Importance of maximum snow accumulation for summer low flows in humid catchments

Abstract

Abstract. Winter snow accumulation obviously has an effect on the following catchment runoff. The question is, however, how long this effect lasts and how important it is compared to rainfall inputs. Here we investigate the relative importance of snow accumulation on one critical aspect of runoff, namely the summer low flow. This is especially relevant as the expected increase of air temperature might result in decreased snow storage. A decrease of snow will affect soil and groundwater storages during spring and might cause low streamflow values in the subsequent warm season. To understand these potential climate change impacts, a better evaluation of the effects of inter-annual variations in snow accumulation on summer low flow under current conditions is central. The objective in this study was (1) to quantify how long snowmelt affects runoff after melt-out and (2) to estimate the sensitivity of catchments with different elevation ranges to changes in snowpack. To find suitable predictors of summer low flow we used long time series from 14 Alpine and pre-Alpine catchments in Switzerland and computed different variables quantifying winter and spring snow conditions. In general, the results indicated that maximum winter snow water equivalent (SWE) influenced summer low flow, but could expectedly only partly explain the observed inter-annual variations. On average, a decrease of maximum SWE by 10 % caused a decrease of minimum discharge in July by 6-9 % in catchments higher than 2000 m a.s.l. This effect was smaller in middle-and lower-elevation catchments with a decrease of minimum discharge by 2-5 % per 10 % decrease of maximum SWE. For higher-and middle-elevation catchments and years with below-average SWE maximum, the minimum discharge in July decreased to 70-90 % of its normal level. Additionally, a reduction in SWE resulted in earlier low-flow occurrence in some cases. One other important factor was the precipitation between maximum SWE and summer low flow. When only dry preceding conditions in this period were considered, the importance of maximum SWE as a predictor of low flows increased. We assessed the sensitivity of individual catchments to the change of maximum SWE using the non-parametric Theil-Sen approach as well as an elasticity index. Both sensitivity indicators increased with increasing mean catchment elevation, indicating a higher sensitivity of summer low flow to snow accumulation in Alpine catchments compared to lower-elevation pre-Alpine catchments.

Climate Elasticity of Low Flows in the Maritime Western U.S. Mountains

“…We find that summer low‐flow elasticity to the annual maximum SWE ( SWE MAX ) ranges from 0.13% to 1.18% (median 0.43%), similar to values reported for the Sierra Nevada mountains and the Swiss Alps (Godsey et al, ; Jenicek et al, , ). The magnitude of low‐flow elasticity to SWE MAX depends on the correlation between low flows and SWE MAX .…”

“…We estimated Q MIN elasticity using ordinary least squares log‐log linear regression between annual time series of Q MIN and SWE MAX , PPT , and PET (Jenicek et al, ). The method is equivalent to the bivariate parametric estimator described by Sankarasubramanian et al ().…”

Snow hydrology signatures for model identification within a limits‐of‐acceptability approach

“…A regional check with discharge data from catchments with similar settings shows, however, a remarkable result: The simulated annual discharge anomalies (with respect to a longer term mean) for the Dischma catchment (with time variable lapse rates) closely follow the trend of the nearby rivers (Figure ), while the observed data completely drifts away. In addition, all published simulations with similar models (with PREVAH and HBV by Orth et al, ;with HBV by Jenicek et al, ) show a comparable deviation from the observed discharge data over the period 2000–2005 (Schaefli et al, ).…”

Bias correcting precipitation forecasts to improve the skill of seasonal streamflow forecasts

“…Jenicek et al, 2016, and references therein), and dynamical methods, which use seasonal meteorological forecasts as input to a hydrological model. More recently, mixed approaches have been investigated to take advantage of initial land surface conditions, seasonal predictions of atmospheric variables and the predictability information contained in large-scale climate features (see Robertson et al, 2013;Yuan et al, 2015, and references therein).…”