Eric W. Harmsen

Urban heat islands developing in coastal tropical cities

Beautiful and breezy cities on small tropical islands, it turns out, may not be exempt from the same local climate change effects and urban heat island effects seen in large continental cities such as Los Angeles or Mexico City. A surprising, recent discovery indicates that this is the case for San Juan, Puerto Rico, a relatively affluent coastal tropical city of about two million inhabitants that is spreading rapidly into the once-rural areas around it. A recent climatological analysis of the surface temperature of the city has revealed that the local temperature has been increasing over the neighboring vegetated areas at a rate of 0.06°C per year for the past 30 years. This is a trend that may be comparable to climate changes induced by global warming.

Calibration of selected pyranometers and satellite derived solar radiation in Puerto Rico

Knowledge of solar radiation at the ground surface is valuable for many disciplines. In this study, ground-based sensors at Fortuna and Mayaguez, Puerto Rico, were used to calibrate daily-integrated satellite-derived solar radiation. The calibration equations yielded R 2 values of 0.88 and 0.83 at Fortuna and Mayaguez, respectively. A regression equation was also derived based on the combined data from the two locations with an R 2 of 0.87. The calibration equations for Fortuna and UPRM were validated using 283 and 227 days of solar radiation data from 2010, respectively. The combined data equation (intended as an island-wide equation) worked well at UPRM but not at Fortuna. We recommend, for locations other than the study areas, that the uncorrected remotely sensed data be used. At the study sites, the uncorrected data produced reasonably accurate results with a maximum 6.22% error between the mean estimated and measured solar radiation.

An algorithm to estimate soil moisture over vegetated areas based on in situ and remote sensing information

An algorithm is proposed for estimating soil moisture over vegetated areas. The algorithm uses in situ and remote sensing information and statistical tools to estimate soil moisture at 1 km spatial resolution and at 20 cm depth over Puerto Rico. Soil moisture within the study region is characterized by spatial and temporal variability. The temporal variability for a given area exhibits long- and short-term variations that can be expressed by two empirical models. The average monthly soil moisture exhibits the long-term variability and is modelled by an artificial neural network (ANN), whereas the short-term variability is determined by hourly variation and is represented by a nonlinear stochastic transfer function model. Monthly vegetation index, land surface temperature, accumulated rainfall and soil texture are the major drivers of the ANN to estimate the monthly soil moisture. Radar, satellite and in situ observations are the major sources of information of the soil moisture empirical models. A self-organized ANN was also used to identify spatial variability to be able to determine a similar transfer function that best resembles the properties of a particular grid point and estimate the hourly soil moisture across the island. Validation techniques reveal an average absolute error of 3.34% of volumetric water content and this result shows that the proposed algorithm is a potential tool for estimating soil moisture over vegetated areas.

Flood alert system using rainfall forecast data in Western Puerto Rico

The Western Puerto Rico area is subject to flooding due to sudden, extreme rainfall events, some of which fail to be detected by NOAAs NEXRAD radar. The use of new radars with higher spatial resolution and covering the low atmosphere are vital for flood forecasting efforts, and for studying and predicting atmospheric phenomena. Recently the University of Puerto Rico in Mayagüez initiated investigations using two (2) types of these radars, Off-the Grid (OTG) and TropiNet (RXM-25), respectively, in the Mayagüez Bay Drainage Basin area. This is the first time that such radar technology will be used for hydrologic analyses and specifically for rainfall forecasting in Puerto Rico. The forecast analysis will be made using time series with autoregressive methods and selecting the stochastic model parameters most appropriate for an optimal prediction; with the radar results, a distributed hydrologic model (Vflo™) is used to obtain the spatial distribution of flooding depth.

Evaluation of Several Dielectric Mixing Models for Estimating Soil Moisture Content in Sand, Loam and Clay Soils

As part of a NOAA-funded project, studies are being conducted at the University of Puerto Rico-Mayaguez Campus using surface-based ground penetrating radar (GPR) to measure soil moisture content. The GPR will eventually be used to verify values of soil moisture at several locations in Puerto Rico using active radar and passive satellite-based sensors. As a part of the estimation process, it is necessary to relate moisture content to the GPR-measured dielectric constant. The motivation for this study was the need to select an appropriate dielectric mixing model for the wide range of soils being considered in the study. An important requirement of the dielectric mixing model was that it works well with input data available from NRCS Soil Survey Reports (e.g., soil texture, available water capacity, etc). The advantage of using this type of data is that it can be readily incorporated into a geographic information system (GIS) to be used with the geo-referenced dielectric data of the surface and satellite-based sensors. This paper provides a review of several dielectric mixing models, and compares moisture content estimates for sand, loam and clay soils, based on dielectric data obtained from a GPR, TDR and Theta Probe™. These results are also compared to soil moisture contents obtained from gravimetric data. Soils were characterized in terms of their chemical and physical properties; information needed by several of the dielectric mixing models. In some cases, especially with the loam soil, wide variations in the dielectric constants and moisture contents were observed.

Analysis of the Heat Index in the Mesoamerica and Caribbean Region

AbstractHourly data collected from ground stations were used to study the maximum daytime heat index Hi in the Mesoamerica and Caribbean Sea (MAC) region for a 35-yr period (1980–2014). Observations of Hi revealed larger values during the rainy season and smaller values during the dry season. The Hi climatology exhibits the largest values in Mesoamerica, followed by the Greater Antilles and then by the Lesser Antilles. The trend in Hi indicates a notable increasing pattern of 0.05°C yr−1 (0.10°F yr−1), and the trends are more prominent in Mesoamerica than in Caribbean countries. This work also includes the analysis of heat index extreme events (HIEE). Usually the extreme values of the heat index are used for advising heat warning events, and it was found that 45 HIEEs occurred during the studied period. The average duration of HIEE was 2.4 days, and the average relative intensity (excess over the threshold) was 2.4°C (4.3°F). It was found that 82% of HIEE lasted 2 or 2.5 days and 80% exhibited relative in...

Performance and Evaluation of Multisensor Precipitation Estimation Algorithm Using a High Density Rain Gauge Network and Hydrologic Simulation

Abstract: A rain gauge network (28 rain gauges) was installed in western Puerto Rico (PR) within a 4km x 4km GOES satellite pixel. Located within the pixel is a well monitored sub-watershed of 3.55 km2, referred to here as the “testbed subwatershed” (TBSW). The rain gauge network was established to evaluate the performance of the GOES-based Hydro-Estimator (HE) rain rate algorithm, and estimated rain rates from NEXRAD radar and the Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) radar network, which has a high spatial resolution (approximated 200 m). Furthermore, the rain gauge network will provide a high temporal and spatial resolution rainfall dataset to be input into a distributed hydrologic model in the TBSW. The focus of this work is to evaluate the performance of the Multisensor Precipitation Estimation (MPE) product at 1hour and 1day temporal resolution within the 4km x 4km HE pixel and at watershed level for 2007. The MPE product is popular within the hydrologic modeling community due to its resolution and mean field bias correction computations in its coverage. Results for 2007 indicate that the highest rainfall measured by the rain gauges within the HE pixel area were September with an average and standard deviation of 241.75 mm and 73.3 mm, respectively; and August with 223.7 mm and 64.66 mm, respectively. While for the same months the Multisensor Precipitation Estimation, produced a total monthly rainfall accumulation and standard deviation of 247.36 mm and 64.4 mm for September, respectively, and 233.68 mm and 36.54 mm for August, respectively. The mean and standard deviation daily field bias for these months were 1.08 and 1.5 for September, respectively, and 0.93 and 1.6 for August, respectively. The bias changed, when considering an hourly analysis, to 1.98 average and 5.45 standard deviation for August and 1.49 average and 3.01 standard deviation for September.

DOWNSCALED CLIMATE CHANGE IMPACTS ON REFERENCE EVAPOTRANSPIRATION AND RAINFALL DEFICITS IN PUERTO RICO

The purpose of this study was to estimate reference evapotranspiration (ET o) and rainfall excess (rainfall – ET o) under climate change condition s for three locations in Puerto Rico: Adjuntas, Mayaguez and Lajas. Reference evapotranspiration was estimated by the Penman -Monteith method. Rainfall and temperature data were statistically downscaled from predictions obtained from the DOE/NCAR PCM global circulation model. The B1 (low), A2 (mid -high) and A 1fi (high) emission scenarios of the Intergovernmental Panel on Climate Change Special Report on Emission Scenarios were evaluated. Results from the analysis indicate that the rainy season will become wetter and the dry season will become drier. The ave rage estimated rainfal l excess (i.e., rainfall – ET o > 0) for all scenarios and locations increased in September (the wettest month) to 356.4 mm for the year 2090 relative to an average rainfall excess of 149.8 mm for 2000. The average rainfall deficit (i .e., rainfall – ETo < 0) in February increased to 72.1 mm for the year 2090 relative to an average rainfall deficit of -26.1 mm for 2000. The implications of these results suggest that additional water could be saved during the wet months, which would be needed to offset increased irrigation requirements during the dry months.

RAINFALL VARIATION WITHIN A 4 KM X 4 KM AREA IN WESTERN PUERTO RICO

Algorithms have been developed to estimate near-real-time rainfall from radar and satellites. These data can be ingested into hydrologic models to estimate flash flooding. A major limitation of the methodology, however, is the relatively poor resolution of the remotely sensed rainfall. Large variations in rainfall can occur within the remotely sensed pixel area, which may be important hydrologically. For example, rain gauge data for a storm on October 22, 2006 measured within a 4 km x 4 km area located in a tropical coastal drainage basin in Western Puerto Rico, revealed large spatial variation. The rain gauge network consisted of sixteen gauges distributed throughout the area. The rainfall average and standard deviation for the storm were 40.6 mm and 25.5 mm, respectively. Maximum and minimum recorded gauge rainfalls were 68.1 mm and 5.9 mm, respectively, and the maximum rainfall gradient within the study area was 65 mm per km. The objective of this paper is to present results of rainfall distribution for selected storms within a 4 km x 4 km study area located near Mayaguez, PR, and to discuss the implications of the results on calibration/validation of quantitative precipitation estimation (QPE). In the future, the rain gauge network will be augmented and will be used to evaluate rainfall estimates from NOAA’s Hydro-Estimator and SCaMPR QPE algorithms, NEXRAD, and the UPRM CASA distributed collaborative adaptive sensing radar network.

The Potential Impact of Climate Change on Agriculture in Puerto Rico

Abstract: This paper discusses the implications of climate change on agriculture in general terms, with special emphasis on agricultural water resources. Specific potential impacts from climate change in Puerto Rico are also discussed. A detailed case study is presented in which crop water requirements are predicted during the next 100 years for three locations in western Puerto Rico: Adjuntas, Mayaguez and Lajas. Rainfall and temperature data were statistically downscaled from predictions obtained from the DOE/NCAR PCM global circulation model. The B1 (low), A2 (mid-high) and A1fi (high) emission scenarios of the Intergovernmental Panel on Climate Change Special Report on Emission Scenarios were evaluated. The average estimated rainfall excess (i.e., rainfall – ETo > 0) for all scenarios and locations increased in September (the wettest month) to 356.4 mm for the year 2090 relative to an average rainfall excess of 149.8 mm for 2000. The average rainfall deficit (i.e., rainfall – ETo < 0) in February increased to -72.1 mm for the year 2090 relative to an average rainfall deficit of -26.1 mm for 2000. The implications of these results suggest that additional water could be saved during the wet months, which would be needed to offset increased irrigation requirements during the dry months.