For the past several years, the Los Angeles Department of Water and
Power and the Inyo County Water Department have used forecasts of water table response to
pumping to develop and evaluate annual pumping plans. These forecasts are based on
multiple linear regression modeling of eighteen shallow monitoring wells that serve as
indicators of water table conditions. As specified in the Inyo/Los Angeles water
agreement, every April LADWP presents Inyo County with projections of runoff and proposed
pumping for the next twelve months. Inyo County evaluates the plan in light of existing
and projected vegetation, groundwater, and soil water conditions. To better predict the
effects of pumping on water table levels, ICWD improved existing multiple regression
models. The improvements were twofold: (1) the number of indicator wells was enlarged by
screening all shallow monitoring wells for suitability as indicator wells, and (2) a
method of evaluating the uncertainty in the predictions was developed.
Figure 7. Four-year simulation of water table recovery to 2.0 meters at monitoring
well 419T, TA wellfield. Second, third, and fourth year's pumping is held at 3,000
acre-feet/year. 2.0 meters is the approximate rooting zone depth at the site.
The multiple regression models use the relationship between the measured water table
fluctuations at monitoring wells and records of pumping and runoff to predict the response
of the water table to a given amount of pumping and recharge. ICWD hydrology staff looked
at 170 shallow monitoring wells for suitability as indicator wells. The periods of record
for the modeled monitoring wells ranged from 6 to 23 years. Multiple regression models
were developed for each well and, based on a set of statistical tests, each of these wells
was classified as (1) affected by both pumping and runoff, (2) affected by pumping, (3)
affected by runoff, or (4) unaffected by either pumping or runoff. Wells affected by
pumping and runoff or affected by pumping were regarded as useful for evaluating pumping
plans. Other non-statistical criteria, such as distance from LADWP wellfields or proximity
to future Lower Owens River Project rewatering areas resulted in a final culling of the
set down to 37 wells that appeared useful as indicator wells. ICWD hydrology staff
incorporated statistical methods into the models that provide both predictions of the
likliest result and estimates in the uncertainty in that prediction. In the future, we can
use such estimates of prediction uncertainty to examine various pumping scenarios, and to
evaluate the probability of certain events occurring. For example, when evaluating a
pumping plan's effect on vegetation health, we may want to know the probability that the
water table will recover to the root zone as well as the likeliest water table elevation.
Figure 7 shows the results of simulations of shallow monitoring well 419T, in the
Taboose-Aberdeen (TA) wellfield. During the first year of the simulation, various rates of
pumping from the TA wellfield are shown along the bottom axis; during the subsequent three
years, TA pumping was held at 3,000 acre-feet per year to allow the water table to
recover. The vertical axis shows the likelihood that the depth to water (DTW) in the well
will not recover to 2.0 meters by the end of the runoff year (starting from a DTW of 1.4
m, approximately the water level extant in April, 1998). The significance of the 2.0 m DTW
is that it is the approximate depth of the rooting zone of the vegetation at the site (an
alkali meadow). It is clear from Figure 7 that a single year of high pumping from the TA
wellfield is likely to affect this site for several years.
In the future, ICWD hydrology staff plans to integrate the multiple regression models
with soil water balance models and evapotranspiration models, and to compare them with
other hydrologic modeling tools. Integration with soil water balance and
evapotranspiration models will be useful for projecting how long a given supply of soil
water can sustain healthy native vegetation. A comparison of regression and other
hydrologic models will allow ICWD hydrology staff to choose the best predictive tool at a
given location for assessing the effects of groundwater pumping.
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