A distributed cellular automata model to simulate potential future impacts of climate change on snow cover area

Collados‐Lara, Antonio‐Juan; Pardo‐Igúzquiza, Eulogio; Pulido-Velázquez, David

doi:10.1016/j.advwatres.2018.12.010

Cited by 37 publications

(25 citation statements)

References 39 publications

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“…The CA model has already been implemented for sand dunes, to simulate large-length scale sand dune dynamics (Werner 1995, Narteau et al 2009 and for terrestrial snow thickness variability (Leguizamón 2005, Collados-Lara et al 2019. Therefore, it is highly recommended to implement a CA model in snow thickness variability in this length scale.…”

Section: Governing Physical Processesmentioning

confidence: 99%

Physical length scales of wind-blown snow redistribution and accumulation on relatively smooth Arctic first-year sea ice

Moon

Nandan

Scharien

et al. 2019

Environ. Res. Lett.

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Snow thickness measurements over relatively smooth Arctic first-year sea ice, obtained near Cambridge Bay in the Canadian Arctic (2014, 2016 and 2017) and near Elson Lagoon in the Alaskan Arctic (2003 and, are analyzed to quantify physical length-scales and their relevant scaling behaviors. We use the multi-fractal temporally weighted detrended fluctuation analysis method to detect two major physical length-scales from the two independent study locations. Our results suggest that physical processes underlying the formation of snow dunes are consistent and that the wind is the main process shaping the snow thickness variability and redistribution. One scale, around 10 m, appears to be related to the formation of the snow 'dunes', while the other scale, between 30 and 100 m, is likely associated with the various interactions of the snow dunes such as merging, calving and lateral linking. Results imply that snow on level sea ice shows self-organized characteristics.

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Section: Governing Physical Processesmentioning

confidence: 99%

Physical length scales of wind-blown snow redistribution and accumulation on relatively smooth Arctic first-year sea ice

Moon

Nandan

Scharien

et al. 2019

Environ. Res. Lett.

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“…Daily and monthly data are commonly employed to address hydrological questions (Hannah et al ., 2011), and the distribution of meteorological variables are modelled from different orographic or landscape patterns using these different temporal resolutions (Benavides et al ., 2007; Collados‐Lara et al ., 2018). Hydrological models incorporate temperature at the basin scale for lumped approaches (Jódar et al ., 2018) to the kilometre scale for distributed approaches (Collados‐Lara et al ., 2019). Sometimes these resolutions are adequate for hydrological assessment, but other cases require finer resolutions (temporal and spatial) to assess the availability of water resources (Young et al ., 2009; Chen et al ., 2019).…”

Section: Introductionmentioning

confidence: 99%

Intra‐day variability of temperature and its near‐surface gradient with elevation over mountainous terrain: Comparing MODIS land surface temperature data with coarse and fine scale near‐surface measurements

Collados‐Lara

Fassnacht

Pulido-Velázquez

et al. 2020

Intl Journal of Climatology

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Where land surface air temperature data are not available, satellite land surface temperature are used. However, the coarse spatial resolution of satellitederived products may yield errors at the local scale. This work shows the differences between the MODIS Land Surface Temperature and Emissivity (MOD11A1) product and ground measurements at two different scales. We used data from 21 SNOTEL stations across the northern Front Range of Colorado to represent the coarse scale and 17 iButton temperature sensors across the Colorado State University Mountain Campus to represent the fine scale. We found significant differences in the temperature and its changes with elevation for the two spatial scales. At the fine scale, cold air drainage can induce an inversion of the temperature gradient with elevation. A higher correlation was found during the nighttime at the fine scale, while, at the coarse scale, higher correlations were observed during the daytime. On windy nights, temperatures do not cool as much as on calmer nights, and the coarse scale nearsurface temperature gradient with elevation persists, though the fine scale inversions do not develop. The near-surface temperature gradients with elevation based on the MODIS pixels are similar to the ground-based data at the coarse scale but not at the fine scale. Thus, one must be cautious in selecting the near-surface temperature gradients with elevation for mountainous terrain when different scales are considered, and a proper validation of satellite products is necessary prior to their use to avoid the propagation of uncertainties.

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“…In-situ data is limited to lower and accessible altitudes and may not truly represent the snow and glacier changes [4] at higher altitudes like those of UIB. Remote sensing data and various modeling approaches such as physical-based models [13], cellular automata (CA) models [16,17], and artificial neural Water 2019, 11,761 3 of 19 networks [18,19] were used previously to analyze the snow cover in different regions of the world. Remote sensing data offers the quantitative examination of physical properties of snow and glaciers in remote areas where accessibility of data is expensive and dangerous [20].…”

Section: Introductionmentioning

confidence: 99%

“…It is considered a real standard for snow cover estimation. Impacts of future climate projections on the snow cover area were studied using the cellular automata (CA) model [16], supported with MODIS satellite data. Daily snow products produced by MODIS sensors were available since 2000.…”

Section: Introductionmentioning

confidence: 99%

Simulating Current and Future River-Flows in the Karakoram and Himalayan Regions of Pakistan Using Snowmelt-Runoff Model and RCP Scenarios

Hayat

Akbar

Tahir

et al. 2019

Water

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Upper Indus Basin (UIB) supplies more than 70% flow to the downstream agricultural areas during summer due to the melting of snow and glacial ice. The estimation of the stream flow under future climatic projections is a pre-requisite to manage water resources properly. This study focused on the simulation of snowmelt-runoff using Snowmelt-Runoff Model (SRM) under the current and future Representative Concentration Pathways (RCP 2.6, 4.5 and 8.5) climate scenarios in the two main tributaries of the UIB namely the Astore and the Hunza River basins. Remote sensing data from Advanced Land Observation Satellite (ALOS) and Moderate Resolution Imaging Spectroradiometer (MODIS) along with in-situ hydro-climatic data was used as input to the SRM. Basin-wide and zone-wise approaches were used in the SRM. For the zone-wise approach, basin areas were sliced into five elevation zones and the mean temperature for the zones with no weather stations was estimated using a lapse rate value of −0.48 °C to −0.76 °C/100 m in both studied basins. Zonal snow cover was estimated for each zone by reclassifying the MODIS snow maps according to the zonal boundaries. SRM was calibrated over 2000–2001 and validated over the 2002–2004 data period. The results implied that the SRM simulated the river flow efficiently with Nash-Sutcliffe model efficiency coefficient of 0.90 (0.86) and 0.86 (0.86) for the basin-wide (zone-wise) approach in the Astore and Hunza River Basins, respectively, over the entire simulation period. Mean annual discharge was projected to increase by 11–58% and 14–90% in the Astore and Hunza River Basins, respectively, under all the RCP mid- and late-21st-century scenarios. Mean summer discharge was projected to increase between 10–60% under all the RCP scenarios of mid- and late-21st century in the Astore and Hunza basins. This study suggests that the water resources of Pakistan should be managed properly to lessen the damage to human lives, agriculture, and economy posed by expected future floods as indicated by the climatic projections.

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A distributed cellular automata model to simulate potential future impacts of climate change on snow cover area

Cited by 37 publications

References 39 publications

Physical length scales of wind-blown snow redistribution and accumulation on relatively smooth Arctic first-year sea ice

Physical length scales of wind-blown snow redistribution and accumulation on relatively smooth Arctic first-year sea ice

Intra‐day variability of temperature and its near‐surface gradient with elevation over mountainous terrain: Comparing MODIS land surface temperature data with coarse and fine scale near‐surface measurements

Simulating Current and Future River-Flows in the Karakoram and Himalayan Regions of Pakistan Using Snowmelt-Runoff Model and RCP Scenarios

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