Conference Agenda

Sea ice plays an important role in global climate change, shipping, navigation and the extraction of natural resources, and influences the detection of other ocean phenomena; for example, sea wave retrieval requires the removal of sea ice ‘pollution’. The Surface Wave Investigation and Monitoring instrument (SWIM) on the China-France Oceanography Satellite (CFOSAT) is a new type of sensor with a small incidence angle detection mode that is different from traditional remote sensors. Sea ice monitoring at small incidence angles has rarely been studied. Therefore, this research focuses on sea ice monitoring in the Arctic based on SWIM data from October 2019 to April 2021. Sea ice type is the key parameter of Arctic Sea ice monitoring. Six waveform features are extracted from the SWIM echo data at small incidence angles, then sea ice classification based on multi-feature combinations is carried out using the chosen KNN classifier. Thus, the optimal classifier-feature assembly at each incidence angle is developed, and the highest overall accuracy is up to 81% at 2°. Sea ice recognition is useful for extracting sea ice density, sea ice extent, sea ice edge and other parameters, and is also applied for the detection of other ocean phenomena. Therefore, based on the above work, a method to distinguish between sea ice and sea water is established. Eleven features are first extracted, and applied for the analysis of the waveform characteristics using the cumulative probability distribution and mutual information measurement. The optimal classifier is the KNN method with Euclidean distance and k equal to 11. Feature combinations are also used to separate sea ice and sea water based on the KNN method to select the optimal combination. Thus, the optimal classifier-feature assembly at each small incidence angle is established, and the highest overall accuracy reaches 97.1%. Moreover, the application of the optimal classifier–feature assemblies is studied. The overall accuracies of sea ice recognition using the optimal classier–feature assemblies in three stages of sea ice development are higher than 90 percent, and the highest reaches 99.9%. Sea ice extents and edges can also be extracted using this method. The consistency of sea ice extents with NSIDC is higher than 94% (the highest is 98.2%), and the accuracies of daily sea ice edge products are higher than NSIDC. Our work not only confirms the ability of sea ice classification and recognition based on the new SWIM data with high accuracies, but also studies the application of SWIM data in sea ice services. SWIM data can be used as a new data source for operational sea ice monitoring.

Icebergs impose a risk on ship navigation and offshore structures. Images from satellite Synthetic Aperture Radar (SAR) which can be acquired independent of light conditions and cloud coverage, are widely used for monitoring icebergs. Automated detection of icebergs in SAR images is normally carried out by utilizing constant false alarm rate (CFAR) filters, which compare the intensity of individual pixels or cluster of pixels against the intensities of the adjacent pixels (i.e. the background) and adaptively set a threshold based on several assumptions regarding the statistical distribution of the background clutter.

Five different CFAR filters were tested for iceberg detection in open water. The algorithms were applied to both a C- and an L-band SAR image acquired over the Labrador Sea in July 2019. The SAR images were dual-polarized (HH and HV) as used at the operational ice and iceberg charting centers and were resampled to the same pixel spacing. The performance of the CFAR filters was assessed by comparing the automated detections to 230 icebergs manually identified in a coinciding optical Sentinel-2 image. The F-score was used for quantifying the success rate as a trade-off between false and missed detections.

Comparing the frequency bands, the L-band data reveal a slightly worse overall performance than C-band. At C-band, the highest F-score was obtained for a CFAR detector based on the gamma distribution, whereas for L-band data, a CFAR detection based on the log-normal distribution resulted in the highest F-score. This indicates that for a given sea state, the sea clutter distributions differ between C- and L-band. The magnitude of the F-score shows high variability, dependent on the PFA and type of algorithm. This is demonstrated by our poster, considering also the runtime of the single algorithms. Potential problems of iceberg detections in SAR images acquired at C- and L-band will be mentioned.

Snow albedo is a significant factor in the land surface energy balance and the water cycle. It is usually parameterized as functions of snow-related variables in land surface models (LSMs). However, the default snow albedo scheme in the widely used Noah LSM shows evident drawbacks in land-atmosphere interactions simulations during snow processes on the complex topographic Tibetan Plateau (TP). We firstly demonstrate that the improved Noah snow albedo scheme performs well in relation to near-surface meteorological elements estimates after including MODIS albedo products and explicit considering snow depth (SD) as an additional factor. Then, we comprehensively evaluate the performance of the improved snow albedo scheme implemented in the coupled WRF/Noah in simulating additional eight snow events on the TP. The modeling results are compared with WRF run with the default Noah scheme and in-situ observations. The improved snow albedo scheme significantly outperforms the default Noah scheme in relation to air temperature, albedo and sensible heat flux (SH) estimates, by alleviating cold bias estimates, albedo overestimates and SH underestimates, respectively. This in turn contributes to more accurate reproductions of snow event evolution. The averaged RMSE relative reductions (and relative increase in correlation coefficients) for air temperature, albedo, SH and SD reach 27% (5%), 32% (69%), 13% (17%) and 21% (108%) respectively. These results demonstrate the strong potential of our improved snow albedo parameterization scheme for snow event simulations on the TP. Our study provides a theoretical reference for researchers committed to further improving the snow albedo parameterization scheme.

Glacier albedo determines the net shortwave radiation and therefore affect glacier energy and mass balance. Glaciers in the Western Nyainqentanglha Mountains (WNM) are one of the most important fresh water resources for the people around Lhasa, however, the relationship between albedo and mass balance is still unclear due to absence of high spatial resolution and accurate glacier products. In this study, we firstly optimized the procedure to retrieve glacier mass balance for Chinese high resolution stereo images (ZY-3/TLA) uniquely provided by the Dragon Program, and then analyzed its change in the WNM during 2000-2017. Secondly, we improved albedo retrieval method for Sentinel 2/MSI, Landsat 8/OLI and MODIS data, and then analyzed long term variability of albedo in the WNM. Finally, explored their relationships according to these two results.

The results showed that: 1) ZY-3 TLA data can generate 5 m spatial resolution DEM and is very promising to extract high accurate mass balance estimates. 2) The glaciers in the WNM experienced accelerated mass loss in 2000-2017, and the thinning rates in the ablation regions were apparently larger than in the accumulation regions. 3) The improved glacier albedo retrievals were in good agreement with field observations and gave a better accuracy in terms of spatial and temporal coverage. 4) Glacier albedo experienced large inter-annual fluctuations and a significant decreasing trend in 2001-2021. 5) Good correlation between albedo and mass balance indicates that decreasing albedo is a key driver of mass loss in this region.

Global surface soil moisture (SSM) products with higher accuracy are needed urgently for agricultural water resource management, environment, and climate analysis applications such as the global climate change monitoring, drought monitoring and vegetation growth monitoring. Temporal and spatial sampling by the space-borne instruments designed to retrieve SSM is limited by the orbit of the satellite and by the operation of the sensor system. This constraints the generation of global, daily SSM data products. To obtain a global SSM product with higher coverage and accuracy this study integrated five SSM products with good performance in global coverage and accuracy, i.e., the SSM retrievals from the data acquired by the Soil Moisture and Ocean Salinity (SMOS), Advanced Scatterometer (ASCAT), FengYun 3-B (FY3-B), ESA-CCI and Soil Moisture Active and Passive mission (SMAP). These five SSM data products were retrieved using different algorithms, but they were combined to produce a (2011~2018) time-series of daily global SSM by applying the TCA and Linear Weight Fusion (LWF). First, we merged the global SMOS, FY3-B, and ASCAT SSM products from 2011 till 2018 using the TCA-based LWF algorithm. Then, the first merged SSM product, ESA-CCI, and SMAP SSM products were 2^nd merged using the same fusion method but for the period 2015~2018. The Global Daily-scale Soil Moisture Fusion Dataset (GDSMFD) with 25km spatial resolution (2011~2018) was produced. Finally, we used five metrics to evaluate and compare the SMOS, FY3-B, ASCAT, ESA-CCI, SMAP, the GDSMFD SSM products against in-situ soil moisture measurements at the sites of ten observation networks, which belong to the International Soil Moisture Network (ISMN). Results indicated that the GDSMFD was consistent with in-situ soil moisture measurements, the minimum of root mean square error values of GDSMFD was only 0.036 cm3/cm3. Moreover, the GDSMFD had a good global coverage with mean Global Coverage Fraction (GCF) of 0.672 and the maximum GCF of 0.837. GDSMFD performed well in accuracy and global coverage fraction, making it valuable in applications to the global climate change monitoring, drought monitoring and hydrological monitoring. GDSMFD product was released at the National Tibetan Plateau Data Center (DOI: 10.11888/Terre.tpdc.271935)

Glacier and snow are sensitive indicators of regional climate variability. In the early 21st century, glaciers in the West Kunlun and Pamir regions showed stable or even slightly positive mass budgets, and this is anomalous in a worldwide context of glacier recession. The analysis was focused on the high mountain ranges surrounding the Tarim Basin, where the spatial distribution of snow cover is quite variable. The study was based on multi-temporal remote sensing data to monitor glacier and snow cover area in the Tarim Basin high mountain area. MODIS data was used to calculate the Normalized Difference Snow Index (NDSI) and a threshold was applied to extract the Tarim Basin glacier area and seasonal snow cover. Between 2000 and 2020, the total area of the Tarim Basin Glacier declined at a rate of 0.94% per year. Because of differences in atmospheric circulation and environmental conditions, changes in the glacier area of the Tarim Basin show differences across five sub-regions. The rate of glacier area loss was fastest (2.98% per year) in the East Tian Shan, while Pamir Mountains and East Kunlun had the slowest rates, i.e. 0.50% per year and 0.81% per year, respectively.

Little is known about how rain, snow- and ice melt vary sub-seasonally and along the altitudinal gradient in high-elevation watersheds. Such basins play a key role in sustaining water supply to mountain communities and downstream ecosystems in High Mountain Asia.

We simulate mountain hydrology using a land surface model that constrains energy and mass fluxes using physical representations of both cryospheric and biospheric processes at 100 m spatial resolution. We study the upper Langtang catchment (~4000-7000 m a.s.l.) in the Nepalese Himalayas, and simulate a detailed water balance for one hydrological year (2018/2019), revealing the relative importance of precipitation, snow, ice, soil moisture and vegetation for different elevations and seasons. We use the model to study how snow and glacier processes affect the hydrological cycle and how vegetation can mediate water yield from the high mountains of a glacierized Himalayan catchment downstream. This bridges the modelling gap between snow- and glacier dynamics, which generate the runoff, and vegetation processes, which interfere with runoff production and water uses at lower elevations.

Our land surface modelling approach provides detailed insights into the importance of each of the energy and mass balance components for the catchment water budget and reveals high altitudinal and subseasonal variability in the hydrologic partition of the high-elevation Langtang valley. Our simulations indicated that the depletion of the cryospheric water budget is dominated by snow melt, but at high elevations primarily dictated by snow and ice sublimation. At the catchment scale we found that water loss through evapotranspiration, dominated by snow sublimation at high elevations and evapotranspiration from vegetation at the lowest altitudinal zone, exceeds the water production from ice melt by > 50%. This shows that vegetation is relevant in determining the amount of runoff transferred further downstream, even for high elevation, extensively glacierized Himalayan catchments.

Glaciers are key components of the Asian water towers and provide water to large downstream communities for domestic, agricultural and industrial uses. In the Nepal Himalaya, the Indian Summer Monsoon dominates climate, and results in a complex meteorology and simultaneous accumulation and ablation that complicate the quantification of snow processes. Assessing solid precipitation input, especially in the upper accumulation area (> 6000 m a.s.l.), remains key to understanding recent mass losses. Catchment-scale glacio-hydrological modelling in the Himalaya has to date mostly relied on temperature-index or intermediate-complexity enhanced temperature-index methods, but recent studies have shown that such approaches can lead to inaccurate amounts of melt, especially at high elevations where refreezing, sublimation and avalanches influence the snow depth variability. The Trakarding–Trambau Glacier system experienced significant mass loss over the last decades, and recent field measurements of meteorology and glacier change present the opportunity to examine these problems with physically-based and spatially-resolved atmospheric and glacio-hydrological modelling.

We combine a novel non-hydrostatic atmospheric model (NHM; atmospheric core of the cryosphere-oriented regional climate model NHM-SMAP) and an advanced land surface model at cloud-permitting hyper-resolution (~ 100 m) to explore the role of snow processes in the water balance of this glacierized catchment. We force the land-surface model of the catchment with dynamically downscaled, hourly outputs from NHM for the 2018-2019 hydrological year.

We evaluate the NHM output using available in-situ meteorological observations and evaluate the land surface model skills and process representation with in-situ mass balance observations, remotely sensed surface elevation change and snow cover. Coupling of the two types of models is unprecedented in the Himalaya, and holds promise to reveal processes that cannot be explicitly assessed by simpler models or forcing data. We investigate the contribution of sublimation and precipitation partition to the glacier mass balance and catchment runoff, and analyze the difference in mass balance and its drivers between the debris-covered and debris free-glaciers. To place this very novel type of simulations into the context of current research, we compare our NHM-forced simulations with simulations forced by station data and ERA5-Land reanalysis. Finally, we evaluate the effect of spatial resolution (50 m, 100 m, 200 m) on model performance and process representation.

Our results highlight the potential of sophisticated models based on the calculations of energy and mass fluxes to unravel the complex processes that shape the response of Himalayan catchments, and provide an assessment of their skills as a function of spatial resolution.

Supraglacial debris thickness is an important control on the surface melt rates of debris-covered glaciers, which are common features of the cryosphere in High-Mountain Asia. Here we present a new dataset of altitudinally-distributed supraglacial debris thickness for the region, generated using remote sensing and numerical modelling techniques. Our modelled debris thickness data are consistent with in-situ data by less than 0.1 m 79% of the time at 14 glaciers, and show similar altitudinal patterns and central values. We show that debris thickness increases as surface velocity decreases, and that debris is thicker on glaciers in a more advanced stage of their debris-cover evolution.

Recent advances in remote sensing approaches have enable large-scale and glacier-specific assessments of glacier volume changes in response to climatic changes. However, these geodetic-differencing thinning patterns integrate the distinct influences of mass balance processes (ie ablation and accumulation) and ice dynamics (advection and flux divergence), rendering them useful for glacier model calibration or validation only at the glacier or larger scales.

In this study, we leverage high-quality digital elevation change and glacier surface velocity datasets along with multi-model ice thickness estimates to isolate the annualized local glacier mass balance for glaciers. Our implementation of the continuity equation builds on recent advances and is applicable to derive altitudinal or distributed mass balance profiles at local and regional scales. We demonstrate the applicability and utility of this approach for several case studies, yielding understanding for glacier health and vital target datasets for improved glacier model calibration and validation.

First, we show that a basic implementation of the continuity equation, applicable with existing regional-scale glacier-change datasets, can yield important insights into glacier health. Specifically, we derive multidecadal effective altitudinal mass balance profiles and leverage these to quantify the equilibrium line altitude (ELA) and accumulation area ratio (AAR) for over 5000 glaciers across High Mountain Asia, and additionally determine the ablation balance ratio, a metric contextualizing the glacier mass balance in terms of the rate of mass turnover.

Second, we apply the continuity equation to derive mass balance gradients for a global subset of glaciers. For this, we leverage new higher-quality large-scale glacier change datasets relevant for the recent five-year period (2016-2020). Over this timeline, firn density profiles and mass balance gradients generally remain stable, making these results suitable for large-scale glacier model calibration.

In a third demonstration, we apply the continuity equation to precise glacier thinning and velocity datasets derived from high resolution photogrammetric products (e.g. derived from Pleiades, Deimos, and UAV datasets) for selected catchments in High Mountain Asia with high-quality ground observations. These short-timescale applications (1-2 years) require more sophisticated flow corrections, but provide annualized specific net mass balance information at high spatial resolution (2m) and are able to represent areas inaccessible by traditional measurements (e.g. avalanche cones). As such, these results provide crucial targets for advanced glacier models of high process complexity.

Taken together these three examples showcase the utility of the continuity equation to bridge the gap between traditional glaciological measurements and new remote sensing datasets. The approach can estimate fully-distributed net annual mass balance at high spatial resolution and over broad domains, independent of traditional melt and mass balance models. As such, our results provide vital new, extensive calibration targets that reduce equifinality risk posed by models calibrated to geodetic measurements alone. These target datasets (e.g. ELAs, AARs, mass balance gradients, local mass balance) are suited for glacier models of varying complexity and process representation.

Soil moisture plays an important role in controlling water and energy exchange between the land and the atmosphere. Characterizing large scale soil moisture is important for many applications, e.g, agricultural and water resources management, drought and flood forecasting. Assimilation (DA) of remotely sensed soil moisture observations into land surface models (LSMs) can improve soil moisture estimation, however, in many studies assimilation of remotely sensed soil moisture improves evapotranspiration prediction hardly. Most LSMs have an over-simplified representation of groundwater dynamics, and the propagation of remote sensing information into neighboring and deeper soil zones depends on the lateral flow and subsurface physical processes represented in the model. In this study, we assimilated soil moisture information into the stand-alone Community Land model (CLM) and the land surface-subsurface model CLM-ParFlow, components of the Terrestrial Systems Modeling Platform (TSMP). The CLM-ParFlow uses Richard’s equation to simulate variably saturated three-dimensional flow in the subsurface and uses a two dimensional kinematic wave approximation for overland flow and river routing. The experiment is conducted for a temperate region (150 km × 150 km) in Western Germany, with a horizontal grid resolution of 500 m, for the period from Mar 2018 to Nov 2018. The SMAP soil moisture data are assimilated with the Ensemble Kalman Filter (EnKF) on a daily basis. We compared the simulated soil moisture content with in situ soil moisture measurements derived from Cosmic Ray Neutron Sensors (CRNS), and simulated ET with observations by Eddy Covariance (EC) stations. It is found that soil moisture characterization improved by DA, but metrics are not better for CLM-ParFlow than CLM stand-alone. Nevertheless, spatial soil moisture patterns by CLM-ParFlow look more realistic than the ones simulated by CLM. DA was able to further improve the characterization of soil moisture contents and improves ET estimation under drought conditions. In addition, with coupled land surface-subsurface models the impact of soil moisture assimilation on simulated groundwater levels and river discharge, which are not represented well by classical land surface models, could also be evaluated.

This work focuses on relatively accurate evaluating the penetration depths of both C-band and X-band radar for glacier areas of the south-eastern Tibet Plateau and further estimate multi-temporal glacier mass balance for the largest lake-terminating glacier in the TP based on the geodetic method. Our results demonstrate that there are either an underestimation of 60% or an overestimation of 202% for the previous C-band penetration corrections. We also found that the rate of mass loss of Yanong Glacier has doubled since 2000, and the interannual mass change has shown a highly volatile and accelerating trend.