Snow Multidata Mapping and Modeling (S3M) 5.1: a distributed cryospheric model with dry and wet snow, data assimilation, glacier mass balance, and debris-driven melt
<p>By shifting winter precipitation into summer freshet, the cryosphere supports life across the world. The sensitivity of this mechanism to climate and the role played by the cryosphere in the Earth's energy budget have motivated the development of a broad spectrum of predictive models....
Main Authors: | , , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Copernicus Publications
2022-06-01
|
Series: | Geoscientific Model Development |
Online Access: | https://gmd.copernicus.org/articles/15/4853/2022/gmd-15-4853-2022.pdf |
_version_ | 1818157164308463616 |
---|---|
author | F. Avanzi S. Gabellani F. Delogu F. Silvestro E. Cremonese U. Morra di Cella U. Morra di Cella S. Ratto H. Stevenin |
author_facet | F. Avanzi S. Gabellani F. Delogu F. Silvestro E. Cremonese U. Morra di Cella U. Morra di Cella S. Ratto H. Stevenin |
author_sort | F. Avanzi |
collection | DOAJ |
description | <p>By shifting winter precipitation into summer freshet, the cryosphere supports life across the world.
The sensitivity of this mechanism to climate and the role played by the cryosphere in the Earth's energy budget have motivated the development of a broad spectrum of predictive models. Such models represent seasonal snow and glaciers with various complexities and generally are not integrated with hydrologic models describing the fate of meltwater through the hydrologic budget. We present Snow Multidata Mapping and Modeling (S3M) v5.1, a spatially explicit and hydrology-oriented cryospheric model that simulates seasonal snow and glacier evolution through time and that can be natively coupled with distributed hydrologic models. Model physics include precipitation-phase partitioning, snow and glacier mass balances, snow rheology and hydraulics, a hybrid temperature-index and radiation-driven melt parametrization, and a data-assimilation protocol.
Comparatively novel aspects of S3M are an explicit representation of the spatial patterns of snow liquid-water content, the implementation of the <span class="inline-formula">Δ<i>h</i></span> parametrization for distributed ice-thickness change, and the inclusion of a distributed debris-driven melt factor.
Focusing on its operational implementation in the northwestern Italian Alps, we show that S3M provides robust predictions of the snow and glacier mass balances at multiple scales, thus delivering the necessary information to support real-world hydrologic operations.
S3M is well suited for both operational flood forecasting and basic research, including future scenarios of the fate of the cryosphere and water supply in a warming climate.
The model is open source, and the paper comprises a user manual as well as resources to prepare input data and set up computational environments and libraries.</p> |
first_indexed | 2024-12-11T15:09:50Z |
format | Article |
id | doaj.art-2232568ae5e142bba4fcdbc876d301f1 |
institution | Directory Open Access Journal |
issn | 1991-959X 1991-9603 |
language | English |
last_indexed | 2024-12-11T15:09:50Z |
publishDate | 2022-06-01 |
publisher | Copernicus Publications |
record_format | Article |
series | Geoscientific Model Development |
spelling | doaj.art-2232568ae5e142bba4fcdbc876d301f12022-12-22T01:00:47ZengCopernicus PublicationsGeoscientific Model Development1991-959X1991-96032022-06-01154853487910.5194/gmd-15-4853-2022Snow Multidata Mapping and Modeling (S3M) 5.1: a distributed cryospheric model with dry and wet snow, data assimilation, glacier mass balance, and debris-driven meltF. Avanzi0S. Gabellani1F. Delogu2F. Silvestro3E. Cremonese4U. Morra di Cella5U. Morra di Cella6S. Ratto7H. Stevenin8CIMA Research Foundation, Via Armando Magliotto 2, 17100 Savona, ItalyCIMA Research Foundation, Via Armando Magliotto 2, 17100 Savona, ItalyCIMA Research Foundation, Via Armando Magliotto 2, 17100 Savona, ItalyCIMA Research Foundation, Via Armando Magliotto 2, 17100 Savona, ItalyClimate Change Unit, Environmental Protection Agency of Aosta Valley, Loc. La Maladière, 48-11020 Saint-Christophe, ItalyCIMA Research Foundation, Via Armando Magliotto 2, 17100 Savona, ItalyClimate Change Unit, Environmental Protection Agency of Aosta Valley, Loc. La Maladière, 48-11020 Saint-Christophe, ItalyRegione Autonoma Valle d'Aosta, Centro funzionale regionale, Via Promis 2/a, 11100 Aosta, ItalyRegione Autonoma Valle d'Aosta, Centro funzionale regionale, Via Promis 2/a, 11100 Aosta, Italy<p>By shifting winter precipitation into summer freshet, the cryosphere supports life across the world. The sensitivity of this mechanism to climate and the role played by the cryosphere in the Earth's energy budget have motivated the development of a broad spectrum of predictive models. Such models represent seasonal snow and glaciers with various complexities and generally are not integrated with hydrologic models describing the fate of meltwater through the hydrologic budget. We present Snow Multidata Mapping and Modeling (S3M) v5.1, a spatially explicit and hydrology-oriented cryospheric model that simulates seasonal snow and glacier evolution through time and that can be natively coupled with distributed hydrologic models. Model physics include precipitation-phase partitioning, snow and glacier mass balances, snow rheology and hydraulics, a hybrid temperature-index and radiation-driven melt parametrization, and a data-assimilation protocol. Comparatively novel aspects of S3M are an explicit representation of the spatial patterns of snow liquid-water content, the implementation of the <span class="inline-formula">Δ<i>h</i></span> parametrization for distributed ice-thickness change, and the inclusion of a distributed debris-driven melt factor. Focusing on its operational implementation in the northwestern Italian Alps, we show that S3M provides robust predictions of the snow and glacier mass balances at multiple scales, thus delivering the necessary information to support real-world hydrologic operations. S3M is well suited for both operational flood forecasting and basic research, including future scenarios of the fate of the cryosphere and water supply in a warming climate. The model is open source, and the paper comprises a user manual as well as resources to prepare input data and set up computational environments and libraries.</p>https://gmd.copernicus.org/articles/15/4853/2022/gmd-15-4853-2022.pdf |
spellingShingle | F. Avanzi S. Gabellani F. Delogu F. Silvestro E. Cremonese U. Morra di Cella U. Morra di Cella S. Ratto H. Stevenin Snow Multidata Mapping and Modeling (S3M) 5.1: a distributed cryospheric model with dry and wet snow, data assimilation, glacier mass balance, and debris-driven melt Geoscientific Model Development |
title | Snow Multidata Mapping and Modeling (S3M) 5.1: a distributed cryospheric model with dry and wet snow, data assimilation, glacier mass balance, and debris-driven melt |
title_full | Snow Multidata Mapping and Modeling (S3M) 5.1: a distributed cryospheric model with dry and wet snow, data assimilation, glacier mass balance, and debris-driven melt |
title_fullStr | Snow Multidata Mapping and Modeling (S3M) 5.1: a distributed cryospheric model with dry and wet snow, data assimilation, glacier mass balance, and debris-driven melt |
title_full_unstemmed | Snow Multidata Mapping and Modeling (S3M) 5.1: a distributed cryospheric model with dry and wet snow, data assimilation, glacier mass balance, and debris-driven melt |
title_short | Snow Multidata Mapping and Modeling (S3M) 5.1: a distributed cryospheric model with dry and wet snow, data assimilation, glacier mass balance, and debris-driven melt |
title_sort | snow multidata mapping and modeling s3m 5 1 a distributed cryospheric model with dry and wet snow data assimilation glacier mass balance and debris driven melt |
url | https://gmd.copernicus.org/articles/15/4853/2022/gmd-15-4853-2022.pdf |
work_keys_str_mv | AT favanzi snowmultidatamappingandmodelings3m51adistributedcryosphericmodelwithdryandwetsnowdataassimilationglaciermassbalanceanddebrisdrivenmelt AT sgabellani snowmultidatamappingandmodelings3m51adistributedcryosphericmodelwithdryandwetsnowdataassimilationglaciermassbalanceanddebrisdrivenmelt AT fdelogu snowmultidatamappingandmodelings3m51adistributedcryosphericmodelwithdryandwetsnowdataassimilationglaciermassbalanceanddebrisdrivenmelt AT fsilvestro snowmultidatamappingandmodelings3m51adistributedcryosphericmodelwithdryandwetsnowdataassimilationglaciermassbalanceanddebrisdrivenmelt AT ecremonese snowmultidatamappingandmodelings3m51adistributedcryosphericmodelwithdryandwetsnowdataassimilationglaciermassbalanceanddebrisdrivenmelt AT umorradicella snowmultidatamappingandmodelings3m51adistributedcryosphericmodelwithdryandwetsnowdataassimilationglaciermassbalanceanddebrisdrivenmelt AT umorradicella snowmultidatamappingandmodelings3m51adistributedcryosphericmodelwithdryandwetsnowdataassimilationglaciermassbalanceanddebrisdrivenmelt AT sratto snowmultidatamappingandmodelings3m51adistributedcryosphericmodelwithdryandwetsnowdataassimilationglaciermassbalanceanddebrisdrivenmelt AT hstevenin snowmultidatamappingandmodelings3m51adistributedcryosphericmodelwithdryandwetsnowdataassimilationglaciermassbalanceanddebrisdrivenmelt |