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Accueil du site → Doctorat → États-Unis → 2016 → Frameworks for Improving Multi-Index Drought Monitoring Using Remote Sensing Observations.

University of California – Irvine (2016)

Frameworks for Improving Multi-Index Drought Monitoring Using Remote Sensing Observations.

Farahmand, Alireza

Titre : Frameworks for Improving Multi-Index Drought Monitoring Using Remote Sensing Observations.

Auteur : Farahmand, Alireza

Université de soutenance : University of California – Irvine

Grade : Doctor of Philosophy (PhD) 2016

Présentation
Droughts are among the most common and devastating natural disasters. Reducing damages associated with droughts relies on monitoring and prediction information as well as plans to cope with droughts. The overarching goal of this dissertation is to improve current capabilities in drought monitoring using space-based observations, with a focus on integrating remotely sensed data products that are not commonly being used for drought monitoring. The first chapter of this dissertation, surveys current and emerging drought monitoring approaches using remotely-sensed observations from climatological and ecosystem perspectives. Current and future satellite missions offer opportunities to develop composite and multi-sensor (or multi-index) drought assessment models. While there are immense opportunities, there are major challenges including data continuity, unquantified uncertainty, sensor changes, and community acceptability. One of the major limitations of many of the currently available satellite observations is their short length of record. A number of relevant satellite missions and sensors (e.g., Atmospheric Infrared Sounder (AIRS), Gravity Recovery and Climate Experiment) provide only slightly over a decade of data, which may not be sufficient to study droughts from a climatological perspective. However, they still provide valuable information about relevant hydrologic and ecological processes linked to this natural hazard. Therefore, there is a need for models and algorithms that combine multiple data sets and/or assimilate satellite observations into model simulations to generate long-term climate data records. To address this gap, Chapter 2 introduces Standardized Drought Analysis Toolbox (SDAT), which includes a generalized framework for deriving nonparametric univariate and multivariate standardized drought indices. Current indicators suffer from deficiencies including some prior distributional assumption, temporal inconsistency, and statistical incomparability. Different indicators have varying scales and ranges and their values cannot be compared with each other directly. Most drought indicators rely on a representative parametric probability distribution function that fits the data. However, a parametric distribution function may not fit the data, especially in continental/global scale studies. Particularly, when the sample size is relatively small as in the case of many satellite precipitation products. SDAT is based on a nonparametric framework that can be applied to different climatic variables including precipitation, soil moisture and relative humidity, without having to assume representative parametric distributions. The most attractive feature of the framework is that it leads to statistically consistent drought indicators based on different variables. We show that using SDAT with satellite observation leads to more reliable drought information, compared to the commonly used parametric methods. We argue that satellite observations not currently used for operational drought monitoring, such as…

Présentation et version intégrale

Page publiée le 12 novembre 2016, mise à jour le 25 octobre 2019