the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
06 Oct 2021
06 Oct 2021
Hydrological modeling using the SWAT Model in urban and peri-urban environments: The case of Kifissos experimental sub-basin (Athens, Greece)
- 1Laboratory of Engineering Geology and Hydrogeology, School of Mining and Metallurgical Engineering, National Technical University of Athens, Heroon Polytechniou Str. 9, 15780 Zografou, Athens, Greece
- 2Laboratory of Hydrology and Water Resources Development, School of Civil Engineering, National Technical University of Athens, Heroon Polytechneiou Str. 9, 157 80 Zographou, Greece
- 1Laboratory of Engineering Geology and Hydrogeology, School of Mining and Metallurgical Engineering, National Technical University of Athens, Heroon Polytechniou Str. 9, 15780 Zografou, Athens, Greece
- 2Laboratory of Hydrology and Water Resources Development, School of Civil Engineering, National Technical University of Athens, Heroon Polytechneiou Str. 9, 157 80 Zographou, Greece
Abstract. SWAT (Soil and Water Assessment Tool) is a continuous time, semi-distributed river basin model that has been widely used to evaluate the effects of alternative management decisions on water resources. This study, demonstrates the application of SWAT model for streamflow simulation in an experimental basin with daily and hourly rainfall observations to investigate the influence of rainfall resolution on model performance. The model was calibrated for 2018 and validated for 2019 using the SUFI-2 algorithm in the SWAT-CUP program. Daily surface runoff was estimated using the Curve Number method and hourly surface runoff was estimated using the Green and Ampt Mein Larson method. A sensitivity analysis conducted in this study showed that the parameters related to groundwater flow were more sensitive for daily time intervals and channel routing parameters were more influential for hourly time intervals. Model performance statistics and graphical techniques indicated that the daily model performed better than the sub-daily model. The Curve Number method produced higher discharge peaks than the Green and Ampt Mein Larson method and estimated better the observed values. Overall, the general agreement between observations and simulations in both models suggests that the SWAT model appears to be a reliable tool to predict discharge over long periods of time.
Evgenia Koltsida et al.
Status: open (until 01 Dec 2021)
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RC1: 'Comment on hess-2021-482', Anonymous Referee #1, 18 Oct 2021
reply
The paper is interesting and pleasant to read. I have only minor doubts that could be addressed before publication.
first of all, in the introduction (lines 33-45) the Authors provide an overview of experimental watersheds dealing with hydrological observations. Although the topic is interesting, I suggest to shorten this paragraph because the cited watersheds ar not considered in the present manuscript and the topic cuold be misleading for the reader.
Second, in introduction (lines 72-73) I believe that aims and novelty of the manuscript should be more enphasized.
Third, in paragraph 2.2, I found a little confusing the instruments description and the data that later are used in the manuscript. The Authors mention (lines 101-106) water level and water velocity sensor that are installed in the experimemental watershed, but specify only units in mm, what about the velocity? is the sensor present and used?
Forth (lines 296-297), the Authors mention an interesting effect of precipitation time step that could affect the result, but in my opinion the results could be affected also by the classic difficulty in obtaining reliable estimations of GAML parameters based on the soil type and heterogeneity. Eventually this issue could be discussed here.
Fifth (line 337) the Authors mention observational errors; could they specify if this could be attributed to the estimation of channel and hillslope flow velocities?
other minor corrections are:
line 81: route?Â
line 97: specify the acronym.
line 190: changing or constant?
line 198: do the numbers refer to discharge?
Â
Evgenia Koltsida et al.
Data sets
Weather data National Observatory of Athens, NOA https://www.meteo.gr
Discharge data Open Hydrosystem Information Network https://openhi.net/
Soil data Food and Agriculture Organization, FAO http://www.fao.org/
DEM data Shuttle Radar Topography Mission, SRTM https://earthexplorer.usgs.gov/
Land use data Corine Land Cover, CLC 2018 https://land.copernicus.eu/
Model code and software
SWAT code USDA Agricultural Research Service and Texas A&M AgriLife Research http://swat.tamu.edu/
Evgenia Koltsida et al.
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