Overconsumption Gravely Threatens Water Security in the Binational Rio Grande-Bravo Basin

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Richter, Karem Abdelmohsen, Sameer Dhakal, James S. Famiglietti, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7313321/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Nov, 2025 Read the published version in Discover Water → Version 1 posted 17 You are reading this latest preprint version Abstract The Rio Grande-Bravo basin shared by the United States and Mexico is experiencing a severe water crisis demanding urgent attention. In recent decades, water storage reservoirs, aquifers, and annual streamflow volumes have been substantially depleted, leaving little buffer for continued over-consumption of renewable water supplies. Despite the great scarcity of water and intensifying water shortages in this basin, a full accounting of the river’s consumptive uses and losses has never been undertaken. In this study we assemble detailed water consumption estimates from a broad array of sources to describe how surface and ground water were consumed for both direct uses (agricultural, municipal, commercial, thermoelectric power generation) and indirect uses (reservoir evaporation and riparian evapotranspiration) in each of 14 sub-basins during recent decades. We find that only half (48%) of water directly consumed for anthropogenic purposes is supported by renewable replenishment; the other half (52%) has been unsustainable, meaning that it is causing depletion of reservoirs, aquifers, and river flows. The over-consumption of renewable water supplies is primarily due to irrigated agriculture, which accounts for 87% of direct water consumption in the basin. At the same time, water shortages have contributed to the loss of 18% of farmland in the river’s headwaters in Colorado, 36% along the Rio Grande in New Mexico, and 49% in the Pecos River tributary in New Mexico and Texas. Farmland contraction in the US portion of the basin has resulted in lowered irrigation consumption and many cities have been able to reduce their water use as well, but irrigation in the Mexican portion of the basin has increased greatly, causing basin-wide consumption to remain high. This severe water crisis presents an opportunity for envisioning a more secure and sustainable water future for the basin, but a swift transition will be needed to avoid damaging consequences for farms, cities, and ecosystems. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Water scarcity has a long history in the binational Rio Grande-Bravo (RGB)[10] basin. The RGB is the fifth longest river in the United States (~3,000 km), but its paltry flow of water belies its ‘grand’ name; the river conveys less water than the 50 th longest river in the country [1]. The river flowed perennially throughout its length until the late 19 th century, when intensive irrigation in the San Luis Valley headwaters in Colorado began to dry the snowmelt-fed river during the irrigation season, resulting in diminished flows as far downstream as El Paso, Texas (Figure 1) [2,3]. As water consumption increased throughout the rest of the basin during the past century, the river began to dry up entirely since the 1950s [4] in the “Forgotten Reach” from Fort Quitman to Presidio (Figure 1) and has intermittently failed to reach its delta at the Gulf of Mexico [5,6] since 2001. In recent decades, river drying has expanded to previously perennial stretches in New Mexico and the Big Bend region [7,8]. Today, only 15% of the estimated natural flow of the river remains at Anzalduas, Mexico near the river’s delta at the Gulf of Mexico [9]. A multi-decadal megadrought has substantially reduced water supplies in the RGB. Since 2000, snowmelt inflows from the basin’s headwaters in Colorado have been 17% lower than during the 20 th century [10]. Climate scientists have reframed the long-running drought as the onset of long-term aridification and are forecasting additional river flow diminishment of 16-28% in coming decades as the climate continues to warm [11]. Despite attempts by both urban and agricultural water users to reduce their consumption [12], a persistent imbalance between water consumption and replenishment has led to severely depleted reservoirs, aquifers, and river flows throughout the RGB basin. As a result, farmers dependent upon irrigation and numerous cities face an existential water crisis. In the San Luis Valley of Colorado (Figure 1), diminished river flows have led to increased groundwater pumping, causing aquifer levels to plummet [13]. The Colorado state engineer has threatened to shut off hundreds of groundwater wells if the aquifer supporting irrigated farms cannot be stabilized [14]. In New Mexico, reservoir storage fell to just 13% of capacity by the end of 2024, and reservoirs in the Rio Conchos sub-basin of Mexico have fallen to under 20% of their capacity, increasing the vulnerability of drying up completely with just one or two more dry years. Reduced water supplies have also led to political and legal disputes among state and national governments. In 2013, Texas sued New Mexico in the US Supreme Court over repeated failure to receive its annual water allocations as specified under the interstate Rio Grande Compact [15]; the case has yet to be decided. Mexico’s shortfalls in delivering water to the US as specified under the 1944 “Treaty on Utilization of Waters of the Colorado and Tijuana Rivers and of the Rio Grande” has raised tension between the two national governments [15–17]. 1.1 Impacts on biodiversity, farmers, and cities The RGB basin is renowned for its biological diversity and endemism, supporting more than 130 mammals, 3,000 plant and 500 bird species [2]. Wetlands and riparian forests supported by the river are critically important to birds migrating along the Central Flyway [18]. Nearly half of the basin’s native fish species are found nowhere else, but flow depletion has become a major factor in the imperilment of at least 75 freshwater species supported by the river system [19]. The river and underlying aquifers provide drinking water for more than 11 million people in Mexico and 4 million in the US and are used to irrigate more than 7800 km 2 (1.9 million acres) of farmland in the two countries [2]. As a result of decreased river flows and depleted water storage, farmers on both sides of the border have experienced severe reductions in irrigation supplies from surface water, leading to increased groundwater pumping [17]. In many recent years, no surface water has been available after June in a region with a growing season typically lasting through October. Summer monsoon rains in 2025 provided farmers in the Middle Rio Grande Conservancy District in central New Mexico two extra weeks of irrigation until mid-July, while farmers in the Rio Conchos basin of Mexico faced the likelihood of not receiving any surface water supplies at all. Rapidly growing urban populations and industries in the RGB basin are also becoming increasingly vulnerable to water scarcity [20]. Despite the great scarcity of water and intensifying water shortages in the RGB basin, a full accounting of consumptive water uses and losses has never been undertaken. In this study we assemble detailed estimates of water consumption (water withdrawals minus return flows) from a broad array of sources to reveal how surface and ground water was consumed in each of 14 sub-basins during recent decades. We have also developed estimates of changes in reservoir storage, groundwater volumes, and river outflows to quantify the degree to which annual water consumption has exceeded annual replenishment in recent decades. Results 2.1 Accounting for consumptive water uses An important first principle in resolving water scarcity is to develop a sound understanding of how water is consumed in a river basin [21]. Such accounting enables consideration of which water-use sectors may be able to reduce water use to the degree needed to rebalance consumption with replenishment. In this study we have fully accounted for all water consumed in the Rio Grande-Bravo basin, based on three “direct use” categories and two “indirect use” categories. The direct use categories – in which water is directly used for human purposes – include municipal & commercial; thermoelectric generation at power plants; and crop irrigation including a small volume of water exported out of the basin for agricultural use. The agricultural use is further differentiated by individual crops grown in the basin to help facilitate discussions about the potential water benefits of transitioning to alternative crop mixes [22]. The indirect categories include reservoir evaporation and riparian evapotranspiration (ET). The annual average volume of water consumed in each category has been estimated for the entire basin and each sub-basin shown in Figure 2, with results summarized in Figure 3 and Table 1 (a tabular summary in Imperial units is provided in Supplementary Information Table SI-1). 2.2 Irrigated agriculture dominates water consumption As indicated by Table 1 and Figure 3, agriculture is the dominant direct water consumer in the basin, accounting for 87% of direct consumption and 39% of total consumptive use basin wide. In some sub-basins such as the Rio Grande in Colorado, or the Rio Conchos in Mexico, virtually all direct consumption goes to irrigated farms. Overall, agricultural consumption is nearly seven times the volume of all other direct uses combined. Within agriculture, cattle-feed crops (i.e., alfalfa, grass hay, and pasture) account for 56% of irrigation water consumed. These crops are particularly dominant in the Northern New Mexico and Middle New Mexico sub-basins, where they consume almost all irrigation water (Table 1). 1. Trends in water consumption As mentioned previously, snowmelt runoff (the primary source of water supply) has decreased in the RGB basin by 17% in the 21 st century. At the same time, direct water consumption has been increasing at the basin level since 2000, at the average rate of 25.8 million cubic meters/year (MCM/yr), equivalent to 20,954 acre-feet/year (AF/yr). Direct consumption has decreased on the US side at an average rate of 43.1 MCM/yr (34,960 AF/yr), primarily due to reductions in the total area of irrigated farmland. However, these gains were more than offset by increases on the Mexican side of 68.9 MCM/yr (55,914 AF/yr). Figure 4 illustrates consumptive use trends in each direct water-use category, and Figure 5 shows trends in agricultural consumption, which is by far the largest direct water-use category (trends in individual crops are illustrated in Supplementary Information Figure SI-1). 2. Indirect water uses are substantial Indirect uses and losses of water account for 56% of overall water consumption in the RGB basin. Reservoir evaporation accounts for 12% of water consumption. The two biggest reservoirs on the river – Amistad and Falcon – account for 38% of all reservoir evaporation. Riparian ET is the single largest water consumption category, accounting for 44% of total water consumption. 2. Impacts of Overconsumption on Reservoir and Aquifer Storage and Basin Outflows A persistent imbalance between water consumption and supply has severely depleted water sources in the RGB basin. The volume being depleted ‘feeds’ or enables the overconsumption, creating a feedback loop which in turn further accelerates depletion [23]. We estimated the annual average volume of depletion in each water source during 2002-2024 that helped support the consumptive uses summarized in Table 1. The three trends used to evaluate overconsumption include volumetric changes in reservoir storage, aquifer storage, and river outflows. We assessed annual average volumetric changes for each of these three components for the four focal areas identified in Figure 2 and the entire RGB basin, for a common period of 2002-2024. We note that some of the most severe depletions in these water sources occurred during 2000-2001, but data available on groundwater losses do not begin until 2002 (see Methods) so trends in the three water sources were assessed with 2002 as the starting point. 3.1 Loss of reservoir storage An estimated 12% of all water stored in basin reservoirs has been lost since 2002 (average annual loss of 177.6 MCM/yr (143,945 AF/yr). The loss of reservoir storage has been most severe in New Mexico, where 71% of the reservoir storage that existed at the start of 2002 was gone by the end of 2024 (Figure 6). 1. Groundwater depletion Groundwater depletion in the RGB basin has been much more severe (roughly 15 times greater) than reservoir depletion (Figure 7). As surface water supplies have declined over recent decades, groundwater pumping has increased substantially [24]. An estimated 2,700 MCM/yr (2,189,000 AF/yr) of groundwater loss occurred during 2002-2024, with the Pecos River (950 MCM/yr; 770,179 AF/yr), Rio Grande New Mexico (630 MCM/yr; 510,750 AF/yr), and the Rio Conchos (490 MCM/yr; 397,250 AF/yr) experiencing the greatest overdrafts. 3.3 Depletion of river outflows In addition to the loss of water stored in reservoirs and aquifers, it is also important to assess trends in annual river flow volumes. Changes in total outflow provide another indicator of the sustainability of water consumption within a region and can be counted as another component of water overdraft in the basin. River outflow measured at the RGB basin outlet near Brownsville, Texas indicates annual volumetric outflow declines averaging 22.4 MCM/yr (18,147 AF/yr) since 2002. We note that this indicates ongoing (further) depletion of an already heavily depleted river that had lost ~85% of its flow prior to 2002. 3.4 Quantifying the proportion of direct consumption that is unsustainable The three sub-components of change used to evaluate overconsumption include reservoir storage, aquifer storage, and river outflow volumes. Summing these three sub-components at the full basin level results in water depletion totaling 2,900 MCM/yr (2,351,092 AF/yr) since 2002. By dividing the total average volume of depletion by the average volume of direct water consumption in each sub-area we obtain estimates of the degree (%) of overconsumption (i.e., unsustainable use). At the whole basin scale, 52% of direct consumption is unsustainable. However, overconsumption is much greater in some sub-areas of the basin such as in the Pecos River in New Mexico and Texas, where 140% of all water directly consumed is unsustainable (overconsumption is 66% of total water consumption), and in the Rio Grande New Mexico sub-basins where 85% of direct consumption is unsustainable. Table 2 provides over-consumption estimates for the overall basin and four focal areas identified in Figure 2. Figure 8 illustrates the role of water depletion in supporting the RGB basin’s overconsumption. Table 2. Estimates of water resource trends in sub-areas of the RGB basin . All values in MCM/yr with AF/yr in parentheses. The Rio Grande in New Mexico focal area includes three sub-basins as indicated in Figure 2: Rio Grande Northern New Mexico, Rio Grande Middle New Mexico, and Rio Grande Southern New Mexico. The Pecos River focal area includes two sub-basins: Pecos River New Mexico and Pecos River Texas. Annual values are based on the slope of linear regression trendlines across 23 years of daily or monthly volumetric data. Region Reservoir trend Aquifer trend River outflow trend Total volumetric change Total direct water consumption Percent over-consumption (unsustainable) Rio Grande, Colorado +0.3 (+223) -110 (-89,179) +0.9 (+ 718) -108.8 (-88,238) 995.9 (807,412) 11% Rio Grande, New Mexico -25.5 (-20,662) -630 (-510,750) -9.7 (-7,852) -665.2 (-539,264) 780.0 (632,411) 85% Pecos River, New Mexico & Texas +2.8 (+2,268) -950 (-770,179) -0.6 (-472) -947.8 -768,383 675.7 (547,811) 140% Rio Conchos +1.6 (+1,260) -490 (-397,250) -0.01 (-11) -488.5 (-396,001) 898.4 (728,375) 54% Entire Basin -177.6 (-143,945) -2700 (-2,189,000) -22.4 (-18,147) -2900.0 (-2,351,092) 5574.6 (4,519,434) 52% Discussion and Conclusions The water scarcity challenges within the RGB basin have received much less attention from media outlets and national policymakers as compared to the Colorado River Basin (CRB) in the American Southwest. This can largely be explained by the comparatively smaller volume of water it carries (natural flows of 11,225 MCM/yr (9.1 million AF/yr)) in the RGB [25] vs. 18,996 MCM/yr (15.4 million AF/yr)) in the CRB [26]), as well as the smaller population it serves with drinking water (15 million in RGB vs. 40 million in CRB ) and the area of irrigated farmland it supports (7,800 km 2 in RGB vs. 22,300 km 2 in CRB) [2]. However, the water crisis facing the RGB is arguably more severe and urgent than the CRB, as illustrated by these conditions: 1. Groundwater in the San Luis Valley of Colorado has been depleted at a rate of 110 MCM/yr ((89,179 AF/year), equivalent to 11% of the annual average of direct water consumption in the valley (Table 2). The state’s engineer has threatened to shut off hundreds of groundwater wells if the aquifers cannot be stabilized and restored to near 1976 levels [27,28]. A water conservation program has been charging farmers to pump groundwater and has paid farmers since 2012 to fallow farm fields voluntarily, yet aquifer levels continue to fall [29]. Efforts to recover these aquifers have been greatly impacted by 17% declines in snowmelt runoff this century. 2. New Mexico has fallen into water debt to Texas under the terms of the interstate Rio Grande Compact, leading to a lawsuit before the US Supreme Court [15]. New Mexico’s cumulative debt to Texas of 153 MCM (124,000 AF) as of December 2024 is equivalent to 20% of the annual average direct water consumption in sub-basins along the Rio Grande. New Mexico’s water debt poses an extremely difficult challenge for the state given that it has lost much of its reservoir and aquifer storage in recent decades, and climate warming is projected to further reduce the state’s water supplies by 16-28% in coming decades [11]. 3. In recent years, Mexico has fallen into mounting water debt to the US under the terms of the 1944 Treaty on Utilization of Waters of the Colorado and Tijuana Rivers and of the Rio Grande [30]. Mexico’s water debt is causing political conflict between the two countries. The current federal administration in the US asserts that Texas farmers along the lower Rio Grande-Bravo have been unduly suffering from water shortages [31]. Texas farmers in the lower valley experienced losses of nearly USD$500 million in 2024 due to insufficient irrigation supply [32]. At the same time, Mexican farmers are also experiencing irrigation shortfalls and associated crop losses [17]. The severe water crisis emerging in the RGB basin requires urgent action. At the end of 2024, total reservoir storage in the basin stood at just 26% of capacity (~4.24 million AF; Figure 6) and has continued to decline in 2025. Within the Rio Conchos sub-basin in Mexico, reservoirs were at 20% of capacity at the end of 2024. In New Mexico, reservoirs were at 13% of capacity within the Rio Grande sub-basins, and 12% in the Pecos River sub-basins in New Mexico and Texas. Without a substantial reduction of water consumption, a repeat of the four-year rate of reservoir depletion experienced in 2010-2013 would completely wipe out all remaining water stored in the RGB basin. All remaining water stored in New Mexico reservoirs could be gone in just one or two low runoff years. Notably, large cities such as Albuquerque and El Paso have been able to substantially lower their water use even while their populations have grown rapidly in recent decades. For example, Albuquerque’s population grew by 40% while its water use declined by 17% [33], and El Paso’s population grew by 36% while water use declined by 6% [34]. Additional urban water conservation could help alleviate water over-consumption in the basin. However, with continued rapid population growth and development of water-intensive data centers [35] most cities will be challenged to simply hold water use levels constant in coming years. Even if cities and power plants in the basin were able to stop using water altogether, it would reduce the basin’s overconsumption by only 25%. Most of the needed water savings must come from irrigated agriculture, given its dominant 87% share of direct water consumption. Proven strategies for reducing consumption in irrigated agriculture exist [22,36] but must be rapidly deployed at sufficient scale and financially sustained by governments, companies, and credit institutions to rebalance the basin’s water budget at sub-basin, state, and binational levels. Achieving necessary levels of reduction in consumptive use will be extremely difficult given the high levels of over-consumption existing presently (Table 2), as well as expected declines in water availability due to climate change and associated increases in fire frequency and severity (see Supplemental Information for discussion of fire impacts). Understandably, farmers are strongly resistant to mandatory cutbacks in water use as irrigation is essential to their livelihoods in the dry climate of the RGB basin. For instance, when the Mexican federal government began releasing water from a large dam on the Rio Conchos in 2020 to comply with the international water treaty between Mexico and the US, more than 2,000 protestors fearing loss of their scarce irrigation supplies erupted into violence, resulting in the death of a protestor [30,37]. Political leaders and water managers have been loath to mandate water-use reductions in the face of such resistance and hostility. Voluntary water conservation programs that pay farmers to temporarily fallow farmland have been implemented in both Colorado and New Mexico, but these programs have never achieved targeted reduction levels due to an inability to offer sufficient financial incentives to participants. Given the recent elimination or reduction of US federal programs that have helped finance these water conservation programs, the funding challenge will fall largely on already-strained state and local budgets. If water consumption cannot be substantially reduced through regulatory or incentivized means, a likely outcome will be continued loss of farmland due to insolvency [38] from lowered crop production and other factors, including the aging of farmers and lack of affordable farm labor. Our analysis reveals that during 2000-2019, Colorado lost 18% of its farmland in the RGB basin, New Mexico lost 28% along its Rio Grande sub-basins, and the Pecos River sub-basin lost 49%. At this rate of farmland retirement and associated reductions in water consumption (Figure 5), assuming no changes in other consumptive uses, the Rio Grande sub-basin in Colorado might eliminate the volume of annual deficit shown in Table 2 in 11 years. However, it would require 59 years to attain balance between water consumption and replenishment in the Pecos River sub-basin in New Mexico and Texas, and 95 years along the Rio Grande sub-basins in New Mexico. These projections do not account for climate change-induced reductions in water replenishment in coming decades. Reaching a balance between consumption and replenishment in Mexican sub-basins will require a reversal of the recent trends of increasing irrigation in the Mexican sub-basins. It is important to acknowledge that urban and agricultural water conservation programs have thus far been unable to produce the reductions in water consumption required to stabilize – much less restore – the water sources within the RGB [27]. It is also highly unlikely that imported or desalinated water sources will resolve this crisis, given that the costs of such importation or treatment will not be affordable for farmers. The unaffordability of new supplies leaves only three plausible non-exclusive options: (1) transform the agricultural landscape to produce crops that require little to no irrigation [22]; (2) financially incentivize ‘deficit irrigation’ to substantially reduce the duration of irrigation during the growing season [39]; or (3) permanently convert some portion of irrigated farmland to other uses such as wildlife habitat, solar farms, or other purposes that require much less water. If water consumption for municipal, commercial, and thermoelectric power generation remains constant, irrigation reductions would need to be at the level of over-consumption indicated in Table 2. Given severe groundwater depletion across the RGB basin, an immediate first step could be to place a moratorium on new wells in over-drafted areas of the basin (Figure 7). Moratoriums would need to be supplemented with sub-basin groundwater caps (limits) that reduce the total volume of pumping to a level that halts groundwater depletion and stabilizes aquifer levels. Wight et al. (2025) [40] surveyed nearly 50 global case studies where caps on water diversions and pumping have been imposed; many of these cases focused on aquifer management, and many have successfully averted or reversed groundwater depletion. However, it is also important to note the causes of failure highlighted by Wight et al., including inabilities to enforce such restrictions adequately. Moratoriums have been set for many over-exploited aquifers in Mexico including in the RGB basin, but these efforts have been plagued by enforcement shortcomings [41]. This water crisis presents an opportunity for the residents of the RGB to envision a new, more sustainable water future. The ‘Multi-benefit Land Repurposing Program’ underway in the water-stressed Central Valley of California provides one example of productive community dialogue around possible future scenarios [42]. The “Exploratory Scenario Planning” approach being advanced by the Lincoln Institute of Land Policy in various communities in the western US similarly offers ways to engage local communities in planning for their water future [43]. Any transformational strategies will require careful and inclusive planning, provision of strong financial incentives for farming communities to facilitate needed changes, and wide support from water management agencies and decision makers to ensure water and food security for the region. Alternate pathways toward a sustainable water future are available for the RGB basin, but time is of the essence in correcting the highly unsustainable conditions that presently exist. Methods 5.1 Estimating municipal & commercial water consumption for US sub-basins Municipal and commercial consumptive use estimates were sourced from the United States Geological Survey’s (USGS) public supply reanalysis dataset for 2000–2020 [44,45]. This dataset applies a machine learning model to refine water use estimates using existing USGS data and supplemental inputs from local, state, and federal sources, aiming to improve spatial and temporal resolution and standardize estimation methods across sectors. The public supply delivery model provides monthly estimates of withdrawals and consumptive use (in million gallons per day) for domestic, commercial, industrial, institutional, and irrigation (MCI/CII) uses at the Hydrologic Unit Code 12-digit (HUC12) or Water Service Area (WSA) scale. For this study, we used total consumptive use (surface water + groundwater) estimates at the HUC12 level, reflecting the complex and dynamic interactions between surface and groundwater in the basin. For each HUC12 unit h , we aggregated the monthly public supply consumptive use estimates for each year t and multiplied the sum by 365.25 to convert to annual total use in million gallons. This produced annual consumptive use estimates for each HUC12 from 2000 to 2020. These values were spatially referenced by merging with the USGS’s Watershed Boundary Dataset [46], which defines the geographic extents of each HUC12. To assign these calculated values to the relevant sub-basins in our study area, we used spatial intersection [47]. Because some HUC12s extend across multiple sub-basins, we apportioned their annual consumptive use proportionally based on the area of overlap. Specifically, for each intersecting pair of HUC12 h and sub-basin s , the portion of h 's annual use attributed to s was weighted by the ratio of the area of h within s to the total area of h . It is important to note that our MCI estimates do not include self-supplied domestic and industrial water use, which we know are present in the basin [48], so are almost certainly an underestimate. 5.2 Estimating municipal & commercial water consumption in Mexican sub-basins Estimates of 2000-2020 annual water use for domestic and manufacturing water consumption in Mexico were obtained from the WaterGAP global hydrologic modeling team at the University of Kassel, Germany [49]. The gridded WaterGAP water use data were aggregated to the RGB sub-basins in a Geographic Information System. 5.3 Estimating thermometric power plant water consumption for US sub-basins Thermoelectric power plant water use values for the US were obtained from the recent USGS reanalysis of the thermoelectric power plant water use dataset [50]. This dataset provides monthly water withdrawal and consumption values from 2008 to 2020 at the HUC-12 scale. Using the HUC-12 basins on the US side of the RGB basin, we summed the thermoelectric water use values into their respective HUC-8 basin-scale membership. To obtain water use estimates for missing data between 2000 and 2007, we developed a backcasting Seasonal Autoregressive Integrated Moving Average (SARMIA) time series model [51,52]. Individual backcasting SARIMA models were fit to each HUC-12 sub-basin with recorded thermoelectric power plant records to capture local temporal dynamics and improve accuracy in reconstructing historical trends. For detailed model specifications and results, see Supplemental Information discussion including Table SI-3 and Figures SI-3–SI-10. 5.4 Estimating thermoelectric power plant water consumption for Mexican sub-basins Annual estimates of thermoelectric water consumption for the Mexican portion of the RGB basin from 2000 to 2020 were obtained from the Estadísticas del Agua en México annual reports published by the Sistema Nacional de Información del Agua [53]. However, there were missing annual reports for the years 2000, 2001, 2002, and 2020, and the annual reports of 2004, 2005, 2008, 2009, and 2013 reported a withdrawal value but not a consumptive value for the RGB basin. We applied a linear interpolation across the 2000-2020 period to address these data gaps. The resulting annual values for the RGB basin were then proportionally allocated to individual Mexican HUC-8 basins based on the installed total generation capacity (in megawatts) and the operational start year of each thermoelectric power plant. Information on installed plant generation capacity and commissioning dates was manually collected from the Global Energy Monitor Wiki [54]. For a detailed explanation of the interpolation and proportional allocation processes, see Supplemental Information discussion including Tables SI-4 and SI-5. 5.5 Estimating crop water consumption for US sub-basins We determined volumetric crop water requirements (VCWR) for 30 major crops (see Table 3 for acreage estimated for the RGB basin), accounting for 94.2% of the total irrigated area in the US [55], for the period 2000-2019. The VCWR for each crop was calculated by taking the product of the crop water requirement (CWR), measured as the depth of consumed water per unit area, by a crop’s irrigated harvested area within each 2.5 arc-minute grid cell within the conterminous United States (CONUS). The CWR for these crops was adapted from Modeled Irrigated Agriculture of the United States (MirAG-US), which provides monthly blue and green CWR at a 2.5 arc-minute resolution for CONUS [56]. The irrigated harvested areas for the 30 major crops were obtained from HarvestGRID, which provides yearly crop-specific irrigated acreage, at a 2.5 arc-minute resolution across CONUS [57]. We aggregated the monthly blue and green CWR to obtain yearly CWR and calculated the crop-specific VCWR using the corresponding yearly CWR and irrigated harvested area. For pastureland, which MirAG-US does not account for but is widely irrigated in the basin, we estimated VCWR by leveraging CWR, both blue and green, for alfalfa from MirAG-US. For locations where pasture/hay was cultivated within individual subbasins, we obtained the annual green CWR for pastureland by averaging the green CWR for alfalfa at a subbasin scale. We estimated the annual blue CWR for pastureland at the subbasin level by adjusting the blue CWR for alfalfa using the reported applied irrigation depth from the Irrigation and Water Management Surveys for the years 2003 [58], 2008 [59], 2013 [60], and 2018 [55]. The adjustment factor was derived by calculating the state-average irrigation depth ratio between pastureland and alfalfa in New Mexico, where most of the study area is located. Subsequently, this adjustment factor was multiplied by the annual alfalfa blue CWR for each 2.5 arc-minute grid cell and then averaged across the subbasin. To identify irrigated pasturelands at the subbasin level, we combined National Land Cover Database (NLCD) [61] 30m resolution rasters with the corresponding Landsat-based National Irrigation Dataset (LANID) [62] 30m resolution rasters. Since NLCD classifies both pasture and hay under a single category, we calculated the effective pastureland area of each subbasin by subtracting irrigated other hay areas from irrigated pasture/hay areas from NLCD. For 2008-2019, a combination of the Cropland Data Layer (CDL) [63] and LANID were used to identify irrigated other hay, which was then subtracted from NLCD’s pasture/hay category. For pre-2008 years when CDL data were not available, we used average subbasin-level irrigated area for other hays calculated from available years, which was then subtracted from NLCD to estimate irrigated pastureland. The VCWR for pastureland was determined at the subbasin level according to Equation 1. Where, subscripts denote the crop type and superscripts NLCD and CDL denote the data sources of irrigated crop acreage We note that the adjustment factor is applied only to blue CWRs. Table 3. Average crop acreage for 26 major crops and pastureland in the Rio-Grande across 2000-2019 . Note that only 26 of the 30 crops reported by MirAG-US and HarvestGRID are grown in the region. Crop Crop Area (Acres) Percentage Alfalfa 299,117 30.8% Other Hay 134,323 13.8% Cotton 94,035 9.7% Corn 76,956 7.9% Pasture 76,731 7.9% Sorghum 69,566 7.2% Potato 60,195 6.2% Barley 49,538 5.1% Pecan 39,280 4.0% Sugarcane 28,078 2.9% Winter Wheat 12,073 1.2% Spring Wheat 9,332 1.0% Oats 5,434 0.6% Oranges 4,793 0.5% Durum Wheat 4,673 0.5% Dry Beans 1,836 0.2% Sunflower 1,551 0.2% Canola 1,119 0.1% Soybean 1,035 0.1% Peanuts 918 0.1% Sugarbeet 594 0.1% Sweet Corn 508 0.1% Apples 477 0.1% Tomato 130 0.01% Grapes 45 0.00% Peas 4 0.00% Total 972,351 100.0% 5.6 Estimating crop water consumption for Mexican sub-basins Actual evapotranspiration (ET) for major annual crops in Mexico was simulated using the Environmental Policy Integrated Climate (EPIC) model [64,65] at a 30-arcminute spatial resolution, with crop-specific parameters assigned to each crop type. Perennial crops, including fruit trees and alfalfa, were modeled separately using the CropGBWater model—a Python-based global gridded tool designed to estimate green and blue water consumption in crop production [66]. To distinguish between rainfed and irrigated agriculture, we used municipal-level agricultural statistics from Mexico [67] to generate harvested area maps at a 5-arcminute resolution for the major crops. Combined with the ET outputs from the crop models, these maps were used to compute green and blue water consumption for crop production at 5-arcminute resolution. The resulting grid-level estimates were then aggregated to the sub-basin scale using sub-basin polygon boundaries. These estimates were further verified using data from the Comision Nacional del Agua’s Agricultural Statistics database [68], using some of the main irrigation districts for comparisons. 5.7 Estimating reservoir evaporation To estimate monthly evaporative water loss from reservoirs we utilized the Global Lake Evaporation Volume dataset [69]. This dataset provides evaporation estimates for global lakes and reservoirs derived from the HydroLAKES database [70]. We spatially associated each lake with its corresponding sub-basin by overlaying the HydroLAKES dataset with a delineated RGB sub-basin layer. Through this spatial intersection, we identified 37 reservoirs within the basin (Table SI-2). We computed the annual mean evaporation during 2000-2018 for each of these reservoirs using their monthly evaporation values over the study period. 5.8 Estimating riparian evapotranspiration Riparian and wetland vegetation along the RGB river corridor were mapped at ~30 m resolution using spatial data products and the Google Earth Engine Python API [71]. The riparian corridor was delineated using Strahler stream order information within the USGS’s National Hydrography Dataset Plus version 2 [72] and Mexico’s RED Digital Hydrographic database [73]. The total width of the riparian corridor for each stream segment was established based on the corresponding Strahler stream order designation [74]. The valley floor within the river corridor was delineated using the Global Shuttle Radar Topography Mission Landforms dataset (~90 m resolution) [75]. Riparian and wetland vegetation within the riparian corridor’s valley floor was mapped annually over the 2000-2022 period by masking out cropland, bare ground, impervious surfaces, and water/snow/ice from the Global 30m Land Cover Change Dataset [76]. Total annual ET (AF/yr) of riparian and wetland vegetation mapped within the RGB sub-basins were estimated over the 2000-2022 period using OpenET [77] (~30 m resolution) for the United States and the Penman-Monteith-Leuning Evapotranspiration V2 (PML-V2) product [78–80] (~500 m resolution) for Mexico. OpenET estimates were prioritized for riparian and wetland vegetation, and PML-V2 estimates were used where OpenET data were not available. The PML-V2 estimates include a sum of the vegetation transpiration (Ec), soil evaporation (Es), and interception from vegetation canopy (Ei) bands. Total annual precipitation was also estimated for riparian and wetland vegetation over the 2000-2022 period using gridMET [81] (~4.6 km resolution). For each basin, the median evapotranspiration and precipitation values for riparian and wetland vegetation were calculated. Precipitation-derived ET is highly variable across riparian and wetland vegetation and depends on the species and water table depth [82–84]. Based on our review of literature cited here, we assume that half of the annual precipitation supports riparian ET, either directly as enhanced soil moisture or indirectly as monsoon runoff. Total annual ET was calculated per basin by subtracting half of the precipitation depth from the ET depth and then converted into volumetric units by multiplying the annual riparian area. 5.9 Estimating inter-basin transfer volumes Using the Interbasin Transfer Database Standard Version 1.0 (IBTDS 1.0) [85], we identified 54 unique interbasin water transfer (IBT) projects comprising 274 transfer links within the RGB basin. Of the 274 identified links, 16 links (corresponding to 14 unique IBT projects) had water transfer volumes: 13 included average and daily time series data, while 3 had only average transfer volumes. Among the 13 links with daily time series data, 12 links (corresponding to 10 unique projects) contained records covering all or part of the 2000–2019 study period. For each of these 12 links, we assessed the completeness of daily flow data and retained only those years with complete daily records (i.e., no missing values). We then calculated total annual transfer volumes for each valid year and computed the mean annual IBT volume by averaging across the valid years. Many of the IBTs in the RGB basin are small and may represent seasonal water transfers. While the larger IBT projects are well represented in the dataset, particularly those with associated flow data, some small IBTS may not be captured by IBTDS 1.0. 5.10 Estimating depletion of river outflow volumes Annual runoff volumes were obtained from the USGS’s “Water Data for the Nation” webpages [86] and the International Boundary & Water Commission [87]. Data were obtained from monitoring stations closest to each river’s exit from a sub-basin that included measurements for the 2002-2024 period of record. These gauges include the Rio Grande at Lobatos, CO; Rio Grande below Elephant Butte Reservoir, New Mexico; Rio Conchos near Ojinaga, Mexico; Pecos River near Girvin, Texas; and Rio Grande at Brownsville, Texas. The average annual rate of change was estimated using the slopes of the linear regression lines. 5.11 Estimating reservoir depletion volumes Daily estimates of reservoir volume were obtained from multiple sources including the US Bureau of Reclamation’s “Reclamation Information Sharing Environment (RISE)” webpages [88], the Natural Resources Conservation Service’s “Air & Water Database Report Generator” webpages [89], the Texas Water Development Board’s “Water Data for Texas” webpages [90], and the International Boundary & Water Commission’s “Water Data Portal” webpages [87]. A listing of reservoirs used in estimating evaporation is provided in Table SI-2. The average annual rate of change was estimated using the slopes of the linear regression lines. 5.12 Estimating groundwater depletion volumes We followed Rodell and Famiglietti [91], Yeh et al. [92], Rodell et al. [93], and Rodell et al. [94] to estimate groundwater storage changes using NASA GRACE and GRACE Follow-On (GRACE/FO) satellite observations from 2002 to 2024. The GRACE/FO missions provide monthly observations of Earth's gravity field, which are used to estimate changes in TWS at local and global scale [95,96]. These GRACE data are particularly useful for monitoring groundwater and other water components that are difficult to measure directly [97,98]. The GRACE/FO mascon solutions offer improved spatial pixels resolution and reduced leakage compared to earlier GRACE spherical harmonic data (GRACE-SH), enhancing hydrological applications in complex hydrological systems [99]. In this work, we used the average of three mascon solutions: NASA’s Jet Propulsion Laboratory (JPL-RL06.1M [100], the University of Texas Center for Space Research (CSR-RL06.02M [101]), and NASA Goddard Space Flight Center (GSFC-RL06v2.0M [99,102,103] for our TWS estimates. We analyzed total water storage (TWS) variations across the entire RGB and its sub-basins. Groundwater storage (GWS) variations were derived by subtracting measured surface water reservoir storage (SWRS) and modeled soil moisture storage (SMS) and from TWS. The SWRS variations were estimated by compiling daily storage data for major RGB reservoirs from data sources stated in section 5.11 above. SMS was obtained from the North American Land Data Assimilation System (NLDAS) [104], which includes three land surface models (NOAH, VIC, and MOSAIC) [91–94]. NLDAS integrates multiple land surface models to provide high-resolution hydrometeorological variables across the continent. It offers gridded outputs of soil moisture, evapotranspiration, and surface runoff, among others. This case study used SMS from three NLDAS models to isolate groundwater storage variations. Snow water equivalent (SWE) was not included in this analysis, as it contributes minimally to total water storage across most of the RGB, particularly outside the headwater regions, and is commonly excluded in similar large-scale groundwater assessments [94]. To isolate long-term trends and suppress the seasonal signal, we applied Seasonal-Trend decomposition using LOESS (STL) to all monthly time series of all water compartments (TWS, SWRS, SMS, and GWS). STL's an iterative, non-parametric decomposition procedure that separates a time series into three components (trend, seasonal, residual). This process involved first filling in any missing monthly values using linear interpolation. Next, the time series were decomposed into seasonal and trend components using the STL (Seasonal-Trend decomposition based on LOESS) method. Finally, the deseasonalized component, representing the long-term trend, was extracted from the original series [105,106]. The resulting nonseasonal time series were then used to calculate annual and decadal trends in water storage across the basins. Uncertainty in TWS was assessed by calculating the standard deviation among the three GRACE/FO solutions. For SMS, uncertainty was estimated as the standard deviation among the three NLDAS models (NOAH, VIC, and MOSAIC). Since reservoir storage datasets did not include error estimates, we followed Liu et al. [98] in assigning a 15% uncertainty to the SWRS values. The overall uncertainty in GWS (σGWS) was determined by combining the individual uncertainties from TWS, SWRS, and SMS in quadrature (Equation 2) Declarations 6.1 Funding Statement K.F.F., E.C.S., and R.R.R. were supported by a grant from the National Science Foundation's Sustainable Regional Systems Program, the Transformation Network, which aims to support convergent research and education that will advance sustainable regional systems science, engineering, and education (NSF Grant #2115169).Landon Marston was supported by National Science Foundation Grant RISE-2108196. 6.2 Ethics statement Not applicable. 6.3 Author contributions B.D.R. conceived of the study, wrote the main manuscript, assembled and analyzed data, and prepared tables and figures. K.A. assembled and analyzed data and prepared figures. S.D. assembled and analyzed data and prepared figures. J.S.F. analyzed data and edited manuscript. K.F.F. assembled and analyzed data and edited manuscript. H.G. assembled and analyzed data. L.M. analyzed data and edited manuscript. M.M.M. assembled and analyzed data. E.P. assembled and analyzed data, prepared figures, and edited manuscript. M.M.R. assembled and analyzed data and edited manuscript. B.L.R. analyzed data and edited manuscript. R.R.R. analyzed data and edited manuscript. N.S. assembled and analyzed data. E.C.S. assembled and analyzed data and edited manuscript. 6.4 Competing interests The authors have no competing interests as defined by Discover, or other interests that might be perceived to influence the results and/or discussion reported in this paper. 6.5 Dual publication Nothing in this manuscript has been previously published nor under consideration for publication elsewhere. 6.6 Authorship The corresponding author confirms that he has read the journal policies and is submitting their manuscript in accordance with those policies. 6.7 Third party material All of the material in this manuscript is owned by the authors and no permissions are required. 6.8 Data availability Data sets generated during the current study are available from the corresponding author on reasonable request. 6.9 Acknowledgements The authors would like to acknowledge Dr. Pete Caldwell of the US Forest Service's Southern Research Station for assisting with accessing WaterGAP data for municipal and commercial uses in Mexico. 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Response of deep aquifers to climate variability. Science of The Total Environment. 2019;677:530–44. https://doi.org/10.1016/j.scitotenv.2019.04.316 Footnotes [10] The river is known as the Rio Grande in the US but is the Rio Bravo in Mexico. We have combined these names as Rio Grande-Bravo (RGB) in this paper. Table 1 Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.docx Table1.docx Cite Share Download PDF Status: Published Journal Publication published 20 Nov, 2025 Read the published version in Discover Water → Version 1 posted Editorial decision: Revision requested 16 Sep, 2025 Reviews received at journal 15 Sep, 2025 Reviews received at journal 09 Sep, 2025 Reviews received at journal 09 Sep, 2025 Reviewers agreed at journal 09 Sep, 2025 Reviewers agreed at journal 08 Sep, 2025 Reviews received at journal 05 Sep, 2025 Reviewers agreed at journal 04 Sep, 2025 Reviews received at journal 04 Sep, 2025 Reviewers agreed at journal 04 Sep, 2025 Reviewers agreed at journal 26 Aug, 2025 Reviewers agreed at journal 19 Aug, 2025 Reviewers invited by journal 17 Aug, 2025 Editor invited by journal 12 Aug, 2025 Editor assigned by journal 12 Aug, 2025 Submission checks completed at journal 12 Aug, 2025 First submitted to journal 06 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7313321","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":502791084,"identity":"d12e35f5-47fd-4b2e-84b6-e90ac6f404b5","order_by":0,"name":"Brian D. Richter","email":"data:image/png;base64,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","orcid":"","institution":"Sustainable Waters","correspondingAuthor":true,"prefix":"","firstName":"Brian","middleName":"D.","lastName":"Richter","suffix":""},{"id":502791088,"identity":"64139b93-3ce0-484b-9f38-fca10f0276c1","order_by":1,"name":"Karem Abdelmohsen","email":"","orcid":"","institution":"Arizona State University","correspondingAuthor":false,"prefix":"","firstName":"Karem","middleName":"","lastName":"Abdelmohsen","suffix":""},{"id":502791089,"identity":"86f2eb03-dbf0-44c0-9875-f4d6f0ed1dcf","order_by":2,"name":"Sameer Dhakal","email":"","orcid":"","institution":"Virginia Tech","correspondingAuthor":false,"prefix":"","firstName":"Sameer","middleName":"","lastName":"Dhakal","suffix":""},{"id":502791092,"identity":"e23020c0-2da3-4851-980b-e5df578922c7","order_by":3,"name":"James S. 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Mekonnen","email":"","orcid":"","institution":"University of Alabama","correspondingAuthor":false,"prefix":"","firstName":"Mesfin","middleName":"M.","lastName":"Mekonnen","suffix":""},{"id":502791103,"identity":"0547af9f-81d9-4998-825d-463bbeddb3a9","order_by":8,"name":"Enrique Prunes","email":"","orcid":"","institution":"World Wide Fund for Nature","correspondingAuthor":false,"prefix":"","firstName":"Enrique","middleName":"","lastName":"Prunes","suffix":""},{"id":502791104,"identity":"9f5a0237-34e4-49c8-9914-cddc775ec6d5","order_by":9,"name":"Melissa M. Rohde","email":"","orcid":"","institution":"Rohde Environmental Consulting","correspondingAuthor":false,"prefix":"","firstName":"Melissa","middleName":"M.","lastName":"Rohde","suffix":""},{"id":502791105,"identity":"0a110184-4ed0-4e7c-b8f0-9f56f8dbb2b4","order_by":10,"name":"Benjamin L. Ruddell","email":"","orcid":"","institution":"Northern Arizona University","correspondingAuthor":false,"prefix":"","firstName":"Benjamin","middleName":"L.","lastName":"Ruddell","suffix":""},{"id":502791106,"identity":"fd826b73-d3ea-477d-aedd-ede6187c2090","order_by":11,"name":"Richard R. Rushforth","email":"","orcid":"","institution":"Arizona State University","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"R.","lastName":"Rushforth","suffix":""},{"id":502791107,"identity":"08cb5925-6625-490d-ba71-b6f1a920eaef","order_by":12,"name":"Natalie Shahbol","email":"","orcid":"","institution":"World Wide Fund for Nature","correspondingAuthor":false,"prefix":"","firstName":"Natalie","middleName":"","lastName":"Shahbol","suffix":""},{"id":502791108,"identity":"43a25177-124c-47b2-976a-cde753fbef22","order_by":13,"name":"Eric C. Sjöstedt","email":"","orcid":"","institution":"Northern Arizona University","correspondingAuthor":false,"prefix":"","firstName":"Eric","middleName":"C.","lastName":"Sjöstedt","suffix":""}],"badges":[],"createdAt":"2025-08-06 23:23:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7313321/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7313321/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s43832-025-00301-2","type":"published","date":"2025-11-20T15:59:04+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89880762,"identity":"d33dad9c-49f2-4133-9c33-30286807c5e5","added_by":"auto","created_at":"2025-08-26 05:50:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":18249784,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMap of the Rio Grande-Bravo basin\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7313321/v1/4cfb658469c8c2e2d04ca5fd.png"},{"id":89882026,"identity":"8ec6a259-03bc-4d31-a3d0-06256123d5cf","added_by":"auto","created_at":"2025-08-26 05:58:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":13841353,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eSub-basin accounting units.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e This map illustrates the locations of the 14 sub-basin accounting units used in quantifying water consumption, as well as four focal areas for which additional analysis and summary statistics have been compiled. The area identified as “Closed Basin” was excluded from the analysis because water in this basin is hydrologically disconnected from other sub-basins.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7313321/v1/fc224bb319f7ae0951180475.png"},{"id":89880755,"identity":"8f52100e-9ac5-4b97-b9cf-c317a57c4446","added_by":"auto","created_at":"2025-08-26 05:50:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1046610,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConsumptive water uses across the RGB basin\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e. (a) Consumptive use percentages across the entire RGB basin; (b) Consumptive use percentages for the Rio Grande Colorado sub-basin; (c) Consumptive use percentages for the Rio Grande New Mexico focal area delineated in Figure 2; (d) Consumptive use percentages for the Rio Conchos sub-basin. Percentages may not total to exactly 100% due to rounding.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7313321/v1/ee27ba21816771ad8410ac6e.png"},{"id":89880766,"identity":"f804f2fb-5446-44ed-a3d9-67138c76df90","added_by":"auto","created_at":"2025-08-26 05:50:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":749637,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAnnual volumes of water consumption in each water-use sector.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Values in million cubic meters. M\u0026amp;C MX=municipal and commercial consumption in Mexico; M\u0026amp;C US=municipal and commercial uses in the US; Thermo MX=thermoelectric power generation in Mexico; Thermo US=thermoelectric power generation in the US; Crops MX=irrigated crops in Mexico; Crops US=irrigated crops in the US.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7313321/v1/d6573366e45f6089bb3e31e2.png"},{"id":89880770,"identity":"d7898804-d940-4378-866b-61171524f06a","added_by":"auto","created_at":"2025-08-26 05:50:37","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":708186,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eTrends in agricultural water consumption\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e. On the US side of the RGB basin, agricultural water consumption decreased, but consumption has increased substantially on the Mexico side. As graphed here, “New Mexico” does not include the Pecos River within New Mexico and Texas, which is graphed separately. The “Lower Valley” on the US side includes the Devils River sub-basin and the Amistad Reservoir to the Gulf sub-basin. On the Mexican (MX) side, the Lower Valley includes the Amistad Reservoir to the Gulf sub-basin as well as the Rio Salado and Rio San Juan sub-basins.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7313321/v1/b892bee2b04812305224f354.png"},{"id":89880765,"identity":"4a55fcf8-403a-4272-a04e-26dfaeef31e8","added_by":"auto","created_at":"2025-08-26 05:50:36","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":271746,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eReservoir depletion in New Mexico\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e. Higher levels of snowmelt runoff in the Rio Grande’s headwaters during 2005-2009 helped to temporarily bolster reservoir levels in Colorado and New Mexico. However, the volume of water stored in RGB reservoirs has been declining rapidly since 2010. Total water storage in the Rio Grande New Mexico focal area fell to 13% of capacity by the end of 2024 and has continued to decline in 2025.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7313321/v1/6dc1b616c35c1cb1815f71a7.png"},{"id":89882028,"identity":"2ac3aa29-52ac-4614-a766-b2245443d300","added_by":"auto","created_at":"2025-08-26 05:58:36","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2032472,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eGroundwater depletion.\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e The volume of groundwater storage (GWS) in RGB aquifers has declined substantially during 2002-2024, with particularly severe depletion in the Pecos River, Rio Grande New Mexico, and Rio Conchos of Mexico. Map indicates millimeters of change in groundwater levels, based on data from the (US) National Aeronautical and Space Administration’s Gravity Recovery and Climate Experiment (GRACE and GRACE Follow-On) satellite missions.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7313321/v1/9dcf2c4cc0dd80c0cddd44f7.png"},{"id":89882030,"identity":"9fd17022-eac4-4e2e-b2ff-d4fdf38e123c","added_by":"auto","created_at":"2025-08-26 05:58:36","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":793101,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eWater sources and consumptive uses in MCM/year\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e. This diagram illustrates the role that water source depletion is playing in enabling the over-consumption of water in the RGB basin since 2002. The water sourced from the RGB’s river flow and groundwater is deemed to be the sustainable portion of water supply. The remainder of water needed to support consumptive uses is being supported by depletions of annual river volumes, aquifers, and reservoirs in the basin.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7313321/v1/b93c02a383224069d4f606e1.png"},{"id":96651247,"identity":"2ecb5045-5b7c-49bb-b20c-13132cc292b7","added_by":"auto","created_at":"2025-11-24 16:14:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":36849880,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7313321/v1/fb7dd50e-eb05-4590-bcf6-c0f130c1197d.pdf"},{"id":89880768,"identity":"5253d245-3060-4c55-b439-903bb5eed99a","added_by":"auto","created_at":"2025-08-26 05:50:36","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":8572707,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7313321/v1/cd278b8273a3bc4d34fa9a12.docx"},{"id":89880756,"identity":"adc4708a-9691-436b-9a6a-4624532052d4","added_by":"auto","created_at":"2025-08-26 05:50:36","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":189961,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7313321/v1/16d85aa95432a8db1eeac3fe.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Overconsumption Gravely Threatens Water Security in the Binational Rio Grande-Bravo Basin","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWater scarcity has a long history in the binational Rio Grande-Bravo (RGB)[10] basin. The RGB is the fifth longest river in the United States (~3,000 km), but its paltry flow of water belies its \u0026lsquo;grand\u0026rsquo; name; the river conveys less water than the 50\u003csup\u003eth\u003c/sup\u003e longest river in the country [1]. The river flowed perennially throughout its length until the late 19\u003csup\u003eth\u003c/sup\u003e century, when intensive irrigation in the San Luis Valley headwaters in Colorado began to dry the snowmelt-fed river during the irrigation season, resulting in diminished flows as far downstream as El Paso, Texas (Figure 1) [2,3]. As water consumption increased throughout the rest of the basin during the past century, the river began to dry up entirely since the 1950s [4] in the \u0026ldquo;Forgotten Reach\u0026rdquo; from Fort Quitman to Presidio (Figure 1) and has intermittently failed to reach its delta at the Gulf of Mexico [5,6] since 2001. In recent decades, river drying has expanded to previously perennial stretches in New Mexico and the Big Bend region [7,8]. Today, only 15% of the estimated natural flow of the river remains at Anzalduas, Mexico near the river\u0026rsquo;s delta at the Gulf of Mexico [9].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA multi-decadal megadrought has substantially reduced water supplies in the RGB. Since 2000, snowmelt inflows from the basin\u0026rsquo;s headwaters in Colorado have been 17% lower than during the 20\u003csup\u003eth\u003c/sup\u003e century [10]. Climate scientists have reframed the long-running drought as the onset of long-term aridification and are forecasting additional river flow diminishment of 16-28% in coming decades as the climate continues to warm [11].\u003c/p\u003e\n\u003cp\u003eDespite attempts by both urban and agricultural water users to reduce their consumption [12], a persistent imbalance between water consumption and replenishment has led to severely depleted reservoirs, aquifers, and river flows throughout the RGB basin. As a result, farmers dependent upon irrigation and numerous cities face an existential water crisis. In the San Luis Valley of Colorado (Figure 1), diminished river flows have led to increased groundwater pumping, causing aquifer levels to plummet [13]. The Colorado state engineer has threatened to shut off hundreds of groundwater wells if the aquifer supporting irrigated farms cannot be stabilized [14]. In New Mexico, reservoir storage fell to just 13% of capacity by the end of 2024, and reservoirs in the Rio Conchos sub-basin of Mexico have fallen to under 20% of their capacity, increasing the vulnerability of drying up completely with just one or two more dry years.\u003c/p\u003e\n\u003cp\u003eReduced water supplies have also led to political and legal disputes among state and national governments. In 2013, Texas sued New Mexico in the US Supreme Court over repeated failure to receive its annual water allocations as specified under the interstate Rio Grande Compact [15]; the case has yet to be decided. Mexico\u0026rsquo;s shortfalls in delivering water to the US as specified under the 1944 \u0026ldquo;Treaty on Utilization of Waters of the Colorado and Tijuana Rivers and of the Rio Grande\u0026rdquo; has raised tension between the two national governments [15\u0026ndash;17].\u003c/p\u003e\n\u003cp\u003e1.1 Impacts on biodiversity, farmers, and cities\u003c/p\u003e\n\u003cp\u003eThe RGB basin is renowned for its biological diversity and endemism, supporting more than 130 mammals, 3,000 plant and 500 bird species [2]. Wetlands and riparian forests supported by the river are critically important to birds migrating along the Central Flyway [18]. Nearly half of the basin\u0026rsquo;s native fish species are found nowhere else, but flow depletion has become a major factor in the imperilment of at least 75 freshwater species supported by the river system [19].\u003c/p\u003e\n\u003cp\u003eThe river and underlying aquifers provide drinking water for more than 11 million people in Mexico and 4 million in the US and are used to irrigate more than 7800 km\u003csup\u003e2\u003c/sup\u003e (1.9 million acres) of farmland in the two countries [2]. As a result of decreased river flows and depleted water storage, farmers on both sides of the border have experienced severe reductions in irrigation supplies from surface water, leading to increased groundwater pumping [17]. In many recent years, no surface water has been available after June in a region with a growing season typically lasting through October. Summer monsoon rains in 2025 provided farmers in the Middle Rio Grande Conservancy District in central New Mexico two extra weeks of irrigation until mid-July, while farmers in the Rio Conchos basin of Mexico faced the likelihood of not receiving any surface water supplies at all. Rapidly growing urban populations and industries in the RGB basin are also becoming increasingly vulnerable to water scarcity [20].\u003c/p\u003e\n\u003cp\u003eDespite the great scarcity of water and intensifying water shortages in the RGB basin, a full accounting of consumptive water uses and losses has never been undertaken. In this study we assemble detailed estimates of water consumption (water withdrawals minus return flows) from a broad array of sources to reveal how surface and ground water was consumed in each of 14 sub-basins during recent decades. We have also developed estimates of changes in reservoir storage, groundwater volumes, and river outflows to quantify the degree to which annual water consumption has exceeded annual replenishment in recent decades.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003e2.1 Accounting for consumptive water uses\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAn important first principle in resolving water scarcity is to develop a sound understanding of how water is consumed in a river basin [21]. Such accounting enables consideration of which water-use sectors may be able to reduce water use to the degree needed to rebalance consumption with replenishment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this study we have fully accounted for all water consumed in the Rio Grande-Bravo basin, based on three \u0026ldquo;direct use\u0026rdquo; categories and two \u0026ldquo;indirect use\u0026rdquo; categories. The direct use categories \u0026ndash; in which water is directly used for human purposes \u0026ndash; include municipal \u0026amp; commercial; thermoelectric generation at power plants; and crop irrigation including a small volume of water exported out of the basin for agricultural use. The agricultural use is further differentiated by individual crops grown in the basin to help facilitate discussions about the potential water benefits of transitioning to alternative crop mixes [22]. The indirect categories include reservoir evaporation and riparian evapotranspiration (ET). The annual average volume of water consumed in each category has been estimated for the entire basin and each sub-basin shown in Figure 2, with results summarized in Figure 3 and Table 1 (a tabular summary in Imperial units is provided in Supplementary Information Table SI-1).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.2 Irrigated agriculture dominates water consumption\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAs indicated by Table 1 and Figure 3, agriculture is the dominant direct water consumer in the basin, accounting for 87% of direct consumption and 39% of total consumptive use basin wide. In some sub-basins such as the Rio Grande in Colorado, or the Rio Conchos in Mexico, virtually all direct consumption goes to irrigated farms. Overall, agricultural consumption is nearly seven times the volume of all other direct uses combined.\u003c/p\u003e\n\u003cp\u003eWithin agriculture, cattle-feed crops (i.e., alfalfa, grass hay, and pasture) account for 56% of irrigation water consumed. These crops are particularly dominant in the Northern New Mexico and Middle New Mexico sub-basins, where they consume almost all irrigation water (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e1. Trends in water consumption\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAs mentioned previously, snowmelt runoff (the primary source of water supply) has decreased in the RGB basin by 17% in the 21\u003csup\u003est\u003c/sup\u003e century. At the same time, direct water consumption has been increasing at the basin level since 2000, at the average rate of 25.8 million cubic meters/year (MCM/yr), equivalent to 20,954 acre-feet/year (AF/yr). Direct consumption has decreased on the US side at an average rate of 43.1 MCM/yr (34,960 AF/yr), primarily due to reductions in the total area of irrigated farmland. However, these gains were more than offset by increases on the Mexican side of 68.9 MCM/yr (55,914 AF/yr). Figure 4 illustrates consumptive use trends in each direct water-use category, and Figure 5 shows trends in agricultural consumption, which is by far the largest direct water-use category (trends in individual crops are illustrated in Supplementary Information Figure SI-1).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2. Indirect water uses are substantial\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIndirect uses and losses of water account for 56% of overall water consumption in the RGB basin. Reservoir evaporation accounts for 12% of water consumption. The two biggest reservoirs on the river \u0026ndash; Amistad and Falcon \u0026nbsp;\u0026ndash; account for 38% of all reservoir evaporation. Riparian ET is the single largest water consumption category, accounting for 44% of total water consumption.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2. \u003cstrong\u003eImpacts of Overconsumption on Reservoir and Aquifer Storage and Basin Outflows\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA persistent imbalance between water consumption and supply has severely depleted water sources in the RGB basin. The volume being depleted \u0026lsquo;feeds\u0026rsquo; or enables the overconsumption, creating a feedback loop which in turn further accelerates depletion [23]. We estimated the annual average volume of depletion in each water source during 2002-2024 that helped support the consumptive uses summarized in Table 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe three trends used to evaluate overconsumption include volumetric changes in reservoir storage, aquifer storage, and river outflows. We assessed annual average volumetric changes for each of these three components for the four focal areas identified in Figure 2 and the entire RGB basin, for a common period of 2002-2024. We note that some of the most severe depletions in these water sources occurred during 2000-2001, but data available on groundwater losses do not begin until 2002 (see Methods) so trends in the three water sources were assessed with 2002 as the starting point.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.1 Loss of reservoir storage\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAn estimated 12% of all water stored in basin reservoirs has been lost since 2002 (average annual loss of 177.6 MCM/yr (143,945 AF/yr). The loss of reservoir storage has been most severe in New Mexico, where 71% of the reservoir storage that existed at the start of 2002 was gone by the end of 2024 (Figure 6).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e1. Groundwater depletion\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eGroundwater depletion in the RGB basin has been much more severe (roughly 15 times greater) than reservoir depletion (Figure 7). As surface water supplies have declined over recent decades, groundwater pumping has increased substantially [24]. An estimated 2,700 MCM/yr (2,189,000 AF/yr) of groundwater loss occurred during 2002-2024, with the Pecos River (950 MCM/yr; 770,179 AF/yr), Rio Grande New Mexico (630 MCM/yr; 510,750 AF/yr), and the Rio Conchos (490 MCM/yr; 397,250 AF/yr) experiencing the greatest overdrafts.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.3 Depletion of river outflows\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn addition to the loss of water stored in reservoirs and aquifers, it is also important to assess trends in annual river flow volumes. Changes in total outflow provide another indicator of the sustainability of water consumption within a region and can be counted as another component of water overdraft in the basin. River outflow measured at the RGB basin outlet near Brownsville, Texas indicates annual volumetric outflow declines averaging 22.4 MCM/yr (18,147 AF/yr) since 2002. We note that this indicates ongoing (further) depletion of an already heavily depleted river that had lost ~85% of its flow prior to 2002.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.4 Quantifying the proportion of direct consumption that is unsustainable\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe three sub-components of change used to evaluate overconsumption include reservoir storage, aquifer storage, and river outflow volumes. Summing these three sub-components at the full basin level results in water depletion totaling 2,900 MCM/yr (2,351,092 AF/yr) since 2002. By dividing the total average volume of depletion by the average volume of direct water consumption in each sub-area we obtain estimates of the degree (%) of overconsumption (i.e., unsustainable use). At the whole basin scale, 52% of direct consumption is unsustainable. However, overconsumption is much greater in some sub-areas of the basin such as in the Pecos River in New Mexico and Texas, where 140% of all water directly consumed is unsustainable (overconsumption is 66% of total water consumption), and in the Rio Grande New Mexico sub-basins where 85% of direct consumption is unsustainable. Table 2 provides over-consumption estimates for the overall basin and four focal areas identified in Figure 2. Figure 8 illustrates the role of water depletion in supporting the RGB basin\u0026rsquo;s overconsumption.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Estimates of water resource trends in sub-areas of the RGB basin\u003c/strong\u003e. All values in MCM/yr with AF/yr in parentheses. The Rio Grande in New Mexico focal area includes three sub-basins as indicated in Figure 2: Rio Grande Northern New Mexico, Rio Grande Middle New Mexico, and Rio Grande Southern New Mexico. The Pecos River focal area includes two sub-basins: Pecos River New Mexico and Pecos River Texas. Annual values are based on the slope of linear regression trendlines across 23 years of daily or monthly volumetric data.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"708\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1864%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eRegion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0169%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eReservoir trend\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7119%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAquifer trend\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1695%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eRiver outflow trend\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5593%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTotal volumetric change\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2542%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTotal direct water consumption\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.1017%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePercent over-consumption\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(unsustainable)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1864%;\"\u003e\n \u003cp\u003eRio Grande, Colorado\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0169%;\"\u003e\n \u003cp\u003e+0.3\u003c/p\u003e\n \u003cp\u003e(+223)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7119%;\"\u003e\n \u003cp\u003e-110\u003c/p\u003e\n \u003cp\u003e(-89,179)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1695%;\"\u003e\n \u003cp\u003e+0.9\u003c/p\u003e\n \u003cp\u003e(+ 718)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5593%;\"\u003e\n \u003cp\u003e-108.8\u003c/p\u003e\n \u003cp\u003e(-88,238)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2542%;\"\u003e\n \u003cp\u003e995.9\u003c/p\u003e\n \u003cp\u003e(807,412)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.1017%;\"\u003e\n \u003cp\u003e11%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1864%;\"\u003e\n \u003cp\u003eRio Grande,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNew Mexico\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0169%;\"\u003e\n \u003cp\u003e-25.5\u003c/p\u003e\n \u003cp\u003e(-20,662)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7119%;\"\u003e\n \u003cp\u003e-630\u003c/p\u003e\n \u003cp\u003e(-510,750)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1695%;\"\u003e\n \u003cp\u003e-9.7\u003c/p\u003e\n \u003cp\u003e(-7,852)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5593%;\"\u003e\n \u003cp\u003e-665.2\u003c/p\u003e\n \u003cp\u003e(-539,264)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2542%;\"\u003e\n \u003cp\u003e780.0\u003c/p\u003e\n \u003cp\u003e(632,411)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.1017%;\"\u003e\n \u003cp\u003e85%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1864%;\"\u003e\n \u003cp\u003ePecos River,\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNew Mexico \u0026amp; Texas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0169%;\"\u003e\n \u003cp\u003e+2.8\u003c/p\u003e\n \u003cp\u003e(+2,268)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7119%;\"\u003e\n \u003cp\u003e-950\u003c/p\u003e\n \u003cp\u003e(-770,179)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1695%;\"\u003e\n \u003cp\u003e-0.6\u003c/p\u003e\n \u003cp\u003e(-472)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5593%;\"\u003e\n \u003cp\u003e-947.8\u003c/p\u003e\n \u003cp\u003e-768,383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2542%;\"\u003e\n \u003cp\u003e675.7\u003c/p\u003e\n \u003cp\u003e(547,811)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.1017%;\"\u003e\n \u003cp\u003e140%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1864%;\"\u003e\n \u003cp\u003eRio Conchos\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0169%;\"\u003e\n \u003cp\u003e+1.6\u003c/p\u003e\n \u003cp\u003e(+1,260)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7119%;\"\u003e\n \u003cp\u003e-490\u003c/p\u003e\n \u003cp\u003e(-397,250)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1695%;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003cp\u003e(-11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5593%;\"\u003e\n \u003cp\u003e-488.5\u003c/p\u003e\n \u003cp\u003e(-396,001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2542%;\"\u003e\n \u003cp\u003e898.4\u003c/p\u003e\n \u003cp\u003e(728,375)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.1017%;\"\u003e\n \u003cp\u003e54%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1864%;\"\u003e\n \u003cp\u003eEntire Basin\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.0169%;\"\u003e\n \u003cp\u003e-177.6\u003c/p\u003e\n \u003cp\u003e(-143,945)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7119%;\"\u003e\n \u003cp\u003e-2700\u003c/p\u003e\n \u003cp\u003e(-2,189,000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1695%;\"\u003e\n \u003cp\u003e-22.4\u003c/p\u003e\n \u003cp\u003e(-18,147)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5593%;\"\u003e\n \u003cp\u003e-2900.0\u003c/p\u003e\n \u003cp\u003e(-2,351,092)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.2542%;\"\u003e\n \u003cp\u003e5574.6\u003c/p\u003e\n \u003cp\u003e(4,519,434)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.1017%;\"\u003e\n \u003cp\u003e52%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion and Conclusions","content":"\u003cp\u003eThe water scarcity challenges within the RGB basin have received much less attention from media outlets and national policymakers as compared to the Colorado River Basin (CRB) in the American Southwest. This can largely be explained by the comparatively smaller volume of water it carries (natural flows of 11,225 MCM/yr (9.1 million AF/yr)) in the RGB [25] vs. 18,996 MCM/yr (15.4 million AF/yr)) in the CRB [26]), as well as the smaller population it serves with drinking water (15 million in RGB vs. 40 million in CRB ) and the area of irrigated farmland it supports (7,800 km\u003csup\u003e2\u003c/sup\u003e in RGB vs. 22,300 km\u003csup\u003e2\u003c/sup\u003e in CRB) [2]. However, the water crisis facing the RGB is arguably more severe and urgent than the CRB, as illustrated by these conditions:\u003c/p\u003e\n\u003cp\u003e1. Groundwater in the San Luis Valley of Colorado has been depleted at a rate of 110 MCM/yr ((89,179 AF/year), equivalent to 11% of the annual average of direct water consumption in the valley (Table 2). The state\u0026rsquo;s engineer has threatened to shut off hundreds of groundwater wells if the aquifers cannot be stabilized and restored to near 1976 levels [27,28]. A water conservation program has been charging farmers to pump groundwater and has paid farmers since 2012 to fallow farm fields voluntarily, yet aquifer levels continue to fall [29]. Efforts to recover these aquifers have been greatly impacted by 17% declines in snowmelt runoff this century. \u003c/p\u003e\n\u003cp\u003e2. New Mexico has fallen into water debt to Texas under the terms of the interstate Rio Grande Compact, leading to a lawsuit before the US Supreme Court [15]. New Mexico\u0026rsquo;s cumulative debt to Texas of 153 MCM (124,000 AF) as of December 2024 is equivalent to 20% of the annual average direct water consumption in sub-basins along the Rio Grande. New Mexico\u0026rsquo;s water debt poses an extremely difficult challenge for the state given that it has lost much of its reservoir and aquifer storage in recent decades, and climate warming is projected to further reduce the state\u0026rsquo;s water supplies by 16-28% in coming decades [11].\u003c/p\u003e\n\u003cp\u003e3. In recent years, Mexico has fallen into mounting water debt to the US under the terms of the 1944 Treaty on Utilization of Waters of the Colorado and Tijuana Rivers and of the Rio Grande [30]. Mexico\u0026rsquo;s water debt is causing political conflict between the two countries. The current federal administration in the US asserts that Texas farmers along the lower Rio Grande-Bravo have been unduly suffering from water shortages [31]. Texas farmers in the lower valley experienced losses of nearly USD$500 million in 2024 due to insufficient irrigation supply [32]. At the same time, Mexican farmers are also experiencing irrigation shortfalls and associated crop losses [17].\u003c/p\u003e\n\u003cp\u003eThe severe water crisis emerging in the RGB basin requires urgent action. At the end of 2024, total reservoir storage in the basin stood at just 26% of capacity (~4.24 million AF; Figure 6) and has continued to decline in 2025. Within the Rio Conchos sub-basin in Mexico, reservoirs were at 20% of capacity at the end of 2024. In New Mexico, reservoirs were at 13% of capacity within the Rio Grande sub-basins, and 12% in the Pecos River sub-basins in New Mexico and Texas. Without a substantial reduction of water consumption, a repeat of the four-year rate of reservoir depletion experienced in 2010-2013 would completely wipe out all remaining water stored in the RGB basin. All remaining water stored in New Mexico reservoirs could be gone in just one or two low runoff years. \u003c/p\u003e\n\u003cp\u003eNotably, large cities such as Albuquerque and El Paso have been able to substantially lower their water use even while their populations have grown rapidly in recent decades. For example, Albuquerque\u0026rsquo;s population grew by 40% while its water use declined by 17% [33], and El Paso\u0026rsquo;s population grew by 36% while water use declined by 6% [34]. Additional urban water conservation could help alleviate water over-consumption in the basin. However, with continued rapid population growth and development of water-intensive data centers [35] most cities will be challenged to simply hold water use levels constant in coming years. Even if cities and power plants in the basin were able to stop using water altogether, it would reduce the basin\u0026rsquo;s overconsumption by only 25%.\u003c/p\u003e\n\u003cp\u003eMost of the needed water savings must come from irrigated agriculture, given its dominant 87% share of direct water consumption. Proven strategies for reducing consumption in irrigated agriculture exist [22,36] but must be rapidly deployed at sufficient scale and financially sustained by governments, companies, and credit institutions to rebalance the basin\u0026rsquo;s water budget at sub-basin, state, and binational levels. Achieving necessary levels of reduction in consumptive use will be extremely difficult given the high levels of over-consumption existing presently (Table 2), as well as expected declines in water availability due to climate change and associated increases in fire frequency and severity (see Supplemental Information for discussion of fire impacts). \u003c/p\u003e\n\u003cp\u003eUnderstandably, farmers are strongly resistant to mandatory cutbacks in water use as irrigation is essential to their livelihoods in the dry climate of the RGB basin. For instance, when the Mexican federal government began releasing water from a large dam on the Rio Conchos in 2020 to comply with the international water treaty between Mexico and the US, more than 2,000 protestors fearing loss of their scarce irrigation supplies erupted into violence, resulting in the death of a protestor [30,37]. Political leaders and water managers have been loath to mandate water-use reductions in the face of such resistance and hostility. Voluntary water conservation programs that pay farmers to temporarily fallow farmland have been implemented in both Colorado and New Mexico, but these programs have never achieved targeted reduction levels due to an inability to offer sufficient financial incentives to participants. Given the recent elimination or reduction of US federal programs that have helped finance these water conservation programs, the funding challenge will fall largely on already-strained state and local budgets.\u003c/p\u003e\n\u003cp\u003eIf water consumption cannot be substantially reduced through regulatory or incentivized means, a likely outcome will be continued loss of farmland due to insolvency [38] from lowered crop production and other factors, including the aging of farmers and lack of affordable farm labor. Our analysis reveals that during 2000-2019, Colorado lost 18% of its farmland in the RGB basin, New Mexico lost 28% along its Rio Grande sub-basins, and the Pecos River sub-basin lost 49%. At this rate of farmland retirement and associated reductions in water consumption (Figure 5), assuming no changes in other consumptive uses, the Rio Grande sub-basin in Colorado might eliminate the volume of annual deficit shown in Table 2 in 11 years. However, it would require 59 years to attain balance between water consumption and replenishment in the Pecos River sub-basin in New Mexico and Texas, and 95 years along the Rio Grande sub-basins in New Mexico. These projections do not account for climate change-induced reductions in water replenishment in coming decades. Reaching a balance between consumption and replenishment in Mexican sub-basins will require a reversal of the recent trends of increasing irrigation in the Mexican sub-basins.\u003c/p\u003e\n\u003cp\u003eIt is important to acknowledge that urban and agricultural water conservation programs have thus far been unable to produce the reductions in water consumption required to stabilize \u0026ndash; much less restore \u0026ndash; the water sources within the RGB [27]. It is also highly unlikely that imported or desalinated water sources will resolve this crisis, given that the costs of such importation or treatment will not be affordable for farmers. The unaffordability of new supplies leaves only three plausible non-exclusive options: (1) transform the agricultural landscape to produce crops that require little to no irrigation [22]; (2) financially incentivize \u0026lsquo;deficit irrigation\u0026rsquo; to substantially reduce the duration of irrigation during the growing season [39]; or (3) permanently convert some portion of irrigated farmland to other uses such as wildlife habitat, solar farms, or other purposes that require much less water. If water consumption for municipal, commercial, and thermoelectric power generation remains constant, irrigation reductions would need to be at the level of over-consumption indicated in Table 2.\u003c/p\u003e\n\u003cp\u003eGiven severe groundwater depletion across the RGB basin, an immediate first step could be to place a moratorium on new wells in over-drafted areas of the basin (Figure 7). Moratoriums would need to be supplemented with sub-basin groundwater caps (limits) that reduce the total volume of pumping to a level that halts groundwater depletion and stabilizes aquifer levels. Wight et al. (2025) [40] surveyed nearly 50 global case studies where caps on water diversions and pumping have been imposed; many of these cases focused on aquifer management, and many have successfully averted or reversed groundwater depletion. However, it is also important to note the causes of failure highlighted by Wight et al., including inabilities to enforce such restrictions adequately. Moratoriums have been set for many over-exploited aquifers in Mexico including in the RGB basin, but these efforts have been plagued by enforcement shortcomings [41].\u003c/p\u003e\n\u003cp\u003eThis water crisis presents an opportunity for the residents of the RGB to envision a new, more sustainable water future. The \u0026lsquo;Multi-benefit Land Repurposing Program\u0026rsquo; underway in the water-stressed Central Valley of California provides one example of productive community dialogue around possible future scenarios [42]. The \u0026ldquo;Exploratory Scenario Planning\u0026rdquo; approach being advanced by the Lincoln Institute of Land Policy in various communities in the western US similarly offers ways to engage local communities in planning for their water future [43]. Any transformational strategies will require careful and inclusive planning, provision of strong financial incentives for farming communities to facilitate needed changes, and wide support from water management agencies and decision makers to ensure water and food security for the region. Alternate pathways toward a sustainable water future are available for the RGB basin, but time is of the essence in correcting the highly unsustainable conditions that presently exist.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003e5.1 Estimating municipal \u0026amp; commercial water consumption for US sub-basins\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMunicipal and commercial consumptive use estimates were sourced from the United States Geological Survey\u0026rsquo;s (USGS) public supply reanalysis dataset for 2000\u0026ndash;2020 [44,45]. This dataset applies a machine learning model to refine water use estimates using existing USGS data and supplemental inputs from local, state, and federal sources, aiming to improve spatial and temporal resolution and standardize estimation methods across sectors. The public supply delivery model provides monthly estimates of withdrawals and consumptive use (in million gallons per day) for domestic, commercial, industrial, institutional, and irrigation (MCI/CII) uses at the Hydrologic Unit Code 12-digit (HUC12) or Water Service Area (WSA) scale. For this study, we used total consumptive use (surface water + groundwater) estimates at the HUC12 level, reflecting the complex and dynamic interactions between surface and groundwater in the basin.\u003c/p\u003e\n\u003cp\u003eFor each HUC12 unit \u003cem\u003eh\u003c/em\u003e, we aggregated the monthly public supply consumptive use estimates for each year \u003cem\u003et\u003c/em\u003e and multiplied the sum by 365.25 to convert to annual total use in million gallons. This produced annual consumptive use estimates for each HUC12 from 2000 to 2020. These values were spatially referenced by merging with the USGS\u0026rsquo;s Watershed Boundary Dataset [46], which defines the geographic extents of each HUC12.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo assign these calculated values to the relevant sub-basins in our study area, we used spatial intersection [47]. Because some HUC12s extend across multiple sub-basins, we apportioned their annual consumptive use proportionally based on the area of overlap. Specifically, for each intersecting pair of HUC12 \u003cem\u003eh\u0026nbsp;\u003c/em\u003eand sub-basin \u003cem\u003es\u003c/em\u003e, the portion of \u003cem\u003eh\u003c/em\u003e\u0026apos;s annual use attributed to \u003cem\u003es\u0026nbsp;\u003c/em\u003ewas weighted by the ratio of the area of \u003cem\u003eh\u0026nbsp;\u003c/em\u003ewithin \u003cem\u003es\u0026nbsp;\u003c/em\u003eto the total area of \u003cem\u003eh\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eIt is important to note that our MCI estimates do not include self-supplied domestic and industrial water use, which we know are present in the basin [48], so are almost certainly an underestimate.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e5.2 Estimating municipal \u0026amp; commercial water consumption in Mexican sub-basins\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eEstimates of 2000-2020 annual water use for domestic and manufacturing water consumption in Mexico were obtained from the WaterGAP global hydrologic modeling team at the University of Kassel, Germany [49]. The gridded WaterGAP water use data were aggregated to the RGB sub-basins in a Geographic Information System.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e5.3 Estimating thermometric power plant water consumption for US sub-basins\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThermoelectric power plant water use values for the US were obtained from the recent USGS reanalysis of the thermoelectric power plant water use dataset [50]. This dataset provides monthly water withdrawal and consumption values from 2008 to 2020 at the HUC-12 scale. Using the HUC-12 basins on the US side of the RGB basin, we summed the thermoelectric water use values into their respective HUC-8 basin-scale membership. To obtain water use estimates for missing data between 2000 and 2007, we developed a backcasting Seasonal Autoregressive Integrated Moving Average (SARMIA) time series model [51,52]. \u0026nbsp; Individual backcasting SARIMA models were fit to each HUC-12 sub-basin with recorded thermoelectric power plant records to capture local temporal dynamics and improve accuracy in reconstructing historical trends. For detailed model specifications and results, see Supplemental Information discussion including Table SI-3 and Figures SI-3\u0026ndash;SI-10.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e5.4 Estimating thermoelectric power plant water consumption for Mexican sub-basins\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAnnual estimates of thermoelectric water consumption for the Mexican portion of the RGB basin from 2000 to 2020 were obtained from the Estad\u0026iacute;sticas del Agua en M\u0026eacute;xico annual reports published by the Sistema Nacional de Informaci\u0026oacute;n del Agua [53]. However, there were missing annual reports for the years 2000, 2001, 2002, and 2020, and the annual reports of 2004, 2005, 2008, 2009, and 2013 reported a withdrawal value but not a consumptive value for the RGB basin. We applied a linear interpolation across the 2000-2020 period to address these data gaps. The resulting annual values for the RGB basin were then proportionally allocated to individual Mexican HUC-8 basins based on the installed total generation capacity (in megawatts) and the operational start year of each thermoelectric power plant. Information on installed plant generation capacity and commissioning dates was manually collected from the Global Energy Monitor Wiki [54]. For a detailed explanation of the interpolation and proportional allocation processes, see Supplemental Information discussion including Tables SI-4 and SI-5.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e5.5 Estimating crop water consumption for US sub-basins\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe determined volumetric crop water requirements (VCWR) for 30 major crops (see Table 3 for acreage estimated for the RGB basin), accounting for 94.2% of the total irrigated area in the US [55], for the period 2000-2019. The VCWR for each crop was calculated by taking the product of the crop water requirement (CWR), measured as the depth of consumed water per unit area, by a crop\u0026rsquo;s irrigated harvested area within each 2.5 arc-minute grid cell within the conterminous United States (CONUS). The CWR for these crops was adapted from Modeled Irrigated Agriculture of the United States (MirAG-US), which provides monthly blue and green CWR at a 2.5 arc-minute resolution for CONUS [56]. The irrigated harvested areas for the 30 major crops were obtained from HarvestGRID, which provides yearly crop-specific irrigated acreage, at a 2.5 arc-minute resolution across CONUS [57]. We aggregated the monthly blue and green CWR to obtain yearly CWR and calculated the crop-specific VCWR using the corresponding yearly CWR and irrigated harvested area.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor pastureland, which MirAG-US does not account for but is widely irrigated in the basin, we estimated VCWR by leveraging CWR, both blue and green, for alfalfa from MirAG-US. For locations where pasture/hay was cultivated within individual subbasins, we obtained the annual green CWR for pastureland by averaging the green CWR for alfalfa at a subbasin scale. We estimated the annual blue CWR for pastureland at the subbasin level by adjusting the blue CWR for alfalfa using the reported applied irrigation depth from the Irrigation and Water Management Surveys for the years 2003 [58], 2008 [59], 2013 [60], and 2018 [55]. The adjustment factor was derived by calculating the state-average irrigation depth ratio between pastureland and alfalfa in New Mexico, where most of the study area is located. Subsequently, this adjustment factor was multiplied by the annual alfalfa blue CWR for each 2.5 arc-minute grid cell and then averaged across the subbasin.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo identify irrigated pasturelands at the subbasin level, we combined National Land Cover Database (NLCD) [61] 30m resolution rasters with the corresponding Landsat-based National Irrigation Dataset (LANID) [62] 30m resolution rasters. Since NLCD classifies both pasture and hay under a single category, we calculated the effective pastureland area of each subbasin by subtracting irrigated other hay areas from irrigated pasture/hay areas from NLCD. For 2008-2019, a combination of the Cropland Data Layer (CDL) [63] and LANID were used to identify irrigated other hay, which was then subtracted from NLCD\u0026rsquo;s pasture/hay category. For pre-2008 years when CDL data were not available, we used average subbasin-level irrigated area for other hays calculated from available years, which was then subtracted from NLCD to estimate irrigated pastureland. The VCWR for pastureland was determined at the subbasin level according to Equation 1.\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003cp\u003eWhere, subscripts denote the crop type and superscripts NLCD and CDL denote the data sources of irrigated crop acreage We note that the adjustment factor is applied only to blue CWRs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Average crop acreage for 26 major crops and pastureland in the Rio-Grande across 2000-2019\u003c/strong\u003e. Note that only 26 of the 30 crops reported by MirAG-US and HarvestGRID are grown in the region.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"321\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33.9564%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrop\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40.4984%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrop Area (Acres)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.5452%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33.9564%;\"\u003e\n \u003cp\u003eAlfalfa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40.4984%;\"\u003e\n \u003cp\u003e299,117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.5452%;\"\u003e\n \u003cp\u003e30.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33.9564%;\"\u003e\n \u003cp\u003eOther Hay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40.4984%;\"\u003e\n \u003cp\u003e134,323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.5452%;\"\u003e\n \u003cp\u003e13.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33.9564%;\"\u003e\n \u003cp\u003eCotton\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40.4984%;\"\u003e\n \u003cp\u003e94,035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.5452%;\"\u003e\n \u003cp\u003e9.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33.9564%;\"\u003e\n \u003cp\u003eCorn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40.4984%;\"\u003e\n \u003cp\u003e76,956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.5452%;\"\u003e\n \u003cp\u003e7.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33.9564%;\"\u003e\n \u003cp\u003ePasture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40.4984%;\"\u003e\n \u003cp\u003e76,731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.5452%;\"\u003e\n \u003cp\u003e7.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33.9564%;\"\u003e\n \u003cp\u003eSorghum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40.4984%;\"\u003e\n \u003cp\u003e69,566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.5452%;\"\u003e\n \u003cp\u003e7.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33.9564%;\"\u003e\n \u003cp\u003ePotato\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40.4984%;\"\u003e\n \u003cp\u003e60,195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.5452%;\"\u003e\n \u003cp\u003e6.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33.9564%;\"\u003e\n \u003cp\u003eBarley\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40.4984%;\"\u003e\n \u003cp\u003e49,538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.5452%;\"\u003e\n \u003cp\u003e5.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33.9564%;\"\u003e\n \u003cp\u003ePecan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40.4984%;\"\u003e\n \u003cp\u003e39,280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.5452%;\"\u003e\n \u003cp\u003e4.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33.9564%;\"\u003e\n \u003cp\u003eSugarcane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40.4984%;\"\u003e\n \u003cp\u003e28,078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.5452%;\"\u003e\n \u003cp\u003e2.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33.9564%;\"\u003e\n \u003cp\u003eWinter Wheat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40.4984%;\"\u003e\n \u003cp\u003e12,073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.5452%;\"\u003e\n \u003cp\u003e1.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33.9564%;\"\u003e\n \u003cp\u003eSpring Wheat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40.4984%;\"\u003e\n \u003cp\u003e9,332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.5452%;\"\u003e\n \u003cp\u003e1.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33.9564%;\"\u003e\n \u003cp\u003eOats\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40.4984%;\"\u003e\n \u003cp\u003e5,434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.5452%;\"\u003e\n \u003cp\u003e0.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33.9564%;\"\u003e\n \u003cp\u003eOranges\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40.4984%;\"\u003e\n \u003cp\u003e4,793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.5452%;\"\u003e\n \u003cp\u003e0.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33.9564%;\"\u003e\n \u003cp\u003eDurum Wheat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40.4984%;\"\u003e\n \u003cp\u003e4,673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.5452%;\"\u003e\n \u003cp\u003e0.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33.9564%;\"\u003e\n \u003cp\u003eDry Beans\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40.4984%;\"\u003e\n \u003cp\u003e1,836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.5452%;\"\u003e\n \u003cp\u003e0.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33.9564%;\"\u003e\n \u003cp\u003eSunflower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40.4984%;\"\u003e\n \u003cp\u003e1,551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.5452%;\"\u003e\n \u003cp\u003e0.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33.9564%;\"\u003e\n \u003cp\u003eCanola\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40.4984%;\"\u003e\n \u003cp\u003e1,119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.5452%;\"\u003e\n \u003cp\u003e0.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33.9564%;\"\u003e\n \u003cp\u003eSoybean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40.4984%;\"\u003e\n \u003cp\u003e1,035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.5452%;\"\u003e\n \u003cp\u003e0.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33.9564%;\"\u003e\n \u003cp\u003ePeanuts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40.4984%;\"\u003e\n \u003cp\u003e918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.5452%;\"\u003e\n \u003cp\u003e0.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33.9564%;\"\u003e\n \u003cp\u003eSugarbeet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40.4984%;\"\u003e\n \u003cp\u003e594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.5452%;\"\u003e\n \u003cp\u003e0.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33.9564%;\"\u003e\n \u003cp\u003eSweet Corn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40.4984%;\"\u003e\n \u003cp\u003e508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.5452%;\"\u003e\n \u003cp\u003e0.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33.9564%;\"\u003e\n \u003cp\u003eApples\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40.4984%;\"\u003e\n \u003cp\u003e477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.5452%;\"\u003e\n \u003cp\u003e0.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33.9564%;\"\u003e\n \u003cp\u003eTomato\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40.4984%;\"\u003e\n \u003cp\u003e130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.5452%;\"\u003e\n \u003cp\u003e0.01%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33.9564%;\"\u003e\n \u003cp\u003eGrapes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40.4984%;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.5452%;\"\u003e\n \u003cp\u003e0.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33.9564%;\"\u003e\n \u003cp\u003ePeas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40.4984%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.5452%;\"\u003e\n \u003cp\u003e0.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 33.9564%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 40.4984%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e972,351\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25.5452%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.0%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cem\u003e5.6 Estimating crop water consumption for Mexican sub-basins\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eActual evapotranspiration (ET) for major annual crops in Mexico was simulated using the Environmental Policy Integrated Climate (EPIC) model [64,65] at a 30-arcminute spatial resolution, with crop-specific parameters assigned to each crop type. Perennial crops, including fruit trees and alfalfa, were modeled separately using the CropGBWater model\u0026mdash;a Python-based global gridded tool designed to estimate green and blue water consumption in crop production [66].\u003c/p\u003e\n\u003cp\u003eTo distinguish between rainfed and irrigated agriculture, we used municipal-level agricultural statistics from Mexico [67] to generate harvested area maps at a 5-arcminute resolution for the major crops. Combined with the ET outputs from the crop models, these maps were used to compute green and blue water consumption for crop production at 5-arcminute resolution. The resulting grid-level estimates were then aggregated to the sub-basin scale using sub-basin polygon boundaries. These estimates were further verified using data from the Comision Nacional del Agua\u0026rsquo;s Agricultural Statistics database [68], using some of the main irrigation districts for comparisons.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e5.7 Estimating reservoir evaporation\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo estimate monthly evaporative water loss from reservoirs we utilized the Global Lake Evaporation Volume dataset [69]. This dataset provides evaporation estimates for global lakes and reservoirs derived from the HydroLAKES database [70]. We spatially associated each lake with its corresponding sub-basin by overlaying the HydroLAKES dataset with a delineated RGB sub-basin layer. Through this spatial intersection, we identified 37 reservoirs within the basin (Table SI-2). We computed the annual mean evaporation during 2000-2018 for each of these reservoirs using their monthly evaporation values over the study period.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e5.8 Estimating riparian evapotranspiration\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eRiparian and wetland vegetation along the RGB river corridor were mapped at ~30 m resolution using spatial data products and the Google Earth Engine Python API [71]. The riparian corridor was delineated using Strahler stream order information within the USGS\u0026rsquo;s National Hydrography Dataset Plus version 2 [72] and Mexico\u0026rsquo;s RED Digital Hydrographic database [73]. The total width of the riparian corridor for each stream segment was established based on the corresponding Strahler stream order designation [74]. The valley floor within the river corridor was delineated using the Global Shuttle Radar Topography Mission Landforms dataset (~90 m resolution) [75]. Riparian and wetland vegetation within the riparian corridor\u0026rsquo;s valley floor was mapped annually over the 2000-2022 period by masking out cropland, bare ground, impervious surfaces, and water/snow/ice from the Global 30m Land Cover Change Dataset [76].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTotal annual ET (AF/yr) of riparian and wetland vegetation mapped within the RGB sub-basins were estimated over the 2000-2022 period using OpenET [77] (~30 m resolution) for the United States and the Penman-Monteith-Leuning Evapotranspiration V2 (PML-V2) product [78\u0026ndash;80] (~500 m resolution) for Mexico. OpenET estimates were prioritized for riparian and wetland vegetation, and PML-V2 estimates were used where OpenET data were not available. The PML-V2 estimates include a sum of the vegetation transpiration (Ec), soil evaporation (Es), and interception from vegetation canopy (Ei) bands. Total annual precipitation was also estimated for riparian and wetland vegetation over the 2000-2022 period using gridMET [81] (~4.6 km resolution). For each basin, the median evapotranspiration and precipitation values for riparian and wetland vegetation were calculated. Precipitation-derived ET is highly variable across riparian and wetland vegetation and depends on the species and water table depth [82\u0026ndash;84]. Based on our review of literature cited here, we assume that half of the annual precipitation supports riparian ET, either directly as enhanced soil moisture or indirectly as monsoon runoff. Total annual ET was calculated per basin by subtracting half of the precipitation depth from the ET depth and then converted into volumetric units by multiplying the annual riparian area.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e5.9 Estimating inter-basin transfer volumes\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eUsing the Interbasin Transfer Database Standard Version 1.0 (IBTDS 1.0) [85], we identified 54 unique interbasin water transfer (IBT) projects comprising 274 transfer links within the RGB basin. Of the 274 identified links, 16 links (corresponding to 14 unique IBT projects) had water transfer volumes: 13 included average and daily time series data, while 3 had only average transfer volumes. Among the 13 links with daily time series data, 12 links (corresponding to 10 unique projects) contained records covering all or part of the 2000\u0026ndash;2019 study period. For each of these 12 links, we assessed the completeness of daily flow data and retained only those years with complete daily records (i.e., no missing values). We then calculated total annual transfer volumes for each valid year and computed the mean annual IBT volume by averaging across the valid years. Many of the IBTs in the RGB basin are small and may represent seasonal water transfers. While the larger IBT projects are well represented in the dataset, particularly those with associated flow data, some small IBTS may not be captured by IBTDS 1.0.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e5.10 Estimating depletion of river outflow volumes\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAnnual runoff volumes were obtained from the USGS\u0026rsquo;s \u0026ldquo;Water Data for the Nation\u0026rdquo; webpages [86] and the International Boundary \u0026amp; Water Commission [87]. Data were obtained from monitoring stations closest to each river\u0026rsquo;s exit from a sub-basin that included measurements for the 2002-2024 period of record. These gauges include the Rio Grande at Lobatos, CO; Rio Grande below Elephant Butte Reservoir, New Mexico; Rio Conchos near Ojinaga, Mexico; Pecos River near Girvin, Texas; and Rio Grande at Brownsville, Texas. The average annual rate of change was estimated using the slopes of the linear regression lines.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e5.11 Estimating reservoir depletion volumes\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDaily estimates of reservoir volume were obtained from multiple sources including the US Bureau of Reclamation\u0026rsquo;s \u0026ldquo;Reclamation Information Sharing Environment (RISE)\u0026rdquo; webpages [88], the Natural Resources Conservation Service\u0026rsquo;s \u0026ldquo;Air \u0026amp; Water Database Report Generator\u0026rdquo; webpages [89], the Texas Water Development Board\u0026rsquo;s \u0026ldquo;Water Data for Texas\u0026rdquo; webpages [90], and the International Boundary \u0026amp; Water Commission\u0026rsquo;s \u0026ldquo;Water Data Portal\u0026rdquo; webpages [87]. A listing of reservoirs used in estimating evaporation is provided in Table SI-2. The average annual rate of change was estimated using the slopes of the linear regression lines.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e5.12 Estimating groundwater depletion volumes\u003c/em\u003e\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;We followed Rodell and Famiglietti [91], Yeh et al. [92], Rodell et al. [93], and Rodell et al. [94] to estimate groundwater storage changes using NASA GRACE and GRACE Follow-On (GRACE/FO) satellite observations from 2002 to 2024. The GRACE/FO missions provide monthly observations of Earth\u0026apos;s gravity field, which are used to estimate changes in TWS at local and global scale [95,96]. These GRACE data are particularly useful for monitoring groundwater and other water components that are difficult to measure directly [97,98]. The GRACE/FO mascon solutions offer improved spatial pixels resolution and reduced leakage compared to earlier GRACE spherical harmonic data (GRACE-SH), enhancing hydrological applications in complex hydrological systems [99].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this work, we used the average of three mascon solutions: NASA\u0026rsquo;s Jet Propulsion Laboratory (JPL-RL06.1M [100], the University of Texas Center for Space Research (CSR-RL06.02M [101]), and NASA Goddard Space Flight Center (GSFC-RL06v2.0M [99,102,103] for our TWS estimates. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe analyzed total water storage (TWS) variations across the entire RGB and its sub-basins. Groundwater storage (GWS) variations were derived by subtracting measured surface water reservoir storage (SWRS) and modeled soil moisture storage (SMS) and from TWS. The SWRS variations were estimated by compiling daily storage data for major RGB reservoirs from data sources stated in section 5.11 above. SMS was obtained from the North American Land Data Assimilation System (NLDAS) [104], which includes three land surface models (NOAH, VIC, and MOSAIC) [91\u0026ndash;94]. NLDAS integrates multiple land surface models to provide high-resolution hydrometeorological variables across the continent. It offers gridded outputs of soil moisture, evapotranspiration, and surface runoff, among others. This case study used SMS from three NLDAS models to isolate groundwater storage variations. Snow water equivalent (SWE) was not included in this analysis, as it contributes minimally to total water storage across most of the RGB, particularly outside the headwater regions, and is commonly excluded in similar large-scale groundwater assessments [94].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;To isolate long-term trends and suppress the seasonal signal, we applied Seasonal-Trend decomposition using LOESS (STL) to all monthly time series of all water compartments (TWS, SWRS, SMS, and GWS). STL\u0026apos;s an iterative, non-parametric decomposition procedure that separates a time series into three components (trend, seasonal, residual). This process involved first filling in any missing monthly values using linear interpolation. Next, the time series were decomposed into seasonal and trend components using the STL (Seasonal-Trend decomposition based on LOESS) method. Finally, the deseasonalized component, representing the long-term trend, was extracted from the original series [105,106]. The resulting nonseasonal time series were then used to calculate annual and decadal trends in water storage across the basins.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Uncertainty in TWS was assessed by calculating the standard deviation among the three GRACE/FO solutions. For SMS, uncertainty was estimated as the standard deviation among the three NLDAS models (NOAH, VIC, and MOSAIC). Since reservoir storage datasets did not include error estimates, we followed Liu et al. [98] in assigning a 15% uncertainty to the SWRS values. The overall uncertainty in GWS (\u0026sigma;GWS) was determined by combining the individual uncertainties from TWS, SWRS, and SMS in quadrature (Equation 2)\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003e6.1 Funding Statement\u003c/em\u003e\u003c/p\u003e\n\n\u003cp\u003eK.F.F., E.C.S., and R.R.R. were supported by a grant from the National Science Foundation\u0026apos;s Sustainable Regional Systems Program, the Transformation Network, which aims to support convergent research and education that will advance sustainable regional systems science, engineering, and education (NSF Grant #2115169).Landon Marston was supported by National Science Foundation Grant RISE-2108196. \u003c/p\u003e\n\n\u003cp\u003e\u003cem\u003e6.2 Ethics statement\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\n\u003cp\u003e\u003cem\u003e6.3 Author contributions\u003c/em\u003e\u003c/p\u003e\n\n\u003cp\u003eB.D.R. conceived of the study, wrote the main manuscript, assembled and analyzed data, and prepared tables and figures. K.A. assembled and analyzed data and prepared figures. S.D. assembled and analyzed data and prepared figures. J.S.F. analyzed data and edited manuscript. K.F.F. assembled and analyzed data and edited manuscript. H.G. assembled and analyzed data. L.M. analyzed data and edited manuscript. M.M.M. assembled and analyzed data. E.P. assembled and analyzed data, prepared figures, and edited manuscript. M.M.R. assembled and analyzed data and edited manuscript. B.L.R. analyzed data and edited manuscript. R.R.R. analyzed data and edited manuscript. N.S. assembled and analyzed data. E.C.S. assembled and analyzed data and edited manuscript.\u003c/p\u003e\n\n\u003cp\u003e\u003cem\u003e6.4 Competing interests\u003c/em\u003e\u003c/p\u003e\n\n\u003cp\u003eThe authors have no competing interests as defined by Discover, or other interests that might be perceived to influence the results and/or discussion reported in this paper.\u003c/p\u003e\n\n\u003cp\u003e\u003cem\u003e6.5 Dual publication\u003c/em\u003e\u003c/p\u003e\n\n\u003cp\u003eNothing in this manuscript has been previously published nor under consideration for publication elsewhere.\u003c/p\u003e\n\n\u003cp\u003e\u003cem\u003e6.6 Authorship\u003c/em\u003e\u003c/p\u003e\n\n\u003cp\u003eThe corresponding author confirms that he has read the journal policies and is submitting their manuscript in accordance with those policies.\u003c/p\u003e\n\n\u003cp\u003e\u003cem\u003e6.7 Third party material\u003c/em\u003e\u003c/p\u003e\n\n\u003cp\u003eAll of the material in this manuscript is owned by the authors and no permissions are required.\u003c/p\u003e\n\n\u003cp\u003e\u003cem\u003e6.8 Data availability\u003c/em\u003e\u003c/p\u003e\n\n\u003cp\u003eData sets generated during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\n\u003cp\u003e\u003cem\u003e6.9 Acknowledgements\u003c/em\u003e\u003c/p\u003e\n\n\u003cp\u003eThe authors would like to acknowledge Dr. Pete Caldwell of the US Forest Service\u0026apos;s Southern Research Station for assisting with accessing WaterGAP data for municipal and commercial uses in Mexico. Dr. Samuel Sandoval-Solis of the University of California at Davis has provided helpful citations and editing.\u003c/p\u003e\n\n\u003cp\u003e\u003cem\u003e6.10 Consent to publish\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe publication of this manuscript has been approved by all authors.\u003c/p\u003e\n\n\u003cp\u003e\u003cem\u003e6.11 Consent to participate\u003c/em\u003e\u003c/p\u003e\n\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWikipedia contributors. List of rivers of the United States by discharge [Internet]. Wikipedia. 2024. https://en.wikipedia.org/w/index.php?title=List_of_rivers_of_the_United_States_by_discharge\u0026amp;oldid=1261439320. Accessed 30 Jun 2025.\u003c/li\u003e\n\u003cli\u003eRichter BD, Prunes E, Liu N, Caldwell P, Wei D, Davis KF, et al. Opportunities for Restoring Environmental Flows in the Rio Grande\u0026ndash;Rio Bravo Basin Spanning the US\u0026ndash;Mexico Border. 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Science of The Total Environment. 2019;677:530\u0026ndash;44. https://doi.org/10.1016/j.scitotenv.2019.04.316\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003cp\u003e[10] The river is known as the Rio Grande in the US but is the Rio Bravo in Mexico. We have combined these names as Rio Grande-Bravo (RGB) in this paper.\u003c/p\u003e\n"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-water","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diwa","sideBox":"Learn more about [Discover Water](https://www.springer.com/43832)","snPcode":"","submissionUrl":"","title":"Discover Water","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7313321/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7313321/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The Rio Grande-Bravo basin shared by the United States and Mexico is experiencing a severe water crisis demanding urgent attention. In recent decades, water storage reservoirs, aquifers, and annual streamflow volumes have been substantially depleted, leaving little buffer for continued over-consumption of renewable water supplies. Despite the great scarcity of water and intensifying water shortages in this basin, a full accounting of the river’s consumptive uses and losses has never been undertaken. In this study we assemble detailed water consumption estimates from a broad array of sources to describe how surface and ground water were consumed for both direct uses (agricultural, municipal, commercial, thermoelectric power generation) and indirect uses (reservoir evaporation and riparian evapotranspiration) in each of 14 sub-basins during recent decades. We find that only half (48%) of water directly consumed for anthropogenic purposes is supported by renewable replenishment; the other half (52%) has been unsustainable, meaning that it is causing depletion of reservoirs, aquifers, and river flows. The over-consumption of renewable water supplies is primarily due to irrigated agriculture, which accounts for 87% of direct water consumption in the basin. At the same time, water shortages have contributed to the loss of 18% of farmland in the river’s headwaters in Colorado, 36% along the Rio Grande in New Mexico, and 49% in the Pecos River tributary in New Mexico and Texas. Farmland contraction in the US portion of the basin has resulted in lowered irrigation consumption and many cities have been able to reduce their water use as well, but irrigation in the Mexican portion of the basin has increased greatly, causing basin-wide consumption to remain high. This severe water crisis presents an opportunity for envisioning a more secure and sustainable water future for the basin, but a swift transition will be needed to avoid damaging consequences for farms, cities, and ecosystems.","manuscriptTitle":"Overconsumption Gravely Threatens Water Security in the Binational Rio Grande-Bravo Basin","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-26 05:50:31","doi":"10.21203/rs.3.rs-7313321/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-16T06:28:36+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-15T15:07:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-09T13:48:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-09T13:20:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"223358208699216105156860759004313387601","date":"2025-09-09T12:57:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"37126925659323587542655862123992619141","date":"2025-09-08T16:20:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-05T23:11:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"121760907908813506132736818895613648266","date":"2025-09-04T21:11:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-04T15:03:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"89179299122723027422397775366925476803","date":"2025-09-04T14:36:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"143115142370161577190195050963288028953","date":"2025-08-26T22:27:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"49521585705573603128161515438136306324","date":"2025-08-19T23:19:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-17T23:01:11+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-12T09:34:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-12T07:06:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-12T07:05:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Water","date":"2025-08-06T23:11:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-water","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diwa","sideBox":"Learn more about [Discover Water](https://www.springer.com/43832)","snPcode":"","submissionUrl":"","title":"Discover Water","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"afba95e8-5c7a-4965-8261-55a8b2e61b09","owner":[],"postedDate":"August 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-24T16:12:10+00:00","versionOfRecord":{"articleIdentity":"rs-7313321","link":"https://doi.org/10.1007/s43832-025-00301-2","journal":{"identity":"discover-water","isVorOnly":false,"title":"Discover Water"},"publishedOn":"2025-11-20 15:59:04","publishedOnDateReadable":"November 20th, 2025"},"versionCreatedAt":"2025-08-26 05:50:31","video":"","vorDoi":"10.1007/s43832-025-00301-2","vorDoiUrl":"https://doi.org/10.1007/s43832-025-00301-2","workflowStages":[]},"version":"v1","identity":"rs-7313321","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7313321","identity":"rs-7313321","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}