Water scarcity is a defining characteristic of the Western United States (US) of America (Longreads 2018). It is also an existential threat becoming more exacerbated due to the increasingly frequent, widespread, and severe drought conditions resulting from intensifying global heating and human-induced climate change (National Oceanic and Atmospheric Administration [NOAA] 2021). The region is living through the worst megadrought (long-term multidecadal dryness/ prolonged periods of dryness of more than two decades) in over 1,200 years (Williams et al. 2022, 232). Recent estimates show that about 45% of the contiguous U.S. experienced moderate to extreme drought (NOAA 2022; National Drought Mitigation Center [NDMC] 2022) (Figure 1), and much of this is concentrated in the West where 65% of the region is facing extreme drought and more acutely in the Southwest (Figures 2 and 3).

Figure 1. Map of the intensity and impacts the droughts have in the US (NDMC. 2022)
Figure 1. Map of the intensity and impacts the droughts have in the US (NDMC. 2022)

 

Figure 2: Map of the intensity of the droughts in the western US, (NDMC, NOAA. 2022)
Figure 2: Map of the intensity of the droughts in the western US, (NDMC, NOAA. 2022)

 

Figure 3. Percent of U.S western land area under drought (U.S. Department of Agriculture 2022) 
Figure 3. Percent of U.S western land area under drought (U.S. Department of Agriculture 2022) 

 

The drought condition in the West of the U.S. is forecast to intensify “with the rest of the century” (Canon, Gabrielle 2022), and will further aggravate the already terrifying situation of reduced precipitation, increased evaporation rates and shrinking surface water reservoirs like lakes (Lake Meads: Figure 4), rivers (Colorado Rivers), rainfed ephemeral and stock ponds on fields, farms, and rangelands. As climate externalities such as water shortages, heat waves, and wildfires continue to ravage, there is a growing fear of irrecoverable economic losses within the community of farmers and ranchers losing farm- and rangelands that depend on the disappearing surface water for crop irrigation.

Figure 4. Drought map and satellite images of the shrinking Lake Mead (Nevada-Utah Border) (The Economist. 2022)
Figure 4. Drought map and satellite images of the shrinking Lake Mead (Nevada-Utah Border) (The Economist. 2022)

 

“The only persistent drought we have had in the last 60 years, would be the last 3 years, and it is the first and only drought with three recorded nonevent summer monsoons.” – Bob Prosser (NASA DEVELOP National Program, 2021).

"Everyone else I've talked to says in 85 years, it (drought condition) has not been this bad. We have 85 years' worth of our own drought data that says we've never done this ... not to this extent."- Atkin.  (The Cable News Network (CNN) 2021).

Stock ponds and ranching

Stock ponds (Figure 5) are often located in remote areas and provide water supply to maintain quality animal health and production from livestock raised on pastures and rangelands. Pasture- and rangelands can be found in many Southwestern states of the U.S. including Arizona, Utah, and Texas (Utah State University n.d; Aberle et al. 2021, 2; Walker 2021, 65). 

Figure 5. Remote stock pond within a grazing allotment in Arizona (Lisa Bolton and NASA DEVELOP National Program, 2021)
Figure 5. Remote stock pond within a grazing allotment in Arizona (Lisa Bolton and NASA DEVELOP National Program, 2021)

“Whether cattle are grazing native range, fescue, Bermuda grass or any other kind of pasture, its value as a grazing resource is dependent upon the availability of livestock water. For the grazer, an ample supply of stock water is a blessing, while a shortage presents a serious challenge.” – Troy Smith (Smith 2011, 1)

Because of the remoteness of these water sources, the challenging task of regularly monitoring them to ensure adequate availability of water for livestock is becoming even more arduous under increasingly intensified drought conditions. Traditional monitoring practices are often costly, time-consuming, and energy-intensive as they involve weekly or monthly trips to ranches. Therefore, there is a need for the adoption of innovative, technology-based stock pond monitoring and management solutions. (Utah State University n.d; Aberle et al. 2021, 3). To this end, remote sensing technology or optical satellite imagery has been deployed to offer rapid and cost-effective solutions for spatial detection and regular monitoring of remote rainfed stock ponds’ surface water extent. 

Remote sensing technology to monitor stock ponds

Remote sensing technology is a powerful tool that enables the monitoring and assessment of various environmental features and resources, providing timely and accurate information for effective resource management. Use cases of this technology in agriculture and livestock management reveal reductions in cost and labor which are often associated with remote livestock reservoir monitoring. Similarly, sensor-based satellite communication technologies can be integrated into automated alert systems which enable efficient transmission of relevant water-related data and real-time decision-making. Examples include the Satellite Radio Stock Watering System (SRSWS), and Information Data Technologies' (IDT) flow meter and pressure transducer systems. SRSWS is a solar-powered system equipped with a mini-satellite unit for data transmission generated from a transducer sensor to a website. The SRSWS monitors emails or calls the rancher with an alert message about the depleted stock pond fill or water level. This is in use in southern Utah, USA (Utah State University n.d). IDT systems transmit data over a satellite system that consists of a satellite antenna, pressure transducer (sensor type) control box, and valve box often configured to give periodic (8- or 24- hourl intervals) readings via a website and exported to a spreadsheet for more analysis. The website shows a graphical output of daily water usage and reservoir water height, as well as monthly usage for the previous year.

Pilot systems of the above-described technology have been deployed in Crockett County, Texas, USA (Walker 2021, 68). While these tools offer innovative solutions for traditional stock reservoir monitoring practices, especially addressing challenges related to time and energy demand, their adoption and deployment can still be limited by cost associated with equipment installation and depreciation. For example, the total annual cost for an IDT unit ranges from 440 – 690 US Dollars (Walker 2021, 66). Given the hardware requirements and maintenance of these tools, emerging remote sensing-based app technologies will offer more flexibility with significant cost reduction and no hardware requirement except for users’ mobile phones.

One of the most recent works relying on remotely sensed data for addressing drought-induced water scarcity challenges in the Southwest U.S., specifically in Arizona, was conducted by the 2021 Summer cohort of NASA DEVELOP National Program. The study (Aberle et al. 2021, 1) captured seasonal and interannual variations of stock ponds surface water area (Figure 6) through a time series analysis using Landsat 8 OLI, Sentinel-1 C-band Synthetic Aperture Radar (C-SAR), and Sentinel-2 Multispectral Instrument (MSI) (Figure 7). The research products were time series chart (Figure 8) and software tool (Surface Water Identification and Forecasting Tool, SWIFT) developed in Google Earth Engine . The tool will enable ranchers and wildlife managers to remotely monitor stock ponds water extent as regularly as new satellite imagery and/or data becomes available, which will be every 10 days when using Sentinel-2, for instance.

 

Figure 6. (a) “Map of all digitized points used for training the classification models and the accuracy assessment. Blue points represent “water” and orange points represent “non-water” points. The yellow star represents the zoomed in region shown in panel (b)”. (Aberle et al. 2021, 7)
Figure 6. (a) “Map of all digitized points used for training the classification models and the accuracy assessment. Blue points represent “water” and orange points represent “non-water” points. The yellow star represents the zoomed in region shown in panel (b)”. (Aberle et al. 2021, 7).

 

Figure 7. (a) “Pre-processed true color mosaic of optical (Landsat 8 and Sentinel-2) imagery. (b) Classified optical imagery. (c) Pre-processed, false color Sentinel-1 imagery displayed using the VV and VH polarization bands. (d) Classified Sentinel-1 imagery. For classified imagery (b and d), black represents non-water points and white represents classified surface water. All images were captured in January 2018. The regional map (bottom) shows the zoomed in area located in Arizona shown in panels (a) - (d) represented by the yellow star” (Aberle et al. 2021, 9). 
Figure 7. (a) “Pre-processed true color mosaic of optical (Landsat 8 and Sentinel-2) imagery. (b) Classified optical imagery. (c) Pre-processed, false-color Sentinel-1 imagery displayed using the VV and VH polarization bands. (d) Classified Sentinel-1 imagery. For classified imagery (b and d), black represents non-water points and white represents classified surface water. All images were captured in January 2018. The regional map (bottom) shows the zoomed-in area located in Arizona shown in panels (a) - (d) represented by the yellow star” (Aberle et al. 2021, 9). 

 

Figure 8. (a) “Map of the study area with the Arizona state borders marked. (b) Time series of average monthly total surface areas of water in the study area since March 2013 with the 2-month moving average (purple). The monthly moving average precipitation for the study area from gridMET (Abatzoglou, 2013) is shown along the right y-axis (blue)” (Aberle et al. 2021, 8).
Figure 8. (a) “Map of the study area with the Arizona state borders marked. (b) Time series of average monthly total surface areas of water in the study area since March 2013 with the 2-month moving average (purple). The monthly moving average precipitation for the study area from gridMET (Abatzoglou, 2013) is shown along the right y-axis (blue)” (Aberle et al. 2021, 8).

 

Remote sensing data-dependent technologies: Advantages, implementation, and challenges

“A monitoring tool would certainly help to know what your waters are doing, because in a year like this we are so busy hauling water to ponds and finding out where water reserves are; we don’t have enough time to plan.” – Kit Metzger (NASA DEVELOP National Program 2021).

SWIFT was initially devised to empower stakeholders (i.e., ranchers, farmers, and wildlife managers) to monitor in near real-time (as satellite data becomes available), the availability of water in stock ponds in Arizona, but it can also be deployed in other regions of the Southwest U.S. where similar challenge of drought-induced water scarcity exists. Deployment in other parts of the world may require additional high-resolution optical data and algorithm/method modification.

The ability of users to effectively monitor livestock reservoirs with SWIFT will empower them with information to deploy strategies that will ensure more sustainable and efficient water resources management for livestock production and even wildlife conservation (Aberle et al. 2021, 6). In addition to surface water extent monitoring to guarantee water supply for livestock rearing. Information on the proximity to water in a remote location could also aid firefighting operations of fire departments in a region bedeviled by incessant wildfires (ibid).

Although SWIFT is awaiting implementation by local stakeholders (Diablo Trust, Kaibab National Forest, Range Program, The Flying M Ranch, Bar T Bar Ranch, etc.) in Arizona, other technologies like the SRSWS and IDT systems have been successfully deployed. When compared to traditional means of stock water monitoring where ranchers rely on past occurrences, road trips, or on-ranch travel to assess ponds' water levels, the IDT system is considered highly sophisticated. It can work in any location and detect water leaks in a timely manner. However, the operational requirement for users to log on to a website to access their stock water level information, is a reservation users continue to nurse (Walker 2020, 68). Users prefer information delivered to their emails and cell phones rather than periodic website visits. Despite this operational challenge noted for IDT which can be addressed through operational design improvement, huge economic and labor-saving advantages exist in the adoption of remote water monitoring systems like IDT, SRSWS, and SWIFT (when fully operational). Ranchers can be guaranteed timely and reliable information on livestock’s access to water, which is considered the most important nutrient for livestock (ibid).

Conclusion

Drought-induced water scarcity is a critical environmental issue that occurs when an extended period of dry weather leads to a significant decrease in water availability. This often wreaks severe havoc on ecosystems, agriculture, communities, and various water-dependent economic sectors. Addressing this environmental challenge requires a mix of efficient water management strategies including the use of space-based technologies for environmental change monitoring and assessment. Remote sensing data acquisition and processing, and analysis using sophisticated algorithms and specialized apps can guarantee more accurate, cheaper, and more timely information for environmental resources management, unlike traditional livestock reservoir monitoring methods.

While the solutions offered by remote sensing technology do not eliminate the rising threat of drought-induced water shortages, they are a significant adaptation strategy that farmers and ranchers could adopt at low or no cost for business sustainability and wildlife managers for wildlife conservation. Effective (adaptive) management strategies coupled with climate change mitigation measures are crucial to minimize the impacts of droughts and ensure sustainable water resources and livestock management.

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