Uncertainty In Hydraulic Properties Research Articles

Episodic extreme rainfall events drive groundwater recharge in arid zone environments of central Australia

Understanding the factors controlling groundwater recharge in arid vadose zones is critical for sustainable groundwater management in the face of climate variability and change. Here we estimated groundwater recharge in data sparse regions under arid climate by implementing a multi-model approach using HYDRUS-1D for a century-long time series of meteorological data and site-specific regolith hydraulic properties of a bare soil and a Mulga savanna-type soil in central Australia. Grain‐size analysis provided a contiguous 12-m deep profile of hydraulic properties that were generated by means of pedotransfer functions. To account for conceptual model uncertainty in hydraulic properties, eleven pedotransfer functions were applied. Climate data from three stations accounted for spatial heterogeneity in local climate of the Ti Tree Basin study area. Simulated water fluxes in the vadose zone indicated that only rainfall events of more than 150–200 mm resulted in groundwater recharge. Recharge was linked to extreme rainfall associated with monsoonal cyclones. Based on the 130-year climate record, average recharge for the savanna-type vegetation ranged from 4.3 to 7.4 mm/a across the three climate stations, with an overall mean of 4.6 mm/a. The bare soil had an overall mean recharge of 29.5 mm/a, ranging from 23.5 to 35.8 mm/a. Results from this study provide a better understanding of the highly episodic recharge in arid environments and are critical input to sustainably manage groundwater resources.

The role of uncertainty in bedrock depth and hydraulic properties on the stability of a variably-saturated slope

We investigate the uncertainty in bedrock depth and soil hydraulic parameters on the stability of a variably-saturated slope in Rio de Janeiro, Brazil. We couple Monte Carlo simulation of a three-dimensional flow model with numerical limit analysis to calculate confidence intervals of the safety factor using a 22-day rainfall record. We evaluate the marginal and joint impact of bedrock depth and soil hydraulic uncertainty. The mean safety factor and its 95% confidence interval evolve rapidly in response to the storm events. Explicit recognition of uncertainty in the hydraulic properties and depth to bedrock increases significantly the probability of failure.

Comparing approaches for modeling spatially distributed direct recharge in a semi-arid region (Okanagan Basin, Canada)

Spatially distributed recharge is compared at two different scales using three different modeling approaches within the semi-arid Okanagan Basin, British Columbia, Canada. Regional recharge was modeled by mapping results for one-dimensional soil columns from the water-balance code HELP (Hydrologic Evaluation of Landfill Performance, V3.80D). The regional model was then compared to two, independently derived, local-scale models to ensure local trends were captured in the regional model, and to compare modeling methods. Average annual recharge, predicted by the regional model, varied from no recharge to 186 mm/yr. For the north Okanagan (Vernon area), regional estimates were compared to Richards’ equation-based MIKE-SHE (V2007) estimates, which showed a significant difference in average annual recharge: 7 mm/yr (MIKE-SHE) and 109 mm/yr (HELP). In the south Okanagan (Oliver area), regional estimates were compared to high-resolution, local HELP estimates. Similar values of average annual recharge were obtained: 34 mm/yr (local) and 42 mm/yr (regional). A comparison with measured actual evapotranspiration data in the north Okanagan, showed HELP over-predicted recharge compared to MIKE-SHE by under-predicting evapotranspiration during summer months. Thus, the use of HELP in semi-arid areas may be limited if accurate estimates of recharge are needed. However, results may give satisfactory groundwater model calibrations results because of high uncertainty in hydraulic properties.

Evaluation of Model Complexity and Input Uncertainty of Field‐Scale Water Flow and Salt Transport

Prediction of large‐scale vadose zone water flow and salt transport is affected by errors due to uncertainties in model structure, that is, complexity of representation of hydrologic processes and model input uncertainties such as parameter values, boundary conditions, and initial conditions. Selection of an appropriate level of model complexity must consider these two sources of uncertainty. We illustrate this selection process for the prediction of field‐scale crop transpiration and salt drainage in irrigated agriculture given a limited number of point‐scale measurements. Various levels of model input uncertainty in hydraulic properties and infiltration rates are considered, representative of a range of spatial heterogeneities. Model complexity is defined relative to a “true” model, in terms of the level of spatial and temporal averaging used in the approximate model. This set‐up allows for the separation of the total model error into different terms representing the model input error and the structural model error. Results show that the relative contribution of structural model error to the total model error decreases as spatial heterogeneity or uncertainty in the model input increases. Model input uncertainty may be reduced by taking a larger number of point‐scale measurements. Our analysis further illustrates that there exists an optimal trade‐off between model complexity and model input uncertainty. It suggests that complex models may be replaced by more simple ones as long as the resulting structural model error is smaller than the prediction errors due to input uncertainty. The methodology provides a straightforward and objective approach to identifying an optimal level of model complexity as a function of the degree of field‐scale heterogeneity and data availability.

Error analysis of simplified unsaturated flow models under large uncertainty in hydraulic properties : Govindaraju, R S; Or, D; Kavvas, M L; Rolston, D E; Biggar, J Water Resour ResV28, N11, Nov 1992, P2913–2924

Error analysis of simplified unsaturated flow models under large uncertainty in hydraulic properties : Govindaraju, R S; Or, D; Kavvas, M L; Rolston, D E; Biggar, J Water Resour ResV28, N11, Nov 1992, P2913–2924

Error analyses of simplified unsaturated flow models under large uncertainty in hydraulic properties

The functional relationships between soil hydraulic properties (K, θ and ψ) are needed for analyzing unsaturated water flow in soils and are generally of a nonlinear form. These relationships, estimated from field‐measured data, usually show large variability because of measurement errors and natural spatial heterogeneity exhibited by many field soils. A probabilistic approach is often employed to account for this uncertainty in parameters whereby the coefficients in the resulting partial differential equation are treated as random quantities (usually characterized by their moments). The objective of this analysis is to evaluate the errors resulting when simplified solutions based on simple forms of the hydraulic properties are used rather than nonlinear and numerically complicated models. As an example of a parametric functional relationship for K‐θ‐ψ, we adopt the Brooks‐Corey formulation in which a single parameter λ is considered as random to emulate the spread encountered in practice. For data that show a large enough spread and when one is interested in the mean behavior of K, θ or ψ, simple flow models such as the Green‐Ampt model and the linear model are proposed as alternatives to the more accurate (but numerically demanding) Richards' equation. To compare models, scaled error definitions are introduced and are expressed as a percentage of the saturated hydraulic conductivity of the soil. These time dependent errors are then computed under various boundary conditions. In general, the Green‐Ampt solution is found to yield smaller errors than the linear model when compared with solutions from Richards' equation. It is concluded that the use of simplified models is justified when field data exhibit sufficient spread, and a tight bound cannot be placed on the parameter estimates.