Hydrological model simulations are often complicated by inevitable uncertainties in initial conditions, boundary conditions, and parameter fields. A proper identification and quantification of such uncertainties are nowadays a must for any modern hydrologist. In this mini symposium, beside presentations focusing on how uncertainty quantification can be properly performed for problems typical of hydrological sciences (e.g., flow and transport in porous media, river and karst spring discharge predictions, surface water-groundwater interaction…), we want to emphasize why uncertainty quantification is relevant in hydrology and its implication for engineering applications.
The minisimposium received funding from the International Graduate School of Science and Engineering of the Technical University of Munich.
Environmental risk assessment (ERA) procedures are often based on safety thresholds, typically set in relation with ecotoxicological indicators. In this work we present a new sensitivity index to rank the contribution of uncertain input parameters for the probability of a target quantity to exceed safety limits. The proposed index leverages on recently proposed moment-based sensitivity indices and falls into the category of global sensitivity indicators. Therefore, it allows considering nonlinear input-output maps typically encountered when dealing with contaminants reactive transport in soil and aquifers. We tested the application of our proposed indicator to soil and aquifer pollution by pesticides in agricultural soils. First, we specifically target soil and groundwater contamination by glyphosate (GLP) and its toxic metabolite aminomethylphosphonic acid (AMPA) in a wheat field under uncertain soil hydraulic parameters. Target quantities are GLP and AMPA aqueous concentrations in the soil column and their leaching below the root zone. In scenarios of dry and wet conditions, parameter-specific sensitivity quantify the contribution of each parameter in the different conditions. The analysis is then expanded to geographically distributed assessments of soil and water contamination. The discussion will emphasize how these results may be then used to direct future environmental monitoring operations and detailed characterization in contamination hotspots.
The location of groundwater divides is especially important to define catchment boundaries and groundwater protection zones. In geological units with high hydraulic conductivity such as karst systems, groundwater flow can lead to considerable fluctuations of the groundwater divide. Usually, the groundwater divide can be derived from surface topography, hydraulic head observations, tracer tests, and groundwater flow models calibrated to the mentioned observations and tests. However, observed data is usually financially limited, causing a scarce availability of informative data. For this reason is vital to design monitoring networks effectively. In this work, we present a methodology to determine optimal monitoring strategies that minimizes the uncertainty in locating of the groundwater divide in a real catchment with a very limited number of observation wells. The methodology is a formal minimization of expected posterior uncertainty within a sampling-based Bayesian framework. Preliminary results show that, for our case study and three monitoring wells, uncertainty reduction is maximized when the first well is close to the topographic surface water divide, the second one in the valley, and the third one between the first two wells. The bad news is that, despite of optimizations, legal constraints on admissible locations for drilling wells limit the uncertainty reduction of the prior uncertainty.
A quantitative understanding of the processes connecting the land surface and the subsurface and leading to the changes in water storage requires a cross-disciplinary approach focused on the dynamic link between atmospheric conditions, subsurface, and evapotranspiration, the largest component of surface energy balance. While integrated hydrologic models show promise in this context, they are, however, plagued by parametric uncertainties. The relationships between subsurface and surface flows and hydrodynamic parameters are well documented in the literature, nevertheless, little is known about how these uncertainties propagate into land surface processes especially evapotranspiration. In this work, we relied on an integrated hydrologic model to simulate the transfer of water and energy from the aquifer to the lower atmosphere and to assess how hydrodynamic parameter uncertainties affect evapotranspiration, but also its main components: evaporation and transpiration. We do so by relying on a global sensitivity analysis, which consists in computing Sobol and AMA metrics with a surrogate model constructed using polynomial chaos expansion. Our results show that the effects of vertical hydraulic conductivity, porosity, and Van Genuchten mainly govern the evapotranspiration. However, we remark that while evaporation shows similar behavior than the evapotranspiration, the transpiration is very sensitive to aquifers parameters and groundwater flow, especially at certain times.
In polar coastal circulation modeling, an open question concerns the reliability of air-ice-ocean momentum transfer (wind drag) formulae. This uncertainty in turns affects accuracy of predicted storm surge due to extratropical cyclones. Here, we formulate the inference and prediction of wind drag as a stochastic inverse problem, and propose a solution algorithm combining feature extraction techniques, to learn the quantities of interest from time series data, with the high-delity ADvanced CIRCulation (ADCIRC) model.