scholarly journals Use of a supercomputer to advance parameter optimisation using genetic algorithms

2007 ◽  
Vol 9 (4) ◽  
pp. 319-329 ◽  
Author(s):  
Achela K. Fernando ◽  
A. W. Jayawardena
Keyword(s):  
Computational Time ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Mean Flow ◽  
Tank Model ◽  
Serial Processing ◽  
Parameter Optimisation ◽  
Modified Genetic Algorithm ◽  
Parallel Ga ◽  
Reduced Time

Parameter optimisation is a significant but time-consuming process that is inherent in conceptual hydrological models representing rainfall–runoff processes. This study presents two modifications to achieve optimised results for a Tank Model in less computational time. Firstly, a modified genetic algorithm (GA) is developed to enhance the fitness of the population consisting of possible solutions in each generation. Then the parallel processing capabilities of an IBM 9076 SP2 computer are used to expedite implementation of the GA. A comparison of processing time between a serial IBM RS/6000 390 computer and an IBM 9076 SP2 supercomputer reveals that the latter can be up to 8 times faster. The effectiveness of the modified GA is tested with two Tank Models for a hypothetical catchment and a real catchment. The former showed that the parallel GA reaches a lower overall error in reduced time. The overall RMSE, expressed as a percentage of actual mean flow rate, improves from 31.8% in a serial processing computer to 29.5% on the SP2 supercomputer. The case of the real catchment – Shek-Pi-Tau Catchment in Hong Kong – reveals that the supercomputer enhances the swiftness of the GA and achieves its objective within a couple of hours.

scholarly journals Aplicação do Tank Model como Ferramenta de Gestão na Bacia do Rio Perdizes – Cambará do Sul/RS

2021 ◽  
Vol 14 (2) ◽  
pp. 1143
Author(s):  
Karla Campagnolo ◽  
Sofia Melo Vasconcellos ◽  
Vinicius Santanna Castiglio ◽  
Marina Refatti Fagundes ◽  
Masato Kobiyama
Keyword(s):  
River Basin ◽  
National Park ◽  
Management Tool ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Moisture Index ◽  
Tank Model ◽  
Calibration And Validation ◽  
Historical Series ◽  
Soil Moisture Index

A representação do processo precipitação-vazão por meio de modelos hidrológicos conceituais visa quantificar o volume escoado em uma bacia como consequência de uma determinada precipitação. Aliados a eles, os índices têm sido uma ferramenta útil para quantificar eventos extremos, como o Soil Moisture Index (TMI) que foi formulado a partir do modelo hidrológico Tank Model. Desta forma, o objetivo deste trabalho foi aplicar o Tank Model para a bacia do rio Perdizes, em Cambará do Sul (RS), e avaliar o desempenho do TMI para prever a ocorrência de cheias, limiar este utilizado para o fechamento da Trilha do rio do Boi, no Parque Nacional de Aparados da Serra (PNAS). Os dados utilizados na simulação foram obtidos pelas estações meteorológica e fluviométrica instaladas na bacia. Após a calibração e validação de três séries históricas no Tank Model, os valores obtidos do TMI foram comparados com os dias que a Trilha foi fechada, a partir de altos níveis registrados no rio Perdizes. O TMI demonstrou que o nível utilizado para fechar a Trilha do rio do Boi correspondeu a cheias em 72% das vezes. Portanto, o TMI mostrou bom desempenho ao indicar a ocorrência de cheias na área estudada, sendo uma ferramenta útil para a tomada de decisões na gestão do PNAS.  Application of the Tank Model as a Management Tool in the Perdizes River Basin - Cambará do Sul/RS.ABSTRACTThe representation of the rainfall-runoff process by means of conceptual hydrological models aims to quantify the volume drained in a basin as result of a specific precipitation. Allied to them, the indices have been a useful tool to quantify extreme events, such as the Tank Moisture Index (TMI) which was formulated from the Tank Model. Thus, the objective of this work was to apply the Tank Model to the Perdizes river basin, in Cambará do Sul (RS), and to evaluate the performance of the TMI to predict the occurrence of floods, the threshold used for the closure of the Rio do Boi trail, in the Aparados da Serra National Park (PNAS). The data used in the simulation were obtained at the meteorological and fluviometric stations installed in the basin. After the calibration and validation of three historical series in the Tank Model, the values obtained in the TMI were compared with the days when the Trail was closed, from high levels recorded in the Perdizes river. The average TMI values demonstrated that the level used to close the Rio do Boi Trail corresponded to floods 72% of the time, and the median, 75%. Therefore, the TMI showed good performance in indicating the occurrence of floods in the study area, being a useful tool for decision making in the PNAS management.Keywords: Tank Moisture Index, trail closure, Aparados da Serra National Park.

scholarly journals Simplifying a hydrological ensemble prediction system with a backward greedy selection of members – Part 1: Optimization criteria

2011 ◽  
Vol 15 (11) ◽  
pp. 3307-3325 ◽  
Author(s):  
D. Brochero ◽  
F. Anctil ◽  
C. Gagné
Keyword(s):  
Computational Time ◽  
Ensemble Prediction ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Weather Forecasts ◽  
Prediction Systems ◽  
The Relationship ◽  
Selection Of

Abstract. Hydrological Ensemble Prediction Systems (HEPS), obtained by forcing rainfall-runoff models with Meteorological Ensemble Prediction Systems (MEPS), have been recognized as useful approaches to quantify uncertainties of hydrological forecasting systems. This task is complex both in terms of the coupling of information and computational time, which may create an operational barrier. The main objective of the current work is to assess the degree of simplification (reduction of the number of hydrological members) that can be achieved with a HEPS configured using 16 lumped hydrological models driven by the 50 weather ensemble forecasts from the European Centre for Medium-range Weather Forecasts (ECMWF). Here, Backward Greedy Selection (BGS) is proposed to assess the weight that each model must represent within a subset that offers similar or better performance than a reference set of 800 hydrological members. These hydrological models' weights represent the participation of each hydrological model within a simplified HEPS which would issue real-time forecasts in a relatively short computational time. The methodology uses a variation of the k-fold cross-validation, allowing an optimal use of the information, and employs a multi-criterion framework that represents the combination of resolution, reliability, consistency, and diversity. Results show that the degree of reduction of members can be established in terms of maximum number of members required (complexity of the HEPS) or the maximization of the relationship between the different scores (performance).

A Conceptual Tank Model for Urban Storm Water System

2013 ◽  
Vol 777 ◽  
pp. 430-433
Author(s):  
Xing Po Liu
Keyword(s):  
Low Impact Development ◽  
Water System ◽  
Storm Water ◽  
Rainfall Runoff ◽  
Sewer System ◽  
Tank Model ◽  
Objective Model ◽  
Principles Of Design ◽  
Urban Storm ◽  
Storm Sewer

In order to cope with urban flooding, water scarcity and rainfall-runoff pollution comprehensively, a conceptual tank model of urban storm water system is proposed. Tank model is a multi-layer, multi-objective model, so design of urban storm water system is more complex than that of urban storm sewer system. Some principles of design of urban storm water system are discussed, such as Low Impact Development, Smart storm water management, and so on.

scholarly journals Fuzzy committees of specialized rainfall-runoff models: further enhancements and tests

2013 ◽  
Vol 17 (11) ◽  
pp. 4441-4451 ◽  
Author(s):  
N. Kayastha ◽  
J. Ye ◽  
F. Fenicia ◽  
V. Kuzmin ◽  
D. P. Solomatine
Keyword(s):  
Hydrological Model ◽  
Weighting Function ◽  
Hydrological Models ◽  
Membership Functions ◽  
Rainfall Runoff ◽  
Objective Functions ◽  
Hydrological Regimes

Abstract. Often a single hydrological model cannot capture the details of a complex rainfall–runoff relationship, and a possibility here is building specialized models to be responsible for a particular aspect of this relationship and combining them to form a committee model. This study extends earlier work of using fuzzy committees to combine hydrological models calibrated for different hydrological regimes – by considering the suitability of the different weighting function for objective functions and different class of membership functions used to combine the specialized models and compare them with the single optimal models.

scholarly journals Multi-time-scale input approaches for hourly-scale rainfall–runoff modeling based on recurrent neural networks

2021 ◽  
Author(s):  
Kei Ishida ◽  
Masato Kiyama ◽  
Ali Ercan ◽  
Motoki Amagasaki ◽  
Tongbi Tu
Keyword(s):  
Time Series ◽  
Time Scale ◽  
Time Series Data ◽  
Series Data ◽  
Computational Time ◽  
Rainfall Runoff ◽  
Runoff Modeling ◽  
Input Time ◽  
Target Data ◽  
Resolution Data

Abstract This study proposes two effective approaches to reduce the required computational time of the training process for time-series modeling through a recurrent neural network (RNN) using multi-time-scale time-series data as input. One approach provides coarse and fine temporal resolutions of the input time-series data to RNN in parallel. The other concatenates the coarse and fine temporal resolutions of the input time-series data over time before considering them as the input to RNN. In both approaches, first, the finer temporal resolution data are utilized to learn the fine temporal scale behavior of the target data. Then, coarser temporal resolution data are expected to capture long-duration dependencies between the input and target variables. The proposed approaches were implemented for hourly rainfall–runoff modeling at a snow-dominated watershed by employing a long short-term memory network, which is a type of RNN. Subsequently, the daily and hourly meteorological data were utilized as the input, and hourly flow discharge was considered as the target data. The results confirm that both of the proposed approaches can reduce the required computational time for the training of RNN significantly. Lastly, one of the proposed approaches improves the estimation accuracy considerably in addition to computational efficiency.

scholarly journals Estimating the Relative Uncertainties Sourced from GCMs and Hydrological Models in Modeling Climate Change Impact on Runoff

2012 ◽  
Vol 13 (1) ◽  
pp. 122-139 ◽  
Author(s):  
Jin Teng ◽  
Jai Vaze ◽  
Francis H. S. Chiew ◽  
Biao Wang ◽  
Jean-Michel Perraud
Keyword(s):  
Climate Change ◽  
Climate Change Impact ◽  
Daily Rainfall ◽  
Low Flow ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Runoff Modeling ◽  
Change Impact ◽  
The Difference ◽  
Climate Series

Abstract This paper assesses the relative uncertainties from GCMs and from hydrological models in modeling climate change impact on runoff across southeast Australia. Five lumped conceptual daily rainfall–runoff models are used to model runoff using historical daily climate series and using future climate series obtained by empirically scaling the historical climate series informed by simulations from 15 GCMs. The majority of the GCMs project a drier future for this region, particularly in the southern parts, and this is amplified as a bigger reduction in the runoff. The results indicate that the uncertainty sourced from the GCMs is much larger than the uncertainty in the rainfall–runoff models. The variability in the climate change impact on runoff results for one rainfall–runoff model informed by 15 GCMs (an about 28%–35% difference between the minimum and maximum results for mean annual, mean seasonal, and high runoff) is considerably larger than the variability in the results between the five rainfall–runoff models informed by 1 GCM (a less than 7% difference between the minimum and maximum results). The difference between the rainfall–runoff modeling results is larger in the drier regions for scenarios of big declines in future rainfall and in the low-flow characteristics. The rainfall–runoff modeling here considers only the runoff sensitivity to changes in the input climate data (primarily daily rainfall), and the difference between the hydrological modeling results is likely to be greater if potential changes in the climate–runoff relationship in a warmer and higher CO2 environment are modeled.

Using machine learning to downscale simulations of climate change induced changes to the shallow groundwater table

2021 ◽  
Author(s):  
Raphael Schneider ◽  
Hans Jørgen Henriksen ◽  
Julian Koch ◽  
Lars Troldborg ◽  
Simon Stisen
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Hydrological Model ◽  
Shallow Groundwater ◽  
Groundwater Table ◽  
Training Data ◽  
Computational Time ◽  
Hydrological Models ◽  
Shallow Groundwater Table ◽  
Induced Changes

<p>The DK-model (https://vandmodel.dk/in-english) is a national water resource model, covering all of Denmark. Its core is a distributed, integrated surface-subsurface hydrological model in 500m horizontal resolution. With recent efforts, a version at a higher resolution of 100m was created. The higher resolution was, amongst others, desired by end-users and to better represent surface and surface-near phenomena such as the location of the uppermost groundwater table. Being presently located close to the surface across substantial parts of the country and partly expected to rise, the groundwater table and its future development due to climate change is of great interest. A rising groundwater table is associated with potential risks for infrastructure, agriculture and ecosystems. However, the 25-fold jump in resolution of the hydrological model also increases the computational effort. Hence, it was deemed unfeasible to run the 100m resolution hydrological model nation-wide with an ensemble of climate models to evaluate climate change impact. The full ensemble run could only be performed with the 500m version of the model. To still produce the desired outputs at 100m resolution, a downscaling method was applied as described in the following.</p><p>Five selected subcatchment models covering around 9% of Denmark were run with five selected climate models at 100m resolution (using less than 3% of the computational time for hydrological models compared to a national, full ensemble run at 100m). Using the simulated changes at 100m resolution from those models as training data, combined with a set of covariates including the simulated changes in 500m resolution, Random Forest (RF) algorithms were trained to downscale simulated changes from 500m to 100m.</p><p>Generalizing the trained RF algorithms, Denmark-wide maps of expected climate change induced changes to the shallow groundwater table at 100m resolution were modelled. To verify the downscaling results, amongst others, the RF algorithms were successfully validated against results from a sixth hydrological subcatchment model at 100m resolution not used in training the algorithms.</p><p>The experience gained also opens for various other applications of similar algorithms where computational limitations inhibit running distributed hydrological models at fine resolutions: The results suggest the potential to downscale other model outputs that are desired at fine resolutions.</p>

Can hydrological model identifiability be improved? Stress-testing the concept of stochastic calibration

2021 ◽  
Author(s):  
Vasileios Kourakos ◽  
Andreas Efstratiadis ◽  
Ioannis Tsoukalas
Keyword(s):  
Dependence Structure ◽  
Hydrological Models ◽  
Proof Of Concept ◽  
Rainfall Runoff ◽  
Predictive Capacity ◽  
New Approach ◽  
Stochastic Inputs ◽  
Promising Strategy ◽  
Synthetic Time Series ◽  
Temporal Windows

<p>Hydrological calibrations with historical data are often deemed insufficient for deducing safe estimations about a model structure that imitates, as closely as possible, the anticipated catchment behaviour. Ιn order to address this issue, we investigate a promising strategy, using as drivers synthetic time series, which preserve the probabilistic properties and dependence structure of the observed data. The key idea is calibrating a model on the basis of synthetic rainfall-runoff data, and validating against the full observed data sample. To this aim, we employed a proof of concept on few representative catchments, by testing several lumped conceptual hydrological models with alternative parameterizations and across two time-scales, monthly and daily. Next, we attempted to reinforce the validity of the recommended methodology by employing monthly stochastic calibrations in 100 MOPEX catchments. As before, a number of different hydrological models were used, for the purpose of proving that calibration with stochastic inputs is independent of the chosen model. The results highlight that in most cases the new approach leads to stronger parameter identifiability and stable predictive capacity across different temporal windows, since the model is trained over much extended hydroclimatic conditions.</p>

scholarly journals Comparative evaluation of conceptual and physical rainfall–runoff models

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
R. K. Jaiswal ◽  
Sohrat Ali ◽  
Birendra Bharti
Keyword(s):  
Water Balance ◽  
Swat Model ◽  
Water Balance Model ◽  
Optimization Techniques ◽  
Balance Model ◽  
Series Data ◽  
Climatic Data ◽  
Rainfall Runoff ◽  
Tank Model ◽  
Australian Water

AbstractThe design of water resource structures needs long-term runoff data which is always a problem in developing countries due to the involvement of huge cost of operation and maintenance of gauge discharge sites. Hydrological modelling provides a solution to this problem by developing relationship between different hydrological processes. In the past, several models have been propagated to model runoff using simple empirical relationships between rainfall and runoff to complex physical model using spatially distributed information and time series data of climatic variables. In the present study, an attempt has been made to compare two conceptual models including TANK and Australian water balance model (AWBM) and a physically distributed but lumped on HRUs scale SWAT model for Tandula basin of Chhattisgarh (India). The daily data of reservoirs levels, evaporation, seepage and releases were used in a water balance model to compute runoff from the catchment for the period of 24 years from 1991 to 2014. The rainfall runoff library (RRL) tool was used to set up TANK model and AWBM using auto and genetic algorithm, respectively, and SWAT model with SWATCUP application using sequential uncertainty fitting as optimization techniques. Several tests for goodness of fit have been applied to compare the performance of conceptual and semi-distributed physical models. The analysis suggested that TANK model of RRL performed most appropriately among all the models applied in the analysis; however, SWAT model having spatial and climatic data can be used for impact assessment of change due to climate and land use in the basin.

scholarly journals Preliminary Study of Computational Time Steps in a Physically Based Distributed Rainfall–Runoff Model

Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1269 ◽  
Author(s):  
Yun Choi ◽  
Mun-Ju Shin ◽  
Kyung Kim
Keyword(s):  
Optimal Parameter ◽  
Computational Time ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Stream Network ◽  
Time Step ◽  
Simulation Results ◽  
Rainfall Runoff Model ◽  
Parameter Values ◽  
Runoff Model

The choice of the computational time step (dt) value and the method for setting dt can have a bearing on the accuracy and performance of a simulation, and this effect has not been comprehensively researched across different simulation conditions. In this study, the effects of the fixed time step (FTS) method and the automatic time step (ATS) method on the simulated runoff of a distributed rainfall–runoff model were compared. The results revealed that the ATS method had less peak flow variability than the FTS method for the virtual catchment. In the FTS method, the difference in time step had more impact on the runoff simulation results than the other factors such as differences in the amount of rainfall, the density of the stream network, or the spatial resolution of the input data. Different optimal parameter values according to the computational time step were found when FTS and ATS were used in a real catchment, and the changes in the optimal parameter values were smaller in ATS than in FTS. The results of our analyses can help to yield reliable runoff simulation results.

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