A Sub-Hourly Precipitation Dataset from a Pluviographic Network in Central Chile
Abstract
1. Introduction
2. Data Description
2.1. Data Source and Instrumentation
2.2. Dataset Characteristics
- Temporal patterns: storm onset, peak intensity timing, and recession characteristics;
- Short-duration intensities: precipitation rates over durations from 5 min to several hours;
- Event structure: the internal structure of rainfall events, including multiple intensity peaks and dry periods within storms;
- Seasonal variations: detailed precipitation dynamics across Chile’s Mediterranean climate seasons.
3. Methodology for Digitizing Pluviograph Strip Charts
3.1. Strip Chart Digitization Process
3.1.1. Preprocessing and Scanning
3.1.2. Automated Trace Extraction
- Image preprocessing: Noise reduction, contrast enhancement, and binarization to isolate the ink trace from background elements and grid lines.
- Coordinate system calibration: Identification of reference time marks (horizontal axis) and precipitation quantity trace marks (vertical axis) to establish precise scaling factors in both dimensions.
- Trace identification and extraction: Implementation of edge detection and line-tracing algorithms to identify and extract the continuous pen trajectory representing accumulated precipitation.
- Siphon event detection: Automated recognition of the characteristic vertical drops in the trace that represent siphoning events, necessary for calculating total accumulated precipitation.
- Conversion to time series: Transformation of the extracted geometric trace into a continuous time series of accumulated precipitation at 5 min intervals.
- Derivation of intensity values: Calculation of 5 min precipitation intensity values through temporal aggregation and differentiation of the accumulated series.
3.1.3. Quality Control and Manual Verification
- Automated consistency checks: Application of logical tests to identify physically implausible values, discontinuities, and other anomalies requiring further examination.
- Visual verification: Manual inspection of extracted traces superimposed on the original chart images for a subset of charts, with emphasis on high-intensity events and problematic sections identified during automated checks.
- Comparison with daily records: Validation of 24-hour accumulated totals against independent daily precipitation measurements from co-located standard rain gauges where available.
- Cross-station comparison: Evaluation of spatial consistency through comparison of simultaneous records from proximate stations during significant precipitation events.
- Expert review: Final examination of questionable records by experienced hydrometeorological analysts, with manual corrections applied where necessary.
3.2. Temporal Aggregation and Dataset Compilation
4. Data Format
- datetime: timestamp in ISO 8601 format (YYYY-MM-DD HH:MM:SS);
- rainfall: precipitation amount in millimeters (2 decimal places) accumulated over the preceding 5 min interval.
Listing 1. Sample of the 5 min rainfall data format from the dataset showing precipitation records from 25 March 1985. |
5. User Notes
5.1. Recommended Applications
5.2. Machine Learning Applications
5.3. Assumptions and Considerations
5.4. Analytical Method Development
5.5. Limitations
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Brunet, M.; Jones, P. Data Rescue Initiatives: Bringing Historical Climate Data into the 21st Century. Clim. Res. 2011, 47, 29–40. [Google Scholar] [CrossRef]
- World Meteorological Organization. Guide to Instruments and Methods of Observation: Volume I—Measurement of Meteorological Variables; Number WMO No 8 in Technical Reports; WMO: Geneva, Switzerland, 2018. [Google Scholar]
- Pizarro-Tapia, R.; González-Leiva, F.; Valdés-Pineda, R.; Ingram, B.; Sangüesa, C.; Vallejos, C. A Rainfall Intensity Data Rescue Initiative for Central Chile Utilizing a Pluviograph Strip Charts Reader (PSCR). Water 2020, 12, 1887. [Google Scholar] [CrossRef]
- Sangüesa, C.; Pizarro, R.; Ingram, B.; Balocchi, F.; García-Chevesich, P.; Pino, J.; Ibáñez, A.; Vallejos, C.; Mendoza, R.; Bernal, A.; et al. Streamflow Trends in Central Chile. Hydrology 2023, 10, 144. [Google Scholar] [CrossRef]
- Dirección General de Aguas. Manual Básico Para Instrucción de Hidromensores; Technical report, DGA; Departamento de Hidrología, Ministerio de Obras Públicas de Chile (MOP): Santiago, Chile, 1991. [Google Scholar]
- Lanciotti, S.; Ridolfi, E.; Russo, F.; Napolitano, F. Intensity–duration–frequency curves in a data-rich era: A review. Water 2022, 14, 3705. [Google Scholar] [CrossRef]
- Pizarro, R.; Ingram, B.; Gonzalez-Leiva, F.; Valdés-Pineda, R.; Sangüesa, C.; Delgado, N.; García-Chevesich, P.; Valdés, J.B. WEBSEIDF: A Web-Based System for the Estimation of IDF Curves in Central Chile. Hydrology 2018, 5, 40. [Google Scholar] [CrossRef]
- Dirks, K.; Hay, J.; Stow, C.; Harris, D. High-resolution studies of rainfall on Norfolk Island: Part II: Interpolation of rainfall data. J. Hydrol. 1998, 208, 187–193. [Google Scholar] [CrossRef]
- Jaklič, A.; Šajn, L.; Derganc, G.; Peer, P. Automatic Digitization of Pluviograph Strip Charts. Meteorol. Appl. 2016, 23, 57–64. [Google Scholar] [CrossRef]
- Sušin, N.; Peer, P. Open-Source Tool for Interactive Digitisation of Pluviograph Strip Charts. Weather 2018, 73, 222–226. [Google Scholar] [CrossRef]
- Brönnimann, S.; Brugnara, Y.; Allan, R.J.; Brunet, M.; Compo, G.P.; Crouthamel, R.I.; Jones, P.D.; Jourdain, S.; Luterbacher, J.; Siegmund, P.; et al. A Roadmap to Climate Data Rescue Services. Geosci. Data J. 2018, 5, 28–39. [Google Scholar] [CrossRef]
- Rahimifard, S.; Trollman, H. UN Sustainable Development Goals: An engineering perspective. Int. J. Sustain. Eng. 2018, 11, 1–3. [Google Scholar] [CrossRef]
- Boisier, J.P.; Rondanelli, R.; Garreaud, R.D.; Muñoz, F. Anthropogenic and natural contributions to the Southeast Pacific precipitation decline and recent megadrought in central Chile. Geophys. Res. Lett. 2016, 43, 413–421. [Google Scholar] [CrossRef]
- Hák, T.; Janoušková, S.; Moldan, B. Sustainable Development Goals: A need for relevant indicators. Ecol. Indic. 2016, 60, 565–573. [Google Scholar] [CrossRef]
- Shortridge, J. Observed trends in daily rainfall variability result in more severe climate change impacts to agriculture. Clim. Change 2019, 157, 429–444. [Google Scholar] [CrossRef]
- Kerry, R.; Ingram, B.; Garcia-Cela, E.; Magan, N.; Ortiz, B.V.; Scully, B. Determining future aflatoxin contamination risk scenarios for corn in Southern Georgia, USA using spatio-temporal modelling and future climate simulations. Sci. Rep. 2021, 11, 13522. [Google Scholar] [CrossRef]
- Deidda, R.; Mascaro, G.; Piga, E.; Querzoli, G. An Automatic System for Rainfall Signal Recognition from Tipping Bucket Gage Strip Charts. J. Hydrol. 2007, 333, 400–412. [Google Scholar] [CrossRef]
- Van Piggelen, H.E.; Brandsma, T.; Manders, H.; Lichtenauer, J.F. Automatic Curve Extraction for Digitizing Rainfall Strip Charts. J. Atmos. Ocean. Technol. 2011, 28, 891–906. [Google Scholar] [CrossRef]
- Haralick, R.M.; Shapiro, L.G. Image Segmentation Techniques. Comput. Vision Graph. Image Process. 1985, 29, 100–132. [Google Scholar] [CrossRef]
- Roy, P.; Goswami, S.; Chakraborty, S.; Azar, A.T.; Dey, N. Image Segmentation Using Rough Set Theory: A Review. Int. J. Rough Sets Data Anal. 2014, 1, 62–74. [Google Scholar] [CrossRef]
- Marchewka, A.; Pasela, R. Extraction of Data from Limnigraf Chart Images. In Image Processing and Communications Challenges 5. Advances in Intelligent Systems and Computing; Choraś, R.S., Ed.; Springer: Berlin/Heidelberg, Germany, 2014; Volume 233, pp. 263–269. [Google Scholar] [CrossRef]
- Gonzalez, R.C.; Woods, R.E. Digital Image Processing, 3rd ed.; Prentice-Hall, Inc.: Upper Saddle River, NJ, USA, 2008. [Google Scholar]
- Sonka, M.; Hlavac, V.; Boyle, R. Image Pre-Processing. In Image Processing, Analysis and Machine Vision; Springer: Boston, MA, USA, 1993; pp. 56–111. [Google Scholar] [CrossRef]
- Krig, S. Computer Vision Metrics: Survey, Taxonomy, and Analysis; Apress Media LLC: New York, NY, USA, 2014. [Google Scholar]
- Pizarro, R.; Valdés, R.; García-Chevesich, P.; Vallejos, C.; Sangüesa, C.; Morales, C.; Balocchi, F.; Abarza, A.; Fuentes, R. Latitudinal Analysis of Rainfall Intensity and Mean Annual Precipitation in Chile. Chil. J. Agric. Res. 2007, 72, 252–261. [Google Scholar] [CrossRef]
- Pizarro, R.; Garcia-Chevesich, P.; Valdes, R.; Dominguez, F.; Hossain, F.; Ffolliott, P.; Olivares, C.; Morales, C.; Balocchi, F.; Bro, P. Inland Water Bodies in Chile Can Locally Increase Rainfall Intensity. J. Hydrol. 2013, 481, 56–63. [Google Scholar] [CrossRef]
- Pizarro, R.; Valdés, R.; Abarza, A.; Garcia-Chevesich, P. A Simplified Storm Index Method to Extrapolate Intensity-Duration-Frequency (IDF) Curves for Ungauged Stations in Central Chile. Hydrol. Process. 2015, 29, 641–652. [Google Scholar] [CrossRef]
- Munang, R.; Nkem, J.N.; Han, Z. Using Data Digitalization to Inform Climate Change Adaptation Policy: Informing the Future Using the Present. Weather Clim. Extrem. 2013, 1, 17–18. [Google Scholar] [CrossRef]
- Ashcroft, L.; Allan, R.; Bridgman, H.; Gergis, J.; Pudmenzky, C.; Thornton, K. Current Climate Data Rescue Activities in Australia. Adv. Atmos. Sci. 2016, 33, 1323–1324. [Google Scholar] [CrossRef]
- Wilkinson, C.; Brönnimann, S.; Jourdain, S.; Roucaute, E.; Crouthamel, R.; IEDRO Team; Brohan, P.; Valente, A.; Brugnara, Y.; Brunet, M.; et al. Best Practice Guidelines for Climate Data Rescue v1, of the Copernicus Climate Change Service Data Rescue Service. Technical Report C3S_DC3S311a_Lot1.3.4.1_2019_v1-contract: 2019/C3S_311a_Lot1_Met Office/SC2, Copernicus Climate Change Service. 2019; Available online: http://www.c3.urv.cat/docs/publicacions/2019/Deliverable_BestPracticeGuidelines_Part1.pdf (accessed on 28 May 2025).
- ISO 8601; ISO. Available online: https://www.iso.org/iso-8601-date-and-time-format.html (accessed on 28 May 2025).
- Fletcher, T.D.; Andrieu, H.; Hamel, P. Understanding, management and modelling of urban hydrology and its consequences for receiving waters: A state of the art. Adv. Water Resour. 2013, 51, 261–279. [Google Scholar] [CrossRef]
- Ochoa-Rodriguez, S.; Wang, L.P.; Gires, A.; Pina, R.D.; Reinoso-Rondinel, R.; Bruni, G.; Ichiba, A.; Gaitan, S.; Cristiano, E.; van Assel, J.; et al. Impact of spatial and temporal resolution of rainfall inputs on urban hydrodynamic modelling outputs: A multi-catchment investigation. J. Hydrol. 2015, 531, 389–407. [Google Scholar] [CrossRef]
- Dunkerley, D. Identifying individual rain events from pluviograph records: A review with analysis of data from an Australian dryland site. Hydrol. Process. Int. J. 2008, 22, 5024–5036. [Google Scholar] [CrossRef]
- Nhat, L.M.; Tachikawa, Y.; Sayama, T.; Takara, K. A Simple Scaling Charateristics of Rainfall in Time and Space to Derive Intensity Duration Frequency Relationships. Annu. J. Hydraul. Eng. JSCE 2007, 51, 73–78. [Google Scholar] [CrossRef]
- Westra, S.; Fowler, H.J.; Evans, J.P.; Alexander, L.V.; Berg, P.; Johnson, F.; Kendon, E.J.; Lenderink, G.; Roberts, N. Future changes to the intensity and frequency of short-duration extreme rainfall. Rev. Geophys. 2014, 52, 522–555. [Google Scholar] [CrossRef]
- Sun, Q.; Zhang, X.; Zwiers, F.; Westra, S.; Alexander, L.V. A global, continental, and regional analysis of changes in extreme precipitation. J. Clim. 2021, 34, 243–258. [Google Scholar] [CrossRef]
- Bonilla, C.A.; Johnson, O.I. Soil erodibility mapping and its correlation with soil properties in Central Chile. Geoderma 2012, 189, 116–123. [Google Scholar] [CrossRef]
- Angulo-Martínez, M.; López-Vicente, M.; Vicente-Serrano, S.M.; Beguería, S. Mapping rainfall erosivity at a regional scale: A comparison of interpolation methods in the Ebro Basin (NE Spain). Hydrol. Earth Syst. Sci. 2009, 13, 1907–1920. [Google Scholar] [CrossRef]
- Shi, X.; Chen, Z.; Wang, H.; Yeung, D.Y.; Wong, W.K.; Woo, W.c. Convolutional LSTM network: A machine learning approach for precipitation nowcasting. Adv. Neural Inf. Process. Syst. 2015, 28, 802–810. [Google Scholar]
- Ravuri, S.; Lenc, K.; Willson, M.; Kangin, D.; Lam, R.; Mirowski, P.; Fitzsimons, M.; Athanassiadou, M.; Kashem, S.; Madge, S.; et al. Skilful precipitation nowcasting using deep generative models of radar. Nature 2021, 597, 672–677. [Google Scholar] [CrossRef]
- Crochemore, L.; Ramos, M.H.; Pappenberger, F. Bias correcting precipitation forecasts to improve the skill of seasonal streamflow forecasts. Hydrol. Earth Syst. Sci. 2016, 20, 3601–3618. [Google Scholar] [CrossRef]
- Kramer, R.J.; Soden, B.J.; Pendergrass, A.G. Evaluating Climate Model Simulations of the Radiative Forcing and Radiative Response at Earth’s Surface. J. Clim. 2019, 32, 4089–4102. [Google Scholar] [CrossRef]
- Estévez, E.; Marcos, M. Model-based validation of industrial control systems. IEEE Trans. Ind. Inform. 2011, 8, 302–310. [Google Scholar] [CrossRef]
- Chen, B.; Liu, C. Warm organized rain systems over the tropical eastern Pacific. J. Clim. 2016, 29, 3403–3422. [Google Scholar] [CrossRef]
- Serrano-Notivoli, R.; Beguería, S.; Saz, M.Á.; Longares, L.A.; de Luis, M. SPREAD: A high-resolution daily gridded precipitation dataset for Spain–an extreme events frequency and intensity overview. Earth Syst. Sci. Data 2017, 9, 721–738. [Google Scholar] [CrossRef]
- Zhang, C.J.; Zeng, J.; Wang, H.Y.; Ma, L.M.; Chu, H. Correction model for rainfall forecasts using the LSTM with multiple meteorological factors. Meteorol. Appl. 2020, 27, e1852. [Google Scholar] [CrossRef]
- Kratzert, F.; Klotz, D.; Brenner, C.; Schulz, K.; Herrnegger, M. Rainfall–runoff modelling using long short-term memory (LSTM) networks. Hydrol. Earth Syst. Sci. 2018, 22, 6005–6022. [Google Scholar] [CrossRef]
- Kratzert, F.; Klotz, D.; Herrnegger, M.; Sampson, A.K.; Hochreiter, S.; Nearing, G.S. Toward improved predictions in ungauged basins: Exploiting the power of machine learning. Water Resour. Res. 2019, 55, 11344–11354. [Google Scholar] [CrossRef]
- Lanza, L.G.; Cauteruccio, A. Accuracy assessment and intercomparison of precipitation measurement instruments. In Precipitation Science; Elsevier: Amsterdam, The Netherlands, 2022; pp. 3–35. [Google Scholar]
- Reichstein, M.; Camps-Valls, G.; Stevens, B.; Jung, M.; Denzler, J.; Carvalhais, N.; Prabhat, F. Deep learning and process understanding for data-driven Earth system science. Nature 2019, 566, 195–204. [Google Scholar] [CrossRef]
- George, D.; Shen, H.; Huerta, E. Classification and unsupervised clustering of LIGO data with Deep Transfer Learning. Phys. Rev. D 2018, 97, 101501. [Google Scholar] [CrossRef]
- Joe, P.; Baklanov, A.; Grimmond, S.; Bouchet, V.; Molina, L.T.; Schluenzen, K.H.; Mills, G.; Tan, J.; Golding, B.; Masson, V.; et al. Guidance on integrated urban hydro-meteorological, climate and environmental services: Challenges and the way forward. In Urban Climate Science for Planning Healthy Cities; Springer: Berlin/Heidelberg, Germany, 2021; pp. 311–338. [Google Scholar] [CrossRef]
- Lanza, L.G.; Vuerich, E. The WMO field intercomparison of rain intensity gauges. Atmos. Res. 2009, 94, 534–543. [Google Scholar] [CrossRef]
- Garreaud, R.D.; Vuille, M.; Compagnucci, R.; Marengo, J. Present-day south american climate. Palaeogeogr. Palaeoclimatol. Palaeoecol. 2009, 281, 180–195. [Google Scholar] [CrossRef]
- Vicuña, S.; Garreaud, R.D.; McPhee, J. Climate change impacts on the hydrology of a snowmelt driven basin in semiarid Chile. Clim. Change 2011, 105, 469–488. [Google Scholar] [CrossRef]
- Koutsoyiannis, D. On the appropriateness of the Gumbel distribution for modelling extreme rainfall. In Hydrological Risk: Recent Advances in Peak River Flow Modelling, Prediction and Real-Time Forecasting. Assessment of the Impacts of Land-Use and Climate Changes; Editoriale Bios: Castrolibero, Italy, 2003; pp. 24–25. [Google Scholar]
- Overeem, A.; Buishand, A.; Holleman, I. Rainfall depth-duration-frequency curves and their uncertainties. J. Hydrol. 2008, 348, 124–134. [Google Scholar] [CrossRef]
- Debele, B.; Srinivasan, R.; Parlange, J.Y. Accuracy evaluation of weather data generation and disaggregation methods at finer timescales. Adv. Water Resour. 2007, 30, 1286–1300. [Google Scholar] [CrossRef]
- Hingray, B.; Haha, M.B. Statistical performances of various deterministic and stochastic models for rainfall series disaggregation. Atmos. Res. 2005, 77, 152–175. [Google Scholar] [CrossRef]
- Ingram, B.; Cornford, D.; Evans, D. Fast algorithms for automatic mapping with space-limited covariance functions. Stoch. Environ. Res. Risk Assess. 2008, 22, 661–670. [Google Scholar] [CrossRef]
- Wagner, P.D.; Fiener, P.; Wilken, F.; Kumar, S.; Schneider, K. Comparison and evaluation of spatial interpolation schemes for daily rainfall in data scarce regions. J. Hydrol. 2012, 464, 388–400. [Google Scholar] [CrossRef]
- Guerreiro, S.B.; Fowler, H.J.; Barbero, R.; Westra, S.; Lenderink, G.; Blenkinsop, S.; Lewis, E.; Li, X.F. Detection of continental-scale intensification of hourly rainfall extremes. Nat. Clim. Change 2018, 8, 803–807. [Google Scholar] [CrossRef]
- Barbero, R.; Fowler, H.; Lenderink, G.; Blenkinsop, S. Is the intensification of precipitation extremes with global warming better detected at hourly than daily resolutions? Geophys. Res. Lett. 2017, 44, 974–983. [Google Scholar] [CrossRef]
- Molina-Sanchis, I.; Lázaro, R.; Arnau-Rosalén, E.; Calvo-Cases, A. Rainfall timing and runoff: The influence of the criterion for rain event separation. J. Hydrol. Hydromechanics 2016, 64, 226. [Google Scholar] [CrossRef]
- Gaál, L.; Molnar, P.; Szolgay, J. Selection of intense rainfall events based on intensity thresholds and lightning data in Switzerland. Hydrol. Earth Syst. Sci. 2014, 18, 1561–1573. [Google Scholar] [CrossRef]
- García-Marín, A.P.; Jiménez-Hornero, F.; Ayuso-Muñoz, J. Multifractal analysis as a tool for validating a rainfall model. Hydrol. Process. Int. J. 2008, 22, 2672–2688. [Google Scholar] [CrossRef]
- He, F.; Mohamadzadeh, N.; Sadeghnejad, M.; Ingram, B.; Ostovari, Y. Fractal Features of Soil Particles as an Index of Land Degradation under Different Land-Use Patterns and Slope-Aspects. Land 2023, 12, 615. [Google Scholar] [CrossRef]
- Veneziano, D.; Langousis, A.; Furcolo, P. Multifractality and rainfall extremes: A review. Water Resour. Res. 2006, 42. [Google Scholar] [CrossRef]
- Gires, A.; Tchiguirinskaia, I.; Schertzer, D.; Schellart, A.; Berne, A.; Lovejoy, S. Influence of small scale rainfall variability on standard comparison tools between radar and rain gauge data. Atmos. Res. 2014, 138, 125–138. [Google Scholar] [CrossRef]
- Rasmussen, R.; Baker, B.; Kochendorfer, J.; Meyers, T.; Landolt, S.; Fischer, A.P.; Black, J.; Thériault, J.M.; Kucera, P.; Gochis, D.; et al. How well are we measuring snow: The NOAA/FAA/NCAR winter precipitation test bed. Bull. Am. Meteorol. Soc. 2012, 93, 811–829. [Google Scholar] [CrossRef]
- Shepherd, J.M.; Pierce, H.; Negri, A.J. Rainfall modification by major urban areas: Observations from spaceborne rain radar on the TRMM satellite. J. Appl. Meteorol. 2002, 41, 689–701. [Google Scholar] [CrossRef]
Station Name | Longitude | Latitude | Availability | Altitude (masl) | Filename |
---|---|---|---|---|---|
Bullileo | 71°24′0.0″ W | 36°17′0.0″ S | 1971–2009 | 600 | Bullileo.csv |
Colorado | 71°15′0.0″ W | 35°38′0.0″ S | 1969–2009 | 420 | Colorado.csv |
Pirque | 70°35′0.3″ W | 33°40′1.9″ S | 1985–2009 | 659 | Pirque.csv |
Talca | 71°37′0.0″ W | 35°26′0.0″ S | 1982–2009 | 130 | Talca.csv |
San Manuel | 71°38′57.8″ W | 36°21′26.0″ S | 1995–2009 | 270 | SanManuel.csv |
Melozal | 71°47′1.7″ W | 35°46′27.9″ S | 1971–2009 | 110 | Melozal.csv |
Parral | 71°49′44.3″ W | 36°11′35.3″ S | 1974–2009 | 175 | Parral.csv |
Pencahue | 71°50′5.7″ W | 35°22′31.0″ S | 1974–2009 | 55 | Pencahue.csv |
Potrero Grande | 71°5′51.8″ W | 35°11′0.0″ S | 1971–2009 | 460 | PotreroGrande.csv |
Cerro Calán | 70°32′0.3″ W | 33°24′1.9″ S | 1992–2009 | 848 | CerroCalan.csv |
Los Panguiles | 71°1′0.4″ W | 33°26′1.9″ S | 1985–2009 | 190 | LosPanguiles.csv |
Melipilla | 71°13′0.4″ W | 33°42′1.9″ S | 1985–2009 | 168 | Melipilla.csv |
Pudahuel DMC | 70°47′39.7″ W | 33°23′30.8″ S | 1986–2009 | 480 | PudahuelDMC.csv |
Rungue | 70°54′0.3″ W | 33°1′1.8″ S | 1984–2009 | 700 | Rungue.csv |
1 h | 6 h | 24 h | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Station | N | R2 | RMSE | NSE | R2 | RMSE | NSE | R2 | RMSE | NSE | ||
Cerro Calán | 18 | 0.895 | 0.94 | 0.834 | 0.930 | 0.73 | 0.867 | 0.950 | 0.30 | 0.949 | ||
Cerro El Padre | 40 | 0.943 | 1.13 | 0.939 | 0.985 | 0.27 | 0.985 | 0.994 | 0.12 | 0.994 | ||
Embalse Ancoa | 38 | 0.868 | 1.38 | 0.866 | 0.845 | 1.17 | 0.844 | 0.962 | 0.34 | 0.960 | ||
Embalse Coihueco | 38 | 0.937 | 1.88 | 0.931 | 0.981 | 0.43 | 0.977 | 0.985 | 0.25 | 0.983 | ||
Melipilla | 34 | 0.857 | 1.47 | 0.797 | 0.842 | 0.71 | 0.799 | 0.947 | 0.28 | 0.935 | ||
Potrero Grande | 38 | 0.858 | 1.73 | 0.846 | 0.903 | 1.74 | 0.725 | 0.890 | 0.51 | 0.888 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2025 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 (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Sangüesa, C.; Ibañez, A.; Pizarro, R.; Vidal-Silva, C.; Garcia-Chevesich, P.; Mendoza, R.; Toledo, C.; Pino, J.; Paredes, R.; Ingram, B. A Sub-Hourly Precipitation Dataset from a Pluviographic Network in Central Chile. Data 2025, 10, 95. https://doi.org/10.3390/data10070095
Sangüesa C, Ibañez A, Pizarro R, Vidal-Silva C, Garcia-Chevesich P, Mendoza R, Toledo C, Pino J, Paredes R, Ingram B. A Sub-Hourly Precipitation Dataset from a Pluviographic Network in Central Chile. Data. 2025; 10(7):95. https://doi.org/10.3390/data10070095
Chicago/Turabian StyleSangüesa, Claudia, Alfredo Ibañez, Roberto Pizarro, Cristian Vidal-Silva, Pablo Garcia-Chevesich, Romina Mendoza, Cristóbal Toledo, Juan Pino, Rodrigo Paredes, and Ben Ingram. 2025. "A Sub-Hourly Precipitation Dataset from a Pluviographic Network in Central Chile" Data 10, no. 7: 95. https://doi.org/10.3390/data10070095
APA StyleSangüesa, C., Ibañez, A., Pizarro, R., Vidal-Silva, C., Garcia-Chevesich, P., Mendoza, R., Toledo, C., Pino, J., Paredes, R., & Ingram, B. (2025). A Sub-Hourly Precipitation Dataset from a Pluviographic Network in Central Chile. Data, 10(7), 95. https://doi.org/10.3390/data10070095