| |
2004
Earth Feature Story Special: The
Benefits of Hydrologic Predictability Each
year, millions of gallons of water that flow through our rivers and tributaries
offer a massive source of renewable energyhydropower. Its clean and
efficient, currently supplying about 10 percent of the electricity in the United
States and up to 70 percent of electricity demand in the Pacific Northwest. Unlike
some other energy sources, hydropower does not produce greenhouse gases or air
pollution. With so much at stake for U.S. citizens and those who manage water
systems, improving long-lead forecasts of river streamflow can allow operators
of large reservoirs to plan for expected inflows and produce hydropower more efficiently.
Anticipated seasonal inflows are also of importance to large users of water, such
as irrigators and farmers and hydropower reservoirs also contribute to local economies.
A study of one medium-sized hydropower project in Wisconsin showed that the recreational
value to residents and visitors exceeded $6.5 million annually. HOW
HYDROPOWER WORKS
| | |
Item
1 | CREDIT:
U.S. Dept. of Energy | The
most common type of hydroelectric power plant is an impoundment facility. This
is typically a large system, and uses a dam to store river water in a reservoir.
Water released from the reservoir flows through a turbine, spinning it, which
in turn activates a generator to produce electricity. The water may be released
either to meet changing electricity needs or to maintain a constant reservoir
level. Advances
in long-lead climate forecasting have made it possible to produce useful streamflow
forecasts further in advance, but many are based on only one or two variables,
such as snowpack estimates. Additionally, few studies have explored the economic
value of hydrologic predictability. To examine the economic benefit of incorporating
additional data into long-lead forecasts, scientists Edwin Maurer of Santa Clara
University and Dennis Lettenmaier at the University of Washington, linked remote
conditions, such as tropical sea surface temperatures, land surface moisture levels
and regional climate to streamflow in the Missouri River basin. Many studies
have suggested that combining better land moisture data with forecasts of distinct,
long-term weather patterns created by atmospheric phenomena like El Nino/Southern
Oscillation (ENSO) would be of value to the water management community. We attempted
to quantify this, gaining insight into the relative effects of each new source
of information, said Maurer. El
Nino / Southern Oscillation (ENSO) marks a see-saw shift in surface air pressure
between Darwin, Australia and the South Pacific Island of Tahiti. When the pressure
is high at Darwin it is low at Tahiti and vice versa. El NiÒo, and its
sister event La NiÒa, are the extreme phases of this southern oscillation,
with El Nino referring to a warming of the eastern tropical Pacific, and La Nina
a cooling. Lettenmaier said the ENSO condition is one of the most valued
tools in any long-term forecast because it has major consequences on weather patterns
and is generally well understood.
|
|
|
|
Items
2 and 3 IMAGES
CREDIT: NASA JPL | These
sea surface temperature (SST) anomalies--departures from the mean state--change
atmospheric wind patterns in the mid-latitudes in winter and spring, shifting
the way moist air gets transported in the atmosphere, directly affecting U.S.
precipitation, snow accumulation and soil moisture, which in turn impacts water
runoff into river basins. The
researchers used data from the NOAA/NASA North American Land Data Assimilation
project (NLDAS) to produce model simulations that considered several atmospheric
variables, including soil moisture. Errors in long-lead forecasts are often
traced back in part to inaccuracies in the observed initial conditions used to
make the forecast, said Lettenmaier. NLDAS is working to refine this information.
Snow and soil moisture data are currently produced using images from NASAs
Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Microwave
Scanning Radiometer (AMSR). NASAs Hydrosphere State Mission (HYDROS), scheduled
for launch in 2010, will provide the first global views of Earth's changing soil
moisture and land surface freeze/thaw conditions, leading to breakthroughs in
water, energy and carbon management.
| | |
Item
3 | The
Moderate Resolution Imaging Spectroradiometer
(MODIS), flying aboard NASAs Terra and Aqua satellites, measures snow cover
over the entire globe every day, cloud cover permitting. At spatial resolutions
of up to 500 meters per pixel, MODIS allows scientists to distinguish between
snow and clouds, both of which appear bright white when seen from above at true-color
wavelengths. The
image above shows snow cover (white pixels) across North America from February
2-9, 2002. Click the links above to access time series animations showing changes
in snow cover during the winter of 2001-02. As you play the movies, notice how
snow cover begins to build up in the early fall and melts by late spring in the
Northern Hemisphere. |
|
|
| Item
4 | This
image was derived from the "8-Day CMG Snow Cover" field of the MODIS/Terra
Snow Cover 8-Day L3 Global 0.05Deg CMG data set, available from NSIDC. The product
consists of a global map of snow cover extent, expressed as a fraction of snow
in each CMG cell, for an eight-day period. | The
studys findings, published in the January 2004 issue of Journal of Climate
show that use of climate forecast information combined with better snow water
content and soil moisture data can improve runoff predictions in ways that might
yield some economic value. A typical goal of water management is to keep reservoirs
as full as possible, while minimizing spill, to maintain maximum hydropower production.
Hydropower's operational flexibility and its unique ability to change output allows
water managers to respond effectively to forecast data. For example, if water
system managers were warned of an upcoming dry season, they would likely retain
higher than normal water levels, usually by reducing the amount of water typically
evacuated from the system. Clearly, improved and additional predictability,
and thus a reduced uncertainty in flow volumes, translates into greater hydropower
benefits, said Maurer. Some
river basins, including the Missouri, have such large storage capacities that
variations in annual inflow have little impact on hydropower generation. In the
study, hydropower benefits increased by only 1.8 percent, from $530 million to
$540 million.
| | |
Item
5 | This
image displays the snow cover from February 21, 2003 in white. Areas with snow
cover on February 21, 2002 but not on February 21, 2003 are shown in blue. Credit:
NASA |
| | |
Item
6 | Monthly
simulated and historic Missouri River main stem system volumes, 1968-1997. Credit:
Edwin P. Maurer, Santa Clara University |
| | |
Item
7 | Monthly
energy generation of Missouri River main stem dams, historic and simulated.
Credit:
Edwin P. Maurer, Santa Clara University | But,
smaller water systems are more influenced by changing environmental conditions
and thus carry greater potential benefits from improved long-lead forecasts. To
simulate the effects of predictability on a smaller system, a hypothetical, reduced
storage system was developed. A 7.1 percent increase in hydropower benefits was
observed, representing about $25.7 million annually. Of this total, incorporating
currently available climate forecasts, such as the development of El Nino or La
Nina conditions, was shown to be the most important factor in predicting future
streamflow. Accurate information on soil moisture provided the bulk of the remaining
benefits, while snow water content data carried less hydropower benefit. It should
also be noted that actions taken by water resource managers in light of forecasts
are at least partially driven by the operating rules governing their reservoir
system.
| | |
Item
8 | Ratio
of system volume to annual system inflow versus the percent difference between
perfect and no forecast skill for past studies and the current study.
Credit:
Edwin P. Maurer, Santa Clara University |
| | |
Item
9 | Total
system hydropower benefits for reduced-volume Missouri River main stem dams under
different levels of predictive skill. Credit: Edwin P. Maurer, Santa Clara University |
A
seasonal analysis conducted by Maurer and Lettenmaier pointed to a general decrease
in predictability with increasing lead time, high levels of predictability for
winter runoff at short lead times, and the importance soil moisture and snow water
content for improved runoff predictability. Furthermore, the economic payoffs
for improved predictions were found to be most significant in the winter and spring,
when climate and land surface conditions can provide information about the large
inflows in the spring and summer.
| | |
Item
9 | Average
annual Hydropower benefits above a no forecast skill scenario with the specified
level of predictability in the current month, and no skill in other months.
Credit:
Edwin P. Maurer, Santa Clara University | While
the study found that forecasts considering additional variables carry some economic
benefits for water managers in hydropower generation, Maurer says correlating
their findings to smaller systems could definitely provide insight on other
issues that are of great importance to emergency planners and communities, such
as flood control.
While
the study only considered hydropower impacts of added hydrologic predictability,
since they are the dominant benefits for the Missouri River reservoirs, improved
long-lead forecasts of anticipated river inflows can also impact downstream navigation,
habitat protection and recreation. The reservoirs formed by hydroelectric dams
also often provide many water-based recreational opportunities including fishing,
water sports, and boating. Many hydro operators own a significant amount of land
around many reservoirs that is open to the public for uses including hiking, hunting
and skiing. These may be larger factors in other basins, and would bring new dimensions
to the value of predictability and the importance of better defining the land
surface moisture and SST conditions. For
more information contact: Mike
Bettwy NASA Goddard Space Flight Center Greenbelt, MD 20771 Phone:
301-286-3026
To
read about what El Nino is, and how it disrupts weather worldwide, click here.
|