UC Irvine

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 near-surface air relative humidity data from the Atmospheric Infrared Sounder (AIRS) mission, provide opportunities to improve early drought warning. In the third chapter of this dissertation, we outline a new drought monitoring framework for early drought onset detection using AIRS relative humidity data. The early warning and onset detection of drought is of particular importance for effective agriculture and water resource management. Previous studies show that the Standard Precipitation Index (SPI), a measure of precipitation deficit, detects drought onset earlier than other indicators. Here satellite-based near surface air relative humidity data can further improve drought onset detection and early warning. This chapter introduces the Standardized Relative Humidity Index (SRHI) based on the NASA's AIRS observations. SRHI relies on SDAT's nonparametric framework, introduced in Chapter 2. The results indicate that the SRHI typically detects the drought onset earlier than SPI. While the AIRS mission was not originally designed for drought monitoring, its relative humidity data offers a new and unique avenue for drought monitoring and early warning. Early warning aspects of SRHI may have merit for integration into current drought monitoring systems.

One of the research opportunities identified in Chapter 1 is using current (and future) satellite missions to develop composite and multi-indicator drought models. In Chapter 4, we outline a framework for assessing impacts of droughts on forest health using a multi-sensor approach. This framework relies on the relationship between climate conditions (e.g., temperature, precipitation, relative humidity, Vapor Pressure Deficit) and forest health based on greenness of vegetation. Wildfires, tree mortality and forest productivity increase during drought periods. Using the proposed multi-index approach, Chapter 4 aims to investigate the effects of recent summer, dry-season and winter droughts on the forest health in western United States. We use Vapor Pressure Deficit (VPD) as an indicator that combines temperature and relative humidity for forest stress assessment. Normalized Difference Vegetation Index (NDVI) is commonly used for assessing vegetation health. During summer and growing season, VPD values are generally high. The results show that the VPD and NDVI provide consistent information on forest health. In addition to VPD, we use conditional probability of NDVI in high temperature and low relative humidity percentiles over the summer and the growing season. We show that combining temperature and relative humidity using a conditional probability approach offers multi-sensor information on forest condition. During winter, on the other hand, VPD and temperature is relatively lower. NDVI distributions in winter were found to be more associated with precipitation as opposed to relative humidity and temperature. We believe the a joint indicator based on temperature and relative humidity can be considered as a link between climate condition and actual impact on the ecosystem.