Submit to Water Review for Water Edit a Special Issue

Journal Browser

► Journal Browser

Special Issue "Use of Meta-Heuristic Techniques in Rainfall-Runoff Modelling"

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".

Deadline for manuscript submissions: closed (20 December 2015) | Viewed by 79802

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special issue Use of Meta-Heuristic Techniques in Rainfall-Runoff Modelling book cover image

Share This Special Issue

Special Issue Editor

Prof. Dr. Kwok-wing Chau

E-Mail Website
Guest Editor

Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong
Interests: artificial intelligence; hydrology; soft computing; water quality; meta-heuristic algorithm; hydrodynamic; rainfall; runoff
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Each year, extreme floods, which appear to be occurring more frequently in recent years (owing to climate change), lead to enormous economic damage and human suffering around the world. It is therefore imperative to be able to accurately predict both the occurrence time and magnitude of peak discharge in advance of an impending flood event. The use of meta-heuristic techniques in rainfall-runoff modeling is a growing field of endeavor in water resources management. These techniques can be used to calibrate data-driven rainfall-runoff models to improve forecasting accuracies. This Special Issue of the journal, Water, is designed to fill the analytical void by including papers concerning advances in the contemporary use of meta-heuristic techniques in rainfall-runoff modeling. The information and analyses are intended to contribute to the development and implementation of effective hydrological predictions, and thus, of appropriate precautionary measures.

Prof. Dr. Kwok-Wing Chau
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

rainfall-runoff
meta-heuristic
data-driven
modeling
flood
prediction

Published Papers (15 papers)

Download All Papers

Editorial

Jump to: Research

Open AccessEditorial

Use of Meta-Heuristic Techniques in Rainfall-Runoff Modelling

Kwok-wing Chau

Water 2017, 9(3), 186; https://doi.org/10.3390/w9030186 - 06 Mar 2017

Cited by 96 | Viewed by 5146

Abstract

Each year, extreme floods, which appear to be occurring more frequently in recent years (owing to climate change), lead to enormous economic damage and human suffering around the world. It is therefore imperative to be able to accurately predict both the occurrence time and magnitude of peak discharge in advance of an impending flood event. The use of meta-heuristic techniques in rainfall-runoff modeling is a growing field of endeavor in water resources management. These techniques can be used to calibrate data-driven rainfall-runoff models to improve forecasting accuracies. This Special Issue of the journal Water is designed to fill the analytical void by including papers concerning advances in the contemporary use of meta-heuristic techniques in rainfall-runoff modeling. The information and analyses can contribute to the development and implementation of effective hydrological predictions, and thus, of appropriate precautionary measures. Full article

(This article belongs to the Special Issue Use of Meta-Heuristic Techniques in Rainfall-Runoff Modelling)

Research

Jump to: Editorial

Open AccessArticle

Regional Patterns of Baseflow Variability in Mexican Subwatersheds

and

Water 2016, 8(3), 98; https://doi.org/10.3390/w8030098 - 11 Mar 2016

Cited by 1 | Viewed by 5068

Abstract

One of the challenges faced by subwatershed hydrology is the discovery of patterns associated with climate and landscape variability with the available data. This study has three objectives: (1) to evaluate the annual recession curves; (2) to relate the recession parameter (RP) with physiographic characteristics of 21 Mexican subwatersheds in different climate regions; and (3) to formulate a Baseflow (BF) model based on a top-down approach. The RP was calibrated utilizing the largest magnitude curves. The RP was related to topographical, climate and soil variables. A non-linear model was employed to separate the baseflow which considers RP as a recharge rate. Our results show that RP increases with longitude and decreases with latitude. RP displayed a sustained non-linear behavior determined by precipitation rate and evapotranspiration replace the P/E parts with \(\frac{P}{E}\) over years and subwatersheds. The model was fit to a parameter concurrent with invariance and space-time symmetry conditions. The dispersion of our model was associated with the product of replace the P/E parts with \(\frac{P}{E}\) by the aquifer’s transmissivity. We put forward a generalized baseflow model, which made the discrimination of baseflow from direct flow in subwatersheds possible. The proposed model involves the recharge-storage-discharge relation and could be implemented in basins where there are no suitable ground-based data. Full article

(This article belongs to the Special Issue Use of Meta-Heuristic Techniques in Rainfall-Runoff Modelling)

► Show Figures

Graphical abstract

Open AccessArticle

A CN-Based Ensembled Hydrological Model for Enhanced Watershed Runoff Prediction

Muhammad Ajmal

Taj Ali Khan

and

Tae-Woong Kim

Water 2016, 8(1), 20; https://doi.org/10.3390/w8010020 - 15 Jan 2016

Cited by 11 | Viewed by 5040

Abstract

A major structural inconsistency of the traditional curve number (CN) model is its dependence on an unstable fixed initial abstraction, which normally results in sudden jumps in runoff estimation. Likewise, the lack of pre-storm soil moisture accounting (PSMA) procedure is another inherent limitation of the model. To circumvent those problems, we used a variable initial abstraction after ensembling the traditional CN model and a French four-parameter (GR4J) model to better quantify direct runoff from ungauged watersheds. To mimic the natural rainfall-runoff transformation at the watershed scale, our new parameterization designates intrinsic parameters and uses a simple structure. It exhibited more accurate and consistent results than earlier methods in evaluating data from 39 forest-dominated watersheds, both for small and large watersheds. In addition, based on different performance evaluation indicators, the runoff reproduction results show that the proposed model produced more consistent results for dry, normal, and wet watershed conditions than the other models used in this study. Full article

(This article belongs to the Special Issue Use of Meta-Heuristic Techniques in Rainfall-Runoff Modelling)

► Show Figures

Figure 1

Open AccessArticle

Application of the Entropy Method to Select Calibration Sites for Hydrological Modeling

and

Water 2015, 7(12), 6719-6735; https://doi.org/10.3390/w7126652 - 26 Nov 2015

Cited by 8 | Viewed by 4877

Abstract

Selecting an optimum number of calibration sites for hydrological modeling is challenging. Modelers often spend a lot of time and effort on trial and error because there is no guide. We propose a novel entropy method to automate the selection of the optimum combination of calibration sites. To illustrate, the proposed entropy method is applied using discharge data from one river basin in Korea. First, different combinations of discharge-gauging sites were grouped based on the maximum information estimated by the entropy method. Then, a hydrological model was set up for the study basin and was calibrated by estimating optimal parameters using a genetic algorithm at the discharge-gauging sites. The calibration result confirmed that the model’s performance was best when it was calibrated using the site number and combination suggested by the entropy method. In addition, the entropy method was useful in reducing the time and effort of model calibration. Therefore, we suggest and confirm the applicability of the entropy method in selecting calibration sites for hydrological modeling. Full article

(This article belongs to the Special Issue Use of Meta-Heuristic Techniques in Rainfall-Runoff Modelling)

► Show Figures

Figure 1

Open AccessArticle

Coupled Heuristic Prediction of Long Lead-Time Accumulated Total Inflow of a Reservoir during Typhoons Using Deterministic Recurrent and Fuzzy Inference-Based Neural Network

Chien-Lin Huang

Nien-Sheng Hsu

and

Chih-Chiang Wei

Water 2015, 7(11), 6516-6550; https://doi.org/10.3390/w7116516 - 17 Nov 2015

Cited by 5 | Viewed by 4583

Abstract

This study applies Real-Time Recurrent Learning Neural Network (RTRLNN) and Adaptive Network-based Fuzzy Inference System (ANFIS) with novel heuristic techniques to develop an advanced prediction model of accumulated total inflow of a reservoir in order to solve the difficulties of future long lead-time highly varied uncertainty during typhoon attacks while using a real-time forecast. For promoting the temporal-spatial forecasted precision, the following original specialized heuristic inputs were coupled: observed-predicted inflow increase/decrease (OPIID) rate, total precipitation, and duration from current time to the time of maximum precipitation and direct runoff ending (DRE). This study also investigated the temporal-spatial forecasted error feature to assess the feasibility of the developed models, and analyzed the output sensitivity of both single and combined heuristic inputs to determine whether the heuristic model is susceptible to the impact of future forecasted uncertainty/errors. Validation results showed that the long lead-time–predicted accuracy and stability of the RTRLNN-based accumulated total inflow model are better than that of the ANFIS-based model because of the real-time recurrent deterministic routing mechanism of RTRLNN. Simulations show that the RTRLNN-based model with coupled heuristic inputs (RTRLNN-CHI, average error percentage (AEP)/average forecast lead-time (AFLT): 6.3%/49 h) can achieve better prediction than the model with non-heuristic inputs (AEP of RTRLNN-NHI and ANFIS-NHI: 15.2%/31.8%) because of the full consideration of real-time hydrological initial/boundary conditions. Besides, the RTRLNN-CHI model can promote the forecasted lead-time above 49 h with less than 10% of AEP which can overcome the previous forecasted limits of 6-h AFLT with above 20%–40% of AEP. Full article

(This article belongs to the Special Issue Use of Meta-Heuristic Techniques in Rainfall-Runoff Modelling)

► Show Figures

Figure 1

Open AccessArticle

Estimation of Rainfall Associated with Typhoons over the Ocean Using TRMM/TMI and Numerical Models

Nan-Ching Yeh

Chung-Chih Liu

and

Wann-Jin Chen

Water 2015, 7(11), 6017-6038; https://doi.org/10.3390/w7116017 - 03 Nov 2015

Cited by 4 | Viewed by 4181

Abstract

This study quantitatively estimated the precipitation associated with a typhoon in the northwestern Pacific Ocean by using a physical algorithm which included the Weather Research and Forecasting model, Radiative Transfer for TIROS Operational Vertical Sounder model, and data from the Tropical Rainfall Measuring Mission (TRMM)/TRMM Microwave Imager (TMI) and TRMM/Precipitation Radar (PR). First, a prior probability distribution function (PDF) was constructed using over three million rain rate retrievals from the TRMM/PR data for the period 2002–2010 over the northwestern Pacific Ocean. Subsequently, brightness temperatures for 15 typhoons that occurred over the northwestern Pacific Ocean were simulated using a microwave radiative transfer model and a conditional PDF was obtained for these typhoons. The aforementioned physical algorithm involved using a posterior PDF. A posterior PDF was obtained by combining the prior and conditional PDFs. Finally, the rain rate associated with a typhoon was estimated by inputting the observations of the TMI (attenuation indices at 10, 19, 37 GHz) into the posterior PDF (lookup table). Results based on rain rate retrievals indicated that rainband locations with the heaviest rainfall showed qualitatively similar horizontal distributions. The correlation coefficient and root-mean-square error of the rain rate estimation were 0.63 and 4.45 mm·h⁻¹, respectively. Furthermore, the correlation coefficient and root-mean-square error for convective rainfall were 0.78 and 7.25 mm·h⁻¹, respectively, and those for stratiform rainfall were 0.58 and 9.60 mm·h⁻¹, respectively. The main contribution of this study is introducing an approach to quickly and accurately estimate the typhoon precipitation, and remove the need for complex calculations. Full article

(This article belongs to the Special Issue Use of Meta-Heuristic Techniques in Rainfall-Runoff Modelling)

► Show Figures

Figure 1

Open AccessArticle

An Hourly Streamflow Forecasting Model Coupled with an Enforced Learning Strategy

Ming-Chang Wu

and

Gwo-Fong Lin

Water 2015, 7(11), 5876-5895; https://doi.org/10.3390/w7115876 - 28 Oct 2015

Cited by 19 | Viewed by 4405

Abstract

Floods, one of the most significant natural hazards, often result in loss of life and property. Accurate hourly streamflow forecasting is always a key issue in hydrology for flood hazard mitigation. To improve the performance of hourly streamflow forecasting, a methodology concerning the development of neural network (NN) based models with an enforced learning strategy is proposed in this paper. Firstly, four different NNs, namely back propagation network (BPN), radial basis function network (RBFN), self-organizing map (SOM), and support vector machine (SVM), are used to construct streamflow forecasting models. Through the cross-validation test, NN-based models with superior performance in streamflow forecasting are detected. Then, an enforced learning strategy is developed to further improve the performance of the superior NN-based models, i.e., SOM and SVM in this study. Finally, the proposed flow forecasting model is obtained. Actual applications are conducted to demonstrate the potential of the proposed model. Moreover, comparison between the NN-based models with and without the enforced learning strategy is performed to evaluate the effect of the enforced learning strategy on model performance. The results indicate that the NN-based models with the enforced learning strategy indeed improve the accuracy of hourly streamflow forecasting. Hence, the presented methodology is expected to be helpful for developing improved NN-based streamflow forecasting models. Full article

(This article belongs to the Special Issue Use of Meta-Heuristic Techniques in Rainfall-Runoff Modelling)

► Show Figures

Figure 1

Open AccessArticle

Applying a Correlation Analysis Method to Long-Term Forecasting of Power Production at Small Hydropower Plants

and

Water 2015, 7(9), 4806-4820; https://doi.org/10.3390/w7094806 - 02 Sep 2015

Cited by 10 | Viewed by 5156

Abstract

Forecasting long-term power production of small hydropower (SHP) plants is of great significance for coordinating with large-medium hydropower (LHP) plants. Accurate forecasting can solve the problems of waste-water and abandoned electricity and ensure the safe operation of the power system. However, it faces a series of challenges, such as lack of sufficient data, uncertainty of power generation, no regularity of a single station and poor forecasting models. It is difficult to establish a forecasting model based on classical and mature prediction models. Therefore, this paper introduces a correlation analysis method for forecasting power production of SHP plants. By analyzing the correlation between SHP and LHP plants, a safe conclusion can be drawn that the power production of SHP plants show similar interval inflow to LHP plants in the same region. So a regression model is developed to forecast power production of SHP plants by using the forecasting inflow values of LHP plants. Taking the SHP plants in Yunnan province as an example, the correlation between SHP and LHP plants in a district or county are analyzed respectively. The results show that this correlation method is feasible. The proposed forecasting method has been successfully applied to forecast long-term power production of SHP plants in the 13 districts of the Yunnan Power Grid. From the results, the rationality, accuracy and generality of this method have been verified. Full article

(This article belongs to the Special Issue Use of Meta-Heuristic Techniques in Rainfall-Runoff Modelling)

► Show Figures

Figure 1

Open AccessArticle

Heuristic Methods for Reservoir Monthly Inflow Forecasting: A Case Study of Xinfengjiang Reservoir in Pearl River, China

and

Water 2015, 7(8), 4477-4495; https://doi.org/10.3390/w7084477 - 17 Aug 2015

Cited by 37 | Viewed by 5860

Abstract

Reservoir monthly inflow is rather important for the security of long-term reservoir operation and water resource management. The main goal of the present research is to develop forecasting models for the reservoir monthly inflow. In this paper, artificial neural networks (ANN) and support vector machine (SVM) are two basic heuristic forecasting methods, and genetic algorithm (GA) is employed to choose the parameters of the SVM. When forecasting the monthly inflow data series, both approaches are inclined to acquire relatively poor performances. Thus, based on the thought of refined prediction by model combination, a hybrid forecasting method involving a two-stage process is proposed to improve the forecast accuracy. In the hybrid method, the ANN and SVM are, first, respectively implemented to forecast the reservoir monthly inflow data. Then, the processed predictive values of both ANN and SVM are selected as the input variables of a newly-built ANN model for refined forecasting. Three models, ANN, SVM, and the hybrid method, are developed for the monthly inflow forecasting in Xinfengjiang reservoir with 71-year discharges from 1944 to 2014. The comparison of results reveal that three models have satisfactory performances in the Xinfengjiang reservoir monthly inflow prediction, and the hybrid method performs better than ANN and SVM in terms of five statistical indicators. Thus, the hybrid method is an efficient tool for the long-term operation and dispatching of Xinfengjiang reservoir. Full article

(This article belongs to the Special Issue Use of Meta-Heuristic Techniques in Rainfall-Runoff Modelling)

► Show Figures

Figure 1

Open AccessArticle

Daily Reservoir Runoff Forecasting Method Using Artificial Neural Network Based on Quantum-behaved Particle Swarm Optimization

and

Water 2015, 7(8), 4232-4246; https://doi.org/10.3390/w7084232 - 31 Jul 2015

Cited by 74 | Viewed by 7302

Abstract

Accurate daily runoff forecasting is of great significance for the operation control of hydropower station and power grid. Conventional methods including rainfall-runoff models and statistical techniques usually rely on a number of assumptions, leading to some deviation from the exact results. Artificial neural network (ANN) has the advantages of high fault-tolerance, strong nonlinear mapping and learning ability, which provides an effective method for the daily runoff forecasting. However, its training has certain drawbacks such as time-consuming, slow learning speed and easily falling into local optimum, which cannot be ignored in the real world application. In order to overcome the disadvantages of ANN model, the artificial neural network model based on quantum-behaved particle swarm optimization (QPSO), ANN-QPSO for short, is presented for the daily runoff forecasting in this paper, where QPSO was employed to select the synaptic weights and thresholds of ANN, while ANN was used for the prediction. The proposed model can combine the advantages of both QPSO and ANN to enhance the generalization performance of the forecasting model. The methodology is assessed by using the daily runoff data of Hongjiadu reservoir in southeast Guizhou province of China from 2006 to 2014. The results demonstrate that the proposed approach achieves much better forecast accuracy than the basic ANN model, and the QPSO algorithm is an alternative training technique for the ANN parameters selection. Full article

(This article belongs to the Special Issue Use of Meta-Heuristic Techniques in Rainfall-Runoff Modelling)

► Show Figures

Figure 1

Open AccessArticle

Daily Runoff Forecasting Model Based on ANN and Data Preprocessing Techniques

and

Water 2015, 7(8), 4144-4160; https://doi.org/10.3390/w7084144 - 28 Jul 2015

Cited by 15 | Viewed by 5461

Abstract

There are many models that have been used to simulate the rainfall-runoff relationship. The artificial neural network (ANN) model was selected to investigate an approach of improving daily runoff forecasting accuracy in terms of data preprocessing. Singular spectrum analysis (SSA) as one data preprocessing technique was adopted to deal with the model inputs and the SSA-ANN model was developed. The proposed model was compared with the original ANN model without data preprocessing and a nonlinear perturbation model (NLPM) based on ANN, i.e., the NLPM-ANN model. Eight watersheds were selected for calibrating and testing these models. Comparative study shows that the learning and training ability of ANN models can be improved by SSA and NLPM techniques significantly, and the performance of the SSA-ANN model is much better than the NLPM-ANN model, with high foresting accuracy. The SSA-ANN1 model, which only considers rainfall as model input, was compared with the SSA-ANN2 model, which considers both rainfall and previous runoff as model inputs. It is shown that the Nash-Sutcliffe criterion of the SSA-ANN2 model is much higher than that of the SSA-ANN1 model, which means that the proper selection of previous runoff data as rainfall-runoff model inputs can significantly improve model performance since they usually are highly auto-correlated. Full article

(This article belongs to the Special Issue Use of Meta-Heuristic Techniques in Rainfall-Runoff Modelling)

► Show Figures

Figure 1

Open AccessArticle

Parameter Automatic Calibration Approach for Neural-Network-Based Cyclonic Precipitation Forecast Models

Der-Chang Lo

Chih-Chiang Wei

and

En-Ping Tsai

Water 2015, 7(7), 3963-3977; https://doi.org/10.3390/w7073963 - 17 Jul 2015

Cited by 21 | Viewed by 5694

Abstract

This paper presents artificial neural network (ANN)-based models for forecasting precipitation, in which the training parameters are adjusted using a parameter automatic calibration (PAC) approach. A classical ANN-based model, the multilayer perceptron (MLP) neural network, was used to verify the utility of the proposed ANN–PAC approach. The MLP-based ANN used the learning rate, momentum, and number of neurons in the hidden layer as its major parameters. The Dawu gauge station in Taitung, Taiwan, was the study site, and observed typhoon characteristics and ground weather data were the study data. The traditional multiple linear regression model was selected as the benchmark for comparing the accuracy of the ANN–PAC model. In addition, two MLP ANN models based on a trial-and-error calibration method, ANN–TRI1 and ANN–TRI2, were realized by manually tuning the parameters. We found the results yielded by the ANN–PAC model were more reliable than those yielded by the ANN–TRI1, ANN–TRI2, and traditional regression models. In addition, the computing efficiency of the ANN–PAC model decreased with an increase in the number of increments within the parameter ranges because of the considerably increased computational time, whereas the prediction errors decreased because of the model’s increased capability of identifying optimal solutions. Full article

(This article belongs to the Special Issue Use of Meta-Heuristic Techniques in Rainfall-Runoff Modelling)

► Show Figures

Figure 1

Open AccessArticle

Spatial Disaggregation of Areal Rainfall Using Two Different Artificial Neural Networks Models

Sungwon Kim

and

Vijay P. Singh

Water 2015, 7(6), 2707-2727; https://doi.org/10.3390/w7062707 - 05 Jun 2015

Cited by 15 | Viewed by 6135

Abstract

The objective of this study is to develop artificial neural network (ANN) models, including multilayer perceptron (MLP) and Kohonen self-organizing feature map (KSOFM), for spatial disaggregation of areal rainfall in the Wi-stream catchment, an International Hydrological Program (IHP) representative catchment, in South Korea. A three-layer MLP model, using three training algorithms, was used to estimate areal rainfall. The Levenberg–Marquardt training algorithm was found to be more sensitive to the number of hidden nodes than were the conjugate gradient and quickprop training algorithms using the MLP model. Results showed that the networks structures of 11-5-1 (conjugate gradient and quickprop) and 11-3-1 (Levenberg-Marquardt) were the best for estimating areal rainfall using the MLP model. The networks structures of 1-5-11 (conjugate gradient and quickprop) and 1-3-11 (Levenberg–Marquardt), which are the inverse networks for estimating areal rainfall using the best MLP model, were identified for spatial disaggregation of areal rainfall using the MLP model. The KSOFM model was compared with the MLP model for spatial disaggregation of areal rainfall. The MLP and KSOFM models could disaggregate areal rainfall into individual point rainfall with spatial concepts. Full article

(This article belongs to the Special Issue Use of Meta-Heuristic Techniques in Rainfall-Runoff Modelling)

► Show Figures

Figure 1

Open AccessArticle

Subgrid Parameterization of the Soil Moisture Storage Capacity for a Distributed Rainfall-Runoff Model

and

Water 2015, 7(6), 2691-2706; https://doi.org/10.3390/w7062691 - 29 May 2015

Cited by 8 | Viewed by 5029

Abstract

Spatial variability plays an important role in nonlinear hydrologic processes. Due to the limitation of computational efficiency and data resolution, subgrid variability is usually assumed to be uniform for most grid-based rainfall-runoff models, which leads to the scale-dependence of model performances. In this paper, the scale effect on the Grid-Xinanjiang model was examined. The bias of the estimation of precipitation, runoff, evapotranspiration and soil moisture at the different grid scales, along with the scale-dependence of the effective parameters, highlights the importance of well representing the subgrid variability. This paper presents a subgrid parameterization method to incorporate the subgrid variability of the soil storage capacity, which is a key variable that controls runoff generation and partitioning in the Grid-Xinanjiang model. In light of the similar spatial pattern and physical basis, the soil storage capacity is correlated with the topographic index, whose spatial distribution can more readily be measured. A beta distribution is introduced to represent the spatial distribution of the soil storage capacity within the grid. The results derived from the Yanduhe Basin show that the proposed subgrid parameterization method can effectively correct the watershed soil storage capacity curve. Compared to the original Grid-Xinanjiang model, the model performances are quite consistent at the different grid scales when the subgrid variability is incorporated. This subgrid parameterization method reduces the recalibration necessity when the Digital Elevation Model (DEM) resolution is changed. Moreover, it improves the potential for the application of the distributed model in the ungauged basin. Full article

(This article belongs to the Special Issue Use of Meta-Heuristic Techniques in Rainfall-Runoff Modelling)

► Show Figures

Figure 1

Open AccessArticle

Grey Forecast Rainfall with Flow Updating Algorithm for Real-Time Flood Forecasting

Jui-Yi Ho

and

Kwan Tun Lee

Water 2015, 7(5), 1840-1865; https://doi.org/10.3390/w7051840 - 27 Apr 2015

Cited by 15 | Viewed by 4985

Abstract

The dynamic relationship between watershed characteristics and rainfall-runoff has been widely studied in recent decades. Since watershed rainfall-runoff is a non-stationary process, most deterministic flood forecasting approaches are ineffective without the assistance of adaptive algorithms. The purpose of this paper is to propose an effective flow forecasting system that integrates a rainfall forecasting model, watershed runoff model, and real-time updating algorithm. This study adopted a grey rainfall forecasting technique, based on existing hourly rainfall data. A geomorphology-based runoff model can be used for simulating impacts of the changing geo-climatic conditions on the hydrologic response of unsteady and non-linear watershed system, and flow updating algorithm were combined to estimate watershed runoff according to measured flow data. The proposed flood forecasting system was applied to three watersheds; one in the United States and two in Northern Taiwan. Four sets of rainfall-runoff simulations were performed to test the accuracy of the proposed flow forecasting technique. The results indicated that the forecast and observed hydrographs are in good agreement for all three watersheds. The proposed flow forecasting system could assist authorities in minimizing loss of life and property during flood events. Full article

(This article belongs to the Special Issue Use of Meta-Heuristic Techniques in Rainfall-Runoff Modelling)

► Show Figures