Assessing the impact of climate change – and its uncertainty – on snow cover areas by using cellular automata models and stochastic weather generators
- 1. Instituto Geológico y Minero de España, Urb. Alcázar del Genil, 4. Edificio Zulema Bajo, 18006 Granada (Spain)
- 2. Instituto Geológico y Minero de España, Ríos Rosas, 23, 28003 Madrid (Spain)
Description
Highlights: • Generation of potential future local scenarios from regional climate models • Generation of multiple future series by using a stochastic weather generator • Correction of the bias of the stochastic weather generators outputs • Impacts of climate change on snow cover area by using a cellular automata model • Average elevation shift of snow of around 400 m in Sierra Nevada (Spain) Climate change will modify the spatiotemporal distribution of water resources in the future. Snow availability in alpine systems plays an important role for water dependent ecosystems, water demand supply, tourism, and hydropower. The assessment of the impact of climate change (and its uncertainty) on snow is a key subject in determining suitable adaptation strategies in these systems. In this paper, we propose a new methodology for assessing the impact of climate change on snow cover areas (SCAs). We have developed the Monte Carlo method analysis to combine several approaches to generate multiple input series and propagate them within a previously calibrated SCA cellular automata model. This generates potential future local scenarios from regional climate models. These scenarios are used to generate multiple series by using a stochastic weather generator. The methodology also includes an approach to correct the outputs bias of the stochastic weather generators when it is needed. Finally, the historical and the corrected multiple future weather series are used to simulate the impact on the SCA by using a cellular automata model. It is a novel approach that allows us to quantify the impact and uncertainty of climate change on the SCA. The methodology has been applied to the Sierra Nevada (southern Spain), which is the most southern alpine mountain range in Europe. In the horizon 2071–2100, under the RCP 8.5 emission scenario, we estimate mean reductions of SCA that will move from 42 to 66% from December to February. The reductions are higher for the rest of the year (from March to May reductions of between 47 and 95% and from September to November reductions of between 54 and 100%). These SCA changes may be roughly equivalent to an elevation shift of snow of around 400 m.
Availability note (English)
Available from http://dx.doi.org/10.1016/j.scitotenv.2021.147776Additional details
Identifiers
- DOI
- 10.1016/j.scitotenv.2021.147776;
- PII
- S0048969721028473;
Publishing Information
- Journal Title
- Science of the Total Environment
- Journal Volume
- 788
- Journal Page Range
- vp.
- ISSN
- 0048-9697
- CODEN
- STENDL
INIS
- Country of Publication
- Netherlands
- Country of Input or Organization
- International Atomic Energy Agency (IAEA)
- INIS RN
- 54059010
- Subject category
- S54: ENVIRONMENTAL SCIENCES;
- Descriptors DEI
- CLIMATE MODELS; ECOSYSTEMS; GREENHOUSE EFFECT; HYDROELECTRIC POWER; MONTE CARLO METHOD; SNOW; STOCHASTIC PROCESSES; WATER REQUIREMENTS; WATER RESOURCES; WEATHER
- Descriptors DEC
- ATMOSPHERIC PRECIPITATIONS; CALCULATION METHODS; CLIMATIC CHANGE; DEMAND; ELECTRIC POWER; ENERGY SOURCES; MATHEMATICAL MODELS; POWER; RENEWABLE ENERGY SOURCES; RESOURCES
Optional Information
- Copyright
- Copyright (c) 2021 Elsevier B.V. All rights reserved.