Editing Drought (section)

== Indices and monitoring ==
[[File:Unl drought monitor D2 2000-2024.png|thumb|Percent of U.S. experiencing drought intensity of at least level D2 (severe drought), during the weeks of 2000 to 2024.]]

Several indices have been defined to quantify and monitor drought at different spatial and temporal scales. A key property of drought indices is their spatial comparability, and they must be statistically robust.<ref name=":3" /> Drought indices include:<ref name=":3" />
* [[Palmer drought index]] (sometimes called the Palmer drought severity index (PDSI)): a regional drought index commonly used for monitoring drought events and studying areal extent and severity of drought episodes.<ref name="auto">{{Cite journal|last1=Mishra|first1=Ashok K.|last2=Singh|first2=Vijay P.|date=September 2010|title=A review of drought concepts|url=https://linkinghub.elsevier.com/retrieve/pii/S0022169410004257|journal=Journal of Hydrology|volume=391|issue=1–2|pages=202–216|doi=10.1016/j.jhydrol.2010.07.012|bibcode=2010JHyd..391..202M}}</ref> The index uses precipitation and temperature data to study moisture supply and demand using a simple water balance model.<ref name="auto" /><ref>{{Cite journal|last=Van Loon|first=Anne F.|date=July 2015|title=Hydrological drought explained: Hydrological drought explained|journal=Wiley Interdisciplinary Reviews: Water|volume=2|issue=4|pages=359–392|doi=10.1002/wat2.1085|doi-access=free|bibcode=2015WIRWa...2..359V}}</ref><ref>{{Cite journal|last1=Liu|first1=Yi|last2=Ren|first2=Liliang|last3=Ma|first3=Mingwei|last4=Yang|first4=Xiaoli|last5=Yuan|first5=Fei|last6=Jiang|first6=Shanhu|date=January 2016|title=An insight into the Palmer drought mechanism based indices: comprehensive comparison of their strengths and limitations|url=http://link.springer.com/10.1007/s00477-015-1042-4|journal=Stochastic Environmental Research and Risk Assessment|volume=30|issue=1|pages=119–136|doi=10.1007/s00477-015-1042-4|bibcode=2016SERRA..30..119L|issn=1436-3240}}</ref>
* [[Keetch-Byram Drought Index]]: an index that is calculated based on rainfall, air temperature, and other [[meteorological]] factors.<ref>{{cite journal|author1=Keetch, John J.|author2=Byram, George M.|year=1968|title=A Drought Index for Forest Fire Control|url=http://www.srs.fs.usda.gov/pubs/viewpub.jsp?index=40|journal=Res. Pap. Se-38. Asheville, Nc: U.S. Department of Agriculture, Forest Service, Southeastern Forest Experiment Station. 35 P|publisher=USDA Forest Service Southern Research Station|volume=038|access-date=August 11, 2016|quote=(Date: 1968) Res. Paper SE-38. 32 pp. Asheville, NC: U.S. Department of Agriculture, Forest Service}}</ref>
* Standardized precipitation index (SPI): It is computed based on precipitation, which makes it a simple and easy-to-apply indicator for monitoring and prediction of droughts in different parts of the world. The [[World Meteorological Organization]] recommends this index for identifying and monitoring meteorological droughts in different climates and time periods.<ref name=":3" />
* [[Standardised Precipitation Evapotranspiration Index|Standardized Precipitation Evapotranspiration Index]] (SPEI): a multiscalar drought index based on climatic data. The SPEI accounts also for the role of the increased atmospheric [[Potential evapotranspiration|evaporative demand]] on drought severity.<ref name=":3" /> Evaporative demand is particularly dominant during periods of precipitation deficit. The SPEI calculation requires long-term and high-quality precipitation and atmospheric evaporative demand datasets. These can be obtained from ground stations or gridded data based on reanalysis as well as satellite and multi-source datasets.<ref name=":3" />
* Indices related to vegetation: root-zone soil moisture, vegetation condition index (VDI) and vegetation health index (VHI). The VCI and VHI are computed based on vegetation indices such as the normalized difference vegetation index (NDVI) and temperature datasets.<ref name=":3" />
* Deciles index
* Standardized runoff index
High-resolution drought information helps to better assess the spatial and temporal changes and variability in drought duration, severity, and magnitude at a much finer scale. This supports the development of site-specific adaptation measures.<ref name=":3" />

The application of multiple indices using different datasets helps to better manage and monitor droughts than using a single dataset, This is particularly the case in regions of the world where not enough data is available such as Africa and South America. Using a single dataset can be limiting, as it may not capture the full spectrum of drought characteristics and impacts.<ref name=":3" />

Careful monitoring of moisture levels can also help predict increased risk for wildfires.