# The Curve Number Concept as a Driver for Delineating Hydrological Response Units

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Overview of Schematization and Parameterization Approaches in Hydrological Modelling

^{2}. It was found that HRUs are a reliable means for regional hydrological watershed modelling, allowing spatial up- and down-scaling [53,55]. Bongartz [56] compared the topographic-based and homogeneous HRUs and reported that, for watersheds with areas less than 200 km

^{2}, homogeneous HRUs provided better representation of the watershed processes.

_{j}, where n

_{j}is the number of classes corresponding to the i-th layer. It is worth mentioning that, in contrast to classical parameterization approaches, HRUs do not represent contiguous geographical areas (while sub-basins are by definition contiguous). Instead, they represent basin partitions with common characteristics, and thus common parameter values. The intersection of the sub-basin and HRU layers represents the minor geographical elements in the modelling procedure, driven by the rainfall and Potential Evapotranspiration (PET) of the corresponding sub-basin and responding according to the parameter values of the corresponding HRU. The shape of this non-contiguous element is of no interest, since at each time step, the runoff generated by HRUs is integrated over sub-basins and propagated to their outlet.

## 3. Curve Number Approach for HRU Delineation

#### 3.1. The Standard CN Approach and Its Shortcomings

#### 3.2. Analytical Method for CN Assessment

#### 3.3. GIS-Based Procedure for Extracting CN Maps

#### 3.4. Validation Based on Observed Flood Events

^{2}), the quite significant extent of highly-permeable geological formations and the steep slopes. The key task was the evaluation of the NRCS-CN method for extracting the effective rainfall, combined with the unit hydrograph theory, and the development of empirical formulas within this procedure to better represent the peculiarities of Mediterranean catchments that are mainly affected by flash floods [72].

#### 3.5. HRU Delineation Approaches Based on CN Classes

#### 3.6. Which Is the Recommended Number of HRUs?

## 4. Case Study

#### 4.1. Modelling Approach

#### 4.2. Study Area and Data

^{2}and an average elevation of 770 m, reaching a maximum elevation of 1715 m and a minimum of 93 m at the Latomeio Baka outlet. Terrain slope ranges from almost flat on the lowland and riverside areas to 76% on the steep mountain slopes, with the mean slope calculated at 22%. Nedontas originates in the western slopes of Taygetos and is mainly formed by three tributaries: its headwaters comprise the Nedousa and Alagonia tributaries, as well as spring flows in their sub-basins; the upper reach of Nedontas is joined by the lower reach of Karveliotis stream emanating from the SE area of the basin. The length of the main river course is about 26 km; the river discharges to the Gulf of Messenia, traversing the city of Kalamata, a regional economic center of SW Peloponnese, with 55,000 inhabitants.

^{3}/s).

#### 4.3. Model Setup and Other Assumptions

^{2}and by setting additional nodes at the three flow station sites, as illustrated in Figure 4. Their main properties are summarized in Table 3. Spatially-averaged precipitation time series of hourly resolution were extracted at the sub-basin scale, using the Thiessen polygon method. For the assignment of PET time series over sub-basins, we employed a parametric radiation-based approach that uses extraterrestrial solar radiation (which is periodic function of latitude and time), temperature and three parameters [86,87]. Areal temperature data were also estimated through the Thiessen polygon method, while the model parameters have been derived by fitting the model against Penman–Monteith data from the neighboring meteorological station of Kalamata. Finally, hourly discharge time series, used within calibrations, were constructed based on raw hydrometric data, i.e., 15-min interval river stage observations and sparse flow measurements were used to construct rating curves at the three stations.

#### 4.4. Preparation of CN Map

#### 4.5. Calibration Experiment 1: Varying the Number of HRUs

#### 4.6. Calibration Experiment 2: Contrasting Alternative HRU Delineation Approaches

#### 4.7. Investigation of Model Results for CN-Based Parameterization

## 5. Summary and Conclusions

## Supplementary Materials

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## Appendix A. Brief Description of the HYDROGEIOS Model

**Figure A1.**Representation of modelling components and associated fluxes within basin partitions. Meteorological inputs (Precipitation (PET)) vary across sub-basins, while model parameters, shown in callouts, vary across HRUs.

## References

- Beven, K.J. Changing Ideas in Hydrology—The Case of Physicallybased Models. J. Hydrol.
**1989**, 105, 157–172. [Google Scholar] [CrossRef] - Boyle, D.P.; Gupta, H.V.; Sorooshian, S.; Koren, V.; Zhang, Z.; Smith, M. Towards Improved Streamflow Forecasts: The Value of Semidistributed Modeling. Water Resour. Res.
**2001**, 37, 2749–2759. [Google Scholar] [CrossRef] - Ajami, N.K.; Gupta, H.; Wagener, T.; Sorooshian, S. Calibration of a Semi-Distributed Hydrologic Model for Streamflow Estimation along a River System. J. Hydrol.
**2004**, 298, 112–135. [Google Scholar] [CrossRef] - Nalbantis, I.; Efstratiadis, A.; Rozos, E.; Kopsiafti, M.; Koutsoyiannis, D. Holistic versus Monomeric Strategies for Hydrological Modelling of Human-Modified Hydrosystems. Hydrol. Earth Syst. Sci.
**2011**, 15, 743–758. [Google Scholar] [CrossRef] - Eckhardt, K.; Arnold, J.G. Automatic Calibration of a Distributed Catchment Model. J. Hydrol.
**2001**, 251, 103–109. [Google Scholar] [CrossRef] - Fatichi, S.; Vivoni, E.R.; Ogden, F.L.; Ivanov, V.Y.; Mirus, B.; Gochis, D.; Downer, C.W.; Camporese, M.; Davison, J.H.; Ebel, B.; et al. An Overview of Current Applications, Challenges, and Future Trends in Distributed Process-Based Models in Hydrology. J. Hydrol.
**2016**, 537, 45–60. [Google Scholar] [CrossRef] - Refsgaard, J.C. Parameterisation, Calibration and Validation of Distributed Hydrological Models. J. Hydrol.
**1997**, 198, 69–97. [Google Scholar] [CrossRef] - Beven, K.J.; Binley, A.M. The Future of Distributed Models: Model Calibration and Uncertainty Prediction. Hydrol. Process.
**1992**, 6, 279–298. [Google Scholar] [CrossRef] - Efstratiadis, A.; Nalbantis, I.; Koukouvinos, A.; Rozos, E.; Koutsoyiannis, D. HYDROGEIOS: A Semi-Distributed GIS-Based Hydrological Model for Modified River Basins. Hydrol. Earth Syst. Sci.
**2008**, 12, 989–1006. [Google Scholar] [CrossRef] - Arnold, J.G.; Allen, P.M.; Bernhardt, G. A Comprehensive Surface-Groundwater Flow Model. J. Hydrol.
**1993**, 142, 47–69. [Google Scholar] [CrossRef] - Neitsch, S.L.; Arnold, J.G.; Kiniry, J.R.; Williams, J.R. Soil and Water Assessment Tool Theoretical Documentation, Version 2005; Texas Water Resources Institute: Temple, TX, USA, 2005. [Google Scholar]
- Zhang, H.L.; Wang, Y.J.; Wang, Y.Q.; Li, D.X.; Wang, X.K. The Effect of Watershed Scale on HEC-HMS Calibrated Parameters: A Case Study in the Clear Creek Watershed in Iowa, US. Hydrol. Earth Syst. Sci.
**2013**, 17, 2735–2745. [Google Scholar] [CrossRef] - Beven, K.J. Prophecy, Reality and Uncertainty in Distributed Hydrological Modelling. Adv. Water Resour.
**1993**, 16, 41–51. [Google Scholar] [CrossRef] - Hromadka, T.V. San Bernardino County Hydrology Manual; Williamson and Schmid: Irvine, CA, USA, 1986. [Google Scholar]
- Dehotin, J.; Braud, I. Which Spatial Discretization for Distributed Hydrological Models? Proposition of a Methodology and Illustration for Medium to Large Scale Catchments. Hydrol. Earth Syst. Sci.
**2008**, 12, 769–796. [Google Scholar] [CrossRef] - Cho, H.; Olivera, F. Effect of the Spatial Variability of Land Use, Soil Type, and Precipitation on Streamflows in Small Watersheds. J. Am. Water Resour. Assoc.
**2009**, 45, 673–686. [Google Scholar] [CrossRef] - Han, J.-C.; Huang, G.-H.; Zhang, H.; Li, Z.; Li, Y.-P. Effects of Watershed Subdivision Level on Semi-Distributed Hydrological Simulations: Case Study of the SLURP Model Applied to the Xiangxi River Watershed, China. Hydrol. Sci. J.
**2014**, 59, 108–125. [Google Scholar] [CrossRef] - Li, Z.; Li, L.; Huang, P.; Li, Q. Effect of Watershed Subdivision on Confluence Parameter. J. Hohai Univ.
**2014**, 42, 283–288. [Google Scholar] - Savenije, H.H.G. HESS Opinions “Topography Driven Conceptual Modelling (FLEX-Topo)”. Hydrol. Earth Syst. Sci.
**2010**, 14, 2681–2692. [Google Scholar] [CrossRef] - Rennό, C.D.; Nobre, A.D.; Cuartas, L.A.; Soares, J.V.; Hodnett, M.G.; Tomasella, J.; Waterloo, M.J. HAND, a New Terrain Descriptor Using SRTM-DEM: Mapping Terra-Firme Rainforest Environments in Amazonia. Remote Sens. Environ.
**2008**, 112, 3469–3481. [Google Scholar] [CrossRef] - Donnelly, C.; Andersson, J.C.M.; Arheimer, B. Using Flow Signatures and Catchment Similarities to Evaluate the E-HYPE Multi-Basin Model across Europe. Hydrol. Sci. J.
**2016**, 61, 255–273. [Google Scholar] [CrossRef] - Gharari, S.; Hrachowitz, M.; Fenicia, F.; Savenije, H.H.G. Hydrological Landscape Classification: Investigating the Performance of HAND Based Landscape Classifications in a Central European Meso-Scale Catchment. Hydrol. Earth Syst. Sci.
**2011**, 15, 3275–3291. [Google Scholar] [CrossRef] - Bingner, R.L.; Garbrecht, J.; Arnold, J.G.; Srinivasan, R. Effect of Watershed Subdivision on Simulation Runoff and Fine Sediment Yield. Trans. Am. Soc. Agric. Eng.
**1997**, 40, 1329–1335. [Google Scholar] [CrossRef] - Boyd, M.J.; Pilgrim, D.H.; Cordery, I. A Storage Routing Model Based on Catchment Geomorphology. J. Hydrol.
**1979**, 42, 209–230. [Google Scholar] [CrossRef] - Goodrich, D.C.; Woolhiser, D.A.; Sorooshian, S. Model Complexity Required to Maintain Hydrologic Response. In Proceedings ASCE National Conference on Hydraulic Engineering; Abt, S.A., Gessler, J., Eds.; American Society of Civil Engineers (ASCE): Colorado Springs, CO, USA, 1988; pp. 431–463. [Google Scholar]
- Goodrich, D.C. An Overview of the USDA-ARS Climate Change and Hydrology Program and Analysis of Model Complexity as a Function of Basin Scale. In Proceedings of the Workshop on the Effects of Global Climate Change on Hydrology and Water Resources at Catchment Scale, Tsukuba, Japan, 3–6 February 1992; pp. 233–242. [Google Scholar]
- Norris, G.R. A Proccess for Interfacing a Hydrologic Model to a Geographic Information System. Mater’s Thesis, Oklahoma State University, Stillwater, OK, USA, 1992. [Google Scholar]
- Norris, G.R.; Haan, C.T. Impact of Subdivining Watersheds on Estimated Hydrographs. Am. Soc. Agric. Eng.
**1993**, 9, 443–445. [Google Scholar] [CrossRef] - Zhang, Z.; Koren, V.; Smith, M.; Reed, S.; Wang, D. Use of Next Generation Weather Radar Data and Basin Disaggregation to Improve Continuous Hydrograph Simulations. J. Hydrol. Eng.
**2004**, 9, 103–115. [Google Scholar] [CrossRef] - Arabi, M.; Govindaraju, R.S.; Hantush, M.M.; Engel, B.A. Role of Watershed Subdivision on Modeling the Effectiveness of Best Management Practices With Swat 1. J. Am. Water Resour. Assoc.
**2006**, 45268, 513–528. [Google Scholar] [CrossRef] - Cho, J.; Lowrance, R.R.; Bosch, D.D.; Strickland, T.C.; Her, Y.; Vellidis, G. Effect of Watershed Subdivision and Filter Width on Swat Simulation of a Coastal Plain watershed1. J. Am. Water Resour. Assoc.
**2010**, 46, 586–602. [Google Scholar] [CrossRef] - FitzHugh, T.W.; Mackay, D.S. Impacts of Input Parameter Spatial Aggregation on an Agricultural Nonpoint Source Pollution Model. J. Hydrol.
**2000**, 236, 35–53. [Google Scholar] [CrossRef] - Kalin, L.; Govindaraju, R.S.; Hantush, M.M. Effect of Geomorphologic Resolution on Modeling of Runoff Hydrograph and Sedimentograph over Small Watersheds. J. Hydrol.
**2003**, 276, 89–111. [Google Scholar] [CrossRef] - Kumar, S.; Merwade, V. Impact of Watershed Subdivision and Soil Data Resolution on Swat Model Calibration and Parameter Uncertainty. J. Am. Water Resour. Assoc.
**2009**, 45, 1179–1196. [Google Scholar] [CrossRef] - Muleta, M.K.; Nicklow, J.W.; Bekele, E.G. Sensitivity of a Distributed Watershed Simulation Model to Spatial Scale. J. Hydrol. Eng.
**2007**, 12, 163–172. [Google Scholar] [CrossRef] - Thieken, A.H.; Lücke, A.; Diekkrüger, B.; Richter, O. Scaling Input Data by GIS for Hydrological Modelling. Hydrol. Process.
**1999**, 13, 611–630. [Google Scholar] [CrossRef] - Nour, M.H.; Smith, D.W.; El-Din, M.G.; Prepas, E.E. Effect of Watershed Subdivision on Water-Phase Phosphorus Modelling: An Artificial Neural Network Modelling Application. J. Environ. Eng. Sci.
**2008**, 7, 95–108. [Google Scholar] [CrossRef] - Kite, G.W. Manual for the SLURP Hydrological Model; The National Health Research Institutes (NHRI): Saskatoon, SK, Canada, 1997. [Google Scholar]
- Bathurst, J.C. Sensitivity Analysis of the Systeme Hydrologique Europeen for an Upland Catchment. J. Hydrol.
**1986**, 87, 103–123. [Google Scholar] [CrossRef] - Bruneau, P.; Gascuel-Odoux, C.; Robin, P.; Merot, P.; Beven, K. Sensitivity to Space and Time Resolution of a Hydrological Model Using Digital Elevation Data. Hydrol. Process.
**1995**, 9, 69–81. [Google Scholar] [CrossRef] - Manguerra, H.B.; Engel, B.A. Hydrologic Parameterization of Watersheds for Runoff Prediction Using SWAT. J. Am. Water Resour. Assoc.
**1998**, 34, 1149–1162. [Google Scholar] [CrossRef] - Molnár, D.K.; Julien, P.Y. Grid-Size Effects on Surface Runoff Modeling. J. Hydrol. Eng.
**2000**, 5, 8–16. [Google Scholar] [CrossRef] - Tao, T.; Kouwen, N. Remote Sensing and Fully Distributed Modelling for Flood Forecasting. J. Water Resour. Plan. Manag.
**1989**, 115, 809–823. [Google Scholar] [CrossRef] - Zhang, W.; Montgomery, D.R. Digital Elevation Model Grid Size, Landscape Representation, and Hydrologic Simulations. Water Resour. Res.
**1994**, 30, 1019–1028. [Google Scholar] [CrossRef] - Wood, E.F.; Sivapalan, M.; Beven, K.; Band, L. Effects of Spatial Variability and Scale with Implications to Hydrologic Modeling. J. Hydrol.
**1988**, 102, 29–47. [Google Scholar] [CrossRef] - Sasowsky, K.C.; Gardner, T.W. Watershed Configuration and Geographic Information System Parameterization for SPUR Model Hydrologic Simulations. J. Am. Water Resour. Assoc.
**1991**, 27, 7–18. [Google Scholar] [CrossRef] - Reggiani, P.; Sivapalan, M.; Hassanizadeh, S.M. A Unifying Framework for Watershed Thermodynamics: Balance Equations for Mass, Momentum, Energy and Entropy, and the Second Law of Thermodynamics. Adv. Water Resour.
**1998**, 22, 367–398. [Google Scholar] [CrossRef] - Reggiani, P.; Hassanizadeh, S.M.; Sivapalan, M.; Gray, W.G. A Unifying Framework for Watershed Thermodynamics: Constitutive Relationships. Adv. Water Resour.
**1999**, 23, 15–39. [Google Scholar] [CrossRef] - Reggiani, P.; Sivapalan, M.; Hassanizadeh, S. Conservation Equations Governing Hillslope Responses: Exploring the Physical Basis of Water Balance. Water Resour. Res.
**2000**, 36, 1845–1863. [Google Scholar] [CrossRef] - Reggiani, P.; Rientjes, T.H.M. Flux Parameterization in the Representative Elementary Watershed Approach: Application to a Natural Basin. Water Resour. Res.
**2005**, 41, 1–18. [Google Scholar] [CrossRef] - Daniel, E.B. Watershed Modeling and Its Applications: A State-of-the-Art Review. Open Hydrol. J.
**2011**, 5, 26–50. [Google Scholar] [CrossRef] - Leavesley, G.H.; Lichty, R.W.; Troutman, B.M.; Saindon, L.G. Precipitation-Runoff Modeling System: User’s Manual; United States Geological Survey (USGS): Denver, CO, USA, 1983.
- Flügel, W.-A. Delineating Hydrological Response Units by Geographical Information System Analyses for Regional Hydrological Modelling Using PRMS/MMS in the Drainage Basin of the River Brosl, Germany. Hydrol. Process.
**1995**, 9, 423–436. [Google Scholar] [CrossRef] - Leavesley, G.H.; Stannard, L.G. The Precipitation-Runoff Modeling System-PRMS. In Computer Models of Watershed Hydrology; Singh, V.P., Ed.; Water Resources Publication: Highlands Ranch, CO, USA, 1995; p. 1144. [Google Scholar]
- Flügel, W.-A. Combining GIS with Regional Hydrological Modelling Using Hydrological Response Units (HRUs): An Application from Germany. Math. Comput. Simul.
**1997**, 43, 297–304. [Google Scholar] [CrossRef] - Bongartz, K. Applying Different Spatial Distribution and Modelling Concepts in Three Nested Mesoscale Catchments of Germany. Phys. Chem. Earth
**2003**, 28, 1343–1349. [Google Scholar] [CrossRef] - Srinivasan, R.; Muttiah, R.S.; Dyke, P.T.; Walker, C.; Arnold, J. Hydrologic Unit Model for the United States (HUMUS); Texas Agricultural Experiment Station: Temple, TX, USA, 2000. [Google Scholar]
- Neitsch, S.L.; Arnold, J.G.; Kiniry, J.R.; Williams, J.R.; King, K.W. Soil and Water Assessment Tool Theoretical Documentation, Version 2000; Texas Water Resources Institute: Temple, TX, USA, 2002. [Google Scholar]
- Gitau, M.W. A Quantitative Assessment of BMP Effectiveness for Phosphorus Pollution Control: The Town Brook Watershed; The Pennsylvannia State University: State College, PA, USA, 2003. [Google Scholar]
- Gassman, P.W.; Reyes, M.R.; Green, C.H.; Arnold, J.G. The Soil and Water Assessment Tool: Historical Development, Applications, and Future Research Directions. Trans. Asabe
**2007**, 50, 1211–1250. [Google Scholar] [CrossRef] - Gassman, P.W. A Simulation Assessment of the Boone River Watershed: Baseline Calibration/Validation Results and Issues, and Future Needs. Ph.D. Thesis, Iowa State University, Ameas, IA, USA, 2008. [Google Scholar]
- The National Regulator for Compulsory Specifications (NRCS). SCS National Engineering Handbook, Section 4; NRCS: Washington, DC, USA, 1972. [Google Scholar]
- The National Regulator for Compulsory Specifications (NRCS). National Engineering Handbook: Part 630—Hydrology; The National Regulator for Compulsory Specifications (NRCS): Washington, DC, USA, 2004. [Google Scholar]
- Soulis, K.X.; Valiantzas, J.D. SCS-CN Parameter Determination Using Rainfall-Runoff Data in Heterogeneous Watersheds—The Two-CN System Approach. Hydrol. Earth Syst. Sci.
**2012**, 16, 1001–1015. [Google Scholar] [CrossRef][Green Version] - Banasik, K.; Rutkowska, A.; Kohnová, S. Retention and Curve Number Variability in a Small Agricultural Catchment: The Probabilistic Approach. Water
**2014**, 6, 1118–1133. [Google Scholar] [CrossRef] - Montgomery, D.R.; Dietrich, W.E. Runoff Generation in a Steep, Soil-Mantled Landscape. Water Resour. Manag.
**2002**, 38, 1168. [Google Scholar] [CrossRef] - Huang, M.; Gallichand, J.; Wang, Z.; Goulet, M. A Modification to the Soil Conservation Service Curve Number Method for Steep Slopes in the Loess Plateau of China. Hydrol. Process.
**2006**, 20, 579–589. [Google Scholar] [CrossRef] - Xu, Y.; Fu, B.; Gao, G.; He, C. Watershed Discretization Based on Multiple Factors and Its Application in the Chinese Loess Plateau. Hydrol. Earth Syst. Sci.
**2011**, 8, 9063–9087. [Google Scholar] [CrossRef] - Deshmukh, D.S.; Chaube, U.C.; Hailu, A.E.; Gudeta, D.A.; Kassa, M.T. Estimation and Comparison of Curve Numbers Based on Dynamic Land Use Land Cover Change, Observed Rainfall-Runoff Data and Land Slope. J. Hydrol.
**2013**, 492, 89–101. [Google Scholar] [CrossRef] - Verma, S.; Verma, R.K.; Mishra, S.K.; Singh, A.; Jayaraj, G.K. A Revisit of NRCS-CN Inspired Models Coupled with RS and GIS for Runoff Estimation. Hydrol. Sci. J.
**2017**, 62, 1891–1930. [Google Scholar] [CrossRef] - Merheb, M.; Moussa, R.; Abdallah, C.; Colin, F.; Perrin, C.; Baghdadi, N. Hydrological Response Characteristics of Mediterranean Catchments at Different Time Scales: A Meta-Analysis. Hydrol. Sci. J.
**2016**, 61, 2520–2539. [Google Scholar] [CrossRef] - Efstratiadis, A.; Koussis, A.D.; Koutsoyiannis, D.; Mamassis, N. Flood Design Recipes vs. Reality: Can Predictions for Ungauged Basins Be Trusted? Nat. Hazards Earth Syst. Sci.
**2014**, 14, 1417–1428. [Google Scholar] [CrossRef] - Efstratiadis, A.; Koukouvinos, A.; Michaelidi, E.; Galiouna, E.; Tzouka, K.; Koussis, A.D.; Mamassis, N.; Koutsoyiannis, D. Description of Regional Approaches for the Estimation of Characteristic Hydrological Quantities, DEUCALION—Assessment of Flood Flows in Greece under Conditions of Hydroclimatic Variability: Development of Physically-Established Conceptual-Probabilistic; Department of Water Resources and Environmental Engineering—National Technical University of Athens, National Observatory of Athens: Athens, Greece, 2014. [Google Scholar]
- Kowalik, T.; Walega, A. Estimation of CN Parameter for Small Agricultural Watersheds Using Asymptotic Functions. Water
**2015**, 7, 939–955. [Google Scholar] [CrossRef] - Efstratiadis, A.; Koutsoyiannis, D. One Decade of Multiobjective Calibration Approaches in Hydrological Modelling: A Review. Hydrol. Sci. J.
**2010**, 55, 58–78. [Google Scholar] [CrossRef] - Jakeman, A.J.; Hornberger, G.M. How Much Complexity Is Warranted in a Rainfall-Runoff Model? Water Resour. Res.
**1993**, 29, 2637–2649. [Google Scholar] [CrossRef] - Fenicia, F.; Kavetski, D.; Savenije, H.H.G.; Clark, M.P.; Schoups, G.; Pfister, L.; Freer, J. Catchment Properties, Function, and Conceptual Model Representation: Is There a Correspondence? Hydrol. Process.
**2014**, 28, 2451–2467. [Google Scholar] [CrossRef] - Wheater, H.S.; Bishop, K.H.; Beck, M.B. He Identification of Conceptual Hydrological Models for Surface Water Acidification. Hydrol. Process.
**1986**, 1, 89–109. [Google Scholar] [CrossRef] - Wagener, T.; Boyle, D.P.; Lees, M.J.; Wheater, H.S.; Gupta, H.V.; Sorooshian, S. A Framework for Development and Application of Hydrological Models. Hydrol. Earth Syst. Sci.
**2001**, 5, 13–26. [Google Scholar] [CrossRef] - Fenicia, F.; Kavetsi, D.; Savenije, H.H.G.; Pfister, L. From Spatially Variable Streamflow Todistributed Hydrological Models: Analysis of Key Modeling Decisions. Water Resour. Res.
**2016**, 52, 1–36. [Google Scholar] [CrossRef] - Pollacco, J.A.P.; Mohanty, B.P.; Efstratiadis, A. Weighted Objective Function Selector Algorithm for Parameter Estimation of SVAT Models with Remote Sensing Data. Water Resour. Res.
**2013**, 49, 6959–6978. [Google Scholar] [CrossRef] - Silvestro, F.; Gabellani, S.; Rudari, R.; Delogu, F.; Laiolo, P.; Boni, G. Uncertainty Reduction and Parameter Estimation of a Distributed Hydrological Model with Ground and Remote-Sensing Data. Hydrol. Earth Syst. Sci.
**2015**, 19, 1727–1751. [Google Scholar] [CrossRef] - Efstratiadis, A.; Koukouvinos, A.; Dimitriadis, P.; Rozos, E.; Koussis, A.D. Theoretical Documentation of Hydrological-Hydraulic Simulation Model, DEUCALION—Assessment of Flood Flows in Greece under Conditions of Hydroclimatic Variability: Development of Physically-Established Conceptual-Probabilistic Framework and Computational; Department of Water Resources and Environmental Engineering—National Technical University of Athens, National Observatory of Athens: Athens, Greece, 2014. [Google Scholar]
- Koutsoyiannis, D.; Andreadakis, A.; Mavrodmou, R.; Christofides, A.; Mamassis, N.; Efstratiadis, A.; Koukouvinos, A.; Karavokiros, G.; Kozanis, S.; Mamais, D.; et al. National Programme for the Management and Protection of Water Resources; Department of Water Resources and Environmental Engineering—National Technical University of Athens: Athens, Greece, 2008. [Google Scholar]
- Efstratiadis, A.; Koussis, A.D.; Lykoudis, S.; Koukouvinos, A.; Christofides, A.; Karavokiros, G.; Kappos, N.; Mamassis, N.; Koutsoyiannis, D. Hydrometeorological Network for Flood Monitoring and Modeling. In Proceedings of First International Conference on Remote Sensing and Geoinformation of Environment; (SPIE) Society of Photo-Optical Instrumentation Engineers: Paphos, Cyprus, 2013; Volume 8795. [Google Scholar]
- Tegos, A.; Efstratiadis, A.; Koutsoyiannis, D. A Parametric Model for Potential Evapotranspiration Estimation Based on a Simplified Formulation of the Penman-Monteith Equation. In Evapotranspiration—An Overview; Alexandris, S.G., Ed.; InTech: Hongkong, China, 2013; p. 24. [Google Scholar]
- Tegos, A.; Malamos, N.; Efstratiadis, A.; Tsoukalas, I.; Karanasios, A.; Koutsoyiannis, D. Parametric Modelling of Potential Evapotranspiration: A Global Survey. Water
**2017**, 9, 795. [Google Scholar] [CrossRef] - Freer, J.; Beven, K.J.; Ambroise, B. Bayesian Estimation of Uncertainty in Runoff Prediction and the Value of Data: An Application of the GLUE Approach. Water Resour. Manag.
**1996**, 32, 2161–2173. [Google Scholar] [CrossRef] - Moriasi, D.N.; Arnold, J.G.; Van Liew, M.W.; Bingner, R.L.; Harmel, R.D.; Veith, T.L. Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations. Trans. Am. Soc. Agric. Biol. Eng.
**2007**, 50, 885–900. [Google Scholar] - Winchell, M.; Srinivasan, R.; Di Luzio, M.; Arnold, J.G. ArcSwat Interface for SWAT 2012: Users Guide; Blackland Research Centre, Texas Agrivcultural Experiment Station: Temple, TX, USA, 2013. [Google Scholar]
- Boyle, D.P.; Gupta, H.V.; Sorooshian, S. Toward Improved Calibration of Hydrologic Models: Combining the Strengths of Manual and Automatic Methods. Water Resour. Res.
**2000**, 36, 3663–3674. [Google Scholar] [CrossRef] - Mazi, K.; Koussis, A.D.; Restrepo, P.J.; Koutsoyiannis, D. A Groundwater-Based, Objective-Heuristic Parameter Optimisation Method for the PRMS Model: The Akrotiri Basin, Cyprus Application. J. Hydrol.
**2004**, 290, 243–258. [Google Scholar] [CrossRef] - Mazi, K.; Koussis, A.D.; Restrepo, P.J.; Koutsoyiannis, D. Erratum: A Groundwater-Based, Objective-Heuristic Parameter Optimisation Method for the PRMS Model: The Akrotiri Basin, Cyprus Application. J. Hydrol.
**2004**, 299, 160–161. [Google Scholar] [CrossRef] - Rozos, E.; Efstratiadis, A.; Nalbantis, I.; Koutsoyiannis, D. Calibration of a Semi-Distributed Model for Conjunctive Simu-Lation of Surface and Groundwater Flow. Hydrol. Sci. J.
**2004**, 49, 819–842. [Google Scholar] [CrossRef] - Rozos, E.; Koutsoyiannis, D. A Multicell Karstic Aquifer Model with Alternative Flow Equations. J. Hydrol.
**2006**, 325, 340–355. [Google Scholar] [CrossRef] - Koussis, A.D. Assessment and Review of the Hydraulics of Storage Flood Routing 70 Years after the Presentation of the Muskingum Method. Hydrol. Sci. J.
**2009**, 54, 43–61. [Google Scholar] [CrossRef] - Koussis, A.D. Reply to the Discussion of “Assessment and Review of the Hydraulics of Storage Flood Routing 70 Years after the Presentation of the Muskingum Method” by M. Perumal. Hydrol. Sci. J.
**2010**, 55, 1431–1441. [Google Scholar] [CrossRef] - Nash, J.E.; Sutcliffe, J.V. River Flow Forecasting through Conceptual Models Part I—A Discussion of Principles. J. Hydrol.
**1970**, 10, 282–290. [Google Scholar] [CrossRef] - Kottegoda, N.T. Stochastic Water Resources Technology; McMillan Press: Hong Kong, China, 1980. [Google Scholar]
- Efstratiadis, A.; Koutsoyiannis, D. An Evolutionary Annealing-Simplex Algorithm for Global Optimisation of Water Resource Systems. In Proceedings of the Fifth International Conference on Hydroinformatics; International Water Association: Cardiff, UK, 2002; pp. 1423–1428. [Google Scholar]
- Tsoukalas, I.; Kossieris, P.; Efstratiadis, A.; Makropoulos, C. Surrogate-Enhanced Evolutionary Annealing Simplex Algorithm for Effective and Efficient Optimization of Water Resources Problems on a Budget. Environ. Model. Softw.
**2016**, 77, 122–142. [Google Scholar] [CrossRef]

**Figure 1.**(

**a**) Layers of geographic information for permeability classes (${i}_{\mathrm{PERM}}$), vegetation density classes (${i}_{\mathrm{VEG}}$) and drainage capacity classes (${i}_{\mathrm{SLOPE}}$); (

**b**) layer overlay; (

**c**) CN parameter map.

**Figure 3.**HRU delineation based on the CN map: (

**a**) CN parameter classes and (

**b**) configuration of two HRUs, using CN = 63 as the threshold to determine the associated CN classes.

**Figure 4.**Digital map of Nedontas River Basin, also showing monitoring stations (meteorological and hydrometric) and the main modelling components (junctions, sub-basins, river segments).

**Figure 5.**(

**a**) Water permeability classes; (

**b**) land cover classes; (

**c**) terrain slope (%) classes; (

**d**) CN parameter map of the initial Nedontas River Basin map of 18 CN classes.

**Figure 7.**HRU delineation of Nedontas River Basin: (

**a**) SWAT GIS overlay and threshold definition method, 31 HRUs; (

**b**) HYDROGEIOS union of layers method, four HRUs.

**Figure 8.**Computed vs. observed discharge series at the basin outlet (Latomeio Baka) for HRU delineation with: (

**a**) unique combination; (

**b**) union of layers; (

**c**) CN approach.

**Figure 9.**Computed vs. observed discharge series at Karveliotis monitoring station for HRU delineation with: (

**a**) unique combination; (

**b**) union of layers; (

**c**) CN approach.

**Figure 10.**Computed vs. observed discharge series at Alagonia monitoring station for HRU delineation with: (

**a**) unique combination; (

**b**) union of layers; (

**c**) CN approach.

**Table 1.**Coding of the physiographic characteristics for the estimation of parameter Curve Number (CN) for reference conditions (Antecedent Soil Moisture (AMC) Type II and initial abstraction ratio 20%).

Permeability Class | ${\mathit{i}}_{\mathbf{P}\mathbf{E}\mathbf{R}\mathbf{M}}$ | Vegetation Class | ${\mathit{i}}_{\mathbf{V}\mathbf{E}\mathbf{G}}$ | Drainage Capacity Class | ${\mathit{i}}_{\mathbf{S}\mathbf{L}\mathbf{O}\mathbf{P}\mathbf{E}}$ |
---|---|---|---|---|---|

Very high | 1 | Dense | 1 | Negligible | 1 |

High | 2 | Moderate | 2 | Low | 2 |

Moderate | 3 | Low | 3 | Moderate | 3 |

Low | 4 | Sparse | 4 | High | 4 |

Very low | 5 | Negligible | 5 | Very high | 5 |

Basin | Area (km^{2}) | Examined Events | Average Runoff Coefficient | Average Observed CN (Adjusted) | Reference CN |
---|---|---|---|---|---|

Nedontas | 120.8 | 11 | 0.121 | 53 | 61 |

Karveliotis | 15.3 | 10 | 0.171 | 52 | 65 |

Alagonia | 20.9 | 10 | 0.350 | 74 | 70 |

Lousios | 166.3 | 11 | 0.156 | 70 | 63 |

Sarantapotamos | 144.6 | 12 | 0.059 | 62 | 48 |

Oinoe | 51.2 | 12 | 0.012 | 52 | 45 |

Chalandri stream | 5.2 | 1 | 0.215 | 88 | 62 |

Drafi | 15.7 | 10 | 0.094 | 52 | 54 |

Lykorema | 7.9 | 11 | 0.101 | 78 | 52 |

Peristerona (Cyprus) | 77.1 | 14 | 0.407 | 72 | 71 |

Xeros (Cyprus) | 68.5 | 10 | 0.279 | 70 | 66 |

Sub-Basin | Name | Area (km^{2}) | Mean Elevation (m) | Main Stream Length (m) | Mean Slope (%) |
---|---|---|---|---|---|

W60 | Nedousa | 19.47 | 1008.9 | 4296 | 23.1 |

W180 | Alagonia | 20.72 | 1092.4 | 497 | 18.2 |

W130 | Karveliotis | 14.92 | 1096.5 | 1721 | 21.2 |

W170 | Downstream of Alagonia | 10.91 | 739.4 | 2807 | 20.3 |

W120 | Downstream of Karveliotis | 11.81 | 710.1 | 5755 | 25.9 |

W70 | Downstream of Nedousa | 12.53 | 741.9 | 4589 | 26.0 |

W90 | Latomeio Baka | 28.2 | 654.8 | 5921 | 18.4 |

**Table 4.**Overall model performance, expressed in terms of the composite error function, considering alternative parameterizations, by means of the number of HRUs.

HRUs | CN Classes | HRU Parameters | No. of Trials | Error Measure in Calibration | Error Measure in Validation |
---|---|---|---|---|---|

1 | 29-91 | 7 | 2000 | 2.465 | 3.674 |

2 | 29–61 | 14 | 4000 | 2.072 | 3.043 |

64–91 | |||||

3 | 29–57 | 21 | 6000 | 1.985 | 2.675 |

61–67 | |||||

70–91 | |||||

4 | 29–55 | 28 | 8000 | 2.094 | 3.621 |

58–64 | |||||

67–70 | |||||

73–91 | |||||

5 | 29–49 | 35 | 10,000 | 2.390 | 3.519 |

52–58 | |||||

61–64 | |||||

67–70 | |||||

73–91 |

Hydrometric Station | Calibration Period | Validation Period | ||
---|---|---|---|---|

Efficiency | High Flow Efficiency | Efficiency | High Flow Efficiency | |

One HRU | ||||

Basin outlet: Latomeio Baka | 0.524 | 0.297 | 0.591 | −0.521 |

Karveliotis | 0.672 | −0.166 | 0.687 | 0.218 |

Alagonia | 0.662 | 0.054 | 0.708 | 0.416 |

Two URHs | ||||

Basin outlet: Latomeio Baka | 0.798 | 0.810 | 0.560 | −0.793 |

Karveliotis | 0.887 | 0.676 | 0.712 | 0.259 |

Alagonia | 0.674 | 0.083 | 0.722 | 0.496 |

Three HRUs | ||||

Basin outlet: Latomeio Baka | 0.672 | 0.722 | 0.706 | −0.067 |

Karveliotis | 0.892 | 0.761 | 0.809 | 0.571 |

Alagonia | 0.721 | 0.248 | 0.736 | 0.571 |

Four HRUs | ||||

Basin outlet: Latomeio Baka | 0.773 | 0.814 | 0.643 | −0.407 |

Karveliotis | 0.869 | 0.631 | 0.738 | 0.285 |

Alagonia | 0.673 | 0.145 | 0.724 | 0.513 |

Five HRUs | ||||

Basin outlet: Latomeio Baka | 0.716 | 0.762 | 0.567 | −0.563 |

Karveliotis | 0.819 | 0.521 | 0.793 | 0.622 |

Alagonia | 0.737 | 0.160 | 0.672 | 0.495 |

Unique Combination | Union of Layers | CN Approach | |
---|---|---|---|

No. of HRUs | 31 | 4 | 3 |

No. of HRU parameters | 217 | 28 | 21 |

Total No. of parameters | 233 | 44 | 37 |

Relative effort within hybrid calibration * | ~3 | ~1.5 | 1 |

**Table 7.**Optimal values of efficiency, high flow efficiency and average bias for three HRU delineation approaches.

Hourly Runoff | Calibration Period | Validation Period | ||
---|---|---|---|---|

Efficiency | High Flow Eff. | Efficiency | High Flow Eff. | |

HRU delineation | Unique combination | |||

Basin outlet: Latomeio Baka | 0.768 | 0.809 | 0.719 | 0.068 |

Karveliotis | 0.908 | 0.663 | 0.813 | 0.580 |

Alagonia | 0.754 | 0.253 | 0.680 | 0.141 |

HRU delineation | Union of layers | |||

Basin outlet: Latomeio Baka | 0.801 | 0.757 | 0.771 | 0.195 |

Karveliotis | 0.655 | 0.693 | 0.762 | 0.368 |

Alagonia | 0.658 | 0.090 | 0.726 | 0.411 |

HRU delineation | CN based | |||

Basin outlet: Latomeio Baka | 0.814 | 0.816 | 0.802 | 0.241 |

Monitoring station: Karveliotis | 0.892 | 0.789 | 0.837 | 0.579 |

Monitoring station: Alagonia | 0.797 | 0.300 | 0.753 | 0.440 |

HRU | CN 29–59 | CN 61–69 | CN 73–91 |
---|---|---|---|

Total Area (km^{2}) | 40.2 | 54.8 | 22.9 |

Max. infiltration ratio (mm/h) | 80.0 | 61.3 | 39.4 |

Interception capacity (mm) | 50.0 | 39.4 | 5.0 |

Soil capacity up to spill (mm) | 393.5 | 377.0 | 50.0 |

Perc. of infiltration to the lower zone (%) | 78.8 | 54.083 | 5.292 |

Soil capacity up to interflow (mm) | 500.0 | 428.4 | 110.6 |

Recession rate for interflow (%) | 0.426 | 0.231 | 2.571 |

Recession rate for percolation (%) | 0.187 | 0.116 | 0.055 |

Variable | Equivalent Depths (mm) | Volumes (hm^{3}) | |
---|---|---|---|

Precipitation | 1690.3 | 200.1 | |

Evapotranspiration | 623.0 | 73.8 | |

Percolation | 607.1 | 71.9 | |

Surface runoff | 385.5 | 45.6 | |

Spring runoff | 249.6 | 29.6 | |

Underground losses | 343.7 | 49.9 | |

Soil storage change | 183.6 | 21.7 | |

Groundwater storage change | 29.3 | 3.5 | |

Outlet runoff | 557.2 | 66.0 | |

(a) | |||

Overall Water Balance | |||

Output Variable | Percentage of Precipitation (%) | ||

Evapotranspiration | 34.3 | ||

River outflow | 30.7 | ||

Underground losses | 23.2 | ||

Storage change | 11.7 | ||

(b) |

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Savvidou, E.; Efstratiadis, A.; Koussis, A.D.; Koukouvinos, A.; Skarlatos, D. The Curve Number Concept as a Driver for Delineating Hydrological Response Units. *Water* **2018**, *10*, 194.
https://doi.org/10.3390/w10020194

**AMA Style**

Savvidou E, Efstratiadis A, Koussis AD, Koukouvinos A, Skarlatos D. The Curve Number Concept as a Driver for Delineating Hydrological Response Units. *Water*. 2018; 10(2):194.
https://doi.org/10.3390/w10020194

**Chicago/Turabian Style**

Savvidou, Eleni, Andreas Efstratiadis, Antonis D. Koussis, Antonis Koukouvinos, and Dimitrios Skarlatos. 2018. "The Curve Number Concept as a Driver for Delineating Hydrological Response Units" *Water* 10, no. 2: 194.
https://doi.org/10.3390/w10020194