Evaluation and Intercomparison of Multiple Snow Water Equivalent Products over the Tibetan Plateau

2019 ◽  
Vol 20 (10) ◽  
pp. 2043-2055 ◽  
Author(s):  
Qingyun Bian ◽  
Zhongfeng Xu ◽  
Long Zhao ◽  
Yong-Fei Zhang ◽  
Hui Zheng ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Snow Water Equivalent ◽  
Climate Model ◽  
Asian Summer Monsoon ◽  
Accurate Estimation ◽  
The Tibetan Plateau ◽  
Seasonal Forecasts ◽  
Snow Condition ◽  
Snow Water

Abstract Snow cover affects the thermal conditions of the Tibetan Plateau through snow–albedo feedback and snowmelt, which, in turn, modulates the Asian summer monsoon climate. An accurate estimation of the snow condition on the Tibetan Plateau is therefore of great importance in both seasonal forecasts and climate studies. Estimation of snow water equivalent (SWE) over the Tibetan Plateau is challenging due to the high altitude, complex terrain, and insufficient in situ observations. Multiple SWE products derived from satellite estimates, reanalyses, regional climate model simulations, and land data assimilations are intercompared in terms of daily, seasonal, and annual variations and are then evaluated against in situ SWE observations. The results show a relatively consistent seasonal to interannual variability of the SWE estimates among the products. The discrepancies in magnitude are large, however, especially in winter and spring. Evaluation against in situ SWE observations indicates that none of these products is capable of accurately characterizing both the spatial pattern and temporal variations.

Towards a long term global snow climate data record from satellite data generated within the Snow Climate Change Initiative

2020 ◽  
Author(s):  
Gabriele Schwaizer ◽  
Lars Keuris ◽  
Thomas Nagler ◽  
Chris Derksen ◽  
Kari Luojus ◽  
...  
Keyword(s):  
Time Series ◽  
Satellite Data ◽  
Snow Water Equivalent ◽  
Climate Model ◽  
Process Models ◽  
Climate Data ◽  
Seasonal Snow ◽  
Snow Water

<p>Seasonal snow is an important component of the global climate system. It is highly variable in space and time and sensitive to short term synoptic scale processes and long term climate-induced changes of temperature and precipitation. Current snow products derived from various satellite data applying different algorithms show significant discrepancies in extent and snow mass, a potential source for biases in climate monitoring and modelling. The recently launched ESA CCI+ Programme addresses seasonal snow as one of 9 Essential Climate Variables to be derived from satellite data.</p><p>In the snow_cci project, scheduled for 2018 to 2021 in its first phase, reliable fully validated processing lines are developed and implemented. These tools are used to generate homogeneous multi-sensor time series for the main parameters of global snow cover focusing on snow extent and snow water equivalent. Using GCOS guidelines, the requirements for these parameters are assessed and consolidated using the outcome of workshops and questionnaires addressing users dealing with different climate applications. Snow extent product generation applies algorithms accounting for fractional snow extent and cloud screening in order to generate consistent daily products for snow on the surface (viewable snow) and snow on the surface corrected for forest masking (snow on ground) with global coverage. Input data are medium resolution optical satellite images (AVHRR-2/3, AATSR, MODIS, VIIRS, SLSTR/OLCI) from 1981 to present. An iterative development cycle is applied including homogenisation of the snow extent products from different sensors by minimizing the bias. Independent validation of the snow products is performed for different seasons and climate zones around the globe from 1985 onwards, using as reference high resolution snow maps from Landsat and Sentinel- 2as well as in-situ snow data following standardized validation protocols.</p><p>Global time series of daily snow water equivalent (SWE) products are generated from passive microwave data from SMMR, SSM/I, and AMSR from 1978 onwards, combined with in-situ snow depth measurements. Long-term stability and quality of the product is assessed using independent snow survey data and by intercomparison with the snow information from global land process models.</p><p>The usability of the snow_cci products is ensured through the Climate Research Group, which performs case studies related to long term trends of seasonal snow, performs evaluations of CMIP-6 and other snow-focused climate model experiments, and applies the data for simulation of Arctic hydrological regimes.</p><p>In this presentation, we summarize the requirements and product specifications for the snow extent and SWE products, with a focus on climate applications. We present an overview of the algorithms and systems for generation of the time series. The 40 years (from 1980 onwards) time series of daily fractional snow extent products from AVHRR with 5 km pixel spacing, and the 20-year time series from MODIS (1 km pixel spacing) as well as the coarse resolution (25 km pixel spacing) of daily SWE products from 1978 onwards will be presented along with first results of the multi-sensor consistency checks and validation activities.</p>

scholarly journals Projected Changes in Snow Water Equivalent over the Tibetan Plateau under Global Warming of 1.5° and 2°C

2020 ◽  
Vol 33 (12) ◽  
pp. 5141-5154
Author(s):  
Qinglong You ◽  
Fangying Wu ◽  
Hongguo Wang ◽  
Zhihong Jiang ◽  
Nick Pepin ◽  
...  
Keyword(s):  
Global Warming ◽  
Tibetan Plateau ◽  
Snow Water Equivalent ◽  
Coupled Model ◽  
The Tibetan Plateau ◽  
Model Simulations ◽  
The Future ◽  
Snow Water ◽  
Positive Correlations ◽  
Projected Changes

AbstractSnow water equivalent (SWE) is a critical parameter for characterizing snowpack, which has a direct influence on the hydrological cycle, especially over high terrain. In this study, SWE from 18 coupled model simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5) is validated against the Canadian Sea Ice and Snow Evolution Network (CanSISE) SWE. The model simulations under RCP8.5 and RCP4.5 are employed to investigate projected changes in spring/winter SWE over the Tibetan Plateau (TP) under global warming of 1.5° and 2°C. Most CMIP5 models overestimate the CanSISE SWE. A decrease in mean spring/winter SWE for both RCPs over most regions of the TP is predicted in the future, with most significant reductions over the western TP, consistent with pronounced warming in that region. This is supported by strong positive correlations between SWE and mean temperature in the future in both seasons. Compared with the preindustrial period, spring/winter SWE over the TP under global warming of 1.5° and 2°C will reduce significantly, at faster rates than over China as a whole and the Northern Hemisphere. SWE changes over the TP do not show a simple elevation dependency under global warming of 1.5° and 2°C, with maximum changes in the elevation band of 4000–4500 m. Moreover, there are also strong positive correlations between projected SWE and historical mean SWE, indicating that the initial conditions of SWE are an important parameter of future SWE under specific global warming scenarios.

Improved Simulation of the East Asian Summer Monsoon Rainfall with Satellite-Derived Snow Water Equivalent Data*

2009 ◽  
Vol 137 (6) ◽  
pp. 1790-1804 ◽  
Author(s):  
Kazuyoshi Souma ◽  
Yuqing Wang
Keyword(s):  
Tibetan Plateau ◽  
Summer Monsoon ◽  
Snow Water Equivalent ◽  
Asian Summer Monsoon ◽  
Lower Troposphere ◽  
Seasonal Prediction ◽  
Summer Monsoon Rainfall ◽  
Early Summer ◽  
Shortwave Radiation ◽  
The Tibetan Plateau

Abstract The effect of Eurasian spring snow amount on the summer monsoon rainfall over East Asia has been studied both observationally and numerically. The results indicate that the Eurasian spring snow amount could be important for seasonal prediction of East Asian summer monsoon (EASM) rainfall. Therefore, accurately initializing snow could be critical to improving seasonal prediction of EASM rainfall by numerical models. An attempt has been made in this study to initialize snow in a regional climate model using snow water equivalent (SWE) data derived from a microwave imager. Results from an ensemble seasonal prediction experiment for the 2005 EASM show that the satellite-derived SWE data can be effectively used to initialize a dynamical seasonal prediction model, which leads to improved seasonal prediction of EASM rainfall. Possible effects of snow anomalies over the Tibetan Plateau on EASM rainfall were also studied through a comparative ensemble simulation in which snow was initialized by spinning up the same model from the previous winter. It is found that the anomalous snow amount over the Tibetan Plateau could lead to cooling of the surface and lower troposphere not only over the Tibetan Plateau but also in the surrounding areas because of the reduced net surface shortwave radiation associated with the high snow albedo. This would result in positive anomalies in geopotential height and weaken the cyclonic monsoon circulation in the lower troposphere in East Asia, causing a rainfall increase in South China but a reduction in the Yangtze River Valley in early summer (May–June). The difference in rainfall in midsummer (July–August) was not significant when compared with that in early summer. The surface heat budget indicates that the reduced net surface shortwave radiation is largely balanced by the reduced surface sensible heat flux.

What is the impact of earth observation and in-situ data assimilation on seasonal hydrological forecast quality?

2021 ◽  
Author(s):  
Jude Lubega Musuuza ◽  
Louise Crochemore ◽  
Ilias G. Pechlivanidis
Keyword(s):  
Snow Water Equivalent ◽  
Seasonal Forecasts ◽  
Meteorological Forcing ◽  
In Situ Data ◽  
Snow Water ◽  
Reservoir Inflow ◽  
The Impact ◽  
Forecast Quality

<p>Earth Observations (EO) have become popular in hydrology because they provide information in locations where direct measurements are either unavailable or prohibitively expensive to make. Recent scientific advances have enabled the assimilation of EOs into hydrological models to improve the estimation of initial states and fluxes which can further lead to improved  forecasting of different variables. When assimilated, the data exert additional controls on the quality of the forecasts; it is hence important to apportion the effects according to model forcings and the assimilated datasets. Here,  we investigate the hydrological response and seasonal predictions over the snowmelt driven Umeälven catchment in northern Sweden. The HYPE hydrological model is driven  by two meteorological forcings: (i) a downscaled GCM meteorological product based on the bias-adjusted ECMWF SEAS5 seasonal forecasts, and (ii) historical meteorological data based on the Extended Streamflow Prediction (ESP) technique. Six datasets are assimilated consisting of four EO products (fractional snow cover, snow water equivalent, and the actual and potential evapotranspiration) and two in-situ measurements (discharge and reservoir inflow). We finally assess the impacts of the meteorological forcing data and the assimilated EO and in-situ data on the quality of streamflow and reservoir inflow seasonal forecasting skill for the period 2001-2015. The results show that all assimilations generally improve the skill but the improvement varies depending on the season and assimilated variable. The lead times until when the data assimilations influence the forecast quality are also different for different datasets and seasons; as an example, the impact from assimilating snow water equivalent persists for more than 20 weeks during the spring. We finally show that the assimilated datasets exert more control on the forecasting skill than the meteorological forcing data, highlighting the importance of initial hydrological conditions for this snow-dominated river system.</p>

scholarly journals Assessment of MERRA-2 and ERA5 to Model the Snow Water Equivalent in the High Atlas (1981–2019)

Water ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 890
Author(s):  
Mohamed Wassim Baba ◽  
Abdelghani Boudhar ◽  
Simon Gascoin ◽  
Lahoucine Hanich ◽  
Ahmed Marchane ◽  
...  
Keyword(s):  
River Discharge ◽  
Snow Water Equivalent ◽  
High Elevation ◽  
Water Runoff ◽  
High Atlas ◽  
Seasonal Snow ◽  
Snow Covered Area ◽  
Covered Area ◽  
Snow Water

Melt water runoff from seasonal snow in the High Atlas range is an essential water resource in Morocco. However, there are only few meteorological stations in the high elevation areas and therefore it is challenging to estimate the distribution of snow water equivalent (SWE) based only on in situ measurements. In this work we assessed the performance of ERA5 and MERRA-2 climate reanalysis to compute the spatial distribution of SWE in the High Atlas. We forced a distributed snowpack evolution model (SnowModel) with downscaled ERA5 and MERRA-2 data at 200 m spatial resolution. The model was run over the period 1981 to 2019 (37 water years). Model outputs were assessed using observations of river discharge, snow height and MODIS snow-covered area. The results show a good performance for both MERRA-2 and ERA5 in terms of reproducing the snowpack state for the majority of water years, with a lower bias using ERA5 forcing.

Exploring the use of gpr and satellite-based snow data for snowmelt runoff predictions

2021 ◽  
Author(s):  
Ilaria Clemenzi ◽  
David Gustafsson ◽  
Jie Zhang ◽  
Björn Norell ◽  
Wolf Marchand ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Data Assimilation ◽  
Snow Water Equivalent ◽  
Distribution Model ◽  
Accurate Estimation ◽  
Snowmelt Runoff ◽  
Snow Water ◽  
Snow Distribution ◽  
The Impact

<p>Snow in the mountains is the result of the interplay between meteorological conditions, e.g., precipitation, wind and solar radiation, and landscape features, e.g., vegetation and topography. For this reason, it is highly variable in time and space. It represents an important water storage for several sectors of the society including tourism, ecology and hydropower. The estimation of the amount of snow stored in winter and available in the form of snowmelt runoff can be strategic for their sustainability. In the hydropower sector, for example, the occurrence of higher snow and snowmelt runoff volumes at the end of the spring and in the early summer compared to the estimated one can substantially impact reservoir regulation with energy and economical losses. An accurate estimation of the snow volumes and their spatial and temporal distribution is thus essential for spring flood runoff prediction. Despite the increasing effort in the development of new acquisition techniques, the availability of extensive and representative snow and density measurements for snow water equivalent estimations is still limited. Hydrological models in combination with data assimilation of ground or remote sensing observations is a way to overcome these limitations. However, the impact of using different types of snow observations on snowmelt runoff predictions is, little understood. In this study we investigated the potential of assimilating in situ and remote sensing snow observations to improve snow water equivalent estimates and snowmelt runoff predictions. We modelled the seasonal snow water equivalent distribution in the Lake Överuman catchment, Northern Sweden, which is used for hydropower production. Simulations were performed using the semi-distributed hydrological model HYPE for the snow seasons 2017-2020. For this purpose, a snowfall distribution model based on wind-shelter factors was included to represent snow spatial distribution within model units. The units consist of 2.5x2.5 km<sup>2</sup> grid cells, which were further divided into hydrological response units based on elevation, vegetation and aspect. The impact on the estimation of the total catchment mean snow water equivalent and snowmelt runoff volume were evaluated using for data assimilation, gpr-based snow water equivalent data acquired along survey lines in the catchment in the early spring of the four years, snow water equivalent data obtained by a machine learning algorithm and satellite-based fractional snow cover data. Results show that the wind-shelter based snow distribution model was able to represent a similar spatial distribution as the gpr survey lines, when assessed on the catchment level. Deviations in the model performance within and between specific gpr survey lines indicate issues with the spatial distribution of input precipitation, and/or need to include explicit representation of snow drift between model units. The explicit snow distribution model also improved runoff simulations, and the ability of the model to improve forecast through data assimilation.</p>

scholarly journals Direct radiative effects of dust aerosols emitted from the Tibetan Plateau on the East Asian summer monsoon – a regional climate model simulation

2017 ◽  
Author(s):  
Hui Sun ◽  
Xiaodong Liu ◽  
Zaitao Pan
Keyword(s):  
Tibetan Plateau ◽  
Regional Climate Model ◽  
East Asian Summer Monsoon ◽  
Asian Monsoon ◽  
Climate Model ◽  
Asian Summer Monsoon ◽  
Regional Climate ◽  
East Asian ◽  
Dust Aerosols ◽  
Asian Summer

Abstract. While dust aerosols emitted from major Asian sources such as Taklimakan and Gobi Deserts have been shown to have strong effect on Asian monsoon and climate, the role of dust emitted from Tibetan Plateau (TP) itself, where aerosols can directly interact with the TP heat pump because of their physical proximity both in location and elevation, has not been examined. This study uses the dust coupled RegCM4.1 regional climate model to simulate the spatiotemporal distribution of dust aerosols originating in the TP and their radiative effects on the East Asian summer monsoon (EASM) during both heavy and light dust years. Two 20-year simulations with and without the dust emission from TP showed that direct radiative cooling in the mid-troposphere induced by the TP locally produced dust aerosols resulted in an overall anticyclonic circulation anomaly in the low-troposphere centered over the TP region. The northeasterly anomaly in the EASM region reduces its strength considerably. The simulations found a significant negative correlation between the TP column dust load produced by local emissions and the corresponding anomaly in the EASM index (R=−0.41). The locally generated TP dust can cause surface cooling far downstream in eastern Mongolia and northeastern China through stationery Rossby wave propagation. Although contribution to the total Asian dust source from within TP (mainly Qaidam Basin) is relatively small, its impacts on Asian monsoon and climate seems disproportionately large, likely owning to its higher elevation within TP itself.

scholarly journals Spatiotemporal patterns of High Mountain Asia's snowmelt season identified with an automated snowmelt detection algorithm, 1987–2016

2017 ◽  
Vol 11 (5) ◽  
pp. 2329-2343 ◽  
Author(s):  
Taylor Smith ◽  
Bodo Bookhagen ◽  
Aljoscha Rheinwalt
Keyword(s):  
Time Series ◽  
Tibetan Plateau ◽  
Climate Model ◽  
Single Point ◽  
Passive Microwave ◽  
Spatiotemporal Patterns ◽  
Signal Separation ◽  
High Mountain ◽  
The Tibetan Plateau ◽  
Microwave Data

Abstract. High Mountain Asia (HMA) – encompassing the Tibetan Plateau and surrounding mountain ranges – is the primary water source for much of Asia, serving more than a billion downstream users. Many catchments receive the majority of their yearly water budget in the form of snow, which is poorly monitored by sparse in situ weather networks. Both the timing and volume of snowmelt play critical roles in downstream water provision, as many applications – such as agriculture, drinking-water generation, and hydropower – rely on consistent and predictable snowmelt runoff. Here, we examine passive microwave data across HMA with five sensors (SSMI, SSMIS, AMSR-E, AMSR2, and GPM) from 1987 to 2016 to track the timing of the snowmelt season – defined here as the time between maximum passive microwave signal separation and snow clearance. We validated our method against climate model surface temperatures, optical remote-sensing snow-cover data, and a manual control dataset (n = 2100, 3 variables at 25 locations over 28 years); our algorithm is generally accurate within 3–5 days. Using the algorithm-generated snowmelt dates, we examine the spatiotemporal patterns of the snowmelt season across HMA. The climatically short (29-year) time series, along with complex interannual snowfall variations, makes determining trends in snowmelt dates at a single point difficult. We instead identify trends in snowmelt timing by using hierarchical clustering of the passive microwave data to determine trends in self-similar regions. We make the following four key observations. (1) The end of the snowmelt season is trending almost universally earlier in HMA (negative trends). Changes in the end of the snowmelt season are generally between 2 and 8 days decade−1 over the 29-year study period (5–25 days total). The length of the snowmelt season is thus shrinking in many, though not all, regions of HMA. Some areas exhibit later peak signal separation (positive trends), but with generally smaller magnitudes than trends in snowmelt end. (2) Areas with long snowmelt periods, such as the Tibetan Plateau, show the strongest compression of the snowmelt season (negative trends). These trends are apparent regardless of the time period over which the regression is performed. (3) While trends averaged over 3 decades indicate generally earlier snowmelt seasons, data from the last 14 years (2002–2016) exhibit positive trends in many regions, such as parts of the Pamir and Kunlun Shan. Due to the short nature of the time series, it is not clear whether this change is a reversal of a long-term trend or simply interannual variability. (4) Some regions with stable or growing glaciers – such as the Karakoram and Kunlun Shan – see slightly later snowmelt seasons and longer snowmelt periods. It is likely that changes in the snowmelt regime of HMA account for some of the observed heterogeneity in glacier response to climate change. While the decadal increases in regional temperature have in general led to earlier and shortened melt seasons, changes in HMA's cryosphere have been spatially and temporally heterogeneous.

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