scholarly journals Application of PCSWMM for the 1-D and 1-D–2-D Modeling of Urban Flooding in Damansara Catchment, Malaysia

2021 ◽  
Vol 11 (19) ◽  
pp. 9300
Author(s):  
Lariyah Mohd Sidek ◽  
Lloyd Hock Chye Chua ◽  
Aqilah Syasya Mohd Azizi ◽  
Hidayah Basri ◽  
Aminah Shakirah Jaafar ◽  
...  
Keyword(s):  
River Flow ◽  
Flood Hazard ◽  
Developed Countries ◽  
Coefficient Of Determination ◽  
Efficiency Coefficient ◽  
Urban Flood ◽  
Storm Water Management Model ◽  
Flood Depth ◽  
Comparison Results ◽  
Model Efficiency

Coupled with climate change, the urbanization-driven increase in the frequency and intensity of floods can be seen in both developing and developed countries, and Malaysia is no exemption. As part of flood hazard mitigation, this study aimed to simulate the urban flood scenarios in Malaysia’s urbanized catchments. The flood simulation was performed using the Personal Computer Storm Water Management Model (PCSWMM) modeling of the Damansara catchment as a case study. An integrated hydrologic-hydraulic model was developed for the 1-D river flow modeling and 1-D–2-D drainage overflow modeling. The reliability of the 1-D river flow model was confirmed through the calibration and validation, in which the water level in TTDI Jaya was satisfactorily predicted, supported by the coefficient of determination (R2), Nash–Sutcliffe model efficiency coefficient (NSE), and relative error (RE). The performance of the 1-D–2-D model was further demonstrated based on the flood depth, extent, and risk caused by the drainage overflow. Two scenarios were tested, and the comparison results showed that the current drainage effectively reduced the drainage overflow due to the increased size of drains compared to the historic drainage in 2015. The procedure and findings of this study could serve as references for the application in flood mitigation planning worldwide, especially for developing countries.

scholarly journals Predicting coastal urban floods using artificial neural network: The case study of Macau, China

2021 ◽  
Vol 11 (10) ◽  
Author(s):  
Weijun Dai ◽  
Zhiming Cai
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Flood Hazard ◽  
Scale Model ◽  
Coefficient Of Determination ◽  
Urban Flood ◽  
Flood Depth ◽  
Flood Disasters ◽  
Artificial Neural

AbstractUsing data-driven models to predict floods in advance is one of the current effective methods and hot researches to reduce urban flood disasters. In order to improve the prediction accuracy, it is necessary to select the appropriate flood hazard factors and the number of training samples to construct the prediction model. In our current research, an artificial neural network (i.e., the back-propagation neural network, BPNN) model was developed to predict the flood depth in the next hour. A case study of the urban flood during six typhoons in Macau of China was conducted to prove the performance of the proposed model. The flood depth was collected as output; after analyzing their correlation to the flood typhoon optimum track, urban weather, tides, geographic height and water depth increment of the submerged area were used as input. As a result, four models trained with different sample numbers were developed for training and testing. The model performances were examined using average absolute error, root mean square error and the coefficient of determination. The results show that in this case study, the 30-min scale model provides reliable predictions and can provide useful decision support for the prevention and mitigation of flood disasters in coastal urban.

Study on the Application of Clustering Method in the Determination of Uncertainty Parameters of SWMM Model

2021 ◽  
Author(s):  
Chengshuai Liu ◽  
Bingyan Ma ◽  
Caihong Hu ◽  
Qiang Wu ◽  
Yue Sun ◽  
...  
Keyword(s):  
Absolute Error ◽  
Coefficient Of Determination ◽  
Efficiency Coefficient ◽  
Clustering Method ◽  
Urban Flood ◽  
Storm Water Management Model ◽  
Tuning Method ◽  
Manual Adjustment ◽  
Simulation Results ◽  
Uncertainty Parameters

Abstract Storm Water Management Model (SWMM) is one of the most commonly used models in urban flood simulation. However, because the calibration and verification of the model's uncertainty parameters are extremely dependent on the measured flood data, it is difficult to apply the model in areas lacking data. This study proposes a parameter sample clustering method based on peer research results to determine the uncertainty parameters of SWMM, and compares the simulation results with the simulation results of the manual adjustment method based on measured data. The research shows that the Absolute error (AE), Relative error (RE), Nash efficiency coefficient (NSE), and Coefficient of determination (R2) of the water depth simulated by the parameter sample clustering method are 0.040m, 9.08%, 0.949, 0.967 compared with the measured value, respectively. The value of AE, RE, NSE, and R2 of the manual tuning method during the calibration simulation period are 0.066m, 15.95%, 0.881 and 0.924, respectively. Therefore, the parameter sample clustering method has a better simulation effect than manual tuning method, and it can be further promoted in areas without flood data.

scholarly journals Evaluating Hydrological Models for Deriving Water Resources in Peninsular Spain

2019 ◽  
Vol 11 (10) ◽  
pp. 2872 ◽  
Author(s):  
Julio Pérez-Sánchez ◽  
Javier Senent-Aparicio ◽  
Francisco Segura-Méndez ◽  
David Pulido-Velazquez ◽  
Raghavan Srinivasan
Keyword(s):  
Aridity Index ◽  
Information Criterion ◽  
Climatic Conditions ◽  
Coefficient Of Determination ◽  
Efficiency Coefficient ◽  
Run Off ◽  
Margin Of Error ◽  
Model Efficiency ◽  
Flow Duration Curves ◽  
Peninsular Spain

Water availability is essential for the appropriate analysis of its sustainable management. We performed a comparative study of six hydrological balance models (Témez, ABCD, GR2M, AWBM, GUO-5p, and Thornthwaite-Mather) in several basins with different climatic conditions within Spain in the 1977–2010 period. We applied six statistical indices to compare the results of the models: the Akaike information criterion (AIC), the Bayesian information criterion (BIC), Nash–Sutcliffe model efficiency coefficient (NSE), coefficient of determination (R2), percent bias (PBIAS), and the relative error between observed and simulated run-off volumes (REV). Furthermore, we applied the FITEVAL software to determine the uncertainty of the model. The results show that when the catchments are more humid the obtained results are better. The GR2M model gave the best fit in peninsular Spain in a UNEP aridity index framework above 1, and NSE values above 0.75 in a 95% confidence interval classify GR2M as very good for humid watersheds. The use of REV is also a key index in the assessment of the margin of error. Flow duration curves show good performance in the probabilities of exceedance lower than 80% in wet watersheds and deviations in low streamflows account for less than 5% of the total streamflow.

scholarly journals Soybean yield in the Matopiba region under climate changes

2020 ◽  
Vol 24 (1) ◽  
pp. 8-14
Author(s):  
Vicente de P. R. da Silva ◽  
Roberta A. e Silva ◽  
Girlene F. Maciel ◽  
Enio P. de Souza ◽  
Célia C. Braga ◽  
...  
Keyword(s):  
Climate Models ◽  
Co2 Concentration ◽  
Climatic Conditions ◽  
Coefficient Of Determination ◽  
Climate Change Scenarios ◽  
Efficiency Coefficient ◽  
Soybean Yield ◽  
Aquacrop Model ◽  
Model Efficiency ◽  
Rcp 8.5

ABSTRACT The climatic conditions along the cycle are the main factors responsible for the final production of any crop. This study aimed to evaluate the current conditions and the effects of climate change scenarios on the yield of soybean grown in the Matopiba region, located between the states of Tocantins, south and northeast of Maranhão, south of Piauí and west of Bahia, Brazil. The AquaCrop model of FAO, version 5.0, was calibrated with data of 2014 and validated with those of 2016, using climate, soil and crop management parameters collected in two experimental campaigns conducted between June and October in 2014 and 2016 in Palmas, TO, Brazil. The performance of the model was evaluated using the following statistical indicators: prediction error (PE), coefficient of determination (R2), normalized root mean square error (NRMSE), Nash-Sutcliffe model efficiency coefficient (EF) and Willmott’s index of agreement (d). It was verified that the AquaCrop model underestimates soybean grain yield under severe water stress conditions throughout the growing cycle. The increase in CO2 concentration and in the air temperature, projected by the climate models HadGEM2-ES and MIROC5 under the scenario of stabilization (RCP 4.5) and the scenario of progression (RCP 8.5), have contributed to the increase in soybean yield by the end of this century.

scholarly journals Methodology for the construction of an urban flood hazard chart

RBRH ◽  
2019 ◽  
Vol 24 ◽  
Author(s):  
Rejane Cristina Siqueira ◽  
Priscilla Macedo Moura ◽  
Talita Fernanda das Graças Silva
Keyword(s):  
Flood Hazard ◽  
Economic Loss ◽  
Water Levels ◽  
Urban Flood ◽  
Loss Of Life ◽  
Storm Water Management Model ◽  
Accumulated Rainfall ◽  
Belo Horizonte ◽  
Rain Events ◽  
Precipitation Events

ABSTRACT Flood events are one of the major causes of economic loss and the loss of life worldwide. Unfortunately, their occurrence has become increasingly more frequent and of greater magnitude. In order to minimize the population’s exposure to danger, it is necessary to invest in tools that aid in the decision-making process related to urban drainage management. The present work proposes a methodology for the construction of a Flood Hazard Chart for urban watersheds. The Storm Water Management Model (SWMM) was applied to the Cachoeirinha Watershed (Belo Horizonte, Brazil). The model was calibrated and validated using precipitation data and water levels recorded in monitoring stations located in the study area. The Nash-Sutcliffe Coefficients for the calibration and validation were 0.72 and 0.70, respectively. The performance of the model was satisfactory, although the model was not able to represent the more intense rain events that led to emergency and overflow warnings. Modeling results allowed the construction of the hazard chart, which defined hazard ranges or warning levels of hazard as a function of accumulated rainfall and duration. The constructed graph was assessed from real precipitation events and proved to be valid, since most events corresponded with the defined warning levels in the chart. The Flood Hazard Chart proposed in this research is a valuable tool for flood risk management as it has the potential to reduce exposure to flood disasters.

scholarly journals High-Resolution Hydrological-Hydraulic Modeling of Urban Floods Using InfoWorks ICM

2021 ◽  
Vol 13 (18) ◽  
pp. 10259
Author(s):  
Lariyah Mohd Sidek ◽  
Aminah Shakirah Jaafar ◽  
Wan Hazdy Azad Wan Abdul Majid ◽  
Hidayah Basri ◽  
Mohammad Marufuzzaman ◽  
...  
Keyword(s):  
High Resolution ◽  
Flood Hazard ◽  
Digital Terrain Model ◽  
Hydraulic Modeling ◽  
Flood Hazards ◽  
Tropical Country ◽  
Urban Flood ◽  
Terrain Model ◽  
Flood Depth ◽  
Infoworks Icm

Malaysia, being a tropical country located near the equatorial doldrums, experiences the annual occurrence of flood hazards due to monsoon rainfalls and urban development. In recent years, environmental policies in the country have shifted towards sustainable flood risk management. As part of the development of flood forecasting and warning systems, this study presented the urban flood simulation using InfoWorks ICM hydrological−hydraulic modeling of the Damansara catchment as a case study. The response of catchments to the rainfall was modeled using the probability distributed moisture (PDM) model due to its capability for large catchments with long-term runoff prediction. The interferometric synthetic aperture radar (IFSAR) technique was used to obtain high-resolution digital terrain model (DTM) data. The calibrated and validated model was first applied to investigate the effectiveness of the existing regional ponds on flood mitigation. For a 100-year flood, the extent of flooded areas decreased from 12.41 km2 to 3.61 km2 as a result of 64-ha ponds in the catchment, which is equivalent to a 71% reduction. The flood hazard maps were then generated based on several average recurrence intervals (ARIs) and uniform rainfall depths, and the results showed that both parameters had significant influences on the magnitude of flooding in terms of flood depth and extent. These findings are important for understanding urban flood vulnerability and resilience, which could help in sustainable management planning to deal with urban flooding issues.

scholarly journals Modeling of Lake Malombe Annual Fish Landings and Catch per Unit Effort (CPUE)

Forecasting ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 39-55
Author(s):  
Rodgers Makwinja ◽  
Seyoum Mengistou ◽  
Emmanuel Kaunda ◽  
Tena Alemiew ◽  
Titus Bandulo Phiri ◽  
...  
Keyword(s):  
Time Series ◽  
Information Criterion ◽  
Coefficient Of Determination ◽  
Series Data ◽  
Efficiency Coefficient ◽  
Arima Models ◽  
Catch Per Unit Effort ◽  
Annual Fish ◽  
Unit Effort ◽  
The Mean

Forecasting, using time series data, has become the most relevant and effective tool for fisheries stock assessment. Autoregressive integrated moving average (ARIMA) modeling has been commonly used to predict the general trend for fish landings with increased reliability and precision. In this paper, ARIMA models were applied to predict Lake Malombe annual fish landings and catch per unit effort (CPUE). The annual fish landings and CPUE trends were first observed and both were non-stationary. The first-order differencing was applied to transform the non-stationary data into stationary. Autocorrelation functions (AC), partial autocorrelation function (PAC), Akaike information criterion (AIC), Bayesian information criterion (BIC), square root of the mean square error (RMSE), the mean absolute error (MAE), percentage standard error of prediction (SEP), average relative variance (ARV), Gaussian maximum likelihood estimation (GMLE) algorithm, efficiency coefficient (E2), coefficient of determination (R2), and persistent index (PI) were estimated, which led to the identification and construction of ARIMA models, suitable in explaining the time series and forecasting. According to the measures of forecasting accuracy, the best forecasting models for fish landings and CPUE were ARIMA (0,1,1) and ARIMA (0,1,0). These models had the lowest values AIC, BIC, RMSE, MAE, SEP, ARV. The models further displayed the highest values of GMLE, PI, R2, and E2. The “auto. arima ()” command in R version 3.6.3 further displayed ARIMA (0,1,1) and ARIMA (0,1,0) as the best. The selected models satisfactorily forecasted the fish landings of 2725.243 metric tons and CPUE of 0.097 kg/h by 2024.

Urban flood hazard analysis in present and future climate after statistical downscaling: a case study in Ha Tinh city, Vietnam

2021 ◽  
Vol 18 (4) ◽  
pp. 257-274
Author(s):  
T. T. A. Le ◽  
N. T. Lan-Anh ◽  
V. Daskali ◽  
B. Verbist ◽  
K. C. Vu ◽  
...  
Keyword(s):  
Statistical Downscaling ◽  
Flood Hazard ◽  
Hazard Analysis ◽  
Future Climate ◽  
Urban Flood

scholarly journals Uncertainty Assessment of Urban Hydrological Modelling from a Multiple Objective Perspective

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1393 ◽  
Author(s):  
Bo Pang ◽  
Shulan Shi ◽  
Gang Zhao ◽  
Rong Shi ◽  
Dingzhi Peng ◽  
...  
Keyword(s):  
Flood Management ◽  
Uncertainty Assessment ◽  
Multiple Objective ◽  
Hydrological Models ◽  
Validation Period ◽  
Urban Flood ◽  
Calibration Period ◽  
Storm Water Management Model ◽  
Glue Method ◽  
Generalized Likelihood Uncertainty Estimation

The uncertainty assessment of urban hydrological models is important for understanding the reliability of the simulated results. To satisfy the demand for urban flood management, we assessed the uncertainty of urban hydrological models from a multiple-objective perspective. A multiple-criteria decision analysis method, namely, the Generalized Likelihood Uncertainty Estimation-Technique for Order Preference by Similarity to Ideal Solution (GLUE-TOPSIS) was proposed, wherein TOPSIS was adopted to measure the likelihood within the GLUE framework. Four criteria describing different urban stormwater characteristics were combined to test the acceptability of the parameter sets. The TOPSIS was used to calculate the aggregate employed in the calculation of the aggregate likelihood value. The proposed method was implemented in the Storm Water Management Model (SWMM), which was applied to the Dahongmen catchment in Beijing, China. The SWMM model was calibrated and validated based on the three and two flood events respectively downstream of the Dahongmen catchment. The results showed that the GLUE-TOPSIS provided a more precise uncertainty boundary compared with the single-objective GLUE method. The band widths were reduced by 7.30 m3/s in the calibration period, and by 7.56 m3/s in the validation period. The coverages increased by 20.3% in the calibration period, and by 3.2% in the validation period. The median estimates improved, with an increase of the Nash–Sutcliffe efficiency coefficients by 1.6% in the calibration period, and by 10.0% in the validation period. We conclude that the proposed GLUE-TOPSIS is a valid approach to assess the uncertainty of urban hydrological model from a multiple objective perspective, thereby improving the reliability of model results in urban catchment.

scholarly journals Role of Runoff–Infiltration Partitioning and Resolved Overland Flow on Land–Atmosphere Feedbacks: A Case Study with the WRF-Hydro Coupled Modeling System for West Africa

2016 ◽  
Vol 17 (5) ◽  
pp. 1489-1516 ◽  
Author(s):  
Joel Arnault ◽  
Sven Wagner ◽  
Thomas Rummler ◽  
Benjamin Fersch ◽  
Jan Bliefernicht ◽  
...  
Keyword(s):  
West Africa ◽  
Overland Flow ◽  
River Flow ◽  
Efficiency Coefficient ◽  
Wet Season ◽  
Coupled Modeling ◽  
Study Region ◽  
Outer Domain ◽  
Terrestrial Water

Abstract The analysis of land–atmosphere feedbacks requires detailed representation of land processes in atmospheric models. The focus here is on runoff–infiltration partitioning and resolved overland flow. In the standard version of WRF, runoff–infiltration partitioning is described as a purely vertical process. In WRF-Hydro, runoff is enhanced with lateral water flows. The study region is the Sissili catchment (12 800 km2) in West Africa, and the study period is from March 2003 to February 2004. The WRF setup here includes an outer and inner domain at 10- and 2-km resolution covering the West Africa and Sissili regions, respectively. In this WRF-Hydro setup, the inner domain is coupled with a subgrid at 500-m resolution to compute overland and river flow. Model results are compared with TRMM precipitation, model tree ensemble (MTE) evapotranspiration, Climate Change Initiative (CCI) soil moisture, CRU temperature, and streamflow observation. The role of runoff–infiltration partitioning and resolved overland flow on land–atmosphere feedbacks is addressed with a sensitivity analysis of WRF results to the runoff–infiltration partitioning parameter and a comparison between WRF and WRF-Hydro results, respectively. In the outer domain, precipitation is sensitive to runoff–infiltration partitioning at the scale of the Sissili area (~100 × 100 km2), but not of area A (500 × 2500 km2). In the inner domain, where precipitation patterns are mainly prescribed by lateral boundary conditions, sensitivity is small, but additionally resolved overland flow here clearly increases infiltration and evapotranspiration at the beginning of the wet season when soils are still dry. The WRF-Hydro setup presented here shows potential for joint atmospheric and terrestrial water balance studies and reproduces observed daily discharge with a Nash–Sutcliffe model efficiency coefficient of 0.43.

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