Sensitivity of the Penman-Monteith reference evapotranspiration equation to meteorological variables for Puerto Rico

Michelle M. Irizarry-Ortiz; Eric Harmsen

doi:10.3390/hydrology10050101

Home/Archive/USGS

USGS · 70243873

Sensitivity of the Penman-Monteith reference evapotranspiration equation to meteorological variables for Puerto Rico

Michelle M. Irizarry-Ortiz·Eric Harmsen

View original source ↗Export metadata ↓

Abstract

Spatiotemporal variations in reference evapotranspiration ( ET o ) are sensitive to the meteorological data used in its estimation. The sensitivity of the ASCE standardized ET o equation to meteorological variables from GOES-PRWEB dataset was evaluated for the island of Puerto Rico. Island wide, ET o is most sensitive to daily mean relative humidity ( RH mean ), followed by solar radiation, daily maximum ( T max ) and minimum ( T min ) air temperatures, and wind speed with average absolute relative sensitivity coefficients (SCs) of 0.98, 0.57, 0.50, 0.27, and 0.12, respectively. The derived SCs guided the prioritization of bias correction of meteorological data for ET o estimation from two downscaled climate models (CNRM and CESM). The SCs were applied to evaluate how meteorological variables contribute to model errors and projected future changes in ET o from 1985–2005 to 2040–2060 at irrigated farms in the south. Both models project a 5.6% average increase in annual ET o due to projected increases in T max and T min and a decrease in RH mean . Despite ET o being most sensitive to relative changes in RH mean , the contributions from RH mean , T max , and T min to future changes in ET o are similar. CESM projects increases in ET o in March, November, and December, increasing the potential for crop water stress. Study limitations are discussed.

Explore related subjects

Hydrology Article

Source-reported bounding extent: 17.926875° to 18.515757° latitude; -67.940799° to -65.221568° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Michelle M. Irizarry-Ortiz, Eric Harmsen. 2023-04-25. Sensitivity of the Penman-Monteith reference evapotranspiration equation to meteorological variables for Puerto Rico. https://doi.org/10.3390/hydrology10050101

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

USGS2025

Regional analysis of the dependence of peak-flow quantiles on climate with application to adjustment to climate trends

Standard flood-frequency analysis methods rely on an assumption of stationarity, but because of growing understanding of climatic persistence and concern regarding the effects of climate change, the need for methods to detect and model nonstationary flood frequency has become widely recognized. In this study, a regional statistical method for estimating the effects of climate variations on annual maximum (peak) flows that allows for the effect to vary by quantile is presented and applied. The method uses a panel–quantile regression framework based on a location-scale model with two fixed effects per basin. The model was fitted to 330 selected gauged basins in the midwestern United States, filtered to remove basins affected by reservoir regulation and urbanization. Precipitation and discharge simulated using a water-balance model at daily and annual time scales were tested as climate variables. Annual maximum daily discharge was found to be the best predictor of peak flows, and the quantile regression coefficients were found to depend monotonically on annual exceedance probability. Application of the models to gauged basins is demonstrated by estimating the peak-flow distributions at the end of the study period (2018) and, using the panel model, to the study basins as-if-ungauged by using leave-one-out cross validation, estimating the fixed effects using static basin characteristics, and parameterizing the water-balance model discharge using median parameters. The errors of the quantiles predicted as-if-ungauged approximately doubled compared to the errors of the fitted panel model.

Hydrology↗

USGS2024

Insight into sources of benzene, TCE, and PFOA/PFAS in groundwater at Naval Air Station Whiting Field, Florida, through numerical particle-tracking simulations

Past waste-disposal activities at Naval Air Station Whiting Field (NASWF) have led to elevated concentrations of contaminants in the underlying sand and gravel aquifer. Contaminants include two of the most commonly detected chemicals in groundwater in many countries (benzene and trichloroethylene (TCE)) and the “forever chemicals” per- and poly-fluoroalkyl substances (PFAS) such as perfluorooctanoic acid (PFOA) and perfluorooctane sulfonic acid (PFOS). A MODFLOW model (the Whiting Field Groundwater Model (WFGM)) was previously developed for NASWF and the surrounding area to simulate groundwater flow. To obtain insight into groundwater flow pathways for the identification of potential source areas, the MODPATH particle-tracking application was applied to the WFGM for three public supply wells and three monitoring wells at NASWF. The travel time to recharge areas was estimated using concentrations of the groundwater age-dating solutes tritium (as helium ingrowth) and chlorofluorocarbons detected in the monitoring wells. Simulated travel times agree with the groundwater ages and indicate that the calibrated WFGM reasonably represents groundwater flow velocities and pathways. The MODPATH simulations confirm suspected on-base source areas to explain chemical detection in the monitoring wells. In contrast, the particle-tracking simulations indicate that potential source areas to the public supply wells include both on- and off-base sources. This is important because PFAS chemicals can have multiple sources, including land application of sludge-based fertilizers. This approach that combines groundwater age dating with particle-tracking simulations can be applied at similar sites characterized by benzene-, TCE-, and PFAS-contaminated groundwater.

Florida↗

USGS2024

Ratingcurve: A Python package for fitting streamflow rating curves

Streamflow is one of the most important variables in hydrology, but it is difficult to measure continuously. As a result, nearly all streamflow time series are estimated from rating curves that define a mathematical relationship between streamflow and some easy-to-measure proxy like water surface elevation (stage). Despite the existence of automated methods, most rating curves are still fit manually, which can be time-consuming and subjective. Although several automated methods exist, they vary greatly in performance because of the non-convex nature of the problem. In this work, we develop a parameterization of the segmented power law that works reliably with minimal data, which could serve operationally or as a benchmark for evaluating other methods. The model, along with test data and tutorials, is available as an open-source Python package called ratingcurve . The implementation uses a modern probabilistic machine-learning framework, which is relatively easy to modify so that others can improve upon it.

Hydrology↗