Parallelisation of a distributed hydrologic model

Cui, Zhengtao; Vieux, Baxter E.; Neeman, Henry; Moreda, Fekadu

doi:10.1504/ijcat.2005.006802

Cited by 19 publications

(9 citation statements)

References 20 publications

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“…The usage of GPUs for processing a 2D flood simulation model is presented in [18] and [19]. Other methods of parallelization are described in [20] and [21].…”

Section: Hydrologic Modelsmentioning

confidence: 99%

Calibration of SWAT Hydrological Models in a Distributed Environment Using the gSWAT Application

Bacu¹,

Mihon²,

Ștefănuț³

et al. 2013

SpecialIssue

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Abstract-Topics such as the sustainability and vulnerability of land management practices on water quality and quantity are very important in these days both for decision makers and for citizens. The enviroGRIDS FP7 project addresses some of these topics in the Black Sea Catchment area. One of the software tools developed in this project is gSWAT. It allows the calibration of SWAT hydrological models in a flexible development environment and uses distributed computational infrastructures to speedup the simulations. The development of SWAT (Soil Water Assessment Tool) hydrological models is a well-known procedure for the hydrological specialists and this paper highlights, from the end-users point of view, the scenarios related with the calibration procedures available in the gSWAT application.

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“…The usage of GPUs for processing a 2D flood simulation model is presented in [18] and [19]. Other methods of parallelization are described in [20] and [21].…”

Section: Hydrologic Modelsmentioning

confidence: 99%

Calibration of SWAT Hydrological Models in a Distributed Environment Using the gSWAT Application

Bacu¹,

Mihon²,

Ștefănuț³

et al. 2013

SpecialIssue

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“…This 16 has been proved effective by several studies (e.g. Apostolopoulos and Georgakakos, 17 1997; Vivoni et al, 2005;Cui et al, 2005;Kolditz et al, 2007). The study of 18 Apostolopoulos and Georgakakos (1997) However, the size of the subbasins and the degree of their related drainage 2 network complexes, which affect the computation time in hydrological simulation, 3 within a basin may vary greatly.…”

Section: Introductionmentioning

confidence: 95%

Dynamic parallelization of hydrological model simulations

Wang

et al. 2011

Environmental Modelling & Software

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This paper introduces the development of a dynamic parallel algorithm for conducting 2 hydrological model simulations. This new algorithm consists of a river network 3 decomposition method and an enhanced master-slave paradigm. The decomposition 4 method is used to divide a basin river network into a large number of subbasins, and the 5 enhanced master-slave paradigm is adopted to realize the function of this new dynamic 6 basin decomposition method through using the Message-Passing Interface (MPI) and 7 C++ language. This new algorithm aims to balance computation load and then to 8 achieve a higher speedup and efficiency of parallel computing in hydrological 9 simulation for the river basins which are delineated by high-resolution drainage 10 networks. This paper uses a modified binary tree codification method developed by Li 11 et al. (2010) to code drainage networks, and the basin width function to estimate the 12 possible maximum parallel speedup and the associated efficiency. As a case study, with 13 a hydrological model, the Digital Yellow River Model, this new dynamic parallel 14 algorithm is applied to the Chabagou basin in northern China. The application results 15 reveal that the new algorithm is efficient in the dynamic dispatching of simulation tasks 16 to computing processes, and that the parallel speedup and efficiency are comparable 17 with the estimations made by using the basin width function.

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“…Distributed physically-based hydrological models (DHMs) appeared in the 1960s and have been the object of critics due to their complexity and difficulty of use [1]. Nowadays, the availability of higher resolution spatio-temporal datasets, the appearance of high performance computers, and the development of parallel-computing [2][3][4][5] have opened the possibility of using these models for large size basins and long-term hydrological continuous simulations. Thus, challenges such as the influence of land-use changes [6,7] or the impact of climate change [8,9] on the involved hydrological processes can be analyzed with these approaches.…”

Section: Introductionmentioning

confidence: 99%

“…In the case of event-based approaches, some studies have combined non-complex stochastic storm generators (i.e., [19,20]) or complex rainfall generators (i.e., [21]); with semidistributed (i.e., [22]) or distributed models (i.e., [23]). In the case of continuous simulations, in order to reduce the computational cost, most authors have worked with lumped or semidistributed models (i.e., [24,25]), and some of them have worked with distributed models by using high performance computers [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Dependence Between Extreme Rainfall Events and the Seasonality and Bivariate Properties of Floods. A Continuous Distributed Physically-Based Approach

Gabriel-Martín

Sordo‐Ward

Garrote

et al. 2019

Water

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This paper focuses on proposing the minimum number of storms necessary to derive the extreme flood hydrographs accurately through event-based modelling. To do so, we analyzed the results obtained by coupling a continuous stochastic weather generator (the Advanced WEather GENerator) with a continuous distributed physically-based hydrological model (the TIN-based real-time integrated basin simulator), and by simulating 5000 years of hourly flow at the basin outlet. We modelled the outflows in a basin named Peacheater Creek located in Oklahoma, USA. Afterwards, we separated the independent rainfall events within the 5000 years of hourly weather forcing, and obtained the flood event associated to each storm from the continuous hourly flow. We ranked all the rainfall events within each year according to three criteria: Total depth, maximum intensity, and total duration. Finally, we compared the flood events obtained from the continuous simulation to those considering the N highest storm events per year according to the three criteria and by focusing on four different aspects: Magnitude and recurrence of the maximum annual peak-flow and volume, seasonality of floods, dependence among maximum peak-flows and volumes, and bivariate return periods. The main results are: (a) Considering the five largest total depth storms per year generates the maximum annual peak-flow and volume, with a probability of 94% and 99%, respectively and, for return periods higher than 50 years, the probability increases to 99% in both cases; (b) considering the five largest total depth storms per year the seasonality of flood is reproduced with an error of less than 4% and (c) bivariate properties between the peak-flow and volume are preserved, with an error on the estimation of the copula fitted of less than 2%.

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Parallelisation of a distributed hydrologic model

Cited by 19 publications

References 20 publications

Calibration of SWAT Hydrological Models in a Distributed Environment Using the gSWAT Application

Calibration of SWAT Hydrological Models in a Distributed Environment Using the gSWAT Application

Dynamic parallelization of hydrological model simulations

Dependence Between Extreme Rainfall Events and the Seasonality and Bivariate Properties of Floods. A Continuous Distributed Physically-Based Approach

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