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Seaside Model Report<br />

October 26, 2007<br />

Model Development<br />

shown on Figure 1.18b. The <strong>model</strong> was calibrated in part to simulate similar areas where<br />

<strong>groundwater</strong> levels are below sea level.<br />

Hydraulic parameters were identified from a weighted least-squares fit of the<br />

computed heads to the measured <strong>groundwater</strong> levels. The computer program PEST<br />

(Watermark Numerical Computing, 2002) was used to perform the calibration. PEST<br />

identifies parameter values such that the <strong>model</strong> fit minimizes the objective function<br />

n<br />

i=<br />

1<br />

[ ( )] 2<br />

w h h<br />

S = ∑ −<br />

(3.6)<br />

i<br />

ci<br />

mi<br />

where S is the sum of weighted squared residuals, h ci is the computed head for a<br />

particular well <strong>and</strong> time, h mi is the measured <strong>groundwater</strong> level for the particular well <strong>and</strong><br />

time, w i is the weight assigned to the measured <strong>groundwater</strong> level, <strong>and</strong> n is the number of<br />

measured <strong>groundwater</strong> levels. The weight assigned to a measurement is related to the<br />

uncertainty in the measurement according to the relation<br />

w i<br />

1<br />

= (3.7)<br />

σ<br />

2<br />

i<br />

where σ<br />

i<br />

is the st<strong>and</strong>ard error of the uncertainty in the measurement.<br />

All <strong>groundwater</strong>-level measurements are uncertain, <strong>and</strong> the uncertainty has at<br />

least two sources. Firstly, uncertainty arises in measuring or recording <strong>groundwater</strong><br />

depths <strong>and</strong> in measuring, estimating, or recording measuring-point altitudes.<br />

Additionally, uncertainty arises in the documentation of the location <strong>and</strong> construction of<br />

wells. However, for the Seaside <strong>groundwater</strong> <strong>basin</strong>, this uncertainty type most likely is<br />

small. Secondly, uncertainty arises because <strong>groundwater</strong>-level measurements represent<br />

spatial <strong>and</strong> temporal point processes, but the computed heads represent spatially <strong>and</strong><br />

temporally average processes (Anderson <strong>and</strong> Woessner, 2002, pp.229-230, Gelhar,<br />

1986), where the averaging depends on the effective spatial <strong>and</strong> temporal scales<br />

represented in the <strong>model</strong>. This uncertainty increases significantly with lower-permeability<br />

<strong>groundwater</strong> systems <strong>and</strong> with more heterogeneous <strong>and</strong> anisotropic <strong>groundwater</strong> systems.<br />

Because the Seaside <strong>groundwater</strong> <strong>basin</strong> can be characterized as a highly heterogeneous,<br />

highly anisotropic, <strong>and</strong> moderately permeable <strong>groundwater</strong> system, <strong>and</strong> because the<br />

effective <strong>model</strong> spatial <strong>and</strong> temporal scales are large, the second uncertainty type is large.<br />

Within the <strong>groundwater</strong>-level data for the Seaside <strong>groundwater</strong> <strong>basin</strong>, the st<strong>and</strong>ard<br />

error for the combined effects of the two uncertainty types is about 16 ft. The first<br />

uncertainty type has a st<strong>and</strong>ard of perhaps 5 ft or less, the second error type has a<br />

st<strong>and</strong>ard error of most likely 15 ft or more. These combine according to the relation<br />

σ = σ 1<br />

+ σ<br />

(3.8)<br />

i i 2i<br />

23

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