HS6.4

HS6 EDI
Remote Sensing of Seasonal Snow 

Snow constitutes a freshwater resource for over a billion of people world-wide. High percentage of this water resource mainly come from seasonal snow located in mid-latitude regions. The current warming situation alerts that these snow water storages are in high risk to be dramatically reduced, affecting not only to water supply but also ecosystem over these areas. Therefore, understanding seasonal snow dynamics, possible changes and implication have become crucial for water resources management. Remote sensing has proven to be the main technique used to monitor the snow properties across mid-large extensions and their hydrological implications, for decades now. Moreover, the recent advances, which are focused on the study of snow properties at higher spatio-temporal scales (e.g., small-scale snow-topography interactions, snow-vegetation interaction, diurnal variation of snow, rain over snow events), are helping to understand better snow acumulation, distribution and ablation dynamics.
This session is focused on studies linking the use of remote sensing of seasonal snow in hydrological applications: techniques and data from different technologies, such as time-lapse imagery, laser scanners, radar, optical photography, thermal and hyperspectral technologies, or other new applications, with the aim of quantifying and better understanding snow characteristics (i.e. snow grain size, snow depth, albedo, pollution load, snow specific area and snow density), snow related processes (snowfall, melting, evaporation and sublimation), snow dynamics, snow hydrological impacts and snow environmental effects.

Co-organized by CR2
Convener: Rafael Pimentel | Co-convener: Claudia Notarnicola
Presentations
| Thu, 26 May, 17:00–18:27 (CEST)
 
Room 2.31

Session assets

Session materials

Presentations: Thu, 26 May | Room 2.31

Chairpersons: Rafael Pimentel, Claudia Notarnicola
17:00–17:10
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EGU22-7319
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solicited
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Virtual presentation
Thomas Nagler and the SnowPEx Team

Satellite observations are the only means for timely and complete observations of the global snow cover. A range of different satellite snow products is available, the performance of which is of vital interest for the global user community. We provide an overview on goals and activities of the SnowPEx+ initiative, dedicated to the intercomparison of northern hemispheric and global satellite snow products, derived from data of long-term operational as well as recently launched satellites. SnowPEx+ is the continuation of SnowPEx (2014-2017), carried out as an international collaborative effort under the umbrella of Global Cryosphere Watch / WMO and funded by ESA.

SnowPEx+ focuses on two parameters of the seasonal snowpack, the snow extent (SE) from medium resolution optical satellite data (Sentinel-3, VIIRS, MODIS, AVHRR, etc.) and the snow water equivalent (SWE) from passive microwave satellite data. Overall, 15 hemispheric and global SE products (binary and fractional SE) and two SWE products are participating in the experiment. For intercomparison, daily SE products are transformed to a common map projection and standardized SnowPEx protocols are applied, elaborated by the international snow product community. The SE product evaluation applies statistical measures for quantifying the agreement between the various products, including the analysis of spatial patterns. Validation of SE products uses as benchmark high resolution snow maps from about 150 globally distributed Landsat scenes acquired in different climate zones, under different solar illumination conditions and over various land cover types. This snow reference data set, based on various retrieval algorithms, is generated and evaluated by the SnowPEx+ High Resolution Snow Products Focus Group. In-situ snow data from several organisations in Europe, North America and Asia are also used for validating the satellite SE and SWE products. SWE products are also inter-compared with gridded snow products from land surface models driven by atmospheric reanalysis data. In addition, the multi-year trends of the various SE and SWE products are evaluated. We provide an overview on the snow products, discuss the validation and intercomparison protocols, and report on preliminary results from the intercomparison and validation of various snow products.

How to cite: Nagler, T. and the SnowPEx Team: SnowPEx+: The International Snow Products Intercomparison and Evaluation Excercise 2015-2020, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7319, https://doi.org/10.5194/egusphere-egu22-7319, 2022.

17:10–17:17
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EGU22-10725
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Highlight
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Presentation form not yet defined
Ana Barros, Carrie Vuoyvich, Michael Durand, Leung Tsang, Paul Houser, and Hans-Peter Marshall

Global snow water equivalent (SWE) data are required for understanding the role of snow in the Earth’s water, energy and carbon cycles, and are critical for informing water resource and snow-related hazards. While exciting progress has been made in recent decades, there are currently no global SWE data at the required frequency, resolution and accuracy to address scientific and operational requirements. These data are needed to inform science and application areas and, taken as a whole, are critical to global water and food security. New higher-resolution microwave instruments can provide this information, especially when combined with modeling.  We now have the capability to put these instruments in space to monitor SWE and volume at the required resolution for improved and useful water prediction globally.  This presentation will describe the requirements of a spaceborne SWE mission, review progress in snow remote sensing technology and algorithms, and describe a potential path forward to meet identified snow data needs.

How to cite: Barros, A., Vuoyvich, C., Durand, M., Tsang, L., Houser, P., and Marshall, H.-P.: Global Snow Water Equivalent Observations from Space, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10725, https://doi.org/10.5194/egusphere-egu22-10725, 2022.

17:17–17:24
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EGU22-3473
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ECS
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Virtual presentation
Chunyu Dong and Jianfeng Luo

Remotely sensed MODIS (Moderate Resolution Imaging Spectroradiometer) data and the NDSI (Normalized Difference Snow Index) based approach have been applied globally for snow cover mapping. However, this method displays severe omission errors in forested areas, due to the forest canopy shading of snow. In this study, we developed a new forest snow mapping algorithm based on MODIS reflectance data, time-lapse observations of forest snow, and a random forest model. We built a time-lapse camera network in the eastern Qilian Mountains in northwestern China to monitor the forest snow processes and obtain the ground truth data. The random forest (RF) model seems to be powerful in capturing the relationships between the MODIS surface reflectance bands and the forest snow presence. The presented approach significantly improved the accuracy of binary snow cover (BSC) mapping in forests. We evaluated the performance of the proposed algorithm with the traditional NDSI-based method. The results show that the new algorithm has a superior performance in forest BSC mapping, compared to the NDSI-based BSC. The proposed RF-BSC can retrieve ~70% of all real forest snow pixels, while the NDSI-BSC can only detect 8-14%. We further investigated the geographical influence (e.g. topography, forest coverage, and solar illumination) of the algorithm performance. This study suggests that the fusion of optical remote sensing data and ground-based observations using machine learning techniques has a great potential in improving the accuracy of land cover mapping.

How to cite: Dong, C. and Luo, J.: Development of a new forest snow mapping algorithm using MODIS data, machine learning and time-lapse photography, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3473, https://doi.org/10.5194/egusphere-egu22-3473, 2022.

17:24–17:31
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EGU22-4520
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ECS
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Presentation form not yet defined
Flora Weissgerber, Céline Monteil, and Alexandre Girard

Snow dynamics is a key parameter for the hydrological model predicting the river flow rate used in dam management. In the MORDOR model used by EDF, the information of the daily snow extent is an input to improve the flow prediction. This information is extracted from MODIS NDSI daily product. Due to cloud cover, this information can be lacking or imprecise for multiple consecutive days over one catchment, reducing the precision of the prediction.

The goal of this study is to detect the snow extent using SAR data, since it can acquire images through clouds. We focus over the Guil catchment in the French Alps. Sentinel-1 interferometric stacks from June 2018 to August 2019 are used for three different orbits.

Previous studies showed the capacity of the ratio between the current image and a reference image acquired in summer to detect wet snow [Nagler2016], or that the ratio between VH and VV could be linked to the height of snow [Lievens2019]. Interferometry has be shown capable to detect snow since the snow covered area can exhibit a lower coherence [Singh2008].

To compare these parameters using a ground truth, we projected the MODIS NDSI data on our S1-stack using a 1m DEM and considered pixels as snowy if the NDSI is above 0.4.

As pointed in other studies [Löw2002, Wang2015], it is very hard to set a threshold for these parameters, mostly because the vegetation exhibits volume scattering and changes the same way as snow. Using SVM, we investigated the capability of these parameters to detect snow in two setups:
- snow detection: the goal is to classify the pixels as snow or snow-free for all the image, using Nagler parameter in VV and VH, the ratio between VH and VV at each date and the polarimetric coherence at each date. For Nagler parameter, the reference image is the temporal average of the images over July and August 2018.
- change detection: the goal is to classify the pixels into 4 classes, snow-free to snow-free, snow to snow, snow-free to snow and snow to snow-free. Considering two consecutive images, this was done using the variation of the VV and VH ratio, the interferometric coherence between these images, and the ratio between the polarimetric coherences of the images.
For each setup, the learning and the testing were done on two samples of 20000 randomly selected pixels, equally distributed between the classes.

For the snow detection method, between 54% and 59% of the pixels are correctly classified, for the three orbits. This result is stable with the choice of the learning sample. For the change detection setup only 30% of the pixels are correctly classified. Moreover, the per-class metrics vary widely from one experience to the other. This variability as well as the low classification results underline the difficulty of the task but can also be linked to the resolution difference between MODIS used as ground truth and S1. To robustify the detection, spatial and temporal regularization seems necessary.

How to cite: Weissgerber, F., Monteil, C., and Girard, A.: Snow extend and snow change mapping with Sentinel-1 imaging using SVM, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4520, https://doi.org/10.5194/egusphere-egu22-4520, 2022.

17:31–17:38
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EGU22-9932
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Virtual presentation
Lars Keuris, Thomas Nagler, Nico Mölg, and Stefan Scheiblauer

Precise snow cover estimations are relevant for many fields of research applications, such as for hydrological and meteorological modelling. Furthermore, snow plays an important role in hydropower management and flood prediction. Snow cover monitoring from satellite imagery has received increasing attention over the past decades. Nowadays, improvements in estimation methodologies and better availability of augmented satellite imagery provide an excellent basis for reliable estimations of fractional snow cover.

In this work we exploit the available spectral information of the Sentinel-2 MSI and Sentinel-3 OLCI for automatic estimation of fractional snow coverage. This is achieved through linear spectral unmixing with local endmembers. Similar implementation of methods that employ spectral unmixing use a reflectance model or a spectral library with pre-selected endmembers. Our approach selects the spectral endmembers from the data itself and applies them depending on the distance from the query point assuming spectral similarities of ground reflectance nearby. Endmembers are found through a pre-classification step based on conservative thresholds in combination with a similarity measure. The linear unmixing problem is solved several times for each query point using different combinations of endmembers detected in the vicinity of the query point; accounting for different illumination conditions and shaded areas. Finally, a careful selection of accurate fractional snow cover estimations is performed. This approach is globally applicable, adjusts to the local environment and illumination conditions and avoids costly endmember modelling or the provision of an external spectral library. The method was tested in different regions in the world using different satellite data including Sentinel-2, Landsat and Sentinel-3 OLCI and were inter-compared with snow information from other sources. In the presentation we will present the method, and examples of fractional snow cover maps. The performance of the method will be shown in comparison with other data and the limitations and capabilities will be discussed.

How to cite: Keuris, L., Nagler, T., Mölg, N., and Scheiblauer, S.: Fractional snow cover estimation through linear spectral unmixing of Sentinel-2 and Sentinel-3 optical satellite data using local endmembers, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9932, https://doi.org/10.5194/egusphere-egu22-9932, 2022.

17:38–17:45
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EGU22-11519
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On-site presentation
Fatemeh Zakeri and Gregoire Mariethoz

Daily snow cover is an essential parameter in hydrology, climate, and environmental studies. Although remote sensing images provide valuable information on snow, they are restricted by clouds, clouds’ shadows, and temporal and spatial coverage. This study synthesizes daily snow cover maps based on climate and near clear sky Sentinel-2 and Landsat images. The motivation of this study is that snow patterns are repeatable between years with similar meteorological characteristics. Accordingly, a distance metric based on climate information is computed, including temperature and precipitation (1km resolution) as well as auxiliary data such as daily MODIS snow cover. This distance quantifies the mismatch between the days when clear sky Landsat or Sentinel-2 data is available, learning days, and days when there is no clear sky satellite data or test days. The proposed methodology is applied on a subset of the Alpine belt called the Western Swiss Alps and on the Jonschwil sub-basin, both located in Switzerland. We have synthesized daily snow cover maps for each of our regions of interest for 20 years since 2000.

To evaluate synthesized snow cover maps, we use leave-one-out cross-validation, comparison with a random selection process, and a degree-day snow model. The leave-one-out assessment shows a good agreement between the actual Landsat and the synthesized one. The synthesized snow cover maps also show that the proposed method output agrees with physical concepts as the physical features have been used along with satellite data in the proposed model. Considering physical features in synthesizing Landsat images is an innovation that allows us to use the methodology to synthesize images for the pre-satellite period. Moreover, random selection assessment shows that considering a metric based on climate and auxiliary data can synthesize snow cover as repeatable patterns depending on meteorological data.

How to cite: Zakeri, F. and Mariethoz, G.: Synthesizing Daily Snow Cover Maps Using Satellite Images and Climate Information, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11519, https://doi.org/10.5194/egusphere-egu22-11519, 2022.

17:45–17:52
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EGU22-12576
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Presentation form not yet defined
Abror Gafurov, Olga Kalashnikova, Djafar Niyazov, Adkham Mamaraimov, Akmal Gafurov, and Uktam Adkhamov

Snow is an important hydrological component in Central Asia. The snowmelt contributes to about 50 % of total water formation in the region, depending on geographic conditions. Many hydro-meteorological phenomena such as floods or drought conditions can be triggered by snowmelt amounts in Central Asia. The amount of snow accumulation in the mountains of Tian-Shan and Pamir also defines the availability of water for summer months to be used for agricultural production or re-filling of reservoirs for energy production in the winter period. Thus, it is of high importance to better understand the seasonal variation of snow and if the over the global average climate warming in the region is affecting the processes related to snow accumulation and melt.

In this study, we analyze 22 years of daily Moderate Resolution Imaging Radiometer (MODIS) snow cover data that was processed using the MODSNOW-Tool, including cloud elimination. Additionally, observed snow depth data from meteorological stations were used to estimate trends related to snow cover change. We used several parameters such as snow cover duration, snow depth, snow cover extent, and snowline elevation to analyze changes.  We conducted this analysis in 18 river basins across the Central Asian domain with each river basin having different geographic conditions and the results show varying tendencies. In many river basins, a clear decrease of snow cover was found to be significant, whereas in some river basins also increase in the snow cover extent in particular months could be identified. We attributed the changes related to snow cover to available historical temperature and precipitation records from meteorological stations to better understand the driving forces. The results of this study indicate seasonal snow cover variations but also potential water shortages in particular months as well as water abundance in months where water demand is not high in Central Asia. 

How to cite: Gafurov, A., Kalashnikova, O., Niyazov, D., Mamaraimov, A., Gafurov, A., and Adkhamov, U.: Climate change-driven seasonal snow cover variations in Central Asia, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12576, https://doi.org/10.5194/egusphere-egu22-12576, 2022.

17:52–17:59
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EGU22-12801
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Presentation form not yet defined
Rafael Pimentel, Javier Aparicio, Pedro Torralbo, Eva Contreras, Fátima Moreno-Pérez, Cristina Aguilar, and María José Polo

Observations worldwide identify snow cover persistence together with snowfall occurrence as the most affected variables by global warming. In particular, Mediterranean mountain areas are pointed as climate warming hotspots. The characteristic snow-patched distribution shown over these areas, which result in different accumulation-ablation cycles during the cold season, usually makes spatial resolution the limiting factor for its correct representation. Remote sensing is the only feasible data source for distributed quantification of snow in mountain regions on medium to large scales, due among other to the limited access to these areas together with the lack of dense ground monitoring stations for snow variables. Among the numerous remote sensing sources, the Landsat constellation is those that better fit both basic requirements for studying snow over these areas, to cover a long period with observation and to have an high spatial resolution. However, the traditional classification algorithms for snow detection are usually based on normalized indexes that  provide a binary classification as snow and no-snow pixels throughout the study area; this simple classification may result in large error in heterogeneous and transitional areas within the snow-dominated domain. Alternatively, the spectral mixture analysis (SMA) approach provides a fraction of snow cover within each pixel and thus, constitutes a step forward to characterize heterogeneous and patchy snow areas in semiarid regions. 

This work analysed the role of mixed pixels, defined as pixels made up of different types of surfaces, in snow cover distribution over Mediterranean mountains. Sierra Nevada Mountain Range in southern Spain has been chosen as representative of a Mediterranean mountain area, which is characterized by strong altitudinal gradients with marked differences between the south (directly affected to the sea) and the north faces are found in the area. The fractional snow cover maps, at 30×30 m and 16 days spatial and temporal resolution respectively, derived from SMA of Landsat TM and ETM+ validated using as high resolution terrestrial photography (Pimentel et al., 2017) has been used for mixed pixel analysis. On the one hand, the results show the importance of mixed pixels, which can constitute more than 50% of the total pixels in some areas of the mountainous range and season of the year. On the other hand, the analysis carried out has allowed the identification of areas more prone to allocate this type of pixels, linking that fact to climatic drivers. 

This work has been funded by project MONADA - "Hydrometeorological trends in mountainous protected areas in Andalusia: examples of co-development of climatic services for strategies of adaptation to climatic change", with the economic collaboration of the European Funding for Rural Development (FEDER) and the Andalusian Ministry of Economic Transformation, Industry, Knowledge and Universities. R+D+i project 2020.

How to cite: Pimentel, R., Aparicio, J., Torralbo, P., Contreras, E., Moreno-Pérez, F., Aguilar, C., and Polo, M. J.: Quantifying the role of mixed pixels in snow cover distribution in semiarid regions: A study case in Sierra Mountain (Spain), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12801, https://doi.org/10.5194/egusphere-egu22-12801, 2022.

17:59–18:06
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EGU22-993
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Highlight
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On-site presentation
Jonas Köhler, Andreas Dietz, and Claudia Kuenzer

The inter and intra-annual dynamics of seasonal snow are of key interest in the tourism-based economies of many Alpine regions as well as for millions of people in the adjacent European lowlands when it comes to freshwater supply and electricity generation. However, accurate snow observations over long periods of time and at large spatial scales are especially challenging in inaccessible mountainous areas. This can be overcome by using data from Earth Observation satellites, which have been constantly monitoring the Earth’s surface for almost 40 years. On a catchment basis, we derive the Snow Line Elevation (SLE) from Landsat data for the entire Alpine region and model the spatio-temporal dynamics in monthly time-series ranging from 1984 to today. Based on the historical observations we model future SLE dynamics comparing different uni-variate and multi-variate approaches and assess them for their ability to generate multi-year forecasts from EO-derived time series data. These forecasts can enable local and regional stakeholders to adapt to a potentially changing snow regime under climate change.

How to cite: Köhler, J., Dietz, A., and Kuenzer, C.: Modeling future Snow Line Elevation dynamics in the Alps based on long remote sensing time-series, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-993, https://doi.org/10.5194/egusphere-egu22-993, 2022.

18:06–18:13
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EGU22-9744
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ECS
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On-site presentation
Leon Bührle, Mauro Marty, Lucie Eberhard, Andreas Stoffel, and Yves Bühler

Abstract.

Snow depths are traditionally determined by point measurements at automatic weather stations or field observations, which cannot capture the complexity of snow depth distribution in alpine terrain. Therefore, remote sensing techniques have become key tools for spatially continuous snow depth mapping. Only satellites, airborne laser scanners (ALS) or photogrammetry from piloted aircrafts are capable of covering large regions of more than 100 km². However, the accuracy of satellite data does not match/achieve the requirements for exact snow depth mapping yet. In comparison to ALS, photogrammetric methods are considerably more economic, but have the disadvantages of light and weather dependence as well as the lacking ability to penetrate high vegetation. Nevertheless, previous studies of photogrammetric snow depth mapping on a large scale have already proven the accurate implementation, but those studies were either limited to only one recording or characterized by a spatial resolution of around 2 m. These properties limit the comparison of snow depth distribution and the analysis of small-scale features.

In our study we apply airborne imagery from the current state-of-the-art survey camera Vexcel Ultracam acquired during the annual peak of winter for the five years from 2017 to 2021 in an area of approximately 300 km2 around Davos, Switzerland. This enables the calculation of outstandingly improved annual snow depth maps. The high spatial resolution of the snow depth maps (0.5 m) in combination with the high-resolution orthophoto (0.25 m) enables the identification of small-scale snow depth features. Additionally, the development of masks for high-vegetated and settled areas in combination with the high accuracy of the unmasked snow depth values (root mean square error of around 0.15 m) represents a significant step forward for reliable snow depth mapping of large alpine regions with photogrammetric methods. Our study focuses on the consistent workflow used for processing the snow depth maps, demonstrates the special characteristics of the snow depth distribution and presents the potential for investigations and applications based on this unique snow depth time-series.

How to cite: Bührle, L., Marty, M., Eberhard, L., Stoffel, A., and Bühler, Y.: Snow depth mapping over large, high-alpine regions by airplane photogrammetry , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9744, https://doi.org/10.5194/egusphere-egu22-9744, 2022.

18:13–18:20
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EGU22-5117
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ECS
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On-site presentation
Laura Sourp, Simon Gascoin, Mohamed Wassim Baba, and César Deschamps-Berger

The Snow Water Equivalent (SWE) is a key variable to characterize water resource availability in mountain catchments. Despite its hydrological significance, the snow cover is poorly monitored in many regions due to a lack of in situ measurements. 

Global climate reanalysis products provide increasingly accurate data but are too coarse to be used directly in mountain regions to reconstruct snow related variables. However these reanalyses have been successfully used to generate high resolution meteorological forcing and run a snowpack model in the central Andes and the High Atlas mountain ranges (Mernild et al. 2017; Baba et al. 2018). 

The method is based on the MicroMet/SnowModel package (Liston and Elder 2006a; 2006b). MicroMet performs spatial interpolation of meteorological variables using the digital elevation model (downscaling) and the other routines of SnowModel computes the snowpack energy and mass balance. We have implemented a tool to improve the automation and scalability of this method to simulate the snow cover distribution in other regions using ERA5 or ERA5-Land. Our snow simulation tool only requires a digital elevation model as input. The land cover is extracted from the Copernicus global land cover map and the meteorological data are retrieved from either ERA5 or ERA5-Land over the period of interest.  

We used three catchments under the influence of Mediterranean climate to evaluate the performance of this tool: Tuolumne (USA), Bassies (France) and Yeso (Chile). For each catchment either the modeled SWE depth or snow depth are compared with the validation data, over periods going from 3 to 8 years.  In the Tuolumne basin, where the dataset is the most accurate with several SWE maps per year, we find a very good agreement at the basin scale (RMSE 40 mm w.e.). However, the mean RMSE in the highest elevation band (3500-4000 m asl) can exceed 500 mm w.e., which we attribute to the  lack of gravitational transport in SnowModel and errors in the spatial distribution of precipitation. To reduce these errors in particular, we are implementing a non-deterministic representation of the precipitation input data to eventually allow the assimilation of globally available remote sensing data. 

This tool will allow us to compute snow reanalyses in key mountain ranges around the Mediterranean sea over the past two decades (Pyrenees, Atlas and Mount Lebanon) and study the influence of topography and climate on the snow cover variability.

How to cite: Sourp, L., Gascoin, S., Baba, M. W., and Deschamps-Berger, C.: Development of a snow reanalysis pipeline using downscaled ERA5 data: application to Mediterranean mountains, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5117, https://doi.org/10.5194/egusphere-egu22-5117, 2022.

18:20–18:27
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EGU22-2078
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Presentation form not yet defined
Chengxuan Lu, Guohua Fang, Jian Ye, and Zitong Yang

Rationale: After the Tropical Rainfall Measuring Mission (TRMM) was successfully launched by NASA and JAXA in 1997, NASA released the first GPM-era global precipitation product (IMERG) in April 2014, aiming to obtain precipitation data with ultra-fine temporal and spatial resolution around the world. Examining the key precipitation data in different climatic areas influenced by the monsoon can effectively help users and algorithm developers maximise the accuracy and characteristics of new satellite remote sensing products. Objective: To this end, this study used the upper and middle Lancang River basin (UMLRB), a transnational river with complex climatic conditions, as the research area to explore the applicability and precipitation distribution of IMERG and TRMM, and evaluate their accuracy. Methods: In this study, various performance indexes were used to comprehensively evaluate the retrieval accuracy of IMERG and TRMM remote sensing precipitation data in UMLRB; these indexes can be divided into two categories according to the evaluation objectives. One type of indexes mainly evaluates the amplitude consistency of precipitation, and the other type of indexes is mainly used to evaluate the occurrence consistency of precipitation. Results: The results indicated that: (1) The temporal distribution of precipitation in different climatic regions was correctly detected by IMERG and TRMM in the UMLRB, and the dry and wet seasons in the climate transition zone were distinct. (2) IMERG and TRMM tended to overestimate moderate rain (1.0-20 mm/d) while underestimating heavy rain (20-50mm/d) and extreme precipitation (> 50mm/d). (3) In terms of the amplitude consistency of precipitation, the detection results of IMERG in the alpine climate zone were not completely consistent with those of TRMM, while those in the climate transition zone were consistent with TRMM. (4) The stronger the precipitation intensity, the worse the accuracy of IMERG and TRMM, especially between heavy rain (20-50 mm/d) and extreme precipitation (> 50mm/d). (5) The IMERG, which had greater application potential in complex climatic conditions, had higher accuracy than TRMM.” Conclusions/Recommendations: Therefore, before using remote sensing precipitation data to study watershed hydrometeorology in monsoon-affected areas, their seasonal distribution, precipitation intensity, and the type of remote sensing data should be carefully considered to verify their accuracy.

How to cite: Lu, C., Fang, G., Ye, J., and Yang, Z.: Accuracy assessment of IMERG and TRMM remote sensing precipitation data under the influence of monsoon over the upper and middle Lancang River basin, China, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2078, https://doi.org/10.5194/egusphere-egu22-2078, 2022.