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The effects of hurricanes Irma and Maria and a severe drought on the temperature, precipitation, and soil moisture (under canopy and in the open) were calculated at 22 sites from 0–1045 m in northeastern Puerto Rico from 2001–2021, against the background short-term trend. Median and minimum air temperatures increased uniformly across the elevational gradient, 1.6 times as fast in the air under the canopy (+0.08 °C/yr) and 2.2 times as fast in the soil under the canopy (+0.11 °C/yr) as for air temperature in the open. There were no substantial moisture trends (average decrease <0.01 mm/yr). The peak effect of the hurricanes on under-canopy air temperature was the same as under-canopy soil temperature at 1000 m (+3, 0.7, 0.4 °C for maximum, median, minimum) but air maximum and minimum temperature peak effects were twice as high at 0 m (and soil temperatures stayed constant). Soil temperature hurricane recovery took longer at higher elevations. The peak effect of the hurricanes and the drought on the soil moisture was the same (but in opposite directions, ±0%), except for the wettest months where drought peak effect was larger and increasing with elevation. Differing patterns with elevation indicate different ecosystem stresses.

 

Abstract Understanding near-surface atmospheric behavior in the tropics is imperative given the role of tropical energy fluxes in Earth’s climate cycles, but this area is complicated by a land–atmosphere interaction that includes rugged topography, seasonal weather drivers, and frequent environmental disturbances. This study examines variation in near-surface atmospheric behaviors in northeastern Puerto Rico using a synthesis of data from lowland and montane locations under different land covers (forest, urban, and rural) during 2008–21, when a severe drought, large hurricanes (Irma and Maria), and the COVID-19 mobility-reducing lockdown occurred. Ceilometer, weather, air quality, radiosonde, and satellite data were analyzed for annual patterns and monthly time series of data and data correlations. The results showed a system that is strongly dominated by easterly trade winds transmitting regional oceanic patterns over terrain. Environmental disturbances affected land–atmosphere interaction for short time periods after events. Events that reduce the land signature (reducing greenness: e.g., drought and hurricanes, or reducing land pollution: e.g., COVID-19 lockdown) were evidenced to strengthen the transmission of the oceanic pattern. The most variation in near-surface atmospheric behavior was seen in the mountainous areas that were influenced by both factors: trade winds, and terrain-induced orographic lifting. As an exception to the rest of the near-surface atmospheric behavior, pollutants other than ozone did not correlate positively or negatively with stronger trade winds at all sites across the region. Instead, these pollutants were hypothesized to be more anthropogenically influenced. Once COVID-19 lockdown had persisted for 3 months, urban pollution decreased and cloud base may have increased. 

Abstract. With projected increasing intensity of hurricanes and largeuncertainty in the path of forest recovery from hurricanes, studies areneeded to understand the fundamental response of forests to canopy openingand debris deposition: the response of the abiotic factors underneath thecanopy. Through two manipulative experiments and instrumenting prior toHurricane Maria (2017) in the Luquillo Experimental Forest (LEF) ofPuerto Rico, this study found a long recovery time of primary abioticfactors (beneath canopy light, throughfall, and temperature) influenced bythe disturbance of canopy opening, as well as complex responses by the secondaryabiotic factors (relative humidity, soil moisture, and leaf saturation)influenced by the disturbance of the primary factors. Recovery took 4–5 years for beneath canopy light, while throughfall recovery took 4–9 yearsand neither had recovered when Hurricane Maria passed 3 years after thesecond experiment. Air and soil temperature seemingly recovered quickly fromeach disturbance (<2.5 years in two experiments for ∼+1 ∘C of change); however, temperature was the most importantmodulator of secondary factors, which followed the long-term patterns of thethroughfall. While the soil remained wetter and relative humidity in the airstayed lower until recovery, leaves in the litter and canopy were wetter anddrier, with evidence that leaves dry out faster in low rainfall and saturatefaster in high rainfall after disturbance. Comparison of satellite and fielddata before and after the 2017 hurricanes showed the utility of satellitesin expanding the data coverage, but the muted response of the satellite datasuggests they measure dense forest as well as thin forest that is not asdisturbed by hurricanes. Thus, quick recovery times recorded by satellitesshould not be assumed representative of all the forest. Data recordsspanning the multiple manipulative experiments followed by HurricaneMaria in the LEF provide evidence that intermediate hurricane frequencyhas the most extreme abiotic response (with evidence on almost all abioticfactors tested) versus infrequent or frequent hurricanes. 

Surface meteorological analyses are an essential input (termed “forcing”) for hydrologic modeling. This study investigated the sensitivity of different hydrologic model configurations to temporal variations of seven forcing variables (precipitation rate, air temperature, longwave radiation, specific humidity, shortwave radiation, wind speed, and air pressure). Specifically, the effects of temporally aggregating hourly forcings to hourly daily average forcings were examined. The analysis was based on 14 hydrological outputs from the Structure for Unifying Multiple Modeling Alternatives (SUMMA) model for the 671 Catchment Attributes and Meteorology for Large-Sample Studies (CAMELS) basins across the contiguous United States (CONUS). Results demonstrated that the hydrologic model sensitivity to temporally aggregating the forcing inputs varies across model output variables and model locations. We used Latin hypercube sampling to sample model parameters from eight combinations of three influential model physics choices (three model decisions with two options for each decision, i.e., eight model configurations). Results showed that the choice of model physics can change the relative influence of forcing on model outputs and the forcing importance may not be dependent on the parameter space. This allows for model output sensitivity to forcing aggregation to be tested prior to parameter calibration. More generally, this work provides a comprehensive analysis of the dependence of modeled outcomes on input forcing behavior, providing insight into the regional variability of forcing variable dominance on modeled outputs across CONUS.