Browsing by Subject "Data Assimilation"

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    Advancing the Predictability of the Earth System Processes: Insights from Optimal Mass Transport Theory
    (2022-03) Tamang, Sagar
    The improvement in forecast lead time by operational forecasting centers is made possible through data assimilation (DA) in which the Earth system model outputs are optimally combined with the satellite observations. However, the presence of non-Gaussian state-space and systematic errors degrades the performance of classic Bayesian DA methodologies, which rely on affine Euclidean penalization of error. In the present thesis, inspired from the field of information geometry, modern DA paradigms over Riemannian manifolds equipped with the Wasserstein metric are proposed, whereby optimal mass transport theory promises to extend the geophysical forecast skills. The Wasserstein metric is a geodesic distance and enables assimilation in a space characterized by families of probability distributions with finite second-order moments leading to full recovery of non-Gaussian forecast probability distributions. Unlike Eulerian penalization of error in the Euclidean space, the Wasserstein metric is sensitive to the translation of probability measures, enabling to formally penalize geophysical biases. The presented frameworks are applied to dissipative and chaotic evolutionary dynamics as well as the high-dimensional quasi-geostrophic model of atmospheric circulation with a wide range of applications in the Earth system models. The results suggest that under systematic errors and non-Gaussian state-space, utilization of the Wasserstein metric in the DA framework can reduce the forecast uncertainty beyond what is possible through the classic DA methodologies. Preliminary evidence also suggests that the framework is more robust to the curse of dimensionality and requires fewer ensemble members than the dimension of the state-space.
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    Evaluating the Impact of Vegetation and Future Climate Change on Groundwater Recharge using a Land-Surface Model
    (2022-01) Anurag, Harsh
    Understanding groundwater recharge is critical for accurate assessment of our valuable groundwater resources. Unfortunately, it’s also one of the most difficult fluxes of hydrological cycle to quantify because it’s influenced by several interacting factors including climate, topography, soil, land-use and vegetation. This thesis uses an integrated land-surface model to understand various factors that impact recharge. Vegetation, through evapotranspiration (ET), controls the amount of water reaching the water table and becoming recharge. Thus, changes in vegetation growth can in turn impact groundwater recharge. Currently, vegetation representation in most recharge modeling studies is specified using climatological leaf area index (LAI) values. This kind of year-to-year repeating vegetation parameterization cannot capture seasonal and inter-annual vegetation responses to dynamic meteorological conditions and can thus neglect the corresponding impact on recharge. The first part of this thesis uses Community Land Model (CLM) to investigate the sensitivity of recharge to seasonal and interannual varying vegetation in Minnesota (USA) across different climate, hydrogeology, and ecoregions. We found that although year-to-year varying vegetation does not affect long-term climatological recharge estimates, it can drive disproportionately large variability in annual and seasonal recharge. Results also show that across the precipitation gradient, vegetation leaf-out in Minnesota is highly sensitive to springtime temperature anomalies, and this phenological response can trigger notable changes in ET and subsequently recharge. Along with characterizing recharge responses to vegetation dynamics, understanding and predicting recharge under future climate conditions is also critical, as climate change is imposing additional stresses on our water resources. In the second part of this thesis, we used Minnesota as a testbed to understand how recharge will respond to changing climate in upper-latitude, low-elevation temperate settings. We compared the simulated future recharge (2026-2055) under two emissions scenarios (RCP4.5 and RCP8.5) with baseline historical conditions (1976-2005) and found that despite consistent projections of higher precipitation, state-average recharge will mostly decline or remain about the same due to warming-induced ET increases. Results also demonstrate that in addition to precipitation and temperature change, moisture feedbacks on ET and the influence of hydrogeological properties and frozen ground dynamics on runoff is essential to consider when quantifying climate change impacts on recharge in temperate zones. The final part of this thesis focuses on snowfall-induced seasonally frozen ground changes and its impact on spring recharge. We conducted simulation experiments with varying snow inputs to test the hypothesis that a smaller snowpack will allow for higher partitioning to runoff versus recharge due to greater ground frost. Results show that smaller snowpacks did lead to lower spring recharge amounts relative to precipitation compared to larger snowpacks, but not due to greater partitioning to runoff as initially hypothesized. Instead, relative recharge decreased alongside relative runoff when snowfall was less, because more of the infiltrated water was lost to ET as the surface soil ice thawed earlier and meltwater infiltrated into the root zone earlier. Overall, the findings in this thesis enhances our understanding of processes controlling groundwater recharge in upper to mid-latitude, low-relief settings such as Minnesota. As demand of groundwater continues to increase, understanding this important process by which aquifers are replenished is imperative for effective and sustainable groundwater resource management.