Sensitivity of Calibrated Parameters and Water Resource Estimates on Different Objective Functions and Optimization Algorithms

Flash floods are natural phenomena with environmental, social and economic impacts. To date, few numerical models are able to simulate hydrological processes at catchment scale at a reasonable time scale to describe flash events with accurate details. Considering a~810 km 2 Mediterranean river coastal basin (southwestern France) as a study case, the objective of the present study was to assess the ability of the sub-daily module of the lumped Soil and Water Assessment Tool (SWAT) to simulate discharge (1) time-continuously, by testing two sub-basin delineation schemes, two catchment sizes, and two output time-steps; and (2) at flood time-scale, by comparing the performances of SWAT to the performances of the event-based fully distributed MARINE model when simulating flash flood events. We showed that there was no benefit of decreasing the size of the minimum drainage area (e.g., from~15 km 2 down to~1 km 2 ) when delineating sub-basins in SWAT. We also showed that both the MARINE and SWAT models were equally able to reproduce peak discharge, flood timing and volume, and that they were both limited by rainfall and soil data. Hence, the SWAT model appears to be a reliable modelling tool to predict discharge over long periods of time in large flash-flood-prone basins.

Section: Discharge Calibration and Validation Results In The Main Chamentioning

confidence: 99%

Simulating Flash Floods at Hourly Time-Step Using the SWAT Model

Boithias

Sauvage

Lenica

et al. 2017

“…Ajami, et al [8] state that neglecting different aspects of uncertainty during the calibration of hydrological models may result in inconsistent outputs. We hence emphasize that it may be prudent for modelers to pay more attention to the existence of uncertainty from multiple sources of data (especially climate data) in combination with other sources of uncertainty such as spatial data resolution [48], objective functions, or optimization algorithms [38]. We also suggest that the calibration of models against more observed variables such as evapotranspiration or soil moisture may help to select better models.…”

Section: Discussionmentioning

confidence: 97%

“…We calibrated the models using parameters sensitive to discharge, selected based on the initial model simulation, the guidelines suggested by Abbaspour et al [17], and the experience gained from previous work in the same river basin [37,38], as explained in Table 3. The snow parameter i.e., "maximum snow melt rate" was set to 5 mm C −1 day −1 based on the work of Vaghefi et al [37] in all eight configurations.…”

Section: C4mentioning

confidence: 99%

Assessing the Uncertainty of Multiple Input Datasets in the Prediction of Water Resource Components

Kamali

Abbaspour

Yang

2017

A large number of local and global databases for soil, land use, crops, and climate are now available from different sources, which often differ, even when addressing the same spatial and temporal resolutions. As the correct database is unknown, their impact on estimating water resource components (WRC) has mostly been ignored. Here, we study the uncertainty stemming from the use of multiple databases and their impacts on WRC estimates such as blue water and soil water for the Karkheh River Basin (KRB) in Iran. Four climate databases and two land use maps were used to build multiple configurations of the KRB model using the soil and water assessment tool (SWAT), which were similarly calibrated against monthly river discharges. We classified the configurations based on their calibration performances and estimated WRC for each one. The results showed significant differences in WRC estimates, even in models of the same class i.e., with similar performance after calibration. We concluded that a non-negligible level of uncertainty stems from the availability of different sources of input data. As the use of any one database among several produces questionable outputs, it is prudent for modelers to pay more attention to the selection of input data.

“…The model calibration process was subject to manifold studies, e.g., discussing underlying calibration algorithms [4,5], the choice of the calibration variable [6,7], the objective functions used for calibration [8,9], varying model input data [10,11] or uncertainties of various sources and their propagation throughout the model [12,13]. However, guidance regarding the question of measurement site selection for the calibration of hydrodynamic sewer models is still scarce.…”

Section: Introductionmentioning

confidence: 99%

“…In order to ensure a multi-perspective view on the calibration results [9], different objective functions (NSE, d, r, rmse, ssq) are evaluated to compare the simulated with the reference water level time series and thus assess each calibration performance.…”

mentioning

confidence: 99%

A Heuristic Method for Measurement Site Selection in Sewer Systems

Vonach

Tscheikner-Gratl

Rauch

et al. 2018

Although calibration of a hydrodynamic model depends on the availability of measurement data representing the system behavior, advice for the planning of necessary measurement campaigns for model calibration is scarce. This work tries to address this question of efficient measurement site selection on a network scale for the objective of calibrating a hydrodynamic model case study in Austria. For this, a model-based approach is chosen, as the method should be able to be used before measurement data is available. An existing model is assumed to represent the real system behavior. Based on this extended availability of "measurement data" in every point of the system, different approaches are established to heuristically assess the suitability of one or more pipes in combination as calibration point(s). These approaches intend to find suitable answers to the question of measurement site selection for this specific case study within a relatively short time and with a reasonable computational effort. As a result, the relevance of the spatial distribution of calibration points is highlighted. Furthermore, particular efficient calibration points are identified and further measurement sites in the underlying network are recommended.