Comparing multi-objective optimization techniques to calibrate a conceptual hydrological model using in situ runoff and daily GRACE data

Abstract

Hydrological models are necessary tools for simulating the water cycle and for understanding changes in water resources. To achieve realistic model simulation results, real-world observations are used to determine model parameters within a "calibration" procedure. Optimization techniques are usually applied in the model calibration step, which assures a maximum similarity between model outputs and observations. Practical experiences of hydrological model calibration have shown that single-objective approaches might not be adequate to tune different aspects of model simulations. These limitations can be as a result of (i) using observations that do not sufficiently represent the dynamics of the water cycle, and/or (ii) due to restricted efficiency of the applied calibration techniques. To address (i), we assess how adding daily Total Water Storage (dTWS) changes derived from the Gravity Recovery And Climate Experiment (GRACE) as an extra observations, besides the traditionally used runoff data, improves calibration of a simple 4-parameter conceptual hydrological model (GR4J, in French: modèle du Génie Ruralà 4 paramètres Journalier) within the Danube River Basin. As selecting a proper calibration approach (in ii) is a challenging task and might have significant influence on the quality of model simulations, for the first time, four evolutionary optimization techniques, including the Non-dominated Sorting Genetic Algorithm II (NSGA-II), the Multi-objective Particle Swarm Optimization (MPSO), the Pareto Envelope-Based Selection Algorithm II (PESA-II), and the Strength Pareto Evolutionary Algorithm II (SPEA-II) along with the Combined objective function and Genetic Algorithm (CGA) are tested to calibrate the model in (i). A number of quality measures are applied to assess cardinality, accuracy, and diversity of solutions, which include the Number of Pareto Solutions (NPS), Generation Distance (GD), Spacing (SP), and Maximum Spread (MS). Our results indicate that according to MS and SP, NSGA-II performs better than other techniques for calibrating GR4J using GRACE dTWS and in situ runoff data. Considering GD as a measure of efficiency, MPSO is found to be the best technique. CGA is found to be an efficient method, while considering the statistics of the GR4J's 4 calibrated parameters to rank the optimization techniques. The Nash-Sutcliffe model efficiency coefficient is also used to assess the predictive power of the calibrated hydrological models, for which our results indicate satisfactory performance of the assessed calibration experiments.

Multiobjective Calibration of a Global Biogeochemical Ocean Model Against Nutrients, Oxygen, and Oxygen Minimum Zones

“…MO approaches gained most attention in economy and operations research but have also been applied for geoscientific problems, mainly and long‐established in the field of hydrology (see, e.g., Efstratiadis & Koutsoyiannis, ; Mostafaie et al, ; Yapo et al, ). Recent contributions also deal with emulators/surrogates of earth system models in order to apply MO in the face of different prediction tasks (see, e.g., Langenbrunner & Neelin, , ; Price et al, ).…”

Analysis of Flood Storage Area Operations in Huai River Using 1D and 2D River Simulation Models Coupled with Global Optimization Algorithms

“…All algorithms are improved versions of their earlier versions. Details of the algorithms are not of main focus here, but interested readers can check the indicated references, as well as some comparative studies [32,33]. Using the MOEA framework, for each of the three tested algorithms several metrics (parameters) describing the progress of the evolution were extracted, among which were generational distance and hyper-volume.…”

Qom—A New Hydrologic Prediction Model Enhanced with Multi-Objective Optimization

“…Another related study is found in Reference [32], where authors analyzed how adding daily Total Water Storage (dTWS) derived from the Gravity Recovery And Climate Experiment (GRACE) as extra observations, besides the traditionally used runoff data, improved the calibration of a conceptual hydrological model within the Danube River Basin. As calibration approach, four popular evolutionary optimization techniques (NSGA-II, MOPSO, PESA-II, SPEA2) were tested to calibrate the model.…”