scholarly journals A flexible and efficient multi-model framework in support of water management

2016 ◽  
Vol 373 ◽  
pp. 1-6 ◽  
Author(s):  
Vincent Wolfs ◽  
Quan Tran Quoc ◽  
Patrick Willems
Keyword(s):  
Water Management ◽  
Drainage System ◽  
Calibration Procedure ◽  
Structure Identification ◽  
Model Structure ◽  
Rainfall Runoff ◽  
Model Framework ◽  
Modelling Framework ◽  
Advanced Model

Abstract. Flexible, fast and accurate water quantity models are essential tools in support of water management. Adjustable levels of model detail and the ability to handle varying spatial and temporal resolutions are requisite model characteristics to ensure that such models can be employed efficiently in various applications. This paper uses a newly developed flexible modelling framework that aims to generate such models. The framework incorporates several approaches to model catchment hydrology, rivers and floodplains, and the urban drainage system by lumping processes on different levels. To illustrate this framework, a case study of integrated hydrological-hydraulic modelling is elaborated for the Grote Nete catchment in Belgium. Three conceptual rainfall-runoff models (NAM, PDM and VHM) were implemented in a generalized model structure, allowing flexibility in the spatial resolution by means of an innovative disaggregation/aggregation procedure. They were linked to conceptual hydraulic models of the rivers in the catchment, which were developed by means of an advanced model structure identification and calibration procedure. The conceptual models manage to emulate the simulation results of a detailed full hydrodynamic model accurately. The models configured using the approaches of this framework are well-suited for many applications in water management due to their very short calculation time, interfacing possibilities and adjustable level of detail.

scholarly journals Baseflow representation module on MODCEL

RBRH ◽  
2018 ◽  
Vol 23 ◽  
Author(s):  
Pedro Lucas Cosmo de Brito ◽  
Marcelo Gomes Miguez ◽  
José Paulo Soares de Azevedo
Keyword(s):  
Groundwater Flow ◽  
Significant Contribution ◽  
Hydrological Model ◽  
Calibration Procedure ◽  
Coefficient Of Determination ◽  
Rainfall Runoff ◽  
Flood Events ◽  
Infiltration Process ◽  
Previous Version

ABSTRACT The land use characteristics of rural watersheds allow infiltration and consequent generation of groundwater flow, which constitutes a significant contribution to the hydrograph. Prior to this study, the MODCEL-COPPE/UFRJ model simulated only runoff, disregarding the losses occurred in rainfall-runoff process. Therefore, its application was more appropriate to urban watersheds, simulating flood events where surface flows prevail. This study aimed at representing the infiltration process and at incorporating the groundwater flow in the MODCEL’s structure, making feasible the rural watersheds simulation thus expanding its applicability as a hydrological model. A case study was performed in a 417 km2 subcatchment of Piabanha River, located at Petrópolis/RJ. It’s a predominantly rural watershed, with 80% of its area covered by forests. The model represented satisfactorily the seasonality and the magnitude of simulated recharges. In the parameter calibration procedure gave a coefficient of determination R2 = 0.75, comparing the calculated flows to the observed flows. During validation period, we obtained a coefficient of determination R2 = 0.76. The fit obtained was superior to that obtained in previous modeling of the same watershed by SMAP and MODCEL (previous version) and it was similar to TOPMODEL. In the hydrograph recession, new MODCEL presented R2 = 0.75, against 0.52 obtained in its previous version.

How good does Automatic Model Structure Identification work? A Benchmark Study with 6915 Model Structures.

2021 ◽  
Author(s):  
Diana Spieler ◽  
Niels Schütze
Keyword(s):  
Optimization Algorithms ◽  
Calibration Procedure ◽  
Mixed Integer ◽  
Structure Identification ◽  
Model Parameters ◽  
Model Structure ◽  
Integer Optimization ◽  
Mixed Integer Optimization ◽  
Standard Calibration ◽  
Model Structures

<p>Recent investigations have shown it is possible to simultaneously calibrate model structures and model parameters to identify appropriate models for a given task (Spieler et al., 2020). However, this is computationally challenging, as different model structures may use a different number of parameters. While some parameters may be shared between model structures, others might be relevant for only a few structures, which theoretically requires the calibration of conditionally active parameters. Additionally, shared model parameters might cause different effects in different model structures, causing their optimal values to differ across structures. In this study, we tested how two current “of the shelf” mixed-integer optimization algorithms perform when having to handle these peculiarities during the automatic model structure identification (AMSI) process recently introduced by Spieler et al. (2020).</p><p>To validate the current performance of the AMSI approach, we conduct a benchmark experiment with a model space consisting of 6912 different model structures.  First, all model structures are independently calibrated and validated for three hydro-climatically differing catchments using the CMA-ES algorithm and KGE as the objective function. This is referred to as standard calibration procedure. We identify the best performing model structure(s) based on validation performance and analyze the range of performance as well as the number of structures performing in a similar range. Secondly, we run AMSI on all three catchments to automatically identify the most feasible model structure based on the KGE performance. Two different mixed-integer optimization algorithms are used – namely DDS and CMA-ES. Afterwards, we compare the results to the best performing models of the standard calibration of all 6912 model structures.</p><p>Within this experimental setup, we analyze if the best performing model structure(s) AMSI identifies are identical to the best performing structures of the standard calibration and if there are differences in performance when using different optimization algorithms for AMSI. We also validate if AMSI can identify the best performing model structures for a catchment at a fraction of the computational cost than the standard calibration procedure requires by using “off the shelf” mixed-integer optimization algorithms.</p><p> </p><p> </p><p> </p><p>Spieler, D., Mai, J., Craig, J. R., Tolson, B. A., & Schütze, N. (2020). Automatic Model Structure Identification for Conceptual Hydrologic Models. Water Resources Research, 56(9). https://doi.org/10.1029/2019WR027009</p>

A study on the effects of urban runoff controls in the sandsli research catchment, Bergen, Norway

1997 ◽  
Vol 36 (8-9) ◽  
pp. 379-384
Author(s):  
Sveinn T. Thorolfsson
Keyword(s):  
Ground Water ◽  
Stormwater Management ◽  
Drainage System ◽  
Urban Runoff ◽  
Source Control ◽  
Urban Stormwater ◽  
Urban Stormwater Management ◽  
Water Conditions

This paper describes a case study on a new alternative drainage system for urban stormwater management, the so-called “Sandsli-system”. The aim of this study is to evaluate the Sandsli system and the effects of the solution on ground water conditions. The study is carried out in the Sandsli research catchment in Bergen, Norway. The idea behind the “Sandsli-system is not to mix the polluted and the clean stormwater combined with a source control for both stormwater quantity and quality. The clean stormwater is percolated as quickly as possible, while the polluted stormwater is collected and conducted to an appropriate site for disposal or treatment. The Sandsli-system was developed as an alternative drainage system to the conventional drainage system. The system has been functioning satisfactorily since 1981 to date. The advantages of the use of the Sandsli-system is highlighted i.e. recharging the stormwater to the ground water. The Sandsli-system is appropriate to locations with climate and geology similar to that found in the coastal part of Norway

scholarly journals System Dynamics-Multiple Objective Optimization Model for Water Resource Management: A Case Study in Jiaxing City, China

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 671
Author(s):  
Xiaoying Zhou ◽  
Feier Wang ◽  
Kuan Huang ◽  
Huichun Zhang ◽  
Jie Yu ◽  
...  
Keyword(s):  
Resource Management ◽  
System Dynamics ◽  
Water Resource ◽  
Water Allocation ◽  
Water Resource Management ◽  
Water Shortage ◽  
Multiple Objective ◽  
Multiple Objective Optimization ◽  
Model Framework

Predicting and allocating water resources have become important tasks in water resource management. System dynamics and optimal planning models are widely applied to solve individual problems, but are seldom combined in studies. In this work, we developed a framework involving a system dynamics-multiple objective optimization (SD-MOO) model, which integrated the functions of simulation, policy control, and water allocation, and applied it to a case study of water management in Jiaxing, China to demonstrate the modeling. The predicted results of the case study showed that water shortage would not occur at a high-inflow level during 2018–2035 but would appear at mid- and low-inflow levels in 2025 and 2022, respectively. After we made dynamic adjustments to water use efficiency, economic growth, population growth, and water resource utilization, the predicted water shortage rates decreased by approximately 69–70% at the mid- and low-inflow levels in 2025 and 2035 compared to the scenarios without any adjustment strategies. Water allocation schemes obtained from the “prediction + dynamic regulation + optimization” framework were competitive in terms of social, economic and environmental benefits and flexibly satisfied the water demands. The case study demonstrated that the SD-MOO model framework could be an effective tool in achieving sustainable water resource management.

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