Climate Change in Colorado
Climate Change in Colorado
Chapter 3 - Changes in Colorado's Water
Climate variable/event Recent trend Projected future change Confidence in change
Spring Snowpack Lower Lower Medium
Runoff Timing Earlier Earlier High
Annual Streamflow Lower Lower Medium
Summer Soil Moisture Lower Lower High
Evaporative Demand Higher Higher Very High


3.1 Overview

Colorado is known as a ‘headwaters’ state because four major river systems have most of their mountain headwaters within its borders: the Colorado River, the Rio Grande, the Arkansas River, and the Platte River. All of these major rivers, and all of their tributaries with headwaters above 8,000 feet, have a snowmelt-dominated hydrology: most of their annual streamflow (~60-80%) originates as meltwater from the seasonal snowpack (Li et al. 2017). Streams whose watersheds are entirely at lower elevations, whether on the eastern plains or in the western plateau region, have flows driven more by rainfall in the warmer months, with less contribution from snowfall and snowmelt.

Most of the water use in Colorado (83%) depends on surface water supply from streams and rivers, often stored in reservoirs (CWCB 2023). The remaining 17% of water supply comes from groundwater wells that tap either alluvial aquifers that are strongly connected to a river or stream, or deeper aquifers that are replenished over much longer time periods.

In this chapter, we describe recent trends and likely future changes in four related dimensions of Colorado’s water resources: snowpack, streamflow, soil moisture, and evapotranspiration. To summarize observed trends, we rely on both recent research studies and new analyses using observations from key datasets. For likely future changes, we rely on recent studies and new analyses using the ensemble of CMIP5-LOCA-VIC hydrologic projections (Vano et al. 2020). See Appendix A for more information about these hydrologic projections.

3.2 Snowpack


Colorado’s snowpack serves as a huge seasonal reservoir that stores about 15 million acre-feet of water on average at the spring peak and then makes that water available later in the year when water demands for agricultural uses and outdoor watering are higher. Precipitation that falls and is stored as snow is also more likely to end up as runoff than precipitation that falls as rain (Li et al. 2017). Colorado’s seasonal snowpack begins accumulating in late fall and typically peaks in April or May. Once rising spring temperatures warm all of the snowpack to 32°F (0°C), the sun’s energy can more effectively drive snowmelt. The snowmelt leads to an abrupt peak in streamflow, typically in May or June. On most streams and rivers in Colorado fed by mountain snowmelt, about 70-80% of the annual runoff comes in the four months from April to July.

Snow-water equivalent (SWE) refers to the amount of liquid water that would result if the snowpack were melted down. SWE is a better measure for hydrologic monitoring than total snowfall or snow depth, since the latter measures don’t account for the highly variable density of snow. The amount of SWE around the seasonal peak (usually April 1 to May 15) is a very useful predictor of the spring and summer runoff. The peak SWE in Colorado’s mountains is typically 10” to 50”, depending on location, elevation, and year. Note that the snow and rain that falls after peak SWE (in April, May, and June) can dramatically shift the runoff outcomes in years with anomalous spring precipitation, such as 2020 (low) and 2015 (high). In Colorado, SWE is measured hourly at 114 automated SNOTEL (SNowpack TELemetry) sites, and monthly at 81 snow courses, all maintained by the Natural Resources Conservation Service (NRCS) Colorado Snow Survey and its cooperators. Nearly all of these SNOTEL and snow course sites are between 8,500’ and 11,500’.

Similar to weather station data, snow data are subject to non-climatic influences (especially changes in vegetation over time, such as beetle-kill or wildfire) that affect snow deposition at the site (Kampf et al. 2022; Julander and Bricco 2006; Pugh and Small 2012; Giovando and Niemann 2022). Thus, monitoring and trend analysis based on multiple sites is more representative of basin-wide conditions compared to single site monitoring and analysis.

Observed snowpack changes

Several recent studies have documented widespread declining trends in April 1 SWE across the West over the past 40 to 70 years (Fyfe et al. 2017; Mote et al. 2018; Zeng et al. 2018; Siler et al. 2019; Musselman et al. 2021). These studies showed that SWE has decreased in most sites in Colorado’s major river basins, though the percentage declines in SWE in Colorado were generally smaller than in most other regions of the West due to Colorado’s relatively high elevations and colder winter climate. These studies also found that warming temperatures were an important cause of the observed SWE declines, while below-normal fall and spring precipitation in the past few decades has also played a role.

An analysis of Colorado’s snowpack updated from the 2014 report is consistent with these recent West-wide studies. Figure 3.1 shows basin-wide SWE from SNOTEL sites and snow courses starting in the 1940s to 1960s for eight of Colorado’s river basins. Note that the year-to-year variability, especially drought years, is highly correlated among the basins. The 21st century (2001-2022) average April 1 SWE for all eight basins is lower, by 3% to 23%, than the 1951-2000 average (Figure 3.1). The largest decreases occurred in the southwestern portion of the state, specifically in the San Juan and Rio Grande basins. Figure 3.1 shows that years with very large snowpacks (>140% of median) — important for refilling reservoirs — have been less frequent since the 1980s. Though as 2019 demonstrates, these big snowpacks can still occur.

Figure 3.1

Observed April 1 snow water equivalent for northern basins (top) and southern basins (bottom) compared to 1991-2020 median.

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Future snowpack projections

Many previous studies have used climate projections (Chapter 2) in combination with hydrologic models to simulate future changes in the hydrology of river basins in Colorado and elsewhere in the interior West. They show that April 1 SWE is likely to decline across Colorado’s river basins due to the systemic impacts of warming temperatures, despite the projected increases in winter and spring precipitation (Battaglin et al. 2011; Lukas et al. 2014; Lute et al. 2015; Alder and Hostetler 2015; Lukas et al. 2020b; Reclamation 2021).

For this report, we analyzed the CMIP5-LOCA-VIC hydrologic projections as noted in Section 3.1. Figure 3.2 shows the projected change in April 1 SWE, under RCP4.5, between the historical baseline (1971-2000) and the 2050-centered period (2035-2064) for the watersheds above key gages within seven major Colorado river basins. In all of the basins, most projections indicate decreased April 1 SWE in the 2050-centered period.

Figure 3.2

Projected future change in April 1st snow-water equivalent (SWE) in watersheds above key gages in seven Colorado river basins for a 2050-centered period (2035-2064) relative to 1971-2000, from an ensemble of 32 CMIP5-LOCA-VIC hydrology projections under a medium-low emissions scenario (RCP4.5). The solid purple and red bars show the middle 80% of the projections (10th-90th percentiles); the two dashes show the minimum and maximum projections; the open squares show the median projections. (Data: GDO-DCP,

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While April 1 SWE is a very commonly used indicator of snowpack and a good predictor of April-July streamflow, it is also a single snapshot in time. Figure 3.3 shows the projected monthly SWE, under RCP4.5, for the 2050-centered period (2035-2064) compared with the historical baseline (1971-2000) for the watershed above the Colorado at Dotsero gage. From November to April, most of the projections for 2050 show lower first-of-month SWE than the historical baseline, reflecting reductions in snow accumulation due mainly to warmer temperatures. For May 1, nearly all projections for 2050 show lower SWE than the historical baseline; the decreases in SWE are greater for April 1. This indicates that snowmelt is starting earlier in the future period. For June 1, all 32 projections show lower SWE than the historical baseline, including projections that showed increased SWE on April 1.

Figure 3.3

Projected future 1st-of-month snow-water equivalent (SWE) for the basin above the Colorado River at Dotsero gage for a 2050-centered period (2035-2064) from an ensemble of 32 CMIP5-LOCA-VIC hydrology projections (purple) under a medium-low emissions scenario (RCP4.5), and the simulated mean monthly SWE for the 1971-2000 period (black). The plotted monthly values are for the 1st of the month. (Data: GDO-DCP,

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Examination of the daily data underlying Figure 3.3 shows that the seasonal peak SWE during the 1971-2000 historical period occurred, on average, on April 9. For the 2050-centered period, 27 of the 32 projections indicate seasonal peak SWE occurring earlier than April 9, by as much as 38 days. The median projection suggests peak SWE will occur 11 days earlier, on March 29.

3.3 Streamflow


The high mountains of Colorado form the headwaters of major rivers and their tributaries that provide water supply for Colorado and over two dozen downstream states and Mexico, including the Colorado, Rio Grande, Arkansas, and North and South Platte rivers. Water from the Colorado River alone is relied upon by over 40 million people. Accurate forecasts of the volume and timing of streamflow are crucial on a daily-to-annual basis for reservoir managers, irrigators, municipal water providers, and flood-warning systems. Similarly, understanding historical variability in streamflow and any potential future changes in streamflow is critical to long-term water planning.

As discussed in the previous section, seasonal (e.g., April-July) and annual streamflows in most of the state’s streams and rivers are driven by snowmelt, so the year-to-year variability in surface water supply in Colorado is strongly related to the variability in the snowpack (e.g., April 1 SWE). Accordingly, seasonal and annual streamflow can be skillfully (though not perfectly) predicted 1-5 months ahead of peak runoff, primarily using SWE and precipitation data. However, precipitation that falls before the start of the snowpack season and after the seasonal SWE peak contributes to runoff as well. The largest source of uncertainty in streamflow forecasts made between January and May is how the subsequent weather will evolve.

The large magnitude of interannual streamflow variability is an ongoing challenge for water managers. As seen in figure 3.4, water year streamflows in Colorado’s river basins vary by up to six-fold from year to year - more than the relative variability in precipitation and snowpack. This is because the fraction of precipitation and snowpack lost to evapotranspiration (see later in this chapter) is greater than average in dry years and less than average in wet years, magnifying the difference in runoff outcomes between dry and wet years. For example, a basin snowpack that is 90% of normal will tend to produce streamflows that are only around 80% of normal (Vano et al. 2012).

Observed Streamflow Changes

Since 2000, the average annual naturalized streamflows in all of Colorado’s major river basins have been lower than the 1951-2000 period (Figure 3.4). Naturalized streamflows are gaged streamflows that have been corrected for upstream diversions, depletions, and reservoir operations, making them more appropriate for monitoring long-term change. The largest relative reductions in flow have been seen in the Arkansas (-19%), South Platte (-18%), San Juan (-15%), and Gunnison (-13%), with smaller reductions in the Yampa (-3%), Colorado headwaters (-5%), and Rio Grande headwaters (-8%). A growing body of evidence indicates that the recent lower streamflows in Colorado have been driven not just by below-normal precipitation, but also by anthropogenic warming (Udall and Overpeck 2017; McCabe et al. 2017; Xiao et al. 2018; Hoerling et al. 2019; Albano et al. 2022; Milly and Dunne 2020). While most of these studies have focused on the Upper Colorado River Basin (i.e., Yampa, Colorado, Gunnison, San Juan) the general findings are applicable to Colorado’s other river basins. The rough consensus that emerges from these studies is that 20-50% of the observed reduction in streamflows since 2000 has been due to warmer temperatures. As discussed in Chapter 2, anthropogenic atmospheric changes may also have some role in the reduced precipitation since 2000, alongside natural variability.

Figure 3.4

Observed naturalized annual (water-year) streamflows for key gages in seven river basins, one from each of Colorado’s water division, from the early 1900s through 2019 or 2021, depending on the gage. The gage records have been corrected for upstream diversions and depletions to reflect the natural hydrology of the watershed. (Data sources: Yampa, Gunnison, San Juan, Colorado: Reclamation; South Platte: Denver Water; Rio Grande: CO DWR; Arkansas: J. Lukas based on Hydrosphere/Aurora Water)

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Future Streamflow Projections

As Colorado’s climate continues to warm over the next several decades (Chapter 2), the warmer temperatures will have increasing systemic impacts on the hydrologic cycle. Given the same amount of precipitation, annual runoff will be lower in a warmer climate—an effect that is already occurring as described above. It is uncertain how much Colorado’s climate will warm (Chapter 2), and it is not precisely known how sensitive the streamflow in Colorado’s river basins is to each increment of warming. However, the science is clear: further warming alone will push the water cycle towards reductions in streamflow and water supply.

Due to the pervasive impacts of warming, most of the plausible climate and hydrologic futures for Colorado’s river basins show decreasing annual runoff. Increases in runoff will occur only if there is a large future increase in precipitation. This general finding has been seen across many studies, using different sets of climate models, different downscaling methods, and different hydrologic models (Nash and Gleick 1991; Christensen et al. 2004; Christensen and Lettenmaier 2007; Reclamation 2011; CWCB 2012; Woodbury et al. 2012; Lukas et al. 2014; Alder and Hostetler 2015; Harding 2015; Lukas et al. 2020b; Reclamation 2021). In studies that have projected future changes in streamflow for multiple basins across Colorado, besides the Colorado River and headwaters, the overall tendency towards lower future flows is not as strong in the northwestern basins (Yampa and White), while it is strongest in the southern basins (San Juan, Rio Grande) (CWCB 2012; Lukas et al. 2014; Harding 2015).

Figure 3.5 shows the projected annual streamflow change under RCP4.5 between the historical baseline (1971-2000) and the 2050-centered period (2035-2064) for gages within seven major Colorado river basins, based on the set the hydrologic projections (CMIP5-LOCA-VIC) described above under future snowpack projections. In every basin, a large majority (65-80%) of the 32 projections indicate decreased streamflow in the 2050-centered period, even though most of the underlying climate model projections show at least slightly higher annual precipitation in the future period (Chapter 2). This difference between the precipitation outcomes and the streamflow outcomes reflects the impact of warming in reducing streamflow for a given amount of precipitation.

Figure 3.5

Projected future annual streamflow change for key gages in seven Colorado river basins for a 2050-centered period (2035-2064) relative to 1971-2000, from an ensemble of 32 CMIP5-LOCA-VIC hydrology projections under a medium-low emissions scenario (RCP4.5). The solid blue and red bars show the middle 80% of the projections (10th-90th percentiles); the two dashes show the minimum (red) and maximum (blue) projections; the open squares show the median projections. (Data: GDO-DCP,

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The relative impacts of warming and precipitation change on projected streamflow can be seen in Figure 3.6, which shows the 32 CMIP5-LOCA-VIC projections of streamflow change for the Colorado River near Dotsero as a function of that projection’s temperature increase and precipitation change. From the statistical relationships between streamflow change, temperature change, and precipitation change underlying this chart, we can estimate that an ensemble-average warming (~4°F) for 2050, relative to 1971-2000, coupled with no precipitation change would result in a streamflow decline of ~13%. Since about 1.5°F of warming has already occurred beyond the 1971-2000 average, this implies that a ~5% streamflow decline has already occurred, with another ~8% decline taking place by 2050, under the 4°F overall warming scenario. These estimates are consistent with previous studies suggesting that each 1°F increment of warming results in a streamflow reduction of 3-5% in Colorado river basins (Woodbury et al. 2012; Vano and Lettenmaier 2014; Milly and Dunne 2020). The minority of the projected climate futures that show increased streamflow in the 2050 period (Figure 3.6; blue bubbles) are all predicated on an increase in annual precipitation of at least 5%, enough to overcome the impacts of warming. Previous studies have found that a precipitation change of 1% results in a streamflow change of 2-3% (Vano and Lettenmaier 2014), consistent with what is seen here.

Figure 3.6

Projected change (%) in annual streamflow (values inside the circles) for the Colorado R. near Dotsero gage, as a function of the projected temperature increase (x-axis) and precipitation change (y-axis), for a 2050-centered period (2035-2064) relative to 1971-2000, from an ensemble of 32 CMIP5-LOCA-VIC hydrology projections under a medium-low emissions scenario (RCP4.5). (Data: GDO-DCP,

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The same analysis of CMIP5-LOCA-VIC projected streamflow, temperature, and precipitation for the South Platte River at South Platte shows a similar pattern as for the Colorado River, though with greater sensitivity to warming. For the South Platte, an ensemble-average warming (~4°F) for 2050 relative to 1971-2000, coupled with no change in precipitation, is associated with a streamflow decline of ~18%. If 1.5°F of that warming has already occurred, that implies a warming-driven streamflow reduction of ~7% to date, with ~11% yet to come by 2050 under the +4°F scenario. The magnitudes of these estimates are consistent with previous studies that have examined climate change impacts for the South Platte (Woodbury et al. 2012; Harding 2015).

As noted earlier in section 2.2, most CMIP5 projections show increased interannual variability in future precipitation for Colorado, implying that streamflow variability would also increase. The projections for the Upper Colorado River Basin (Lees Ferry) as analyzed for Lukas et al. (2020), also from the CMIP5-LOCA-VIC dataset, show increases in the coefficient of variation (CV) of annual streamflows from the late 20th century (1950-1999) to the mid-21st century (2025-2074) in about 70% of the projections.

Future changes in the annual volume of streamflow due to warming will also be accompanied by changes in the timing of streamflow, consistent with the shifts in timing that have already been observed in the past few decades. For streamflow timing, projections based on climate models show near-unanimity regarding the direction of change towards even earlier snowmelt, runoff, and peak streamflow.

Figure 3.7 shows the CMIP5-LOCA-VIC projections of monthly streamflow for the Colorado River near Dotsero for the 2050-centered period compared to the 1971-2000 baseline. There are systematic shifts in the streamflow for almost all months. For March, April, and May, monthly streamflow increases across the model projections as snowmelt and runoff is initiated earlier, and the hydrograph shifts from a sharp peak in June to a more of a “plateau” across May and June, with a May peak in some cases. Flows in June decrease in most projections, and then as the declining limb of the hydrograph shifts and steepens, flows in July, August, and September decrease sharply in all projections. Late fall and winter baseflows (Oct-Feb) are also lower in nearly all projections.

Note that this modeled shift in the hydrograph does not consider the effects of dust-on-snow deposition (Chapter 4), which also acts to shift snowmelt and runoff earlier in the spring. Especially in the southwestern basins of Colorado, which have experienced greater dust-on-snow impacts, a future increasing trend in dust-on-snow events and dust deposition could have a larger effect on shifting the runoff timing than climate change alone and would compound the warming-related shift (Deems et al. 2013; Painter et al. 2018).

Figure 3.7

Projected future monthly streamflows for the Colorado River at Dotsero for a 2050-centered period (2035-2064) from an ensemble of 32 CMIP5-LOCA-VIC hydrology projections (thin blue) under a medium-low emissions scenario (RCP4.5), and the simulated mean streamflow for the 1971-2000 period (black). (Data: GDO-DCP,

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3.4 Soil Moisture


Soil moisture is an important component of Colorado’s hydrologic cycle. Balanced soil water content is essential for the health of agricultural and natural ecosystems. Persistent low soil moisture levels will reduce agricultural yields, stress native vegetation, and reduce both the amount and predictability of Colorado’s water supply (Livneh and Badger 2020; Sazib et al. 2020; Goble et al. 2021). Extreme wet soil moisture anomalies, which are much less common in Colorado, are associated with increased flooding and pest and disease issues (Javelle et al. 2010; Chuang et al. 2012).

Soil moisture has an important feedback effect on weather. Part of the sun’s incoming energy evaporates water from bare soil and leads plants to transpire water from their leaves while carrying out photosynthesis. When soil moisture is unavailable, this fraction of the sun’s energy instead will directly heat the Earth’s surface. Low soil moisture also limits atmospheric water vapor near the ground, an important ingredient for generating thunderstorms. Particularly during summer, dry soils will act to sustain hot, dry atmospheric conditions until a large-scale weather system breaks the pattern (McKinnon et al. 2021).

Much of Colorado is classified as a semi-arid climate, which means some soil moisture limitations are to be expected in a normal year. Surface soil moisture gets recharged only after rain events and then used by plants for transpiration. Plants with access to only shallow soil moisture (less than 36”) must be able to survive for days-to-weeks during the growing season without water. Trees and deep-rooted plants can make use of deeper soil moisture supplied primarily from snowmelt.

In Colorado our understanding of soil moisture averages, seasonal variation, interannual variation, and trends at large spatial scales comes mainly from modeling. Land surface models (e.g., Noah, VIC), which use observed weather data as inputs, are often relied upon to enhance our understanding of Colorado soil moisture. The combination of the Snowpack Telemetry Network (SNOTEL) and Colorado Agricultural Meteorological Network (CoAgMET) offer a limited (< 20 years) observational record at high (8000+ feet) and low elevations. Additionally, it is difficult to interpolate between observations because soil moisture is dependent on soil type, which can change over short distances.

Root-zone soil moisture in Colorado typically peaks in the spring both at high and low elevations. High-elevation soil moisture increases sharply as the snowpack melts. Lower elevation soil moisture, particularly on the eastern plains, remains low through the dry season in winter but increases in response to cool, soaking rains in the spring. During the summer season, soil moisture decreases overall due to evapotranspiration but spikes in response to convective rain events. Soil moisture is typically lowest at the end of the growing season (early fall).

Observed Soil Moisture Changes

Soil moisture models typically do a poor job detecting long-term trends in soil moisture. Fan et al. (2004) showed that on a global scale, soil moisture models capture the seasonal cycle and spatial variability of soil moisture better than trends. Even so, Andreadis and Lettenmaier (2006) found significant decreases in soil moisture in southern Colorado from the mid-20th century to the early 21st century. Tobin et al. (2020) showed significant decreases in soil moisture for eastern Colorado in summer and fall.

Like precipitation, snowpack, and streamflow, soil moisture varies considerably from year to year based on weather patterns. Based on the Western Land Data Assimilation System (WLDAS) model analyses for elevations above 8000’ in Colorado, soil moisture in the uppermost 2 m (80”) has declined from 1980-2022 (Figure 3.8). Water year 2021 saw near record-low fall soil moisture conditions following a poor snowpack year and a hot and dry summer, including a record-warm August for western Colorado (NOAA National Centers for Environmental Information 2023).

Figure 3.8

Colorado volumetric water content in top 2m soils above 8000ft, for 1980-2022. WLDAS model reanalysis soil moisture in blue, with trend line in gray.

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Future Soil Moisture Projections

As the climate continues to warm, the evaporative demand of the atmosphere increases (see section 3.5), which will tend to drive more moisture from soils through both direct evaporation and transpiration from plants, i.e., evapotranspiration. In high-elevation areas where most soil moisture derives from snowmelt, warming temperatures will act to reduce the snowpack through sublimation even before that snow water has a chance to enter the soils.

Previous studies projecting future (~mid-21st century) soil moisture for Colorado’s key runoff-producing basins have consistently shown widespread declines in summer (June-August) soil moisture (typically, down to 2 m/80”) regardless of the ensemble of climate models and hydrologic models used (Ray et al. 2008; Reclamation 2012a; Ayers et al. 2016). These analyses also showed increases in spring (March-May) soil moisture as snowmelt shifts earlier (see section 3.4) and leads to a saturated soil column earlier in the year relative to historical conditions.

Figure 3.9

Projected future changes in monthly soil moisture (0-2 m depth) for the watershed above the Colorado River near Dotsero for a 2050-centered period (2035-2064) from an ensemble of 32 CMIP5-LOCA-VIC hydrology projections under a medium-low emissions scenario (RCP4.5). The solid blue and brown bars show the middle 80% of the model projections (10th-90th percentiles); the two dashes show the minimum and maximum projections; the open squares show the median projections. (Data: GDO-DCP,

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Figure 3.9 shows the CMIP5-LOCA-VIC projections of change in monthly total soil moisture (0-2 m/0-79” depth) for the watershed above the Colorado River near Dotsero for the 2050-centered period, compared to the 1971-2000 baseline. The pattern in monthly and seasonal changes is consistent with the previous studies. Most of the individual projections show increases in spring (April-May) soil moisture, due to earlier snowmelt, but nearly all projections show decreases in the summer, fall, and winter months, driven by warming temperatures. Since soil moisture—along with the snowpack—acts as the interface between the atmosphere and streamflow, it is not surprising that these projected changes in soil moisture appear very similar to the projections of change in monthly streamflow (Fig 3.7 above).

3.5 Evapotranspiration


Evapotranspiration (ET) encompasses evaporation from soils and open water, transpiration from plants and crops, and sublimation from the snowpack. On a statewide basis, about 80% of the precipitation that falls on Colorado returns to the atmosphere through ET before reaching a stream or aquifer. The relatively cool and wet high mountain areas experience smaller fractional ET losses, but typically amount to 30-50% of annual precipitation (Sanford and Selnick 2013).

ET technically refers to the actual loss of water from the land surface — thus, the alternative abbreviation AET (actual evapotranspiration) is used for clarity. The magnitude or rate of AET is constrained by the water that is available to evaporate or transpire (Figure 3.10). After soils and vegetation are fully dried out, no more AET can occur. So cumulative AET over an extended period (e.g., 12 months) will not exceed cumulative precipitation - or in the case of irrigated cropland, precipitation plus the depth of irrigation water.

Evaporative demand (E0) and its equivalent, Potential Evapotranspiration (PET), are measures of the atmosphere’s “thirst” for surface moisture, and thus the potential loss of water from the land’s surface. E0 and PET can exceed and often do exceed AET over any given period. The conceptually similar Reference ET (ET0) is an estimate of the upper bound of ET losses given a particular crop that is fully irrigated. Reference ET is the measure of evaporative demand that is usually reported by agricultural weather station networks such as CoAgMET.

Evaporative demand increases with warmer temperatures, greater solar radiation, lower humidity, and higher winds. Of these, temperature is usually the most important in explaining the level of evaporative demand. PET, E0, and Reference ET are best estimated using a “fully physical” equation such as Penman-Monteith that inputs all four variables: temperature, solar radiation, humidity, and winds; methods using only temperature (Thornwaite, Hargreaves, Blaney-Criddle) have larger errors in real-time monitoring (Sentelhas et al. 2010) and are also problematic for modeling future conditions (Reclamation 2012b).

The tight coupling between temperature and evaporative demand reflects a basic physical relationship, as well as an important feedback mechanism. First, warmer air can hold more moisture than cooler air, as governed by the Clausius-Clapeyron equation. This means that under warmer temperatures — if nothing else changes — evaporative demand increases. This general increase in evaporative demand (PET) with higher temperatures is clearly seen in the seasonal cycle shown in Figure 3.12. Second, when the soils and vegetation dry out seasonally or periodically (i.e., drought) — which often involves increased evaporative demand — a feedback mechanism occurs: More of the sun’s energy heats the surface and the atmosphere above it, rather than going towards evaporating moisture. This drives faster warming, and lower humidity of the air, thus increasing evaporative demand more rapidly (Figure 3.10, right panel).

Figure 3.10

Schematic showing how under high soil moisture and water availability, Actual Evapotranspiration (AET) can have the same magnitude as evaporative demand. With time, if the soils dry out, evaporative demand will often increase (as air temperature rises and humidity decreases in response to the now-dry land surface) but AET decreases, limited by the lower amount of water available at the surface to evaporate and transpire. (Modified from Lukas et al. 2017, The EDDI User Guide).

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AET is much more challenging to measure than evaporative demand. AET can be estimated using a land-surface (hydrology) model with meteorological inputs, or by assimilating satellite observations of land-surface temperature (which reflects evaporation losses) into an energy-balance model. In-situ measurements of AET can be made using Eddy Covariance (EC) methods, in which several instruments at different heights on a tower measure the vertical transfer of energy and moisture between the surface and the atmosphere.

Observed Evapotranspiration Changes

Since rising temperatures, all else equal, will lead to higher evaporative demand (E0, PET, Reference ET), it is reasonable to expect that trends in evaporative demand would reflect the strong warming trend in Colorado over the last several decades. And in fact, upward trends in evaporative demand for Colorado have been found in several studies, using different observational datasets (Ficklin et al. 2015; McCabe and Wolock 2015; Vicente‐Serrano et al. 2020; Albano et al. 2022).

Most recently, a comparison of recent U.S. trends in Reference ET (1980-2020), using five gridded observational datasets, found that the average of the datasets show increasing Reference ET trends across all of Colorado, with the largest increases seen in southeastern Colorado, moderate increases in the northeastern and far southwestern parts of the state, and the smallest increases in the northwest part of the state (Albano et al. 2022). Examination of the four meteorological components of evaporative demand shows that decreased humidity and increases in solar radiation only contributed a small amount to the observed increase in evaporative demand.

Figure 3.11 shows Colorado statewide PET for the growing season (April-September) since 1980, from the gridMET gridded observational dataset, one of the five used in the Albano et al. (2022) study, and whose results are closest to the average across all five datasets. While there is large variability from year to year, the upward trend is clear; statewide PET has increased about 5% over the 1980-2022 period.

Figure 3.11

Observed Colorado statewide Potential Evapotranspiration (PET) over the April-September growing season, 1980-2022. Dashed line shows the linear trend over that period. (Data: gridMET via Climate Toolbox; historical-climate-tracker).

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Future Evapotranspiration Projections

Given the continued and substantial warming projected for Colorado over the next several decades, further increases in evaporative demand in Colorado are extremely likely. Figure 3.12 shows the projected changes monthly PET under RCP4.5 between the historical baseline (1971-2000) and the 2050-centered period (2035-2064) for the watershed of the Colorado River above Dotsero, using the same projection dataset (CMIP5-LOCA-VIC) in the preceding sections.

Figure 3.12

Projected future monthly Potential Evapotranspiration (PET) for the watershed above the Colorado River at Dotsero for a 2050-centered period (2035-2064) from an ensemble of 32 CMIP5-LOCA-VIC hydrology projections (thin red) and median (thick red dashed) under a medium-low emissions scenario (RCP4.5), and the simulated mean streamflow for the 1971-2000 period (black). (Data: GDO-DCP,

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Note the extreme seasonality in PET; about 90% of annual PET occurs from April through September. In each month of the year, all 32 projections show increased PET; total April-September PET increases 10% to 20% by 2050 compared to the 1971-2000 baseline. Comparing the PET change with the temperature change in the same 32 model runs, it is clear that temperature is the primary driver of increased PET. Each 1°F of warming leads to about a 4% increase in PET, with a 4°F warming associated with a 15% increase in PET. A similar downscaled projection dataset (CMIP5-MACA), also paired with the VIC hydrologic model, suggests that increases in PET of roughly similar magnitude (~4%) for each 1°F of warming will occur across all elevations both east and west of the Continental Divide.

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