scholarly journals Variability of the Initial Abstraction Ratio in an Urban and an Agroforested Catchment

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 415 ◽  
Author(s):  
Adam Krajewski ◽  
Anna E. Sikorska-Senoner ◽  
Agnieszka Hejduk ◽  
Leszek Hejduk
Keyword(s):  
Land Use ◽  
Curve Number ◽  
Threshold Value ◽  
Storm Events ◽  
Direct Runoff ◽  
Initial Abstraction ◽  
Land Use Types ◽  
Rainfall Depth ◽  
Storm Rainfall ◽  
Service Curve

The Curve Number method is one of the most commonly applied methods to describe the relationship between the direct runoff and storm rainfall depth. Due to its popularity and simplicity, it has been studied extensively. Less attention has been given to the dimensionless initial abstraction ratio, which is crucial for an accurate direct runoff estimation with the Curve Number. This ratio is most often assumed to be equal to 0.20, which was originally proposed by the method’s developers. In this work, storm events recorded in the years 2009–2017 in two small Polish catchments of different land use types (urban and agroforested) were analyzed for variability in the initial abstraction ratio across events, seasons, and land use type. Our results showed that: (i) estimated initial abstraction ratios varied between storm events and seasons, and were most often lower than the original value of 0.20; (ii) for large events, the initial abstraction ratio in the catchment approaches a constant value after the rainfall depth exceeds a certain threshold value. Thus, when using the Soil Conservation Service-Curve Number (SCS-CN) method, the initial abstraction ratio should be locally verified, and the conditions for the application of the suggested value of 0.20 should be established.

The effects of land use change and precipitation change on direct runoff in Wei River watershed, China

2014 ◽  
Vol 71 (2) ◽  
pp. 289-295 ◽  
Author(s):  
Leihua Dong ◽  
Lihua Xiong ◽  
Upmanu Lall ◽  
Jiwu Wang
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Land Use Change ◽  
Agricultural Land ◽  
Curve Number ◽  
Runoff Generation ◽  
Direct Runoff ◽  
Land Use Types ◽  
Service Curve ◽  
Effects Of Land Use

The principles and degrees to which land use change and climate change affect direct runoff generation are distinctive. In this paper, based on the MODIS data of land use in 1992 and 2003, the impacts of land use and climate change are explored using the Soil Conservation Service Curve Number (SCS-CN) method under two defined scenarios. In the first scenario, the precipitation is assumed to be constant, and thus the consequence of land use change could be evaluated. In the second scenario, the condition of land use is assumed to be constant, so the influence only induced by climate change could be assessed. Combining the conclusions of two scenarios, the effects of land use and climate change on direct runoff volume can be separated. At last, it is concluded: for the study basin, the land use types which have the greatest effect on direct runoff generation are agricultural land and water body. For the big sub basins, the effect of land use change is generally larger than that of climate change; for middle and small sub basins, most of them suffer more from land use change than from climate change.

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 Assimilation of Satellite-Based Data for Hydrological Mapping of Precipitation and Direct Runoff Coefficient for the Lake Urmia Basin in Iran

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1624 ◽  
Author(s):  
Akbari ◽  
Haghighi ◽  
Aghayi ◽  
Javadian ◽  
Tajrishy ◽  
...  
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Curve Number ◽  
Rain Gauge ◽  
Runoff Coefficient ◽  
Lake Urmia ◽  
Direct Runoff ◽  
Rain Gauges ◽  
Elevation Model ◽  
High Elevations ◽  
Service Curve

Water management in arid basins often lacks sufficient hydro-climatological data because, e.g., rain gauges are typically absent at high elevations and inflow to ungauged areas around large closed lakes is difficult to estimate. We sought to improve precipitation and runoff estimation in an arid basin (Lake Urmia, Iran) using methods involving assimilation of satellite-based data. We estimated precipitation using interpolation of rain gauge data by kriging, downscaling the Tropical Rainfall Measuring Mission (TRMM), and cokriging interpolation of in-situ records with Remote Sensing (RS)-based data. Using RS-based data application in estimations gave more precise results, by compensating for lack of data at high elevations. Cokriging interpolation of rain gauges by TRMM and Digitized Elevation Model (DEM) gave 4–9 mm lower Root Mean Square Error (RMSE) in different years compared with kriging. Downscaling TRMM improved its accuracy by 14 mm. Using the most accurate precipitation result, we modeled annual direct runoff with Kennessey and Soil Conservation Service Curve Number (SCS-CN) models. These models use land use, permeability, and slope data. In runoff modeling, Kennessey gave higher accuracy. Calibrating Kennessey reduced the Normalized RMSE (NRMSE) from 1 in the standard model to 0.44. Direct runoff coefficient map by 1 km spatial resolution was generated by calibrated Kennessey. Validation by the closest gauges to the lake gave a NRMSE of 0.41 which approved the accuracy of modeling.

scholarly journals Pemodelan Tutupan Lahan Untuk Menjamin Keberlanjutan Debit Sungai (Suatu Studi Di Sub DAS Cikapundung-Maribaya)

2021 ◽  
Vol 6 (2) ◽  
pp. 121-139
Author(s):  
Aditya Dwifebri Christian Wibowo ◽  
Mahawan Karuniasa ◽  
Dwita Sutjiningsih
Keyword(s):  
Land Use ◽  
River Water ◽  
Water Scarcity ◽  
Soil Conservation ◽  
Curve Number ◽  
Soil Conservation Service ◽  
Land Conversion ◽  
Forest Land ◽  
Service Curve ◽  
Scs Cn

Changes in land use in the Cikapundung watershed, ie changes in forest land to built-up land, have an impact on the quantity of river water. Changes in land use in the Cikapundung River catchment are not ideal conditions for absorbing water. If land conversion is not controlled, it can have a large impact on reducing the availability of water resources for subordinate areas or what is called water scarcity. Analysis that takes into account land use and discharge can be done with several hydrological analysis methods, one of them is the Soil Conservation Service Curve Number (SCS-CN) method. Based on the calculation, the CN value was changed in 2014 from 57.275 to 62.591 where land cover changes began to occur.   Keywords: land use, river water, water scarcity, hydrology, CN value

scholarly journals CN Modeling for Predicting Discharge in Lesti Sub-watershed

2019 ◽  
Vol 8 (12) ◽  
pp. 4890-4896
Keyword(s):  
United States ◽  
Land Use ◽  
Curve Number ◽  
The United States ◽  
United States Department ◽  
Department Of Agriculture ◽  
Mathematical Formula ◽  
States Department ◽  
Soil Group ◽  
Rainfall Depth

This research intends to accurately mapping the Curve Number (CN) that is as the function of cover type, land use treatment, hydrology condition, and hydrologic soil group in the Lesti sub-watershed,. The methodology consists of to build the suitable CN modeling for predicting discharge in the Lesti sub-watershed and then to evaluate the result accurately. The value of CN is obtained from the mathematical formula with the input is rainfall depth and discharge. The result of CN modeling for the Lesti sub-watershed is accurate enough as is made by the United States Department of Agriculture (USDA) in USA. In addition, the CN mapping can be directly used by the engineers of the manager and designer on the water resources structures in Lesti sub-watershed

Elucidating soil moisture dynamics in agricultural landscapes under varying weather patterns

2021 ◽  
Author(s):  
Veronica Fritz ◽  
Thakshajini Thaasan ◽  
Andrew Williams ◽  
Ranjith Udawatta ◽  
Sidath Mendis ◽  
...  
Keyword(s):  
Land Use ◽  
Soil Moisture ◽  
Soil Water ◽  
Water Cycle ◽  
Water Storage ◽  
Soil Water Storage ◽  
Storm Events ◽  
Weather Patterns ◽  
Land Use Types ◽  
Moisture Dynamics

<p>Changing weather patterns and anthropogenic land use change significantly alter the terrestrial water cycle. A key variable that modulates the water cycle on the land surface is soil moisture and its variability in time and space. Hydrological models are used to simulate key components of the water cycle including infiltration, soil storage and uptake by plants. However, uncertainties remain in accurately representing soil moisture dynamics in models. Here, with the aid of several sensors installed at a 30-ha experimental research facility, we attempt to quantify differences in soil water storage across multiple land use types – cropped area, mosaic of turf grass and native plants, and an unkept weeded area as control land use. We will also discuss the accuracy of sensors to correctly measure soil water storage. Our study was conducted at an agricultural experimental station in Columbia, Missouri, USA. We use a variety of instruments to measure weather, evapotranspiration, and soil water. We used boundary layer scintillometers to measure near-surface turbulence, sensors to continuously track soil moisture and temperature, as well as weather stations for precipitation, air temperature, solar radiation and wind speed.  Changes in volumetric water content and soil temperature are measured at 5-minute intervals at 10-, 20-, and 40-cm soil depths to compare soil water storage among the three land use types. We also took soil samples before and after several storm events to calibrate the sensor readings at three sites. We, then, analyzed several storm events over a period of five months and compared the actual soil moisture and soil temperature dynamics at finer time intervals. With additional measurements of weather and boundary layer turbulence, we hope to reveal the landscape and weather control on soil moisture distribution across multiple land uses, and their subsequent impact on plant water uptake. Our preliminary results indicate that continuously disturbed agricultural lands depletes soil moisture at faster rates, which may present challenges in maintaining land productivity in the long term.</p>

On the Ia–S relation of the SCS-CN method

2006 ◽  
Vol 37 (3) ◽  
pp. 261-275 ◽  
Author(s):  
M.K. Jain ◽  
S.K. Mishra ◽  
P. Suresh Babu ◽  
K. Venugopal
Keyword(s):  
Curve Number ◽  
United States Department ◽  
Large Set ◽  
Department Of Agriculture ◽  
Validation Data ◽  
Proposed Model ◽  
Storm Rainfall ◽  
Service Curve ◽  
Scs Cn ◽  
Better Than

The initial abstraction (Ia) versus maximum potential retention (S) relation in the Soil Conservation Service Curve Number (SCS-CN) methodology was revisited, and a new non-linear relation incorporating storm rainfall (P) and S was proposed and tested on a large set of storm rainfall-runoff events derived from the water database of United States Department of Agriculture-Agriculture Research Service (USDA-ARS). Employing root mean square error (RMSE), the performance of both the existing and proposed models was evaluated using the complete database, and for model calibration and validation, data were split into two groups: based on ordered rainfall (P-based) and runoff (Q-based). A specific formulation of the proposed model Ia=λS(P/(P+S))α with λ=0.3 and α=1.5 was found to generally perform better than the existing Ia=0.2S, and therefore was recommended for field applications. When evaluated using the observed Ia data, the proposed version performed significantly better than the existing one.

scholarly journals Statistical and Type II Error Assessment of a Runoff Predictive Model in Peninsula Malaysia

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 812
Author(s):  
Lloyd Ling ◽  
Zulkifli Yusop ◽  
Joan Lucille Ling
Keyword(s):  
Curve Number ◽  
Technological Advancement ◽  
Type Ii ◽  
Error Assessment ◽  
Type Ii Error ◽  
Storm Events ◽  
Rainfall Runoff ◽  
Scs Model ◽  
Rainfall Depth ◽  
Runoff Model

Flood related disasters continue to threaten mankind despite preventative efforts in technological advancement. Since 1954, the Soil Conservation Services (SCS) Curve Number (CN0.2) rainfall-runoff model has been widely used but reportedly produced inconsistent results in field studies worldwide. As such, this article presents methodology to reassess the validity of the model and perform model calibration with inferential statistics. A closed form equation was solved to narrow previous research gap with a derived 3D runoff difference model for type II error assessment. Under this study, the SCS runoff model is statistically insignificant (alpha = 0.01) without calibration. Curve Number CN0.2 = 72.58 for Peninsula Malaysia with a 99% confidence interval range of 67 to 76. Within these CN0.2 areas, SCS model underpredicts runoff amounts when the rainfall depth of a storm is < 70 mm. Its overprediction tendency worsens in cases involving larger storm events. For areas of 1 km2, it underpredicted runoff amount the most (2.4 million liters) at CN0.2 = 67 and the rainfall depth of 55 mm while it nearly overpredicted runoff amount by 25 million liters when the storm depth reached 430 mm in Peninsula Malaysia. The SCS model must be validated with rainfall-runoff datasets prior to its adoption for runoff prediction in any part of the world. SCS practitioners are encouraged to adopt the general formulae from this article to derive assessment models and equations for their studies.

An updated hydrological review on recent advancements in soil conservation service-curve number technique

2010 ◽  
Vol 1 (2) ◽  
pp. 118-134 ◽  
Author(s):  
P. K. Singh ◽  
M. L. Gaur ◽  
S. K. Mishra ◽  
S. S. Rawat
Keyword(s):  
Soil Conservation ◽  
Curve Number ◽  
Estimation Methods ◽  
Soil Conservation Service ◽  
Hydrologic Models ◽  
Relative Significance ◽  
Storm Rainfall ◽  
Service Curve ◽  
Hydrology And Water Resources ◽  
Scs Cn

Although many hydrologic models are available for the estimation of direct runoff from storm rainfall, most models are limited because of their intensive input data and calibration requirements. The Soil Conservation Service-Curve Number (SCS-CN) technique has been applied successfully throughout the entire spectrum of hydrology and water resources, even though originally it was not intended to deal with and solve certain issues such as erosion and sedimentation and environmental engineering. This manuscript includes an updated review of this popular technique with its critical performance analysis under various hydrological applications. The study highlights its provenance and its conceptual and empirical foundations followed by relative significance of the parameter CN and various estimation methods and issues related to the Ia and S relationship. Finally, notable recent advancements available in the literature are discussed for their structural strengths and applicability in real world problems.

scholarly journals Curve Number Estimation for Ungauged Watershed in Semi-Arid Region

2021 ◽  
Vol 7 (6) ◽  
pp. 1070-1083
Author(s):  
Denik Sri Krisnayanti ◽  
Wilhelmus Bunganaen ◽  
John H. Frans ◽  
Yustinus A. Seran ◽  
Djoko Legono
Keyword(s):  
Land Use ◽  
Surface Runoff ◽  
Curve Number ◽  
Eastern Region ◽  
Large Area ◽  
Rainfall Depth ◽  
Standard Response ◽  
Semi Arid Region ◽  
The Relationship ◽  
Semi Arid

The Benanain Watershed is located in East Nusa Tenggara with an area of 3,181 km2 and is divided into 29 sub-watersheds. The East Nusa Tenggara itself is an eastern region of Indonesia with a unique climate condition called semi-arid. The high rainfall intensity occurring in short duration results in large surface runoff and erosion. Floods and erosion in semi-arid areas due to sensitive soils to drought and heavy rainfall extremely. This paper presents the application of the Soil Conservation Services-Curve Number (SCS-CN) real-flood flows through a digital map of soil type, land use, topography, and the heterogeneity of physical condition, especially for ungauged watersheds. The method used is an approach empirical to estimate runoff from the relationship between rainfall, land use, and soil hydrology groups. This watershed has a large area that must analyze every sub-watershed. The land-use of the Benanain watershed is secondary dryland forest by 44.26% and the hydrological soil group on the B group classification with medium to high absorption potential by 46.502% from the total area. The curve number value of the Benanain Watershed ranges from 56.54 to 73.90, where the mean CN value of 65.32. The rainfall (mm) for the 29 sub-watersheds in the Benanain Watershed has decreased by about 74.65% when being surface runoff or only 25.35% of water becomes surface runoff. The relationship between rainfall depth and CN is classified as standard response and trend line (flat slope) equilibrium occurs when rainfall depth value of 56.71 mm and CN is close to 66.30. The high variability of intense rainfall between the rainy season and the dry season had a significant influence on the curve number value in a large watershed area. Further analysis will be more accurate if it is supported by long rainfall data and observation runoff data as a control. Doi: 10.28991/cej-2021-03091711 Full Text: PDF

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