Evaluation of Multi-Satellite Precipitation Products for Streamflow Simulations: A Case Study for the Han River Basin in the Korean Peninsula, East Asia
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Precipitation Data from Rain Gauges
2.3. Satellite-Derived Precipitation Products
2.3.1. TRMM 3B42 Precipitation Products
2.3.2. PERSIANN and PERSIANN-CDR Products
2.3.3. CMADS Precipitation Products
2.4. SWAT Model
2.5. Statistical Measures for Precipitation and Runoff
3. Results
3.1. Evaluation of Different Satellite-Derived Precipitation Data
3.2. SWAT Calibration and Validation
3.3. Streamflow Simulation Using Four Satellite-Derived Rainfall Datasets
4. Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
- González-Rouco, J.F.; Jiménez, J.L.; Quesada, V.; Valero, F. Quality control and homogeneity of precipitation data in the southwest of Europe. J. Clim. 2001, 14, 964–978. [Google Scholar] [CrossRef]
- Price, K.; Purucker, S.T.; Kraemer, S.R.; Babendreier, J.E.; Knightes, C.D. Comparison of radar and gauge precipitation data in watershed models across varying spatial and temporal scales. Hydrol. Process. 2014, 28, 3505–3520. [Google Scholar] [CrossRef]
- Sikorska, A.; Seibert, J. Importance of precipitation data quality for streamflow predictions. Geophys. Res. Abstr. 2015, 17, 13369. [Google Scholar]
- Buarque, D.C.; De Paiva, R.C.D.; Clarke, R.T.; Mendes, C.A.B. A comparison of Amazon rainfall characteristics derived from TRMM, CMORPH and the Brazilian national rain gauge network. J. Geophys. Res. Atmos. 2011, 116. [Google Scholar] [CrossRef]
- Kidd, C.; Bauer, P.; Turk, J.; Huffman, G.J.; Joyce, R.; Hsu, K.L.; Braithwaite, D. Intercomparison of high-resolution precipitation products over northwest Europe. J. Hydrometeorol. 2012, 13, 67–83. [Google Scholar] [CrossRef]
- Thorndahl, S.; Einfalt, T.; Willems, P.; Nielsen, J.E.; Veldhuis, M.C.; Arnbjerg-Nielsen, K.; Rasmussen, M.R.; Molnar, P. Weather radar rainfall data in urban hydrology. Hydrol. Earth Syst. Sci. 2017, 21, 1359–1380. [Google Scholar] [CrossRef]
- Westrick, K.J.; Mass, C.F.; Colle, B.A. The limitation of the WSR-88D radar network for quantitative precipitation measurement over the coastal western United States. Bull. Am. Meteorol. Soc. 1999, 80, 2289–2298. [Google Scholar] [CrossRef]
- Rico-Ramirez, M.A.; Liguori, S.; Schellart, A.N.A. Quantifying radar-rainfall uncertainties in urban drainage flow modeling. J. Hydrol. 2015, 528, 17–28. [Google Scholar] [CrossRef]
- Joyce, R.J.; Janowiak, J.E.; Arkin, P.A.; Xie, P. CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J. Hydrometeorol. 2004, 5, 487–503. [Google Scholar] [CrossRef]
- Kidd, C.; Huffman, G. Global precipitation measurement. Meteorol. Appl. 2011, 18, 334–353. [Google Scholar] [CrossRef]
- Kidd, C.; Levizzani, V. Status of satellite precipitation retrievals. Hydrol. Earth Syst. Sci. 2011, 15, 1109–1116. [Google Scholar] [CrossRef] [Green Version]
- Hou, A.Y.; Kakar, R.K.; Neeck, S.; Azarbarzin, A.A.; Kummerow, C.D.; Kojima, M.; Oki, R.; Nakamura, K.; Iguchi, T. The global precipitation measurement mission. Bull. Am. Meteorol. Soc. 2014, 95, 701–722. [Google Scholar] [CrossRef]
- Su, F.; Hong, Y.; Lettenmaier, D.P. Evaluation of TRMM multisatellite precipitation analysis (TMPA) and its utility in hydrologic prediction in the La Plata basin. J. Hydrometeorol. 2008, 9, 622–639. [Google Scholar] [CrossRef]
- Collischonn, B.; Collischonn, W.; Tucci, C.E.M. Daily hydrological modeling in the Amazon basin using TRMM rainfall estimates. J. Hydrol. 2008, 360, 207–216. [Google Scholar] [CrossRef]
- Scheel, M.L.M.; Rohrer, M.; Huggel, C.; Villar, D.S.; Huffman, G.J. Evaluation of TRMM multi-satellite precipitation analysis (TMPA) performance in the Central Andes region and its dependency on spatial and temporal resolution. Hydrol. Earth Syst. Sci. 2011, 15, 2649–2663. [Google Scholar] [CrossRef] [Green Version]
- Ouma, Y.O.; Owiti, T.; Kibiiy, J.; Ouma, Y.O.; Tateishi, R.; Kipkorir, E. Multitemporal comparative analysis of TRMM-3B42 satellite-estimated rainfall with surface gauge data at basin scales: Daily, decadal and monthly evaluations. Int. J. Remote Sens. 2012, 33, 7662–7684. [Google Scholar] [CrossRef]
- Xue, X.; Hong, Y.; Limaye, A.S.; Gourley, J.J.; Huffman, G.J.; Khan, S.I.; Dorji, C.; Chen, S. Statistical and hydrological evaluation of TRMM-based multi-satellite precipitation analysis over the Wangchu Basin of Bhutan: Are the latest satellite precipitation products ready for use in ungauged basins? J. Hydrol. 2013, 499, 91–99. [Google Scholar] [CrossRef]
- Stisen, S.; Sandholt, I. Evaluation of remote-sensing-based rainfall products through predictive capability in hydrological runoff modelling. Hydrol. Process. 2010, 24, 879–891. [Google Scholar] [CrossRef]
- Behrangi, A.; Khakbaz, B.; Jaw, T.C.; AghaKouchak, A.; Hsu, K.; Sorooshian, S. Hydrologic evaluation of satellite precipitation products over a mid-size basin. J. Hydrol. 2011, 397, 225–237. [Google Scholar] [CrossRef]
- Shen, Y.; Xiong, A.; Wang, Y.; Ha, P. Performance of high-resolution satellite precipitation products over China. J. Geophys. Res. Atmos. 2010, 115. [Google Scholar] [CrossRef]
- Hirpa, F.A.; Gebremichael, M.; Hopson, T. Evaluation of high-resolution satellite precipitation products over very complex terrain in Ethiopia. J. Appl. Meteorol. Climatol. 2010, 49, 1044–1051. [Google Scholar] [CrossRef]
- Meng, X.; Wang, H. Significance of the China meteorological assimilation driving datasets for the SWAT model (CMADS) of East Asia. Water (Switzerland) 2017, 9, 765. [Google Scholar] [CrossRef]
- Meng, X.Y.; Wang, H.; Lei, X.H.; Cai, S.Y.; Wu, H.J. Hydrological modeling in the Manas River Basin using Soil and Water Assessment Tool driven by CMADS. Teh. Vjesn. 2017, 24, 525–534. [Google Scholar]
- Setegn, S.G.; Donoso, M.C. Sustanability of Intergrated Water Resources Management: Water Governace, Climate and Ecohydrology; Springer: Basel, Switzerland, 2015. [Google Scholar]
- Bicknell, B.R.; Imhoff, J.C.; Kittle, J.L.; Donigian, A.S.; Johanson, R.C. Hydrologic Simulation Program—Fortran: User’s Manual for Release 11; Environmental Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency: Athens, GA, USA, 1996.
- Refsgaard, J.C.; Storm, B. MIKE SHE. In Computer Models of Watershed Hydrology; Sing, V.P., Ed.; Water Resource Publications: Highland Ranch, CO, USA, 1995; pp. 806–846. [Google Scholar]
- USACE. HEC-5 Simulation of Flood Control and Conservation System; US Army Corps of Engineers, Hydrologic Engineering Center: Davis, CA, USA, 1998. [Google Scholar]
- Arnold, J.G.; Srinivasan, R.; Muttiah, R.S.; Williams, J.R. Large area hydrologic modeling and assessment part I: Model development. J. Am. Water Resour. Assoc. 1998, 34, 73–89. [Google Scholar] [CrossRef]
- Sivapalan, M.; Takeuchi, K.; Franks, S.W.; Gupta, V.K.; Karambiri, H.; Lakashmi, V.; Liang, X.; McDonnell, J.J.; Mendiondo, E.M.; O’connell, P.E.; et al. IAHS Decade on Predictions in Ungauged Basins (PUB), 2003–2012: Shaping an exciting future for the hydrological sciences. Hydrol. Sci. J. 2003, 48, 857–880. [Google Scholar] [CrossRef]
- Eckhardt, K.; Arnold, J.G. Automatic calibration of a distributed catchment model. J. Hydrol. 2001, 251, 103–109. [Google Scholar] [CrossRef]
- Arnold, J.G.; Allen, P.M. Automated methods for estimating baseflow and ground water recharge from streamflow records. J. Am. Water Resour. Assoc. 1999, 35, 411–424. [Google Scholar] [CrossRef]
- White, K.L.; Chaubey, I. Sensitivity analysis, calibration, and validations for a multisite and multivariable SWAT model. J. Am. Water Resour. Assoc. 2005, 41, 1077–1089. [Google Scholar] [CrossRef]
- Kang, M.S.; Park, S.W.; Lee, J.J.; Yoo, K.H. Applying SWAT for TMDL programs to a small watershed containing rice paddy fields. Agric. Water Manag. 2006, 79, 72–92. [Google Scholar] [CrossRef]
- Kim, N.W.; Chung, I.M.; Won, Y.S.; Arnold, J.G. Development and application of the integrated SWAT-MODFLOW model. J. Hydrol. 2008, 356, 1–16. [Google Scholar] [CrossRef]
- Bae, D.H.; Jung, I.W.; Lettenmaier, D.P. Hydrologic uncertainties in climate change from IPCC AR4 GCM simulations of the Chungju Basin, Korea. J. Hydrol. 2011, 401, 90–105. [Google Scholar] [CrossRef]
- Kim, N.W.; Lee, J.E.; Kim, J.T. Assessment of flow regulation effects by dams in the Han River, Korea, on the downstream flow regimes using SWAT. J. Water Resour. Plan. Manag. 2012, 131, 24–35. [Google Scholar] [CrossRef]
- Shope, C.L.; Maharjan, G.R.; Tenhunen, J.; Seo, B.; Kim, K.; Riley, J.; Arnhold, S.; Koellner, T.; Ok, Y.S.; Peiffer, S.; et al. Using the SWAT model to improve process descriptions and define hydrologic partitioning in South Korea. Hydrol. Earth Syst. Sci. 2014, 18, 539–557. [Google Scholar] [CrossRef] [Green Version]
- Cho, K.H.; Pachepsky, Y.A.; Kim, M.; Pyo, J.; Park, M.H.; Kim, Y.M.; Kim, J.W.; Kim, J.H. Modeling seasonal variability of fecal coliform in natural surface waters using the modified SWAT. J. Hydrol. 2016, 535, 377–385. [Google Scholar] [CrossRef]
- Kim, J.P.; Jung, I.; Park, K.W.; Yoon, S.K.; Lee, D. Hydrological utility and uncertainty of multi-satellite precipitation products in the mountainous region of South Korea. Remote Sens. 2016, 8, 608. [Google Scholar] [CrossRef]
- Smith, P.J.; Panziera, L.; Beven, K.J. Forecasting flash floods using data-based mechanistic models and NORA radar rainfall forecasts. Hydrol. Sci. J. 2014, 59, 1343–1357. [Google Scholar] [CrossRef]
- KOWACO. The Pre-Investigation Report for Groundwater Resources; Korea Water Resources Corporation: Daejeon, Korea, 1993; p. 340. [Google Scholar]
- Korea Meteorological Administration. Annual Report 2016; Korea Meteorological Administration: Seoul, Korea, 2016; p. 49.
- Kim, J.S.; Jain, S.; Yoon, S.K. Warm season streamflow variability in the Korean Han River Basin: Links with atmospheric teleconnections. Int. J. Climatol. 2012, 32, 635–640. [Google Scholar] [CrossRef]
- Lee, K.S.; Bong, Y.S.; Lee, D.; Kim, Y.; Kim, K. Tracing the sources of nitrate in the Han River watershed in Korea, using δ15N-NO3 and δ18O-NO3 values. Sci. Total Environ. 2008, 395, 117–124. [Google Scholar] [CrossRef] [PubMed]
- Heo, B.-H.; Kim, K.-E.; Kang, S.-G. Removals of noises from automatic weather station data and radial velocity data of doppler weather radar using modified median filter. J. Korean Meteorol. Soc. 1999, 35, 127–135. [Google Scholar]
- Li, D.; Christakos, G.; Ding, X.; Wu, J. Adequacy of TRMM satellite rainfall data in driving the SWAT modeling of Tiaoxi catchment (Taihu lake basin, China). J. Hydrol. 2018, 556, 1139–1152. [Google Scholar] [CrossRef]
- Huffman, G.J.; Adler, R.F.; Bolvin, D.T.; Gu, G.; Nelkin, E.J.; Bowman, K.P.; Hong, Y.; Stocker, E.F.; Wolff, D.B. The TRMM multi-satellite precipitation analysis (TMPA): Quasi-global, multi-year, combined-sensor precipitation estimates at fine scale. J. Hydrometeorol. 2007, 8, 38–55. [Google Scholar] [CrossRef]
- Sorooshian, S.; Hsu, K.L.; Gao, X.; Gupta, H.V.; Imam, B.; Braithwaite, D. Evaluation of PERSIANN system satellite-based estimates of tropical rainfall. Bull. Am. Meteorol. Soc. 2000, 81, 2035–2046. [Google Scholar] [CrossRef]
- Ashouri, H.; Hsu, K.L.; Sorooshian, S.; Braithwaite, D.K.; Knapp, K.R.; Cecil, L.D.; Nelson, B.R.; Prat, O.P. PERSIANN-CDR: Daily precipitation climate data record from multisatellite observations for hydrological and climate studies. Bull. Am. Meteorol. Soc. 2015, 96, 69–83. [Google Scholar] [CrossRef]
- Neitsch, S.L.; Arnold, J.G.; Kiniry, J.R.; Williams, J.R. Soil and Water Assessment Tool Theoretical Documentation Version 2009; Texas Water Resources Institute: College Station, TX, USA, 2011. [Google Scholar]
- Abbaspour, K.C.; Johnson, C.A.; van Genuchten, M.T. Estimating uncertain flow and transport parameters using a sequential uncertainty fitting procedure. Vadose Zone J. 2004, 3, 1340–1352. [Google Scholar] [CrossRef]
- Yang, J.; Reichert, P.; Abbaspour, K.C.; Xia, J.; Yang, H. Comparing uncertainty analysis techniques for a SWAT application to the Chaohe Basin in China. J. Hydrol. 2008, 358, 1–23. [Google Scholar] [CrossRef]
- Narsimlu, B.; Gosain, A.K.; Chahar, B.R.; Singh, S.K.; Srivastava, P.K. SWAT model calibration and uncertainty analysis for streamflow prediction in the Kunwari River Basin, India, using sequential uncertainty fitting. Environ. Process. 2015, 2, 79–95. [Google Scholar] [CrossRef]
- Wu, H.; Chen, B. Evaluating uncertainty estimates in distributed hydrological modeling for the Wenjing River watershed in China by GLUE, SUFI-2, and ParaSol methods. Ecol. Eng. 2015, 76, 110–121. [Google Scholar] [CrossRef]
- Khoi, D.N.; Thom, V.T. Parameter uncertainty analysis for simulating streamflow in a river catchment of Vietnam. Glob. Ecol. Conserv. 2015, 4, 538–548. [Google Scholar] [CrossRef]
- Willmott, C.J.; Matsuura, K. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Clim. Res. 2005, 30, 79–82. [Google Scholar] [CrossRef]
- Baik, J.; Choi, M. Spatio-temporal variability of remotetely sensed precipitation data from COMS and TRMM: Case study of Korean peninsula in East Asia. Adv. Space Res. 2015, 56, 125–1138. [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. ASABE 2007, 50, 885–900. [Google Scholar] [CrossRef]
- Dai, A. Precipitation characteristics in eighteen coupled climate models. J. Clim. 2006, 19, 4605–4630. [Google Scholar] [CrossRef]
- Dinku, T.; Chidzambwa, S.; Ceccato, P.; Connor, S.J.; Ropelewski, C.F. Validation of high-resolution satellite rainfall products over complex terrain. Int. J. Remote Sens. 2008, 29, 4097–4110. [Google Scholar] [CrossRef]
- Ma, L.; Ascough Ii, J.C.; Ahuja, L.R.; Shaffer, M.J.; Hanson, J.D.; Rojas, K.W. Root Zone Water Quality Model sensitivity analysis using Monte Carlo simulation. Trans. ASAE 2000, 43, 883–895. [Google Scholar] [CrossRef]
- Hromadka, T.V.; McCuen, R.H. Uncertainty estimates for surface runoff models. Adv. Water Resour. 1998, 11, 2–14. [Google Scholar] [CrossRef]
- Maskey, S.; Guinot, V.; Price, R.K. Treatment of precipitation in rainfall-runoff modelling: A fuzzy set approach. Adv. Water Resour. 2004, 27, 889–898. [Google Scholar] [CrossRef]
- Jones, P.D.; Lister, D.H.; Wilby, R.L.; Kostopoulou, E. Extended riverflow reconstructions for England and Wales, 1865–2002. Int. J. Climatol. 2006, 25, 219–231. [Google Scholar] [CrossRef]
- Andreassian, V.; Perrin, C.; Michel, C.; Usart-Sanchez, I.; Lavabre, J. Impact of imperfect rainfall knowledge on the efficency and the parameters of watershed models. J. Hydrol. 2001, 250, 206–223. [Google Scholar] [CrossRef]
- Gebregiorgis, A.S.; Hossain, F. Understanding the dependence of satellite rainfall uncertainty on topography and climate for hydrologic model simulation. IEEE Trans. Geosci. Remote Sens. 2013, 51, 704–718. [Google Scholar] [CrossRef]
- Xu, S.G.; Niu, Z.; Shen, Y. Understanding the dependence of the uncertainty in a satellite precipitation data set on the underlying surface and a correction method based on geogrphically weighted reggression. Int. J. Remote Sens. 2014, 35, 6508–6521. [Google Scholar] [CrossRef]
- Bitew, M.M.; Gebremichael, M. Assessment of satellite rainfall products for streamflow simulation in medium watersheds of the Ethiopian highlands. Hydrol. Earth Syst. Sci. 2011, 15, 1147–1155. [Google Scholar] [CrossRef] [Green Version]
- Artan, G.; Gadain, H.; Smith, J.L.; Asante, K.; Bandaragoda, C.J.; Verdin, J.P. Adequacy of satellite derived rainfall data for streamflow modeling. Nat. Hazards 2007, 43, 167–185. [Google Scholar] [CrossRef]
Dataset | Version | Spatial/Temporal Resolution | Areal Coverage | Time Coverage | Sources |
---|---|---|---|---|---|
TRMM | 3B42 V7 | 0.250/daily | Near Global | 1998–present | Huffman et al. [47] |
PERSIANN | - | 0.250/daily | Near Global | 2000–present | Sorooshian et al. [48] |
PERSIANN-CDR | CDR | 0.250/daily | Near Global | 1983–2017 | Ashouri et al. [49] |
CMADS | V1.1 | 0.250/daily | East Asia | 2008–2014 | Meng [22] |
Data Type | Data Description | Scale | Data Source |
---|---|---|---|
Topography map | Digital elevation map (DEM) | 90 m | USGS-HydroSHEDS |
Land-use/Land cover map | Land use/Land cover classification 2010 | 1:1,250,000 | Korea Ministry of Environment |
Soil map | Soil types (2007) | 10 km | Food and Agriculture Organization |
Meteorology | Daily precipitation, Minimum and maximum temperature, Solar radiation, Relative humidity, Wind speed | 1990–2013 | Korea Meteorological Administration and Water Resources Management Information System |
Hydrological data | Discharge, Dam operation, Reservoir characteristics | 2008–2013 | Water Resources Management Information System |
Satellite Event | Observation Event | Marginal Total | |
---|---|---|---|
Yes (p ≥ 1.0 mm) | No (p < 1.0 mm) | ||
Yes (p ≥ 1.0 mm) | a | b | a + b |
No (p < 1.0 mm) | c | d | c + d |
Marginal total | a + c | b + d | n = a + b + c + d |
Parameters | Parameter Description | Initial Range | Calibrated Range | Best Value |
---|---|---|---|---|
r CN2 | Initial SCS CN II value | −0.20 to 0.20 | −0.07 to 0.79 | 0.18 |
v ALPHA_BF | Baseflow alpha factor | 0.0035 to 0.80 | 0.28 to 0.80 | 0.72 |
v CH_K2 | Effective hydraulic conductivity of the main channel | −0.01 to 500 | −0.01 to 268 | 4.65 |
v CH_N2 | Manning’s value for main channels | −0.01 to 0.30 | −0.10 to 0.17 | 0.03 |
v CANMX | Maximum canopy storage | 0 to 100 | 0 to 55.40 | 55.19 |
v CH_N1 | Manning’s value for tributary channels | 0.01 to 30 | 10 to 30 | 28.46 |
Code | Station | Calibration (2008–2010) | Validation (2011–2013) | ||||
---|---|---|---|---|---|---|---|
NSE | R2 | PBIAS (%) | NSE | R2 | PBIAS (%) | ||
SG6 | PanUn | 0.53 | 0.54 | −7.50 | - | - | - |
SG8 | YeongWeol1 | 0.57 | 0.59 | 14.70 | 0.64 | 0.72 | −33.41 |
SG9 | YeongChun | 0.59 | 0.59 | 3.50 | 0.61 | 0.71 | −26.99 |
SG10 | DalCheon | 0.63 | 0.67 | 24.30 | 0.75 | 0.78 | −17.19 |
SG11 | Mokgyegyo | 0.94 | 0.95 | 14.70 | 0.61 | 0.73 | −10.10 |
SG15 | Yeojudaegyo | 0.82 | 0.82 | −0.20 | - | - | - |
SG17 | Heukcheongyo | 0.51 | 0.53 | 20.00 | 0.61 | 0.61 | −7.73 |
SG19 | WeonTong | 0.53 | 0.67 | 11.30 | 0.50 | 0.51 | 21.47 |
SG20 | NaeLinCheon | 0.69 | 0.70 | 17.10 | 0.58 | 0.66 | −12.44 |
SG23 | Jueumchigyo | 0.50 | 0.51 | 16.90 | 0.56 | 0.56 | 18.90 |
SG25 | Bangokgyo | 0.67 | 0.71 | 24.20 | 0.58 | 0.63 | −6.78 |
SG26 | Daeseongri | 0.71 | 0.73 | 0.60 | 0.61 | 0.73 | −14.71 |
SG28 | Sumthlgyo | 0.63 | 0.67 | 20.50 | 0.75 | 0.78 | 20.77 |
SG31 | Gwangjingyo | 0.56 | 0.63 | −14.10 | 0.56 | 0.77 | −24.96 |
SG33 | Jungranggyo | 0.56 | 0.58 | 18.40 | 0.90 | 0.90 | −11.80 |
SG37 (outlet) | Haengjudaegyo | 0.58 | 0.59 | −13.00 | 0.77 | 0.81 | −38.80 |
Statistics | Gauged Rainfall (Calibration) | Gauged Rainfall (Validation) | TRMM | CMADS | PERSIANN | PERSIANN-CDR |
---|---|---|---|---|---|---|
P-factor | 0.54 | 0.47 | 0.51 | 0.40 | 0.39 | 0.33 |
R-factor | 0.47 | 0.56 | 0.42 | 0.42 | 0.37 | 0.43 |
Code | Station | Product | R2 | NSE | PBIAS (%) |
---|---|---|---|---|---|
SG6 | PanUn | Rain gauge | 0.54 | 0.53 | −7.50 |
PERSIANN | 0.44 | 0.23 | 55.70 | ||
PERSIANN-CDR | 0.21 | 0.19 | 35.00 | ||
TRMM 3B42 V7 | 0.48 | 0.42 | −52.70 | ||
CMADS | 0.39 | 0.37 | 11.20 | ||
SG8 | YeongWeol1 | Rain gauge | 0.66 | 0.61 | −9.36 |
PERSIANN | 0.55 | 0.21 | 67.20 | ||
PERSIANN-CDR | 0.55 | 0.21 | 67.20 | ||
TRMM 3B42 V7 | 0.46 | 0.45 | −18.40 | ||
CMADS | 0.49 | 0.43 | 23.20 | ||
SG9 | YeongChun | Rain gauge | 0.65 | 0.60 | −11.75 |
PERSIANN | 0.49 | 0.25 | 59.50 | ||
PERSIANN-CDR | 0.28 | 0.25 | 31.30 | ||
TRMM 3B42 V7 | 0.59 | 0.54 | −33.00 | ||
CMADS | 0.44 | 0.42 | 20.90 | ||
SG10 | DalCheon | Rain gauge | 0.73 | 0.69 | 3.56 |
PERSIANN | 0.41 | 0.15 | 68.90 | ||
PERSIANN-CDR | 0.29 | 0.23 | 46.30 | ||
TRMM 3B42 V7 | 0.33 | 0.33 | −14.80 | ||
CMADS | 0.36 | 0.33 | 10.10 | ||
SG11 | Mokgyegyo | Rain gauge | 0.84 | 0.78 | 2.30 |
PERSIANN | 0.61 | 0.18 | 71.10 | ||
PERSIANN-CDR | 0.60 | 0.42 | 50.10 | ||
TRMM 3B42 V7 | 0.81 | 0.79 | −7.30 | ||
CMADS | 0.70 | 0.62 | 35.50 | ||
SG15 | Yeojudaegyo | Rain gauge | 0.82 | 0.82 | −0.20 |
PERSIANN | 0.06 | 0.04 | 40.50 | ||
PERSIANN-CDR | 0.05 | 0.03 | 31.20 | ||
TRMM 3B42 V7 | 0.60 | 0.43 | −13.57 | ||
CMADS | 0.31 | 0.31 | −29.30 | ||
SG17 | Heukcheongyo | Rain gauge | 0.57 | 0.56 | 6.14 |
PERSIANN | 0.08 | 0.03 | 69.90 | ||
PERSIANN-CDR | 0.06 | 0.03 | 58.80 | ||
TRMM 3B42 V7 | 0.48 | 0.42 | 12.70 | ||
CMADS | 0.30 | 0.38 | 41.80 | ||
SG19 | WeonTong | Rain gauge | 0.59 | 0.52 | 16.39 |
PERSIANN | 0.40 | 0.04 | 83.20 | ||
PERSIANN-CDR | 0.44 | 0.17 | 71.20 | ||
TRMM 3B42 V7 | 0.59 | 0.58 | 28.30 | ||
CMADS | 0.57 | 0.49 | 27.10 | ||
SG20 | NaeLinCheon | Rain gauge | 0.68 | 0.64 | 2.33 |
PERSIANN | 0.32 | 0.12 | 72.60 | ||
PERSIANN-CDR | 0.23 | 0.17 | 54.10 | ||
TRMM 3B42 V7 | 0.59 | 0.58 | −4.50 | ||
CMADS | 0.31 | 0.45 | 19.50 | ||
SG23 | Jueumchigyo | Rain gauge | 0.52 | 0.51 | 17.90 |
PERSIANN | 0.04 | 0.02 | 73.80 | ||
PERSIANN-CDR | 0.04 | 0.02 | 59.90 | ||
TRMM 3B42 V7 | 0.58 | 0.42 | 11.30 | ||
CMADS | 0.50 | 0.43 | 22.90 | ||
SG25 | Bangokgyo | Rain gauge | 0.67 | 0.63 | 8.71 |
PERSIANN | 0.14 | 0.02 | 73.70 | ||
PERSIANN-CDR | 0.08 | 0.02 | 62.00 | ||
TRMM 3B42 V7 | 0.56 | 0.55 | 16.00 | ||
CMADS | 0.47 | 0.41 | 20.70 | ||
SG26 | Daeseongri | Rain gauge | 0.73 | 0.66 | −7.06 |
PERSIANN | 0.45 | 0.35 | 48.90 | ||
PERSIANN-CDR | 0.50 | 0.44 | 36.00 | ||
TRMM 3B42 V7 | 0.55 | 0.51 | −8.90 | ||
CMADS | 0.53 | 0.51 | 24.30 | ||
SG28 | Sumthlgyo | Rain gauge | 0.73 | 0.69 | 20.64 |
PERSIANN | 0.13 | 0.20 | 75.30 | ||
PERSIANN-CDR | 0.07 | 0.10 | 67.40 | ||
TRMM 3B42 V7 | 0.62 | 0.57 | 25.10 | ||
CMADS | 0.59 | 0.55 | 23.90 | ||
SG31 | Gwangjingyo | Rain gauge | 0.70 | 0.56 | −14.53 |
PERSIANN | 0.08 | 0.05 | 38.10 | ||
PERSIANN-CDR | 0.06 | 0.01 | 12.70 | ||
TRMM 3B42 V7 | 0.57 | 0.44 | −51.20 | ||
CMADS | 0.49 | 0.42 | −10.70 | ||
SG33 | Jungranggyo | Rain gauge | 0.74 | 0.73 | 3.30 |
PERSIANN | 0.19 | 0.04 | 75.80 | ||
PERSIANN-CDR | 0.30 | 0.15 | 62.50 | ||
TRMM 3B42 V7 | 0.29 | 0.27 | 13.40 | ||
CMADS | 0.39 | 0.30 | 21.80 | ||
SG37 (Outlet) | Haengjudaegyo | Rain gauge | 0.70 | 0.68 | −25.90 |
PERSIANN | 0.23 | 0.16 | 52.60 | ||
PERSIANN-CDR | 0.18 | 0.16 | 32.90 | ||
TRMM 3B42 V7 | 0.46 | 0.47 | −32.50 | ||
CMADS | 0.22 | 0.32 | 14.60 | ||
Average | Rain gauge | 0.68 | 0.64 | 0.31 | |
PERSIANN | 0.29 | 0.13 | 64.18 | ||
PERSIANN-CDR | 0.25 | 0.16 | 48.66 | ||
TRMM 3B42 V7 | 0.54 | 0.49 | −8.13 | ||
CMADS | 0.44 | 0.42 | 17.34 |
© 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
Vu, T.T.; Li, L.; Jun, K.S. Evaluation of Multi-Satellite Precipitation Products for Streamflow Simulations: A Case Study for the Han River Basin in the Korean Peninsula, East Asia. Water 2018, 10, 642. https://doi.org/10.3390/w10050642
Vu TT, Li L, Jun KS. Evaluation of Multi-Satellite Precipitation Products for Streamflow Simulations: A Case Study for the Han River Basin in the Korean Peninsula, East Asia. Water. 2018; 10(5):642. https://doi.org/10.3390/w10050642
Chicago/Turabian StyleVu, Thom Thi, Li Li, and Kyung Soo Jun. 2018. "Evaluation of Multi-Satellite Precipitation Products for Streamflow Simulations: A Case Study for the Han River Basin in the Korean Peninsula, East Asia" Water 10, no. 5: 642. https://doi.org/10.3390/w10050642
APA StyleVu, T. T., Li, L., & Jun, K. S. (2018). Evaluation of Multi-Satellite Precipitation Products for Streamflow Simulations: A Case Study for the Han River Basin in the Korean Peninsula, East Asia. Water, 10(5), 642. https://doi.org/10.3390/w10050642