@phdthesis{mccandless_utilizing_2014,
	type = {Thesis},
	title = {Utilizing {GRACE} {TWS}, {NDVI}, and precipitation for drought identification and classification in {Texas}},
	url = {https://repositories.lib.utexas.edu/handle/2152/26179},
	abstract = {Drought is one of the most widespread and least understood natural phenomena. Many indices using multiple data types have been created, and their success at identifying periods of extreme wetness and dryness has been well documented. In recent years, researchers have begun to assess the potential of total water storage (TWS) anomalies in drought monitoring method- ologies. The Gravity Recovery and Climate Experiment (GRACE) provides temporally and spatially consistent TWS measurements across the globe, and studies have shown GRACE TWS anomalies are suited to identify drought.

GRACE TWS is used with MODIS-derived normalized difference veg- etation index (NDVI) and NOAA/NWS precipitation data to create a new drought index, the Merged-dataset Drought Index (MDI). Each dataset corre- lates with a different type of drought, giving robustness to MDI. MDI is based on dataset deviations from a monthly climatology and is objective and easy to calculate. MDI is studied across Texas, which is broken into five dataset- defined sub-regions. Multiple drought events are identified from 2002 - 2014, with the most severe beginning in October 2010. A new drought severity clas- sification scheme is proposed based on MDI, and it is organized to match the current US Drought Monitor Classification Scheme.

MDI shows strong correlation with existing drought indices, notably the Palmer Drought Severity Index (PDSI). MDI consistently identifies droughts in different sub-regions of Texas, but shows better performance in regions with large GRACE TWS signals. MDI performance is enhanced through a weighting scheme that relies more on GRACE TWS. Even with this scheme, MDI and PDSI exhibit occasional behavioral differences.

Drought analysis using MDI shows for the first time that GRACE data provides information on a sub-regional scale in Texas, an area with low signal amplitudes. Past studies have shown TWS capable of identifying drought, but MDI is the first index to quantitatively use GRACE TWS in a manner consistent with current practices of identifying drought. MDI also establishes a framework for a future, completely remote-sensing based index that can enable temporally and spatially consistent drought identification across the globe. This study is useful as well for establishing a baseline for the necessary spatial resolution required from future geodetic space missions for use in drought identification at smaller scales.},
	language = {en},
	urldate = {2023-07-06},
	author = {McCandless, Sarah Elizabeth},
	month = aug,
	year = {2014},
	note = {Accepted: 2014-09-30T16:21:03Z},
	keywords = {Terrestrial Ecoregions (CEC 1997)},
}

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GRACE TWS is used with MODIS-derived normalized difference veg- etation index (NDVI) and NOAA/NWS precipitation data to create a new drought index, the Merged-dataset Drought Index (MDI). Each dataset corre- lates with a different type of drought, giving robustness to MDI. MDI is based on dataset deviations from a monthly climatology and is objective and easy to calculate. MDI is studied across Texas, which is broken into five dataset- defined sub-regions. Multiple drought events are identified from 2002 - 2014, with the most severe beginning in October 2010. A new drought severity clas- sification scheme is proposed based on MDI, and it is organized to match the current US Drought Monitor Classification Scheme. MDI shows strong correlation with existing drought indices, notably the Palmer Drought Severity Index (PDSI). MDI consistently identifies droughts in different sub-regions of Texas, but shows better performance in regions with large GRACE TWS signals. MDI performance is enhanced through a weighting scheme that relies more on GRACE TWS. Even with this scheme, MDI and PDSI exhibit occasional behavioral differences. Drought analysis using MDI shows for the first time that GRACE data provides information on a sub-regional scale in Texas, an area with low signal amplitudes. Past studies have shown TWS capable of identifying drought, but MDI is the first index to quantitatively use GRACE TWS in a manner consistent with current practices of identifying drought. MDI also establishes a framework for a future, completely remote-sensing based index that can enable temporally and spatially consistent drought identification across the globe. This study is useful as well for establishing a baseline for the necessary spatial resolution required from future geodetic space missions for use in drought identification at smaller scales.","language":"en","urldate":"2023-07-06","author":[{"propositions":[],"lastnames":["McCandless"],"firstnames":["Sarah","Elizabeth"],"suffixes":[]}],"month":"August","year":"2014","note":"Accepted: 2014-09-30T16:21:03Z","keywords":"Terrestrial Ecoregions (CEC 1997)","bibtex":"@phdthesis{mccandless_utilizing_2014,\n\ttype = {Thesis},\n\ttitle = {Utilizing {GRACE} {TWS}, {NDVI}, and precipitation for drought identification and classification in {Texas}},\n\turl = {https://repositories.lib.utexas.edu/handle/2152/26179},\n\tabstract = {Drought is one of the most widespread and least understood natural phenomena. Many indices using multiple data types have been created, and their success at identifying periods of extreme wetness and dryness has been well documented. In recent years, researchers have begun to assess the potential of total water storage (TWS) anomalies in drought monitoring method- ologies. The Gravity Recovery and Climate Experiment (GRACE) provides temporally and spatially consistent TWS measurements across the globe, and studies have shown GRACE TWS anomalies are suited to identify drought.\n\nGRACE TWS is used with MODIS-derived normalized difference veg- etation index (NDVI) and NOAA/NWS precipitation data to create a new drought index, the Merged-dataset Drought Index (MDI). Each dataset corre- lates with a different type of drought, giving robustness to MDI. MDI is based on dataset deviations from a monthly climatology and is objective and easy to calculate. MDI is studied across Texas, which is broken into five dataset- defined sub-regions. Multiple drought events are identified from 2002 - 2014, with the most severe beginning in October 2010. A new drought severity clas- sification scheme is proposed based on MDI, and it is organized to match the current US Drought Monitor Classification Scheme.\n\nMDI shows strong correlation with existing drought indices, notably the Palmer Drought Severity Index (PDSI). MDI consistently identifies droughts in different sub-regions of Texas, but shows better performance in regions with large GRACE TWS signals. MDI performance is enhanced through a weighting scheme that relies more on GRACE TWS. Even with this scheme, MDI and PDSI exhibit occasional behavioral differences.\n\nDrought analysis using MDI shows for the first time that GRACE data provides information on a sub-regional scale in Texas, an area with low signal amplitudes. Past studies have shown TWS capable of identifying drought, but MDI is the first index to quantitatively use GRACE TWS in a manner consistent with current practices of identifying drought. MDI also establishes a framework for a future, completely remote-sensing based index that can enable temporally and spatially consistent drought identification across the globe. This study is useful as well for establishing a baseline for the necessary spatial resolution required from future geodetic space missions for use in drought identification at smaller scales.},\n\tlanguage = {en},\n\turldate = {2023-07-06},\n\tauthor = {McCandless, Sarah Elizabeth},\n\tmonth = aug,\n\tyear = {2014},\n\tnote = {Accepted: 2014-09-30T16:21:03Z},\n\tkeywords = {Terrestrial Ecoregions (CEC 1997)},\n}\n\n\n\n\n\n\n\n","author_short":["McCandless, S. 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