To develop a scientific understanding of Earth’s system and its response to natural or human-induced changes, and to improve prediction of climate, weather, and natural hazards.
| Science Objectives | Approach | Data Needed | Data/output generated | Expected Outcomes |
|---|---|---|---|---|
| To quantify historical and future spatial / temporal variability in climate over the HMA | ||||
| To determine the physical processes driving changes in atmospheric, cryospheric and hydrospheric in the HMA region. | ||||
| To partition the components of the water budget of watersheds in the HMA and how that varies in space and time | ||||
| To predict impacts of changing hydrological cyle on human and biogeophysical systems | ||||
| To determine new ways to utilize remote sensing data to calibrate and validate models of the HMA system | ||||
| To discover couplings and feedbacks between physical and human systems in the HMA region |
Validating a Glacier Melt Toolbox For High Mountain Asia Using a Remote-Sensing-Driven Data Integration Framework
| Science Objectives | Approach | Data Needed | Data/output generated | Expected Outcomes |
|---|---|---|---|---|
| To quantify the mass variations of the entire HMA region with a focus on isolating mass changes due to hydrology and cryosphere. | Forward modeling of GRACE Level 1B data together with all available observed/modeled hydrological information. | Best estimates of cryosphere and hydrology variations in the region along with associated uncertainties, up to daily resolution, up to 1 km spatial. | Residuals of mass not currently captured by existing cryosphere/hydrology observations and models @ 1x1 degree mascon spatial and monthly temporal resolution across entire HMA. | Information on deficiencies in existing models /datasets; Formal mechanism for using GRACE to validate model output; iterative procedure can be implemented. |
| To assess HMA glacier mass balance on ~50-year, decadal, annual and seasonal timescales. | Generate high-resolution DEMs from DigitalGlobe imagery and integrate with other available DEMs for elevation change analysis. | <ul><li>DigitalGlobe imagery from Polar Geospatial Center (Morin, Osmanoglu/Montesano/Neigh)</li><li> SNODAS snowcover data for DEM co-registration (Painter) </li><li>Historical DEMs from declassified ~1970s imagery (Rupper/Maurer)</li><li> SRTM, ICESat-1, ICESat-2</li></ul> | <ul><li>2, 8, 32-m DEMs</li><li>0.5-m orthoimages</li><li>DEM time series and regional mosaics</li><li>Volume/mass change estimates</li></ul> | <ul><li>Regional glacier mass balance estimates</li><li>Cal/val for GMELT models</li><li>New capacity for high-resolution geomorphological analyses (lakes, debris cover, landslides, etc)</li></ul> |
| To quantify groundwater contributions to terrestrial water storage change in HMA | Collect and analyze available groundwater observations and conduct baseflow recession analysis from available stream gauges | Observation wells and longterm streamflow records, ideally a few points within major sub-basins | Monthly groundwater storage changes (spatial resolution partially dependent on data availability) | Improved groundwater data to contribute to GRACE forward modeling efforts |
| To assess downstream impacts of changing water availability on agricultural systems | <ul><li>Estimate demand for groundwater and surface water</li><li>develop future demand scenarios from projected water availablity</li><li>crop modeling to assess management strategies</li></ul> | Projections of changes in streamflow, landcover maps, population projects, historical crop yields (if doing crop modeling) | Maps of land use scenarios as a function of changes in water availability | <ul><li>mechanism to translate the impacts of scientific findings to societal applications</li><li>improved understanding of future water availability, and the changing dependence between surface water and groundwater</li></ul> |
Quantifying the Role of Dust on Precipitation, Snow, and Runoff in High Mountain Asia
| Science Objectives | Approach | Data Needed | Data/output generated | Expected Outcomes |
|---|---|---|---|---|
| To identify the sources of aerosols globally | Collect and analyze remote sensing products following Ginoux et al. Rev. Geophys., 2012 | MODIS, SeaWiFS, TOMS, OMI, CALIOP, MODIS LAI, MODIS snow, AMSRE soil moisture, HYDE3.2 land use, GMTED2010 topography | Global 0.1 x 0.1 deg resolution dust source inventory | <ul><li>Provides a common gridded inventory of dust sources at high resolution (cf. Ginoux et al., 2012)</li><li>Will provide natural and anthropogenic attribution based on land use datasets</li><li>Can be used as sources for emission parameterization and validation of dust</li></ul> |
| To identify the frequency and strength of deposition and origins of aerosols | Run a nudged global GCM from 1980-2015 to generate meteorological conditions and separately with tagging techniques to track the origin of aerosols (cf. Li et al., JGR-Atmos, 2010) | NCEP reanalysis, monthly/daily aerosol optical depth (MODIS, SeaWiFS, CALIOP, AERONET) | Global climate model output for HMA region at 50 km resolution for meteorological data and aerosols from 1980-2015 | <ul><li>Statistics of HMA dust deposition events to determine ‘major’ events</li><li>Contribution of each major sources to aerosol deposition over the Himalaya</li><li> Can be used to calibrate and implement a surface albedo change parameterization in the land surface model component of our global modeling work</li></ul> |
| Validate snowcover and snowpack in GCM model | compare GCM output with available snow data sets | MODIS, output from other HiMAT teams, Rutgers snow product | Regional biases in snowpack simulations will be quantified | <ul><li>Pinpoint regions of simulation issues for future improvement</li><li>Allows for calculations of error bars and bias correction for future climate projections</li></ul> |
| To quantify how aerosols interact with the HMA hydroclimate | Run the global climate model with and without aerosol deposition events affecting surface albedo at fixed global radiative forcing | Parameterization from previous objective outcome, global climate model simulations | Global climate model output for HMA region at 50 km resolution for meteorological data and aerosols for a control simulation with and without aerosol deposition (cf. Kapnick et al., Nat. Geo., 2014) | <ul><li>Improves our understanding of how surface albedo changes from aerosols affect hydroclimate</li><li>Improved understanding of the role of dust in altering snowpack and HMA water availability</li></ul> |
| To assess future HMA hydroclimate | Run global climate model with and without deposition events from present to 2100 | Parameterization from previous objective outcome, global climate model simulations | Global climate model output for HMA region at 50 km resolution for meteorological data and aerosols for a climate change simulation with and without aerosol deposition | <ul><li>Improves our understanding of how surface albedo changes from aerosols will affect hydroclimate and regional climate sensitivity in the future</li><li>Quantify the role of dust in future HMA water availability</li></ul> |
Interlinked glacier dynamics, lakes, mountain hazards, and critical vulnerabilities in the Himalaya
| HiMAT program objectives and requirements | Science and applications questions or goals | Approach/solution, task name | Data and modeling needs |
|---|---|---|---|
| Investigate glaciers, snow, precipitation: Changes, Water resources, Induced impacts, Human vulnerabilities, Biogeophysical systems | <ul><li>How are Himalayan glacier lakes and glaciers changing on seasonal, annual, and 30-year time scales? Lake area, depth, and volume; Glacier length, area, mass balance</li><li> Potential GLOF triggers: How have glacial lakes been affected by mass movements (landslides and avalanches), and by moraine downwasting or thermokarst/pond development?</li><li>How have satellite-era snow/ice melt floods compared to rainfall-caused floods and outbursts from glacial lakes and glacier-associated landslide dammed lakes? What are the different flood types’ geographic reaches and magnitudes? </li></ul> | <ul><li>EMPIRICAL STUDIES: TASK 1a. Satellite time series of glacial lakes: Time series of lake areas, turbidity proxy, seasonally resolved, satellite era. Published lake depths and volumes.</li><li>Task 1b. Satellite time series of moraine dams, lateral moraines, mountain slopes: Use high repeat frequency images to assess landslides and avalanches. Selected images: Semi-auto snow, ice, debris, water mapping of avalanches, rockfalls, supraglacial ponds. </li><li>Task 1c. Satelite times series of glaciers/snow. Annual, decadal trends in seasonal snowcover, glacier extent, avalanches, glacier lakes, glacier flow & length, downstream river wetted area. Selected glacier thickness/volume change. </li></ul> | <ul><li>Landsat, ASTER, ALI, WV imaging, 100 images per lake. Lake area, albedo/color time series. </li><li>Landsat, ALI, ASTER time series. WorldView for detailed look and interpretation. WV repeat DEMs of selected moraine dams of interest. </li><li>Landsat, ALI, ASTER, WV: cryosphere fluctuations. Populate NSIDC GLIMS glacier database. ASTER GDEM, SRTM topo. Precipitation & snowmelt floods; GLOF models.</li></ul> |
Multi-sensor impact analysis of cascading hazards and transportation corridors in Nepal
| Science Objectives | Approach | Data Needed | Data/output generated | Expected Outcomes |
|---|---|---|---|---|
| To map landslides along key road corridors (main highways and new road construction) | Fusion of remote sensing data to catalog recent landslides, merge with existing records of landslide activity | Optical imagery, SAR data, and high-resolution digital elevation models | Landslide inventories | Better understanding of where, when, and how many landslides are occurring |
| To model landslide susceptibility | Determine terrain’s susceptibility to landslide initiation with locally calibrated empirical models, then generate runout simulations | DEM, Land cover, Soils, Geology, landslide volume (or range of volumes) to delineate runout | Susceptibility map of each study area | Better infrastructure planning, due to ranking of road segments by probability of disruption |
| To incorporate landslide triggering events into a real-time dynamic model | Establish triggering relationships for hydrometeorological and seismic variables | Locally calibrated datasets for rain (including forecasts), snow, snowmelt, and seismicity | Nowcast that shows the current landslide hazard | Broad situational awareness of landslide hazard in real-time for multiple stakeholders |
| To characterize risk and vulnerability related to landslides and identify potential economic impacts in the transportation network due to the disruptions from landslides | Combine spatial data on hazard and exposure with economic analyses to assess road networks at risk to cascading landslide hazards | Population, Road networks (OSM), economic data (if available) from the World Bank | Indicators of Exposure, Vulnerability, and Risk | Better understanding of the geography of risk along road networks within Nepal |
Quantifying hydrologic and cryospheric changes and associated mechanisms over High Mountain Asia using remote sensing, data assimilation and meteorological modeling
| Science Objectives | Approach | Data Needed | Data/output generated | Expected Outcomes |
|---|---|---|---|---|
| <ul><li>Quantify estimates of multi-decadal changes in land surface snow and glaciers</li><li>Understand the impact of these changes on elevation dependent warming and associated land-atmosphere feedbacks</li></ul> | <ul><li>Conduct a comprehensive land reanalysis using the NASA LIS data assimilation environment using advanced machine learning tools at 1km spatial resolution</li><li>Conduct coupled regional mesoscale simulations using the NASA NU-WRF environment</li></ul> | Passive microwave, optical, IR and thermal observations (SMMR, SSMI, AMSR-E, AMSR2, MODIS, VIIRS, Landsat), Meteorological inputs (precipitation, radiation), Land surface parameters (vegetation, topography, soils, albedo, LAI), terrestrial water storage (GRACE) | Snow Water Equivalent, snow depth, snow melt, snow cover, snow grain size, soil moisture, terrestrial water storage, latent, sensible fluxes, evaporation, surface and subsurface runoff, canopy evaporation, surface temperature, vegetation temperature, soil temperature, Reference datasets of snow/ice changes, runoff, altimetry, temperature, snow cover | <ul><li>Quantification of changes in water availability over river basins originating from HMA</li><li>Regional water budget component estimates and changes in their variability</li><li>Quantify the impact of elevation dependent warming and snow-albedo feedbacks over HMA</li><li>Development of techniques for the optimal exploitation of information content of remote sensing measurements</li></ul> |
Understanding and forecasting changes in High Mountain Asia snow hydrology via a novel Bayesian reanalysis and modeling approach
| Science Objectives | Approach | Data Needed | Data/output generated | Expected Outcomes |
|---|---|---|---|---|
| To assess the representavity of the MERRA-2 reanalysis over HMA snow-dominated basins | Analyze the relationship between large-scale MERRA-2 estimates and local in-situ meteorological data | MERRA-2 reanalysis dataset, in-situ meteorological data | Uncertainty functions for the MERRA-2 dataset over the HMA region | Identifica,on of the error structure of MERRA-2 over the HMA region |
| To identify the modes of spatiotemporal variability in SWE and snow accumulation and melt processes over the HMA region | Develop a snow reanalysis dataset over the HMA region by combining model estimates and fSCA observations within a data assimilation framework | Landsat fSCA time series, static inputs (DEM, slope, etc.), dynamic inputs (MERRA-2 forcing data) | Daily SWE and fSCA time series between 1984-2016 for the HMA domain, other snow states from reanalysis as well possible | Identification of the physiographic and climatic drivers behind snow accumulation and melt over the region, distributed climatology of snow accumulation and melt consistent with remotely sensed depletion record |
| To assess how snow accumulation and melt processes have changed during the last ~30 years | Evaluate the reanalysis results using trend testing, identify areas of changing accumulation and melt patterns | Snow reanalysis outputs | Statistical trend test results and spatiotemporal paVerns of change over the HMA region | Identification of regions with declining snow reserves, changing accumulation or melt patterns |
| To quantify how SWE accumulation modulates streamflow over the HMA region | Relate the SWE outputs from the reanalysis to streamflow measurements throughout the region | Snow reanalysis outputs and observed streamflow volumes | Mathematical relationships between SWE metrics and streamflow metrics | Quantification of the relationship between snow and streamflow throughout the HMA region and identification of regions where snow plays a significant role instreamflow generation |
| To improve process parameterization of RCMs over the HMA region | Diagnosis of RCM simulations using SWE reanalysis results | RCM simulations, snow reanalysis outputs | RCM simulations | Improvement of RCM process parameterization |
| To identify the underlying atmospheric drivers of variability in SWE accumulation and melt patterns over the region | Use RCM outputs and MERRA-2 atmospheric fields to diagnose drivers behind accumulation and melt patterns over the region | RCM simulations, MERRA-2 atmospheric variables, snow reanalysis outputs | Relationship between snowpack states and atmospheric variables | Identification of main atmospheric drivers behind snow accumulation and melt over the region, relationship between snow and large-scale climatic indices |
Understanding High Mountain Asia: Assessing climate-induced river flow change and associated economic output.
| Science Objectives | Approach | Data Needed | Data/output generated | Expected Outcomes |
|---|---|---|---|---|
| Development of a framework for evaluating impacts of climate change on snow and glaciers and resultant impact on river flow regimes | <ul><li>Generate and project atmospheric variables appropriate for analysis in rugged mountain regions using a high-resolution regional climate model</li><li> Calibrate seasonal and annual river flows using remote sensing and hydrologic modeling </li><li>forecast annual flow for mid- and end of century using estimated data on snow and glacier melt</li></ul> | <ul><li>Radar backscatter at weekly frequency (high resolution) and daily frequency (coarse resolution)</li><li>Surface measurements of glacier state from weather stations (temperature, radiation)</li><li> Downscaled WRF simulation output and projection</li><li> River flow measurements</li></ul> | <ul><li>Surface freeze/thaw status derived from microwave remote sensing: 1) Snow melt state over glaciated and non-glaciated areas; 2) Land surface freeze/thaw state over non-glaciated areas</li><li> Snow and glacial hydrology output from Utah Energy Balance model</li><li>Projected changes in runoff from snow and glaciers under future climate change scenarios</li></ul> | Assessment of the impacts of climate forcing on melting of snow and glaciers and associated river flow using a combination of remote sensing observations and field data |
| Evaluate impacts of changes in river flow on hydroelectric power generation and downstream biodiversity (fish) | <ul><li>A hydropower process model will be used to estimate hydropower generation under future flow regimes</li><li> Use the Index of River Functionality (IRF) to evaluate suitability of flow conditions for meeting fisheries management goals</li></ul> | <ul><li>Streamflow projections determined from from various climate change scenarios</li><li> Capacity of hydropower facilities within selected HMA basins</li><li> Historic fishery and wetland conditions data</li></ul> | <ul><li> Potential for hydropower generation and associated revenue</li><li> Suitability scores for fisheries resources</li><li> Annual estimates of fishery biomass or harvest based upon suitability of conditions</li></ul> | <ul><li> Hydropower valuation </li><li> Impact of flow variability on downstream biodiversity (fisheries) and other ecosystem indicators (e.g., wetland conditions)</li></ul> |
| Valuation of the impact of climate change on the economic value of hydropower and downstream ecosystem services related to fisheries | <ul><li>Calibrate and project the impact of flow variability on electricity generation using Process modeling</li><li> Economic value will be estimated using an integrated assessment framework</li></ul> | <ul><li> Historic electricity market data and energy production levels </li><li> Historic economic value estimates for fisheries</li></ul> | The yearly and integrated economic value of hydropower, fisheries, agriculture and ecosystem services under future flow regimes. | <ul><li> Estimate the economic value of river system under projected river flow scenario </li><li> Estimate the change in economic value of downstream fisheries under projected river flow scenario </li><li> Estimate the change in net present value of the benefits from hydropower generation under projected changes in river flow scenarios</li></ul> |
| Science Objectives | Approach | Data Needed | Data/output generated | Expected Outcomes |
|---|---|---|---|---|
| To derive data products based on satellite remote sensing observations to provide optimal constraints for the modeling framework | use (a) high-resolution spaceborne imagery to derive DSMs for glacier volume change; (b) radar imagery in combination with existing snow extent maps to obtain snow cover extent and in combination with a DSM snow line altitudes, and (c) optical imagery to map glacier debris cover extent | Worldview-1, -2, -3, Landsat, Envisat, Sentinel-1 | Digital Elevation Models Snow Cover Extent Maps Snow Line Altitudes Glacier Debris Cover Maps | Independent glacier volume change estimates by DEM differencing, high resolution snow cover/snow line altitude maps. Annual glacier debris cover maps. |
| To elucidate the regional scale monsoon driven climate dynamics with focus on precipitation patterns across the HMA region | applying a regional climate model with unprecedented spatial resolution application of a high resolution, convection permitting, regional climate model to provide precipitation and other meteorological data | TRMM 3B42 MODIS AIRS CMIP5 | high resolution precipitation simulations and other atmospheric parameters | |
| To assess recent glacier changes and forecast future glacier evolution | develop a new model framework that will model the mass evolution of every glacier in the HMA based on air temperature and precipitation, and simple approaches to account for glacier geometry changes | sowline altitude, Debris cover, Grace, In-situ data (ICIMOD), Glacier area and width (RGI) Ice Thickness | Glacier mass-change over the simulation years | framework that, as a whole, will exceed any of the existing large-scale glacier models in functionality and flexibility , and directly feed into GMELT |
| To quantify the hydrological response to climate and glacier changes and forecast how those changes impact human water availability downstream of HMA. | coordinate WBM with the climate, glacier and snow extent models to explore the complete hydrological system of water in HMA and the human impacts | Precipitation Temperature Snow dynamics Land cover types, including crop land and irrigated crop land Dam and reservoir locations | estimates of time varying water availability across the region | assess the vulnerability of humans in the downstream regions in response to changes in HMA |
| Science Objectives | Approach | Data Needed | Data/output generated | Expected Outcomes |
|---|---|---|---|---|
| OBJ1: Quantify and document seasonal variation of fractional snow covered area, snow grain size, and radiative forcing by light-absorbing particles in the HMA area. | Analyze optical remote sensing retrievals across HMA. Retrievals will come from SCAG and DRFS algorithms (Painter et al., 2009; Painter et al., 2013). | MODIS (Terra and Aqua) MOD09GA surface reflectance. NPP and JPSS-1 VIIRS surface reflectance. Landsat 8 OLI surface reflectance. | Time series of fractional snow covered area, dust/BC radiative forcing, snow grain size, albedo for entire HMA region at 500, 750, and 1500 m resolution. Basin scale and stratified distillations of products. Trend analyses. Products for validation and constraint of mesoscale climate modeling. | Improved understanding of trends in physical retrievals of critical snow properties (extent and controls on albedo and melt rates). |
| OBJ2: Validate physically-based mesoscale modeling with remote sensing and determine optimal calibration to minimize errors | Assess mesoscale climate model results with remotely sensed fSCA, radiative forcing by dust/BC, grain size, and albedo, and in situ impurity concentrations and albedo measurements. | WRF-Chem CLM SNICAR model runs over HMA for 2000-present. MODSCAG, MODDRFS, VIIRSCAG, VIIRSDRFS, OLISCAG, OLIDRFS outputs at native resolution and coarsened resolution to match WCCS modeling grid. | Uncertainty and error analysis of WRF-Chem CLM SNICAR modeling of snowmelt and energy balance across HMA. Calibration metrics to minimize errors while constrained by remotely sensed retrievals of snow properties. | With the physically based modeling constrained by physically based remote sensing and in situ measurements, we have climate modeling that orients toward explicit energy balance treatment and capacity to address Objective 3. |
| OBJ3: Understand relative contributions of variation in energy balance components to variation in snow and ice melt across the HMA in present day | Use the remote sensing- and in situ measurement-constrained mesoscale climate/RT modeling to understand how changes in GHG and dust/BC are impacting present day changes in snow and ice melt. | WRF-Chem CLM SNICAR model runs over HMA for 2000-present, having been constrained and re-calibrated for error minimization. | Paritiioning of anomalies in snow warming and snowmelt rates into changs in GHG warming and dust/BC radiative forcing impacts on direct heating/melting and grain size coarsening. | The physically-based modeling, with constraint from remotely sensed retrievals and in situ measurements, will allow us to understand present day contributions of changes to the Earth system. |
| OBJ4: Understand the future changes to snowmelt across HMA under scenarios of increased GHGs and changes in BC and dust emissions. | With the RS and in situ constrained and optimized mesoscale modeling,we can explore the likely changes in snowmelt, runoff, and glacier loss under expected scenarios of changes in GHG concentrations and dust/BC deposition. | WRF-Chem CLM SNICAR model runs over HMA for 2000-present, having been constrained and re-calibrated for error minimization. Projected ranges of GHG change scenarios and dust/BC change scenarios. | Snowmelt, glacier melt, and runoff results under independent and combined scenarios of GHG and dust/BC changes in ranges of monthly, seasonal, and annual resolutions. | These results will allow us to more quantitatively project expected changes to the HMA cryosphere and runoff regime under climate and particulate changes. In turn, downstream hydrology, ecosystem, and economic processes can be better understood. |
Precipitation and Glacier Change in High Mountain Asia Over the Modern Era
| Science Objectives | Approach | Data Needed | Data/output generated | Expected Outcomes |
|---|---|---|---|---|
| To quantify the temporal and spatial variability in precipitation over HMA, and impacts of this variability on glacier mass balance over the modern era (1980-present) | Model precipitation using WRF and Bayesian statistical models; model glacier surface energy and mass balance; generate DEMs from historical satellite imagery; validate climate and glacier model output | Precipitation products (station, reanalysis, remote sensing), glacier areas over time, gridded climate data, glacier debris thickness and aerial extent, glacier/snow albedo | High-res gridded weather and climate products over modern era, glacier mass balance change (modeled and observed) over modern era, glacier energy balance terms, glacier meltwater flux | Improved understanding of precipitation variability/trends and processes, robust estimates of glacier sensitivity to climate variability and change, contribution of glaciers to water resources and sea level rise |
Radiation, Aerosol Joint Observation-Modeling Exploration over Glaciers in Himalayan Asia
| Science Objectives | Approach | Data Needed | Data/output generated | Expected Outcomes |
|---|---|---|---|---|
| To provide better understanding of aerosol/snow/ice properties over the HMA region using satellite products | Performing satellite retrievals by employing the state-of-the-art enhanced-Deep Blue algorithm | Satellite L1B reflectance from MODIS/VIIRS (as input) and ground-based measurements of aerosol and snow impurity (below, as validation) | MODIS/VIIRS RGB imagery, retrievals of AOD, Angstrom Exponent, snow effective grain size, impurity equivalent (dust, soot), and surface radiative forcing at 1 km resolution over HMA | Large-scale information to help constrain the model simulations of changes in HMA region due to the presence of light-absorbing aerosols |
| To conduct field deployment in spring 2018 for providing initialization and validation data in retrieval and modeling studies | Deploying, in collaboration with regional groups, a network of sunphotometer, spectrometer, radiometer at selected sites | Laboratory analysis of snow impurity and grain size (critically needed) | Retrieved aerosol/ cirrus properties, surface bidirectional reflectance and irradiance fields over a network of selected sites | Critical ground-truth data over a network of aerosol inflow, transport and deposition sites for validation/ initialization studies |
| To assess the roles of aerosol, snow impurity and radiation interactions in regional climate and to provide predictions for guiding field experiment, as well as the post-mission mini-reanalysis | Conducting simulations of GEOS-5, with and without aerosol radiative and snow impurity effects, and conducting a post-mission reanalysis including field observations | Satellite and ground-based measurements of AOD, snow impurity and albedo, and irradiance fields as the model constraints and validations | Simulated and assimilated AOD, atmospheric state parameters, snow impurity (dust, EC/OC), surface albedo, solar and terrestrial heat fluxes, snowmelt flux and precipitation | Knowledge of the impact of snow impurity and aerosol radiative effects on snowmelt, land surface temperature and wetness, and subsequent development of Indian summer monsoon. |