Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications

Sensitivity analysis (SA) aims to identify the key parameters that affect model performance and it plays important roles in model parameterization, calibration, optimization, and uncertainty quantification. However, the increasing complexity of hydrological models means that a large number of parameters need to be estimated. To better understand how these complex models work, efficient SA methods should be applied before the application of hydrological modeling. This study provides a comprehensive review of global SA methods in the field of hydrological modeling. The common definitions of SA and the typical categories of SA methods are described. A wide variety of global SA methods have been introduced to provide a more efficient evaluation framework for hydrological modeling. We review, analyze, and categorize research into global SA methods and their applications, with an emphasis on the research accomplished in the hydrological modeling field. The advantages and disadvantages are also discussed and summarized. An application framework and the typical practical steps involved in SA for hydrological modeling are outlined. Further discussions cover several important and often overlooked topics, including the relationship between parameter identification, uncertainty analysis, and optimization in hydrological modeling, how to deal with correlated parameters, and time-varying SA. Finally, some conclusions and guidance recommendations on SA in hydrological modeling are provided, as well as a list of important future research directions that may facilitate more robust analyses when assessing hydrological modeling performance

A Compact Dication Source for Ba2+^{2+} Tagging and Heavy Metal Ion Sensor Development

We present a tunable metal ion beam that delivers controllable ion currents in the picoamp range for testing of dry-phase ion sensors. Ion beams are formed by sequential atomic evaporation and single or multiple electron impact ionization, followed by acceleration into a sensing region. Controllability of the ionic charge state is achieved through tuning of electrode potentials that influence the retention time in the ionization region. Barium, lead, and cobalt samples have been used to test the system, with ion currents identified and quantified using a quadrupole mass analyzer. Realization of a clean $\mathrm{Ba^{2+}}$ ion beam within a bench-top system represents an important technical advance toward the development and characterization of barium tagging systems for neutrinoless double beta decay searches in xenon gas. This system also provides a testbed for investigation of novel ion sensing methodologies for environmental assay applications, with dication beams of Pb$^{2+}$ and Cd$^{2+}$ also demonstrated for this purpose

A Bayesian-Monte Carlo approach to assess uncertainties in process-based, continuous simulation models.

A Bayesian-Monte Carlo approach was carried out to assess uncertainties in process-based, continuous simulation models. This was achieved by using the 93.13 version of the Water Erosion Prediction Project (WEPP) model when applied to a small semi-arid rangeland watershed nested in the Walnut Gulch Experimental Watershed, near Tombstone, AZ. Two techniques were evaluated to calibrate the model and identify the probability distributions of parameters based on the concept of model output classification ("acceptable" or "not acceptable"). Technique I consisted of Monte Carlo simulation with correlated parameter deviates generation. Technique II applied Monte Carlo simulation with correlated parameter deviates generation within a Bayesian framework to update parameter probability distributions every time that the model produced an acceptable realization. Based on the results, both techniques were able to calibrate the model and to identify parameter distributions, however; Technique I was computational more expensive than Technique II. This resulted because Technique II searched for parameter deviates within the region of the prior distributions more likely to produce acceptable model realizations. The contribution of parameter error and model error to total model uncertainty was assessed by using the mean square error equation. Errors were uniform during continuous simulations, errors never increased or decreased with the time of simulation. However, errors are larger toward components of higher levels of aggregation (soil erosion calculations). This resulted in larger errors in sediment yield predictions. Lack of homoscedasticity was observed, the largest errors for the largest rainfall events. This is more evident for peak runoff and sediment yield than for runoff volume. Also, a larger contribution of model error to total prediction uncertainty for peak runoff and sediment yield predictions was observed. Prediction intervals of runoff volume indicated that WEPP does acceptable responses in estimating infiltration variables. Almost all observed runoff volume data were inside the 90% prediction intervals. Prediction intervals for peak runoff revealed that WEPP rarely comprised the observed data within the range of predictions. Because the large errors in estimating sediment yield, most of the observed data never fell inside the prediction intervals

Sensitivity analysis of the WEPP Watershed model

Uncertainty in the hydrologic and soil erosion predictions of the WEPP Watershed model due to errors in model parameter estimation was identified through a sensitivity analysis based on the Monte-Carlo method. Identification of parameter sensitivities provides guidance in the collection of parameter data in places where the model is intended to simulate soil erosion. Changes in model predictions caused by changes in model parameters were quantified for model applications in semi-arid rangeland watersheds. The magnitude of the changes in model parameters was defined by the spatial variability of parameters in a watershed. Model sensitivities in predicting overland flow and soil erosion on hillslopes and channels are presented considering rainfall characteristics. The results show that WEPP predictions are very sensitive to attributes that define a storm event (amount, duration, and ip). Model sensitivity to soil erosion parameters also depends of the type of storm event

Sensitivity analysis of EUROSEM using Monte Carlo simulation I : hydrological, soil and vegetation parameters.:hydrological, soil and vegetation parameters

Knowledge about model uncertainty is essential for erosion modelling and provides important information when it comes to parameterizing models. In this paper a sensitivity analysis of the European soil erosion model (EUROSEM) is carried out using Monte Carlo simulation, suitable for complex non-linear models, using time-dependent driving variables. The analysis revealed some important characteristics of the model. The variability of the static output parameters was generally high, with the hydrologic parameters being the most important ones, especially saturated hydraulic conductivity and net capillary drive followed by the percentage basal area for the hydrological and vegetation parameters and detachability and cohesion for the soil erosion parameters. Overall, sensitivity to vegetation parameters was insignificant. The coefficient of variation for the sedigraph was higher than for the hydrograph, especially from the beginning of the rainstorm and up to the peak, and may explain difficulties encountered when trying to match simulated hydrographs and sedigraphs with observed ones. The findings from this Monte Carlo simulation calls for improved within-storm modelling of erosion processes in EUROSEM. Information about model uncertainty will be incorporated in a new EUROSEM user interface

Sensitivity of North American agriculture to ENSO-based climate scenarios and their socio-economic consequences: Modeling in an integrated assessment framework

A group of Canadian, US and Mexican natural resource specialists, organized by the Pacific Northwest National Laboratory (PNNL) under its North American Energy, Environment and Economy (NA3E) Program, has applied a simulation modeling approach to estimating the impact of ENSO-driven climatic variations on the productivity of major crops grown in the three countries. Methodological development is described and results of the simulations presented in this report. EPIC (the Erosion Productivity Impact Calculator) was the agro-ecosystem model selected-for this study. EPIC uses a daily time step to simulate crop growth and yield, water use, runoff and soil erosion among other variables. The model was applied to a set of so-called representative farms parameterized through a specially-assembled Geographic Information System (GIS) to reflect the soils, topography, crop management and weather typical of the regions represented. Fifty one representative farms were developed for Canada, 66 for the US and 23 for Mexico. El Nino-Southern Oscillation (ENSO) scenarios for the EPIC simulations were created using the historic record of sea-surface temperature (SST) prevailing in the eastern tropical Pacific for the period October 1--September 30. Each year between 1960 and 1989 was thus assigned to an ENSO category or state. The ENSO states were defined as El Nino (EN, SST warmer than the long-term mean), Strong El Nino (SEN, much warmer), El Viejo (EV, cooler) and Neutral (within {+-}0.5 C of the long-term mean). Monthly means of temperature and precipitation were then calculated at each farm for the period 1960--1989 and the differences (or anomalies) between the means in Neutral years and EN, SEN and EV years determined. The average monthly anomalies for each ENSO state were then used to create new monthly statistics for each farm and ENSO-state combination. The adjusted monthly statistics characteristic of each ENSO state were then used to drive a stochastic-weather simulator that provided 30 years of daily-weather data needed to run EPIC. Maps and tables of the climate anomalies by farm show climatic conditions that differ considerably by region, season and ENSO state