Sea-level fingerprints, the spatial patterns of sea level change resulting from rapid melting of glaciers and ice sheets, play an important role in understanding past and projecting future changes in relative sea level (RSL). Over century timescales, the viscous flow of Earth’s interior is a small component of the total deformation due to ice loading in most regions, so fingerprints computed using elastic Earth models are accurate. However, in regions where the viscosity is orders of magnitude lower than the global average, the viscous component of deformation can be significant, in which case it is important to consider models of viscoelastic deformation. There is evidence that the glaciated regions of Alaska, Western Canada and USA, and the Southern Andes are situated on top of mantle regions in which the local viscosity is several orders of magnitude lower than typical global mean values. The goal of this work is to determine the importance of viscous flow in computing RSL fingerprints associated with future ice mass loss from these regions. Version 5.0 of the Randolph Glacier Inventory is used to estimate the ice load distribution required for calculating sea-level fingerprints. For the glaciated regions that have lower than average viscosity, fingerprints were calculated using an elastic Earth model and a 3D viscoelastic model to quantify the influence of viscous flow on the predicted sea level changes. Using glacier mass loss values for the intermediate future climate scenario Representative Concentration Pathway (RCP) 4.5, the global sea level response was computed at 2100 CE relative to 2010 CE due to melting from all glacier regions. On comparing the results of the two models it was found that ice-load-induced viscous flow contributes significantly (more than a few cm) to the RSL fingerprints only in near-field regions. However, in these regions, the non-elastic contribution can be 10s of cm. For example, at Juneau, USA the elastic calculation gave relative sea level changes of ∼ −45 cm, compared to ∼ −120 cm based on the viscoelastic calculation.

Quantifying the contribution of glaciers to water resources is particularly important in locations where glaciers may provide a large percentage of total river discharge. In some remote locations, direct field measurements of melt rates are difficult to acquire, so alternate approaches are needed. Positive degree-day modeling (PDD) of glacier melt is a valuable tool to making first order approximations of the volume of melt coming from glaciers. In this study, a PDD-melt model is applied to glaciers in the Indus River watershed located in Afghanistan, China, India, and Pakistan. Here, millions of people rely on the water from the Indus River, which previous work suggests may be heavily dependent on glacier melt from high mountain regions in the northern part of the watershed. In this region, the PDD melt model calculates the range of melt volumes from more than 45,000 km2 of glaciated area. It relies on a limited suite of input variables for glaciers in the region: elevation, temperature, temperature lapse rate, melt factor, and surface area. Three global gridded climate datasets were used to determine the bounds of temperature at each glacier: UEA CRU CL 2.0, UEA CRU TS 2.1, and NCEP/NCAR 40 year reanalysis. The PDD melt model was run using four different melt scenarios: mean, minimum, maximum, and randomized. These scenarios account for differences in melt volume not captured by temperature, and take uncertainties in all input parameters into account to bound the possible melt volume. The spread in total melt volume from the model scenarios ranges between 27 km3 and 439 km3. While the difference in these calculations is large, it is highly likely the real value falls within this range. Importantly, even the smallest model volume output is a significant melt water value. This suggests that even when forcing the absolute smallest volume of melt, the glacier contribution to the Indus watershed is significant. In addition to providing information about melt volume, this model helps to highlight glaciers with the greatest contribution to total melt. Despite differences in the individual climate models, the spatial pattern in glacier melt is similar, with glaciers contributing the majority of total melt volume occurring in similar geographic regions regardless of which temperature dataset is used. For regions where glacier areas are reasonably well-constrained, contributions from individual glaciers can be quantified. Importantly, less than 5% of glaciers contribute at least 70% of the total melt volume in the watershed. The majority of these glaciers are in Pakistan, the region with the largest percentage of known glaciers with large surface areas at lower elevations. In addition to calculating current melt volumes over large glaciated areas, this model can also be used to determine future melt rates under differing climate scenarios. By applying suggested future regional temperature change to the temperature data, the impact on average melt rate over the watershed was found to increase from 3.02 m/year to 4.69 m/year with up to 2 °C temperature increase. Assuming glacier area remains relatively constant over short time periods, this would amount to a 145 km3 increase in melt volume.

The Hindu-Kush Himalayan region possesses a large resource of snow and ice, which acts as a freshwater reservoir for irrigation, domestic water consumption or hydroelectric power for billions of people in South Asia. Monitoring hydrologic resources in this region is challenging because of the difficulty of installing and maintaining a climate and hydrologic monitoring network, limited transportation and communication infrastructure and difficult access to glaciers. As a result of the high, rugged topographic relief, ground observations in the region are extremely sparse. Reanalysis data offer the potential to compensate for the data scarcity, which is a barrier in hydrological modeling and analysis for improving water resources management. Reanalysis weather data products integrate observations with atmospheric model physics to produce a spatially and temporally complete weather record in the post-satellite era. This dissertation creates an integrated hydrologic modeling system that tests whether streamflow prediction can be improved by taking advantage of the National Aeronautics and Space Administration (NASA) remote sensing and reanalysis weather data products in physically based energy balance snow melt and hydrologic models. This study also enhances the energy balance snowmelt model by adding capability to quantify glacier melt. The novelty of this integrated modeling tool resides in allowing the user to isolate various components of surface water inputs (rainfall, snow and glacier ice melt) in a cost-free, open source graphical-user interface-based system that can be used for government and institutional decision-making. Direct, physically based validation of this system is challenging due to the data scarcity in this region, but, to the extent possible, the model was validated through comparison to observed streamflow and to point measurements at locations in the United States having available data

京都大学 / 新制・課程博士 / 博士(工学) / 甲第23429号 / 工博第4884号 / 新制||工||1763(附属図書館) / 京都大学大学院工学研究科社会基盤工学専攻 / (主査)教授 田中 茂信, 准教授 田中 賢治, 准教授 佐山 敬洋 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

The reliance on freshwater released by mountain glaciers and ice caps demands that the effects of climate change on these thermally-sensitive systems are evaluated thoroughly. Coupling climate variability to processes of mass and energy exchange at the glacier scale is challenged, however, by a lack of climate data at an appropriately fine spatial resolution. The thesis addresses this challenge through attempting to reconcile this scale mismatch: glacier boundary-layer observations of meteorology and ablation at Vestari Hagafellsjökull, Iceland, and Storglaciären, Sweden, are related to synoptic-scale meteorological variability recorded in gridded, reanalysis data. Specific attention is directed toward synoptic controls on: i) near-surface air temperature lapse rates; ii) stationarity of temperature-index melt model parameters; and iii) glacier-surface ablation. A synoptic weather-typing procedure, which groups days of similar reanalysis meteorology into weather categories , forms the basis of the analytical approach adopted to achieve these aims. Lapse rates at Vestari Hagafellsjökull were found to be shallowest during weather categories characterised by warm, cloud-free weather that encouraged katabatic drainage; steep lapse rates were encountered in weather categories associated with strong synoptic winds. Quantitatively, 26% to 38% of the daily lapse-rate variability could be explained by weather-category and regression-based models utilizing the reanalysis data: a level of skill sufficient to effect appreciable improvements in the accuracy of air temperatures extrapolated vertically over Vestari Hagafellsjökull. Weather categories also highlighted the dynamic nature of the temperature-ablation relationship. Notably, the sensitivity of ablation to changes in air temperature was observed to be non-stationary between weather categories, highlighting vulnerabilities of temperature-index models. An innovative solution to this limitation is suggested: the relationship between temperature and ablation can be varied as a function of weather-category membership. This flexibility leads to an overall improvement in the simulation of daily ablation compared to traditional temperature-index formulations (up to a 14% improvement in the amount of variance explained), without the need for additional meteorological data recorded in-situ. It is concluded that weather categories are highly appropriate for evaluating synoptic controls on glacier meteorology and surface energetics; significant improvements in the parameterization of boundary-layer meteorology and ablation rates are realised through their application.