Abstract
Drought is a familiar climatic phenomenon in the United States Southwest, with complex human-environment interactions that extend beyond just the physical drought events. Due to continued climate variability and change, droughts are expected to become more frequent and/or severe in the future. Decision-makers are charged with mitigating and adapting to these more extreme conditions and to do that they need to understand the specific impacts drought has on regional and local scales, and how these impacts compare to historical conditions. Tremendous progress in drought monitoring strategies has occurred over the past several decades, with more tools providing greater spatial and temporal resolutions for a variety of variables, including drought impacts. Many of these updated tools can be used to develop improved drought climatologies for decision-makers to use in their drought risk management actions. In support of a Food-Energy-Water (FEW) systems study for New Mexico, this article explores the use of updated drought monitoring tools to analyze data and develop a more holistic drought climatology applicable for New Mexico. Based upon the drought climatology, droughts appear to be occurring with greater frequency and magnitude over the last two decades. This improved drought climatology information, using New Mexico as the example, increases the understanding of the effects of drought on the FEW systems, allowing for better management of current and future drought events and associated impacts.
Introduction
Since 2000, 16 drought events classified as “Billion Dollar Disasters” have occurred across the United States (U.S.) according to a national web-based archive of these types of disasters for a variety of natural hazards (NOAA NCEI, ). The estimated economic losses from the 16 drought-specific events total $133.2 billion. These losses illustrate that drought is a key disaster that can have dramatic local and regional impacts across the U.S. Droughts are different from other hazards in that they can develop slowly, extend over large regions, and have a long duration, making it very difficult for officials to tally both the direct and indirect impacts that result (Ding et al., ). Because of their widespread and long-lasting nature, droughts also affect multiple sectors of a region's economy. Therefore, it is important to consider drought impacts when analyzing the dynamics of Food-Energy-Water (FEW) systems in a location.
The complex and costly impacts of the recent drought events across the U.S. highlight the need for a proactive drought risk management approach to help officials be better prepared for future drought events (Wilhite, ). Historically, most efforts of drought management have focused on responding to drought impacts after an event, which has often meant that these responses have been uncoordinated and untimely, and little is done to reduce potential impacts in the next events (Wilhite and Pulwarty, ; Wilhite et al., ). Drought risk management, however, places the attention on improving drought early warning, drought planning, and drought mitigation strategies that will hopefully reduce future drought impacts (Wilhite et al., ).
Drought early warning is often considered the foundation of successful drought risk management (Hayes et al., ). It consists of both the assessment of current conditions (or drought monitoring) as well as an outlook for future conditions. In the past several decades, there have been remarkable advancements in the capacity of early warning, particularly with new drought monitoring tools, remote sensing technologies, and improved understanding of oceanic-atmospheric interactions to assist with drought outlooks. The advancements have particularly highlighted the improved spatial and temporal resolutions that are now available with many of these tools (Hayes et al., ).
An important aspect of drought early warning is having a solid understanding of a location's drought climatology (i.e., historical drought events' frequency, magnitude, duration, and impacts), so that current and future drought conditions are placed into a proper historical perspective and impacts can be anticipated or projected based upon drought severity levels (Steinemann et al., ; Svoboda et al., ; Martin et al., ). Decision-makers often ask specific questions during a drought event that link to the climatology such as how the current severity compares with historical records, how often the current severity has occurred in the past, when was the last time the drought event was this severe, what impacts have occurred, and what does the past record indicate as to what can be expected ahead (Svoboda et al., ). These questions relate directly to the decision-makers' understanding drought and having access to drought climatologies. Timely answers with key information derived from an accurate drought climatology, especially when combined with real-time drought early warning information, provide powerful resources for decision-makers to use for improving drought response and drought risk management strategies.
Drought and Food-Energy-Water Systems of New Mexico, U.S.A
This analysis was conducted within the context of providing an improved understanding of how natural hazard events such as drought can affect the sustainability and resilience of New Mexico's FEW systems (Geli et al., ). This article thus highlights the value of an updated drought climatology for the state while also investigating the role drought (among other factors) has had, and will have, on the FEW systems across the state. New Mexico is specifically included in 11 of the Billion Dollar drought designations since 2000, and the three-year 2011–13 period that severely affected New Mexico had an estimated $59.6 billion of losses nationally from the direct and indirect impacts that occurred (NOAA NCEI, ). For New Mexico, drought impacts affect crop and livestock productivity (Sawalhah et al., ; Zaied et al., 2019, 2020; Gedefaw et al., ); water supplies for public and ecosystem consumption; human, animal, and wildlife health; forests and wildfires; and recreation and tourism. These sectors have also become more vulnerable to natural hazards (i.e., droughts and wildfires) given changes occurring in the climate (NMOSE and NMWRRI, ). Figure 1 is a time series that highlights drought has been a regular feature in New Mexico since the beginning of the U.S. Drought Monitor (USDM) record in 2000 (USDM, ), and the recent 2018–2019 drought reached the “extreme” (D3) and “exceptional” (D4) categories for parts of the state. In addition to this recent drought, parts of New Mexico were in the exceptional category during 2002, 2003–2004, 2006, 2011–2012, and 2013.
Figure 1
These recent droughts in New Mexico, and the availability of new drought early warning tools and information for use by decision-makers, emphasize the importance of building a strong drought climatology. This article provides a historical drought perspective for New Mexico within the context of providing an improved understanding of FEW systems response during drought, and how a drought climatology can potentially improve drought risk management. Updating the state's drought history is also important for using drought early warning information to establish a baseline that can also be helpful for multiple other applications, such as developing indicators and thresholds of resiliency for FEW systems monitoring shifts in the dynamic equilibrium of these systems, and can serve as a model (and provide guidelines) for other states and regions on how to update the drought climatology in their locations (Svoboda et al.,
New Mexico is diverse in its topography, climate, and economic sectors. Its economy is driven by a number of principal sectors that include energy, agriculture, mining, and recreation (USDA NASS,
However, the state is challenged by limited and variable water supplies that affect the sustainability of its interconnected FEW systems. The World Resources Institute (WRI, 2020), for example, identifies much of New Mexico in the “extremely high” category for current water quantity risks. This challenge is highlighted by the fact that the state of New Mexico has been in at least “Moderate Drought” (D1) for 999 weeks since January 2000 (93%), as designated by the USDM, more than any other state in the U.S. (USDM,
Therefore, the goals of this article are to: (1) highlight how the availability of data and tools have rapidly advanced over the past two decades; (2) demonstrate how these data and tools can now be leveraged to build improved drought climatologies using a New Mexico example; and (3) illustrate how these drought climatologies can support drought risk management.
Data
Tremendous progress in drought early warning strategies has occurred over the past two decades, with more tools providing greater spatial and temporal resolution for a variety of variables, including drought impacts. For a considerable amount of time, the Palmer Drought Severity Index (PDSI) (Palmer,
This influx of available drought early warning information and data, while advantageous, does come with a suite of corresponding limitations as well (Finnessey et al.,
Table 1
| WestWide drought tracker | U.S. drought monitor | Climate at a glance | Drought risk atlas | NCEI | Centers for disease control | ||
|---|---|---|---|---|---|---|---|
| 1895 - present Gridded data, 4 km (PRISM) File Format: NetCDF | 2000 – present Data: shapefile | Period of record dependent upon individual station Station based data | Period of record dependent upon individual station Station based data | Period of record dependent upon variable File format: HTML, XML, JSON | 1989–2016 File format: CSV, RDF, RSS, TSV (for excel), EM | ||
| Temporal scale | - Annual - Monthly | - Weekly | - Annual - Monthly | - Annual - Monthly - Weekly | - Annual - Weekly - YTD - Aggregate by number of months | - Monthly - Weekly - Quarter (seasonal) | |
| Spatial coverage | - CONUS - State - County - Climate division - HUC | - National - State - Climate region and division - County - FEMA region - HUC (2, 4, 6, and 8 digits) - NWS region - River forecast center - Urban areas - USACE district and division - USDA climate hubs - Other regions | - Climate region and division - State - County - FEMA region - HUC (2, 4, 6, and 8 digits) - NWS region - River forecast center - Urban areas | - Individual stations | - FEMA region - HUC (2, 4, 6, and 8 digits) | - FEMA region - HUC (2, 4, 6, and 8 digits) | |
| Climate data | - Temperature (anomaly, avg., max., and min.) - Precipitation (total and anomaly) - Heating degree days - Colling degree days - Growing degree days | - PDSI, SPI, and other climatological inputs; the Keech-Byram Drought Index for fire, satellite-based assessments of vegetation health, and various indicators of soil moisture; and hydrologic data, SWSI & Snowpack | - Temperature (avg., max., and min.) - Precipitation - Cooling degree days - Heating degree days | - Temperature (anomaly, avg., max. and min) - Precipitation (total and anomaly) - Decilies (1–96 months) - Standardized Streamflow Index | - Daily summaries - Global summaries (month & year) - Normals (hourly, daily, monthly, seasonally, annually) - Precipitation (15-minute intervals, hourly) - Weather Radar | - Population-weighted global horizontal irradiance - Population-weighted UV irradiance - Flood vulnerability - Future projections of extreme heat & precipitation - Heat stress, heat vulnerability, and heat-related mortality - Historical extreme heat days, events, precipitation - Temperature distribution | |
| Drought indices | - PDSI - sc-PDSI - Palmer-Z - SPI (1–72 months) - SPEI (1–72 months) | - USDM (Drought categories D0-D4) - DSCI | - PDSI - PHDI - PMDI - Palmer-Z | - PDSI - sc-PDSI - PHDI - PMDI - SPI (1–96 months) - SPEI (1–96 months) | - PDSI - sc-PDSI - SPI (1–96 months) - SPEI (1–96 months) - Drought monitor | - PDSI - SPEI - SPI - USDM - Max. number of consecutive months of mild drought or worse - Number of months of mild drought or worse |
Climate and drought data sources.
Enhancement of Existing Drought Monitoring Tools
There are a large number of drought and climate resources that are beneficial in drought monitoring and risk management. Many of the historically popular drought indices and indicators have been improved to incorporate new available data, temporal and spatial scales, and enhanced computing power. The U.S. Drought Monitor (USDM), Drought Risk Atlas (DRA), and Drought Impact Reporter (DIR) are three tools that are continually evolving in order to provide useful and robust data for scientists and decision-makers.
The U.S. Drought Monitor
In the U.S., one of the most widely-used drought monitoring tools is the U. S. Drought Monitor (NDMC,
Figure 2

A USDM map of New Mexico for 2 October 2018 and an associated table with relevant statistics comparing the drought coverage with other past time periods (NDMC,
The USDM's website [http://droughtmonitor.unl.edu] and associated web-based tools include an archive of the weekly maps and a timeline product of the drought intensities for a variety of scales. The maps, data tables, and time series can also be produced at national, county, river basin, and several different climate-related scales. The USDM is used by several government agencies to trigger a variety of drought responses (NDMC,
The Drought Risk Atlas (DRA)
The Drought Risk Atlas (DRA) tool was developed by the NDMC to provide decision-makers with more details about historical drought events (Svoboda et al.,
Figure 3

An example of the Drought Risk Atlas [http://droughtatlas.unl.edu/data/climate.aspx] that highlights the monthly PDSI heat map of PDSI values for Roswell, NM, from 1988 to 2017.
Drought Impacts and the Integration of Monitoring Tools
The USDM and DRA are powerful drought monitoring tools that can help in developing drought climatologies, but they lack one key element that is beneficial for improving decision making: drought impacts. Redmond (
Given this challenge, the NDMC launched the Drought Impact Reporter (DIR) in 2005 as the nation's first comprehensive, web-based archive of drought impacts. The DIR is a moderated database of events drawn from a variety of sources, documenting the occurrence of drought impacts. Moderators scan daily results of automated news searches for evidence of a quantifiable or observable change at a specific place and time that can be attributed to drought. When they find a report that meets these criteria, they add it to the DIR as an impact (Smith et al.,
Recent Drought Monitoring Tools for Drought Climatologies
This section features two of the more recently developed tools that can assist decision-makers with building drought climatologies.
The Drought Severity and Coverage Index (DSCI)
The USDM as it is represented in Figures 1, 2 is very helpful illustrating how information on drought severity can be provided at different spatial scales, but the information remains primarily a qualitative comparison of historical drought conditions. It is difficult to compare conditions between two or more separate drought events, either in one location or in multiple locations, and then make a determination regarding which events were more severe (Martin et al.,
Figure 4 demonstrates the transformation process for New Mexico of the categorical USDM time series (Figure 1) to the continuous DSCI time series using Equation 1. Two advantages can result from converting the percent of an area in each USDM drought category into the DSCI: (1) it provides a single numerical value describing current drought extent and intensity and (2) it allows for drought to be quantified over time. While the USDM provides real-time maps of the spatial extent of drought, it does not provide a simple way to analyze drought over time as do some of the other drought indices. With that in mind, the DSCI is a new tool that increases the capacity of the USDM for further drought monitoring and analysis. The usefulness of the USDM is limited when a long-term historical context is needed since it has only been operational since 2000.
Figure 4

Weekly time series that show how the USDM categorical plot is transformed into the continuous DSCI. The continuous DSCI (USDM) values are 0–99 (None), 100–199 (D0), 200–299 (D1), 300–399 (D2), 400–499 (D3), and 500 (D4).
The West Wide Drought Tracker (WWDT)
Another tool that can be very useful for a decision-makers is the WestWide Drought Tracker [WWDT, https://wrcc.dri.edu/wwdt/] (Abatzoglou et al.,
While there are a number of other climate and drought tools that can be used by decision-makers, the tools described here were selected because of the types of available data, the spatial and temporal scales available, and the robustness of the available data. The data and tools discussed happen to be focused on the United States, but significant progress in drought monitoring is also being made around the world, and several examples of these are highlighted in Table 2. The processes described in this article can be applied to any location having historical data available with the appropriate spatial and temporal resolutions.
Table 2
| Tool/Dataset | References/Links | Description |
|---|---|---|
| North American Drought Monitor (NADM) | Lawrimore et al. ( | Users have access to a monthly drought monitoring composite maps and data for Canada, the United States, and Mexico, with an archive of the NADM dating back to 2002 |
| Monitor de Secas do Nordeste (the Northeast Drought Monitor, or MSNE) | Hayes et al. ( | A tool adapted directly from the USDM process for 13 states in northeastern Brazil and operational since July 2014 |
| Global Drought and Flood Catalog (GDFC) | He et al. ( | Users can access in situ and remote sensing datasets and products of droughts and floods, 1950–2016, to provide an estimate of extremes in regions of the world |
| Anywhere DEWS (AD-EWS) | Sutanto et al. ( | In addition to real-time drought information for multiple aspects of the water cycle, users can get high resolution seasonal drought predictions for Europe |
| European Drought Observatory (EDO)/Global Drought Observatory | EDO GDO | Users have access to current drought information and a database of drought events back to 1950 for Europe and around the world. The GDO is mainly for emergency response information |
| European Drought Report Impact Inventory (EDII)/European Drought Reference Database (EDR) | Stahl et al. ( | The European Drought Center (EDC) provides an archive of drought impacts (EDII) or summaries of historical European droughts and an SPI visualization tool (1958–2009). Users can also submit drought impacts as well |
| African Flood and Drought Monitor (AFDM), Princeton University | Sheffield et al. ( | The AFDM provides drought assessments and forecasts for the African continent at various temporal and spatial scales. Current conditions are compared with historical reconstructions of the water cycle |
International drought monitoring activities.
Methods
The process of building a drought climatology is unique for every location. Ensuring that there are available historical data to provide the appropriate context is a key consideration for highlighting the frequency, magnitude, duration, and impacts of previous drought events. Additional factors to consider in this process include understanding how the drought climatology addresses the drought perspectives and management activities of the decision-makers. It is also important to understand the spatial scale most relevant for the decision-makers. As with a more traditional climatology where both temperature and precipitation are the main components, temperature- and precipitation-related indicators are very important for a drought climatology as they both represent important components of the hydrological cycle and are a good starting point.
For this analysis of New Mexico's drought, all temperature, precipitation, and drought indices data used were taken from the WWDT tool. Although provided for New Mexico on a statewide scale in this case, similar analyses can be conducted for a variety of spatial scales including individual stations, counties, river basins, and climate divisions where historical data are available for more than 30 years. The WWDT data are available for the 1895 through 2019 period, which allows anomalies to be compared to longer averages from 1900 to 1999.
Drought Events Using the Self-Calibrated Palmer Drought Severity Index
One indicator that has been used regularly for assessing drought and building drought climatologies is the PDSI (Palmer,
Figure 5

The Self-Calibrating PDSI (sc-PDSC) for the 1900–2019 period. Positive sc-PDSI values (blue) representing wet conditions and negative sc-PDSI values (red) representing dry conditions.
While the sc-PDSI time series itself is valuable, additional analyses can be made on the time series to provide quantitative comparisons of drought events based on their duration, magnitude, and severity. In this study for New Mexico, the method adapted by Nam et al. (
Using the long-term sc-PDSI time series for New Mexico, the duration of a DEP is the number of consecutive months (two or more) where the sc-PDSI threshold of −2.0 or less was met. The magnitude of a DEP is the absolute value of the sum of the sc-PDSI values for all months within a designated DEP (Equation 2).
Using the duration and magnitude, the severity can be calculated for each DEP using Equation 3. This method is simple and provides a quick and easy way to analyze how droughts compare through history.
Table 3 shows all 31 DEPs as defined by the process described above for New Mexico since 1895. The DEPs in red are designated as the Top 10 events in New Mexico's history. The DEPs for 1895–2019 period were ranked on the calculated drought severity (Equation 3). It is important to note that the 1950s drought, which is often considered New Mexico's worst drought since the beginning of the instrumental record, is broken into two separate drought events using this method. This is because there were 4 months during 1952 when the magnitude threshold was not met. Otherwise, if this gap was not present, the cumulative 1950s drought would rank as the most severe drought event for the state.
Table 3
| Drought event | Drought rank | Start | End | Magnitude | Duration (months) | Severity |
|---|---|---|---|---|---|---|
| DEP01 | 25 | 06/1902 | 11/1902 | 13.74 | 6 | 2.29 |
| DEP02 | 2 | 11/1903 | 08/1904 | 32.37 | 10 | 3.24 |
| DEP03 | 11 | 03/1910 | 03/1911 | 37.34 | 13 | 2.87 |
| DEP04 | 16 | 10/1917 | 10/1918 | 34.79 | 13 | 2.68 |
| DEP05 | 9 | 08/1922 | 02/1923 | 20.28 | 7 | 2.90 |
| DEP06 | 15 | 02/1925 | 07/1925 | 16.17 | 6 | 2.70 |
| DEP07 | 4 | 01/1934 | 04/1935 | 48.75 | 16 | 3.05 |
| DEP08 | 30 | 10/1943 | 11/1943 | 4.37 | 2 | 2.19 |
| DEP09 | 23 | 09/1945 | 09/1946 | 23.95 | 10 | 2.40 |
| DEP10 | 17 | 06/1947 | 12/1947 | 18.37 | 7 | 2.62 |
| DEP11 | 5 | 03/1950 | 03/1952 | 74.91 | 25 | 3.00 |
| DEP12 | 6 | 08/1952 | 09/1957 | 179.64 | 61 | 2.94 |
| DEP13 | 31 | 10/1963 | 02/1964 | 10.75 | 5 | 2.15 |
| DEP14 | 19 | 06/1964 | 01/1965 | 20.65 | 8 | 2.58 |
| DEP15 | 13 | 01/1967 | 07/1967 | 19.32 | 7 | 2.76 |
| DEP16 | 18 | 02/1971 | 08/1971 | 18.37 | 7 | 2.62 |
| DEP17 | 22 | 03/1972 | 07/1972 | 12.44 | 5 | 2.49 |
| DEP18 | 8 | 03/1974 | 08/1974 | 17.44 | 6 | 2.91 |
| DEP19 | 28 | 01/1981 | 02/1981 | 4.43 | 2 | 2.22 |
| DEP20 | 29 | 05/1989 | 07/1989 | 6.57 | 3 | 2.19 |
| DEP21 | 26 | 10/1989 | 01/1990 | 9.05 | 4 | 2.26 |
| DEP22 | 14 | 01/1996 | 08/1996 | 21.9 | 8 | 2.74 |
| DEP23 | 12 | 02/2000 | 10/2000 | 24.94 | 9 | 2.77 |
| DEP24 | 10 | 04/2002 | 01/2003 | 25.9 | 9 | 2.88 |
| DEP25 | 7 | 05/2003 | 03/2004 | 32.25 | 11 | 2.93 |
| DEP26 | 3 | 01/2006 | 07/2006 | 21.85 | 7 | 3.12 |
| DEP27 | 27 | 03/2009 | 11/2009 | 15.78 | 7 | 2.25 |
| DEP28 | 1 | 03/2011 | 10/2013 | 106.17 | 32 | 3.32 |
| DEP29 | 20 | 01/2014 | 08/2014 | 20.43 | 8 | 2.55 |
| DEP30 | 24 | 04/2017 | 09/2017 | 14.37 | 6 | 2.40 |
| DEP31 | 21 | 04/2018 | 09/2018 | 15.21 | 6 | 2.54 |
New Mexico sc-PDSI identified Drought Events and drought severity since 1895.
The DEPs highlighted in red represent the Top 10 DEPs based on the calculated Severity (Equation 3).
However, the method does have one limitation in that duration and magnitude are given the same weight. This means that a drought with a very large magnitude, but short duration can be calculated to have the same severity as a longer drought with moderate magnitude. This is observed comparing DEP02 and DEP12 in Table 3, where DEP02, which only lasted for 10 months, is ranked as more severe than DEP12, which lasted for 61 months.
Drought Events Using the DSCI
As with the sc-PDSI, end-users (i.e., stakeholders, decision-makers, and researchers) can get a time series of the U.S. Drought Monitor for their state or more localized region (Figure 1). This time series record only goes back to 2000, however. To provide a more quantitative perspective of the drought severity categories and the spatial coverage, the Drought Severity and Coverage Index (DSCI) was created and can be used for that current 20-year record. Although the DSCI is available weekly, for this study of New Mexico, monthly averages of the DSCI were used to provide consistency with the sc-PDSI analysis.
Similar to the sc-PDSI analysis, a baseline of two consecutive months reaching the threshold magnitude was established. A DSCI of 200 is equivalent to 100% of an area being in D1 (or Moderate Drought) and a DSCI of 300 is equivalent to 100% of an area being in D2 (“Severe Drought”). Because this study was looking at a statewide DSCI value, and the goal was to set a threshold for drought, the DSCI magnitude threshold was set at 250 to ensure that at least “Moderate Drought” exists for a majority of the state. Table 4 shows 8 DEDs for New Mexico as defined by the temporal step and magnitude threshold described above using Equations 2 and 3. The subscript “D” represents Drought Events specifically identified by the DSCI. The results or the DEDs shown on Table 4 were based on using DSCI instead of sc-PDSI in Equation 2.
Table 4
| Drought event | Drought rank | Start | End | Magnitude | Duration (months) | Severity |
|---|---|---|---|---|---|---|
| DED01 | 4 | 05/2002 | 07/2002 | 1,099 | 3 | 3.66 |
| DED02 | 1 | 08/2003 | 03/2004 | 3,047 | 8 | 3.81 |
| DED03 | 6 | 04/2006 | 06/2006 | 991 | 3 | 3.30 |
| DED04 | 2 | 04/2011 | 12/2011 | 3,346 | 9 | 3.72 |
| DED05 | 8 | 06/2012 | 07/2012 | 607 | 2 | 3.04 |
| DED06 | 3 | 12/2012 | 08/2013 | 3,312 | 9 | 3.68 |
| DED07 | 7 | 04/2014 | 07/2014 | 1,242 | 4 | 3.11 |
| DED08 | 5 | 03/2018 | 08/2018 | 2,019 | 6 | 3.37 |
New Mexico DSCI identified Drought Events (DEDs) and drought severity since 2000.
Results: Building a New Mexico Drought Climatology
As a good place to begin, the temperature and precipitation records are critical for a New Mexico drought climatology because both represent important components of the hydrological cycle and reveal extremes and trends that provide insights to decision-makers regarding past, present, and potentially future drought events. For New Mexico, Figure 6 shows annual temperature and precipitation anomalies on a statewide scale compared to the 1900–1999 average. Figures 6A,B organizes the historical data into two time series that illustrate distinct warmer/cooler or drier/wetter departures over the period of record from 1895 to 2019. The precipitation record oscillates above and below the long-term average, with the most recent years being generally drier than the 1900–1999 average. The temperature record, however, clearly indicates that there is a warming trend occurring. The annual temperature every year since 2000 in New Mexico has been above the 1900–1999 average according to Figure 6.
Figure 6

Annual precipitation and temperature anomalies (1900–1999 average) for New Mexico from 1900 to 2019. (A) shows a timeseries of New Mexico's annual temperature anomalies and (B) precipitation anomalies. (C) Compares annual anomalies based on the Water Year (October-September). All 20th century years are gray and 21st century years are red.
Figure 6C provides a different perspective that visually combines the temperature and precipitation record. Providing alternative perspectives with the data is another important method in a drought climatology to enhance the understanding of the historical context. In this case, Figure 6C uses the historical Water Year, which is an annual representation from October through September of the following year to better represent the water availability in regions like the western U.S., including New Mexico. Many decision-makers manage their activities on a Water Year schedule, so this type of representation would make sense for them. The temperature and precipitation averages for each Water Year are displayed for the 1895–2019 period, with the years since 2000 highlighted in red. In this case, all Water Years in the twenty-first century are warmer than the long-term average, except for 2010, with 10 years being above the long-term average in precipitation (and in the “Wet and Warm” quadrant) and 10 years below the long-term precipitation average (the “Dry and Warm'' quadrant).
Figure 6 shows the co-occurrence of hotter and drier conditions, and highlights the importance of considering their interaction. Increasing temperatures, for example, increase evapotranspiration, which decreases the moisture available for humans, animals, plants, and within the soil.
By evaluating all the above-mentioned drought tools, it can be seen that over the last two decades New Mexico is experiencing more extreme dry periods than wet periods (Figures 5, 6, Tables 3, 4). In Figure 5, the sc-PDSI averaged for New Mexico exceeds +2 just one time after 2000, yet regularly falls below −2.0 for the same time period. Based on the data in Table 3, each decade during the 1900–1999 period averaged 2.2 DEPs, and each DEP averaging 23.1 months with an average severity value of 2.68. Meanwhile, the two decades since 2000 are averaging 4.5 DEPs per decade, each DEP averaging 47.5 months with an average calculated severity of 2.75. It is also notable, that based of the sc-PDSI, four of the Top 10 DEPs occurred since 2000 (Table 3).
There have been significant drought events in New Mexico's history, notably DEP07, DEP11, and DEP12 (Table 3). But based upon the sc-PDSI it appears that droughts are becoming more frequent and severe in magnitude. This is also supported by the calculated drought severity in Table 4. This is in part due to the warming trend New Mexico has been experiencing, especially in the last 20 years. The average temperature from 1900 to 1999 was 11.81°C and the average precipitation was 31.23 mm, where the average temperature from 2000 to 2019 was 12.57°C and average precipitation was 29.79 mm. This combination of an increase in temperature and decrease in precipitation may not seem significant, but for a semi-arid and arid climate that New Mexico's FEW system is built upon, it highly sensitive to these changes in temperature and precipitation.
To further highlight the behavior of the two indices (tools) in representing drought events, Figure 7 shows a comparison between the sc-PDSI and the DSCI timeseries for New Mexico for the 2000–2019 period. Although the sc-PDSI and DSCI values are opposite in magnitude due to their different scales (B), they were consistent in tracking each other for the past 20 years of the USDM data (r = −0.76). This demonstrates that both of these tools are detecting similar drought events with slightly different durations and magnitudes, and illustrates the value of looking at more than one drought index to evaluate drought events. Both sources of data confirm key messages and increase the credibility and confidence in the information for the decision-makers. Generally, this also suggest that the use of convergence of evidence approach in drought monitoring is important.
Figure 7

(A) The Self-Calibrating PDSI (sc-PDSC) for the 1900–2019 period and (B) a comparison between the sc-PDSI and the DSCI (r = 0.76) for the 2000–2019 period over New Mexico.
While it is critical to understand historical drought events and how temperature and precipitation have changed over time, a key component for a robust drought climatology is being able to link these drought events with the corresponding drought impacts. The DIR, while a fairly new tool, provides a database that helps to link impacts to different stages of drought events and their magnitude. Understanding this link can help decision-makers understand a drought's progression and anticipate likely future impacts. Figure 8 depicts all impacts recorded for New Mexico by the DIR since 2005 with an overlay of the DSCI for the same time period. The number of impacts follows the same general trend of the DSCI, and tend to be recorded on the leading edge of a prolonged drought. This makes sense in that impacts in the DIR are recorded as one-off events and plotted here by start date, as end dates are generally unknown. This is an indication that monitoring the conditions leading up to impacts may help provide timely early warning of drought itself, and may actually precede other drought monitoring indicators. It may also relate to different time scales of fast-emerging drought or longer-term drought. Additional analysis is needed to investigate if and when impacts manifest and how this information could be used by decision-makers in drought early warning and drought risk management.
Figure 8

New Mexico monthly drought impacts reported to the DIR and DSCI (2005–2020).
Discussion
New Mexico's economy is highly reliant upon its natural resources. Because of the diverse landscape and arid climate, it is critical for decision-makers to understand the history of previous drought events for effective drought risk management (Finnessey et al.,
The agricultural sector brings in ~$2.5 billion per year into the state's economy and the top two commodities are dairy products and cattle and calves (USDA NASS,
In 2018, New Mexico finished updating the seventh iteration of the New Mexico Drought Plan (NMDP) that was first developed in 1998 (NMOSE and NMWRRI,
The NMDP uses the USDM for its two triggers to initiate the management, adaptation, and mitigation procedures described within the NMDP. These responses are based on the spatial extent of the drought severity categories defined by the USDM. One can use the USDM Tabular Data Archive Tool provided on the USDM (USDM,
Table 5
| Drought event | Beginning week | Ending week | Duration event (Weeks) | Drought stage |
|---|---|---|---|---|
| DEu01 | May 30, 2000 | June 27, 2000 | 5 | Emergency |
| DEu02 | April 2, 2002 | July 2, 2002 | 14 | Emergency |
| July 9, 2002 | July 23, 2002 | 3 | Exceptional | |
| July 30, 2002 | July 30, 2002 | 1 | Emergency | |
| DEu03 | May 20, 2003 | December 16, 2003 | 31 | Emergency |
| December 23, 2003 | March 30, 2004 | 15 | Exceptional | |
| April 6, 2004 | April 6, 2004 | 1 | Emergency | |
| DEu04 | July 13, 2004 | October 5, 2004 | 13 | Emergency |
| DEu05 | March 7, 2006 | July 25, 2006 | 21 | Emergency |
| DEu06 | March 29, 2011 | May 3, 2011 | 6 | Emergency |
| May 10, 2011 | November 22, 2011 | 29 | Exceptional | |
| November 29, 2011 | April 23, 2013 | 74 | Emergency | |
| April 30, 2013 | August 6, 2013 | 15 | Exceptional | |
| August 13, 2013 | September 3, 2013 | 5 | Emergency | |
| DEu07 | February 18, 2014 | July 29, 2014 | 24 | Emergency |
| DEu08 | January 23, 2018 | May 8, 2018 | 16 | Emergency |
| May 15, 2018 | May 22, 2018 | 2 | Exceptional | |
| May 29, 2018 | October 23, 2018 | 22 | Emergency |
Drought Events (DEUs) as designated by the USDM thresholds defined in the 2018 New Mexico Drought Plan.
Figure 9

State of New Mexico USDM D2-D4 and D4 categories with the 2018 NMDP equivalent triggers. The yellow line represents the Emergency trigger of 50% area of D2 conditions. The orange line represents the Exceptional trigger of 20% area of D4 conditions.
The Drought Events (DEUs) identified in the NMDP by the USDM triggers in Table 5 are very similar to the Drought Events (DEPs) as depicted by the sc-PDSI in Table 3 and the DSCI (DEDs) in Table 4. The events shown in Table 5 were defined by weekly conditions, while Tables 3, 4 were monthly values. For Tables 5 and 3, the 2011–2013 drought stands out as the most severe drought in New Mexico since 2000 with durations of ~31 and 32 months, respectively. The DSCI threshold for Table 4 was slightly more restrictive so that the 2011–2013 drought was split into three separate Drought Events during the period for a total of 20 months. No other drought since 2000 in Table 4 had a duration close to that length.
Understanding how and when drought impacts evolve can help decision-makers make better drought management decisions. Early in the USDM's evolution, a table was developed that provided an overview of how drought impacts might link to the various USDM severity levels (Svoboda et al.,
Table 6
| Category | Description | Reported drought IMPACTS in New Mexico |
|---|---|---|
| D0 | Abnormally Dry | ° Low soil moisture ° Increase fire danger |
| D1 | Moderate Drought | ° Supplemental feed and water for Livestock ° Wildfires abundant |
| D2 | Severe Drought | ° Pasture yield limited; selling livestock ° Irrigated crops stunted; dry land crops brown? ° Dust storms ° Abundance and magnitude of wildfires increase; fuel mitigation practices in effect ° Wildlife changing feeding patterns ° Well water decreases |
| D3 | Extreme Drought | ° Livestock suffering; selling herds; high feed costs; emergency CRP grazing ° Low crop yields ° Burn bans and firework restrictions could begin; extreme fire ° Irrigation allotments decrease ° Vegetation and native trees drying |
| D4 | Exceptional Drought | ° Federal lands begin to close for fire precautions; burn bans increase ° Bear encroachment; migratory birds changing patterns ° No surface water left for agriculture; farmers use private wells ° Rio Grande and other large rivers dry |
SDM categories and associated drought impacts for New Mexico (USDM,
Conclusion
New Mexico is highly sensitive to drought conditions due to its variable and limited water supply as well as its normally dry conditions. Climate variability and change increasingly pose significant challenges to the sustainability and resiliency of New Mexico's FEW systems now and into the future, particularly as water resources are further stressed. Drought events and their unique complex human-environment interactions, beyond just the physical drought phenomenon, make them an important climate hazard to focus attention on within the nexus of FEW systems.
Drought early warning and management has advanced significantly over the last 20 years, in large part to the advancement of drought tools. The USDM now has a 20-year history and can be displayed for multiple scales, such as at the state and county levels, and in several different formats. The DSCI is a tool that presents critical information provided by the USDM into a single value for quicker and easier interpretation and response. The simplicity of the DSCI allows decision-makers to use the DSCI as a trigger to initiate drought responses (i.e., NMDP). These advancements in tools lend to more comprehensive drought climatologies to be developed, allowing better understanding how drought has impacted the state of New Mexico in the past and what can be learned from that history. Drought climatologies can be built for any location that has the historical data available, with qualitative (i.e., impacts) and quantitative data providing a more intricate climatology for which more information can be derived by decision-makers.
Until the past few decades, there was not a capability to store and share up-to-date drought impact data. This impact information can elevate already robust drought data, including linking drought indicators with impacts. Our research suggests that monitoring impacts can provide early warning of drought, particularly in conjunction with other drought indicators. Recognizing impacts early can reduce the response time to drought conditions, meaning that decision-makers can take steps to lessen current and future drought impacts.
Drought does not occur in isolation and a state like New Mexico that has an economy heavily dependent upon the land will benefit greatly from having a better understanding of drought and drought impacts throughout the FEW system. Drought climatologies provide valuable information for decision-makers, but there is one limitation that must be considered when building and using historically-derived information, and that is understanding that these climatologies are based upon the assumption of stationarity. Due to climate change, a non-stationarity environment should be taken into consideration when using climatologies to predict future droughts and drought impacts. Regardless, there is still a strong case to understanding how drought has occurred in the past to help with understanding how climate change could influence multiple dimensions of drought.
Another point that was beyond the scope of this study is the discussion about how much the duration of a drought influences the severity, and ultimately the impacts of the drought. A next step is to use the findings from this research and the FEW systems Well-Being Index proposed by Geli et al. (
Future research is needed to establish a more detailed link between drought impact information and drought climatologies in New Mexico. Understanding how and when various drought impacts evolve can help prepare decision-makers and stakeholders alike for possible drought impacts in future drought events. Linking drought impacts with drought climatologies will increase the understanding of the effects of drought on FEW systems, allowing for better management of future drought events and the associated impacts.
Statements
Data availability statement
Publicly available datasets were analyzed in this study. This data can be found at: http://droughtmonitor.unl.edu/; https://wrcc.dri.edu/wwdt/; https://www.fsa.usda.gov/programs-and-services/disaster-assistance-program/index.
Author contributions
LJ conducted the data analysis, in charge of writing the manuscript, and making most of the figures. MH helped to guide the research and manuscript, and provided ideas and edits. HG helped to provide information about New Mexico and how our research applies to New Mexico and the research he is working on. KS provided information on new drought monitoring tools and provided useful data, including advice on the use of the DSCI, to enhance this project. All authors contributed to the article and approved the submitted version.
Funding
This research was partially funded by the National Science Foundation (NSF), awards #173935 and #IIA-1301346.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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Summary
Keywords
drought, drought monitoring, food-energy-water systems, drought impacts, triggers
Citation
Johnson LE, Geli HME, Hayes MJ and Smith KH (2020) Building an Improved Drought Climatology Using Updated Drought Tools: A New Mexico Food-Energy-Water (FEW) Systems Focus. Front. Clim. 2:576653. doi: 10.3389/fclim.2020.576653
Received
26 June 2020
Accepted
09 October 2020
Published
04 December 2020
Volume
2 - 2020
Edited by
Chris C. Funk, United States Geological Survey (USGS), United States
Reviewed by
Jian Peng, University of Oxford, United Kingdom; Ladislaus Benedict Chang'A, Tanzania Meteorological Agency, Tanzania
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Copyright
© 2020 Johnson, Geli, Hayes and Smith.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Lindsay E. Johnson lindsay.johnson@huskers.unl.edu
This article was submitted to Climate Services, a section of the journal Frontiers in Climate
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