Shifting mountain snow patterns in a changing climate from remote sensing retrieval

Retrieval of Fractional Snow Cover over High Mountain Asia Using 1 km and 5 km AVHRR/2 with Simulated Mid-Infrared Reflective Band

Accurate long-term snow-covered-area mapping is essential for climate change studies and water resource management. The NOAA AVHRR/2 provides a unique data source for long-term, large-spatial-scale monitoring of snow-covered areas at a daily scale. However, the value of AVHRR/2 in mapping snow-covered areas is limited, due to its lack of a shortwave infrared band for snow/cloud discrimination. We simulated the reflectance in the 3.75 µm mid-infrared band with a radiative transfer model and then developed three fractional-snow-cover retrieval algorithms for AVHRR/2 imagery at 1 km and 5 km resolutions. These algorithms are based on the multiple endmember spectral mixture analysis algorithm (MESMA), snow index (SI) algorithm, and non-snow/snow two endmember model (TEM) algorithm. Evaluation and comparison of these algorithms were performed using 313 scenarios that referenced snow-cover maps from Landsat-5/TM imagery at 30 m resolution. For all the evaluation data, the MESMA algorithm outperformed the other two algorithms, with an overall accuracy of 0.84 (0.85) and an RMSE of 0.23 (0.21) at the 1 km (5 km) scale. Regarding the effect of land cover type, we found that the three AVHRR/2 fractional-snow-cover retrieval algorithms have good accuracy in bare land, grassland, and Himalayan areas; however, the accuracy decreases in forest areas due to the shading of snow by the canopy. Regarding the topographic effect, the accuracy evaluation indices showed a decreasing and then increasing trend as the elevation increased. The accuracy was worst in the 4000–5000 m range, which was due to the severe snow fragmentation in the High Mountain Asia region; the early AVHRR/2 sensors could not effectively monitor the snow cover in this region. In this study, by increasing the number of bands of AVHRR/2 1 km data for fractional-snow-cover retrieval, a good foundation for subsequent long time series kilometre- resolution snow-cover monitoring has been laid.

Land Surface Snow Phenology Based on an Improved Downscaling Method in the Southern Gansu Plateau, China

Snow is involved in and influences water–energy processes at multiple scales. Studies on land surface snow phenology are an important part of cryosphere science and are a hot spot in the hydrological community. In this study, we improved a statistical downscaling method by introducing a spatial probability distribution function to obtain regional snow depth data with higher spatial resolution. Based on this, the southern Gansu Plateau (SGP), an important water source region in the upper reaches of the Yellow River, was taken as a study area to quantify regional land surface snow phenology variation, together with a discussion of their responses to land surface terrain and local climate, during the period from 2003 to 2018. The results revealed that the improved downscaling method was satisfactory for snow depth data reprocessing according to comparisons with gauge-based data. The downscaled snow depth data were used to conduct spatial analysis and it was found that snow depth was on average larger and maintained longer in areas with higher altitudes, varying and decreasing with a shortened persistence time. Snow was also found more on steeper terrain, although it was indistinguishable among various aspects. The former is mostly located at high altitudes in the SGP, where lower temperatures and higher precipitation provide favorable conditions for snow accumulation. Climatically, factors such as precipitation, solar radiation, and air temperature had significantly singular effectiveness on land surface snow phenology. Precipitation was positively correlated with snow accumulation and maintenance, while solar radiation and air temperature functioned negatively. Comparatively, the quantity of snow was more sensitive to solar radiation, while its persistence was more sensitive to air temperature, especially extremely low temperatures. This study presents an example of data and methods to analyze regional land surface snow phenology dynamics, and the results may provide references for better understanding water formation, distribution, and evolution in alpine water source areas.

Processing of VENµS Images of High Mountains: A Case Study for Cryospheric and Hydro-Climatic Applications in the Everest Region (Nepal)

In the Central Himalayas, glaciers and snowmelt play an important hydrological role, as they ensure the availability of surface water outside the monsoon period. To compensate for the lack of field measurements in glaciology and hydrology, high temporal and spatial resolution optical remotely sensed data are necessary. The French–Israeli VENµS Earth observation mission has been able to complement field measurements since 2017. The aim of this paper is to evaluate the performance of different reflectance products over the Everest region for constraining the energy balance of glaciers and for cloud and snow cover mapping applied to hydrology. Firstly, the results indicate that a complete radiometric correction of slope effects such as the Gamma one (direct and diffuse illumination) provides better temporal and statistical metrics (R2 = 0.73 and RMSE = 0.11) versus ground albedo datasets than a single cosine correction, even processed under a fine-resolution digital elevation model (DEM). Secondly, a mixed spectral-textural approach on the VENµS images strongly improves the cloud mapping by 15% compared with a spectral mask thresholding process. These findings will improve the accuracy of snow cover mapping over the watershed areas downstream of the Everest region.

Vertical difference of climate change impacts on vegetation at temporal-spatial scales in the upper stream of the Mekong River Basin

As the upper section of the Mekong River Basin, the vegetation quality of the Lancang River Basin (LRB) and the related ecological functions are critical for the whole basin. With time-series Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2000 to 2015 and local daily climatic data since 1976, their vertical interaction differences were identified. The results showed that the spatial variation in Normalized difference vegetation index (NDVI) of grassland and forest were sensitive to elevation. The NDVI value in the southern area at elevations less than 3000 m was more than 0.80 and decreased to 0.30-0.60 with elevations higher than 4500 m. The general vegetation quality showed a positive trend under climate change over 16 years. The M-K test of daily precipitation and temperature from 12 local weather stations showed that the basin temperature varied more significantly than precipitation. The temporal correlation between NDVI with precipitation as well as temperature at each pixel indicated that temperature was the dominant factor affecting grassland and forest dynamics in the LRB. The interaction between vegetation and climate was more sensitive at elevations lower than 3000 m. Based on the RCP4.5 scenario, the future temperature distribution was predicted, and its impact on NDVI was simulated at the pixel scale. Under future drier and warmer climate conditions, the responded NDVI in the upper stream with higher elevation may increase soil erosion and decrease streamflow. The NDVI in the downstream area will be improved and be able to adapt to the related climate impacts. Because of the large amount of water and biomass in this basin, higher temperatures will accelerate the decomposition of forest foliar litter. Thus, more organic carbon and forest diffuse pollution will be discharged into the water, potentially affecting the water quality of the whole basin.

On the Interest of Optical Remote Sensing for Seasonal Snowmelt Parameterization, Applied to the Everest Region (Nepal)

In the central part of the Hindu Kush Himalayan region, snowmelt is one of the main inputs that ensures the availability of surface water outside the monsoon period. A common approach for snowpack modeling is based on the degree day factor (DDF) method to represent the snowmelt rate. However, the important seasonal variability of the snow processes is usually not represented when using a DDF method, which can lead to large uncertainties for snowpack simulation. The SPOT-VGT and the MODIS-Terra sensors provide valuable information for snow detection over several years. The aim of this work was to use those data to parametrize the seasonal variability of the snow processes in the hydrological distributed snow model (HDSM), based on a DDF method. The satellite products were corrected and combined in order to implement a database of 8 day snow cover area (SCA) maps over the northern part of the Dudh Koshi watershed (Nepal) for the period 1998–2017. A revisited version of the snow module of the HDSM model was implemented so as to split it into two parameterizations depending on the seasonality. Corrected 8 day SCA maps retrieved from MODIS-Terra were used to calibrate the seasonal parameterization, through a stochastic method, over the period of study (2013–2016). The results demonstrate that the seasonal parameterization reduces the error in the simulated SCA and increases the correlation with the MODIS SCA. The two-set version of the model improved the yearly RMSE from 5.9% to 7.7% depending on the basin, compared to the one-set version. The correlation between the model and MODIS passes from 0.73 to 0.79 in winter for the larger basin, Phakding. This study shows that the use of a remote sensing product can improve the parameterization of the seasonal dynamics of snow processes in a model based on a DDF method.

Snow Cover Evolution in the Gran Paradiso National Park, Italian Alps, Using the Earth Observation Data Cube

Mountainous regions are particularly vulnerable to climate change, and the impacts are already extensive and observable, the implications of which go far beyond mountain boundaries and the environmental sectors. Monitoring and understanding climate and environmental changes in mountain regions is, therefore, needed. One of the key variables to study is snow cover, since it represents an essential driver of many ecological, hydrological and socioeconomic processes in mountains. As remotely sensed data can contribute to filling the gap of sparse in-situ stations in high-altitude environments, a methodology for snow cover detection through time series analyses using Landsat satellite observations stored in an Open Data Cube is described in this paper, and applied to a case study on the Gran Paradiso National Park, in the western Italian Alps. In particular, this study presents a proof of concept of the preliminary version of the snow observation from space algorithm applied to Landsat data stored in the Swiss Data Cube. Implemented in an Earth Observation Data Cube environment, the algorithm can process a large amount of remote sensing data ready for analysis and can compile all Landsat series since 1984 into one single multi-sensor dataset. Temporal filtering methodology and multi-sensors analysis allows one to considerably reduce the uncertainty in the estimation of snow cover area using high-resolution sensors. The study highlights that, despite this methodology, the lack of available cloud-free images still represents a big issue for snow cover mapping from satellite data. Though accurate mapping of snow extent below cloud cover with optical sensors still represents a challenge, spatial and temporal filtering techniques and radar imagery for future time series analyses will likely allow one to reduce the current cloud cover issue.

Detecting Snowfall Events over Mountainous Areas Using Optical Imagery

Snowfall over mountainous areas not only has important implications on the water cycle and the Earth’s radiation balance, but also causes potentially hazardous weather. However, snowfall detection remains one of the most difficult problems in modern hydrometeorology. We present a method for detecting snowfall events from optical satellite data for seasonal snow in mountainous areas. The proposed methodology is based on identifying expanded snow cover or suddenly declined snow grain size using time series images, from which it is possible to detect the location and time of snowfall events. The methodology was tested with Moderate Resolution Imaging Spectroradiometer (MODIS) daily radiance data for an entire hydrologic year from July 2014 to June 2015 in the mountainous area of the Manas River Basin, Northwest China. The study evaluated the recordings of precipitation events at eighteen meteorological stations in the study area prove the effectiveness of the proposed method, showing that there was more liquid precipitation in the second and third quarter, and more solid precipitation in the first and fourth quarter.

Land surface temperature as an indicator of the unsaturated zone thickness: A remote sensing approach in the Atacama Desert

Land surface temperature (LST) seems to be related to the temperature of shallow aquifers and the unsaturated zone thickness (∆Z_uz). That relationship is valid when the study area fulfils certain characteristics: a) there should be no downward moisture fluxes in an unsaturated zone, b) the soil composition in terms of both, the different horizon materials and their corresponding thermal and hydraulic properties, must be as homogeneous and isotropic as possible, c) flat and regular topography, and d) steady state groundwater temperature with a spatially homogeneous temperature distribution. A night time Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image and temperature field measurements are used to test the validity of the relationship between LST and ∆Z_uz at the Pampa del Tamarugal, which is located in the Atacama Desert (Chile) and meets the above required conditions. The results indicate that there is a relation between the land surface temperature and the unsaturated zone thickness in the study area. Moreover, the field measurements of soil temperature indicate that shallow aquifers dampen both the daily and the seasonal amplitude of the temperature oscillation generated by the local climate conditions. Despite empirically observing the relationship between the LST and ∆Z_uz in the study zone, such a relationship cannot be applied to directly estimate ∆Z_uz using temperatures from nighttime thermal satellite images. To this end, it is necessary to consider the soil thermal properties, the soil surface roughness and the unseen water and moisture fluxes (e.g., capillarity and evaporation) that typically occur in the subsurface.

Assessing the impacts of climatic change on mountain water resources

As the evidence for human induced climate change becomes clearer, so too does the realization that its effects will have impacts on numerous environmental and socio-economic systems. Mountains are recognized as very sensitive physical environments with populations whose histories and current social positions often strain their capacity to accommodate intense and rapid changes to their resource base. It is thus essential to assess the impacts of a changing climate, focusing on the quantity of water originating in mountain regions, particularly where snow and ice melt represent a large streamflow component as well as a local resource in terms of freshwater supply, hydropower generation, or irrigation. Increasing evidence of glacier retreat, permafrost degradation and reduced mountain snowpack has been observed in many regions, thereby suggesting that climate change may seriously affect streamflow regimes. These changes could in turn threaten the availability of water resources for many environmental and economic systems, and exacerbate a range of natural hazards that would compound these impacts. As a consequence, socio-economic structures of downstream living populations would be also impacted, calling for better preparedness and strategies to avoid conflicts of interest between water-dependent economic actors. This paper is thus an introduction to the Special Issue of this journal dedicated to the European Union Seventh Framework Program (EU-FP7) project ACQWA (Assessing Climate Impacts on the Quantity and Quality of WAter), a major European network of scientists that was coordinated by the University of Geneva from 2008 to 2014. The goal of ACQWA has been to address a number of these issues and propose a range of solutions for adaptation to change and to help improve water governance in regions where quantity, seasonality, and perhaps quality of water may substantially change in coming decades.