, and their depth‐dependent decay. The calibrated model captures the compartmentalized aquifer structure typical of steep crystalline mountain regions, satisfactorily reproducing spring locations, the expansion and contraction of streams and wetlands, and the catchment's associated streamflow dynamics. Forced with IPCC scenarios (RCP2.6, 4.5, 8.5), simulations suggest that by 2040, half of the historical hydrographic network will experience drier low‐flow conditions, with many upstream springs potentially drying. Projected water table declines alter the characteristic response times of the aquifer by reorganizing subsurface flow paths, strongly shaped by the local geomorphology and topography. Ridge‐dominated and steep sub‐catchments are especially vulnerable to hydrological disconnection, threatening biodiversity‐rich downstream peatlands. This replicable framework provides a robust tool to predict changes in water availability in mountain headwaters, offering critical guidance for sustainable land management and adaptive conservation of groundwater‐dependent ecosystems.

Accurate measurement of river hydrological characteristics is critical for assessing the impacts of flooding caused by meteorological and geomorphological factors. Flow velocity are key indicators in hydrological monitoring. Traditional measurement approaches, such as continuous-wave Doppler radar and pulsed radar systems, are typically mounted on bridges or fixed supports and offer only single-point measurements. These methods often suffer from limited detection range, low accuracy, and poor resistance to environmental interference. To address these limitations, this study proposes a three-dimensional flow detection framework based on multi-input multi-output (MIMO) radar sensors. By leveraging the high reliability and interference resistance of MIMO radar, along with a Space-Velocity-Time (SVT) algorithm that incorporates spatiotemporal information (two-dimensional surface velocity and time), the proposed method enables robust 3D river flow monitoring. In this study, comparative experiments were conducted on four rivers in China with different flow conditions, geomorphic features and weather environments. Results demonstrate that the proposed method achieves a measurement error of less than 5 % compared to acoustic Doppler current profilers (ADCP) and other conventional mechanical approaches, while also offering improved safety and real-time performance. Moreover, an adaptive flow correction algorithm is presented, which uses three optimized prediction models to compute the correction factor and reduces the mean streamflow measurement error to 0.79 % after correction, providing an effective solution for river gauging, flood control and flood resilience.

Abstract. Machine learning is playing an increasing role in hydrology, supplementing or replacing physics-based models. One notable example is the use of recurrent neural networks (RNNs) for forecasting streamflow given observed precipitation and geographic characteristics. Training of such a model over the continental United States (CONUS) has demonstrated that a single set of model parameters can be used across independent catchments, and that RNNs can outperform physics-based models. In this work, we take a next step and study the performance of RNNs for river routing in land surface models (LSMs). Instead of observed precipitation, the LSM-RNN uses instantaneous runoff calculated from physics-based models as an input. We train the model with data from river basins spanning the globe and test it using historical streamflow measurements. The model demonstrates skill at generalization across basins (predicting streamflow in catchments not used in training) and across time (predicting streamflow during years not used in training). We compare the predictions from the LSM-RNN with results from an existing physics-based model calibrated with a similar dataset and find that the LSM-RNN outperforms the physics-based model: a gain in median Nash–Sutcliffe efficiency (NSE) from 0.56 to 0.64 (time-split experiment) and from 0.30 to 0.34 (basin-split experiment). Our results show that RNNs are effective for global streamflow prediction from runoff inputs and motivate the development of complete routing models that can capture nested sub-basis connections.

Water availability is a crucial factor that must be considered in the planning and management of irrigation systems. This study aims to determine water requirements based on a proposed rice–rice–secondary crops cropping pattern in the Oepunu Irrigation Area. This irrigation area encompasses 111 hectares of functional land and 20 hectares of potential land, with the current cropping pattern being rice–secondary crops. To ensure sufficient water supply, a study is necessary to design an effective cropping pattern. This research employed the Modified Penman method to calculate evapotranspiration, the F.J. Mock method to estimate dependable discharge, and direct streamflow measurements. The dependable discharge obtained ranges from 1,02 to 8,38 m³/s. The average discharge from direct measurements during the dry season (May-II to July-II) was 0,26 m³/s. The irrigation water requirements for the alternative cropping patterns are as follows: Alternative I ranges from 0,02–0,32 m³/s, Alternative II from 0,03–0,30 m³/s, and Alternative III from 0,03–0,24 m³/s. Based on the calculations, the maximum irrigable land area is 316.49 hectares for Alternative I, 274.95 hectares for Alternative II, and 948.87 hectares for Alternative III. These figures exceed the available land area, indicating that the selection of a cropping pattern must be based on the planned discharge derived from both irrigation water requirement calculations and field measurements. The planned discharge values are 0,32 m³/s for Alternative I, 0,30 m³/s for Alternative II, and 0,24 m³/s for Alternative III. Based on these planned discharge values, the most suitable cropping pattern is Alternative III, with a planned discharge of 0,24 m³/s and measured available discharge of 0,26 m³/s.

Extreme rainfall events directly increase flood risks and further trigger environmental geological hazards (i.e., landslides and debris flows). Meanwhile, rainfall-induced risks are determined by climate and geographical factors and spatial socioeconomic factors (e.g., population density and gross domestic product). However, the middle stream of Yellow River Basin, where geological hazards frequently occur, lacks systematic analyses of rainfall-induced risks. In this study, we propose a comprehensive quantification framework and apply it to the Loess Plateau of northern China based on 40 years of climate data, streamflow measurements, and multiple spatial and geographical attribute datasets. A deep learning algorithm of long short-term memory (LSTM) was used to predict runoff, and the analytic hierarchy index was utilized to evaluate the comprehensive spatial risk considering natural and socioeconomic factors. Despite a decrease in annual precipitation in our study area of 1.46 mm per year, the intensity of heavy rainfall has increased since the 1980s, characterized by increases in rainstorm intensity (+4.68%), rainfall intensity (+7.07%), and rainfall amount (+5.34%). A comprehensive risk assessment indicated that high-risk areas accounted for 20.30% of the total area, with rainfall, geographical factors, and socioeconomic variables accounting for 53.90%, 29.72%, and 16.38% of risk areas, respectively. Rainfall was the dominant factor that determined the risk, and geographical and socioeconomic properties characterized the vulnerability and resilience of disasters. Our study provided an evaluation framework for multi-hazard risk assessment and insights for the development of disaster prevention and reduction policies.

Modelling occurrence and environmental risk of azithromycin in an intermittent river: Applying hydrological and water quality models.

More than half the world’s lithium resources are found in brine aquifers in Chile, Argentina, and Bolivia. Lithium brine processing requires freshwater, so as lithium exploration increases, accurate estimates of freshwater availability are critical for water management decisions in this region with limited water resources. Here we calculate modern freshwater inflows, such as groundwater recharge and streamflow, for 28 active or prospective lithium-producing basins. We use regional water budget assessments, field streamflow measurements, and global climate and groundwater recharge datasets. Using the freshwater inflow estimates, we calculate water scarcity using the Available Water Remaining methodology. Among all 28 basins, freshwater inflows range from 2 to 33 mm year−1. Our results reveal that commonly used global hydrologic models overestimate streamflow and freshwater availability substantially, leading to inaccurate water scarcity classifications.

The recent improvements in streamflow measurement approaches have boosted the reliability and accuracy of river flow measurement. In this study, long-term measurements of river discharge in the Tokoro River, Japan, were conducted. The key objective of this work is to investigate the extent of river flow measurement in a very shallow and narrow silt stream using the fluvial acoustic tomography system (FAT). Despite the preliminary nature of the measurement results, the recorded data were subject to analysis from three different outlooks. First, examinations were performed under very shallow and high-water conditions. Second, examinations were performed using double acoustic frequency. Third, examinations were performed using multiple independent flow datasets. As a new achievement in terms of advanced monitoring capabilities, it was documented that the measurement by the FAT was possible even in extremely shallow conditions. However, the minimum water depth along the measured cross-section must be ≥9 cm. Moreover, the FAT system demonstrated its capability to monitor streamflow in high water levels. In addition, it was found that using high transmission frequency can provide shorter wavelengths, permitting better spatial resolution and higher velocity resolutions and hence desirable measurement accuracy. Nevertheless, measurements in the presence of high suspended sediment particles were lacking. Alternatively, a lower transmission frequency offers a longer wavelength, which might be less sensitive to small-scale variations and results in an imprecise degree of measurements. Nonetheless, measurements can be accomplished even during the mobilization of a high concentration of suspended sediment matters. Finally, using multiple independent streamflow measurement records, the results proved that the flow measured by the FAT system was in very good agreement with the records acquired using sophisticated measurement approaches such as HADCP and STIV with a very low range of uncertainty.

The dynamics of fluvial erosion responds to soil erosion and surface runoff on hillslopes due to land use and environmental fragility, conditioned by the soil, geology, relief, and rainfall rate. Despite the increasing problems associated with fluvial erosion in Brazil, little information is available on bedload transport in headwater catchments under intense agricultural activity. Therefore, this study sought to characterize the fluvial erosion processes and bedload dynamics in an experimental catchment in southern Brazil located at the edge of the Brazilian Meridional plateau, which is representative of a large area of high environmental fragility and intense agricultural activity in Southern Brazil. The Guarda Mor River drains a headwater catchment (18.5 km2) with undulating and hilly terrain with fragile soils and intense agricultural activity controlling fine and coarse sediment supply downstream. During 11 major rainfall-runoff events, monitoring was conducted to measure streamflow, bedload transport rates, sediment size, and hydraulic parameters, such as Manning's n values and viscous layer thicknesses. A rating curve was established based on 40 streamflow and bedload discharge measurements taken at different water levels and stages along the hydrograph. In addition, a river portion (gravel bed) was characterized as well as the granulometric characteristics of its surface and subsurface layers. The results showed that the transport pattern is influenced by factors other than hydraulic parameters alone, including the interdependence between successive events, armoring effects, and hysteresis. These factors are strongly related to the surface runoff and erosion observed on the hillslopes, which define the streamflow energy and the supply of sand fraction, respectively. A discussion is held on the bedload transport dynamics under non-equilibrium conditions in the modeling of fluvial erosion processes.

Implementation of Standardized Uncertainty Analysis for Streamflow Measurements Acquired with Acoustic Doppler Velocimeters

The quantification of streamflow in river channels is crucial for water resource management, directly influencing the planning of agricultural irrigation, public water supply, and environmental monitoring. However, conventional methods can be expensive and require sophisticated equipment, making the float method a cost-effective alternative for streamflow estimation using simple and easily deployable materials. This study aimed to describe the theoretical and practical procedures for determining streamflow in natural fluvial channels using the float method. The research was conducted in the Piracolino River, in Vilhena/RO, following an experimental design based on the continuity equation, with defined transverse and longitudinal sections. Flow velocity was determined by tracking the displacement of a float between the designated sections, with data corrected using coefficients adjusted to the riverbed characteristics. The results indicated that, although less precise than more advanced techniques, the float method provided reliable streamflow estimates, yielding a calculated value of 2.95 m³/s. The primary limitation of this method lies in external factors such as riverbed irregularities and variations in float shape, which may affect measurement accuracy. Nevertheless, the method proves to be a viable approach for streamflow measurement in small to medium-sized rivers, enabling frequent and cost-effective hydrological monitoring.

ABSTRACT The measurement of a stream flow is a vital component of water quality monitoring, geomorphology, and flooding investigations. However, the direct measurements of stream flow can be costly and challenging. To obtain a continuous record of discharge, typically the recorded stage and discharge are computed from the correlation of stage and discharge in the form of a rating curve. This study focused on the estimation of flow discharges using a rating curve equation based on data recorded at the Erer-Jijiga Bridge hydrometric station at the outlet of the upper Erer Sub-basin in Ethiopia for 2020–2021. The fitted rating curve was then used to estimate discharges from continuously recorded stages. The installation and operation of a gauging station in the Upper Erer Sub-basin will provide local and regional water sector administrators with more reliable estimates of discharge than would be available if the sub-basin was ungauged. This will also assist the local water sector to undertake water resource planning in the Upper Erer River Basin to better use the limited water resources in the region.

Abstract. The recent development of FYRE (French hYdroclimate REanalysis) Climate, a high-resolution ensemble daily reanalysis of precipitation and temperature covering the 1871–2012 period and the whole of France, offers the opportunity to derive streamflow series over the country from 1871 onwards. The FYRE Climate dataset has been used as input for hydrological modelling over a large sample of 661 near-natural French catchments using the GR6J (Génie Rural à 6 Paramètres Journaliers) lumped conceptual model. This approach led to the creation of the 25-member hydrological reconstructions, HydRE (Hydrological REconstruction), spanning the 1871–2012 period. Two sources of uncertainties have been taken into account: (1) the climate uncertainty using forcings from all 25 ensemble members provided by FYRE Climate and (2) the streamflow measurement error by perturbing observations used during the calibration. Further, the hydrological model error based on the relative discrepancies between observed and simulated streamflow has been added to derive the HydREM (Hydrological REconstruction with Model error) streamflow reconstructions. These two reconstructions are compared to other hydrological reconstructions with different meteorological inputs, hydrological reconstructions from a machine learning algorithm, and independent and dependent observations. Overall, the results show the added value of the HydRE and HydREM reconstructions in terms of quality, uncertainty estimation, and representation of extremes, therefore allowing us to better understand the variability in past hydrology over France.

Climate warming induces shifts from snow to rain in cold regions1, altering snowpack dynamics with consequent impacts on streamflow that raise challenges to many aspects of ecosystem services2-4. A straightforward conceptual model states that as the fraction of precipitation falling as snow (snowfall fraction) declines, less solid water is stored over the winter and both snowmelt and streamflow shift earlier in season. Yet the responses of streamflow patterns to shifts in snowfall fraction remain uncertain5-9. Here we show that as snowfall fraction declines, the timing of the centre of streamflow mass may be advanced or delayed. Our results, based on analysis of 1950-2020 streamflow measurements across 3,049 snow-affected catchments over the Northern Hemisphere, show that mean snowfall fraction modulates the seasonal response to reductions in snowfall fraction. Specifically, temporal changes in streamflow timing with declining snowfall fraction reveal a gradient from earlier streamflow in snow-rich catchments to delayed streamflow in less snowy catchments. Furthermore, interannual variability of streamflow timing and seasonal variation increase as snowfall fraction decreases across both space and time. Our findings revise the 'less snow equals earlier streamflow' heuristic and instead point towards a complex evolution of seasonal streamflow regimes in a snow-dwindling world.

The deep transboundary aquifer of regional scale along the Czech Republic–Austria border in Central Europe serves as a thermal-mineral water resource for balneotherapy and plays an important role in the region’s development. The aquifer is composed mostly of Jurassic carbonates at depths from 160 to − 3000 masl. Despite more than two decades of exploitation, no complex analysis of groundwater flow directions and groundwater fluxes ever took place. Now, cross-border cooperation enabled the research team to gather crucial information on the Jurassic aquifer. For a better understanding of the groundwater flow system, a numerical model was developed. To simulate the effect of variable density and viscosity occurring in such a deep aquifer, the SEAWAT numerical model was used. The simulation shows that there is an inflow of low mineralised groundwater from the crystalline outcrops in the northwest and inflow of saline groundwater from southeast. Aquifer discharge was identified along the zone partly corresponding to the course of the Dyje River. To check the model’s accuracy, the river water was sampled together with streamflow measurements. Detected sections of increasing chloride concentration indicate zones of the Jurassic aquifer discharge into the Dyje River. The discharge rate of 85 L/s derived from streamflow and chloride concentrations matches the value computed by the model. The relatively high discharge of the Jurassic aquifer contributes significantly to the high chloride loading observed in the Dyje River.

Abstract. Ice cliffs are melt hot spots that contribute disproportionately to melt on debris-covered glaciers. In this study, we investigate the impact of supraglacial stream hydrology on ice cliffs using in situ and remote sensing observations, streamflow measurements, and a conceptual geomorphic model of ice cliff backwasting applied to ice cliffs on Kennicott Glacier, Alaska. We found that 33 % of ice cliffs (accounting for 69 % of the ice cliff area) are actively influenced by streams, while half are nearer than 10 m from the nearest stream. Supraglacial streams contribute to ice cliff formation and maintenance by horizontal meandering, vertical incision, and debris transport. These processes produce an undercut lip at the ice cliff base and transport clasts up to tens of centimeters in diameter, preventing reburial of ice cliffs by debris. Stream meander morphology reminiscent of sedimentary river channel meanders and oxbow lakes produces sinuous and crescent ice cliff shapes. Stream avulsions result in rapid ice cliff collapse and local channel abandonment. Ice cliffs abandoned by streams are observed to be reburied by supraglacial debris, indicating a strong role played by streams in ice cliff persistence. We also report on a localized surge-like event at the glacier's western margin which drove the formation of ice cliffs from crevassing; these cliffs occur in sets with parallel linear morphologies contrasting with the crescent planform shape of stream-driven cliffs. The development of landscape evolution models may assist in quantifying the total net effect of these processes on steady-state ice cliff coverage and mass balance, contextualizing them with other drivers including supraglacial ponds, differential melt, ice dynamics, and collapse of englacial voids.