Comparative Application of Rain Gauge, Ground- and Space-Borne Radar Precipitation Products for Flood Simulations in a Dam Watershed in South Korea
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data
2.2.1. Land Surface Data
2.2.2. Gauged Data
2.2.3. Ground-Borne Radar Precipitation Product (RAR)
2.2.4. Space-Borne Radar Precipitation Product (IMERG)
2.3. Hydrologic Model
2.4. Methods
2.4.1. Flood Events Selection
2.4.2. Radar Precipitation Products Processing
2.4.3. HEC-HMS Model Development
2.4.4. Flood Simulation and Evaluation
3. Results and Discussion
3.1. Precipitation Estimates
3.1.1. Amounts
3.1.2. Spatial Variability
3.2. Model Development
3.3. Flood Simulations
4. Conclusions
- (1)
- Overall, the Korea Meteorological Administration’s ground-borne weather radar data (RAR) exhibited a slight underestimation of precipitation values compared to gauge point observations, with a difference of 28.2% across all eight flood events (R2 0.86). Consequently, adjustments to related model parameter values were necessary for flood analysis using the RAR data. However, due to the spatial distribution advantages of radar rainfall, aspects related to rainfall-runoff transformation and river channel routing, such as travel time, could be simulated to generate hydrographs that closely resembled the observed discharge without the need to adjust the related parameter values;
- (2)
- For space-borne (i.e., satellite-based) IMERG precipitation data, the total observed amount was underestimated by 15.1% across the eight flood events. However, the correlation coefficient (R2) was 0.46, indicating significant differences from the gauged data. Even with adjustments to parameter values, flood simulations using the IMERG product demonstrated relatively lower correlation and model efficiency compared to the observations. This indicates a significant limitation in using the half-hourly IMERG data for flood modeling in ungauged watersheds. Despite this constraint, it is still inferred that in locations where some discharge data are available, the utilization of the model through verification and calibration is feasible;
- (3)
- When comparing the flood simulation results using the conventional method based on rain gauge observations with those using weather radar and satellite-based precipitation data, the models utilizing both radar data sources exhibited an average Nash–Sutcliffe efficiency (ENS) of 0.863 and 0.776, an R2 of 0.873 and 0.787, and a percent bias (PBIAS) of 7.49% and 7.15%, respectively. The model using the areal-averaged values showed an ENS of 0.895, an R2 of 0.906, and a PBIAS of 7.42%. Although there were some differences, simulations using the RAR data demonstrated relatively satisfactory performance without adjusting parameter values, confirming their utility and efficiency;
- (4)
- Despite varying ratios ranging from 26.0% to 82.2% depending on antecedent rainfall conditions, the analysis of the eight selected flood events revealed the characteristic of watershed flood discharge with an average runoff ratio of 52.5%.
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
References
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Type | Station (Abrev.) | Longitude | Latitude | Elevation [MASL 1] |
---|---|---|---|---|
Precipitation | Gyebuk (GB) | 127°37′46″ E | 35°48′27″ N | 453.00 |
Janggye (JG) | 127°36′03″ E | 35°42′54″ N | 422.00 | |
Cheoncheon (CC) | 127°30′49″ E | 35°40′54″ N | 409.00 | |
Sangjeon (SJ) | 127°29′10″ E | 35°48′11″ N | 334.00 | |
Bugwi (BG) | 127°24′12″ E | 35°51′36″ N | 396.00 | |
Jucheon (JC) | 127°25′34″ E | 35°58′04″ N | 303.00 | |
Ancheon (AC) | 127°32′48″ E | 35°52′01″ N | 313.00 | |
Discharge | Yongdam Dam (YD) | 127°31′40″ E | 35°56′36″ N | 268.50 |
Cheoncheon (CC) | 127°31′38″ E | 35°47′19″ N | 273.50 | |
Donghyang (DH) | 127°32′41″ E | 35°49′59″ N | 291.50 | |
Dochi (DC) | 127°27′27″ E | 35°48′43″ N | 261.10 | |
Seokjeong (SJ) | 127°26′24″ E | 35°51′16″ N | 266.48 | |
Jucheon (JC) | 127°25′58″ E | 35°58′03″ N | 270.86 |
RAR Specifications | |||
---|---|---|---|
File Name: RDR_ROQCZ_CP15AA_$YYYY$MM$DD$HH$NN.bin.gz | |||
Resolution | Description | ||
Temporal | 10 min | ||
Spatial | 1 km | ||
Data structure | Description | ||
Map system | Lambert Conformal Conic projection | ||
Grid cell size | 1 km | ||
X and Y dimension | 1241 and 1761 | ||
Longitude of central meridian | 126.0° E (cell number 460) | ||
Latitude of the projection origin | 38.0° N (cell number 925) | ||
Data table structure | |||
Record | Item | Description | |
1 | Precipitation | float | mm/h |
2 | Radar coverage | unsigned char | 0: inner/1: outer |
3 | Map information | unsigned char | - |
IMERG Specifications | |
---|---|
File Name: 3B-HHR.MS.MRG.3IMERG.$YYYY$MM$DD-S$HH$MM$NN-E$HH$MM$NN.$MMMM.V06B.HDF5.nc4 | |
Resolution | Description |
Temporal | 30 min (final run, 3.5 months latency) |
Spatial | about 10 km (from 90° N–90° S) 60° N–60° S full |
Data structure | Description |
Map system | WGS84 |
Storm Events | Rain Gauge Precipitation Total (mm) | Discharge | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
# | Period | GB | JG | CC | SJ | BG | JC | AC | Areal Average | Peak (m3/s) | Total (mm) |
1 | 17 August 2014 13:00~2014.08.20 12:00 | 97 | 114 | 128 | 100 | 121 | 100 | 101 | 108.3 | 1339.9 | 74.2 |
2 | 24 August 2014 01:00~2014.08.28 24:00 | 90 | 71 | 64 | 106 | 57 | 87 | 101 | 82.2 | 906.0 | 67.6 |
3 | 8 August 2015 13:00~2015.07.11 12:00 | 51 | 37 | 47 | 77 | 59 | 61 | 69 | 57.3 | 269.7 | 21.7 |
4 | 1 July 2016 13:00~2016.07.03 12:00 | 119 | 128 | 153 | 125 | 101 | 124 | 115 | 122.9 | 729.7 | 31.9 |
5 | 16 September 2016 13:00~2016.09.19 24:00 | 147 | 129 | 158 | 148 | 134 | 145 | 162 | 146.1 | 517.3 | 38.3 |
6 | 10 September 2017 01:00~2017.09.12 24:00 | 69 | 77 | 78 | 73 | 73 | 57 | 78 | 71.8 | 341.4 | 29.7 |
7 | 25 August 2018 13:00~2018.08.30 12:00 | 245 | 286 | 331 | 316 | 288 | 269 | 285 | 286.3 | 2192.9 | 168.5 |
8 | 30 August 2018 13:00~2018.09.02 12:00 | 84 | 110 | 98 | 84 | 69 | 100 | 61 | 86.0 | 1127.8 | 68.1 |
Dataset | Data Processing and Program | ||
---|---|---|---|
Conversion | Geo-Referencing | DSS File Generation | |
RAR | Binary to ASCII <NCL script> | Lambert Conformal Conic to SHG grid (Albers Equal-Area) <Python script> | HEC-GridUtil <asc2dssGrid.exe> |
IMERG | netCDF4 to ASCII 1 | WGS84 to SHG grid (ITRF2000) 1 |
Hydrologic Element | Calculation Type | Methods | |
---|---|---|---|
Gauged Data Simulation | Radar-Based Data Simulation | ||
Precipitation | Gauge Weights (Thiessen polygon) | Gridded data (RAR, IMERG) | |
Subbasin | Runoff-depth | SCS Curve Number (CN) | Gridded SCS CN |
Direct-runoff (Transform) | Clark Unit Hydrograph (Clark) | Modified Clark Method (ModClark) | |
Baseflow | Recession | ||
Reach | Routing | Muskingum |
Storm Events | Precipitation Total (mm) | |||||||
---|---|---|---|---|---|---|---|---|
# | Period | Gauged Data | Ground-Borne Radar Data | Space-Borne Radar Data | ||||
Areal Average | Min | Max | Areal Average | Min | Max | Areal Average | ||
1 | 17 August 2014 13:00~2014.08.20 12:00 | 108.3 | 64.8 | 146.9 | 88.7 | 96.5 | 174.2 | 134.4 |
2 | 24 August 2014 01:00~2014.08.28 24:00 | 82.2 | 37.4 | 100.1 | 58.9 | 80.9 | 113.2 | 99.5 |
3 | 8 August 2015 13:00~2015.07.11 12:00 | 57.3 | 16.8 | 65.3 | 38.1 | 9.2 | 81.2 | 39.0 |
4 | 1 July 2016 13:00~2016.07.03 12:00 | 122.9 | 12.3 | 183.5 | 96.9 | 116.7 | 152.3 | 138.9 |
5 | 16 September 2016 13:00~2016.09.19 24:00 | 146.1 | 15.3 | 145.2 | 101.1 | 91.6 | 121.3 | 102.0 |
6 | 10 September 2017 01:00~2017.09.12 24:00 | 71.8 | 5.1 | 77.8 | 50.6 | 32.2 | 56.1 | 42.1 |
7 | 25 August 2018 13:00~2018.08.30 12:00 | 286.3 | 22.7 | 305.9 | 198.6 | 128.4 | 218.9 | 189.4 |
8 | 30 August 2018 13:00~2018.09.02 12:00 | 86.0 | 4.8 | 113.3 | 56.7 | 50.2 | 110.7 | 71.0 |
Hydrologic Element | Process | Initial Parameter Values | |
---|---|---|---|
Gauged Data Simulation | Radar-based Data Simulation | ||
Subbasin | Loss | SCS Curve Number (CN) - CN: determined - Initial abstraction (mm): 0 - Impervious (%): 0 | Gridded SCS CN - CN: determined - Ratio: 0.05 - Factor: 1.0 |
Transform | Clark Unit Hydrograph and ModClark - Time of concentration (HR): determined - Storage coefficient (HR): 2.0 | ||
Baseflow | Recession - Initial discharge (m3/s): observed - Recession constant: 0.2 - Ratio to peak: 0.4 | ||
Reach | Routing | Muskingum - Muskingum K (HR): 0.25 - Muskingum X: 0.25 - Number of subreaches: 1 |
Storm Events # | Gauged Data Simulation | Ground-Borne Radar Data Simulation | Space-Borne Radar Data Simulation | ||||||
---|---|---|---|---|---|---|---|---|---|
ENS | R2 | PBIAS (%) | ENS | R2 | PBIAS (%) | ENS | R2 | PBIAS (%) | |
1 | 0.914 | 0.937 | 16.97 | 0.914 | 0.914 | 4.01 | 0.878 | 0.878 | −1.20 |
2 | 0.941 | 0.945 | −5.61 | 0.919 | 0.925 | 9.54 | 0.842 | 0.843 | −3.14 |
3 | 0.844 | 0.853 | −9.05 | 0.716 | 0.752 | −18.06 | 0.663 | 0.697 | −13.90 |
4 | 0.920 | 0.928 | −7.35 | 0.921 | 0.920 | −2.57 | 0.906 | 0.905 | −0.99 |
5 | 0.893 | 0.902 | −6.69 | 0.912 | 0.921 | −7.28 | 0.611 | 0.629 | 14.35 |
6 | 0.800 | 0.812 | 0.38 | 0.763 | 0.765 | −0.63 | 0.773 | 0.790 | 9.05 |
7 | 0.930 | 0.948 | 11.87 | 0.895 | 0.896 | −0.18 | 0.670 | 0.673 | −7.34 |
8 | 0.921 | 0.925 | 1.43 | 0.865 | 0.891 | −17.64 | 0.861 | 0.878 | −7.19 |
Avg. * | 0.895 | 0.906 | 7.42 | 0.863 | 0.873 | 7.49 | 0.776 | 0.787 | 7.15 |
Storm Events # | Gauged Data Simulation | Ground-Borne Radar Data Simulation | Space-Borne Radar Data Simulation | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
CC | DH | DC | SJ | JC | CC | DH | DC | SJ | JC | CC | DH | DC | SJ | JC | |
1 | 0.81 | 0.55 | 0.61 | 0.88 | 0.68 | 0.93 | 0.90 | 0.60 | 0.80 | 0.35 | 0.88 | 0.82 | 0.73 | 0.69 | 0.27 |
2 | 0.92 | 0.92 | 0.71 | 0.88 | 0.38 | 0.93 | 0.92 | 0.66 | 0.48 | 0.40 | 0.71 | 0.70 | 0.65 | 0.89 | 0.44 |
3 | 0.66 | 0.93 | 0.51 | 0.92 | 0.85 | 0.93 | 0.73 | 0.15 | 0.80 | 0.74 | 0.89 | 0.75 | −0.1 | 0.74 | −0.14 |
4 | 0.74 | 0.81 | N/A * | 0.86 | 0.60 | 0.81 | 0.85 | N/A * | 0.76 | 0.31 | 0.83 | 0.89 | N/A * | 0.77 | 0.79 |
5 | 0.95 | 0.95 | N/A * | 0.08 | 0.65 * | 0.96 | 0.90 | N/A * | 0.61 | −0.42 * | 0.91 | 0.85 | N/A * | −0.28 | −1.18 * |
6 | 0.86 | 0.91 | 0.82 | N/A * | 0.94 | 0.96 | 0.95 | 0.86 | N/A * | 0.86 | 0.95 | 0.90 | 0.61 | N/A * | 0.79 |
7 | −0.74 * | 0.95 | −4.58 * | −2.02 * | N/A * | −0.08 * | 0.79 | −2.89 * | −3.09 * | N/A * | 0.64 * | 0.49 | 0.40 * | 0.57 * | N/A * |
8 | 0.85 | 0.94 | −1.19 * | 0.97 | N/A * | 0.94 | 0.83 | −1.46 * | 0.96 | N/A * | 0.74 | 0.94 | 0.60 * | 0.86 | N/A * |
Avg. | 0.83 | 0.87 | 0.66 | 0.77 | 0.69 | 0.92 | 0.86 | 0.57 | 0.74 | 0.53 | 0.83 | 0.79 | 0.47 | 0.61 | 0.43 |
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Cho, Y. Comparative Application of Rain Gauge, Ground- and Space-Borne Radar Precipitation Products for Flood Simulations in a Dam Watershed in South Korea. Water 2023, 15, 2898. https://doi.org/10.3390/w15162898
Cho Y. Comparative Application of Rain Gauge, Ground- and Space-Borne Radar Precipitation Products for Flood Simulations in a Dam Watershed in South Korea. Water. 2023; 15(16):2898. https://doi.org/10.3390/w15162898
Chicago/Turabian StyleCho, Younghyun. 2023. "Comparative Application of Rain Gauge, Ground- and Space-Borne Radar Precipitation Products for Flood Simulations in a Dam Watershed in South Korea" Water 15, no. 16: 2898. https://doi.org/10.3390/w15162898
APA StyleCho, Y. (2023). Comparative Application of Rain Gauge, Ground- and Space-Borne Radar Precipitation Products for Flood Simulations in a Dam Watershed in South Korea. Water, 15(16), 2898. https://doi.org/10.3390/w15162898