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14 Highlights: 15 • Comparison of Global Sensitivity Analysis (GSA) approaches in a large-scale aquifer 16 • Impacts of uncertain parameters of diverse conceptual models are evaluated via GSA 17 • Moment-based indices inform how parameters influence statistics of model outputs 18 19 20 2 Abstract 21We rely on various Global Sensitivity Analysis (GSA) approaches to detect the way 22 uncertain parameters linked to diverse conceptual geological models influence spatial 23 distributions of hydraulic heads in a three-dimensional complex groundwater system. We 24 showcase our analyses by considering a highly heterogeneous, large scale aquifer system 25 located in Northern Italy. Groundwater flow is simulated considering alternative conceptual 26 models employed to reconstruct the spatial arrangement of the geomaterials forming the 27 internal makeup of the domain and characterizing the distribution of hydraulic conductivities. 28 For each conceptual model, uncertain factors include the values of hydraulic conductivity 29 associated with the geomaterials composing the aquifer as well as the system boundary 30 conditions. We explore the relative influence of parametric uncertainties to steady-state 31 hydraulic head distributions across the set of conceptual models considered by way of three 32 GSA methodologies, i.e., (a) a derivative-based approach, which rests on the Morris indices; 33 (b) the classical variance-based approach, grounded on the evaluation of the Sobol' indices; 34 and (c) a moment-based GSA, which takes into account the influence of uncertain parameters 35 on multiple (statistical) moments of a given model output. Due to computational costs, Sobol' 36 and moment-based indices are obtained numerically through the use of a model-order reduction 37 technique based on the polynomial chaos expansion approach. We find that the sensitivity 38 measures considered convey different yet complementary information. The choice of the 39 conceptual model employed to characterize the lithological reconstruction of the aquifer affects 40 the degree of influence that uncertain parameters can have on modeling results. 41 42 3

Abstract. The distribution of the maximum annual three day snow fall depth H 72 , used for avalanche hazard mapping according to the Swiss procedure (Sp), is investigated for a network of 124 stations in the Alpine part of Switzerland, using a data set dating back to 1931. Stationarity in time is investigated, showing in practice no significant trend for the considered period. Building on previous studies about climatology of Switzerland and using an iterative approach based on statistical tests for regional homogeneity and scaling of H 72 with altitude, seven homogenous regions are identified. A regional approach based on the index value is then developed to estimate the T -years return period quantiles of H 72 at each single site i, H 72i (T ). The index value is the single site sample average µ H 72i . The dimensionless values of H * 72i =H 72i /µ H 72i are grouped in one sample for each region and their frequency of occurrence is accommodated by a General Extreme Value, GEV, probability distribution, including Gumbel. The proposed distributions, valid in each site of the homogeneous regions, can be used to assess the T -years return period quantiles of H * 72i . It is shown that the value of H 72i (T ) estimated with the regional approach is more accurate than that calculated by single site distribution fitting, particularly for high return periods. A sampling strategy based on accuracy is also suggested to estimate the single site index value, i.e. the sample average µ H 72i , critical for the evaluation of the distribution of H 72i . The proposed regional approach is valuable because it gives more accurate snow depth input to dynamics models than the present procedure based on single site analysis, so decreasing uncertainty in hazard mapping procedure.

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Metal sorption of single and binary (competitive) systems for several soils is analyzed to assess the ability of alternative isotherm models to interpret experimental observations. The analysis is performed within a Maximum Likelihood framework and on the basis of model identification (sometimes termed "quality" or "information") criteria. These methodologies allow the assessment of the measurement error variance in the parameter estimation process and the uncertainty arising from the use of alternative (conceptualmathematical) models. We first analyze Cu and Zn sorption in two Israeli soils, Bet Dagan and Yatir, which are slightly alkaline but with substantially different sorption capacities and perform an extensive set of batch experiments in single and binary systems. We then analyze the data set published by Liao and Selim (2009) where Ni and Cd sorption was studied in three different (one neutral and two acidic) soils. Single component data from both sets of experiments are interpreted on the basis of the Langmuir, Freundlich, and Redlich-Peterson (RP) models. The family of binary systems results is analyzed in light of the Sheindorf-Rebhun-Sheintuch (SRS) model, the modified RP model, and the modified and extended Langmuir models. All of the considered models are expressed in terms of initial and equilibrium concentrations, two variables that are measured independently. Maximum Likelihood and model identification criteria (such as Bayesian criteria BIC and KIC, and information theoretic criteria AlC, AlCc, and HIC) are employed to (a) estimate model parameters, (b) rank alternative models, and (c) estimate the relative degree of likelihood of each model by means of a weight, or posterior probability. We show that modeling observation error variance either as a constant or as a function of concentration does not significantly affect parameter estimates for a given model. These different representations of measurement error variance impact the ranking of alternative models based on posterior probability weights. The weights associated with different models can be very similar when a uniform measurement error variance is considered, so that it is difficult to clearly identify a single best model. Abbreviations: ICP, inductively coupled plasma; ICP-MS, inductively coupled plasmamass spectrometry; RP, Redlich-Peterson; SRS, Sheindorf-Rebhun-Sheintuch.A common cause of soil and water resources contamination is the subsequent release of mixtures of heavy metals to the environment, seriously threatening the well-being of humans and ecological systems. In this context, understanding of metal partitioning between solid and aqueous phases is critical for proper design of control and remediation measures.It is known that the adsorption properties of a metal in the presence of other metal(s) are often different from those associated with the adsorption of the individual metal (Sheindorf et al., 1981). Sorption and competitive behavior of met-