(a) τ vs. ε extracted from ten simulation runs with the same parameters on a log-log scale. Power law fit is shown in red, yielding zν = 1.39±0.03 and τ0 = (1.1±0.1) × 10−2t0. (b) Same as (a) but showing ξ vs. ε from 15 runs. At each ε, C7(r) was extracted from each run and averaged together before fitting to find ξ. Power law fits are shown in red, yielding ν = 0.45±0.04 and ξ0 = 0.25±0.05, with ν varied in the fit (solid line) or ξ0 = 0.101±0.005 with fixed ν = 0.66 (dashed line).</p
(A) Dispersity of the inferred values obtained during different runs of C1-5. In total, the calibrat...
Experiment 1: Model fit and optimized parameter distributions for the two-state error-scaling non-ne...
<p>Goodness-of-fit metrics: SSE = Sum of Squared Errors; r<sup>2</sup> = coefficient of determinatio...
(A): Top row: sample histograms of the parameters qR, qC and wC. Yellow represents the entire sample...
<p>VP = variable precision; EP = equal precision; AP = average precision; SP = single precis...
Fig 3 shows the relationship between the computed values of previous study and relative error when t...
<p>This applies to both peak 0 (high, narrow peak) and peak 1 (lower, flatter peak). (with being p...
<p>(<b>A</b>) Simulation fitting result (mean (full line) ± standard deviation (area)) compared to i...
The results of the optimization are shown. The box color indicates velocity distribution used: red—u...
<p>The relative measure of model performance, i.e. the per-bin log-likelihood Δ<i>p</i> (see <a href...
Performance evaluation of the different prediction models on all piezometers (D: The maximum differe...
<p>Note: ΔAIC and ΔBIC represent the difference in AIC or BIC as compared with the best equation. Th...
A: α = 0.3; β = ±log(1.5). B: α = 0.3; β = ±log(1.75). C: α = 0.5; β = ±log(1.5). D: α = 0.5; β = ±l...
<p>In the two sub-figures, the parameter ; <i>η</i> is assigned to values from 1 to 1000; is assign...
<p>(A) The three panel illustrate the relatively shallow way in which the goodness-of-fit, , is degr...
(A) Dispersity of the inferred values obtained during different runs of C1-5. In total, the calibrat...
Experiment 1: Model fit and optimized parameter distributions for the two-state error-scaling non-ne...
<p>Goodness-of-fit metrics: SSE = Sum of Squared Errors; r<sup>2</sup> = coefficient of determinatio...
(A): Top row: sample histograms of the parameters qR, qC and wC. Yellow represents the entire sample...
<p>VP = variable precision; EP = equal precision; AP = average precision; SP = single precis...
Fig 3 shows the relationship between the computed values of previous study and relative error when t...
<p>This applies to both peak 0 (high, narrow peak) and peak 1 (lower, flatter peak). (with being p...
<p>(<b>A</b>) Simulation fitting result (mean (full line) ± standard deviation (area)) compared to i...
The results of the optimization are shown. The box color indicates velocity distribution used: red—u...
<p>The relative measure of model performance, i.e. the per-bin log-likelihood Δ<i>p</i> (see <a href...
Performance evaluation of the different prediction models on all piezometers (D: The maximum differe...
<p>Note: ΔAIC and ΔBIC represent the difference in AIC or BIC as compared with the best equation. Th...
A: α = 0.3; β = ±log(1.5). B: α = 0.3; β = ±log(1.75). C: α = 0.5; β = ±log(1.5). D: α = 0.5; β = ±l...
<p>In the two sub-figures, the parameter ; <i>η</i> is assigned to values from 1 to 1000; is assign...
<p>(A) The three panel illustrate the relatively shallow way in which the goodness-of-fit, , is degr...
(A) Dispersity of the inferred values obtained during different runs of C1-5. In total, the calibrat...
Experiment 1: Model fit and optimized parameter distributions for the two-state error-scaling non-ne...
<p>Goodness-of-fit metrics: SSE = Sum of Squared Errors; r<sup>2</sup> = coefficient of determinatio...