Melt trends portend widespread declines in snow water resources

Abstract In many mountainous regions, winter precipitation accumulates as snow that melts in spring and summer providing water to one billion people globally. As the climate warms and snowmelt occurs earlier, this natural water storage is compromised. While snowpack trend analyses commonly focus on snow water equivalent (SWE), we propose that trends in accumulation season snowmelt serve as a critical indicator of hydrologic change. We compare long-term changes in snowmelt and SWE from snow monitoring stations in western North America. Nearly four-times more stations have increasing winter snowmelt trends than SWE declines; significant (p<0.05) at 42% vs. 12% of stations, respectively. Snowmelt trends are highly sensitive to temperature and an underlying warming signal, while SWE trends are more sensitive to precipitation variability. Thus, continental-scale snow-water resources are in steeper decline than is inferred from widely reported SWE trends alone. More winter snowmelt will complicate future water resources planning and management efforts.

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Long-term (1900-2100) Hydrometeorological and Snow Water Equivalent reconstructions in the French Southern Alps (Mercantour Natural Parc).

10.5194/egusphere-egu21-15872 ◽

2021 ◽

Author(s):

Thibault Mathevet ◽

Cyril Thébault ◽

Jérôme Mansons ◽

Matthieu Le Lay ◽

Audrey Valery ◽

...

Keyword(s):

Climate Change ◽

Time Series ◽

Water Resources ◽

Air Temperature ◽

Snow Water Equivalent ◽

Hydrological Regime ◽

Southern Alps ◽

Snow Line ◽

Snow Water

The aim of this communication is to present a study on climate variability and change on snow water equivalent (SWE) and streamflow over the 1900-2100 period in a mediteranean and moutainuous area. &#160;It is based on SWE and streamflow observations, past reconstructions (1900-2018) and future GIEC scenarii (up to 2100) of some snow courses and hydrological stations situated within the French Southern Alps (Mercantour Natural Parc). This has been conducted by EDF (French hydropower company) and Mercantour Natural Parc.This issue became particularly important since a decade, especially in regions where snow variability had a large impact on water resources availability, poor snow conditions in ski resorts and artificial snow production or impacts on mountainous ecosystems (fauna and flora). As a water resources manager in French mountainuous regions, EDF developed and managed a large hydrometeorological network since 1950. A recent data rescue research allowed to digitize long term SWE manual measurements of a hundred of snow courses within the French Alps. EDF have been operating an automatic SWE sensors network, complementary to historical snow course network. Based on numerous SWE observations time-series and snow modelization (Garavaglia et al., 2017), continuous daily historical SWE time-series have been reconstructed within the 1950-2018 period. These reconstructions have been extented to 1900 using 20 CR (20th century reanalyses by NOAA) reanalyses (ANATEM method, Kuentz et al., 2015) and up to 2100 using GIEC Climate Change scenarii (+4.5 W/m&#178; and + 8.5 W/m&#178; hypotheses). In the scope of this study, Mercantour Natural Parc is particularly interested by snow scenarii in the future and its impacts on their local flora and fauna.Considering observations within Durance watershed and Mercantour region, this communication focuses on: (1) long term (1900-2018) analyses of variability and trend of hydrometeorological and snow variables (total precipitation, air temperature, snow water equivalent, snow line altitude, snow season length, streamflow regimes) , (2) long term variability of snow and hydrological regime of snow dominated watersheds and (3) future trends (2020 -2100) using GIEC Climate Change scenarii.Comparing old period (1950-1984) to recent period (1984-2018), quantitative results within these regions roughly shows an increase of air temperature by 1.2 &#176;C, an increase of snow line height by 200m, a reduction of SWE by 200 mm/year and a reduction of snow season duration by 15 days. Characterization of the increase of snow line height and SWE reduction are particularly important at a local and watershed scale. Then, this communication focuses on impacts on long-term time scales (2050, 2100). This long term change of snow dynamics within moutainuous regions both impacts (1) water resources management, (2) snow resorts and artificial snow production developments or (3) ecosystems dynamics.Connected to the evolution of snow seasonality, the impacts on hydrological regime and some streamflow signatures allow to characterize the possible evolution of water resources in this mediteranean and moutianuous region This study allowed to provide some local quantitative scenarii.

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Measuring snow water equivalent from common-offset GPR records through migration velocity analysis

The Cryosphere ◽

10.5194/tc-11-2997-2017 ◽

2017 ◽

Vol 11 (6) ◽

pp. 2997-3009 ◽

Cited By ~ 4

Author(s):

James St. Clair ◽

W. Steven Holbrook

Keyword(s):

Water Resources ◽

Snow Water Equivalent ◽

Migration Velocity ◽

Snow Surface ◽

Snow Density ◽

Mountainous Regions ◽

Close Relationship ◽

Snow Water ◽

Radar Velocity ◽

Zero Offset

Abstract. Many mountainous regions depend on seasonal snowfall for their water resources. Current methods of predicting the availability of water resources rely on long-term relationships between stream discharge and snowpack monitoring at isolated locations, which are less reliable during abnormal snow years. Ground-penetrating radar (GPR) has been shown to be an effective tool for measuring snow water equivalent (SWE) because of the close relationship between snow density and radar velocity. However, the standard methods of measuring radar velocity can be time-consuming. Here we apply a migration focusing method originally developed for extracting velocity information from diffracted energy observed in zero-offset seismic sections to the problem of estimating radar velocities in seasonal snow from common-offset GPR data. Diffractions are isolated by plane-wave-destruction (PWD) filtering and the optimal migration velocity is chosen based on the varimax norm of the migrated image. We then use the radar velocity to estimate snow density, depth, and SWE. The GPR-derived SWE estimates are within 6 % of manual SWE measurements when the GPR antenna is coupled to the snow surface and 3–21 % of the manual measurements when the antenna is mounted on the front of a snowmobile ∼  0.5 m above the snow surface.

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APHRODITE: Constructing a Long-Term Daily Gridded Precipitation Dataset for Asia Based on a Dense Network of Rain Gauges

Bulletin of the American Meteorological Society ◽

10.1175/bams-d-11-00122.1 ◽

2012 ◽

Vol 93 (9) ◽

pp. 1401-1415 ◽

Cited By ~ 908

Author(s):

Akiyo Yatagai ◽

Kenji Kamiguchi ◽

Osamu Arakawa ◽

Atsushi Hamada ◽

Natsuko Yasutomi ◽

...

Keyword(s):

High Resolution ◽

Water Resources ◽

Middle East ◽

Northern Eurasia ◽

Observation Data ◽

Telecommunication System ◽

Mountainous Regions ◽

Continental Scale ◽

Global Telecommunication System

A daily gridded precipitation dataset covering a period of more than 57 yr was created by collecting and analyzing rain gauge observation data across Asia through the activities of the Asian Precipitation—Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) project. APHRODITE's daily gridded precipitation is presently the only long-term, continental-scale, high-resolution daily product. The product is based on data collected at 5,000–12,000 stations, which represent 2.3–4.5 times the data made available through the Global Telecommunication System network and is used for most daily gridded precipitation products. Hence, the APHRODITE project has substantially improved the depiction of the areal distribution and variability of precipitation around the Himalayas, Southeast Asia, and mountainous regions of the Middle East. The APHRODITE project now contributes to studies such as the determination of Asian monsoon precipitation change, evaluation of water resources, verification of high-resolution model simulations and satellite precipitation estimates, and improvement of precipitation forecasts. The APHRODITE project carries out outreach activities with Asian countries, and communicates with national institutions and world data centers. We have released open-access APHRO_V1101 datasets for monsoon Asia, the Middle East, and northern Eurasia (at 0.5° × 0.5° and 0.25° × 0.25° resolution) and the APHRO_JP_V1005 dataset for Japan (at 0.05° × 0.05° resolution; see www.chikyu.ac.jp/precip/ and http://aphrodite.suiri.tsukuba.ac.jp/). We welcome cooperation and feedback from users.

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Measuring snow water equivalent from common offset GPR records through migration velocity analysis

10.5194/tc-2017-90 ◽

2017 ◽

Author(s):

James St. Clair ◽

W. Steven Holbrook

Keyword(s):

Water Resources ◽

Snow Water Equivalent ◽

Migration Velocity ◽

Snow Surface ◽

Snow Density ◽

Mountainous Regions ◽

Close Relationship ◽

Snow Water ◽

Radar Velocity ◽

Zero Offset

Abstract. Many mountainous regions depend on seasonal snowfall for their water resources. Current methods of predicting the availability of water resources rely on the long-term relationship between stream discharge and snow pack monitoring at isolated locations, which are less reliable during abnormal snow years. Ground-penetrating-radar (GPR) has been shown to be an effective tool for measuring snow water equivalent (SWE) because of the close relationship between snow density and radar velocity. However, the standard methods of measuring radar velocity can be time consuming. Here we apply a migration focusing method originally developed for extracting velocity information from diffracted energy observed in zero-offset seismic sections to the problem of estimating radar velocities in seasonal snow from common-offset GPR data. Diffractions are isolated by plane-wave-destruction filtering and the optimal migration velocity is chosen based on the varimax norm of the migrated image. We then use the radar velocity to estimate snow density, depth, and SWE. The GPR derived SWE estimates are within 3 % of manual SWE measurements when the GPR antenna is coupled to the snow surface and 18 % of the manual measurements when the antenna is mounted on the front of a snowmobile ~ 0.5 meters above the snow surface.

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Snowpack variability and trends at long-term stations in northern Colorado, USA

Proceedings of the International Association of Hydrological Sciences ◽

10.5194/piahs-371-131-2015 ◽

2015 ◽

Vol 371 ◽

pp. 131-136 ◽

Cited By ~ 10

Author(s):

S. R. Fassnacht ◽

M. Hultstrand

Keyword(s):

Snow Depth ◽

Snow Water Equivalent ◽

Entire Period ◽

Northern Colorado ◽

Snow Water ◽

Highly Correlated ◽

The Individual

Abstract. The individual measurements from snowcourse stations were digitized for six stations across northern Colorado that had up to 79 years of record (1936 to 2014). These manual measurements are collected at the first of the month from February through May, with additional measurements in January and June. This dataset was used to evaluate the variability in snow depth and snow water equivalent (SWE) across a snowcourse, as well as trends in snowpack patterns across the entire period of record and over two halves of the record (up to 1975 and from 1976). Snowpack variability is correlated to depth and SWE. The snow depth variability is shown to be highly correlated with average April snow depth and day of year. Depth and SWE were found to be significantly decreasing over the entire period of record at two stations, while at another station the significant trends were an increase over the first half of the record and a decrease over the second half. Variability tended to decrease with time, when significant.

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Disentangling uncertainties in risk-based planning of water resources in the UK

10.5194/egusphere-egu21-10225 ◽

2021 ◽

Author(s):

Francesca Pianosi ◽

Andres Penuela-Fernandez ◽

Christopher Hutton

Keyword(s):

Decision Making ◽

Sensitivity Analysis ◽

Monte Carlo ◽

Water Resources ◽

Water Resources Planning ◽

Adaptive Planning ◽

Water Companies ◽

The Uk ◽

Resources Planning

Proper consideration of uncertainty has become a cornerstone of model-informed planning of water resource systems. In the UK Government&#8217;s 2020 Water Resources Planning Guidelines, the word &#8220;uncertainty&#8221; appears 48 times in 82 pages. This emphasis on uncertainty aligns with the increasing adoption by UK water companies of a &#8220;risk-based&#8221; approach to their long-term decision-making, in order to handle uncertainties in supply-demand estimation, climate change, population growth, etc. The term &#8220;risk-based&#8221; covers a range of methods - such as &#8220;info-gap&#8221;, &#8220;robust decision-making&#8221; or &#8220;system sensitivity analysis&#8221; - that come under different names but largely share a common rationale, essentially based on the use of Monte Carlo simulation. This shift in thinking from previous (deterministic) &#8220;worst-case&#8221; approach to a &#8220;risk-based&#8221; one is important and has the potential to significantly improve water resources planning practice. However its implementation is diminished by a certain lack of clarity about the terminology in use and about the concrete differences (and similarities) among methods. On top of these difficulties, in the next planning-cycle (2021-2026) two further step changes are introduced: (1) water companies are requested to move from a cost-efficiency approach focused on achieving the supply-demand balance, towards a fully multi-criteria approach that more explicitly encompasses other objectives including environmental sustainability; (2) as a further way to handle long-term uncertainties, they are required to embrace an &#8220;adaptive planning&#8221; approach. These changes will introduce two new sets of uncertainties around the robust quantification of criteria, particularly environmental ones, and around the attribution of weights to different criteria. This urgently calls for establishing structured approaches to quantify not only the uncertainty in model outputs, but also the sensitivity of those outputs to different forms of uncertainty in the modelling chain that mostly control the variability of the final outcome &#8211; the &#8220;best value&#8221; plan. Without this understanding of critical uncertainties, the risk is that huge efforts are invested on characterising and/or reducing uncertainties that later turn out to have little impact on the final outcome; or that water managers fall back to using oversimplified representation of those uncertainties as a way to escape the huge modelling burden. In this work, we aim at starting to establish a common rationale to &#8220;risk-based&#8221; methods within the context of a fully multi-criteria approach. We use a proof-of-concept example of a reservoir system in the South-West of England to demonstrate the use of global (i.e. Monte Carlo based) sensitivity analysis to simultaneously quantify output uncertainty and sensitivity, and identify robust decisions. We also discuss the potential of this approach to inform the construction of a &#8220;decision tree&#8221; for adaptive planning.

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ASSESSMENT OF CHANGES IN THE CHARACTERISTICS OF THE WATER, SNOW AND ICE REGIMES IN THE BASINS OF THE SMALL NORTHERN DVINA RIVER UNDER MODERN CONDITIONS AND THEIR POSSIBLE CHANGES IN THE FUTURE

Prirodoobustrojstvo ◽

10.26897/1997-6011-2021-3-117-127 ◽

2021 ◽

pp. 117-127

Author(s):

M. V. GEORGIEVSKY ◽

◽

N. I. GOROSHKOVA ◽

V. A. KHOMYAKOVA ◽

A. V. STRIZHENOK

Keyword(s):

Snow Cover ◽

Climate Warming ◽

Snow Water Equivalent ◽

Winter Precipitation ◽

Spring Runoff ◽

The Future ◽

Snow Water ◽

Project Data ◽

The Impact ◽

Slight Downward Trend

The article presents an analysis of the impact of climate change on the main characteristics of ice phenomena, snow cover and the water regime in the Small Northern Dvina River basin occurring in recent decades. Recently, a significant climate warming has been observed in the basin. As a result, winters are getting warmer and shorter. There is also an increase in winter precipitation and the number of thaws. Climate warming directly affects the duration of snow cover, which decreases both due to the later formation and to the earlier destruction of snow. There is also a slight downward trend in the annual values of the maximum snow water equivalent, which may be the result of an increase in the number of thaws in winter, when a part of the snow cover melts contributing to the winter river runoff. The analysis of the main characteristics of the ice cover on the rivers of the studied basin shows that their changes are similarly to changes in the snow cover: there is a reduction in the freeze-up period due to its later formation and earlier complete destruction. The maximum ice thickness on the rivers of the basin also tends to decrease. There is an increase in winter and a decrease in spring runoff. Predictive estimates of changes in the observed trends in the future are presented in the fi nal part of the article based on the CMIP5 project data.

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On the Role of NAO-Driven Interannual Variability in Rainfall Seasonality on Water Resources and Hydrologic Design in a Typical Mediterranean Basin

Journal of Hydrometeorology ◽

10.1175/jhm-d-17-0078.1 ◽

2018 ◽

Vol 19 (3) ◽

pp. 485-498 ◽

Cited By ~ 4

Author(s):

Roberto Corona ◽

Nicola Montaldo ◽

John D. Albertson

Keyword(s):

Water Resources ◽

Mediterranean Basin ◽

Pearson Correlation ◽

Winter Precipitation ◽

Annual Precipitation ◽

Drought Severity ◽

Water Resources Planning ◽

The Mediterranean ◽

Hydrologic Design ◽

Resources Planning

Abstract In the last several decades, extended dry periods have affected the Mediterranean area with dramatic impacts on water resources. Climate models are predicting further warming, with negative effects on water availability. The authors analyze the hydroclimatic tendencies of a typical Mediterranean basin, the Flumendosa basin located in Sardinia, an island in the center of the Mediterranean Sea, where in the last 30 years a sequence of dry periods has seriously impacted the water management system. Interestingly, in the historic record the annual runoff reductions have been more pronounced than the annual precipitation reductions. This paper performs an analysis that links this runoff decrease to changes in the total annual precipitation and its seasonal structure. The seasonality is a key determinant of the surface runoff process, as it reflects the degree to which rainfall is concentrated during the winter. The observed reductions in winter precipitation are shown here to be well correlated (Pearson correlation coefficient of −0.5) with the North Atlantic Oscillation (NAO) index. Considering the predictability of the winter NAO, there is by extension an opportunity to predict future winter precipitation and runoff tendencies. The recent hydroclimatic trends are shown to impact hydrologic design criteria for water resources planning. The authors demonstrate that there is a dangerous increase of the drought severity viewed from the perspective of water resources planning.

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Towards a long term global snow climate data record from satellite data generated within the Snow Climate Change Initiative

10.5194/egusphere-egu2020-19228 ◽

2020 ◽

Author(s):

Gabriele Schwaizer ◽

Lars Keuris ◽

Thomas Nagler ◽

Chris Derksen ◽

Kari Luojus ◽

...

Keyword(s):

Time Series ◽

Satellite Data ◽

Snow Water Equivalent ◽

Climate Model ◽

Process Models ◽

Climate Data ◽

Seasonal Snow ◽

Snow Water

Seasonal snow is an important component of the global climate system. It is highly variable in space and time and sensitive to short term synoptic scale processes and long term climate-induced changes of temperature and precipitation. Current snow products derived from various satellite data applying different algorithms show significant discrepancies in extent and snow mass, a potential source for biases in climate monitoring and modelling. The recently launched ESA CCI+ Programme addresses seasonal snow as one of 9 Essential Climate Variables to be derived from satellite data.In the snow_cci project, scheduled for 2018 to 2021 in its first phase, reliable fully validated processing lines are developed and implemented. These tools are used to generate homogeneous multi-sensor time series for the main parameters of global snow cover focusing on snow extent and snow water equivalent. Using GCOS guidelines, the requirements for these parameters are assessed and consolidated using the outcome of workshops and questionnaires addressing users dealing with different climate applications. Snow extent product generation applies algorithms accounting for fractional snow extent and cloud screening in order to generate consistent daily products for snow on the surface (viewable snow) and snow on the surface corrected for forest masking (snow on ground) with global coverage. Input data are medium resolution optical satellite images (AVHRR-2/3, AATSR, MODIS, VIIRS, SLSTR/OLCI) from 1981 to present. An iterative development cycle is applied including homogenisation of the snow extent products from different sensors by minimizing the bias. Independent validation of the snow products is performed for different seasons and climate zones around the globe from 1985 onwards, using as reference high resolution snow maps from Landsat and Sentinel- 2as well as in-situ snow data following standardized validation protocols.Global time series of daily snow water equivalent (SWE) products are generated from passive microwave data from SMMR, SSM/I, and AMSR from 1978 onwards, combined with in-situ snow depth measurements. Long-term stability and quality of the product is assessed using independent snow survey data and by intercomparison with the snow information from global land process models.The usability of the snow_cci products is ensured through the Climate Research Group, which performs case studies related to long term trends of seasonal snow, performs evaluations of CMIP-6 and other snow-focused climate model experiments, and applies the data for simulation of Arctic hydrological regimes.In this presentation, we summarize the requirements and product specifications for the snow extent and SWE products, with a focus on climate applications. We present an overview of the algorithms and systems for generation of the time series. The 40 years (from 1980 onwards) time series of daily fractional snow extent products from AVHRR with 5 km pixel spacing, and the 20-year time series from MODIS (1 km pixel spacing) as well as the coarse resolution (25 km pixel spacing) of daily SWE products from 1978 onwards will be presented along with first results of the multi-sensor consistency checks and validation activities.

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Spatial Variability in Seasonal Snowpack Trends across the Rio Grande Headwaters (1984–2017)

Journal of Hydrometeorology ◽

10.1175/jhm-d-20-0077.1 ◽

2020 ◽

Vol 21 (11) ◽

pp. 2713-2733 ◽

Cited By ~ 1

Author(s):

Graham A. Sexstone ◽

Colin A. Penn ◽

Glen E. Liston ◽

Kelly E. Gleason ◽

C. David Moeser ◽

...

Keyword(s):

Spatial Variability ◽

Forest Canopy ◽

Snow Water Equivalent ◽

Rio Grande ◽

Winter Precipitation ◽

Time Step ◽

The North ◽

Spatial Coverage ◽

Snow Water ◽

Snowpack Properties

AbstractThis study evaluated the spatial variability of trends in simulated snowpack properties across the Rio Grande headwaters of Colorado using the SnowModel snow evolution modeling system. SnowModel simulations were performed using a grid resolution of 100 m and 3-hourly time step over a 34-yr period (1984–2017). Atmospheric forcing was provided by phase 2 of the North American Land Data Assimilation System, and the simulations accounted for temporal changes in forest canopy from bark beetle and wildfire disturbances. Annual summary values of simulated snowpack properties [snow metrics; e.g., peak snow water equivalent (SWE), snowmelt rate and timing, and snow sublimation] were used to compute trends across the domain. Trends in simulated snow metrics varied depending on elevation, aspect, and land cover. Statistically significant trends did not occur evenly within the basin, and some areas were more sensitive than others. In addition, there were distinct trend differences between the different snow metrics. Upward trends in mean winter air temperature were 0.3°C decade−1, and downward trends in winter precipitation were −52 mm decade−1. Middle elevation zones, coincident with the greatest volumetric snow water storage, exhibited the greatest sensitivity to changes in peak SWE and snowmelt rate. Across the Rio Grande headwaters, snowmelt rates decreased by 20% decade−1, peak SWE decreased by 14% decade−1, and total snowmelt quantity decreased by 13% decade−1. These snow trends are in general agreement with widespread snow declines that have been reported for this region. This study further quantifies these snow declines and provides trend information for additional snow variables across a greater spatial coverage at finer spatial resolution.

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