Topography significantly influencing low flows in snow-dominated watersheds

Catchments consist of distinct landforms that affect the storage and release of subsurface water. Certain landforms may be the main contributors to streamflow during extended dry periods, and these may vary for different catchments in a given region. We present a unique dataset from snapshot field campaigns during low‐flow conditions in 11 catchments across Switzerland to illustrate this. The catchments differed in size (10 to 110 km2), varied from predominantly agricultural lowlands to Alpine areas, and covered a range of physical characteristics. During each snapshot campaign, we jointly measured streamflow and collected water samples for the analysis of major ions and stable water isotopes. For every sampling location (basin), we determined several landscape characteristics from national geo‐datasets, including drainage area, elevation, slope, flowpath length, dominant land use, and geological and geomorphological characteristics, such as the lithology and fraction of quaternary deposits. The results demonstrate very large spatial variability in specific low‐flow discharge and water chemistry: Neighboring sampling locations could differ significantly in their specific discharge, isotopic composition, and ion concentrations, indicating that different sources contribute to streamflow during extended dry periods. However, none of the landscape characteristics that we analysed could explain the spatial variability in specific discharge or streamwater chemistry in multiple catchments. This suggests that local features determine the spatial differences in discharge and water chemistry during low‐flow conditions and that this variability cannot be assessed a priori from available geodata and statistical relations to landscape characteristics. The results furthermore suggest that measurements at the catchment outlet during low‐flow conditions do not reflect the heterogeneity of the different source areas in the catchment that contribute to streamflow.

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatial variability in specific discharge and streamwater chemistry during low flows: Results from snapshot sampling campaigns in eleven Swiss catchments

Floriancic

Fischer

Molnár

et al. 2019

“…Low flows are typically defined from the minimum flow that is not exceeded for a given duration on an annual basis (e.g., Foster, 1924;Smakhtin, 2001) or from hydrograph recession analysis (e.g., Brutsaert & Nieber, 1977;Kirchner, 2009;Tallaksen, 1995). Characteristics of low flows, such as their frequency, magnitude, and duration, can be related to basin characteristics like elevation, slope, and other topographic drivers (e.g., Li et al, 2018;Mutzner et al, 2013;Staudinger et al, 2017) or to climate characteristics such as seasonal precipitation or temperature patterns (e.g., Giuntoli, Renard, Vidal, & Bard, 2013; Jenicek, Seibert, Zappa, Staudinger, & Jonas, 2016). Water balance models have also been used for low-flow prediction (Fahey et al, 2010;Pushpalatha, Perrin, Le Moine, Mathevet, & Andreassian, 2011), and rainfall-runoff models have been specifically calibrated for low flows (Garcia et al, 2017;Singh & Frevert, 2002;Staudinger, Weiler, & Seibert, 2015).…”

Section: Introductionmentioning

confidence: 99%

Spatio‐temporal variability in contributions to low flows in the high Alpine Poschiavino catchment

Floriancic

Meerveld

Smoorenburg

et al. 2018

In order to predict streamflow accurately during extended dry periods, we need to understand the spatial variability of low flows and the extent to which it is affected by the spatial organization and drainage of catchment subsurface storage areas. This is especially true in Alpine catchments with widely varying topography, lithology, sediment deposits, and soil properties. Field measurements in the Poschiavino catchment in southern Switzerland, during a winter recession period without recharge, provided a unique opportunity to demonstrate the connections between subsurface storage areas, low flows, and their variability. We measured discharge in four nested sub‐catchments during seven field campaigns in the winter of 2013–2014. We analysed stream water electrical conductivity (EC) and water chemistry to identify the areas contributing to low‐flow discharge and estimated their contributions. Sediment cover type and thickness were mapped using a recently developed tool for geomorphology‐based storage classification of mountainous terrain, to determine the physical properties of the subsurface storage areas contributing to low‐flow discharge. Recession analyses combined with water chemistry data allowed the detection of different drainage timescales and the estimation of storage potential of the unconsolidated (Quaternary) deposits. We found substantial spatial variation in storage depletion between the sub‐catchments, ranging from 54 mm to 200 mm for the four‐month monitoring period. Variability in low‐flow contributions from different catchments and different recession behavior could be related to the differences in the estimated storage potential. For some point sources, we could quantify the contributing area and thus quantify low flows at the hillslope scale. Overall, the low‐flow variability is mostly related to the fraction of precipitation that recharges subsurface storage areas and to the properties influencing their drainage. To capture these processes, we suggest low‐flow geomorphological mapping approaches that consider not only morphometric (shape of the landscape) and geologic (properties of the bedrock) controls but also the water storage potential of debris cover and weathered rock.

“…The two Budyko‐based approaches produced very consistent results; that is, about 33–35% increase in streamflow was due to human activities whereas the remaining was due to climate variability. The higher contribution of climate change than human activities is reasonable considering that the vast area of the upper HRB is located in the mountainous regions with a very low population density, whereas climate change is significant and manifests in the form of rapid increases in air temperature and precipitation and substantial variations of the cryospheric processes (Cheng et al, ; Li, Wei, et al, ; Zhang, Zheng, Wang, & Yao, ). Moreover, the finding is in line with the previous studies.…”

Section: Discussionmentioning

confidence: 99%

“…Typically, these methods were used to separate impacts on streamflow. In this research, we also applied them to distinguish impacts on surface run‐off and baseflow, as did by Li et al (). The parameter values for each time‐trend method were estimated using the linear or nonlinear least square regression methods.…”

Section: Methodsmentioning

confidence: 99%

Separating climate change and human contributions to variations in streamflow and its components using eight time‐trend methods

Zhang

Wang

et al. 2018

Separating impacts of human activities and climate change on hydrology is essential for watershed and ecosystem management. Many previous studies have focused on the impacts on total streamflow, however, with little attentions paid to its components (i.e., baseflow and surface run‐off). This study distinguished the contributions of climate change and human activities to the variations in streamflow, baseflow, and surface run‐off in the upstream area of the Heihe River Basin, a typical inland river basin in northwest China, by using eight different forms of time‐trend methods. The isolated contributions to streamflow variation were also compared with those obtained by two Budyko‐based approaches. Our results showed that the time‐trend methods consistently estimated positive contributions of climate variability and human activities to the increases in streamflow and its components but with obviously varying magnitudes. With regard to streamflow, the time‐trend method double‐mass‐curve–Wei, with a physical basis, produced a reasonable smaller contribution of human activities than climate changes, inconsistent with the Budyko‐based approaches. However, all the other time‐trend methods led to contrary results. The contributions to baseflow variation diverged more significantly than those to streamflow and surface run‐off, ranging from 24% to 92% for human activities and from 8% to 76% for climate variability. In terms of surface run‐off, most of the time‐trend approaches produced smaller contributions of human activities (ranging from 21% to 49%) than climate change. The uncertainties associated with the various time‐trend approaches and the baseflow separation algorithm were revealed and discussed, along with some recommendations for future work.