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# Impact of climate change on snow supply in Trojena, Saudi Arabia

Laura Sourp, Abbas Fayad, Simon Gascoin

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## To cite this version:

Laura Sourp, Abbas Fayad, Simon Gascoin. Impact of climate change on snow supply in Trojena, Saudi Arabia. Environmental Research Communications, 2025, 7, pp.091001. 10.1088/2515-7620/adfe3f.hal-05345099

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LETTER • OPEN ACCESS

# Impact of climate change on snow supply in Trojena, Saudi Arabia

To cite this article: Laura Sourp et al 2025 *Environ. Res. Commun.* 7 091001

View the [article online](#) for updates and enhancements.

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LETTER

# Impact of climate change on snow supply in Trojena, Saudi Arabia

Laura Sourp<fnref n="1" />,<fnref n="2" />,* , Abbas Fayad<fnref n="3" /> and Simon Gascoin<fnref n="1" />*

E-mail: laura.sourp@utoulouse.fr and simon.gascoin@utoulouse.fr

**Keywords:** sustainability, artificial snow, middle east, water resource, tourism

Supplementary material for this article is available [online](#)

## Abstract

Trojena is a mountain resort under construction in the Midian Mountains, Saudi Arabia as part of the NEOM project and the host site for the 2029 Asian Winter Games. The development of a ski resort in this desertic region has sparked debate about its sustainability. Here we examine the potential snow supply in Trojena under present and future climate. We use downscaled ERA5 data and a snowpack model accounting for snow management to simulate the artificial snow production. We simulate the snowpack under future climate using the 2050's projected temperature change from the CMIP6 dataset under the SSP2-4.5 scenario. We find that artificial snow production can support a median of 68 skiable days in the highest elevation area of the resort in present conditions and 57 days in the 2050's period with an increase in the interannual variability. In the low elevation area, the median number of skiable days is 52 in present conditions and 0 in the 2050's despite artificial snow production capacity. We conclude that climate change strongly compromises the utility of the planned infrastructure.

## Introduction

Saudi Arabia aims to diversify its economy away from its historical oil dependence towards 'renewable energy, tourism, entertainment, and other key industries' (Saudi Vision 2030 [2025a]). As part of this effort, Trojena is an ambitious mountain resort under development in NEOM located in the Tabuk province. Located 50 kilometers from the Gulf of Aqaba in the Midian Mountains, it spans 60 square kilometers at elevations ranging from 1,500 to 2,600 meters above sea level (figure 1). Trojena will offer an array of outdoor activities, including three months of winter snow skiing with 36 km of slopes planned. Launched in 2022, Trojena is expected to be completed by 2026 and aims at attracting 700,000 tourists per year by 2030 (Saudi Vision 2030 [2025b]). The Trojena project gained international attention when it was announced as the host site for the 2029 Asian Winter Games. The official project's communication promotes 'perfect conditions for a world-class ski resort' (Neom Trojena 2025) through a mix of natural and artificial snow. Whereas the steep topography of the Trojena area might allow snowfalls due to the decrease of air temperature with altitude, a preliminary assessment with MODIS satellite data indicated that the snow cover occurrence frequency near midday (satellite overpass time) over the Trojena area was less than 2 days per year on average during the 2000–2022 period. In addition, several studies highlight that the global ski tourism industry will face increasing risk of snow supply due to climate change, despite snowmaking systems (Steiger et al 2019, François et al 2023, Yang et al 2023). In this context, we sought to examine the feasibility of skiing on snowy slopes in Trojena under present and future climate conditions. For this purpose, we used a numerical snowpack model including a module for grooming and snowmaking (Spandre et al 2016). The model outputs allow us to discuss some environmental implications of this project.

<footnote n="1">¹ Université de Toulouse, CNRS, CNES, IRD, INRAE, Centre d'études spatiales de la biosphère, Toulouse, France</footnote>

<footnote n="2">² Magellium, Ramonville-Saint-Agne, France</footnote>

<footnote n="3">³ Independent researcher, Beirut, Lebanon</footnote>

<footnote>* Authors to whom any correspondence should be addressed.</footnote>
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Method

Reference meteorological forcings

Given the lack of publicly available *in situ* weather data in the Midian mountains region, we based our
snowpack simulations on the ERA5 global climate reanalysis (Hersbach *et al* 2020). ERA5 provides hourly
meteorological variables with a 0.25° resolution dating back to 1940 and has good performance in extratropical
regions (Lavers *et al* 2022, McNicholl *et al* 2022). ERA5 coarse spatial resolution does not capture the fine scale
variability of meteorological variables in mountain regions so its simulated snowpack cannot be used here.
However, ERA5 can be downscaled using a digital elevation model to account for the effect of topography on air
temperature, humidity, radiation, and wind (Wrzesien *et al* 2019, Liu *et al* 2022). Here, we used MicroMet
(Liston and Elder 2006), an algorithm that was successfully used to downscale climate reanalyses including
ERA5 for snowpack simulations in various mountain regions (Mernild *et al* 2017, Baba *et al* 2021, Sourp *et al*
2025). MicroMet distributes meteorological forcings using different physical and empirical submodels (Liston
and Elder 2006). The air temperature, humidity, and precipitation are adjusted using the model cell elevation.
The wind speed is modified according to local terrain. The longwave radiation is estimated from the cloud
fraction, which itself depends on air temperature and dew point temperature. The solar radiation is computed
as a function of the day, hour, latitude, slope, slope azimuth, elevation, and cloud fraction. The partitioning of
precipitation phase (solid and liquid forms) is a function of the downscaled hourly air temperature (Liston *et al*
2020). We used MicroMet, ERA5 and the Copernicus 30 m digital elevation model resampled to 100 m to
<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /><figcaption>**Figure 2.** Flowchart of the simulations.</figcaption></figure>

generate hourly meteorological forcing between 1995 and 2014 in the Trojena area. This formed the forcing for our historical reference run (figure 2). We used ERA5 instead of ERA5-Land despite its coarser resolution because previous work did not evidence a benefit of using ERA5-Land when downscaling to 100 m using SnowModel/MicroMet (Sourp *et al* 2025).

## Projection meteorological forcings

We also computed projected meteorological forcing for the period 2040–2060. The intergovernmental panel on climate change (IPCC) provides monthly temperature increases for different scenarios and horizons relative to several baselines. We extracted the temperature increases of the 50th percentile in the CMIP6 dataset for the land in the Arabian Peninsula compared to the 1995–2014 baseline. We chose a medium term horizon (2041–2060, hereafter 2050) under the scenario SSP2-4.5. We added these monthly temperature increases to the 1995–2014 ERA5 data and ran MicroMet again using the same configuration (figure 2). As a result, air humidity, shortwave and longwave radiation, and precipitation phase were updated according to the projected temperatures by MicroMet.

## Crocus simulations

We extracted the time series at two points from the high-resolution meteorological forcing dataset, the first (P2390) located at the top of the ski resort has an elevation of 2389 m, and the second (P2150) at the bottom of the ski resort with an elevation of 2151 m (figure 1). We used these meteorological forcings to simulate the natural and artificial snowpack. The artificial snowpack was simulated assuming optimal conditions, without logistical, water, or energy limitations. Natural and artificial snowpack simulations were performed using SURFEX/ISBA-Crocus (Vionnet et al 2012). Crocus is a one-dimensional multilayer snowpack model, which computes the energy and mass fluxes along the vertical profile of the snowpack and at its interfaces with the atmosphere and the ground. It simulates the evolution of the snow depth and snow water equivalent, along with phase change, water percolation and snow metamorphism (Spandre et al 2016). Snow management processes, including grooming and snowmaking, were simulated with the Crocus-RESORT model—an extension of Crocus that integrates a snow management module. This module includes a grooming submodule which affects the first 10 to 50 cm of the snowpack (Spandre et al 2016). It also includes a snowmaking submodule for artificial snow production when conditions are favourable. The snowmaking submodule includes production decisions such as production hours, wind speed and wet bulb temperature thresholds, production rate, and water loss efficiency. These parameters depend on the infrastructure of a ski resort (number of snowmaking machines, their technology, and the electrical capacity of the resort) and the choices related to skiers’ experience. Here we used the parameters derived from Alps ski resorts (production hours from 7 p.m. to 8 a.m. local time, 15 km h⁻¹ wind speed threshold, and −4 °C wet bulb temperature threshold) (Spandre et al 2016). We defined a skiable day as a day with at least 300 mm of snow depth (Scott et al 2007, Spandre et al 2019).
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## Model evaluation

Given the lack of *in situ* observations, we evaluated the ERA5-MicroMet-Crocus pipeline (without the RESORT module) using remote sensing data. We leveraged the collection of Landsat images available in Google Earth Engine to determine the presence and absence of snow at 30 m resolution over the reference run period (1995–2014). We extracted snow cover information based on a combination of unsupervised classification and visual control. First, we computed the Normalized Difference Snow Index (NDSI) and used the NDSI threshold of 0.15 to classify a pixel as snow (Bayle *et al* 2025). Based on the visual inspection of the snow cover in the image time series, we observed that the snow cover is usually highly fragmented in the Trojena area and that the occurrence of subpixel snow cover may not be well detected in some cases when the surface is not well illuminated due to topographic shadowing. Hence, to minimize snow omission errors, we considered that snow was present at P2390 (or P2150) on the day of the Landsat image acquisition if there was at least one pixel detected as snow in a 50 m radius centered at this point. We also visually inspected every scene between December and April using a color composite image of bands SWIR/red/green. We removed two scenes with potential snow-cloud confusion and one degraded Landsat 7 scene due to sensor issues (see supplementary figure S1). The visual inspection allowed us to identify a few scenes where snow was present at P2390 or P2150, despite the NDSI was below the 0.15 threshold. We acknowledge that this classification can be up to interpretation. Therefore, we flagged these observations as doubtful (8 dates at P2390 and 2 dates à P2150, dotted lines in figure S2). These Landsat images can be found in supplementary figure S1. We compared the time series of snow detection with the temporal evolution of SWE depths at P2390 and P2150 in the natural snow reference simulation. We applied a threshold of 1 cm w.e. to convert the simulated SWE into snow presence or absence (Barrou Dumont *et al* 2021). We computed the confusion matrix (supplementary figure S3) along with the accuracy and the kappa score to evaluate this reference simulation. To construct these metrics, the doubtful Landsat observations were considered to be snow and the false snow detections were changed to no-data.

## Energy and water consumption

Given the lack of publications about snowmaking technologies in arid climates, our energy and water consumption estimates are based on previous studies in alpine and Scandinavian regions. We used an energy cost of $3 \pm 1 \text{ kWh m}^{-3}$ for snowmaking (François *et al* 2023), $4.5 \pm 2.5 \text{ kWh m}^{-3}$ for desalination (Elsaid *et al* 2020), and estimated the energy cost of lifting water from sea level to 2100 m above sea level considering a pump efficiency ranging from 70% to 90%. We neglected the horizontal transport of water from the Gulf of Aqaba, 50 km away from Trojena. Given these assumptions, we consider that our energy estimates are conservative.

## Evaluation of the artificial lake volume

We downloaded the Sentinel-2 image of 2024-11-27 over the Trojena region, in which we can observe the footprint of construction works corresponding to the future lake shown in promotional images of Trojena. We digitized the visible lake limits at 372 points in the Sentinel-2 images, corresponding to the future lake shoreline. We extracted the elevation values of these points from the Copernicus 30 m DEM. We chose the upper quartile in addition to the average to have an upper-bound estimate of the possible lake elevation and volume. From both lake elevation values and the DEM, we deduced two possible lake outlines that we cropped in the western valley at the estimated location of the future dam. For both lake scenarios, we summed the differences between the lake elevation and the DEM over the lake area and multiplied the result with the DEM pixel area to estimate the total lake volume. Therefore, we obtained two possible values that constitute a likely range of the artificial lake volume in the absence of water input limitations.

## Results

First we present the evaluation of the model and then we analyse the number of skiable days and water consumption in present and future conditions. From the comparison of the model output and the Landsat time series, we obtain an accuracy of 0.87 and 0.95 at P2390 and P2150, respectively, indicating a correct model performance. Figures S2 and S3 show that the model simulates the snow absence but can overestimate the snowpack duration by a few days. The tendency to overestimate SWE with snowpack models in the case of very shallow and ephemeral snowpacks is a known issue (Petersky and Harpold 2018). Despite this deficiency, the model result suggests that the ERA5-MicroMet-Crocus pipeline can adequately represent the natural snowpack variability and its driving meteorological conditions during winter. The observed overestimation of the snow duration means that projected snow duration may be slightly overestimated as well, suggesting that our conclusions below about the viability of the ski resort should be regarded as 'optimistic'.
<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /><figcaption>**Figure 3.** Simulation of ski-related variables under present (1995–2014), and future climate (SSP2-4.5 projections 2040–2060), whiskers displaying the 10–90 percentiles, the green triangle is the mean and the black line is the median.</figcaption></figure>

From the downscaled hourly climate data, we estimate that there was on average 525 h per year below 0°C at the top of the planned resort (noted P2390) and 323 h at the bottom (P2150) over the period 1995–2014 (figure 3). Despite subfreezing temperatures in the Trojena area, natural snow cover is very rare due to the scarce precipitation in this arid to desertic region (ranging between 12 and 63 mm per year in ERA5 between 1995 and 2014). The simulated annual maximum snow water equivalent (SWE) averages 7 mm w.e. at P2390 and only 2.5 mm at P2150 (figure 3). As expected, natural snow is not sufficient for skiing activities, and the Trojena ski resort should rely almost entirely on artificial snow production. Snowmaking typically requires a wet bulb temperature below –4 °C (Spandre et al 2016), which occurs in Trojena on average 405 and 200 h per year respectively at P2390 and P2150 over the reference period. At P2390, snowmaking can drastically increase the annual number of skiable days from 0 to 68 days (median value over 1995–2014, figure 3). However, there remain 10% of years with 0 skiable days due to a lack of cold weather even at this elevation. At P2150, we find a median value of 52 skiable days per year over the same period. However, on average, there are 25% of the years with no skiable day (lower quartile is zero, figure 3). This shows that maintaining a ski resort in the Trojena area is already challenging under present climatic conditions.

Under future climate (year 2050, emission scenario SSP2-4.5), the median number of skiable days drops to zero at P2150, despite artificial snow production. In other words, skiing on snow should not be possible every other year over the entire ski area, even if it is fully equipped with snow cannons. A decrease in the annual number of skiable days is also expected at P2390, but is less pronounced (median value decreases from 68 days to 57 days). However, the interquartile range increases from 21 to 40 days, implying a significant increase in the interannual variability of the number of annual skiable days. Under this scenario, the lower quartile is below 30 days, implying that there is less than a month of snow every four years. In summary, a strong reduction of the number of skiable days per winter in the lower part of the resort combined with an increase in interannual variability compromises the possibility of skiing on snow in the 2050’s (figure 4).

Under the present climate conditions, the median water consumption for artificial snow production is 416 mm per year at P2390 and 349 mm per year at P2150 (median values, figure 3). This seemingly paradoxical difference is due to the lower number of hours below –4° of wet bulb temperature at P2150, which limits the snowmaking capacity. Similarly, the water consumption is expected to drop in the future due to the reduction in the number of hours below –4° wet bulb temperature. Considering 36 km of 20 m wide ski slopes (Saudi Tourism Authority 2024), we estimate the total water consumption for snowmaking to 275,000 m³ per year under present climate and 199,000 m³ in the 2050’s.

## Discussion

Due to a small surface covered by ski slopes, the Trojena resort is expected to have a smaller water consumption for snowmaking than some operational resorts. La Plagne in France, with colder and wetter climate, uses around 570,000 m³ per year (La Folle Entreprise 2022). The Beijing Olympic Winter Games, with its almost
<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /><figcaption>**Figure 4.** Changes at the top and the bottom of the resort. The 90 days in the figure refers to the 3 months of skiing on snow announced.</figcaption></figure>

100% artificial snow due to the dryness of the region, required 2.8 millions m³ (Olympic Winter Games Organizing Committee 2023). However, given the lack of freshwater resources in Trojena region, water for snowmaking should be desalinated and transported from the Gulf of Aqaba to Trojena, which would increase the energy cost. Accounting for the energy consumption of snowmaking, water desalination and lifting from sea level, we estimate the annual energy consumption of snow in Trojena to 3.7 ± 1.2 GWh under present climate and 2.7 ± 0.8 GWh in the future climate of the 2050’s. We note that Trojena’s artificial lake should have a capacity between 88 and 128 millions of m³, which is more than sufficient to meet the needs for snow production. Filling the lake with freshwater for other activities will have a much larger energy cost than snowmaking alone. Although this remains to be investigated, we suspect that a key challenge for this mountain resort will be the maintenance of the artificial lake while meeting its 100% renewable energy and sustainability goals (Neom 2025).

Overall, the model evaluation was conducted using approximately 250 satellite images, including a dozen with snow cover. The agreement between simulated and observed snow presence and absence suggests that the downscaling of the air temperature is realistic. This is important because air temperature is a key variable to estimate artificial snow production (through the wet bulb temperature). We lack *in situ* data to evaluate the accuracy of other relevant variables such as snow depth or snow density, which hinders our ability to evaluate the model more thoroughly. This study primarily aims to provide estimates of water consumption, energy use and sustainability of the resort, rather than precise quantifications of these variables.

## Conclusion

In conclusion, the investments for snow skiing in Trojena are likely to be seriously compromised by climate change. The only possible way to avoid snow-free slopes in Trojena during future winter seasons is a massive usage of snow factories—refrigeration systems which enable snow production no matter what the outside temperature—though this will be at the cost of even higher energy consumption.
<figure><img src="image_16.png" /></figure>

## Data and code availability

This work is based on open data from EU and US providers (ERA5 climate reanalysis, GLO-30 Digital Elevation Model, Sentinel-2 and Landsat imagery). ERA5 data were obtained from the Copernicus data store at the following URL/DOI: https://doi.org/10.24381/cds.adbb2d47. Sentinel-2 data were obtained from the Sentinel Hub. Landsat data were obtained from Google Earth Engine. The Crocus model code is available from http://www.umr-cnrm.fr/surfex/. The SnowModel/MicroMet code used in this study was kindly made available to the authors by G Liston. The ERA5-SnowModel Python code can be found at: SOURP Laura/ ERA_SnowModel_Pipeline_GitLab (https://src.koda.cnrs.fr/laura.sourp.1/era_snowmodel_pipe (Sourp and Gascoin 2024)).

## Acknowledgments

We declare no conflict of interest. We thank Carlo Carmagnola for the support with Crocus and Lucas Wicky for fruitful discussions on this topic.

## Data availability statement

The data that support the findings of this study are openly available at the following URL/DOI: https://doi.org/10.24381/cds.adbb2d47.

## Author contributions

Laura Sourp ID 0009-0001-8698-8253

Conceptualization (equal), Data curation (equal), Formal analysis (equal), Investigation (equal), Methodology (equal), Validation (equal), Visualization (equal), Writing – original draft (lead)

Abbas Fayad ID 0000-0003-0685-9572

Writing – review & editing (equal)

Simon Gascoin ID 0000-0002-4996-6768

Conceptualization (equal), Data curation (equal), Formal analysis (equal), Investigation (equal), Methodology (equal), Supervision (lead), Writing – original draft (supporting)

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