Use of near real-time cloud-free MODIS snow cover data from DLR's Global SnowPack for the early forecast of extreme hydrological events

The MODIS sensor on the NOAA Terra satellite has been providing daily information on global snow cover with a nominal spatial resolution of 500 m since February 2000. Since July 2022, this sensor is also located on NOAA's Aqua Satellite in orbit. The daily snow cover product of both platforms constitutes the basis for the DLR Global SnowPack (GSP) processor. In the course of the GSP processing, the daily data of both MODIS sensors are merged and data gaps (e.g., clouds or polar night) are interpolated over 3 days. From a digital elevation model, the snow height (elevation above which only snow occurs), as well as the snow-free height (elevation below which no snow occurs) are determined. Heights above or below these thresholds are filled accordingly. Finally, remaining gaps are gradually filled by the values of preceding days. Since the year 2022, the daily cloud free GSP data has been made available in near real time (3 days delay due to the preprocessing of the NSIDC) via the GeoService Portal of the Earth Observation Center (EOC). The rapid provision of the information on global snow coverage allows completely new applications of time-critical questions. These include hydrological estimates to what extent the snow conditions in the catchment area influence the drainage behavior. In addition to the satellite data, meteorological and hydrological data of the past 20 years are used to estimate the impact of a changing snow cover on the runoff. In the course of climate change, a delayed onset of snow cover and an earlier snowmelt is likely. Warmer winters also increase the risk of Rain-on-Snow events, which cause a strong increase in the outflow and have more dramatic ecological effects. We will present results for selected river catchment areas with a special focus on hydrological extreme events (droughts and floods), and when their occurrence has been shown early in the development of seasonal snow coverage. Our goal is to provide an automatic early warning system based on near real time GSP for large river catchments with nival-influenced drainage regimes

Evolution of Global Snow Cover - Analysis of 23 Years of DLR's Global SnowPack and Latest Processor Developments

As an essential climate variable, the area covered by snow should be recorded daily and with a sufficient spatial resolution. This is currently only achieved with medium-resolution optical remote sensing sensors. The Global SnowPack processor developed at DLR enables the daily derivation of a gap-free (without data gaps due to clouds or polar night) global snow cover in near real time by combining temporal and topographical interpolation methods. So far, the daily snow product from MODIS on Terra and Aqua has been processed, but now also the daily snow product from Suomi NPP VIIRS is included (Sentinel-3 OLCI will also be included in the near future). As a result, we now have a 23-year data set of global uninterrupted snow cover, which can be used both for trend analysis and for forecasting extreme hydrological events. For the determination of long-term trends, the accuracy and the duration of the time series are decisive, for the hydrological application rather the timeliness. Our pixel-based trend analysis showed most significant developments in the full snow cover season throughout the full hydrological year (Figure 1; meteorological autumn to summer), where more than 30% of the area showed a significant trend of snow cover duration (two-thirds show a decrease). In addition, analyzes at catchment area level were also made. We will present these results and application examples

DLR Global SnowPack - possible applications of the near real-time product

For over 20 years, the MODIS sensors on Terra and Aqua have been providing data on global snow coverage. The daily data provided by the National Snow and Ice Data Center (NSIDC) (currently in version 6.1) is already of very high quality and serves as input data for the DLR Global SnowPack processor. There, remaining data gaps (e.g. due to clouds or polar night) are filled in 4 interpolation steps and cloud-free data is thus provided daily. So far this has only happened retrospectively after the end of a hydrological year (end of the meteorological summer). This data is now made available daily in near real time with a time lag of 3 days in the EOC GeoService Portal. This enables the use of this data in time-critical issues, such as with regard to flood hazards. At the conference we will present the product, the access options and possible applications

Detection of Snow Cover from Historical and Recent AVHHR Data - A Thematic TIMELINE Processor

Global snow cover forms the largest and most transient part of the cryosphere in terms of area. On the local and regional scale, small changes can have drastic effects such as floods and droughts, and on the global scale is the planetary albedo. Daily imagery of snow cover forms the basis of long-term observation and analysis, and only optical sensors offer the necessary spatial and temporal resolution to address decadal developments and the impact of climate change on snow availability. The MODIS sensors have been providing this daily information since 2000; before that, only the Advanced Very High-Resolution Radiometer (AVHRR) from the National Oceanographic and Atmospheric Administration (NOAA) was suitable. In the TIMELINE project of the German Aerospace Center, the historic AVHRR archive in HRPT (High Resolution Picture Transmission) format is processed for the European area and, among other processors, one output is the thematic product 'snow cover' that will be made available in 1 km resolution since 1981. The snow detection is based on the Normalized Difference Snow Index (NDSI), which enables a direct comparison with the MODIS snow product. In addition to the NDSI, ERA5 re-analysis data on the skin temperature and other level 2 TIMELINE products are included in the generation of the binary snow mask. The AVHRR orbit segments are projected from the swath projection into LAEA Europe, aggregated into daily coverages, and from this, the 10-day and monthly snow covers are finally calculated. In this publication, the snow cover algorithm is presented, as well as the results of the first validations and possible applications of the final product

Development of Global Snow Cover - Trends from 23 Years of Global SnowPack

Globally, the seasonal snow cover is the areal largest, the most short-lived and the most variable part of the cryosphere. Remote sensing proved to be a reliable tool to investigate their short-term variations worldwide. The medium-resolution sensor MODIS sensor has been delivering daily snow products since the year 2000. Remaining data gaps due to cloud coverage or polar night are interpolated using the DLR’s Global SnowPack (GSP) processor which produces daily global cloud-free snow cover. With the conclusion of the hydrological year 2022 in the northern hemisphere, the snow cover dynamics of the last 23 hydrological years can now be examined. Trends in snow cover development over different time periods (months, seasons, snow seasons) were examined using the Mann–Kendall test and the Theil–Sen slope. This took place as both pixel based and being averaged over selected hydrological catchment areas. The 23-year time series proved to be sufficient to identify significant developments for large areas. Globally, an average decrease in snow cover duration of −0.44 days/year was recorded for the full hydrological year, even if slight increases in individual months such as November were also found. Likewise, a large proportion of significant trends could also be determined globally at the catchment area level for individual periods. Most drastic developments occurred in March, with an average decrease in snow cover duration by −0.16 days/year. In the catchment area of the river Neman, which drains into the Baltic Sea, there is even a decrease of −0.82 days/year

Global Determination of Snow Cover using Remote Sensing and a Near Real Time Processing Chain

Remote sensing offers the best prerequisites for obtaining comprehensive information on global snow cover. Although microwave remote sensing can provide limited information about the thickness of the snowpack and the water stored (i.e. snow water equivalent), its geometric resolution is not sufficient for an accurate spatial analysis. Optical remote sensing provides the required spatial resolution, but it is often compromised by clouds or at high geographical latitudes by the polar night. In order to obtain cloud-free information on daily snow cover from optical data, the German Aerospace Center developed the already established product Global SnowPack (GSP). It is based on the daily MODIS snow products originating from Terra and Aqua platforms and provided by NSIDC. With the help of sequential algorithms and additional data (digital elevation model, land cover classifications), pixels with clouds or polar night are continuously eliminated. While the Global SnowPack has so far only been calculated retrospectively for the entire hydrological year, there will now also be a near real time product (NRT-GSP). The latest MODIS data (these are available after approx. 2 days) are interpolated on a daily basis using the previous days. The product will be available in the future on the GeoService of the Earth Observation Center. We see an application of this product, for example, in the prediction of extreme hydrological events. In a recently published study, the development of the snow cover of various catchment areas of nival rivers derived from Global SnowPack was incorporated into a snowmelt runoff model. It was found that extreme high and low water events during the annual spring flood were reflected early in the development of the snow cover extent. With the help of the NRT-GSP product, such a development would be recognizable at an early stage and preparations could be made