1. CONCLUSIONS Previous research has shown that, in general, the HE-estimated rainfall does not compare well with measured rain gauge data in Puerto Rico (Cruz Gonzalez, 2006). The purpose of this study was to obtain rainfall data over the area of an HE pixel to evaluate typical spatial and temporal rainfall variability. For the example data presented, rainfall on August 6 th, 2006, exhibited a large degree of variability within the HE pixel (max 55.5 mm, min 9.2 mm). General pixel rainfall statistics were presented, indicating large variability within the pixel. On August 6 th, 2006 measured rain gauge rainfall was 30.83 mm as compared to 0 mm for the Hydro-Estimator. Data will be collected from the rain gauges during the coming year to evaluate how the statistical properties of rainfall vary temporally (e.g. seasonally) and spatially. Comparisons will also be made with the HE, SCaMPR and GMSRA algorithms for selected storms throughout the year. METHODOLODY During late July 2006, sixteen WatchDog tipping bucket rain gauges (Spectrum Technology, Inc.) were installed within the area of one HE pixel. Each rain gauge was equipped with a data logger capable of storing rainfall depth every 5-minutes over a 24 day period. The study area was located near to the University of Puerto Rico’s Mayagüez Campus (UPRM). The pixel area was 4 km x 4 km (16 km 2 ). This area was divided into sixteen evenly spaced squares of 1 km 2 each (Figure 1). To locate the rain gauges the following steps were followed. 1. The center points of the HE pixels were obtained from NOAA-NESDIS. 2. An appropriate HE pixel was selected, which included a relatively large range of topographic relief east of the Mayagüez Bay in western Puerto Rico. 3. Using ArcGIS, sixteen points were located (evenly spaced) within the HE pixel. These sixteen areas will be referred to as sub-areas. 4. With the assistance of a ground positioning system (GPS), students located properties which were as close as possible to the center point locations identified in step no. 3. In each case it was necessary to obtain permission from the property owner before installing the rain gauges. 5. The actual coordinates of the installed rain gauges were recorded and entered into ArcGIS (Figure 2). The data logger clocks were synchronized and programmed to record cumulative rainfall depth every 5 minutes. All rain gauges were placed in areas free from obstructions. It was necessary to located a few of the gauges on roof tops (approximately 5 meters above the ground) owing to inappropriate conditions on the ground. An effort was made to level each of the rain gauges to assure it’s proper functioning. Rainfall Distribution within an Hydro-Estimator Pixel in Western Puerto Rico ABSTRACT Nowcasting flash floods can save money and save lives. Money can be saved by allowing decision makers to implement emergency plans only when necessary, since unnecessary preparations and evacuations are very costly. The technique also allows decision makers to better focus their emergency measures, since locations where flood waters concentrate tend to be storm dependent. An algorithm for rainfall estimation called the Hydro-Estimator (HE) was developed for work in areas of the world where radar and rain gauges are unavailable. The method can potentially be used for nowcasting since 15-minute (or shorter) estimates of rainfall can be obtained. The HE algorithm utilizes data from the GOES geostationary satellite. The HE has been the operational satellite rainfall algorithm of NOAA-NESDIS since 2000 and is available to the National Weather Service forecasters since 2004. Flood forecasting using hydrologic models requires grid spacing which is smaller than the HE pixel (4 km x 4 km). The purpose of this research is to measure the rainfall variability within an HE pixel to evaluate the potential for using the algorithm for nowcasting flash flooding under tropical conditions. Rainfall statistics for nine storms for an HE pixel located in western Puerto Rico (PR) are presented. A comparison is made between the average measured and HE pixel rainfall for August 6 th, 2006. RESULTS Figure 2 shows the final location of the rain gauges within the HE pixel area. Note that some of the rain gauges could not be located close to the center points of the squares because of lack of access. The problem-areas were generally located within undeveloped valleys which could not be accessed. Consequently the final locations of rain gauges were not evenly spaced; however, this resulted in producing a random (beneficial) aspect to the locations of rain gauges within each sub-area. As an example, Figure 3 shows the depth of rainfall measured every 5 minutes by the sixteen gauges on August 6 th, 2006. Figure 4 shows the spatial distribution of total rainfall for the same storm. It is clear that the rainfall in the satellite pixel area can vary significantly. The average and standard deviation for the rainfall were 30.83 mm and 13.63 mm, respectively. Maximum and minimum recorded rainfall were 55.5 mm and 9.2 mm, respectively. Ian Garcia 1, E. W. Harmsen 2 and Jorge Canals Garcia 3 1. Undergraduate Research Assistant, Dept. of Biology, University of Puerto Rico – Mayagüez Campus, 2. Associate Professor, Dept. of Ag. and Biosystems Eng. University of Puerto Rico –Mayagüez Campus 3. Graduate Research Assistant, Dept. of Computer Engineering, University of Puerto Rico – Mayagüez Campus REFERENCES Cruz Gonzalez, B., 2006. Validacion del algoritmo hidro-estimador en la region de puerto rico. tesis Departamento de ININ, Universidad de Puerto Rico, Recinto Universitario de Mayagüez, Julio. Scofield, R. A. and R. J. Kuligowski, 2003. Status and Outlook of Operational Satellite Precipitation Algorithms for Extreme-Precipitation Events. National Environmental Satellite, Data, and Information Service, Camp Springs, Maryland Vila D. and Velasco I., 2002. Some Experiences On Satellite Rainfall Estimation Over South America. 1st IPWG Workshop EUMETSAT SAF NWC, Instituto Nacional de Meteorología, Madrid, Spain. 23-27 September. Figure 2. Air photo showing final locations of the 16 rain gauges. Figure 3. Rainfall measured from rain gauges on August 6 th, 2006. Acknowledgements: We would like to thank the following students for their help on this project: Marcel Giovanni Prieto, Victor Hugo Ramirez, Yaritza Perez, Romara Santiago, Alejandra Roja, Julian Harmsen and Lua Harmsen. Dr. Luis Perez and Dr. Nazario Ramirez for use of their GPS equipment. We also want to thank NOAA CREST for their financial support of this project. Additional support was received from NASA EPSCoR, USDA-TSTAR and NSF-CASA projects. Gauge Figure 1. Study area corresponding to a Hydro-Estimator pixel (4 km x 4 km). Colors represent variations in topography. Long-term Project Goals Validate and enhance quantitative precipitation estimation (QPE) methods in Puerto (HE, SCaMPR, GMSRA) Understand pixel-scale rainfall variability Develop a satellite QPE flash flood Nowcast for a testbed in western PR. Short-Term Project Goals Install 16 digital rain gauges within a HE pixel Collect data from the rain gauges for at least a one year period Evaluate rainfall statistics Compare rain gauge data with HE-estimated data Figure 4. Spatial distribution of rainfall on August 6th, 2006. PROBLEM The HE validation study of Cruz Gonzalez (2006) raised a concern relative to the validation methodology itself; specifically, is it appropriate to compare a single rain gauge value of rainfall with estimates from the HE algorithm, which covers an area of 16 km 2 ? As mentioned above, the inability for the HE algorithm to account for pixel-scale rainfall variability has important implications on nowcasting of flash floods. NESDIS provided us with HE data for Aug 1st through Aug 8th, 2006. A comparison was made with the Aug 6th rain gauge data (Figure 4). The HE did not register any rain on Aug 6th, whereas the rain gauges recorded an average depth of 30.8 mm. Table 1. Pixel rainfall statistics for nine storms. Table 2. Rain gauges where rainfall did not occur during the nine storms. Table 1. provides the pixel rainfall statistics associated with nine storms between August and October 2006. Large variations in rainfall can be observed. It is especially interesting to note that for seven of the nine storms, rainfall was not measured by one or more rain gauges (Table 2). Table 1 also provides the overall average storm duration, total rainfall, standard deviation, maximum, minimum and maximum minus minimum rainfall for the nine storms. This work made use of Engineering Research Centers Shared Facilities supported by the National Science Foundation under NSF Cooperative Agreement No. EEC-0313747. Any Opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect those of the National Science Foundation.