Date of Award

Winter 2006

Project Type

Dissertation

Program or Major

Earth and Environmental Science

Degree Name

Doctor of Philosophy

First Advisor

Charles Vorosmarty

Abstract

Arctic hydrology represents an important component of the larger global climate system, and there are signs that significant water-cycle changes, involving complex feedbacks, have occurred. This dissertation explores the methods to estimate components of the arctic hydrological cycle, the numerous biases and uncertainties associated with the techniques, and suggestions for future research needs. The studies described here focus on quantitative models and methods for predicting the spatial and temporal variability in pan-Arctic hydrology.

This dissertation discusses pan-Arctic water budgets drawn from a hydrological model which is appropriate for applications across the terrestrial Arctic. Including effects from soil-water phase changes results in increases in simulated annual runoff of 7% to 27%. A sensitivity analysis reveals that simulated runoff is far more sensitive to the time-varying climate drivers than to parameterization of the landscape. When appropriate climate data are used, the Pan-Arctic Water Balance Model (PWBM) is able to capture well the variability in seasonal river discharge at the scale of arctic sea basins.

This dissertation also demonstrated a method to estimate snowpack thaw timing from radar data. Discrepancies between thaw timing inferred from the microwave backscatter data and the hydrological model are less than one week. The backscatter signal-to-noise values are highest in areas of higher seasonal snow accumulation, low to moderate tree cover and low topographic complexity. An evaluation of snow water equivalent (SWE) estimates drawn from land surface models and microwave remote sensing data suggests that simulated SWE from a hydrological model like PWBM, when forced with appropriate climate data, is far superior to current snow mass estimate derived from passive microwave data.

Biases arising from interpolations from sparse, uneven networks can be significant. A bias of well over +10 mm yr-1 was found in the early network representations of spatial precipitation across Eurasia. When examining linkages between precipitation and river discharge, these biases limit our confidence in the accuracy of historical precipitation reconstructions. This dissertation assess our current capabilities in estimating components of arctic water cycle and reducing the uncertainties in predictions of arctic climate change.

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