Amin Haghnegahdar

Calibrating Environment Canada's MESH Modelling System over the Great Lakes Basin

Abstract This paper reports on recent progress towards improved predictions of a land surface-hydrological modelling system, Modélisation Environmentale–Surface et Hydrologie (MESH), via its calibration over the Laurentian Great Lakes Basin. Accordingly, a “global” calibration strategy is utilized in which parameters for all land class types are calibrated simultaneously to a number of sub-basins and then validated in time and space. Model performance was evaluated based on four performance metrics, including the Nash-Sutcliffe (NS) coefficient and simulated compared with observed hydrographs. Results from two calibration approaches indicate that in the model validation mode, the global strategy generates better results than an alternative calibration strategy, referred to as the “individual” strategy, in which parameters are calibrated individually to a single sub-basin with a dominant land type and then validated in a different sub-basin with the same dominant land type. The global calibration strategy was relatively successful despite the large number of calibration parameters (51) and relatively small number of model evaluations (1000) used in the automatic calibration procedure. The NS values for spatial validation range from 0.10 to 0.72 with a median of 0.41 for the 15 sub-basins considered. Results also confirm that a careful model calibration and validation is needed before any application of the model.

Insights into sensitivity analysis of Earth and environmental systems models: On the impact of parameter perturbation scale

Abstract This paper investigates the commonly overlooked “sensitivity” of sensitivity analysis (SA) to what we refer to as parameter “perturbation scale”, which can be defined as a prescribed size of the sensitivity-related neighbourhood around any point in the parameter space (analogous to step size Δ x for numerical estimation of derivatives). We discuss that perturbation scale is inherent to any (local and global) SA approach, and explain how derivative-based SA approaches (e.g., method of Morris) focus on small-scale perturbations, while variance-based approaches (e.g., method of Sobol) focus on large-scale perturbations. We employ a novel variogram-based approach, called Variogram Analysis of Response Surfaces (VARS), which bridges derivative- and variance-based approaches. Our analyses with different real-world environmental models demonstrate significant implications of subjectivity in the perturbation-scale choice and the need for strategies to address these implications. It is further shown how VARS can uniquely characterize the perturbation-scale dependency and generate sensitivity measures that encompass all sensitivity-related information across the full spectrum of perturbation scales.

Assimilation of SMOS soil moisture in the MESH model with the ensemble Kalman filter

Over the past decade, satellite soil moisture retrievals have showed great potential to improve land surface and hydrologic modeling, especially through an advanced data assimilation system. Data assimilation can be viewed as a process to optimally merge the model estimate and the observed information based upon some estimate of their error characteristics. This paper presents a case study of assimilating the Soil Moisture and Ocean Salinity (SMOS) satellite soil moisture retrievals (2010-2013) into a coupled land-surface and hydrological model MESH with an ensemble Kalman filter (EnKF). The assimilation experiment is conducted over the Great Lakes basin. The assimilation is validated against in situ soil moisture measurements (53 sites) from the Michigan Automated Weather Network, the Soil Climate Analysis Network, and the Fluxnet-Canada, in terms of the daily-spaced anomaly time series correlation coefficient (soil moisture skill). Results indicate that the assimilation of SMOS retrievals enhances the MESH models soil moisture skill.

Effect of ENSO on annual maximum floods and volume over threshold in the southwestern region of Iran

ABSTRACT In this paper, the effects of the El Niño-Southern Oscillation (ENSO) on the annual maximum flood (AMF) and volume over threshold (VOT) in two major neighbouring river basins in southwest Iran are investigated. The basins are located upstream of the Dez and Karun-I dams and cover over 40u202f000 km2 in total area. The effects of ENSO on the frequency, magnitude and severity (frequency times magnitude) of flood characteristics over the March–April period were analysed. ENSO indices were also correlated with both AMF and VOT. The results indicate that, in the Dez and Karun basins, the El Niño phenomenon intensifies March–April floods compared with neutral conditions. The opposite is true in La Niña conditions. The degree of the effect is more intense in the El Niño period.

Multi-criteria sensitivity analysis as a diagnostic tool for understanding model behavior and characterizing model uncertainty

Complex hydrological models are being increasingly used nowadays for many purposes such as studying the impact of climate and land-use change on water resources. However, building a high-fidelity model, particularly at large scales, remains a challenging task, due to complexities in model functioning and behavior and uncertainties in model structure, parameterization, and data. Global Sensitivity Analysis (GSA), which characterizes how the variation in the model response is attributed to variations in its input factors (e.g., parameters, forcing data), provides an opportunity to enhance the development and application of these complex models. In this paper, we advocate using GSA as an integral part of the modelling process by discussing its capabilities as a tool for diagnosing model structure and detecting potential defects, identifying influential factors, characterizing uncertainty, and selecting calibration parameters. Accordingly, we conduct a comprehensive GSA of a complex land surface-hydrology model, Modelisation Environmentale–Surface et Hydrologie (MESH), which combines the Canadian Land Surface Scheme (CLASS) with a hydrological routing component, WATROUTE. Various GSA experiments are carried out using a new technique, called Variogram Analysis of Response Surfaces (VARS), for alternative hydroclimatic conditions in Canada using multiple criteria, various model configurations, and a full set of model parameters. Results from this study reveal that, in addition to different hydroclimatic conditions and SA criteria, model configurations can also have a major impact on the assessment of sensitivity. GSA can identify aspects of the model internal functioning that are counter-intuitive, and thus, help the modeler to diagnose possible model deficiencies and make recommendations for improving development and application of the model. As a specific outcome of this work, a list of the most influential parameters for the MESH model is developed. This list, along with some specific recommendations, is expected to assist the wide community of MESH and CLASS users, to enhance their modelling applications.

VARS-TOOL: A Toolbox for Comprehensive, Efficient, and Robust Sensitivity and Uncertainty Analysis

Abstract VARS-TOOL is a software toolbox for sensitivity and uncertainty analysis. Developed primarily around the “Variogram Analysis of Response Surfaces” framework, VARS-TOOL adopts a multi-method approach that enables simultaneous generation of a range of sensitivity indices, including ones based on derivative, variance, and variogram concepts, from a single sample. Other special features of VARS-TOOL include (1) novel tools for time-varying and time-aggregate sensitivity analysis of dynamical systems models, (2) highly efficient sampling techniques, such as Progressive Latin Hypercube Sampling (PLHS), that maximize robustness and rapid convergence to stable sensitivity estimates, (3) factor grouping for dealing with high-dimensional problems, (4) visualization for monitoring stability and convergence, (5) model emulation for handling model crashes, and (6) an interface that allows working with any model in any programming language and operating system. As a test bed for training and research, VARS-TOOL provides a set of mathematical test functions and the (dynamical) HBV-SASK hydrologic model.

Global Sensitivity Analysis for High-Dimensional Problems: How tObjectively Group Factors and Measure Robustness and Convergence while Reducing Computational Cost

Abstract Dynamical earth and environmental systems models are typically computationally intensive and highly parameterized with many uncertain parameters. Together, these characteristics severely limit the applicability of Global Sensitivity Analysis (GSA) to high-dimensional models because very large numbers of model runs are typically required to achieve convergence and provide a robust assessment. Paradoxically, only 30 percent of GSA applications in the environmental modelling literature have investigated models with more than 20 parameters, suggesting that GSA is under-utilized on problems for which it should prove most useful. We develop a novel grouping strategy, based on bootstrap-based clustering, that enables efficient application of GSA to high-dimensional models. We also provide a new measure of robustness that assesses GSA stability and convergence. For two models, having 50 and 111 parameters, we show that grouping-enabled GSA provides results that are highly robust to sampling variability, while converging with a much smaller number of model runs.