Quantifying contributions of snowmelt water to streamflow using graphical and chemical hydrograph separation

In this study, we characterize the snowmelt hydrological response of nine headwater watersheds in southeast Wyoming by separating streamflow into three components using a combination of tracer and graphical approaches. First, continuous 15-min records of specific conductance (SC) from 2016 to 2018 were used to separate streamflow into annual contributions, representing water that contributes to streamflow in a given year that entered the watershed in the same year being considered, and perennial contributions, representing water that contributes to streamflow in a given year that entered the watershed in previous years. Then, diurnal streamflow cycles occurring during the snowmelt season were used to graphically separate annual contributions into rapid diurnal snowmelt contributions, representing water with the relatively fastest hydrological response and shortest residence time, and delayed annual contributions, representing water with relatively longer residence time in the watershed before becoming streamflow. On average, mean annual total streamflow was comprised of between 22% and 46% perennial contributions, 7% and 14% rapid diurnal snowmelt contributions, and 46% and 55% delayed annual contributions across the watersheds. A hysteresis index describing SC-discharge patterns indicated that, annually, most watersheds showed negative, concave, anti-clockwise hysteretic direction suggesting faster flow pathways dominate streamflow on the rising limb of the annual hydrograph relative to the falling limb. At the daily timescale during snowmelt-induced diurnal streamflow cycles, hysteresis was negative, but with a clockwise direction, implying that rapid diurnal snowmelt contributions generated from the concurrent daily snowmelt, with lower SC, arrived after delayed annual contribution peaks and preferentially contributed on the falling limb of diurnal cycles. South-facing watersheds were more susceptible to early season snowmelt at slower rates, resulting in less annual and more perennial contributions. Conversely, north-facing watersheds had longer snow persistence and larger proportions of annual contributions and rapid diurnal snowmelt contributions. Watersheds with surficial geology dominated by glacial deposits had a lower proportion of rapid diurnal snowmelt contributions compared to watersheds with large percentages of bedrock surficial geology.     

Modelling suspended sediment concentration using artificial neural networks for Gangotri glacier

The dynamics of suspended sediment involves inherent non‐linearity and complexity because of existence of both spatial variability of the basin characteristics and temporal climatic patterns. This complexity, therefore, leads to inaccurate prediction by the conventional sediment rating curve (SRC) and other empirical methods. Over past few decades, artificial neural networks (ANNs) have emerged as one of the advanced modelling techniques capable of addressing inherent non‐linearity in the hydrological processes. In the present study, feed‐forward back propagation (FFBP) algorithm of ANNs is used to model stage–discharge–suspended sediment relationship for ablation season (May–September) for melt runoff released from Gangotri glacier, one of the largest glaciers in Himalaya. The simulations have been carried out on primary data of suspended sediment concentration (SSC) discharge and stage for ablation season of 11‐year period (1999–2009). Combinations of different input vectors (viz. stage, discharge and SSC) for present and previous days are considered for development of the ANN models and examining the effects of input vectors. Further, based on model performance indices for training and testing phase, a suitable modelling approach with appropriate model input structure is suggested. The conventional SRC method is also used for modelling discharge–sediment relationship and performance of developed models is evaluated by statistical indices, namely; root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R²). Statistically, the performance of ANN‐based models is found to be superior as compared to SRC method in terms of the selected performance indices in simulating the daily SSC. The study reveals suitability of ANN approach for simulation and estimation of daily SSC in glacier melt runoff and, therefore, opens new avenues of research for application of hybrid soft computing models in glacier hydrology. Copyright © 2015 John Wiley & Sons, Ltd.     

Identifying run‐off contributions during melt‐induced run‐off events in a glacierized alpine catchment

We analysed contributions to run‐off using hourly stream water samples from seven individual melt‐induced run‐off events (plus one rainfall event) during 2011, 2012 and 2013 in two nested glacierized catchments in the Eastern Italian Alps. Electrical conductivity and stable isotopes of water were used for mixing analysis and two‐component and three‐component hydrograph separation. High‐elevation snowmelt, glacier melt and autumn groundwater were identified as major end‐members. Discharge and tracers in the stream followed the diurnal variations of air temperature but markedly reacted to rainfall inputs. Hysteresis patterns between discharge and electrical conductivity during the melt‐induced run‐off events revealed contrasting loop directions at the two monitored stream sections. Snowmelt contribution to run‐off was highest in June and July (up to 33%), whereas the maximum contribution of glacier melt was reached in August (up to 65%). The maximum groundwater and rainfall contributions were 62% and 11%, respectively. Run‐off events were generally characterized by decreasing snowmelt and increasing glacier melt fractions from the beginning to the end of the summer 2012, while run‐off events in 2013 showed less variable snowmelt and lower glacier melt contributions than in 2012. The results provided essential insights into the complex dynamics of melt‐induced run‐off events and may be of further use in the context of water resource management in alpine catchments. Copyright © 2015 John Wiley & Sons, Ltd.     

Hydrograph separation using tracers and digital filters to quantify runoff components in a semi‐arid mesoscale catchment

Chemical hydrograph separation using electrical conductivity and digital filters is applied to quantify runoff components in the 1,640 km² semi‐arid Kaap River catchment and its subcatchments in South Africa. A rich data set of weekly to monthly water quality data ranging from 1978 to 2012 (450 to 940 samples per site) was analysed at 4 sampling locations in the catchment. The data were routinely collected by South Africa's national Department of Water and Sanitation, using standard sampling procedures. Chemical hydrograph separation using electrical conductivity (EC) as a tracer was used as reference and a recursive digital filter was then calibrated for the catchment. Results of the two‐component hydrograph separation indicate the dominance of baseflow in the low flow regime, with a contribution of about 90% of total flow; however, during the wet season, baseflow accounts for 50% of total flow. The digital filter parameters were very sensitive and required calibration, using chemical hydrograph separation as a reference. Calibrated baseflow estimates ranged from 40% of total flow at the catchment outlet to 70% in the tributaries. The study demonstrates that routinely monitored water quality data, especially EC, can be used as a meaningful tracer, which could also aid in the calibration of a digital filter method and reduce uncertainty of estimated flow components. This information enhances our understanding of how baseflow is generated and contributed to streamflow throughout the year, which can aid in quantification of environmental flows, as well as to better parameterize hydrological models used for water resources planning and management. Baseflow estimates can also be useful for groundwater and water quality management.