LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Zambrano-Bigiarini, Mauricio; Nauditt, Alexandra; Birkel, Christian; Verbist, Koen; Ribbe, Lars (2017)
Publisher: Copernicus Publications
Languages: English
Types: Article
Subjects: T, G, GE1-350, Geography. Anthropology. Recreation, Environmental technology. Sanitary engineering, Environmental sciences, Technology, TD1-1066
Accurate representation of the real spatio-temporal variability of catchment rainfall inputs is currently severely limited. Moreover, spatially interpolated catchment precipitation is subject to large uncertainties, particularly in developing countries and regions which are difficult to access. Recently, satellite-based rainfall estimates (SREs) provide an unprecedented opportunity for a wide range of hydrological applications, from water resources modelling to monitoring of extreme events such as droughts and floods.

This study attempts to exhaustively evaluate – for the first time – the suitability of seven state-of-the-art SRE products (TMPA 3B42v7, CHIRPSv2, CMORPH, PERSIANN-CDR, PERSIAN-CCS-Adj, MSWEPv1.1, and PGFv3) over the complex topography and diverse climatic gradients of Chile. Different temporal scales (daily, monthly, seasonal, annual) are used in a point-to-pixel comparison between precipitation time series measured at 366 stations (from sea level to 4600 m a.s.l. in the Andean Plateau) and the corresponding grid cell of each SRE (rescaled to a 0.25° grid if necessary). The modified Kling–Gupta efficiency was used to identify possible sources of systematic errors in each SRE. In addition, five categorical indices (PC, POD, FAR, ETS, fBIAS) were used to assess the ability of each SRE to correctly identify different precipitation intensities.

Results revealed that most SRE products performed better for the humid South (36.4–43.7° S) and Central Chile (32.18–36.4° S), in particular at low- and mid-elevation zones (0–1000 m a.s.l.) compared to the arid northern regions and the Far South. Seasonally, all products performed best during the wet seasons (autumn and winter; MAM–JJA) compared to summer (DJF) and spring (SON). In addition, all SREs were able to correctly identify the occurrence of no-rain events, but they presented a low skill in classifying precipitation intensities during rainy days. Overall, PGFv3 exhibited the best performance everywhere and for all timescales, which can be clearly attributed to its bias-correction procedure using 217 stations from Chile. Good results were also obtained by the research products CHIRPSv2, TMPA 3B42v7 and MSWEPv1.1, while CMORPH, PERSIANN-CDR, and the real-time PERSIANN-CCS-Adj were less skillful in representing observed rainfall. While PGFv3 (currently available up to 2010) might be used in Chile for historical analyses and calibration of hydrological models, the high spatial resolution, low latency and long data records of CHIRPS and TMPA 3B42v7 (in transition to IMERG) show promising potential to be used in meteorological studies and water resource assessments. We finally conclude that despite improvements of most SRE products, a site-specific assessment is still needed before any use in catchment-scale hydrological studies.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Abera, W., Brocca, L., and Rigon, R.: Comparative evaluation of different satellite rainfall estimation products and bias correction in the Upper Blue Nile (UBN) basin, Atmos. Res., 178, 471-483, doi:10.1016/j.atmosres.2016.04.017, 2016.
    • Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P.-P., Janowiak, J., Rudolf, B., Schneider, U., Curtis, S., Bolvin, D., Gruber, A., Susskind, J., Arkin, P., and Nelkin, E.: The Version-2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979- Present), J. Hydrometeorol., 4, 1147-1167, doi:10.1175/1525- 7541(2003)004<1147:TVGPCP>2.0.CO;2, 2003.
    • AghaKouchak, A., Farahmand, A., Melton, F. S., Teixeira, J., Anderson, M. C., Wardlow, B. D., and Hain, C. R.: Remote sensing of drought: Progress, challenges and opportunities, Rev. Geophys., 53, 452-480, doi:10.1002/2014RG000456, 2015.
    • Ashouri, H., Hsu, K.-L., Sorooshian, S., Braithwaite, D. K., Knapp, K. R., Cecil, L. D., Nelson, B. R., and Prat, O. P.: PERSIANNCDR: Daily Precipitation Climate Data Record from Multisatellite Observations for Hydrological and Climate Studies, B. Am. Meteorol. Soc., 96, 69-83, doi:10.1175/BAMS-D-13-00068.1, 2015.
    • Beck, H. E., van Dijk, A. I. J. M., Levizzani, V., Schellekens, J., Miralles, D. G., Martens, B., and de Roo, A.: MSWEP: 3- hourly 0.25 global gridded precipitation (1979-2015) by merging gauge, satellite, and reanalysis data, Hydrol. Earth Syst. Sci., 21, 589-615, doi:10.5194/hess-21-589-2017, 2017.
    • Behrangi, A., Khakbaz, B., Jaw, T. C., AghaKouchak, A., Hsu, K., and Sorooshian, S.: Hydrologic evaluation of satellite precipitation products over a mid-size basin, J. Hydrol., 397, 225-237, doi:10.1016/j.jhydrol.2010.11.043, 2011.
    • Bisselink, B., Zambrano-Bigiarini, M., Burek, P., and de Roo, A.: Assessing the role of uncertain precipitation estimates on the robustness of hydrological model parameters under highly variable climate conditions, J. Hydrol., 8, 112-129, doi:10.1016/j.ejrh.2016.09.003, 2016.
    • Blacutt, L. A., Herdies, D. L., de Gonçalves, L. G. G., Vila, D. A., and Andrade, M.: Precipitation comparison for the CFSR, MERRA, TRMM3B42 and Combined Scheme datasets in Bolivia, Atmos. Res., 163, 117-131, doi:10.1016/j.atmosres.2015.02.002, 2015.
    • Boisier, J. P., Rondanelli, R., Garreaud, R. D., and Muñoz, F.: Anthropogenic and natural contributions to the Southeast Pacific precipitation decline and recent megadrought in central Chile, Geophys. Res. Lett., 43, 413-421, doi:10.1002/2015GL067265, 2016.
    • Ceccherini, G., Ameztoy, I., Hernández, C., and Moreno, C.: HighResolution Precipitation Datasets in South America and West Africa based on Satellite-Derived Rainfall, Enhanced Vegetation Index and Digital Elevation Model, Remote Sensing, 7, 6454- 6488, doi:10.3390/rs70506454, 2015.
    • Chaney, N. W., Sheffield, J., Villarini, G., and Wood, E. F.: Development of a High-Resolution Gridded Daily Meteorological Dataset over Sub-Saharan Africa: Spatial Analysis of Trends in Climate Extremes, J. Climate, 27, 5815-5835, doi:10.1175/JCLI-D-13-00423.1, 2014.
    • Chen, S., Hong, Y., Gourley, J. J., Huffman, G. J., Tian, Y., Cao, Q., Yong, B., Kirstetter, P.-E., Hu, J., Hardy, J., Li, Z., Khan, S. I., and Xue, X.: Evaluation of the successive V6 and V7 TRMM multisatellite precipitation analysis over the Continental United States, Water Resour. Res., 49, 8174, doi:10.1002/2012WR012795, 2013.
    • Climate Hazards Group: CHIRPSv2.0 webpage, available at: http: //chg.ucsb.edu/data/chirps/, last access: 1 July 2016.
    • CPC-NCEP-NWS-NOAA-USDC: NOAA CPC Morphing Technique (CMORPH) Global Precipitation Analyses, Tech. rep., Boulder CO, doi:10.5065/D6CZ356W, 2011.
    • Deblauwe, V., Droissart, V., Bose, R., Sonké, B., Blach-Overgaard, A., Svenning, J. C., Wieringa, J. J., Ramesh, B. R., Stévart, T., and Couvreur, T. L. P.: Remotely sensed temperature and precipitation data improve species distribution modelling in the tropics, Glob. Ecol. Biogeogr., 25, 443, doi:10.1111/geb.12426, 2016.
    • Demaria, E. M. C., Rodriguez, D. A., Ebert, E. E., Salio, P., Su, F., and Valdes, J. B.: Evaluation of mesoscale convective systems in South America using multiple satellite products and an object-based approach, J. Geophys. Res.-Atmos., 116, 1-13, doi:10.1029/2010JD015157, 2011.
    • DGA: Atlas del Agua 2016, Santiago, Chile, available at: http:// www.dga.cl/atlasdelagua/, last access: 29 August 2016.
    • Dinku, T., Connor, S. J., and Ceccato, P.: Comparison of CMORPH and TRMM-3B42 over Mountainous Regions of Africa and South America, in: Satellite Rainfall Applications for Surface Hydrology, edited by: Gebremichael, M. and Hossain, F., Springer Netherlands, doi:10.1007/978-90-481-2915-7_11, 193-204, 2010.
    • Ebert, E. E.: Methods for verifying satellite precipitation estimates, in: Measuring precipitation from space, 345-356, Springer, 2007.
    • Ebert, E. E., Janowiak, J. E., and Kidd, C.: Comparison of NearReal-Time Precipitation Estimates from Satellite Observations and Numerical Models, B. Am. Meteorol. Soc., 88, 47-64, doi:10.1175/BAMS-88-1-47, 2007.
    • Emmanuel, I., Andrieu, H., Leblois, E., Janey, N., and Payrastre, O.: Influence of rainfall spatial variability on rainfall-runoff modelling: Benefit of a simulation approach?, J. Hydrol., 531, 337- 348, doi:10.1016/j.jhydrol.2015.04.058, 2015.
    • Fekete, B. M., Vörösmarty, C. J., Roads, J. O., and Willmott, C. J.: Uncertainties in Precipitation and Their Impacts on Runoff Estimates, J. Climate, 17, 294-304, doi:10.1175/1520- 0442(2004)017<0294:UIPATI>2.0.CO;2, 2004.
    • Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., and Michaelsen, J.: The climate hazards infrared precipitation with stations-a new environmental record for monitoring extremes, Sci. Data, 2, 150066, doi:10.1038/sdata.2015.66, 2015.
    • Gebregiorgis, A. S. and Hossain, F.: Understanding the Dependence of Satellite Rainfall Uncertainty on Topography and Climate for Hydrologic Model Simulation, IEEE T. Geosci. Remote, 51, 704-718, doi:10.1109/TGRS.2012.2196282, 2013.
    • Guo, H., Chen, S., Bao, A., Hu, J., Gebregiorgis, A., Xue, X., and Zhang, X.: Inter-Comparison of High-Resolution Satellite Precipitation Products over Central Asia, Remote Sensing, 7, 7181- 7212, doi:10.3390/rs70607181, 2015.
    • Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling, J. Hydrol., 377, 80-91, doi:10.1016/j.jhydrol.2009.08.003, 2009.
    • Habib, E., Henschke, A., and Adler, R. F.: Evaluation of TMPA satellite-based research and real-time rainfall estimates during six tropical-related heavy rainfall events over Louisiana, USA, Atmos. Res., 94, 373-388, doi:10.1016/j.atmosres.2009.06.015, 2009.
    • Harris, I., Jones, P. D., Osborn, T. J., and Lister, D. H.: Updated high-resolution grids of monthly climatic observations - the CRU TS3.10 Dataset, Int. J. Climatol., 34, 623-642, doi:10.1002/joc.3711, 2013.
    • Hijmans, R. J.: raster: Geographic Data Analysis and Modeling, available at: https://CRAN.R-project.org/package=raster (last access: 1 August 2016), R package version 2.5-8, 2016.
    • Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G., and Jarvis, A.: Very high resolution interpolated climate surfaces for global land areas, Int. J. Climatol., 25, 1965-1978, doi:10.1002/joc.1276, 2005.
    • Hong, Y., Hsu, K.-L., Sorooshian, S., and Gao, X.: Precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system, J. Appl. Meteorol., 43, 1834-1853, 2004.
    • Hong, Y., Adler, R. F., Negri, A., and Huffman, G. J.: Flood and landslide applications of near real-time satellite rainfall products, Nat. Hazards, 43, 285-294, doi:10.1007/s11069-006-9106- x, 2007.
    • Hong, Y., Adler, R. F., Huffman, G. J., and Pierce, H.: Applications of TRMM-Based Multi-Satellite Precipitation Estimation for Global Runoff Prediction: Prototyping a Global Flood Modeling System, in: Satellite Rainfall Applications for Surface Hydrology, Springer Netherlands, doi:10.1007/978-90-481- 2915-7_15, 245-265, 2009.
    • Hossain, F. and Huffman, G. J.: Investigating Error Metrics for Satellite Rainfall Data at Hydrologically Relevant Scales, J. Hydrometeorol., 9, 563-575, doi:10.1175/2007JHM925.1, 2008.
    • Hsu, K.-L., Gao, X., Sorooshian, S., and Gupta, H. V.: Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks, J. Appl. Meteorol., 36, 1176-1190, doi:10.1175/1520-0450(1997)036<1176:PEFRSI>2.0.CO;2, 1997.
    • Huffman, G. J.: The Transition in Multi-Satellite Products from TRMM to GPM (TMPA to IMERG), Tech. rep., NASA, available at: https://pmm.nasa.gov/sites/default/files/document_files/ TMPA-to-IMERG_transition.pdf (last access: 20 August 2016), 2015.
    • Huffman, G. J., Adler, R. F., Bolvin, D. T., Gu, G., Nelkin, E. J., Bowman, K. P., Hong, Y., Stocker, E. F., and Wolff, D. B.: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales, J. Hydrometeorol., 8, 38-55, doi:10.1175/JHM560.1, 2007.
    • Huffman, G. J., Adler, R. F., Bolvin, D. T., and Nelkin, E. J.: The TRMM Multi-Satellite Precipitation Analysis (TMPA), in: Satellite Rainfall Applications for Surface Hydrology, edited by: Gebremichael, M. and Hossain, F., Springer Dordrecht Heidelberg, London New York, doi:10.1007/978-90-481-2915-7_1, 3- 22, 2010.
    • INE: Medio Ambiente - Informe Anual 2015, Chile, available at: http://www.ine.cl/canales/chile_estadistico/estadisticas_medio_ ambiente/2015/informe-medio-ambiente2015.pdf (last access: 29 August 2016), 2015.
    • Janowiak, J. E., Kousky, V. E., and Joyce, R. J.: Diurnal cycle of precipitation determined from the CMORPH high spatial and temporal resolution global precipitation analyses, J. Geophys. Res.- Atmos., 110, 23105, doi:10.1029/2005JD006156, 2005.
    • Jarvis, A., Reuter, H., Nelson, A., and Guevara, E.: Hole-filled seamless SRTM data V4, International Centre for Tropical Agriculture (CIAT), available at: http://srtm.csi.cgiar.org (last access: May 2012), 2008.
    • Jolliffe, I. T. and Stephenson, D. B. (Eds.): Forecast verification: A practitioner's guide in atmospheric science, John Wiley & Sons Ltd, England, 2003.
    • Joyce, R. J., Janowiak, J. E., Arkin, P. A., and Xie, P.: CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution, J. Hydrometeorol., 5, 487-503, doi:10.1175/1525- 7541(2004)005<0487:CAMTPG>2.0.CO;2, 2004.
    • Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Leetmaa, A., Reynolds, B., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K. C., Ropelewski, C., Wang, J., Jenne, R., and Joseph, D.: The NCEP/NCAR 40-Year Reanalysis Project, B. Am. Meteorol. Soc., 77, 437-472, doi:10.1175/1520- 0477(1996)077<0437:TNYRP>2.0.CO;2, 1996.
    • Kang, Y., Khan, S., and Ma, X.: Climate change impacts on crop yield, crop water productivity and food security - A review, Prog. Nat. Sci., 19, 1665-1674, doi:10.1016/j.pnsc.2009.08.001, 2009.
    • Khan, S. I., Hong, Y., Wang, J., Yilmaz, K. K., Gourley, J. J., Adler, R. F., Brakenridge, G. R., Policelli, F., Habib, S., and Irwin, D.: Satellite Remote Sensing and Hydrologic Modeling for Flood Inundation Mapping in Lake Victoria Basin: Implications for Hydrologic Prediction in Ungauged Basins, IEEE T. Geosci. Remote, 49, 85-95, doi:10.1109/TGRS.2010.2057513, 2011.
    • Kidd, C., Levizzani, V., Turk, J., and Ferraro, R.: Satellite Precipitation Measurements for Water Resource Monitoring, J. Am. Water Resour. Assoc., 45, 567-579, doi:10.1111/j.1752- 1688.2009.00326.x, 2009.
    • Kidd, C., Bauer, P., Turk, J., Huffman, G. J., Joyce, R., Hsu, K.-L., and Braithwaite, D.: Intercomparison of High-Resolution Precipitation Products over Northwest Europe, J. Hydrometeorol., 13, 67-83, doi:10.1175/JHM-D-11-042.1, 2012.
    • Kling, H., Fuchs, M., and Paulin, M.: Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios, J. Hydrol., 424-425, 264-277, doi:10.1016/j.jhydrol.2012.01.011, 2012.
    • Kundzewicz, Z. W., Mata, L. J., Arnell, N. W., Döll, P., Jimenez, B., Miller, K., Oki, T., S¸ en, Z., and Shiklomanov, I.: The implications of projected climate change for freshwater resources and their management, Hydrol. Sci. J., 53, 3-10, doi:10.1623/hysj.53.1.3, 2008.
    • Kurtzman, D., Navon, S., and Morin, E.: Improving interpolation of daily precipitation for hydrologic modelling: spatial patterns of preferred interpolators, Hydrol. Process., 23, 3281-3291, doi:10.1002/hyp.7442, 2009.
    • Lee, H., Zhang, Y., Seo, D.-J., and Xie, P.: Utilizing satellite precipitation estimates for streamflow forecasting via adjustment of mean field bias in precipitation data and assimilation of streamflow observations, J. Hydrol., 529, 779-794, doi:10.1016/j.jhydrol.2015.08.057, 2015.
    • Legates, D. R. and DeLiberty, T. L.: Precipitation Measurement Biases in the United States, J. Am. Water Resour. Assoc., 29, 855- 861, doi:10.1111/j.1752-1688.1993.tb03245.x, 1993.
    • Li, X.-H., Zhang, Q., and Xu, C.-Y.: Suitability of the TRMM satellite rainfalls in driving a distributed hydrological model for water balance computations in Xinjiang catchment, Poyang lake basin, J. Hydrol., 426-427, 28-38, doi:10.1016/j.jhydrol.2012.01.013, 2012.
    • Lo Conti, F., Hsu, K.-L., Noto, L. V., and Sorooshian, S.: Evaluation and comparison of satellite precipitation estimates with reference to a local area in the Mediterranean Sea, Atmos. Res., 138, 189- 204, doi:10.1016/j.atmosres.2013.11.011, 2014.
    • Maggioni, V., Meyers, P. C., and Robinson, M. D.: A Review of Merged High-Resolution Satellite Precipitation Product Accuracy during the Tropical Rainfall Measuring Mission (TRMM) Era, J. Hydrometeorol., 17, 1101-1117, doi:10.1175/JHM-D-15- 0190.1, 2016.
    • Mantas, V., Liu, Z., Caro, C., and Pereira, A.: Validation of TRMM multi-satellite precipitation analysis (TMPA) products in the Peruvian Andes, Atmos. Res., 163, 132-145, doi:10.1016/j.atmosres.2014.11.012, 2015.
    • Mei, Y., Anagnostou, E. N., Nikolopoulos, E. I., and Borga, M.: Error Analysis of Satellite Precipitation Products in Mountainous Basins, J. Hydrometeorol., 15, 1778-1793, doi:10.1175/JHM-D13-0194.1, 2014.
    • Meng, J., Li, L., Hao, Z., Wang, J., and Shao, Q.: Suitability of TRMM satellite rainfall in driving a distributed hydrological model in the source region of Yellow River, J. Hydrol., 509, 320- 332, doi:10.1016/j.jhydrol.2013.11.049, 2014.
    • Mirza, M. M. Q.: Climate change and extreme weather events: can developing countries adapt?, Climate Policy, 3, 233-248, doi:10.3763/cpol.2003.0330, 2003.
    • Müller, A., Reiter, J., and Weiland, U.: Assessment of urban vulnerability towards floods using an indicatoz-based approach - a case study for Santiago de Chile, Nat. Hazards Earth Syst. Sci., 11, 2107-2123, doi:10.5194/nhess-11-2107-2011, 2011.
    • Naumann, G., Barbosa, P., Carrao, H., Singleton, A., and Vogt, J.: Monitoring Drought Conditions and Their Uncertainties in Africa Using TRMM Data, J. Appl. Meteorol. Climatol., 51, 1867-1874, doi:10.1175/JAMC-D-12-0113.1, 2012.
    • Nikolopoulos, E. I., Anagnostou, E. N., Hossain, F., Gebremichael, M., and Borga, M.: Understanding the Scale Relationships of Uncertainty Propagation of Satellite Rainfall through a Distributed Hydrologic Model, J. Hydrometeorol., 11, 520-532, doi:10.1175/2009JHM1169.1, 2010.
    • Peng, L., Sheffield, J., and Verbist, K. M. J.: Merging station observations with large-scale gridded data to improve hydrological predictions over Chile, in: 2016 AGU Fall Meeting Abstract, 12- 16 December 2016, San Francisco, CA, USA, 2016.
    • Pereira Filho, A. J., Carbone, R. E., Janowiak, J. E., Arkin, P., Joyce, R., Hallak, R., and Ramos, C. G.: Satellite Rainfall Estimates Over South America - Possible Applicability to the Water Management of Large Watersheds, J. Am. Water Resour. Assoc., 46, 344-360, doi:10.1111/j.1752-1688.2009.00406.x, 2010.
    • Prudhomme, C., Giuntoli, I., Robinson, E. L., Clark, D. B., Arnell, N. W., Dankers, R., Fekete, B. M., Franssen, W., Gerten, D., Gosling, S. N., Hagemann, S., Hannah, D. M., Kim, H., Masaki, Y., Satoh, Y., Stacke, T., Wada, Y., and Wisser, D.: Hydrological droughts in the 21st century, hotspots and uncertainties from a global multimodel ensemble experiment, P. Natl. Acad. Sci. USA, 111, 3262-3267, doi:10.1073/pnas.1222473110, 2014.
    • R Core Team: R: A Language and Environment for Statistical Computing, Vienna, Austria, available at: https://www.R-project.org/ (last access: 2 April 2016), 2015.
    • Ren, Z. and Li, M.: Errors and correction of precipitation measurements in China, Adv. Atmos. Sci., 24, 449-458, doi:10.1007/s00376-007-0449-3, 2007.
    • Ringard, J., Becker, M., Seyler, F., and Linguet, L.: Temporal and Spatial Assessment of Four Satellite Rainfall Estimates over French Guiana and North Brazil, Remote Sensing, 7, 16441- 16459, doi:10.3390/rs71215831, 2015.
    • Rogelis, M. C. and Werner, M. G. F.: Spatial Interpolation for RealTime Rainfall Field Estimation in Areas with Complex Topography, J. Hydrometeorol., 14, 85-104, doi:10.1175/JHM-D-11- 0150.1, 2013.
    • Schamm, K., Ziese, M., Raykova, K., Becker, A., Finger, P., Meyer-Christoffer, A., and Schneider, U.: GPCC Full Data Daily Version 1.0 at 1.0 : Daily Land-Surface Precipitation from Rain-Gauges built on GTS-based and Historic Data, doi:10.5676/DWD_GPCC/FD_D_V1_100, 2015.
    • Scheel, M. L. M., Rohrer, M., Huggel, Ch., Santos Villar, D., Silvestre, E., and Huffman, G. J.: Evaluation of TRMM Multisatellite Precipitation Analysis (TMPA) performance in the Central Andes region and its dependency on spatial and temporal resolution, Hydrol. Earth Syst. Sci., 15, 2649-2663, doi:10.5194/hess-15-2649-2011, 2011.
    • Schneider, U., Fuchs, T., Meyer-Christoffer, A., and Rudolf, B.: Global precipitation analysis products of the GPCC, Internet publikation, Global Precipitation Climatology Centre (GPCC), DWD, 2008.
    • Scofield, R. A. and Kuligowski, R. J.: Satellite Precipitation Algorithms for Extreme Precipitation Events, Springer, Netherlands, 485-495, doi:10.1007/978-1-4020-5835-6_38, 2007.
    • Serrat-Capdevila, A., Valdes, J. B., and Stakhiv, E. Z.: Water Management Applications for Satellite Precipitation Products: Synthesis and Recommendations, J. Am. Water Resour. Assoc., 50, 509-525, doi:10.1111/jawr.12140, 2013.
    • Sevruk, B. and Chvíla, B.: Error sources of precipitation measurements using electronic weight systems, Atmos. Res., 77, 39-47, doi:10.1016/j.atmosres.2004.10.026, 2005.
    • Sevruk, B., Ondrás, M., and Chvíla, B.: The WMO precipitation measurement intercomparisons, Atmos. Res., 92, 376-380, doi:10.1016/j.atmosres.2009.01.016, 2009.
    • Sheffield, J., Goteti, G., and Wood, E. F.: Development of a 50-Year High-Resolution Global Dataset of Meteorological Forcings for Land Surface Modeling, J. Climate, 19, 3088, doi:10.1175/JCLI3790.1, 2006.
    • Sorooshian, S., Hsu, K.-L., Gao, X., Gupta, H. V., Imam, B., and Braithwaite, D.: Evaluation of PERSIANN System Satellite-Based Estimates of Tropical Rainfall, B. Am. Meteorol. Soc., 81, 2035-2046, doi:10.1175/1520- 0477(2000)081<2035:EOPSSE>2.3.CO;2, 2000.
    • Sorooshian, S., Hsu, K., Braithwaite, D., Ashouri, H., and NOAA CDR Program: NOAA Climate Data Record (CDR) of Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN-CDR), Version 1 Revision 1, [2003-2014], Tech. rep., NOAA National Centers for Environmental Information, doi:10.7289/V51V5BWQ, 2014.
    • Su, F., Hong, Y., and Lettenmaier, D. P.: Evaluation of TRMM Multisatellite Precipitation Analysis (TMPA) and Its Utility in Hydrologic Prediction in the La Plata Basin, J. Hydrometeorol., 9, 622-640, doi:10.1175/2007JHM944.1, 2008.
    • Tao, H., Fischer, T., Zeng, Y., and Fraedrich, K.: Evaluation of TRMM 3B43 Precipitation Data for Drought Monitoring in Jiangsu Province, China, Water, 8, 221, doi:10.3390/w8060221, 2016.
    • Thiemig, V., Rojas, R., Zambrano-Bigiarini, M., Levizzani, V., and De Roo, A.: Validation of Satellite-Based Precipitation Products over Sparsely Gauged African River Basins, J. Hydrometeorol., 13, 1760-1783, doi:10.1175/JHM-D-12-032.1, 2012.
    • Thiemig, V., Rojas, R., Zambrano-Bigiarini, M., and De Roo, A.: Hydrological evaluation of satellite-based rainfall estimates over the Volta and Baro-Akobo Basin, J. Hydrol., 499, 324-338, doi:10.1016/j.jhydrol.2013.07.012, 2013.
    • Tian, Y. and Peters-Lidard, C. D.: A global map of uncertainties in satellite-based precipitation measurements, Geophys. Res. Lett., 37, 24407, doi:10.1029/2010GL046008, 2010.
    • Tobin, K. J. and Bennett, M. E.: Satellite precipitation products and hydrologic applications, Water Int., 39, 360-380, doi:10.1080/02508060.2013.870423, 2014.
    • Valdés-Pineda, R., Pizarro, R., García-Chevesich, P., Valdés, J. B., Olivares, C., Vera, M., Balocchi, F., Pérez, F., Vallejos, C., Fuentes, R., Abarza, A., and Helwig, B.: Water governance in Chile: Availability, management and climate change, J. Hydrol., doi:10.1016/j.jhydrol.2014.04.016, 2014.
    • Verdin, J., Funk, C., Senay, G., and Choularton, R.: Climate science and famine early warning, Philos. Trans. R. Soc. B, 360, 2155- 2168, doi:10.1098/rstb.2005.1754, 2005.
    • Verworn, A. and Haberlandt, U.: Spatial interpolation of hourly rainfall - effect of additional information, variogram inference and storm properties, Hydrol. Earth Syst. Sci., 15, 569-584, doi:10.5194/hess-15-569-2011, 2011.
    • Ward, E., Buytaert, W., Peaver, L., and Wheater, H.: Evaluation of precipitation products over complex mountainous terrain: A water resources perspective, Adv. Water Resour., 34, 1222-1231, doi:10.1016/j.advwatres.2011.05.007, 2011.
    • Werren, G., Reynard, E., Lane, S. N., and Balin, D.: Flood hazard assessment and mapping in semi-arid piedmont areas: a case study in Beni Mellal, Morocco, Nat. Hazards, 81, 481-511, doi:10.1007/s11069-015-2092-0, 2016.
    • Woldemeskel, F. M., Sivakumar, B., and Sharma, A.: Merging gauge and satellite rainfall with specification of associated uncertainty across Australia, J. Hydrol., 499, 167-176, doi:10.1016/j.jhydrol.2013.06.039, 2013.
    • World Meteorological Organization: Guide to meteorological instruments and methods of observation, WMO-No, 8, Present and past weather, state of the ground, I.14-1-I.14-9, World Meteorological Organization, Geneva, Switzerland, Seventh Edn., 2008.
    • Xue, X., Hong, Y., Limaye, A. S., Gourley, J. J., Huffman, G. J., Khan, S. I., Dorji, C., and 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 3B42V7 ready for use in ungauged basins?, J. Hydrol., 499, 91-99, doi:10.1016/j.jhydrol.2013.06.042, 2013.
    • Yang, Z., Hsu, K., Sorooshian, S., Xu, X., Braithwaite, D., and Verbist, K. M. J.: Bias Adjustment of Satellite-based Precipitation Estimation using Gauge Observations-A Case Study in Chile, J. Geophys. Res.-Atmos., 121, 3790-3806, doi:10.1002/2015JD024540, 2016.
    • Zambrano-Bigiarini, M.: hydroGOF: Goodness-of-fit functions for comparison of simulated and observed hydrological time series, https://CRAN.R-project.org/package=hydroGOF, R package version 0.3-8-7, 2016a.
    • Zambrano-Bigiarini, M.: hydroTSM: Time Series Management, Analysis and Interpolation for Hydrological Modelling, available at: http://CRAN.R-project.org/package=hydroTSM (last access: 1 August 2016), R package version 0.4-8, 2016b.
    • Zambrano-Bigiarini, M., Nauditt, A., Birkel, C., Verbist, K., and Ribbe, L.: Temporal and spatial evaluation of satellitebased rainfall estimates across the complex topographical and climatic gradients of Chile (supplementary material), doi:10.5281/zenodo.251069, 2016.
    • Zhang, A. and Jia, G.: Monitoring meteorological drought in semiarid regions using multi-sensor microwave remote sensing data, Remote Sens. Environ., 134, 12-23, doi:10.1016/j.rse.2013.02.023, 2013.
    • Zhang, X. and Srinivasan, R.: GIS-Based Spatial Precipitation Estimation: A Comparison of Geostatistical Approaches, J. Am. Water Resour. Assoc., 45, 894-906, doi:10.1111/j.1752- 1688.2009.00335.x, 2009.
  • Discovered through pilot similarity algorithms. Send us your feedback.

Share - Bookmark

Cite this article