scholarly journals Assessing the runoff retention of extensive green roofs using runoff coefficients and curve numbers and the impacts of substrate moisture

2020 ◽  
Vol 51 (4) ◽  
pp. 635-647
Author(s):  
Wen Liu ◽  
Qi Feng ◽  
Weiping Chen ◽  
Wei Wei
Keyword(s):  
Curve Number ◽  
Green Roofs ◽  
Runoff Coefficient ◽  
Rainfall Runoff ◽  
Initial Substrate ◽  
Linear Fit ◽  
Substrate Moisture ◽  
Structural Configurations ◽  
Semi Arid Region ◽  
Moisture Conditions

Abstract In this study, rainfall-runoff data of four green roofs with varying structural configurations under dry and wet substrates were analyzed to acquire the effective estimation for Runoff Coefficient (Cv) and Curve Number (CN) parameters. Results showed that for the dry and wet substrates, averaged runoff retention of vegetated green roofs varied from 34.7 to 48.5% and from 14.7 to 30.6%, that for bare green roofs was 64.9 and 35.1%, respectively. For dry and wet substrates, mean Cv of vegetated green roofs was 0.58 and 0.75, respectively. For vegetated green roofs under the wet substrate, average CN values ranged from 96 to 98, meanwhile for dry substrate, average CN varied from 93 to 97. For bare green roof, average CN was 93 for dry substrate and 97 for wet substrate. Predicted runoff using the SCS-CN method exhibited a good linear fit with the observed runoff of green roofs. A significantly positive relationship was found between initial substrate moisture and runoff coefficient as well as CNs. The drier initial substrate moisture conditions corresponded to the lower runoff coefficient and curve numbers. These results would facilitate the proper use of estimated Cv and CN values of green roofs for urban stormwater management in a semi-arid region.

scholarly journals Sara4r: an R graphical user interface (GUI) to estimate watershed surface runoff applying the NRCS – curve number method

2020 ◽  
Author(s):  
Rafael Hernández-Guzmán ◽  
Arturo Ruiz-Luna ◽  
Eduardo Mendoza
Keyword(s):  
User Interface ◽  
Graphical User Interface ◽  
Modular Design ◽  
Curve Number ◽  
Land Use Land Cover ◽  
Rainfall Runoff ◽  
R Software ◽  
Antecedent Moisture ◽  
Curve Number Method ◽  
Moisture Conditions

Abstract This paper introduces a graphical user interface (GUI) for the R software that allows calculating the rainfall-runoff relationship, using the Curve Number method. This GUI is a raster-tool whose outputs are runoff estimates calculated using land use/land cover and hydrologic soil group maps. The package allows the user to select among three different antecedent moisture conditions and includes modifications about the initial abstraction parameter. We tested this GUI with data derived from two watersheds in Mexico and the outputs were compared with those produced using a well-established GIS tool in a vector environment. The results produced by these two approaches were practically the same. The main advantages of our package are: (1) ‘Sara4r’ is faster than previous vector based tools; (2) it is easy to use, even for people with no previous experience using R; (3) the modular design allows the integration of new routines and, (4) it is free and open source.

scholarly journals Formulation of Parsimonious Urban Flash Flood Predictive Model with Inferential Statistics

Mathematics ◽  
2022 ◽  
Vol 10 (2) ◽  
pp. 175
Author(s):  
Lloyd Ling ◽  
Sai Hin Lai ◽  
Zulkifli Yusop ◽  
Ren Jie Chin ◽  
Joan Lucille Ling
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Flash Flood ◽  
Curve Number ◽  
Runoff Coefficient ◽  
Rainfall Runoff ◽  
Inferential Statistics ◽  
Moisture Condition ◽  
Antecedent Moisture ◽  
Runoff Model

The curve number (CN) rainfall–runoff model is widely adopted. However, it had been reported to repeatedly fail in consistently predicting runoff results worldwide. Unlike the existing antecedent moisture condition concept, this study preserved its parsimonious model structure for calibration according to different ground saturation conditions under guidance from inferential statistics. The existing CN model was not statistically significant without calibration. The calibrated model did not rely on the return period data and included rainfall depths less than 25.4 mm to formulate statistically significant urban runoff predictive models, and it derived CN directly. Contrarily, the linear regression runoff model and the asymptotic fitting method failed to model hydrological conditions when runoff coefficient was greater than 50%. Although the land-use and land cover remained the same throughout this study, the calculated CN value of this urban watershed increased from 93.35 to 96.50 as the watershed became more saturated. On average, a 3.4% increase in CN value would affect runoff by 44% (178,000 m3). This proves that the CN value cannot be selected according to the land-use and land cover of the watershed only. Urban flash flood modelling should be formulated with rainfall–runoff data pairs with a runoff coefficient > 50%.

scholarly journals Evaluation of catchment contributing areas and storm runoff in flat terrain subject to urbanisation

2011 ◽  
Vol 15 (2) ◽  
pp. 547-559 ◽  
Author(s):  
O. V. Barron ◽  
D. Pollock ◽  
W. Dawes
Keyword(s):  
Catchment Area ◽  
River Flow ◽  
Spatial Information ◽  
Runoff Coefficient ◽  
Rainfall Runoff ◽  
Rainfall Events ◽  
Analysis Technique ◽  
Modelling Analysis ◽  
Moisture Conditions ◽  
The Impact

Abstract. Contributing Catchment Area Analysis (CCAA) is a spatial analysis technique developed and used for estimation of the hydrological connectivity of relatively flat catchments. It allows accounting for the effect of relief depressions on the catchment rainfall-runoff relationship which is not commonly considered in hydrological modelling. Analysis of distributed runoff was based on USDA runoff curves numbers (USDA, 1986), which utilised the spatial information on land cover and soil types, while CCAA was further developed to define catchment area contributing to river discharge under individual rainfall events. The method was applied to the Southern River catchment, Western Australia, showing that contributing catchment area varied from less than 20% to more than 60% of total catchment area under different rainfall and soil moisture conditions. Such variability was attributed to a compensating effect of relief depressions. CCAA was further applied to analyse the impact of urbanisation on the catchment rainfall-runoff relationship. It was demonstrated that in addition to an increase in runoff coefficient, urbanisation leads to expansion in the catchment area contributing to the river flow. This effect was more evident for the most frequent rainfall events, when an increase in contributing area was responsible for a 30–100% rise in predicted catchment runoff.

Towards a generic rainfall-runoff model for green roofs

2010 ◽  
Vol 62 (4) ◽  
pp. 898-905 ◽  
Author(s):  
H. Kasmin ◽  
V. R. Stovin ◽  
E. A. Hathway
Keyword(s):  
Green Roofs ◽  
Storm Event ◽  
Rainfall Runoff ◽  
Retention Capacity ◽  
Continuous Simulation ◽  
Moisture Storage ◽  
Substrate Moisture ◽  
Rainfall Runoff Model ◽  
Stormwater Retention ◽  
Runoff Model

A simple conceptual model for green roof hydrological processes is shown to reproduce monitored data, both during a storm event, and over a longer continuous simulation period. The model comprises a substrate moisture storage component and a transient storage component. Storage within the substrate represents the roof's overall stormwater retention capacity (or initial losses). Following a storm event the retention capacity is restored by evapotranspiration (ET). However, standard methods for quantifying ET do not exist. Monthly ET values are identified using four different approaches: analysis of storm event antecedent dry weather period and initial losses data; calibration of the ET parameter in a continuous simulation model; use of the Thornthwaite ET formula; and direct laboratory measurement of evaporation. There appears to be potential to adapt the Thornthwaite ET formula to provide monthly ET estimates from local temperature data. The development of a standardized laboratory test for ET will enable differences resulting from substrate characteristics to be quantified.

The influence of soil moisture conditions on the spatio-temporal variability of event rainfall-runoff coefficients in UK catchments

2021 ◽  
Author(s):  
Yanchen Zheng ◽  
Ross Woods ◽  
Jianzhu Li ◽  
Ping Feng
Keyword(s):  
Soil Moisture ◽  
Temporal Variability ◽  
Estimation Methods ◽  
Runoff Coefficient ◽  
Rainfall Runoff ◽  
Design Flood ◽  
Ungauged Catchments ◽  
Flood Estimation ◽  
Spatio Temporal ◽  
Moisture Conditions

<p>Since the bias and uncertainties of the current design flood estimation methods for ungauged catchments are inevitable, estimation of the design flood in ungauged catchments still remains an unsolved problem. The derived distribution approach appears to be the one of the promising design flood estimation methods, as this method can improve the understanding on which processes contribute most to flood in ungauged catchments. Generally, the distribution of rainfall characteristics and lumped rainfall-runoff modelling was incorporated to estimate the flood magnitude in this method. However, we should note that rainfall is not the only driving factor of flood events. Soil moisture conditions are also an important driving factor affecting the rainfall-runoff transformation, and may even control rainfall-runoff coefficients to a higher degree than does rainfall. Hence, here we perform soil moisture analysis at national scale by employing GLDAS-Noah datasets, and link this to observed event runoff coefficients from a large sample of UK catchments. The relationship between soil moisture conditions and rainfall-runoff coefficient was explored to analyse the spatio-temporal variability of runoff coefficient. This study laid the foundation for further development of a practical derived distribution method, by considering the statistical distribution of rainfall-runoff coefficients and the influence of soil moisture conditions.</p>

scholarly journals Rainfall-Runoff Modeling Using the HEC-HMS Model for the Al-Adhaim River Catchment, Northern Iraq

Hydrology ◽  
2021 ◽  
Vol 8 (2) ◽  
pp. 58
Author(s):  
Ahmed Naseh Ahmed Hamdan ◽  
Suhad Almuktar ◽  
Miklas Scholz
Keyword(s):  
Geographical Information Systems ◽  
Hydrological Model ◽  
Daily Rainfall ◽  
Curve Number ◽  
Geographical Information ◽  
Coefficient Of Determination ◽  
Rainfall Runoff ◽  
Time Step ◽  
River Catchment ◽  
Northern Iraq

It has become necessary to estimate the quantities of runoff by knowing the amount of rainfall to calculate the required quantities of water storage in reservoirs and to determine the likelihood of flooding. The present study deals with the development of a hydrological model named Hydrologic Engineering Center (HEC-HMS), which uses Digital Elevation Models (DEM). This hydrological model was used by means of the Geospatial Hydrologic Modeling Extension (HEC-GeoHMS) and Geographical Information Systems (GIS) to identify the discharge of the Al-Adhaim River catchment and embankment dam in Iraq by simulated rainfall-runoff processes. The meteorological models were developed within the HEC-HMS from the recorded daily rainfall data for the hydrological years 2015 to 2018. The control specifications were defined for the specified period and one day time step. The Soil Conservation Service-Curve number (SCS-CN), SCS Unit Hydrograph and Muskingum methods were used for loss, transformation and routing calculations, respectively. The model was simulated for two years for calibration and one year for verification of the daily rainfall values. The results showed that both observed and simulated hydrographs were highly correlated. The model’s performance was evaluated by using a coefficient of determination of 90% for calibration and verification. The dam’s discharge for the considered period was successfully simulated but slightly overestimated. The results indicated that the model is suitable for hydrological simulations in the Al-Adhaim river catchment.

scholarly journals Hydrologic Performance of an Extensive Green Roof under Intensive Rain Events: Results from a Rain-Chamber Simulation

2021 ◽  
Vol 13 (6) ◽  
pp. 3078
Author(s):  
Elena Giacomello ◽  
Jacopo Gaspari
Keyword(s):  
Water Retention ◽  
Green Roof ◽  
Green Roofs ◽  
International Standards ◽  
Dimensionless Number ◽  
Runoff Coefficient ◽  
Retention Performance ◽  
Extensive Green Roof ◽  
Rain Events ◽  
Hydrologic Performance

The water storage capacity of a green roof generates several benefits for the building conterminous environment. The hydrologic performance is conventionally expressed by the runoff coefficient, according to international standards and guidelines. The runoff coefficient is a dimensionless number and defines the water retention performance over a long period. At the scale of single rain events, characterized by varying intensity and duration, the reaction of the green roof is scarcely investigated. The purpose of this study is to highlight how an extensive green roof—having a supposed minimum water performance, compared to an intensive one—responds to real and repetitive rain events, simulated in a rain chamber with controlled rain and runoff data. The experiment provides, through cumulative curve graphs, the behavior of the green roof sample during four rainy days. The simulated rain events are based on a statistical study (summarized in the paper) of 25 years of rain data for a specific location in North Italy characterized by an average rain/year of 1100 mm. The results prove the active response of the substrate, although thin and mineral, and quick draining, in terms of water retention and detention during intense rain events. The study raises questions about how to better express the water performance of green roofs.

scholarly journals Initial abstraction ratio and Curve Number estimation using rainfall and runoff data from a tropical watershed

RBRH ◽  
2019 ◽  
Vol 24 ◽  
Author(s):  
Luiz Claudio Galvão do Valle Junior ◽  
Dulce Buchala Bicca Rodrigues ◽  
Paulo Tarso Sanches de Oliveira
Keyword(s):  
Conversion Factor ◽  
Poor Performance ◽  
Curve Number ◽  
Storm Events ◽  
Rainfall Runoff ◽  
Initial Abstraction ◽  
Rainfall Depth ◽  
Standard Value ◽  
Tropical Watershed ◽  
Runoff Events

ABSTRACT The Curve Number (CN) method is extensively used for predict surface runoff from storm events. However, remain some uncertainties in the method, such as in the use of an initial abstraction (λ) standard value of 0.2 and on the choice of the most suitable CN values. Here, we compute λ and CN values using rainfall and runoff data to a rural basin located in Midwestern Brazil. We used 30 observed rainfall-runoff events with rainfall depth greater than 25 mm to derive associated CN values using five statistical methods. We noted λ values ranging from 0.005 to 0.455, with a median of 0.045, suggesting the use of λ = 0.05 instead of 0.2. We found a S0.2 to S0.05 conversion factor of 2.865. We also found negative values of Nash-Sutcliffe Efficiency (to the estimated and observed runoff). Therefore, our findings indicated that the CN method was not suitable to estimate runoff in the studied basin. This poor performance suggests that the runoff mechanisms in the studied area are dominated by subsurface stormflow.

scholarly journals Influence of Temperature and Moisture Content on Thermal Performance of Green Roof Media

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2421
Author(s):  
Bohan Shao ◽  
Caterina Valeo ◽  
Phalguni Mukhopadhyaya ◽  
Jianxun He
Keyword(s):  
Thermal Conductivity ◽  
Moisture Content ◽  
Green Roof ◽  
Green Roofs ◽  
Power Functions ◽  
Thick Substrate ◽  
Different Temperatures ◽  
Test Cells ◽  
Substrate Moisture ◽  
Phase Transition Zone

The influence of moisture content on substrate thermal conductivity at different temperatures was investigated for four different commercially available substrates for green roofs. In the unfrozen state, as moisture content increased, thermal conductivity increased linearly. In the phase transition zone between +5 and −10 °C, as temperature decreased, thermal conductivity increased sharply during the transition from water to ice. When the substrate was frozen, thermal conductivity varied exponentially with substrate moisture content prior to freezing. Power functions were found between thermal conductivity and temperature. Two equally sized, green roof test cells were constructed and tested to compare various roof configurations including a bare roof, varying media thickness for a green roof, and vegetation. The results show that compared with the bare roof, there is a 75% reduction in the interior temperature’s amplitude for the green roof with 150 mm thick substrate. When a sedum mat was added, there was a 20% reduction in the amplitude of the inner temperature as compared with the cell without a sedum mat.

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