Articles | Volume 25, issue 5
https://doi.org/10.5194/hess-25-2869-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-2869-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
27 May 2021
Research article |
| 27 May 2021
| 27 May 2021
The evaluation of the potential of global data products for snow hydrological modelling in ungauged high-alpine catchments
Michael Weber
Department of Geography, Ludwig-Maximilians-Universität, Munich, Germany
Institute for Hydrology and Water Management, University of Natural Resources and Life Sciences, Vienna, Austria
Matthias Bernhardt
Institute for Hydrology and Water Management, University of Natural Resources and Life Sciences, Vienna, Austria
Karsten Schulz
Institute for Hydrology and Water Management, University of Natural Resources and Life Sciences, Vienna, Austria
Related authors
No articles found.
Hatice Türk, Christine Stumpp, Markus Hrachowitz, Karsten Schulz, Peter Strauss, Günter Blöschl, and Michael Stockinger
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-359, https://doi.org/10.5194/hess-2024-359, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
Using advances in transit time estimation and tracer data, we tested if fast-flow transit times are controlled solely by soil moisture or are also controlled by precipitation intensity. We used soil moisture-dependent and precipitation intensity-conditional transfer functions. We showed that significant portion of event water bypasses the soil matrix through fast flow paths (overland flow, tile drains, preferential flow paths) in dry soil conditions for both low and high-intensity precipitation.
Achille Capelli, Franziska Koch, Patrick Henkel, Markus Lamm, Florian Appel, Christoph Marty, and Jürg Schweizer
The Cryosphere, 16, 505–531, https://doi.org/10.5194/tc-16-505-2022, https://doi.org/10.5194/tc-16-505-2022, 2022
Short summary
Short summary
Snow occurrence, snow amount, snow density and liquid water content (LWC) can vary considerably with climatic conditions and elevation. We show that low-cost Global Navigation Satellite System (GNSS) sensors as GPS can be used for reliably measuring the amount of water stored in the snowpack or snow water equivalent (SWE), snow depth and the LWC under a broad range of climatic conditions met at different elevations in the Swiss Alps.
Christian Voigt, Karsten Schulz, Franziska Koch, Karl-Friedrich Wetzel, Ludger Timmen, Till Rehm, Hartmut Pflug, Nico Stolarczuk, Christoph Förste, and Frank Flechtner
Hydrol. Earth Syst. Sci., 25, 5047–5064, https://doi.org/10.5194/hess-25-5047-2021, https://doi.org/10.5194/hess-25-5047-2021, 2021
Short summary
Short summary
A continuously operating superconducting gravimeter at the Zugspitze summit is introduced to support hydrological studies of the Partnach spring catchment known as the Zugspitze research catchment. The observed gravity residuals reflect total water storage variations at the observation site. Hydro-gravimetric analysis show a high correlation between gravity and the snow water equivalent, with a gravimetric footprint of up to 4 km radius enabling integral insights into this high alpine catchment.
Christoph Klingler, Karsten Schulz, and Mathew Herrnegger
Earth Syst. Sci. Data, 13, 4529–4565, https://doi.org/10.5194/essd-13-4529-2021, https://doi.org/10.5194/essd-13-4529-2021, 2021
Short summary
Short summary
LamaH-CE is a large-sample catchment hydrology dataset for Central Europe. The dataset contains hydrometeorological time series (daily and hourly resolution) and various attributes for 859 gauged basins. Sticking closely to the CAMELS datasets, LamaH includes additional basin delineations and attributes for describing a large interconnected river network. LamaH further contains outputs of a conceptual hydrological baseline model for plausibility checking of the inputs and for benchmarking.
Pirmin Philipp Ebner, Franziska Koch, Valentina Premier, Carlo Marin, Florian Hanzer, Carlo Maria Carmagnola, Hugues François, Daniel Günther, Fabiano Monti, Olivier Hargoaa, Ulrich Strasser, Samuel Morin, and Michael Lehning
The Cryosphere, 15, 3949–3973, https://doi.org/10.5194/tc-15-3949-2021, https://doi.org/10.5194/tc-15-3949-2021, 2021
Short summary
Short summary
A service to enable real-time optimization of grooming and snow-making at ski resorts was developed and evaluated using both GNSS-measured snow depth and spaceborne snow maps derived from Copernicus Sentinel-2. The correlation to the ground observation data was high. Potential sources for the overestimation of the snow depth by the simulations are mainly the impact of snow redistribution by skiers, compensation of uneven terrain, or spontaneous local adaptions of the snow management.
Josef Fürst, Hans Peter Nachtnebel, Josef Gasch, Reinhard Nolz, Michael Paul Stockinger, Christine Stumpp, and Karsten Schulz
Earth Syst. Sci. Data, 13, 4019–4034, https://doi.org/10.5194/essd-13-4019-2021, https://doi.org/10.5194/essd-13-4019-2021, 2021
Short summary
Short summary
Rosalia is a 222 ha forested research watershed in eastern Austria to study water, energy and solute transport processes. The paper describes the site, monitoring network, instrumentation and the datasets: high-resolution (10 min interval) time series starting in 2015 of four discharge gauging stations, seven rain gauges, and observations of air and water temperature, relative humidity, and conductivity, as well as soil water content and temperature, at different depths at four profiles.
Moritz Feigl, Katharina Lebiedzinski, Mathew Herrnegger, and Karsten Schulz
Hydrol. Earth Syst. Sci., 25, 2951–2977, https://doi.org/10.5194/hess-25-2951-2021, https://doi.org/10.5194/hess-25-2951-2021, 2021
Short summary
Short summary
In this study we developed machine learning approaches for daily river water temperature prediction, using different data preprocessing methods, six model types, a range of different data inputs and 10 study catchments. By comparing to current state-of-the-art models, we could show a significant improvement of prediction performance of the tested approaches. Furthermore, we could gain insight into the relationships between model types, input data and predicted stream water temperature.
Christoph Schürz, Bano Mehdi, Jens Kiesel, Karsten Schulz, and Mathew Herrnegger
Hydrol. Earth Syst. Sci., 24, 4463–4489, https://doi.org/10.5194/hess-24-4463-2020, https://doi.org/10.5194/hess-24-4463-2020, 2020
Short summary
Short summary
The USLE is a commonly used model to estimate soil erosion by water. It quantifies soil loss as a product of six inputs representing rainfall erosivity, soil erodibility, slope length and steepness, plant cover, and support practices. Many methods exist to derive these inputs, which can, however, lead to substantial differences in the estimated soil loss. Here, we analyze the effect of different input representations on the estimated soil loss in a large-scale study in Kenya and Uganda.
Benjamin Müller, Matthias Bernhardt, and Karsten Schulz
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-563, https://doi.org/10.5194/hess-2019-563, 2019
Manuscript not accepted for further review
Short summary
Short summary
Time series of thermal remote sensing images include more information than usually used. Land surface related processes are combined into a single image. Activity of these processes change from image to image. Thus, information on land surface characteristics is to be found
somewhere in betweenthe images. We provide an algorithm to test the presence of such characteristics within a set of images. The algorithm can be used for process understanding, model evaluation, data assimilation, etc.
Christoph Schürz, Brigitta Hollosi, Christoph Matulla, Alexander Pressl, Thomas Ertl, Karsten Schulz, and Bano Mehdi
Hydrol. Earth Syst. Sci., 23, 1211–1244, https://doi.org/10.5194/hess-23-1211-2019, https://doi.org/10.5194/hess-23-1211-2019, 2019
Short summary
Short summary
For two Austrian catchments we simulated discharge and nitrate-nitrogen (NO3-N) considering future changes of climate, land use, and point source emissions together with the impact of different setups and parametrizations of the implemented eco-hydrological model. In a comprehensive analysis we identified the dominant sources of uncertainty for the simulation of discharge and NO3-N and further examined how specific properties of the model inputs control the future simulation results.
Abolanle E. Odusanya, Bano Mehdi, Christoph Schürz, Adebayo O. Oke, Olufiropo S. Awokola, Julius A. Awomeso, Joseph O. Adejuwon, and Karsten Schulz
Hydrol. Earth Syst. Sci., 23, 1113–1144, https://doi.org/10.5194/hess-23-1113-2019, https://doi.org/10.5194/hess-23-1113-2019, 2019
Short summary
Short summary
The main objective was to calibrate and validate the eco-hydrological model Soil and Water Assessment Tool (SWAT) with satellite-based actual evapotranspiration (AET) data for the data-sparse Ogun River Basin (20 292 km2) located in southwestern Nigeria. The SWAT model, composed of the Hargreaves PET equation and calibrated using the GLEAM_v3.0a data (GS1), performed well for the simulation of AET and provided a good level of confidence for using the SWAT model as a decision support tool.
Maik Renner, Claire Brenner, Kaniska Mallick, Hans-Dieter Wizemann, Luigi Conte, Ivonne Trebs, Jianhui Wei, Volker Wulfmeyer, Karsten Schulz, and Axel Kleidon
Hydrol. Earth Syst. Sci., 23, 515–535, https://doi.org/10.5194/hess-23-515-2019, https://doi.org/10.5194/hess-23-515-2019, 2019
Short summary
Short summary
We estimate the phase lag of surface states and heat fluxes to incoming solar radiation at the sub-daily timescale. While evapotranspiration reveals a minor phase lag, the vapor pressure deficit used as input by Penman–Monteith approaches shows a large phase lag. The surface-to-air temperature gradient used by energy balance residual approaches shows a small phase shift in agreement with the sensible heat flux and thus explains the better correlation of these models at the sub-daily timescale.
Lu Gao, Jianhui Wei, Lingxiao Wang, Matthias Bernhardt, Karsten Schulz, and Xingwei Chen
Earth Syst. Sci. Data, 10, 2097–2114, https://doi.org/10.5194/essd-10-2097-2018, https://doi.org/10.5194/essd-10-2097-2018, 2018
Short summary
Short summary
High-resolution temperature data sets are important for the Chinese Tian Shan, which has a complex ecological environment system. This study presents a unique high-resolution (1 km, 6-hourly) air temperature data set for this area from 1979 to 2016 based on a robust statistical downscaling framework. The strongest advantage of this method is its independence of local meteorological stations due to a model internal, vertical lapse rate scheme. This method was validated for other mountains.
Frederik Kratzert, Daniel Klotz, Claire Brenner, Karsten Schulz, and Mathew Herrnegger
Hydrol. Earth Syst. Sci., 22, 6005–6022, https://doi.org/10.5194/hess-22-6005-2018, https://doi.org/10.5194/hess-22-6005-2018, 2018
Short summary
Short summary
In this paper, we propose a novel data-driven approach for
rainfall–runoff modelling, using the long short-term memory (LSTM) network, a special type of recurrent neural network. We show in three different experiments that this network is able to learn to predict the discharge purely from meteorological input parameters (such as precipitation or temperature) as accurately as (or better than) the well-established Sacramento Soil Moisture Accounting model, coupled with the Snow-17 snow model.
Stefan Härer, Matthias Bernhardt, Matthias Siebers, and Karsten Schulz
The Cryosphere, 12, 1629–1642, https://doi.org/10.5194/tc-12-1629-2018, https://doi.org/10.5194/tc-12-1629-2018, 2018
Short summary
Short summary
The paper presents an approach which can be used to process satellite-based snow cover maps with a higher-than-today accuracy at the local scale. Many of the current satellite-based snow maps are using the NDSI with a threshold as a tool for deciding if there is snow on the ground or not. The presented study has shown that, firstly, using the standard threshold of 0.4 can result in significant derivations at the local scale and that, secondly, the deviations become smaller for coarser scales.
Karsten Schulz, Reinhard Burgholzer, Daniel Klotz, Johannes Wesemann, and Mathew Herrnegger
Hydrol. Earth Syst. Sci., 22, 2607–2613, https://doi.org/10.5194/hess-22-2607-2018, https://doi.org/10.5194/hess-22-2607-2018, 2018
Short summary
Short summary
The unit hydrograph has been one of the most widely employed modelling techniques to predict rainfall-runoff behaviour of hydrological catchments. We developed a lecture theatre experiment including some student involvement to illustrate the principles behind this modelling technique. The experiment only uses very simple and cheap material including a set of plastic balls (representing rainfall), magnetic stripes (tacking the balls to the white board) and sieves (for ball/water gauging).
Benjamin Müller, Matthias Bernhardt, Conrad Jackisch, and Karsten Schulz
Hydrol. Earth Syst. Sci., 20, 3765–3775, https://doi.org/10.5194/hess-20-3765-2016, https://doi.org/10.5194/hess-20-3765-2016, 2016
Short summary
Short summary
A technology for the spatial derivation of soil texture classes is presented. Information about soil texture is key for predicting the local and regional hydrological cycle. It is needed for the calculation of soil water movement, the share of surface runoff, the evapotranspiration rate and others. Nevertheless, the derivation of soil texture classes is expensive and time-consuming. The presented technique uses soil samples and remotely sensed data for estimating their spatial distribution.
S. Härer, M. Bernhardt, and K. Schulz
Geosci. Model Dev., 9, 307–321, https://doi.org/10.5194/gmd-9-307-2016, https://doi.org/10.5194/gmd-9-307-2016, 2016
Short summary
Short summary
This paper describes a new method to produce spatially and temporally calibrated NDSI-based satellite snow cover maps utilizing simultaneously captured terrestrial photographs as in situ information. First results confirm a high quality of the produced satellite snow cover maps and emphasize the need for calibration of the NDSI threshold value to ensure a high accuracy and reproduciblity. The software "PRACTISE V.2.1" was developed to automatically process the photographs and satellite images.
M. Herrnegger, H. P. Nachtnebel, and K. Schulz
Hydrol. Earth Syst. Sci., 19, 4619–4639, https://doi.org/10.5194/hess-19-4619-2015, https://doi.org/10.5194/hess-19-4619-2015, 2015
Short summary
Short summary
Especially in alpine catchments, areal rainfall estimates often exhibit large errors. Runoff measurements are, on the other hand, one of the most robust observations within the hydrological cycle. We therefore calculate mean catchment rainfall by inverting an HBV-type rainfall-runoff model, using runoff observations as input. The inverse model may e.g. be used to analyse rainfall conditions of extreme flood events or estimation of snowmelt contribution.
B. Müller, M. Bernhardt, and K. Schulz
Hydrol. Earth Syst. Sci., 18, 5345–5359, https://doi.org/10.5194/hess-18-5345-2014, https://doi.org/10.5194/hess-18-5345-2014, 2014
Short summary
Short summary
We present a method to define hydrological landscape units by a time series of thermal infrared satellite data. Land surface temperature is calculated for 28 images in 12 years for a catchment in Luxembourg. Pattern measures show spatio-temporal persistency; principle component analysis extracts relevant patterns. Functional units represent similar behaving entities based on a representative set of images. Resulting classification and patterns are discussed regarding potential applications.
E. Zehe, U. Ehret, L. Pfister, T. Blume, B. Schröder, M. Westhoff, C. Jackisch, S. J. Schymanski, M. Weiler, K. Schulz, N. Allroggen, J. Tronicke, L. van Schaik, P. Dietrich, U. Scherer, J. Eccard, V. Wulfmeyer, and A. Kleidon
Hydrol. Earth Syst. Sci., 18, 4635–4655, https://doi.org/10.5194/hess-18-4635-2014, https://doi.org/10.5194/hess-18-4635-2014, 2014
S. Härer, M. Bernhardt, J. G. Corripio, and K. Schulz
Geosci. Model Dev., 6, 837–848, https://doi.org/10.5194/gmd-6-837-2013, https://doi.org/10.5194/gmd-6-837-2013, 2013
Related subject area
Subject: Snow and Ice | Techniques and Approaches: Modelling approaches
Evaluation of high-resolution snowpack simulations from global datasets and comparison with Sentinel-1 snow depth retrievals in the Sierra Nevada, USA
Inferring sediment-discharge event types in an Alpine catchment from sub-daily time series
Debris cover effects on energy and mass balance of Batura Glacier in the Karakoram over the past 20 years
The application and modification of WRF-Hydro/Glacier to a cold-based Antarctic glacier
Spatio-temporal information propagation using sparse observations in hyper-resolution ensemble-based snow data assimilation
Simulated hydrological effects of grooming and snowmaking in a ski resort on the local water balance
Spatial distribution and controls of snowmelt runoff in a sublimation-dominated environment in the semiarid Andes of Chile
Snow data assimilation for seasonal streamflow supply prediction in mountainous basins
Canopy structure, topography, and weather are equally important drivers of small-scale snow cover dynamics in sub-alpine forests
Climate sensitivity of the summer runoff of two glacierised Himalayan catchments with contrasting climate
A snow and glacier hydrological model for large catchments – case study for the Naryn River, central Asia
Precipitation biases and snow physics limitations drive the uncertainties in macroscale modeled snow water equivalent
Development and parameter estimation of snowmelt models using spatial snow-cover observations from MODIS
Recent hydrological response of glaciers in the Canadian Rockies to changing climate and glacier configuration
Future projections of High Atlas snowpack and runoff under climate change
Trends and variability in snowmelt in China under climate change
Assimilation of citizen science data in snowpack modeling using a new snow data set: Community Snow Observations
Snowpack dynamics in the Lebanese mountains from quasi-dynamically downscaled ERA5 reanalysis updated by assimilating remotely sensed fractional snow-covered area
Learning about precipitation lapse rates from snow course data improves water balance modeling
Snow water equivalents exclusively from snow depths and their temporal changes: the Δsnow model
Application of machine learning techniques for regional bias correction of snow water equivalent estimates in Ontario, Canada
Sensitivity of snow models to the accuracy of meteorological forcings in mountain environments
Snow processes in mountain forests: interception modeling for coarse-scale applications
Satellite-derived products of solar and longwave irradiances used for snowpack modelling in mountainous terrain
Using Gravity Recovery and Climate Experiment data to derive corrections to precipitation data sets and improve modelled snow mass at high latitudes
The role of liquid water percolation representation in estimating snow water equivalent in a Mediterranean mountain region (Mount Lebanon)
Hyper-resolution ensemble-based snow reanalysis in mountain regions using clustering
The sensitivity of modeled snow accumulation and melt to precipitation phase methods across a climatic gradient
Assessment of SWAT spatial and temporal transferability for a high-altitude glacierized catchment
Modeling experiments on seasonal lake ice mass and energy balance in the Qinghai–Tibet Plateau: a case study
A simple model for local-scale sensible and latent heat advection contributions to snowmelt
Assimilation of passive microwave AMSR-2 satellite observations in a snowpack evolution model over northeastern Canada
A simple temperature-based method to estimate heterogeneous frozen ground within a distributed watershed model
Technical note: Representing glacier geometry changes in a semi-distributed hydrological model
Projected cryospheric and hydrological impacts of 21st century climate change in the Ötztal Alps (Austria) simulated using a physically based approach
Scenario approach for the seasonal forecast of Kharif flows from the Upper Indus Basin
The role of glacier changes and threshold definition in the characterisation of future streamflow droughts in glacierised catchments
Modelling hydrologic impacts of light absorbing aerosol deposition on snow at the catchment scale
Liquid water infiltration into a layered snowpack: evaluation of a 3-D water transport model with laboratory experiments
Assessing glacier melt contribution to streamflow at Universidad Glacier, central Andes of Chile
Modelling liquid water transport in snow under rain-on-snow conditions – considering preferential flow
Developing a representative snow-monitoring network in a forested mountain watershed
Subgrid parameterization of snow distribution at a Mediterranean site using terrestrial photography
Assessing the benefit of snow data assimilation for runoff modeling in Alpine catchments
Stable oxygen isotope variability in two contrasting glacier river catchments in Greenland
Spatio-temporal variability of snow water equivalent in the extra-tropical Andes Cordillera from distributed energy balance modeling and remotely sensed snow cover
A conceptual, distributed snow redistribution model
Diagnostic calibration of a hydrological model in a mountain area by hydrograph partitioning
Meltwater run-off from Haig Glacier, Canadian Rocky Mountains, 2002–2013
Modeling the snow surface temperature with a one-layer energy balance snowmelt model
Laura Sourp, Simon Gascoin, Lionel Jarlan, Vanessa Pedinotti, Kat J. Bormann, and Mohamed Wassim Baba
Hydrol. Earth Syst. Sci., 29, 597–611, https://doi.org/10.5194/hess-29-597-2025, https://doi.org/10.5194/hess-29-597-2025, 2025
Short summary
Short summary
Accurate knowledge of the spatial distribution of snow masses across landscapes is important for water management in mountain catchments. We present a new tool for estimating snow water resources without ground measurements. We evaluate the output of this tool using accurate airborne measurements in the Sierra Nevada and find that it provides realistic estimates of snow mass and snow depth at the catchment scale.
Amalie Skålevåg, Oliver Korup, and Axel Bronstert
Hydrol. Earth Syst. Sci., 28, 4771–4796, https://doi.org/10.5194/hess-28-4771-2024, https://doi.org/10.5194/hess-28-4771-2024, 2024
Short summary
Short summary
We present a cluster-based approach for inferring sediment discharge event types from suspended sediment concentration and streamflow. Applying it to a glacierised catchment, we find event magnitude and shape complexity to be the key characteristics separating event types, while hysteresis is less important. The four event types are attributed to compound rainfall–melt extremes, high snowmelt and glacier melt, freeze–thaw-modulated snow-melt and precipitation, and late-season glacier melt.
Yu Zhu, Shiyin Liu, Ben W. Brock, Lide Tian, Ying Yi, Fuming Xie, Donghui Shangguan, and Yiyuan Shen
Hydrol. Earth Syst. Sci., 28, 2023–2045, https://doi.org/10.5194/hess-28-2023-2024, https://doi.org/10.5194/hess-28-2023-2024, 2024
Short summary
Short summary
This modeling-based study focused on Batura Glacier from 2000 to 2020, revealing that debris alters its energy budget, affecting mass balance. We propose that the presence of debris on the glacier surface effectively reduces the amount of latent heat available for ablation, which creates a favorable condition for Batura Glacier's relatively low negative mass balance. Batura Glacier shows a trend toward a less negative mass balance due to reduced ablation.
Tamara Pletzer, Jonathan P. Conway, Nicolas J. Cullen, Trude Eidhammer, and Marwan Katurji
Hydrol. Earth Syst. Sci., 28, 459–478, https://doi.org/10.5194/hess-28-459-2024, https://doi.org/10.5194/hess-28-459-2024, 2024
Short summary
Short summary
We applied a glacier and hydrology model in the McMurdo Dry Valleys (MDV) to model the start and duration of melt over a summer in this extreme polar desert. To do so, we found it necessary to prevent the drainage of melt into ice and optimize the albedo scheme. We show that simulating albedo (for the first time in the MDV) is critical to modelling the feedbacks of albedo, snowfall and melt in the region. This paper is a first step towards more complex spatial modelling of melt and streamflow.
Esteban Alonso-González, Kristoffer Aalstad, Norbert Pirk, Marco Mazzolini, Désirée Treichler, Paul Leclercq, Sebastian Westermann, Juan Ignacio López-Moreno, and Simon Gascoin
Hydrol. Earth Syst. Sci., 27, 4637–4659, https://doi.org/10.5194/hess-27-4637-2023, https://doi.org/10.5194/hess-27-4637-2023, 2023
Short summary
Short summary
Here we explore how to improve hyper-resolution (5 m) distributed snowpack simulations using sparse observations, which do not provide information from all the areas of the simulation domain. We propose a new way of propagating information throughout the simulations adapted to the hyper-resolution, which could also be used to improve simulations of other nature. The method has been implemented in an open-source data assimilation tool that is readily accessible to everyone.
Samuel Morin, Hugues François, Marion Réveillet, Eric Sauquet, Louise Crochemore, Flora Branger, Étienne Leblois, and Marie Dumont
Hydrol. Earth Syst. Sci., 27, 4257–4277, https://doi.org/10.5194/hess-27-4257-2023, https://doi.org/10.5194/hess-27-4257-2023, 2023
Short summary
Short summary
Ski resorts are a key socio-economic asset of several mountain areas. Grooming and snowmaking are routinely used to manage the snow cover on ski pistes, but despite vivid debate, little is known about their impact on water resources downstream. This study quantifies, for the pilot ski resort La Plagne in the French Alps, the impact of grooming and snowmaking on downstream river flow. Hydrological impacts are mostly apparent at the seasonal scale and rather neutral on the annual scale.
Álvaro Ayala, Simone Schauwecker, and Shelley MacDonell
Hydrol. Earth Syst. Sci., 27, 3463–3484, https://doi.org/10.5194/hess-27-3463-2023, https://doi.org/10.5194/hess-27-3463-2023, 2023
Short summary
Short summary
As the climate of the semiarid Andes is very dry, much of the seasonal snowpack is lost to the atmosphere through sublimation. We propose that snowmelt runoff originates from specific areas that we define as snowmelt hotspots. We estimate that snowmelt hotspots produce half of the snowmelt runoff in a small study catchment but represent about a quarter of the total area. Snowmelt hotspots may be important for groundwater recharge, rock glaciers, and mountain peatlands.
Sammy Metref, Emmanuel Cosme, Matthieu Le Lay, and Joël Gailhard
Hydrol. Earth Syst. Sci., 27, 2283–2299, https://doi.org/10.5194/hess-27-2283-2023, https://doi.org/10.5194/hess-27-2283-2023, 2023
Short summary
Short summary
Predicting the seasonal streamflow supply of water in a mountainous basin is critical to anticipating the operation of hydroelectric dams and avoiding hydrology-related hazard. This quantity partly depends on the snowpack accumulated during winter. The study addresses this prediction problem using information from streamflow data and both direct and indirect snow measurements. In this study, the prediction is improved by integrating the data information into a basin-scale hydrological model.
Giulia Mazzotti, Clare Webster, Louis Quéno, Bertrand Cluzet, and Tobias Jonas
Hydrol. Earth Syst. Sci., 27, 2099–2121, https://doi.org/10.5194/hess-27-2099-2023, https://doi.org/10.5194/hess-27-2099-2023, 2023
Short summary
Short summary
This study analyses snow cover evolution in mountainous forested terrain based on 2 m resolution simulations from a process-based model. We show that snow accumulation patterns are controlled by canopy structure, but topographic shading modulates the timing of melt onset, and variability in weather can cause snow accumulation and melt patterns to vary between years. These findings advance our ability to predict how snow regimes will react to rising temperatures and forest disturbances.
Sourav Laha, Argha Banerjee, Ajit Singh, Parmanand Sharma, and Meloth Thamban
Hydrol. Earth Syst. Sci., 27, 627–645, https://doi.org/10.5194/hess-27-627-2023, https://doi.org/10.5194/hess-27-627-2023, 2023
Short summary
Short summary
A model study of two Himalayan catchments reveals that the summer runoff from the glacierized parts of the catchments responds strongly to temperature forcing and is insensitive to precipitation forcing. The runoff from the non-glacierized parts has the exact opposite behaviour. The interannual variability and decadal changes of runoff under a warming climate is determined by the response of glaciers to temperature forcing and that of off-glacier areas to precipitation perturbations.
Sarah Shannon, Anthony Payne, Jim Freer, Gemma Coxon, Martina Kauzlaric, David Kriegel, and Stephan Harrison
Hydrol. Earth Syst. Sci., 27, 453–480, https://doi.org/10.5194/hess-27-453-2023, https://doi.org/10.5194/hess-27-453-2023, 2023
Short summary
Short summary
Climate change poses a potential threat to water supply in glaciated river catchments. In this study, we added a snowmelt and glacier melt model to the Dynamic fluxEs and ConnectIvity for Predictions of HydRology model (DECIPHeR). The model is applied to the Naryn River catchment in central Asia and is found to reproduce past change discharge and the spatial extent of seasonal snow cover well.
Eunsang Cho, Carrie M. Vuyovich, Sujay V. Kumar, Melissa L. Wrzesien, Rhae Sung Kim, and Jennifer M. Jacobs
Hydrol. Earth Syst. Sci., 26, 5721–5735, https://doi.org/10.5194/hess-26-5721-2022, https://doi.org/10.5194/hess-26-5721-2022, 2022
Short summary
Short summary
While land surface models are a common approach for estimating macroscale snow water equivalent (SWE), the SWE accuracy is often limited by uncertainties in model physics and forcing inputs. In this study, we found large underestimations of modeled SWE compared to observations. Precipitation forcings and melting physics limitations dominantly contribute to the SWE underestimations. Results provide insights into prioritizing strategies to improve the SWE simulations for hydrologic applications.
Dhiraj Raj Gyawali and András Bárdossy
Hydrol. Earth Syst. Sci., 26, 3055–3077, https://doi.org/10.5194/hess-26-3055-2022, https://doi.org/10.5194/hess-26-3055-2022, 2022
Short summary
Short summary
In this study, different extensions of the degree-day model were calibrated on snow-cover distribution against freely available satellite snow-cover images. The calibrated models simulated the distribution very well in Baden-Württemberg (Germany) and Switzerland. In addition to reliable identification of snow cover, the melt outputs from the calibrated models were able to improve the flow simulations in different catchments in the study region.
Dhiraj Pradhananga and John W. Pomeroy
Hydrol. Earth Syst. Sci., 26, 2605–2616, https://doi.org/10.5194/hess-26-2605-2022, https://doi.org/10.5194/hess-26-2605-2022, 2022
Short summary
Short summary
This study considers the combined impacts of climate and glacier changes due to recession on the hydrology and water balance of two high-elevation glaciers. Peyto and Athabasca glacier basins in the Canadian Rockies have undergone continuous glacier loss over the last 3 to 5 decades, leading to an increase in ice exposure and changes to the elevation and slope of the glacier surfaces. Streamflow from these glaciers continues to increase more due to climate warming than glacier recession.
Alexandre Tuel, Nabil El Moçayd, Moulay Driss Hasnaoui, and Elfatih A. B. Eltahir
Hydrol. Earth Syst. Sci., 26, 571–588, https://doi.org/10.5194/hess-26-571-2022, https://doi.org/10.5194/hess-26-571-2022, 2022
Short summary
Short summary
Snowmelt in the High Atlas is critical for irrigation in Morocco but is threatened by climate change. We assess future trends in High Atlas snowpack by modelling it under historical and future climate scenarios and estimate their impact on runoff. We find that the combined warming and drying will result in a roughly 80 % decline in snowpack, a 5 %–30 % decrease in runoff efficiency and 50 %–60 % decline in runoff under a business-as-usual scenario.
Yong Yang, Rensheng Chen, Guohua Liu, Zhangwen Liu, and Xiqiang Wang
Hydrol. Earth Syst. Sci., 26, 305–329, https://doi.org/10.5194/hess-26-305-2022, https://doi.org/10.5194/hess-26-305-2022, 2022
Short summary
Short summary
A comprehensive assessment of snowmelt is missing for China. Trends and variability in snowmelt in China under climate change are investigated using historical precipitation and temperature data (1951–2017) and projection scenarios (2006–2099). The snowmelt and snowmelt runoff ratio show significant spatial and temporal variability in China. The spatial variability in snowmelt changes may lead to regional differences in the impact of snowmelt on the water supply.
Ryan L. Crumley, David F. Hill, Katreen Wikstrom Jones, Gabriel J. Wolken, Anthony A. Arendt, Christina M. Aragon, Christopher Cosgrove, and Community Snow Observations Participants
Hydrol. Earth Syst. Sci., 25, 4651–4680, https://doi.org/10.5194/hess-25-4651-2021, https://doi.org/10.5194/hess-25-4651-2021, 2021
Short summary
Short summary
In this study, we use a new snow data set collected by participants in the Community Snow Observations project in coastal Alaska to improve snow depth and snow water equivalence simulations from a snow process model. We validate our simulations with multiple datasets, taking advantage of snow telemetry (SNOTEL), snow depth and snow water equivalence, and remote sensing measurements. Our results demonstrate that assimilating citizen science snow depth measurements can improve model performance.
Esteban Alonso-González, Ethan Gutmann, Kristoffer Aalstad, Abbas Fayad, Marine Bouchet, and Simon Gascoin
Hydrol. Earth Syst. Sci., 25, 4455–4471, https://doi.org/10.5194/hess-25-4455-2021, https://doi.org/10.5194/hess-25-4455-2021, 2021
Short summary
Short summary
Snow water resources represent a key hydrological resource for the Mediterranean regions, where most of the precipitation falls during the winter months. This is the case for Lebanon, where snowpack represents 31 % of the spring flow. We have used models to generate snow information corrected by means of remote sensing snow cover retrievals. Our results highlight the high temporal variability in the snowpack in Lebanon and its sensitivity to further warming caused by its hypsography.
Francesco Avanzi, Giulia Ercolani, Simone Gabellani, Edoardo Cremonese, Paolo Pogliotti, Gianluca Filippa, Umberto Morra di Cella, Sara Ratto, Hervè Stevenin, Marco Cauduro, and Stefano Juglair
Hydrol. Earth Syst. Sci., 25, 2109–2131, https://doi.org/10.5194/hess-25-2109-2021, https://doi.org/10.5194/hess-25-2109-2021, 2021
Short summary
Short summary
Precipitation tends to increase with elevation, but the magnitude and distribution of this enhancement remain poorly understood. By leveraging over 11 000 spatially distributed, manual measurements of snow depth (snow courses) upstream of two reservoirs in the western European Alps, we show that these courses bear a characteristic signature of orographic precipitation. This opens a window of opportunity for improved modeling accuracy and, ultimately, our understanding of the water budget.
Michael Winkler, Harald Schellander, and Stefanie Gruber
Hydrol. Earth Syst. Sci., 25, 1165–1187, https://doi.org/10.5194/hess-25-1165-2021, https://doi.org/10.5194/hess-25-1165-2021, 2021
Short summary
Short summary
A new method to calculate the mass of snow is provided. It is quite simple but gives surprisingly good results. The new approach only requires regular snow depth observations to simulate respective water mass that is stored in the snow. It is called
ΔSNOW model, its code is freely available, and it can be applied in various climates. The method is especially interesting for studies on extremes (e.g., snow loads or flooding) and climate (e.g., precipitation trends).
Fraser King, Andre R. Erler, Steven K. Frey, and Christopher G. Fletcher
Hydrol. Earth Syst. Sci., 24, 4887–4902, https://doi.org/10.5194/hess-24-4887-2020, https://doi.org/10.5194/hess-24-4887-2020, 2020
Short summary
Short summary
Snow is a critical contributor to our water and energy budget, with impacts on flooding and water resource management. Measuring the amount of snow on the ground each year is an expensive and time-consuming task. Snow models and gridded products help to fill these gaps, yet there exist considerable uncertainties associated with their estimates. We demonstrate that machine learning techniques are able to reduce biases in these products to provide more realistic snow estimates across Ontario.
Silvia Terzago, Valentina Andreoli, Gabriele Arduini, Gianpaolo Balsamo, Lorenzo Campo, Claudio Cassardo, Edoardo Cremonese, Daniele Dolia, Simone Gabellani, Jost von Hardenberg, Umberto Morra di Cella, Elisa Palazzi, Gaia Piazzi, Paolo Pogliotti, and Antonello Provenzale
Hydrol. Earth Syst. Sci., 24, 4061–4090, https://doi.org/10.5194/hess-24-4061-2020, https://doi.org/10.5194/hess-24-4061-2020, 2020
Short summary
Short summary
In mountain areas high-quality meteorological data to drive snow models are rarely available, so coarse-resolution data from spatial interpolation of the available in situ measurements or reanalyses are typically employed. We perform 12 experiments using six snow models with different degrees of complexity to show the impact of the accuracy of the forcing on snow depth and snow water equivalent simulations at the Alpine site of Torgnon, discussing the results in relation to the model complexity.
Nora Helbig, David Moeser, Michaela Teich, Laure Vincent, Yves Lejeune, Jean-Emmanuel Sicart, and Jean-Matthieu Monnet
Hydrol. Earth Syst. Sci., 24, 2545–2560, https://doi.org/10.5194/hess-24-2545-2020, https://doi.org/10.5194/hess-24-2545-2020, 2020
Short summary
Short summary
Snow retained in the forest canopy (snow interception) drives spatial variability of the subcanopy snow accumulation. As such, accurately describing snow interception in models is of importance for various applications such as hydrological, weather, and climate predictions. We developed descriptions for the spatial mean and variability of snow interception. An independent evaluation demonstrated that the novel models can be applied in coarse land surface model grid cells.
Louis Quéno, Fatima Karbou, Vincent Vionnet, and Ingrid Dombrowski-Etchevers
Hydrol. Earth Syst. Sci., 24, 2083–2104, https://doi.org/10.5194/hess-24-2083-2020, https://doi.org/10.5194/hess-24-2083-2020, 2020
Short summary
Short summary
In mountainous terrain, the snowpack is strongly affected by incoming shortwave and longwave radiation. Satellite-derived products of incoming radiation were assessed in the French Alps and the Pyrenees and compared to meteorological forecasts, reanalyses and in situ measurements. We showed their good quality in mountains. The different radiation datasets were used as radiative forcing for snowpack simulations with the detailed model Crocus. Their impact on the snowpack evolution was explored.
Emma L. Robinson and Douglas B. Clark
Hydrol. Earth Syst. Sci., 24, 1763–1779, https://doi.org/10.5194/hess-24-1763-2020, https://doi.org/10.5194/hess-24-1763-2020, 2020
Short summary
Short summary
This study used a water balance approach based on GRACE total water storage to infer the amount of cold-season precipitation in four Arctic river basins. This was used to evaluate four gridded meteorological data sets, which were used as inputs to a land surface model. We found that the cold-season precipitation in these data sets needed to be increased by up to 55 %. Using these higher precipitation inputs improved the model representation of Arctic hydrology, particularly lying snow.
Abbas Fayad and Simon Gascoin
Hydrol. Earth Syst. Sci., 24, 1527–1542, https://doi.org/10.5194/hess-24-1527-2020, https://doi.org/10.5194/hess-24-1527-2020, 2020
Short summary
Short summary
Seasonal snowpack is an essential water resource in Mediterranean mountains. Here, we look at the role of water percolation in simulating snow mass (SWE), for the first time, in Mount Lebanon. We use SnowModel, a distributed snow model, forced by station data. The main sources of uncertainty were attributed to rain–snow partitioning, transient winter snowmelt, and the subpixel snow cover. Yet, we show that a process-based model is suitable to simulate wet snowpack in Mediterranean mountains.
Joel Fiddes, Kristoffer Aalstad, and Sebastian Westermann
Hydrol. Earth Syst. Sci., 23, 4717–4736, https://doi.org/10.5194/hess-23-4717-2019, https://doi.org/10.5194/hess-23-4717-2019, 2019
Short summary
Short summary
In this paper we address one of the big challenges in snow hydrology, namely the accurate simulation of the seasonal snowpack in ungauged regions. We do this by assimilating satellite observations of snow cover into a modelling framework. Importantly (and a novelty of the paper), we include a clustering approach that permits highly efficient ensemble simulations. Efficiency gains and dependency on purely global datasets, means that this method can be applied over large areas anywhere on Earth.
Keith S. Jennings and Noah P. Molotch
Hydrol. Earth Syst. Sci., 23, 3765–3786, https://doi.org/10.5194/hess-23-3765-2019, https://doi.org/10.5194/hess-23-3765-2019, 2019
Short summary
Short summary
There is a wide variety of modeling methods to designate precipitation as rain, snow, or a mix of the two. Here we show that method choice introduces marked uncertainty to simulated snowpack water storage (> 200 mm) and snow cover duration (> 1 month) in areas that receive significant winter and spring precipitation at air temperatures at and near freezing. This marked uncertainty has implications for water resources management as well as simulations of past and future hydroclimatic states.
Maria Andrianaki, Juna Shrestha, Florian Kobierska, Nikolaos P. Nikolaidis, and Stefano M. Bernasconi
Hydrol. Earth Syst. Sci., 23, 3219–3232, https://doi.org/10.5194/hess-23-3219-2019, https://doi.org/10.5194/hess-23-3219-2019, 2019
Short summary
Short summary
We tested the performance of the SWAT hydrological model after being transferred from a small Alpine watershed to a greater area. We found that the performance of the model for the greater catchment was satisfactory and the climate change simulations gave insights into the impact of climate change on our site. Assessment tests are important in identifying the strengths and weaknesses of the models when they are applied under extreme conditions different to the ones that were calibrated.
Wenfeng Huang, Bin Cheng, Jinrong Zhang, Zheng Zhang, Timo Vihma, Zhijun Li, and Fujun Niu
Hydrol. Earth Syst. Sci., 23, 2173–2186, https://doi.org/10.5194/hess-23-2173-2019, https://doi.org/10.5194/hess-23-2173-2019, 2019
Short summary
Short summary
Up to now, little has been known on ice thermodynamics and lake–atmosphere interaction over the Tibetan Plateau during ice-covered seasons due to a lack of field data. Here, model experiments on ice thermodynamics were conducted in a shallow lake using HIGHTSI. Water–ice heat flux was a major source of uncertainty for lake ice thickness. Heat and mass budgets were estimated within the vertical air–ice–water system. Strong ice sublimation occurred and was responsible for water loss during winter.
Phillip Harder, John W. Pomeroy, and Warren D. Helgason
Hydrol. Earth Syst. Sci., 23, 1–17, https://doi.org/10.5194/hess-23-1-2019, https://doi.org/10.5194/hess-23-1-2019, 2019
Short summary
Short summary
As snow cover becomes patchy during snowmelt, energy is advected from warm snow-free surfaces to cold snow-covered surfaces. This paper proposes a simple sensible and latent heat advection model for snowmelt situations that can be coupled to one-dimensional energy balance snowmelt models. The model demonstrates that sensible and latent heat advection fluxes can compensate for one another, especially in early melt periods.
Fanny Larue, Alain Royer, Danielle De Sève, Alexandre Roy, and Emmanuel Cosme
Hydrol. Earth Syst. Sci., 22, 5711–5734, https://doi.org/10.5194/hess-22-5711-2018, https://doi.org/10.5194/hess-22-5711-2018, 2018
Short summary
Short summary
A data assimilation scheme was developed to improve snow water equivalent (SWE) simulations by updating meteorological forcings and snowpack states using passive microwave satellite observations. A chain of models was first calibrated to simulate satellite observations over northeastern Canada. The assimilation was then validated over 12 stations where daily SWE measurements were acquired during 4 winters (2012–2016). The overall SWE bias is reduced by 68 % compared to original SWE simulations.
Michael L. Follum, Jeffrey D. Niemann, Julie T. Parno, and Charles W. Downer
Hydrol. Earth Syst. Sci., 22, 2669–2688, https://doi.org/10.5194/hess-22-2669-2018, https://doi.org/10.5194/hess-22-2669-2018, 2018
Short summary
Short summary
Spatial patterns of snow and frozen ground within watersheds can impact the volume and timing of runoff. Commonly used snow and frozen ground simulation methods were modified to better account for the effects of topography and land cover on the spatial patterns of snow and frozen ground. When tested using a watershed in Vermont the modifications resulted in more accurate temporal and spatial simulation of both snow and frozen ground.
Jan Seibert, Marc J. P. Vis, Irene Kohn, Markus Weiler, and Kerstin Stahl
Hydrol. Earth Syst. Sci., 22, 2211–2224, https://doi.org/10.5194/hess-22-2211-2018, https://doi.org/10.5194/hess-22-2211-2018, 2018
Short summary
Short summary
In many glacio-hydrological models glacier areas are assumed to be constant over time, which is a crucial limitation. Here we describe a novel approach to translate mass balances as simulated by the (glacio)hydrological model into glacier area changes. We combined the Δh approach of Huss et al. (2010) with the bucket-type model HBV and introduced a lookup table approach, which also allows periods with advancing glaciers to be represented, which is not possible with the original Huss method.
Florian Hanzer, Kristian Förster, Johanna Nemec, and Ulrich Strasser
Hydrol. Earth Syst. Sci., 22, 1593–1614, https://doi.org/10.5194/hess-22-1593-2018, https://doi.org/10.5194/hess-22-1593-2018, 2018
Short summary
Short summary
Climate change effects on snow, glaciers, and hydrology are investigated for the Ötztal Alps region (Austria) using a hydroclimatological model driven by climate projections for the RCP2.6, RCP4.5, and RCP8.5 scenarios. The results show declining snow amounts and strongly retreating glaciers with moderate effects on catchment runoff until the mid-21st century, whereas annual runoff volumes decrease strongly towards the end of the century.
Muhammad Fraz Ismail and Wolfgang Bogacki
Hydrol. Earth Syst. Sci., 22, 1391–1409, https://doi.org/10.5194/hess-22-1391-2018, https://doi.org/10.5194/hess-22-1391-2018, 2018
Marit Van Tiel, Adriaan J. Teuling, Niko Wanders, Marc J. P. Vis, Kerstin Stahl, and Anne F. Van Loon
Hydrol. Earth Syst. Sci., 22, 463–485, https://doi.org/10.5194/hess-22-463-2018, https://doi.org/10.5194/hess-22-463-2018, 2018
Short summary
Short summary
Glaciers are important hydrological reservoirs. Short-term variability in glacier melt and also glacier retreat can cause droughts in streamflow. In this study, we analyse the effect of glacier changes and different drought threshold approaches on future projections of streamflow droughts in glacierised catchments. We show that these different methodological options result in different drought projections and that these options can be used to study different aspects of streamflow droughts.
Felix N. Matt, John F. Burkhart, and Joni-Pekka Pietikäinen
Hydrol. Earth Syst. Sci., 22, 179–201, https://doi.org/10.5194/hess-22-179-2018, https://doi.org/10.5194/hess-22-179-2018, 2018
Short summary
Short summary
Certain particles that have the ability to absorb sunlight deposit onto mountain snow via atmospheric transport mechanisms and then lower the snow's ability to reflect sunlight, which increases snowmelt. Herein we present a model aiming to simulate this effect and model the impacts on the streamflow of a southern Norwegian river. We find a significant difference in streamflow between simulations with and without the effect of light absorbing particles applied, in particular during spring melt.
Hiroyuki Hirashima, Francesco Avanzi, and Satoru Yamaguchi
Hydrol. Earth Syst. Sci., 21, 5503–5515, https://doi.org/10.5194/hess-21-5503-2017, https://doi.org/10.5194/hess-21-5503-2017, 2017
Short summary
Short summary
We reproduced the formation of capillary barriers and the development of preferential flow through snow using a multi-dimensional water transport model, which was then validated using laboratory experiments of liquid water infiltration into layered, initially dry snow. Simulation results showed that the model reconstructs some relevant features of capillary barriers and the timing of liquid water arrival at the snow base.
Claudio Bravo, Thomas Loriaux, Andrés Rivera, and Ben W. Brock
Hydrol. Earth Syst. Sci., 21, 3249–3266, https://doi.org/10.5194/hess-21-3249-2017, https://doi.org/10.5194/hess-21-3249-2017, 2017
Short summary
Short summary
We present an analysis of meteorological conditions and melt for Universidad Glacier in central Chile. This glacier is characterized by high melt rates over the ablation season, representing a mean contribution of between 10 and 13 % of the total runoff observed in the upper Tinguiririca Basin during the November 2009 to March 2010 period. Few studies have quantified the glacier melt contribution to river runoff in Chile, and this work represents a new precedent for the Andes.
Sebastian Würzer, Nander Wever, Roman Juras, Michael Lehning, and Tobias Jonas
Hydrol. Earth Syst. Sci., 21, 1741–1756, https://doi.org/10.5194/hess-21-1741-2017, https://doi.org/10.5194/hess-21-1741-2017, 2017
Short summary
Short summary
We discuss a dual-domain water transport model in a physics-based snowpack model to account for preferential flow (PF) in addition to matrix flow. So far no operationally used snow model has explicitly accounted for PF. The new approach is compared to existing water transport models and validated against in situ data from sprinkling and natural rain-on-snow (ROS) events. Our work demonstrates the benefit of considering PF in modelling hourly snowpack runoff, especially during ROS conditions.
Kelly E. Gleason, Anne W. Nolin, and Travis R. Roth
Hydrol. Earth Syst. Sci., 21, 1137–1147, https://doi.org/10.5194/hess-21-1137-2017, https://doi.org/10.5194/hess-21-1137-2017, 2017
Short summary
Short summary
We present a coupled modeling approach used to objectively identify representative snow-monitoring locations in a forested watershed in the western Oregon Cascades mountain range. The resultant Forest Elevational Snow Transect (ForEST) represents combinations of forested and open land cover types at low, mid-, and high elevations.
Rafael Pimentel, Javier Herrero, and María José Polo
Hydrol. Earth Syst. Sci., 21, 805–820, https://doi.org/10.5194/hess-21-805-2017, https://doi.org/10.5194/hess-21-805-2017, 2017
Short summary
Short summary
This study analyses the subgrid variability of the snow distribution in a Mediterranean region and formulates a parametric approach that includes these scale effects in the physical modelling of snow by means of accumulation–depletion curves associated with snow evolution patterns, by means of terrestrial photography. The results confirm that the use of these on a cell scale provides a solid foundation for the extension of point snow models to larger areas.
Nena Griessinger, Jan Seibert, Jan Magnusson, and Tobias Jonas
Hydrol. Earth Syst. Sci., 20, 3895–3905, https://doi.org/10.5194/hess-20-3895-2016, https://doi.org/10.5194/hess-20-3895-2016, 2016
Short summary
Short summary
In Alpine catchments, snowmelt is a major contribution to runoff. In this study, we address the question of whether the performance of a hydrological model can be enhanced by integrating data from an external snow monitoring system. To this end, a hydrological model was driven with snowmelt input from snow models of different complexities. Best performance was obtained with a snow model, which utilized data assimilation, in particular for catchments at higher elevations and for snow-rich years.
Jacob C. Yde, Niels T. Knudsen, Jørgen P. Steffensen, Jonathan L. Carrivick, Bent Hasholt, Thomas Ingeman-Nielsen, Christian Kronborg, Nicolaj K. Larsen, Sebastian H. Mernild, Hans Oerter, David H. Roberts, and Andrew J. Russell
Hydrol. Earth Syst. Sci., 20, 1197–1210, https://doi.org/10.5194/hess-20-1197-2016, https://doi.org/10.5194/hess-20-1197-2016, 2016
E. Cornwell, N. P. Molotch, and J. McPhee
Hydrol. Earth Syst. Sci., 20, 411–430, https://doi.org/10.5194/hess-20-411-2016, https://doi.org/10.5194/hess-20-411-2016, 2016
Short summary
Short summary
We present a high-resolution snow water equivalent estimation for the 2001–2014 period over the extratropical Andes Cordillera of Argentina and Chile, the first of its type. The effect of elevation on accumulation is confirmed, although this is less marked in the northern portion of the domain. The 3000–4000 m a.s.l. elevation band contributes the bulk of snowmelt, but the 4000–5000 m a.s.l. band is a significant source and deserves further monitoring and research.
S. Frey and H. Holzmann
Hydrol. Earth Syst. Sci., 19, 4517–4530, https://doi.org/10.5194/hess-19-4517-2015, https://doi.org/10.5194/hess-19-4517-2015, 2015
Short summary
Short summary
Temperature index melt models often lead to snow accumulation in high mountainous elevations. We developed a simple conceptual snow redistribution model working on a commonly used grid cell size of 1x1km. That model is integrated in the hydrological rainfall runoff model COSERO. Applying the model to the catchment of Oetztaler Ache, Austria, could prevent the accumulation of snow in the upper altitudes and lead to an improved model efficiency regarding discharge and snow coverage (MODIS).
Z. H. He, F. Q. Tian, H. V. Gupta, H. C. Hu, and H. P. Hu
Hydrol. Earth Syst. Sci., 19, 1807–1826, https://doi.org/10.5194/hess-19-1807-2015, https://doi.org/10.5194/hess-19-1807-2015, 2015
S. J. Marshall
Hydrol. Earth Syst. Sci., 18, 5181–5200, https://doi.org/10.5194/hess-18-5181-2014, https://doi.org/10.5194/hess-18-5181-2014, 2014
Short summary
Short summary
This paper presents a new 12-year glacier meteorological, mass balance, and run-off record from the Canadian Rocky Mountains. This provides insight into the glaciohydrological regime of the Rockies. For the period 2002-2013, about 60% of glacier meltwater run-off originated from seasonal snow and 40% was derived from glacier ice and firn. Ice and firn run-off is concentrated in the months of August and September, at which time it contributes significantly to regional-scale water resources.
J. You, D. G. Tarboton, and C. H. Luce
Hydrol. Earth Syst. Sci., 18, 5061–5076, https://doi.org/10.5194/hess-18-5061-2014, https://doi.org/10.5194/hess-18-5061-2014, 2014
Short summary
Short summary
This paper evaluates three improvements to an energy balance snowmelt model aimed to represent snow surface temperature while retaining the parsimony of a single layer. Surface heat flow is modeled using a forcing term related to the vertical temperature difference and a restore term related to the temporal gradient of surface temperature. Adjustments for melt water refreezing and thermal conductivity when the snow is shallow are introduced. The model performs well at the three test sites.
Cited articles
Abimbola, O. P., Wenninger, J., Venneker, R., and Mittelstet, A. R.: The
assessment of water resources in ungauged catchments in Rwanda, J. Hydrol.: Reg. Stud., 13, 274–289, https://doi.org/10.1016/j.ejrh.2017.09.001, 2017. a, b
Abrams, M. and Crippen, R.: ASTER GDEM V3 (Aster Global DEM): User Guide, available at: https://lpdaac.usgs.gov/documents/434/ASTGTM_User_Guide_V3.pdf (last access: 25 March 2021), 2019. a
Adams, M. S., Bühler, Y., and Fromm, R.: Multitemporal Accuracy and
Precision Assessment of Unmanned Aerial System Photogrammetry for Slope-Scale
Snow Depth Maps in Alpine Terrain, Pure Appl. Geophys., 175, 3303–3324, https://doi.org/10.1007/s00024-017-1748-y, 2018. a
Arkin, P., Xie, P., and National Center for Atmospheric Research Staff: The Climate Data Guide: CMAP: CPC Merged Analysis of Precipitation, available at:
https://climatedataguide.ucar.edu/climate-data/cmap-cpc-merged-analysis-precipitation, last access: 24 December 2018. a
Bandyopadhyay, J., Rodda, J. C., Kattelmann, R., Kundzewicz, Z., and Kraemer,
D.: Highland waters – a resource of global significance, in: Mountains of the World. A global priority, edited by: Messerli, B. and Ives, J. D.,
Parthenon Publishing, New York, Carnforth, 131–155, https://doi.org/10.1002/(SICI)1099-145X(200003/04)11:2<197::AID-LDR390>3.0.CO;2-U, 1997. a
Barnett, T. P., Adam, J. C., and Lettenmaier, D. P.: Potential impacts of a
warming climate on water availability in snow-dominated regions, Nature, 438,
303–309, https://doi.org/10.1038/nature04141, 2005. a, b
Barrett, A. P.: National Operational Hydrologic Remote Sensing Center SNOw Data Assimilation System (SNODAS) Products at NSIDC: Special Report, NSIDC, Boulder, Colorado, 2003. a
Beniston, M.: Is snow in the Alps receding or disappearing?, Wiley
Interdisciplin. Rev.: Clim. Change, 3, 349–358, https://doi.org/10.1002/wcc.179, 2012. a
Bergstrom, S.: The HBV model (Chapter 13, pp. 443–476), in: Computer models of watershed hydrology, edited by: Singh, V. P., Water Resources Publications, Highlands Ranch, Colorado, USA, 1130 pp., 1995. a
Bernhardt, M., Härer, S., Feigl, M., and Schulz, K.: Der Wert Alpiner
Forschungseinzugsgebiete im Bereich der Fernerkundung, der
Schneedeckenmodellierung und der lokalen Klimamodellierung,
Österreichische Wasser- und Abfallwirtschaft, 70, 515–528,
https://doi.org/10.1007/s00506-018-0510-8, 2018. a
Blöschl, G., Sivapalan, M., Wagener, T., Viglione, A., and Savenije, H. (Eds.): Runoff Predictions in Ungauged Basins: A Synthesis across Processes, Places and Scales, Cambridge University Press, Cambridge,
https://doi.org/10.1017/CBO9781139235761, 2013. a, b
Bormann, K. J., Brown, R. D., Derksen, C., and Painter, T. H.: Estimating
snow-cover trends from space, Nat. Clim. Change, 8, 924–928,
https://doi.org/10.1038/s41558-018-0318-3, 2018. a
Brown, P. D. and Mote, P. W.: The Response of Northern Hemisphere Snow Cover to a Changing Climate, J. Climate, 2008, 2124–2145,
https://doi.org/10.1175/2008JCLI2665.1, 2008. a
Broxton, P. D., Leeuwen, W. J. D., and Biederman, J. A.: Improving Snow Water
Equivalent Maps With Machine Learning of Snow Survey and Lidar Measurements,
Water Resour. Res., 73, 3739–3757, https://doi.org/10.1029/2018WR024146, 2019. a
Brunt, D.: Notes on radiation in the atmosphere. I, Q. J. Roy. Meteorol. Soc., 58, 389–420, https://doi.org/10.1002/qj.49705824704, 1932. a
Bühler, Y., Marty, M., Egli, L., Veitinger, J., Jonas, T., Thee, P., and
Ginzler, C.: Snow depth mapping in high-alpine catchments using digital
photogrammetry, The Cryosphere, 9, 229–243, https://doi.org/10.5194/tc-9-229-2015,
2015. a, b
Buytaert, W., Vuille, M., Dewulf, A., Urrutia, R., Karmalkar, A., and
Célleri, R.: Uncertainties in climate change projections and regional
downscaling in the tropical Andes: implications for water resources
management, Hydrol. Earth Syst. Sci., 14, 1247–1258,
https://doi.org/10.5194/hess-14-1247-2010, 2010. a
Casson, D. R., Werner, M., Weerts, A., and Solomatine, D.: Global re-analysis
datasets to improve hydrological assessment and snow water equivalent
estimation in a sub-Arctic watershed, Hydrol. Earth Syst. Sci., 22, 4685–4697, https://doi.org/10.5194/hess-22-4685-2018, 2018. a, b
Christensen, J. H., Boberg, F., Christensen, O. B., and Lucas-Picher, P.: On
the need for bias correction of regional climate change projections of
temperature and precipitation, Geophys. Res. Lett., 35, L20709, https://doi.org/10.1029/2008GL035694, 2008. a
Currier, W. R., Pflug, J., Mazzotti, G., Jonas, T., Deems, J. S., Bormann,
K. J., Painter, T. H., Hiemstra, C. A., Gelvin, A., Uhlmann, Z., Spaete, L.,
Glenn, N. F., and Lundquist, J. D.: Comparing Aerial Lidar Observations With
Terrestrial Lidar and Snow–Probe Transects From NASA's 2017 SnowEx Campaign,
Water Resour. Res., 55, 6285–6294, https://doi.org/10.1029/2018WR024533, 2019. a
Dadic, R., Mott, R., Lehning, M., and Burlando, P.: Wind influence on snow
depth distribution and accumulation over glaciers, Environ. Res. Lett., 115, 1064, https://doi.org/10.1029/2009JF001261, 2010. a
Danielson, J. J. and Gesch, D. B.: Global Multi-resolution Terrain Elevation
Data 2010 (GMTED2010), available at: https://pubs.usgs.gov/of/2011/1073/pdf/of2011-1073.pdf (last access: 24 May 2021), 2011. a
Dornes, P. F., Pomeroy, J. W., Pietroniro, A., Carey, S. K., and Quinton, W. L.: Influence of landscape aggregation in modelling snow-cover ablation and snowmelt runoff in a sub-arctic mountainous environment, Hydrolog. Sci. J., 53, 725–740, https://doi.org/10.1623/hysj.53.4.725, 2008. a
Dussaillant, J. A., Buytaert, W., Meier, C., and Espinoza, F.: Hydrological
regime of remote catchments with extreme gradients under accelerated change:
the Baker basin in Patagonia, Hydrolog. Sci. J., 57, 1530–1542,
https://doi.org/10.1080/02626667.2012.726993, 2012. a
DWD – Deutscher Wetterdienst: Wetter und Klima im Überblick, available at: https://www.dwd.de, last access: 28 December 2018. a
Ellis, C. R., Pomeroy, J. W., Brown, T., and MacDonald, J.: Simulations of snow accumulation and melt in needleleaf forest environments, Hydrol. Earth Syst. Sci., 14, 925–940, https://doi.org/10.5194/hess-14-925-2010, 2010. a, b
Essery, R. and Etchevers, P.: Parameter sensitivity in simulations of snowmelt, J. Geophys. Res., 109, D20111, https://doi.org/10.1029/2004JD005036, 2004. a
Essery, R., Rutter, N., Pomeroy, J. W., Baxter, R., Stähli, M., Gustafsson, D., Barr, A., Bartlett, P., and Elder, K.: SNOWMIP2: An Evaluation of Forest Snow Process Simulations, B. Am. Meteorol. Soc., 90, 1120–1135, https://doi.org/10.1175/2009BAMS2629.1, 2009. a
Essou, G. R., Brissette, F., and Lucas-Picher, P.: Impacts of combining
reanalyses and weather station data on the accuracy of discharge modelling, J. Hydrol., 545, 120–131, https://doi.org/10.1016/j.jhydrol.2016.12.021, 2017. a, b
Essou, G. R. C., Sabarly, F., Lucas-Picher, P., Brissette, F., and Poulin, A.: Can Precipitation and Temperature from Meteorological Reanalyses Be Used for Hydrological Modeling?, J. Hydrometeorol., 17, 1929–1950,
https://doi.org/10.1175/JHM-D-15-0138.1, 2016. a, b
Eylander, J. B., Peter-Lidard, C. D., and Kumar, S. V.: The AFWA Next
Generation Land Data Assimilation System, available at:
http://www.nrlmry.navy.mil/BACIMO/2005/Proceedings/5 NWP/5.02 NWP Eylander Land Surface Assimilation Paper.pdf, last access: 24 May 2021. a
Fang, X., Pomeroy, J. W., Ellis, C. R., MacDonald, M. K., DeBeer, C. M., and
Brown, T.: Multi-variable evaluation of hydrological model predictions for a
headwater basin in the Canadian Rocky Mountains, Hydrol. Earth Syst. Sci., 17, 1635–1659, https://doi.org/10.5194/hess-17-1635-2013, 2013. a
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S.,
Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S.,
Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf, D.:
The Shuttle Radar Topography Mission, Rev. Geophys., 45, 1485,
https://doi.org/10.1029/2005RG000183, 2007. a, b, c
Fatichi, S., Vivoni, E. R., Ogden, F. L., Ivanov, V. Y., Mirus, B., Gochis, D., Downer, C. W., Camporese, M., Davison, J. H., Ebel, B., Jones, N., Kim, J., Mascaro, G., Niswonger, R., Restrepo, P., Rigon, R., Shen, C., Sulis, M., and Tarboton, D.: An overview of current applications, challenges, and future
trends in distributed process-based models in hydrology, J. Hydrol., 537, 45–60, https://doi.org/10.1016/j.jhydrol.2016.03.026, 2016. a
Fekete, B. M., Robarts, R. D., Kumagai, M., Nachtnebel, H.-P., Odada, E., and
Zhulidov, A V.: Time for in situ renaissance, Science, 349, 685–686, https://doi.org/10.1126/science.aac7358, 2015. a
Feki, H., Slimani, M., and Cudennec, C.: Geostatistically based optimization of a rainfall monitoring network extension: case of the climatically
heterogeneous Tunisia, Hydrol. Res., 48, 514–541, https://doi.org/10.2166/nh.2016.256, 2017. a
Flügel, W.: Delineating hydrological response units by geographical
information system analyses for regional hydrological modelling using
PRMS/MMS in the drainage basin of the River Bröl, Germany, Hydrol. Process., 1995, 423–436, 1995. a
Förster, K., Oesterle, F., Hanzer, F., Schöber, J., Huttenlau, M., and Strasser, U.: A snow and ice melt seasonal prediction modelling system for Alpine reservoirs, Proc. Int. Assoc. Hydrol. Sci., 374, 143–150, https://doi.org/10.5194/piahs-374-143-2016, 2016. a, b
Freudiger, D., Kohn, I., Seibert, J., Stahl, K., and Weiler, M.: Snow
redistribution for the hydrological modeling of alpine catchments, Wires Water, 4, e1232, https://doi.org/10.1002/wat2.1232, 2017. a
Fuka, D. R., Walter, M. T., MacAlister, C., Degaetano, A. T., Steenhuis, T. S., and Easton, Z. M.: Using the Climate Forecast System Reanalysis as weather input data for watershed models, Hydrol. Process., 28, 5613–5623,
https://doi.org/10.1002/hyp.10073, 2013. a, b, c
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S.,
Husak, G., Rowland, J., Harrison, L., Hoell, A., and Michaelsen, J.: The
climate hazards infrared precipitation with stations – a new environmental
record for monitoring extremes, Scient. Data, 2, 150066,
https://doi.org/10.1038/sdata.2015.66, 2015. a, b
Gampe, D., Schmid, J., and Ludwig, R.: Impact of Reference Dataset Selection on RCM Evaluation, Bias Correction, and Resulting Climate Change Signals of
Precipitation, J. Hydrometeorol., 20, 1813–1828, https://doi.org/10.1175/JHM-D-18-0108.1, 2019. a
Gao, L., Bernhardt, M., and Schulz, K.: Downscaling ERA-Interim temperature
data in comlex terrain, Hydrol. Earth Syst. Sci., 16, 4661–4673,
https://doi.org/10.5194/hess-16-4661-2012, 2012. a, b, c
Garen, D. C.: Choosing and Assimilation Forcing Data for Hydrological Prediction, in: Putting Prediction in Ungauged Basins into Practice, edited
by: Pomeroy, J. W., Whitfield, P. H., and Spence, C., Canadian Water Resources Association, ISBN 978-1-896513-38-6, 2013. a
Garnier, B. J. and Ohmura, A.: The evaluation of surface variations in solar
radiation income, Sol. Energy, 13, 21–34, https://doi.org/10.1016/0038-092X(70)90004-6, 1970. a
Gascoin, S., Grizonnet, M., Bouchet, M., Salgues, G., and Hagolle, O.: Theia
Snow collection: high-resolution operational snow cover maps from Sentinel-2
and Landsat-8 data, Earth Syst. Sci. Data, 11, 493–514,
https://doi.org/10.5194/essd-11-493-2019, 2019. a
Germann, U. and Joss, J.: Operational Measurement of Precipitation in
Mountainous Terrain, in: Weather Radar, edited by: Meischner, P., Springer, Berlin, Heidelberg, 52–77, 2004. a
Gesch, D. B., Verdin, K. L., and Greenlee, S. K.: New land surface digital
elevation model covers the Earth, Eos Trans. Am. Geophys. Union, 80, 69–70, https://doi.org/10.1029/99EO00050, 1999. a, b
Girons Lopez, M., Vis, M. J. P., Jenicek, M., Griessinger, N., and Seibert,
J.: Complexity and performance of temperature-based snow routines for runoff
modelling in mountainous areas in Central Europe, Hydrol. Earth Syst. Sci. [preprint], https://doi.org/10.5194/hess-2020-57, 2020. a
Granger, R. J. and Pomeroy, J. W.: Sustainability of the western Canadian
boreal forest under changing hydrological conditions. I. Snow accumulation
and ablation, Sustainability of Water Resources under Increasing Uncertainty,
in: IAHS Publ No. 240, edited by: Rosjberg, D., Boutayeb, N., Gustard, A., Kundzewicz, Z., and Rasmussen, P., IAHS Press, Wallingford, 243–250, 1997. a
Grossi, G., Lendvai, A., Peretti, G., and Ranzi, R.: Snow Precipitation
Measured by Gauges: Systematic Error Estimation and Data Series Correction in
the Central Italian Alps, Water, 9, 461, https://doi.org/10.3390/w9070461, 2017. a, b
Grünewald, T., Stötter, J., Pomeroy, J. W., Dadic, R., Moreno Baños, I., Marturià, J., Spross, M., Hopkinson, C., Burlando, P., and Lehning, M.: Statistical modelling of the snow depth distribution in open alpine terrain, Hydrol. Earth Syst. Sci., 17, 3005–3021, https://doi.org/10.5194/hess-17-3005-2013, 2013. a
Guth, P. L.: Geomorphometry from SRTM, Photogram. Eng. Remote Sens., 72, 269–277, https://doi.org/10.14358/PERS.72.3.269, 2006. a
Haberkorn, A.: European Snow Booklet – an Inventory of Snow Measurements in
Europe, EnviDat, https://doi.org/10.16904/ENVIDAT.59, 2019. a
Hall, D. K., Riggs, G. A., Salomonson, V. V., DiGirolamo, N. E., and Bayr, K. J.: MODIS snow-cover products, Remote Sens. Environ., 83, 181–194, https://doi.org/10.1016/S0034-4257(02)00095-0, 2002. a
Härer, S., Bernhardt, M., Corripio, J. G., and Schulz, K.: PRACTISE –
Photo Rectification And ClassificaTIon SoftwarE (V.1.0), Geosci. Model Dev., 6, 837–848, https://doi.org/10.5194/gmd-6-837-2013, 2013. a
Härer, S., Bernhardt, M., and Schulz, K.: PRACTISE – Photo Rectification
And ClassificaTIon SoftwarE (V.2.1), Geosci. Model Dev., 9, 307–321, https://doi.org/10.5194/gmd-9-307-2016, 2016. a
Härer, S., Bernhardt, M., Siebers, M., and Schulz, K.: On the need for a
time- and location-dependent estimation of the NDSI threshold value for
reducing existing uncertainties in snow cover maps at different scales, The
Cryosphere, 12, 1629–1642, https://doi.org/10.5194/tc-12-1629-2018, 2018. a, b
Hay, L. E. and Clark, M. P.: Use of statistically and dynamically downscaled
atmospheric model output for hydrologic simulations in three mountainous
basins in the western United States, J. Hydrol., 282, 56–75,
https://doi.org/10.1016/S0022-1694(03)00252-X, 2003. a
Hersbach, H., Bell, W., Berrisford, P., Horányi, A. J. M.-S., Nicolas,
J., Radu, R., Schepers, D., Simmons, A., Soci, C., and Dee, D.: Global
reanalysis: goodbye ERA-Interim, hello ERA5, European Centre for Medium-Range Weather Forecasts, Reading, UK, https://doi.org/10.21957/VF291HEHD7, 2019. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P.,
Biavati, G., Bidlot, J., Bonavita, M., Chiara, G., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M.,
Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global
reanalysis, Q. J. Roy. Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/QJ.3803, 2020. a, b, c
Hirsch, R. M. and Costa, J. E.: U.S. stream flow measurement and data
dissemination improve, Eos Trans. AGU, 85, 197–203, https://doi.org/10.1029/2004eo200002, 2004. a
Hirtlreiter, G.: Spät- und postglaziale Gletscherschwankungen im
Wettersteingebirge und seiner Umgebung: Münchner Geographische
Abhandlungen, in: Münchner Geographische Abhandlungen (B), Ludwig-Maximilians-University Munich, Munich, 1992. a
Hopkinson, C., Chasmer, L., Munro, S., and Demuth, M. N.: The influence of DEM resolution on simulated solar radiation-induced glacier melt, Hydrol. Process., 24, 775–788, https://doi.org/10.1002/hyp.7531, 2010. a
Hrachowitz, M. and Weiler, M.: Uncertainty of Precipitation Estimates Caused by Sparse Gauging Networks in a Small, Mountainous Watershed, J. Hydrol. Eng., 16, 460–471, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000331, 2011. a
Hrachowitz, M., Savenije, H., Blöschl, G., McDonnell, J. J., Sivapalan, M., Pomeroy, J. W., Arheimer, B., Blume, T., Clark, M. P., Ehret, U., Fenicia, F., Freer, J. E., Gelfan, A., Gupta, H. V., Hughes, D. A., Hut, R. W., Montanari, A., Pande, S., Tetzlaff, D., Troch, P. A., Uhlenbrook, S.,
Wagener, T., Winsemius, H. C., Woods, R. A., Zehe, E., and Cudennec, C.: A
decade of Predictions in Ungauged Basins (PUB) – a review, Hydrolog. Sci. J., 58, 1198–1255, https://doi.org/10.1080/02626667.2013.803183, 2013. a
Huggel, C., Carey, M., Clague, J. J., and Kaab, A.: The high-mountain
cryosphere: Environmental changes and human risks, Cambridge University Press, Cambridge, https://doi.org/10.1017/CBO9781107588653, 2015. a
Hürkamp, K., Zentner, N., Reckerth, A., Weishaupt, S., Wetzel, K.-F.,
Tschiersch, J., and Stumpp, C.: Spatial and Temporal Variability of Snow
Isotopic Composition on Mt. Zugspitze, Bavarian Alps, Germany, J. Hydrol. Hydromech., 67, 49–58, https://doi.org/10.2478/johh-2018-0019, 2019. a, b
Huss, M., Bookhagen, B., Huggel, C., Jacobsen, D., Bradley, R. S., Clague,
J. J., Vuille, M., Buytaert, W., Cayan, D. R., Greenwood, G., Mark, B. G.,
Milner, A. M., Weingartner, R., and Winder, M.: Toward mountains without
permanent snow and ice, Earth's Future, 5, 418–435, https://doi.org/10.1002/2016EF000514, 2017. a, b
Hüttl, C.: Steuerungsfaktoren und Quantifizierung der chemischen
Verwitterung auf dem Zugspitzplatt (Wettersteingebirge, Deutschland),
in: Münchener Geographische Abhandlungen Reihe B, Ludwig-Maximilians-University Munich, Munich, 1999. a
Kahl, A., Winstral, A., Marks, D., and Dozier, J.: Using Satellite Imagery and the Distributed ISNOBAL Energy Balance Model to Derive SWE Heterogeneity in Mountainous Basins, in: Putting Prediction in Ungauged Basins into Practice, edited by: Pomeroy, J. W., Whitfield, P. H., and Spence, C., Canadian Water Resources Association, ISBN 978-1-896513-38-6, 2013. a
Kim, R. S., Durand, M., and Liu, D.: Spectral analysis of airborne passive
microwave measurements of alpine snowpack: Colorado, USA, Remote Sens. Environ., 205, 469–484, https://doi.org/10.1016/j.rse.2017.07.025, 2018. a
Koch, F., Bach, H., Prasch, M., Weber, M., Braun, L., and Mauser, W.: Climate
Change and Energy – Impact of Snow and Glacier Melting on Hydropower in the
Catchment Area of the Upper, Korrespondenz Wasserwirtschaft, 2011, 319–328,
2011. a
Koch, F., Henkel, P., Appel, F., Schmid, L., Bach, H., Lamm, M., Prasch, M.,
Schweizer, J., and Mauser, W.: Retrieval of Snow Water Equivalent, Liquid
Water Content, and Snow Height of Dry and Wet Snow by Combining GPS Signal
Attenuation and Time Delay, Water Resour. Res., 9, 4465–4487, https://doi.org/10.1029/2018WR024431, 2019. a
König-Langlo, G. and Augstein, F.: Parameterization of the downward
long-wave radiation at the Earth surface in polar regions, Meteorol. Z., 3,
343–347, 1994. a
Konzelmann, T., van de Wal, R., Greuell, W., Bintanja, R., Henneken, E., and
Abeouchi, A.: Parameterization of global and longwave incoming radiation for
the Greenland Ice Sheet, Global Planet. Change, 9, 143–164,
https://doi.org/10.1016/0921-8181(94)90013-2, 1994. a
Kundzewicz, Z. W. and Stakhiv, E. Z.: Are climate models “ready for prime
time” in water resources management applications, or is more research needed?, Hydrolog. Sci. J., 55, 1085–1089, https://doi.org/10.1080/02626667.2010.513211, 2010. a
Kunkel, K. E.: Simple Procedures for Extrapolation of Humidity Variables in the Mountainous Western United States, J. Climate, 2, 656–670,
https://doi.org/10.1175/1520-0442(1989)002<0656:SPFEOH>2.0.CO;2, 1989. a
Kwok, R. and Markus, T.: Potential basin-scale estimates of Arctic snow depth
with sea ice freeboards from CryoSat-2 and ICESat-2: An exploratory analysis,
Adv. Space Res., 62, 1243–1250, https://doi.org/10.1016/j.asr.2017.09.007, 2018. a
Lettenmaier, D. P., Alsdorf, D., Dozier, J., Huffman, G. J., Pan, M., and Wood, E. F.: Inroads of remote sensing into hydrologic science during the WRR era, Water Resour. Res., 51, 7309–7342, https://doi.org/10.1002/2015WR017616, 2015. a
Lievens, H., Demuzere, M., Marshall, H.-P., Reichle, R. H., Brucker, L.,
Brangers, I., de Rosnay, P., Dumont, M., Girotto, M., Immerzeel, W. W.,
Jonas, T., Kim, E. J., Koch, I., Marty, C., Saloranta, T., Schöber, J.,
and de Lannoy, G. J. M.: Snow depth variability in the Northern Hemisphere
mountains observed from space, Nat. Commun., 10, 4629, https://doi.org/10.1038/s41467-019-12566-y, 2019. a, b
Liston, G. E. and Elder, K.: A Meteorological Distribution System for
High-Resolution Terrestrial Modeling (MicroMet), J. Hydrometeorol., 7, 217–234, https://doi.org/10.1175/JHM486.1, 2006. a, b, c, d
Liu, S., Mo, X., Liu, C., Xia, J., and Zhao, W.: How to maximize the predictive values of available data in ungauged basins? – Chinese Lesson, in: Putting Prediction in Ungauged Basins into Practice, edited by: Pomeroy, J. W., Whitfield, P. H., and Spence, C., Canadian Water Resources Association, ISBN 978-1-896513-38-6, 2013. a, b, c, d
López-Moreno, J. I., Pomeroy, J. W., Revuelto, J., and Vicente-Serrano, S. M.: Response of snow processes to climate change: spatial variability in a
small basin in the Spanish Pyrenees, Hydrol. Process., 27, 2637–2650,
https://doi.org/10.1002/hyp.9408, 2013. a
Love, T. B., Kumar, V., Xie, P., and Thiaw, W.: A 20-year daily Africa precipitation climatology using satellite and gauge data, in: Proceedings of the 84th AMS Annual Meeting, vol. Conference on Applied Climatology, Seattle, 4 pp., available at: https://www.cpc.ncep.noaa.gov/products/fews/AFR_CLIM/appl_clim.pdf (last access: 24 May 2021), 2004. a
Ludwig, R. and Schneider, P.: Validation of digital elevation models from SRTM X-SAR for applications in hydrologic modeling, ISPRS J. Photogram. Remote Sens., 60, 339–358, https://doi.org/10.1016/j.isprsjprs.2006.05.003, 2006. a
Lv, Z., Pomeroy, J. W., and Fang, X.: Evaluation of SNODAS Snow Water
Equivalent in Western Canada and Assimilation Into a Cold Region Hydrological
Model, Water Resour. Res., 55, 11166–11187, https://doi.org/10.1029/2019WR025333, 2019. a
MacDonald, M. K., Pomeroy, J. W., and Pietroniro, A.: On the importance of sublimation to an alpine snow mass balance in the Canadian Rocky Mountains, Hydrol. Earth Syst. Sci., 14, 1401–1415, https://doi.org/10.5194/hess-14-1401-2010, 2010. a
Marks, D., Domingo, J., Susong, D., Link, T., and Garen, D.: A spatially
distributed energy balance snowmelt model for application in mountain basins,
Hydrol. Process., 13, 1935–1959,
https://doi.org/10.1002/(SICI)1099-1085(199909)13:12/13<1935::AID-HYP868>3.0.CO;2-C,
1999. a
Marti, R., Gascoin, S., Berthier, E., de Pinel, M., Houet, T., and Laffly, D.: Mapping snow depth in open alpine terrain from stereo satellite imagery, The Cryosphere, 10, 1361–1380, https://doi.org/10.5194/tc-10-1361-2016, 2016. a
Mauser, W., and Prasch, M.: Regional Assessment of Global Change Impacts. The Project GLOWA-Danube, Springer International Publishing, Cham, ISBN 978-3-319-16751-0, https://doi.org/10.1007/978-3-319-16751-0, 2016. a
Merwade, V., Olivera, F., Arabi, M., and Edleman, S.: Uncertainty in Flood
Inundation Mapping: Current Issues and Future Directions, J. Hydrol. Eng., 13, 608–620, https://doi.org/10.1061/(ASCE)1084-0699(2008)13:7(608), 2008. a
Meybeck, M., Green, P., and Vörösmarty, C.: A New Typology for
Mountains and Other Relief Classes, Mt. Res. Dev., 21, 34–45,
https://doi.org/10.1659/0276-4741(2001)021[0034:ANTFMA]2.0.CO;2, 2001. a
Morche, D. and Schmidt, K. H.: Sediment transport in an alpine river before and after a dambreak flood event, Earth Surf. Proc. Land., 37, 347–353, https://doi.org/10.1002/esp.2263, 2012. a
Mott, R., Scipión, D., Schneebeli, M., Dawes, N., Berne, A., and Lehning,
M.: Orographic effects on snow deposition patterns in mountainous terrain,
J. Geophys. Res.-Atmos., 119, 1419–1439, https://doi.org/10.1002/2013JD019880, 2014. a
Muerth, M. J., Gauvin St-Denis, B., Ricard, S., Velázquez, J. A., Schmid,
J., Minville, M., Caya, D., Chaumont, D., Ludwig, R., and Turcotte, R.: On
the need for bias correction in regional climate scenarios to assess climate
change impacts on river runoff, Hydrol. Earth Syst. Sci., 17, 1189–1204, https://doi.org/10.5194/hess-17-1189-2013, 2013. a
Muñoz Sabater, J.: ERA5-Land hourly data from 1981 to present,
ECMWF, https://doi.org/10.24381/CDS.E2161BAC, 2019. a, b
Nagaveni, C., Kumar, K. P., and Ravibabu, M. V.: Evaluation of TanDEMx and SRTM DEM on watershed simulated runoff estimation, J. Earth Syst. Sci., 128, 73, https://doi.org/10.1007/s12040-018-1035-z, 2019. a, b
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual
models: Part 1 – A discussion of principles, J. Hydrol., 10, 282–290, 1970. a
Niu, G.-Y., Yang, Z.-L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M.,
Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y.: The
community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements, Environ. Res. Lett., 116, D12109, https://doi.org/10.1029/2010JD015139, 2011. a
Notarnicola, C.: Hotspots of snow cover changes in global mountain regions over 2000–2018, Remote Sens. Environ., 243, 111781, https://doi.org/10.1016/j.rse.2020.111781, 2020. a, b
Piani, C., Haerter, J. O., and Coppola, E.: Statistical bias correction for
daily precipitation in regional climate models over Europe, Theor. Appl. Climatol., 99, 187–192, https://doi.org/10.1007/s00704-009-0134-9, 2010. a
Poli, P., Hersbach, H., Dee, D. P., Berrisford, P., Simmons, A. J., Vitart, F., Laloyaux, P., Tan, D. G. H., Peubey, C., Thépaut, J.-N., Trémolet, Y., Hólm, E. V., Bonavita, M., Isaksen, L., and Fisher, M.: ERA-20C: An Atmospheric Reanalysis of the Twentieth Century, J. Climate, 29,
4083–4097, https://doi.org/10.1175/JCLI-D-15-0556.1, 2016. a, b, c
Pomeroy, J. W. and Li, L.: Prairie and arctic areal snow cover mass balance
using a blowing snow model, J. Geophys. Res., 105, 26619, https://doi.org/10.1029/2000JD900149, 2000. a
Pomeroy, J. W., Gray, D. M., Brown, T., Hedstrom, N. R., Quinton, W. L.,
Granger, R. J., and Carey, S. K.: The cold regions hydrological model: a
platform for basing process representation and model structure on physical
evidence, Hydrol. Process., 21, 2650–2667, https://doi.org/10.1002/hyp.6787, 2007. a, b, c
Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng,
C.-J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin, J. K., Walker, J. P., Lohmann, D., and Toll, D.: The Global Land Data Assimilation System, B. Am. Meteorol. Soc., 85, 381–394, https://doi.org/10.1175/BAMS-85-3-381, 2004. a, b, c
Rott, H., Nagler, T., Ripper, E., Voglmeier, K., Prinz, R., Fromm, R., Coccia, A., Meta, A., Di Leo, D., and Schuttemeyer, D.: KU- and X-band backscatter analysis and SWE retrieval for Alpine snow, in: IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Piscataway, NJ, 2407–2410, https://doi.org/10.1109/IGARSS.2014.6946957, 2014. a
Sabatini, F.: Setting up and Managing Automatic Weather Stations for Remote
Sites Monitoring: From Niger to Nepal, in: Renewing Local Planning to Face
Climate Change in the Tropics, Green Energy and Technology, edited by: Tiepolo, M., Pezzoli, A., and Tarchiani, V., Springer International Publishing, Cham, 21–39, https://doi.org/10.1007/978-3-319-59096-7_2, 2017. a
Saha, S., Moorthi, S., Pan, H.-L., Wu, X., Wang, J., Nadiga, S., Tripp, P.,
Kistler, R., Woollen, J., Behringer, D., Liu, H., Stokes, D., Grumbine, R.,
Gayno, G., Wang, J., Hou, Y.-T., Chuang, H.-Y., Juang, H.-M. H., Sela, J.,
Iredell, M., Treadon, R., Kleist, D., van Delst, P., Keyser, D., Derber, J., Ek, M., Meng, J., Wei, H., Yang, R., Lord, S., van den Dool, H., Kumar, A., Wang, W., Long, C., Chelliah, M., Xue, Y., Huang, B., Schemm, J.-K., Ebisuzaki, W., Lin, R., Xie, P., Chen, M., Zhou, S., Higgins, W., Zou, C.-Z.,
Liu, Q., Chen, Y., Han, Y., Cucurull, L., Reynolds, R. W., Rutledge, G., and
Goldberg, M.: The NCEP Climate Forecast System Reanalysis, B. Am. Meteorol. Soc., 91, 1015–1058, https://doi.org/10.1175/2010BAMS3001.1, 2010. a, b
Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., Behringer, D., Hou, Y.-T., Chuang, H.-Y., Iredell, M., Ek, M., Meng, J., Yang, R., Mendez, M. P., van den Dool, H., Zhang, Q., Wang, W., Chen, M., and Becker, E.: The NCEP Climate Forecast System Version 2, J. Climate, 27, 2185–2208,
https://doi.org/10.1175/JCLI-D-12-00823.1, 2014. a
Satgé, F., Ruelland, D., Bonnet, M.-P., Molina, J., and Pillco, R.:
Consistency of satellite-based precipitation products in space and over time
compared with gauge observations and snow- hydrological modelling in the Lake
Titicaca region, Hydrol. Earth Syst. Sci., 23, 595–619,
https://doi.org/10.5194/hess-23-595-2019, 2019. a
Schattan, P., Baroni, G., Oswald, S. E., Schöber, J., Fey, C., Kormann, C., Huttenlau, M., and Achleitner, S.: Continuous monitoring of snowpack dynamics in alpine terrain by aboveground neutron sensing, Water Resour. Res., 53, 3615–3634, https://doi.org/10.1002/2016WR020234, 2017. a
Sedlar, J. and Hock, R.: Testing longwave radiation parameterizations under
clear and overcast skies at Storglaciären, Sweden, The Cryosphere, 3,
75–84, https://doi.org/10.5194/tc-3-75-2009, 2009. a
Seyler, F., Muller, F., Cochonneau, G., Guimarães, L., and Guyot, J. L.:
Watershed delineation for the Amazon sub-basin system using GTOPO30 DEM and a
drainage network extracted from JERS SAR images, Hydrol. Process., 23, 3173–3185, https://doi.org/10.1002/hyp.7397, 2009. a
Shaw, T. E., Gascoin, S., Mendoza, P. A., Pellicciotti, F., and McPhee, J.:
Snow Depth Patterns in a High Mountain Andean Catchment from Satellite
Optical Tristereoscopic Remote Sensing, Water Resour. Res., 56, e2019WR024880, https://doi.org/10.1029/2019WR024880, 2020. a
Sheffield, J., Goteti, G., and Wood, E. F.: Development of a 50-Year
High-Resolution Global Dataset of Meteorological Forcings for Land Surface
Modeling, J. Climate, 19, 3088–3111, https://doi.org/10.1175/JCLI3790.1, 2006. a
Shi, J. and Dozier, J.: Mapping seasonal snow with SIR-C/X-SAR in mountainous
areas, Remote Sens. Environ., 59, 294–307, https://doi.org/10.1016/S0034-4257(96)00146-0, 1997. a
Shi, J. and Dozier, J.: Estimation of snow water equivalence using SIR-C/X-SAR. I. Inferring snow density and subsurface properties, IEEE T. Geosci. Remote, 38, 2465–2474, https://doi.org/10.1109/36.885195, 2000. a
Smiatek, G., Kunstmann, H., Knoche, R., and Marx, A.: Precipitation and temperature statistics in high-resolution regional climate models: Evaluation
for the European Alps, J. Geophys. Res., 114, D19107, https://doi.org/10.1029/2008JD011353, 2009. a
Sørensen, R. and Seibert, J.: Effects of DEM resolution on the calculation
of topographical indices: TWI and its components, J. Hydrol., 347, 79–89, https://doi.org/10.1016/j.jhydrol.2007.09.001, 2007. a
Tadono, T., Ishida, H., Oda, F., Naito, S., Minakawa, K., and Iwamoto, H.:
Precise Global DEM Generation by ALOS PRISM, ISPRS Ann. Photogram. Remote Sens. Spat. Inform. Sci., II-4, 71–76, https://doi.org/10.5194/isprsannals-II-4-71-2014, 2014. a, b
Tauro, F., Selker, J., van de Giesen, N., Abrate, T., Uijlenhoet, R., Porfiri, M., Manfreda, S., Caylor, K., Moramarco, T., Benveniste, J., Ciraolo, G., Estes, L., Domeneghetti, A., Perks, M. T., Corbari, C., Rabiei,
E., Ravazzani, G., Bogena, H., Harfouche, A., Brocca, L., Maltese, A.,
Wickert, A., Tarpanelli, A., Good, S., Lopez Alcala, J. M., Petroselli, A.,
Cudennec, C., Blume, T., Hut, R., and Grimaldi, S.: Measurements and
Observations in the XXI century (MOXXI): innovation and multi-disciplinarity
to sense the hydrological cycle, Hydrolog. Sci. J., 63, 169–196,
https://doi.org/10.1080/02626667.2017.1420191, 2018. a
Teutschbein, C., Wetterhall, F., and Seibert, J.: Evaluation of different
downscaling techniques for hydrological climate-change impact studies at the
catchment scale, Clim. Dynam., 37, 2087–2105, https://doi.org/10.1007/s00382-010-0979-8, 2011. a
Thornton, P. E., Running, S. W., and White, M. A.: Generating surfaces of daily meteorological variables over large regions of complex terrain, J. Hydrol., 190, 214–251, https://doi.org/10.1016/S0022-1694(96)03128-9, 1997. a
USGS: GTPO30 Global Digital Elevation Model, available at: https://www.usgs.gov/centers/eros/science/usgs-eros-archive-digital-elevation-global-30-arc-second-elevation (last access: 24 May 2021), 1996. a, b
van de Giesen, N., Hut, R., and Selker, J.: The Trans-African Hydro-Meteorological Observatory (TAHMO), Wiley Interdisciplin. Rev.: Water, 1, 341–348, https://doi.org/10.1002/wat2.1034, 2014. a
Vaze, J., Teng, J., and Spencer, G.: Impact of DEM accuracy and resolution on
topographic indices, Environ. Model. Softw., 25, 1086–1098, https://doi.org/10.1016/j.envsoft.2010.03.014, 2010. a
Viviroli, D., Archer, D. R., Buytaert, W., Fowler, H. J., Greenwood, G. B.,
Hamlet, A. F., Huang, Y., Koboltschnig, G., Litaor, M. I., López-Moreno,
J. I., Lorentz, S., Schädler, B., Schreier, H., Schwaiger, K., Vuille, M., and Woods, R.: Climate change and mountain water resources: overview and
recommendations for research, management and policy, Hydrol. Earth Syst. Sci., 15, 471–504, https://doi.org/10.5194/hess-15-471-2011, 2011. a
Vuyovich, C. M., Jacobs, J. M., and Daly, S. F.: Comparison of passive
microwave and modeled estimates of total watershed SWE in the continental
United States, Water Resour. Res., 50, 9088–9102, https://doi.org/10.1002/2013WR014734, 2014. a
Weber, M., Bernhardt, M., Pomeroy, J. W., Fang, X., Härer, S., and Schulz, K.: Description of current and future snow processes in a small basin in the Bavarian Alps, Environ. Earth Sci., 75, 962, https://doi.org/10.1007/s12665-016-6027-1, 2016. a, b, c, d
Wesemann, J., Herrnegger, M., and Schulz, K.: Hydrological modelling in the
anthroposphere: predicting local runoff in a heavily modified high-alpine
catchment, J. Mount. Sci., 15, 921–938, https://doi.org/10.1007/s11629-017-4587-5, 2018. a
Wetzel, K.-F.: On the Hydrology of the Partnach Area in the Wetterstein
Mountains (Bavarian Alps), Erdkunde, 58, 172–186, https://doi.org/10.3112/erdkunde.2004.02.05, 2004. a
Whitfield, P. H., Moore, R. D., and Shook, K.: Summary and Synthesis of
Workshop Break Out Group Discussions, in: Putting Prediction in Ungauged
Basins into Practice, edited by: Pomeroy, J. W., Whitfield, P. H., and Spence, C., Canadian Water Resources Association, ISBN 978-1-896513-38-6, 271–304, 2013. a
Winstral, A., Elder, K., and Davis, R. E.: Spatial Snow Modeling of
Wind-Redistributed Snow Using Terrain-Based Parameters, J. Hydrometeorol.,
3, 524–538, https://doi.org/10.1175/1525-7541(2002)003<0524:SSMOWR>2.0.CO;2, 2002. a
Wortmann, M., Bolch, T., Menz, C., Tong, J., and Krysanova, V.: Comparison and Correction of High-Mountain Precipitation Data Based on Glacio-Hydrological Modeling in the Tarim River Headwaters (High Asia), J.
Hydrometeorol., 19, 777–801, https://doi.org/10.1175/JHM-D-17-0106.1, 2018. a
Wrobel, J.-P.: Berichtüber den Markierungsversuch auf dem Zugspitzplatt im Sommer 1980, Bayerisches Geologisches Landesamt, München, 1–6, 1980. a
Zhang, F., Zhang, H., Hagen, S. C., Ye, M., Wang, D., Gui, D., Zeng, C., Tian, L., and Liu, J.: Snow cover and runoff modelling in a high mountain catchment with scarce data: effects of temperature and precipitation parameters, Hydrol. Process., 29, 52–65, https://doi.org/10.1002/hyp.10125, 2015. a, b
Short summary
We compared a suite of globally available meteorological and DEM data with in situ data for physically based snow hydrological modelling in a small high-alpine catchment. Although global meteorological data were less suited to describe the snowpack properly, transferred station data from a similar location in the vicinity and substituting single variables with global products performed well. In addition, using 30 m global DEM products as model input was useful in such complex terrain.
We compared a suite of globally available meteorological and DEM data with in situ data for...
