Sensitivity analysis (SA) is a commonly used approach for identifying important parameters that dominate model behaviors. We use a newly developed software package, a Problem Solving environment for Uncertainty Analysis and Design Exploration (PSUADE), to evaluate the effectiveness and efficiency of ten widely used SA methods, including seven qualitative and three quantitative ones. All SA methods are tested using a variety of sampling techniques to screen out the most sensitive (i.e., important) parameters from the insensitive ones. The Sacramento Soil Moisture Accounting (SAC-SMA) model, which has thirteen tunable parameters, is used for illustration. The South Branch Potomac River basin near Springfield, West Virginia in the U.S. is chosen as the study area. The key findings from this study are: (1) For qualitative SA methods, Correlation Analysis (CA), Regression Analysis (RA), and Gaussian Process (GP) screening methods are shown to be not effective in this example. Morris One-At-a-Time (MOAT) screening is the most efficient, needing only 280 samples to identify the most important parameters, but it is the least robust method. Multivariate Adaptive Regression Splines (MARS), Delta Test (DT) and Sum-Of-Trees (SOT) screening methods need about 400–600 samples for the same purpose. Monte Carlo (MC), Orthogonal Array (OA) and Orthogonal Array based Latin Hypercube (OALH) are appropriate sampling techniques for them; (2) For quantitative SA methods, at least 2777 samples are needed for Fourier Amplitude Sensitivity Test (FAST) to identity parameter main effect. McKay method needs about 360 samples to evaluate the main effect, more than 1000 samples to assess the two-way interaction effect. OALH and LPτ (LPTAU) sampling techniques are more appropriate for McKay method. For the Sobol' method, the minimum samples needed are 1050 to compute the first-order and total sensitivity indices correctly. These comparisons show that qualitative SA methods are more efficient but less accurate and robust than quantitative ones

Surrogate modeling uses cheap “surrogates” to represent the response surface of simulation models. It involves several steps, including initial sampling, regression and adaptive sampling. This study evaluates an adaptive surrogate modeling based optimization (ASMO) method on two benchmark problems: the Hartman function and calibration of the SAC-SMA hydrologic model. Our results show that: 1) Gaussian Processes are the best surrogate model construction method. A minimum Interpolation Surface method is the best adaptive sampling method. Low discrepancy Quasi Monte Carlo methods are the most suitable initial sampling designs. Some 15–20 times the dimension of the problem may be the proper initial sample size; 2) The ASMO method is much more efficient than the widely used Shuffled Complex Evolution global optimization method. However, ASMO can provide only approximate optimal solutions, whose precision is limited by surrogate modeling methods and problem-specific features; and 3) The identifiability of model parameters is correlated with parameter sensitivity

The South‐to‐North Water Diversion Middle Route Project (MRP), which started its operation in December 2014, was designed to transfer water from Danjiangkou Reservoir (DR) in Hanjiang River Basin to North China Plain (NCP) to alleviate water shortage and long‐term groundwater depletion in the water‐receiving region. This study investigates the effectiveness of actual MRP operation during 2015–2018 using the observed water budget data collected from DR and the groundwater level data from 559 monitoring wells. Assuming that MRP was in operation during 2005–2014, ensemble water diversion simulations were performed to study the sensitivity of MRP effectiveness to two important factors: the downstream water demand of DR (D_wd) and the ratio (I_r) of water diversion volume (Q_d) replacing groundwater pumping in NCP. Even though the observed and simulated mean annual Q_d during 2015–2018 (i.e., 4.3 and 7.0 km³/year, respectively) failed to meet the original water delivery target of 9.5 km³/year due to its short operation and the coincidence with a dry cycle, MRP is effective in groundwater recovery as an increasing trend (+0.3 km³/year) in groundwater storage (GWS) was observed in NCP during 2015–2018. MRP's effectiveness is sensitive to D_wd and I_r. D_wd should not exceed 23.0 km³/year to guarantee Q_d reaching the original target, and I_r should not be less than 33% to guarantee GWS recovery. Those findings suggest that a reasonable decrease of D_wd and an increase of I_r are the recommended pathway to ensure the effectiveness of MRP in meeting both water delivery and groundwater recovery targets.

[1] With growing interest in understanding the magnitudes and sources of uncertainty in hydrological modeling, the difficult problem of characterizing model structure adequacy is now attracting considerable attention. Here, we examine this problem via a model‐structure‐independent approach based in information theory. In particular, we (a) discuss how to assess and compute the information content in multivariate hydrological data, (b) present practical methods for quantifying the uncertainty and shared information in data while accounting for heteroscedasticity, (c) show how these tools can be used to estimate the best achievable predictive performance of a model (for a system given the available data), and (d) show how model adequacy can be characterized in terms of the magnitude and nature of its aleatory uncertainty that cannot be diminished (and is resolvable only up to specification of its density), and its epistemic uncertainty that can, in principle, be suitably resolved by improving the model. An illustrative modeling example is provided using catchment‐scale data from three river basins, the Leaf and Chunky River basins in the United States and the Chuzhou basin in China. Our analysis shows that the aleatory uncertainty associated with making catchment simulations using this data set is significant (∼50%). Further, estimated epistemic uncertainties of the HyMod, SAC‐SMA, and Xinanjiang model hypotheses indicate that considerable room for model structural improvements remain.