Assimilation of point SWE data into a distributed snow cover model comparing two contrasting methods

In alpine and high-latitude regions, water resource decision making often requires large-scale estimates of snow amounts and melt rates. Such estimates are available through distributed snow models which in some situations can be improved by assimilation of remote sensing observations. However, in regions with frequent cloud cover, complex topography, or large snow amounts satellite observations may feature information of limited quality. In this study, we examine whether assimilation of snow water equivalent (SWE) data from ground observations can improve model simulations in a region largely lacking reliable remote sensing observations. We combine the model output with the point data using three-dimensional sequential data assimilation methods, the ensemble Kalman filter, and statistical interpolation. The filter performance was assessed by comparing the simulation results against observed SWE and snow-covered fraction. We find that a method which assimilates fluxes (snowfall and melt rates computed from SWE) showed higher model performance than a control simulation not utilizing the filter algorithms. However, an alternative approach for updating the model results using the SWE data directly did not show a significantly higher performance than the control simulation. The results show that three-dimensional data assimilation methods can be useful for transferring information from point snow observations to the distributed snow model. Key Points Evaluating methods for assimilating snow observations into distributed models Assimilation can improve model skill also at locations without observations Assimilation of fluxes appears more successful than assimilation of state

Glacier monitoring and capacity building: important ingredients for sustainable mountain development

Glacier observation data from major mountain regions of the world are key to improving our understanding of glacier changes: they deliver fundamental baseline information for climatological, hydrological, and hazard assessments. In many mountain ecosystems, as well as in the adjacent lowlands, glaciers play a crucial role in freshwater provision and regulation. This article first presents the state of the art on glacier monitoring and related strategies within the framework of the Global Terrestrial Network for Glaciers (GTN-G). Both in situ measurements of changes in glacier mass, volume, and length as well as remotely sensed data on glacier extents and changes over entire mountain ranges provide clear indications of climate change. Based on experiences from capacity-building activities undertaken in the Tropical Andes and Central Asia over the past years, we also review the state of the art on institutional capacity in these regions and make further recommendations for sustainable mountain development. The examples from Peru, Ecuador, Colombia, and Kyrgyzstan demonstrate that a sound understanding of measurement techniques and of the purpose of measurements is necessary for successful glacier monitoring. In addition, establishing durable institutions, capacity-building programs, and related funding is necessary to ensure that glacier monitoring is sustainable and maintained in the long term. Therefore, strengthening regional cooperation, collaborating with local scientists and institutions, and enhancing knowledge sharing and dialogue are envisaged within the GTN-G. Finally, glacier monitoring enhances the resilience of the populations that depend on water resources from glacierized mountains or that are affected by hazards related to glacier changes. We therefore suggest that glacier monitoring be included in the development of sustainable adaptation strategies in regions with glaciated mountains

Towards a webcam-based snow cover monitoring network: methodology and evaluation

Snow cover variability has a significant impact on climate and the environment and is of great socioeconomic importance for the European Alps. Terrestrial photography offers a high potential to monitor snow cover variability, but its application is often limited to small catchment scales. Here, we present a semiautomatic procedure to derive snow cover maps from publicly available webcam images in the Swiss Alps and propose a procedure for the georectification and snow classification of such images. In order to avoid the effort of manually setting ground control points (GCPs) for each webcam, we implement a novel registration approach that automatically resolves camera parameters (camera orientation; principal point; field of view, FOV) by using an estimate of the webcams' positions and a high-resolution digital elevation model (DEM). Furthermore, we propose an automatic image-to-image alignment to correct small changes in camera orientation and compare and analyze two recent snow classification methods. The resulting snow cover maps indicate whether a DEM grid is snow-covered, snow-free, or not visible from webcams' positions. GCPs are used to evaluate our novel automatic image registration approach. The evaluation reveals a root mean square error (RMSE) of 14.1 m for standard lens webcams (FOV<48∘) and a RMSE of 36.3 m for wide-angle lens webcams (FOV≥48∘). In addition, we discuss projection uncertainties caused by the mapping of low-resolution webcam images onto the high-resolution DEM. Overall, our results highlight the potential of our method to build up a webcam-based snow cover monitoring network

Snow Extent Variability in Lesotho Derived from MODIS Data (2000–2014)

In Lesotho, snow cover is not only highly relevant to the climate system, but also affects socio-economic factors such as water storage for irrigation or hydro-electricity. However, while sound knowledge of annual and inter-annual snow dynamics is strongly required by local stakeholders, in-situ snow information remains limited. In this study, satellite data are used to generate a time series of snow cover and to provide the missing information on a national scale. A snow retrieval method, which is based on MODIS data and considers the concept of a normalized difference snow index (NDSI), has been implemented. Monitoring gaps due to cloud cover are filled by temporal and spatial post-processing. The comparison is based on the use of clear sky reference images from Landsat-TM and ENVISAT-MERIS. While the snow product is considered to be of good quality (mean accuracy: 68%), a slight bias towards snow underestimation is observed. Based on the daily product, a consistent time series of snow cover for Lesotho from 2000–2014 was generated for the first time. Analysis of the time series showed that the high annual variability of snow coverage and the short duration of single snow events require daily monitoring with a gap-filling procedure

Worldwide assessment of national glacier monitoring and future perspectives

It is widely accepted that glaciers are retreating throughout the world and that their decline causes serious impacts on many societies. Knowledge of glacier distribution and quantification of glacier changes is crucial to assessing the impact of glacier shrinkage on the transboundary hydrological cycle and related issues, such as irrigation, energy production, and natural hazard prevention. Therefore, glacier monitoring is vital to the development of sustainable adaptation strategies in regions with glaciated mountains. Baseline documentation is needed to assess the current status of glacier monitoring. The aim of this study is to assess the status of national implementations of the international monitoring strategy developed by the Global Terrestrial Network for Glaciers (GTN-G) to make the data easily accessible to a broader audience, to identify gaps in the monitoring setup, and to guide countries in improving their monitoring schemes. We developed a standardized procedure to evaluate existing glacier data from international data repositories; these freely accessible data on glacier distribution and changes (as of 2015) for all glacierized countries and regions form the basis of this study. The resulting country profiles are analyzed in relation to the existing GTN-G monitoring strategy. Gaps between the current implementation of glacier monitoring and implementation targets are compiled in a solid gap analysis, which allows countries to be categorized as having poorly developed monitoring, needing improvement, or having well-developed monitoring. Three pilot cases (Kyrgyzstan, Bolivia, and Switzerland) are presented in detailed country profiles

Probabilistic approach to cloud and snow detection on Advanced Very High Resolution Radiometer (AVHRR) imagery

Derivation of probability estimates complementary to geophysical data sets has gained special attention over the last years. Information about a confidence level of provided physical quantities is required to construct an error budget of higher-level products and to correctly interpret final results of a particular analysis. Regarding the generation of products based on satellite data a common input consists of a cloud mask which allows discrimination between surface and cloud signals. Further the surface information is divided between snow and snow-free components. At any step of this discrimination process a misclassification in a cloud/snow mask propagates to higher-level products and may alter their usability. Within this scope a novel probabilistic cloud mask (PCM) algorithm suited for the 1 km × 1 km Advanced Very High Resolution Radiometer (AVHRR) data is proposed which provides three types of probability estimates between: cloudy/clear-sky, cloudy/snow and clear-sky/snow conditions. As opposed to the majority of available techniques which are usually based on the decision-tree approach in the PCM algorithm all spectral, angular and ancillary information is used in a single step to retrieve probability estimates from the precomputed look-up tables (LUTs). Moreover, the issue of derivation of a single threshold value for a spectral test was overcome by the concept of multidimensional information space which is divided into small bins by an extensive set of intervals. The discrimination between snow and ice clouds and detection of broken, thin clouds was enhanced by means of the invariant coordinate system (ICS) transformation. The study area covers a wide range of environmental conditions spanning from Iceland through central Europe to northern parts of Africa which exhibit diverse difficulties for cloud/snow masking algorithms. The retrieved PCM cloud classification was compared to the Polar Platform System (PPS) version 2012 and Moderate Resolution Imaging Spectroradiometer (MODIS) collection 6 cloud masks, SYNOP (surface synoptic observations) weather reports, Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) vertical feature mask version 3 and to MODIS collection 5 snow mask. The outcomes of conducted analyses proved fine detection skills of the PCM method with results comparable to or better than the reference PPS algorithm