Allowing near-surface geophysics students to learn ground penetrating radar (GPR) data processing through hands-on exercises poses a challenge because of the high cost of professional GPR software. Free processing and visualization packages are scarce and often either require other commercial software such as Matab, which many students may not have on their own computers, or they are limited to specific operating systems. To allow students to process GPR data on their own computers, for example data collected as part of a class-based research project or online available data, the processing software needs to be (1) free, (2) platform independent, (3) easy to install and use, and should record the steps the students take to obtain their results in order to facilitate reproducibility and grading. GPRPy (https://github.com/NSGeophysics/GPRPy) is an open-source ground penetrating radar data processing and visualization software that has a graphical user interface and which is also scriptable. It is python-based, making it platform independent and independent of commercial programming environments. The graphical user interface allows for writing out humanly readable python scripts that can be run from the command line to automatize the data processing and visualization. GPRPy is hosted on the collaborative software development website GitHub, allowing educators and researchers to create and adapt their own versions to their needs.

Ash-producing volcanic eruptions are a major natural hazard and the magnitude of the explosivity of these eruptions have been indicated for decades by a semi-quantitative “Volcanic Explosivity Index (VEI)” based on a number of interdependent factors, (eruption duration, volume of ejected material, eruptive column height, tropospheric and stratospheric injection, and qualitative descriptors). We present a simple empirical formulation for a fully quantitative VEI (𝑉𝐸𝐼Hc), based solely on eruption column height. This method directly translates the height of the eruption column into VEI, a useful formulation for immediate understanding of the explosivity of eruptions as they are taking place. This new scale is calibrated for convenience and familiarity to span the same range of values as the existing, semi-quantitative, VEI scale. In addition to being a simple and quantitative approach, this new method, translating column height directly into VEI, has the advantage of being calculated in real time during an eruption. This will improve immediate response by aviation, hazard management, and others to mitigate societal impact of hazardous eruptions.

Coastal areas are highly vulnerable to flooding, due to hydrological extreme events such heavy rainfalls and/or storm surges which are supposed to be increasing in the next future due to the emission in atmosphere of anthropogenic greenhouse gases. In this study, in order to assess the future hydraulic risk in coastal regions, as well as, to identify optimal defense/adaptation policies, a risk analysis model is developed to calculate the present day and future flood risk, accounting for climate change uncertainties and mitigation measures. Such model juxtaposes a number of coupled/nested models as: a) a stacking daily rainfall downscaling model which combines simulations from multiple predictive models, as Random Forest, extreme gradient boosting and Non-homogeneous Hidden Markov Model (NHMM) (Cioffi et al. 2018); b) a Bivariate Point Process model (BPPM) (Zheng et al., 2014) that calculates Joint probability density function between extreme daily rainfall amount and daily extreme storm tide depth; c) a simulation-optimization model - in which multi-objective GA optimization model (Deb et al., 2002) and 2D hydraulic model are combined (Cioffi et al. 2018) - calculates sets of Pareto optimal solutions which are obtained by defining two optimality criteria consisting in: minimizing both the cost of the flood defense infrastructure system and the flooding hydraulic risk. ; d) a mathematical decision model which is aimed to identify the best policies of mitigation of hydraulic risk and the timing, taking into account the uncertainties in hydrological extreme event predictions. The risk analysis model is applied to the study case of Mazzocchio area which is the most depressed area (about 10000 ha) within the Pontinia Plain, a large reclamation region in the south of Lazio (Italy), particularly vulnerable to extreme events - as extreme rainfall amount and sea level rise due to storm surge at the sea outfall of the river- which in the past caused the crisis of hydraulic network system with flooding of large areas and collapse of levees. XXI Century projections of daily rainfall amount and sea level for the RCP 8.5-IPCC scenarios were performed using ensemble of 35 GCM simulations (CESM1 CAM5 BGC 20C + RCP8.5 Large Ensemble) (Kay et al., 2015).

Red mangroves, Rhizophora mangle, in coastal areas have proven to be resilient for many decades. Mangroves accumulate mud, peat and plant matter through aerial roots (Pneumatophores) consist of rigid and flexible roots oscillating in the water. The oscillation of the flexible root not only decreases erosion but also provide a source of renewable energy. These oscillations can essentially be harvested as electrical power from tidal currents of the shallow coastal water in the form of pollution free energy by converting the kinetic energy of the moving fluid into voltage. We present an elastically mounted rigid patch of circular cylinders as simplified flexible mangrove roots to understand the role of flexibility on the vortex-induced vibration phenomena for energy harvesting. We performed kinematics and PIV measurements inside a water channel for the cylinders limited to a transverse oscillation inside a water tunnel at constant velocities . We investigate the effect of Reynolds number, mass ratio, damping ratio, and natural frequency of the system on the range of the achievable voltage. Higher flexibility resulted in increasing the frequency of oscillations with a reduction in the reduced velocity. This bio-inspired design has potentials for future electricity generations, as tidal currents are more predictable than wind energy and solar power as well as environmentally friendly due to minimal alterations to ecosystems and marine life.

Excess sediment is one of the main agents of water pollution, affects the aquatic ecosystem and causes siltation problems in reservoirs and rivers. Bottom sediments are coarse and easily deposited, being the major responsible for morphological alterations in rivers. Therefore, is important to estimate the amount of sediment transported in other to establish mitigation strategies and recovery of affected areas. There are several methodologies for estimating bedload transport: equations, numerical modeling, direct physical sampling, and indirect method measuring dune tracking. Most studies measure bedload transport using the equipment Helley-Smith, generally taken as reference value for other methods. However, Helley-Smith causes changes in the flow field, often resulting in overestimates in sand bed rivers. Those overestimates were not yet described and quantified in detail. This article tries to solve this gap by observing the Helley-Smith sampler at the time of sampling. For this, we used a video camera recording processed by Particle Image Velocimetry (PIV) technique to qualitatively evaluate changes that the intrusive sampler causes in the displacement of bottom sediments in a sand bed river. When the sampler was properly allocated to the bottom, PIV resulting velocity maps of bedload particle transport identified the acceleration of the bottom particles as they approached the mouth of the sampler. We also noticed areas of convergence and vorticity of the sediments towards the sampler’s mouth, which may overestimate sampling. When the sampler’s mouth was disconnected from the bed due to the morphology of the bottom, we find regions of divergence in the displacement of the sediments creating unrealistic results for bedload transport analysis. In addition, the presence of organic matter can be underestimated, which causes difficulties in analyzing the quality of the collections because it is not possible to guarantee an equity in the positioning of the equipment at the bottom of the channel. We thus recommend complementation Helley-Smith measurements with surrogate methods, for example such as using ADCPs or optical methods, as the one described here.

We present preliminary results from an electrokinetic laboratory rock characterization approach, which uses electroosmosis and streaming potential to estimate permeability. The device has two modes. In its first mode it measures differential pressure across a sample while applying electric current or acoustic pressure, while in its second mode it measures voltage across the sample when applying electric current or acoustic pressure. The two modes result in estimates of the electrokinetic coupling coefficients, which can jointly be used to estimate the permeability of the sample. Our collaboration includes analytical and numerical approaches simulating aspects of the experiments being carried out in the laboratory at Sandia. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Mongolia has a complex tectonic history. The lithosphere was formed from multiple plate collisions in the Neoproterozoic - Early Paleozoic associated with the Central Asian Orogenic Belt. The region has since been modified by Mesozoic rifting, Cenozoic magmatism, and major strike-slip faulting along terrane boundaries and sutures. Central and Western Mongolia are part of the larger high elevation, low-relief Mongolian Plateau. To gain deeper understanding of modern deformation within the Hangay Dome in Central Mongolia, two years of teleseismic, regional, and local seismicity, recorded by a dense array of 72 temporary broadband seismic stations, was used to determine the distribution of seismicity and crustal structure. Results from receiver function analysis indicate the Hangay Dome has a crustal thickness ranging from 41-59 km. The thickest crust resides under areas of high topography and generally thins to the east. Average Vp/Vs ratios range from 1.77-1.8. We located the 7680 events detected by the array using a local 1D velocity model. Many events outline the Bulnay and Bogd faults, where historic Mw 8 earthquakes have occurred. Considerable seismicity is observed on the South Hangay – Bayan Hongor Fault System, including a Mw 4.6 earthquake. Seismicity is also observed along the Egiin Davaa and Mogod Faults. Preliminary results from a joint tomographic inversion for earthquake location and 3D velocity structure show a relatively uniform crust, where P-wave velocities in the uppermost crust range from 5.8-6 km/s. In these preliminary inversions, large portions of the region show Vp exceeds 7.0 km/s in the lower 10-15 km of the crust. The depth to the Moho is consistent with results from the receiver function analysis. Lateral velocity variations generally align with terrane boundaries and faults, such as the South Hangay - Bayan Hongor Fault System. Seismicity relocated in the inversion outline the South Hangay, Egiin Davaa, and Bulnay Faults. In addition, a cluster of seismicity locates between the Egiin Davaa and Hag Nuur faults, where no fault has previously been mapped. Seismicity in the Hangay Dome is generally confined to the upper 20 km, suggesting a rheological transition from brittle to ductile at this depth.

Larsen A and B ice shelves were affected by surface melt which preconditioned them for rapid disintegration due to hydrofracture and densification. Recently, warm and dry foehn winds have been discovered to melt the vulnerable Larsen C Ice Shelf (LCIS) surface via sensible heat transfer during polar night. The climatological extent and intensity of polar night surface melt and their effects on the ice surface energy budget are unknown. Here we quantify the spatial pattern and temporal variability of foehn winds and associated melt events during polar night to understand the ice shelf surface mass balance and indirect implications for ice shelf vulnerability. Our Foehn Detection Algorithm (FonDA) uses events identified from in situ Automated Weather Stations (AWS) to calibrate foehn detection from reanalysis data covering all of Antarctica and Greenland. We present a climatology of foehn-driven surface melt days, melt water equivalent, fraction of melt that occurs during polar night, and the surface energy budget. Preliminary results show that foehns perturb sensible heat fluxes by up to 300 Wm-2 and surface air temperatures by up to 13 °C in the absence of shortwave radiation.

The vertical distribution of plant roots in the soil profile is a key trait modulating plant contributions to soil carbon storage, drought and nutrient stress resistance, yield, and fitness. However, direct sampling of deep roots requires massive effort, so existing data are sparse and many researchers have adopted modeling approaches to fill data gaps and generate hypotheses about how soil properties change the biogeochemical, agricultural, ecological, and hydrologic consequences of root depth. Such models are useful only if they correctly represent the processes of interest and give accurate predictions of the root systems they simulate. Most current root growth models represent soil as a uniform and unrestrictive medium. This is often a reasonable simplification when modeling roots grown in pots or artificial media, but is less so for field soils which often increase in density, hardness, and heterogeneity with depth. To better predict the effect of soil hardness on root distribution, we updated the structural-functional root growth model OpenSimRoot to explicitly predict soil hardness from soil bulk density, water content, porosity, and depth. Root growth impedance is curently represented by linear scaling of the root elongation rate according to soil hardness. Future work will incorporate configurable growth responses and allow hardness to control changes in root diameter and growth direction, thus allowing the model to examine the fitness implications of carbon reallocation in complex structured soils. Our updated OpenSimRoot captured >50% of observed variation in penetrometer resistance from field soils. When we incorporated soil hardness into simulations of maize growth, we observed a substantial reduction in the predicted root:shoot ratio that overwhelmed previous model predictions of increased water uptake from steeper root angles. These findings reinforce that models considering costs and benefits of deep rooting should routinely consider soil hardess.

Pressure ridges are deformation features within the sea ice pack created through the collision of sea ice floes. Pressure ridges play an important role in ice drift and influence the mass and energy budgets of the Arctic Ocean. Over the past decade annual airborne surveys over Arctic sea ice have been conducted in late winter (March and April) by NASA’s Operation IceBridge (OIB) mission. A total of 74 OIB flights between 2010 and 2018 surveyed tens of thousands of kilometers of sea ice, providing observations of pressure ridges at a higher spatial and temporal resolution than previous airborne studies. Here we utilize Digital Mapping System (DMS) imagery to identify shadows cast by pressure ridge sails and, then, use these shadows to derive sail height. Over 64,000 DMS images were analyzed, allowing for more than 33 million individual sail height measurements to be calculated. We present the full sail-height distributions of new pressure ridges recently formed across a range of ice conditions on first-year (FYI) and multiyear ice (MYI), and we assess year-to-year variability. We find distinct characteristics depending on the ice type in which the pressure ridge formed. The mean and standard deviation of sail heights on FYI is ~20-30 cm lower than those formed on MYI. Maximum sail heights on FYI are ~1.5 m lower on average. Arctic sea ice is getting younger, shifting from predominantly MYI to predominantly FYI. Our results may inform new model parameterizations of pressure ridges on sea ice in the changing Arctic, thereby supporting advances in sea ice forecasting.

The Lower Mekong is facing an increasing impact of droughts and at the regional level, the Mekong River Commission (MRC) is mandated to work with government agencies on creating and distributing flood, drought, water resource governance and use to improve policy and practice. The MRC is striving to provide regional, locally calibrated and downscaled information on drought forecasts and real-time monitoring through a portal. The Regional Drought and Crop Yield Information System (RDCYIS) is built on regionally and locally calibrated Regional Hydrologic Extreme Assessment System (RHEAS) framework that integrates the Variable Infiltration Capacity (VIC) and Decision Support System for Agro-technology Transfer (DSSAT) models, allowing both nowcast and forecast of drought. This model is co-developed by NASA Jet Propulsion Laboratory (JPL) and the SERVIR-Mekong teams. In this work, we outline how the MRC Drought Team’s requirements were met via RHEAS. Driven with earth observation data, the main aim of this service is to improve present regional and national drought monitoring and forecasting services to Lower Mekong countries for their water allocation and drought mitigation information needs. We provide an overview of the model calibration and validation methodology, and we find reasonable reliability of the soil moisture model results with the satellite based observations from the SMAP and SMOS retrievals. Through this support to MRC in integrating new drought assessment, monitoring and warning methodologies using RHEAS, more data and analyses will be available to support them to develop improved advice on drought early warning to the National Mekong Committees across the Mekong countries. MRC’s assistance is envisaged to enable comprehensive, accurate and useful warnings for the decision-makers at local and provincial level to take effective action. Ultimately this service is expected to assist farmers to make preemptive decisions about their water use, cropping and planting patterns and market decisions which should reduce crop loss and support livelihoods from farming, including from appropriate compensation to farmers from the governments, wherever this is in effect.

Fire emissions are a major contributor to atmospheric composition, affecting atmospheric oxidizing capacity and air quality. Transported amounts from Northern Hemisphere boreal fires can reach the pristine Arctic atmosphere as well as impact air quality in populated regions. Carbon monoxide (CO) is a useful trace gas emitted from fires that can be used to link extreme fire events with climate variability. We use our recently developed statistical tool to investigate the climate drivers of satellite measured CO variability in two Northern Hemisphere boreal fire regions: northwest Canada and Siberia. Our focus is on quantifying the ability of climate mode indices for the Pacific, Atlantic, Indian and Arctic Oceans in predicting CO amounts in these regions. Climate mode indices El Niño Southern Oscillation (ENSO), Tropical North Atlantic (TNA), the Dipole Mode Index (DMI) and the Arctic Oscillation (AO) are used to develop statistical models of column CO interannual variability from the Measurements of Pollution In The Troposphere (MOPITT) satellite instrument, for the time period covering 2001-2017. In addition, we assess the ability of fire emission inventories to reproduce CO, including the Fire Inventory from NCAR (FINN), the NASA Quick Fire Emissions Dataset (QFED) and the Copernicus Atmosphere Monitoring Service (CAMS) Global Fire Assimilation System (GFAS). These are implemented in the NCAR Community Atmosphere Model with chemistry (CAM-chem) and subsequently evaluated against MOPITT CO observations. Emission uncertainty contribution to inter-inventory differences are quantified, and the modeled contribution of fires to CO interannual variability is determined.

End Users of Climate data have nowadays to struggle with accessing the data they need for their research because of the rapid increase in data volumes. The whole climate data archive is expected to reach a volume of 30 Pb in 2018 and up to 2000 Pb in 2022 (estimated). On-demand data processing solutions as close as possible to the data storage are emerging, thanks to newly developed standards, provenance and infrastructures. In Europe several initiatives are taking place to support scientific on-demand data analytics at the European scale. They offer the huge potential of interoperability, as for example the DARE e-science platform (http://project-dare.eu), designed for efficient and traceable development of complex experiments and domain-specific services on the Cloud. Also, the IS-ENES (https://is.enes.org) consortium has developed a platform to ease access to climate data for the climate impact community (C4I: https://climate4impact.eu). The platform is based on existing standards (ISO and OGC), such as WPS (Web Processing Service). DARE will integrate services from the EUDAT CDI, enabling generic access and cross-domain interoperability, as well as providing compliance and integration with the future EOSC platform. The DARE platform will use containerization technologies, so that it can be easily deployed on heterogeneous architectures. A scientific pilot has been designed within the DARE project for the ENES community (climate domain). The objectives are to enable delegation of on-demand computational-intensive calculations to the DARE platform, from the IS-ENES C4I interface, seamlessly. The DARE architecture and the solutions being implemented will be presented, along with the generic and agile approach taken to implement the pilot.

The alluvial wetlands are one of the most important ecosystems of the world and are in abundance in the vast Indo-Gangetic plains. The wetlands of this region are of variable sizes and characteristics but currently face similar problems of drying-out and fragmentation. It is empirical to understand the evolutionary pathways and hydrological connectivity of these wetlands for planning and execution of management and restoration for them. These pathways have been studied for a wetland namely, the Kaabar Tal, situated in the Kosi-Gandak interfan region of the eastern Gangetic plains. Its geomorphic evolutionary pathways have been established using satellite imageries, DEMs, toposheets, and high resolution aerial imagery obtained using unmanned aerial vehicle (UAV). Various geomorphic units characterized by an assemblage of geomorphic features have been mapped for the Kaabar Tal and its surroundings. Seasonal, annual, and decadal variability in the hydrological status of this wetland were estimated for a time-period of 1976-2017 using the historical Landsat datasets. Seasonal variability in hydrological connectivity structure of the wetland with its catchment for the time-period of 1989 to 2017 was estimated in a GIS framework. The structural connectivity was estimated using the technique of diffusion kernel interpolation. The dynamic connectivity was estimated using the Getis-Ord Gi* statistic and Mann-Kendall trend test using the concepts of space-time cubes. The detailed geomorphic mapping revealed that this wetland primarily originated through fluvial processes. A historical reconstruction of its hydrological status revealed that in the recent times the wetland is getting fragmented, and the connectivity potential of different areas of the catchment is a function of the prevalent land-use and land-cover (LULC) pattern and seasonality. Therefore, the heterogeneity and complexity of the geomorphic units of the wetland and the historical LULC patterns of the catchment should be considered in designing any management and restoration plan.

Venus is the most Earth-like of the terrestrial planets, though very little is known about its surface composition. Thanks to recent advances in laboratory spectroscopy and spectral analysis techniques, this is about to change. Although the atmosphere prohibits observations of the surface with traditional imaging techniques over much of the EM spectral range, five transparent windows between ~0.86 µm and ~1.18 µm occur in the atmosphere’s CO2 spectrum. New high temperature laboratory spectra from the Planetary Spectroscopy Laboratory at DLR show that spectra in these windows are highly diagnostic for surface mineralogy [1]. The Venus Emissivity Mapper (VEM) [2] builds on these recent advances VEM is the first flight instrument specially designed to focus solely on mapping Venus’ surface using the windows around 1 µm. Operating in situ from Venus orbit, VEM will provide a global map of composition as well as redox state of the surface, enabling a comprehensive picture of surface-atmosphere interaction on Venus. VEM will return a complex data set containing surface, atmospheric, cloud, and scattering information. Total planned data volume for a typical mission scenario exceeds 1TB. Classical analysis techniques have been successfully used for VIRTIS on Venus Express [3-5] and could be employed with the VEM data. However, application of machine learning approaches to this rich dataset is vastly more efficient, as has already been confirmed with laboratory data. Binary classifiers [6] demonstrate that at current best estimate errors, basalt spectra are confidently discriminated from basaltic andesites, andesites, and rhyolite/granite. Applying the approach of self-organizing maps to the increasingly large set of laboratory measurements allows searching for additional mineralogical indicators, especially including their temperature dependence. [1] Dyar M. D. et al. 2017 LPS XLVIII, #1512. [2] Helbert, J. et al. 2016. San Diego, CA, SPIE. [3] Smrekar, S.E., et al. Science, 2010 328(5978), 605-8. [4] Helbert, J., et al., GRL, 2008 35(11). [5] Mueller, N., et al., JGR, 2008 113. [6] Dyar M. D. et al. 2017 LPS XLVIII, #3014.

The Western US accounts for a significant amount of the forested biomass and carbon uptake within the conterminous United States. Warming and drying climate trends combined with a legacy of fire suppression have left Western forests particularly vulnerable to disturbance from insects, fire and drought mortality. These challenging conditions may significantly weaken this region’s ability to uptake carbon from the atmosphere and warrant continued monitoring. Traditional methods of carbon monitoring are limited by the complex terrain of the Rocky Mountains that lead to complex atmospheric flows coupled with heterogeneous climate and soil conditions. Recently, solar induced fluorescence (SIF) has been found to be a strong indicator of GPP, and with the increased availability of remotely-sensed SIF, provides an opportunity to estimate GPP and ecosystem function across the Western US. Although the SIF-GPP empirical linkage is strong, the mechanistic understanding between SIF and GPP is lacking, and ultimately depends upon changes in leaf chemistry that convert absorbed radiation into photochemistry, heat (via non-photochemical quenching (NPQ)), leaf damage or SIF. Understanding of the mechanistic detail is necessary to fully leverage observed SIF to constrain model estimates of GPP and improve representation of ecosystem processes. Here, we include an improved fluorescence model within CLM 4.5 to simulate seasonal changes in SIF at a sub-alpine forest in Colorado. We find that when the model includes a representation of sustained NPQ the simulated fluorescence is much closer to the seasonal pattern of SIF observed from the GOME-2 satellite platform and a custom tower mounted spectrometer system. We also find that average air temperature may be used as a predictor of sustained NPQ when observations are not available. This relationship to air temperature is promising because it may allow for efficient spatial upscaling of SIF simulations, given widespread availability of temperature data, but not NPQ observations. Further improvements to the fluorescence model should focus upon distinguishing between the impacts of NPQ versus the de-activation of photosystems brought on by high-stress environmental conditions.

Over 270 major dams have been constructed in the Southeastern United States (SEUS) during the past century, changing natural flow patterns and affecting stream temperatures. Projected increases in air temperature combined with changes in precipitation may result in water scarcity and affect maximum stream temperatures during the summer for some regions in the SEUS. Currently existing reservoirs mitigate water shortages during drought by releasing more water but reducing residence time, the ratio of reservoir volume to inflow. Regulating stream temperature in the summer can be done by either increasing residence time or releasing more water. In this study, we investigate the extent to which the current reservoir infrastructure can be used to mitigate the impacts of climate change under current reservoir regulations as well as the range of operating rules that could minimize climate change impacts on both streamflow and river temperature. We use the Variable Infiltration Capacity (VIC) hydrological model to simulate runoff, which is then used as input to a large-scale river routing-reservoir model (MOSART-WM) to simulate reservoir operations and produce regulated streamflow. VIC and MOSART-WM outputs are then used as input to a stream temperature model that accounts for thermal stratification in reservoirs (RBM-res). Climate change projections are based on two representative concentration pathways (RCPs) and multiple global climate models from the Coupled Model Intercomparison Project Phase 5 (CMIP5). We compare modeled changes with those from a model implementation that does not include any reservoirs and which therefore lacks any flow regulation (VIC->MOSART-RBM) to evaluate the resilience of current reservoir infrastructures. We also evaluate different reservoir operating rules (residence time versus low flow mitigation) to investigate the extent to which the current reservoir system can be used to mitigate the impacts of climate changes on both streamflow and stream temperature.

Modeling future glacier changes on regional or global scales is challenging due to a scarcity of data for calibrating model parameters. This makes it difficult to derive accurate model parameters and also to convey the model uncertainty associated with those parameters. Previous global-scale models have typically optimized a single glacier or region specific set of model parameters to project global-scale glacier mass changes. As part of the development of a new open source global-scale glacier evolution model this research implements a Markov Chain Monte Carlo method to address this problem. We use geodetic mass-balance observations to determine probability distributions of a set of model parameters at each glacier. We then form an ensemble of model simulations by sampling parameters from these distributions. Using an ensemble allows us to create a range of mass balance changes for each glacier and quantify the model uncertainty. Applying this calibration technique, we use temperature and precipitation projections of one global climate model to model the mass changes until year 2100 of thousands of glaciers in High Mountain Asia. By using climate projections forced by multiple emission scenarios, we compare the uncertainties in changing climate caused by possible emission scenarios with the uncertainties in the glacier evolution model, and evaluate the results of different climate scenarios on glacier evolution.