Impacts of Snowmelt on Peak Flows in a Forest Watershed

Sui, Jueyi; Koehler, Gero

doi:10.1007/s11269-006-9080-9

Cited by 8 publications

(5 citation statements)

References 12 publications

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“…A 30% snowmelt contribution to runoff was found by Rössler et al . () for a ROS event in the Swiss Alps, while Sui and Koehler () found snowmelt contributions between 30 and 70% in a forest region of southeast Germany.…”

Section: Resultsmentioning

confidence: 99%

Spatio‐temporal controls of snowmelt and runoff generation during rain‐on‐snow events in a mid‐latitude mountain catchment

Garvelmann

Pohl

Weiler

2015

Hydrological Processes

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Abstract:A network of 30 standalone snow monitoring stations was used to investigate the snow cover distribution, snowmelt dynamics, and runoff generation during two rain-on-snow (ROS) events in a 40 km 2 montane catchment in the Black Forest region of southwestern Germany. A multiple linear regression analysis using elevation, aspect, and land cover as predictors for the snow water equivalent (SWE) distribution within the catchment was applied on an hourly basis for two significant ROS flood events that occurred in December 2012. The available snowmelt water, liquid precipitation, as well as the total retention storage of the snow cover were considered in order to estimate the amount of water potentially available for the runoff generation. The study provides a spatially and temporally distributed picture of how the two observed ROS floods developed in the catchment. It became evident that the retention capacity of the snow cover is a crucial mechanism during ROS. It took several hours before water was released from the snowpack during the first ROS event, while retention storage was exceeded within 1 h from the start of the second event. Elevation was the most important terrain feature. South-facing terrain contributed more water for runoff than north-facing slopes, and only slightly more runoff was generated at open compared to forested areas. The results highlight the importance of snowmelt together with liquid precipitation for the generation of flood runoff during ROS and the large temporal and spatial variability of the relevant processes.

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Section: Resultsmentioning

confidence: 99%

Spatio‐temporal controls of snowmelt and runoff generation during rain‐on‐snow events in a mid‐latitude mountain catchment

Garvelmann

Pohl

Weiler

2015

Hydrological Processes

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“…If the net incoming energy is positive, this adds heat to the system and eventually leads to melting of the snowpack. The rate and quantity of snowmelt depends on the amount of energy added to the system (Koivusalo and Kakkonen 2002;Valeo and Ho 2004;Sui and Koehler 2007;Zhao et al 2009). Following from this approach, the amount of snowmelt (Q Tot ) in equivalent millimeter of water (mme) is computed by the following expression:…”

Section: Snowmelt Estimation Following the Energy Budget Approachmentioning

confidence: 99%

Comparison of Process-Based and Temperature-Index Snowmelt Modeling in SWAT

Debele¹,

Srinivasan

Gosain

2009

Water Resour Manage

130

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Snowmelt hydrology is an important part of hydrological analyses where significant proportion of precipitation is expected to fall in a snow form. Many models have long been introduced to enable the simulation of snowmelt processes in the watershed ranging from simple temperature based equations to complex and sophisticated process-based equations. Usually, mixed results have been reported whether or not the difference between results achieved by incorporating data demanding models vis-à-vis simple temperature-index models is justifiable. In this study, we compared the performances of physically based energy budget and simpler temperature-index based snowmelt calculation approaches within the SWAT model at three sites in two different continents. The results indicate insignificant differences between the two approaches. The temperature-index based snowmelt computation method had the overall model efficiency coefficients ranging from 0.49 to 0.73 while the energy budget based approach had efficiency coefficients ranging from 0.33 to 0.59 only. The magnitude of the differences varied based on where the models were applied. However, comparison between two process-based snowmelt estimation procedures (with and without the inclusion of aspect and slope as factors dictating the incoming solar energy) indicate that accounting for ground surface slope and aspect in the snowmelt model slightly improved the results. We conclude that for most B. Debele B. Debele et al. practical applications where net solar radiation, not turbulent heat flux, dominates the snowmelt dynamics, a simpler temperature-index snowmelt estimation model is sufficient.

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“…However, variation in specific conductance does not always indicate baseflow changes [40], and detailed investigations must examine hillslope processes at various scales [41][42][43]. Furthermore, a changing climate will alter the timing and nature of baseflow [19,22,[44][45][46][47][48].…”

Section: Discussionmentioning

confidence: 99%

Baseflow from Snow and Rain in Mountain Watersheds

Flynn,

Fassnacht,

MacDonald

et al. 2024

Water

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After peak snowmelt, baseflow is the primary contributor to streamflow in snow-dominated watersheds. These low flows provide important water for municipal, agricultural, and recreational purposes once peak flows have been allocated. This study examines the correlation between peak snow water equivalent (SWE), post-peak SWE precipitation, and baseflow characteristics, including any yearly lag in baseflow. To reflect the hydrologic processes that are occurring in snow-dominated watersheds, we propose using a melt year (MY) beginning with the onset of snowmelt contributions (the first deviation from baseflow) and ending with the onset of the following year’s snowmelt contributions. We identified the beginning of an MY and extracted the subsequent baseflow values using flow duration curves (FDCs) for 12 watersheds of varying sizes across Colorado, USA. Based on the findings, peak SWE and summer rain both dictate baseflow, especially for the larger watersheds evaluated, as identified by higher correlations with the MY-derived baseflow. Lags in the correlation between baseflow and peak SWE are best identified when low-snow years are investigated separately from high-snow years. The MY is a different and more effective approach to calculating baseflow using FDCs in snow-dominated watersheds in Colorado.

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Impacts of Snowmelt on Peak Flows in a Forest Watershed

Cited by 8 publications

References 12 publications

Spatio‐temporal controls of snowmelt and runoff generation during rain‐on‐snow events in a mid‐latitude mountain catchment

Spatio‐temporal controls of snowmelt and runoff generation during rain‐on‐snow events in a mid‐latitude mountain catchment

Comparison of Process-Based and Temperature-Index Snowmelt Modeling in SWAT

Baseflow from Snow and Rain in Mountain Watersheds

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