Ag Water Webinars — The #Colorado Ag Water Alliance

From the Colorado Ag Water Alliance:

May 5 – Webinar – 12PM – 12:40PM

South Platte River Salinity… Should Agriculture Be Concerned?

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May 19 – Webinar – 12PM – 1PM

Demonstrating Ag Progress on Water Quality: Modeling the Effectiveness of EQIP-funded conservation practices

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Dragon Line irrigation system. Photo credit:

With shrinking #snowpack, #drought predictability melting away — @CUBoulderNews

Photo credit: CU Boulder News

Here’s the release from the University of Colorado (Kelsey Simpkins):

On April 1, water managers across the West use the amount of snowpack present as a part of a simple equation to calculate the available water supply for a given region that year. Historically, this method has accurately predicted whether large areas of the western U.S. will experience drought and to what degree. But new research from CU Boulder suggests that during the 21st century, our ability to predict drought using snow will literally melt away.

By mid-century, over two-thirds of western U.S. states that depend on snowmelt as a water source will see a significant reduction in their ability to predict seasonal drought using snowpack, according to the new study out today in Nature Climate Change. As we approach 2100, this area impacted by reduced drought prediction ability will increase to over 80%.

While measurements of soil moisture, rainfall and temperature can all help assess the chances of coming drought, even when those are taking into consideration, two-thirds of western states are projected to lose much of their ability to predict it.

“Although these other measurements increase a forecast’s accuracy, the loss of snow is something that we’re not going to be able to compensate for easily,” said Ben Livneh, author of the paper and a Fellow in the Cooperative Institute for Research In Environmental Sciences (CIRES).

Snowpack is a crucial source of water for the western U.S., where as much as 75% of freshwater originates as snow. It is also the most relied upon element of annual drought prediction in the region.

Coastal areas that receive water from nearby snowy mountains, like northern California, and regions at lower elevations, like the Washington Cascade Mountains, will be most affected. This is due to the fact that in these areas, less precipitation will fall as snow and they will lose their snow sooner from warming temperatures.

Higher elevations, including the Colorado and Northern Rocky Mountains, will keep their snowpack for longer and be able to continue relying on it as part of their predictive equations. But by the end of the century, even Colorado will not be immune to losing significant snowpack, and therefore, losing accuracy in its seasonal drought prediction.

“If you don’t accurately predict a year without drought, there’s less impact,” said Livneh, an assistant professor of Civil, Environmental and Architectural Engineering. “But there is so much to lose in a drought year by not being prepared for it.”

The point of prediction
The paper is the first to assess what vanishing snowpack might mean for future drought predictability.

Using 28 climate models looking at critical water-producing areas of the mountainous western U.S., Livneh and co-author Andrew Badger, formerly at CIRES, now an associate scientist in the Hydrological Sciences Lab at the NASA Goddard Space Flight Center, simulated snow pack, melt water, stream flow, water storage and evaporation. They calibrated these models more than 20 times against historical data from 1950 to present, to see if they could accurately predict how snowpack impacted streamflow in the past before applying these models to the future. Once satisfied with the models, they ran them up to 2100.

The researchers found that further into the future, snowpack alone became less and less accurate at predicting drought due to the reduction, and eventually, the complete loss of snow at many lower elevations. Between 2035 and 2065, 69% of the western U.S. will see a reduction in accurate seasonal drought prediction on the basis of snow information, with the affected areas increasing to 83% of the greater West between 2070 and 2099.

This reduction in drought prediction ability will affect everything from agriculture and drinking water supplies, to hydropower and flood control. It might increase our reliance on reservoirs, which could fill at different times of year and complicate how cities and states receive their water.

Regions which rely primarily on snow for drought pediction should be looking not only to other methods, but also to places nearby that observe snow at higher elevations, recommends Livneh.

The researchers hope to directly work with regional water managers in Colorado, which will be less affected, as well as those in the Pacific Northwest – which may see some of the biggest impacts of lost snowpack on drought predictability – to plan and adjust to this quickly changing equation.

“This is one way in which the connection to climate change is very clear, and the changing snow landscape has a major impact. Our drinking water, our water supply, for example, is something we take for granted,” said Livneh. “That’s something people should think about: Is that always going to be the case?”

How much #coronavirus testing is enough? States could learn from retailers as they ramp up — The Conversation #COVID19

To control the coronavirus spread, the U.S. needs to get the most value out of the limited testing capacity it has.
Steve Pfost/Newsday RM via Getty Images

Siqian Shen, University of Michigan

As states develop plans to restart their economies, the big fear is that coronavirus cases will surge again. To keep the pandemic under control, strategic testing systems will be needed, and they will need to be scaled up fast.

But how many people should be tested? Who should be tested? And what should that testing system look like?

There isn’t one simple answer. The notion of “widespread testing” has a different meaning for big metropolitan areas, such as New York City and Detroit, than for rural areas like Montana or Alaska. For testing systems to be efficient, they need to be tailored to the demographics, circumstances and disease spread patterns for each.

As policymakers figure out the best design for each state or county, they could learn a lot from the retail industry, where strategic decisions such as where to locate warehouses and distribution centers are being made by companies like Amazon in the face of uncertain customer demand.

I have been researching complex systems design in health care, transportation and energy supply management and have found that good models using mathematics and data can help design such systems, even with the kind of information uncertainty we see with the new coronavirus spread.

It isn’t as simple as ‘test everyone’

Policymakers and researchers have floated several plans for reopening the country, with a range of ideas for how to conduct coronavirus testing. On one end of the spectrum, a plan sketched out by Nobel Prize-winning economist Paul Romer calls for 7% of the U.S. population to be tested at random daily. That’s roughly 23 million people every day.

Another plan, released Monday by a team coordinated by Harvard University’s center for ethics, recommended 5 million tests per day by early June and 20 million a day by August.

While random testing of that magnitude can provide a broad snapshot of the disease, it is not realistic for a country the size of the U.S. in the short term. Countries that are conducting random testing on large percentages of their populations, such as Singapore, Iceland and South Korea, have far fewer people and less complex demographics than the U.S.

The U.S. also has had serious challenges with its testing system, including a flawed diagnostic test approved by the CDC, scarce supplies for manufacturing test kits and running tests, and limited access to screening.

As of mid-April, U.S. public and corporate labs together were only testing about 150,000 coronavirus samples a day. In all, some 4 million tests had been conducted in the U.S., about 20% of them positive.

That high rate of positive tests, 20%, suggests that many COVID-19 cases probably aren’t being identified. The World Health Organization recommends conducting enough tests that no more than 10% come back positive. In the U.S., that would require more than 500,000 per day, according to recent estimates from Harvard public health researchers.

At this point, the U.S. needs to get the most value out of the limited testing capability it has. Some options include prioritizing states with the most infections and deaths from COVID-19; areas where health systems are overwhelmed; or groups that have the most contact with others, such as health workers and grocery store clerks.

Who should be tested?

One purpose of the mathematical models I work with is to help policymakers determine which population groups should be prioritized and how.

There are different reasons to prioritize different groups. For example, older adults and people with chronic illnesses have a higher likelihood of developing severe conditions if they get COVID-19. First responders, health workers, teachers and others who have close contact with large numbers of people also have a high chance of getting and spreading the disease. People living in close quarters such as nursing homes or prisons also run a high risk of infection.

Once priority groups are identified, models can show where to locate testing facilities and how to allocate the tests kits and other resources most efficiently.

Learning from retail

There is a large body of research that has traditionally been used to site retail stores, optimize inventory and production and manage stock levels as demand for products fluctuates by season.

Establishing a testing system for COVID-19 presents a similar set of challenges. The same mathematical models used to help retail companies like Amazon meet demand quickly for a wide range of products could incorporate test-manufacturing capacities, the needs of different populations and goals such as increasing testing speed and coverage.

One question is where to conduct COVID-19 testing. Another set of questions involves the capacity of those facilities, such as how many test kits they need and what their staffing levels should be to process enough tests while minimizing people’s travel distance.

There is one big point of uncertainty, and it’s the same question that widespread testing is helping to clarify: How will the disease spread?

Retail companies know the demand patterns for most of their products quite well, given repeated orders and seasonal trends. But U.S. public health officials have only a few months of partial data on the spread and impact of this new coronavirus.

Adding to the complications, test results can create a biased picture of the pandemic depending on how many tests are done in each area and who was tested.

An iterative process is necessary to learn to analyze the data and predict the disease’s spread.

Whether the U.S. can scale up testing fast enough to keep the coronavirus spread under control mainly relies on strategic testing capabilities and how well we can design that testing system.

[Get facts about coronavirus and the latest research. Sign up for The Conversation’s newsletter.]The Conversation

Siqian Shen, Associate Professor of Industrial and Operations Engineering, University of Michigan

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Changes in snowmelt threaten farmers in western U.S. — @ColoradoStateU #snowpack #runoff #ColoradoRiver #COriver #aridification #ActOnClimate

The findings pinpointed basins around the world most at risk of not having enough water available at the right times for irrigation because of changes in snowmelt patterns. Two of those high-risk areas are the San Joaquin and Colorado river basins in the western United States. Photo: Kevin Bidwell/ Pexels

Here’s the release from Colorado State University (Mary Guiden):

For decades, scientists have thought that changes in snowmelt due to climate change could negatively impact agriculture. Now, a new study reveals the risks to agriculture around the world from changes in snowmelt, finding that farmers in parts of the western United States who rely on snowmelt to help irrigate crops will be among the hardest hit in the world by climate change.

In a study published April 20 in Nature Climate Change, an interdisciplinary team of researchers analyzed monthly irrigation water demand with snowmelt runoff across global basins from 1985 to 2015. The goal was to determine where irrigated agriculture has depended on snowmelt runoff in the past and how that might change with a warming climate.

Researchers then projected changes in snowmelt and rainfall runoff if the Earth warms by 2 or 4 degrees Celsius – about 3 ½ or 7 degrees Fahrenheit – which will potentially put snow-dependent basins at risk.

The findings pinpointed basins around the world most at risk of not having enough water available at the right times for irrigation because of changes in snowmelt patterns. Two of those high-risk areas are the San Joaquin and Colorado river basins in the western United States.

Nathan Mueller is a researcher in the Warner College and College of Agricultural Sciences. Photo: Joe Mendoza/CSU Photography

Nathan Mueller, senior author on the study and an assistant professor at Colorado State University, said that the study reveals how many of our important agricultural regions rely on snowmelt for irrigation. “Our research shows how efforts to halt climate change could benefit farmers and the food supply by preserving snowmelt as a vital water resource for agriculture,” he added.

Yue Qin, lead author and an assistant professor of geography at The Ohio State University, said that in many areas of the world, agriculture depends on snowmelt runoff happening at certain times and at certain magnitudes. “But climate change is going to cause less snow and early melting in some basins, which could have profound effects on food production,” she said.

Map of the San Joaquin River basin in central California, United States, made using public domain USGS National Map data. By Shannon1 – Own work, CC BY-SA 4.0,

Under the 4-degree Celsius warming scenario, the researchers project that the share of irrigation water demand met by snowmelt in the San Joaquin Basin decreases from 33 percent to 18 percent. In the Colorado Basin, the share of water demand met by snowmelt decreases from 38 percent to 23 percent.

Other basins in which agriculture is at particular risk because of changes in snowmelt are located in southern Europe, western China and Central Asia.
Depending on both the magnitude and the timing, rainfall runoff may be able to compensate for declines in snowmelt runoff in meeting irrigation water demand – but only for some basins, the researchers calculated.

“In many basins, future changes in rainfall do not compensate for the lost snowmelt during crop growing seasons,” Mueller said.

The San Joaquin, for example, is one basin where increases in rainfall runoff won’t be able to make up for snowmelt decline when irrigation is most needed.

The researchers evaluated the potential availability of reservoir storage and groundwater to help satisfy the additional irrigation need created by less snowmelt and early melting. In some basins, those additional requirements would pose great challenges in trying to make up for changing snowmelt patterns.

“Irrigation demands not met by rainfall or snowmelt currently already represent more than 40 percent of reservoir water storage in many Asian and North American basins,” said Steven Davis, a co-author and associate professor of earth system science at University of California, Irvine. “And in a warming world, agriculture won’t be the only added demand on reservoirs and other alternative water supplies like groundwater.”

In the San Joaquin Basin, findings suggested that 14 percent of irrigation water demand must be met by new alternative sources under a 4-degree Celsius warming scenario. In the Colorado Basin, the figure would be 9 percent.

The study also examined which crops globally were at most at risk because of snowmelt changes resulting from climate change. Findings showed that rice and cotton in northern hemisphere summer, as well as wheat and managed grassland in spring, were particularly snow-dependent.

For the study, researchers used data on monthly rainfall and snowmelt runoff globally from 1985 to 2015 from a dataset called TerraClimate. They then calculated monthly irrigation water consumption for a variety of crops.

Graphic credit: Western Water Assessment

Comparing historical runoff and total surface water consumption, they estimated monthly snowmelt and rainfall runoff consumption as well as demand for alternative water sources in each major river basin.

They then used climate models to project snowmelt and rainfall runoff in each basin if global mean temperatures rise 2 degrees or 4 degrees Celsius above pre-industrial conditions.

Mueller, who is a researcher in the Department of Ecosystem Science and Sustainability and the Department of Soil and Crop Sciences, notes that the results of the study can help prioritize research and policy attention to identify possible ways to adapt within at-risk basins. “There is no one-size-fits-all solution to the challenges created by changes in snowmelt, and many adaptation options incur tradeoffs,” Mueller said. “Strategies need to be developed regionally in the context of the unique challenges facing each basin at risk.”

The study was supported by the Foundation for Food and Agriculture Research, the U.S. National Science Foundation and the German Federal Ministry of Education and Research.

Other co-authors on the study were from University of California, Irvine; University of California, Merced; University of Idaho; University of Göttingen; Dartmouth College; and Columbia University.

Liza Mitchell, education and outreach coordinator with the Roaring Fork Conservancy, left, and a participant in the Water Education Colorado SNOTEL workshop measure the snow-water equivalent of different layers of the snowpack. The liquid content of snow from this site measured roughly 21 percent. (March 2018)

From The Denver Post (Bruce Finley):

Colorado River Basin farmers will be hardest hit by climate warming, along with food growers in Central Asia and the southern Andes, due to high dependence on shrinking snow as a source of irrigation water, new research has found.

Century-old practices of anticipating drought by monitoring mountain snowpack will be increasingly precarious, researchers also found.

And western agricultural leaders on Monday warned, in letters to President Donald Trump and Congress, that deteriorating pipelines, canals, reservoirs and other water infrastructure threaten the U.S. food supply.

“The western United States is in a tough spot with climate change and changes in snowmelt affecting water resources,” said Colorado State University environmental scientist Nathan Mueller, author of one snowmelt study published Monday in the journal Nature Climate Change.

“But the agriculture in the West is really important for our nation’s food supply. It is imperative to try to adapt to these changes as best we can — and try to limit warming.”

Graphic via Nature Climate


On Monday, a Western Growers coalition of 150 agricultural groups — including the Colorado Farm Bureau, the Colorado Fruit and Vegetable Association and several water districts — urged Trump and Congress to address aging western water infrastructure using coronavirus stimulus funds.

Coalition leaders pointed to changing water flows in the West that imperil food production, advising swift repairs and replacements of head gates, pipelines, canals and reservoirs to help producers endure dry times.