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SCS-CN parameter determination from observed rainfall runoff data. A critical review.

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One the most difficult challenges in applied hydrology is predicting runoff in ungauged or poorly gauged watersheds. Thus, simple approaches for runoff estimation are especially useful in hydrologic applications. A simple, well established, and widely used technique for predicting the direct runoff depths of rainfall events is the Soil Conservation Service - Curve Number (SCS-CN) method. Due to its straightforward but well-proven approach, readily available and well documented environmental inputs, and incorporation of numerous variables influencing runoff generation into a single CN parameter, it quickly rose to prominence among engineers and practitioners. Tables can be used to identify the CN parameter values corresponding to prevailing soil, land cover and land management conditions. However, it is always better to estimate the CN value using observed rainfall-runoff (P-Q) data when available. Estimating appropriate CN values for additional soil – land cover conditions and additional regions is also critical for extending and updating the method’s documentation given that the SCS-CN approach is extremely sensitive to variations in the CN values.However, even when the CN value is determined from measured P-Q data, the estimated CN values vary substantially from storm to storm on any given watershed. For this reason, various methods to estimate the CN value characterizing each watershed have been proposed up to know, and many theories on the reasons behind the observed relationships between CN and P for each watershed have been stated. Though, after many years of research, there isn’t still a unique agreed method to estimate the CN values characterizing a watershed or a soil-land cover complex, while the proposed methods lead to different CN values and in many cases neglect spatial variability. Further, an increasing number of modified SCS-CN versions are continuously developed, and new parameters are introduced complicating the situation even more.Accordingly, this study attempts to collect, categorize, and systematically analyze the huge number of studies on SCS-CN method published in the last 30 years. We selected this period as 30 years ago, in 1993, R.H. Hawkins published his emblematic study on the “Asymptotic determination of runoff curve numbers from data” (J. Irrigat. Drain. Div. ASCE, 119(2): 334–345). In this review study, specific attention is given to the methods focusing on CN value determination from measured P-Q data. The advantages and limitations of the various approaches are investigated, as well as trends and gaps in existing literature. The analysed methods are classified and the main paths are identified. Based on the obtained results, conclusions on the current status are being made, and the more promising approaches are highlighted.  Then, ideas on future research pathways towards the target of a unified CN values determination approach are discussed.
Title: SCS-CN parameter determination from observed rainfall runoff data. A critical review.
Description:
One the most difficult challenges in applied hydrology is predicting runoff in ungauged or poorly gauged watersheds.
Thus, simple approaches for runoff estimation are especially useful in hydrologic applications.
A simple, well established, and widely used technique for predicting the direct runoff depths of rainfall events is the Soil Conservation Service - Curve Number (SCS-CN) method.
Due to its straightforward but well-proven approach, readily available and well documented environmental inputs, and incorporation of numerous variables influencing runoff generation into a single CN parameter, it quickly rose to prominence among engineers and practitioners.
Tables can be used to identify the CN parameter values corresponding to prevailing soil, land cover and land management conditions.
However, it is always better to estimate the CN value using observed rainfall-runoff (P-Q) data when available.
Estimating appropriate CN values for additional soil – land cover conditions and additional regions is also critical for extending and updating the method’s documentation given that the SCS-CN approach is extremely sensitive to variations in the CN values.
However, even when the CN value is determined from measured P-Q data, the estimated CN values vary substantially from storm to storm on any given watershed.
For this reason, various methods to estimate the CN value characterizing each watershed have been proposed up to know, and many theories on the reasons behind the observed relationships between CN and P for each watershed have been stated.
Though, after many years of research, there isn’t still a unique agreed method to estimate the CN values characterizing a watershed or a soil-land cover complex, while the proposed methods lead to different CN values and in many cases neglect spatial variability.
Further, an increasing number of modified SCS-CN versions are continuously developed, and new parameters are introduced complicating the situation even more.
Accordingly, this study attempts to collect, categorize, and systematically analyze the huge number of studies on SCS-CN method published in the last 30 years.
We selected this period as 30 years ago, in 1993, R.
H.
Hawkins published his emblematic study on the “Asymptotic determination of runoff curve numbers from data” (J.
Irrigat.
Drain.
Div.
ASCE, 119(2): 334–345).
In this review study, specific attention is given to the methods focusing on CN value determination from measured P-Q data.
The advantages and limitations of the various approaches are investigated, as well as trends and gaps in existing literature.
The analysed methods are classified and the main paths are identified.
Based on the obtained results, conclusions on the current status are being made, and the more promising approaches are highlighted.
  Then, ideas on future research pathways towards the target of a unified CN values determination approach are discussed.

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