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  • 1
    Online Resource
    Online Resource
    American Meteorological Society ; 2015
    In:  Earth Interactions Vol. 19, No. 3 ( 2015-01-01), p. 1-33
    In: Earth Interactions, American Meteorological Society, Vol. 19, No. 3 ( 2015-01-01), p. 1-33
    Abstract: As is true of many tropical regions, northeastern Puerto Rico is an ecologically sensitive area with biological life that is highly elevation dependent on precipitation and temperature. Climate change has the potential to increase the risk of losing endemic species and habitats. Consequently, it is important to explore the pattern of trends in precipitation and temperature along an elevation gradient. Statistical derivatives of a frequently sampled dataset of precipitation and temperature at 20 sites along an elevation gradient of 1000 m in northeastern Puerto Rico were examined for trends from 2001 to 2013 with nonparametric methods accounting for annual periodic variations such as yearly weather cycles. Overall daily precipitation had an increasing trend of around 0.1 mm day−1 yr−1. The driest months of the annual dry, early, and late rainfall seasons showed a small increasing trend in the precipitation (around 0.1 mm day−1 yr−1). There was strong evidence that precipitation in the driest months of each rainfall season increased faster at higher elevations (0.02 mm day−1 more increase for 100-m elevation gain) and some evidence for the same pattern in precipitation in all months of the year but at half the rate. Temperature had a positive trend in the daily minimum (around 0.02°C yr−1) and a negative trend in the daily maximum whose size is likely an order of magnitude larger than the size of the daily minimum trend. Physical mechanisms behind the trends may be related to climate change; longer-term studies will need to be undertaken in order to assess the future climatic trajectory of tropical forests.
    Type of Medium: Online Resource
    ISSN: 1087-3562
    Language: English
    Publisher: American Meteorological Society
    Publication Date: 2015
    detail.hit.zdb_id: 2025258-4
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  • 2
    Online Resource
    Online Resource
    American Meteorological Society ; 2016
    In:  Earth Interactions Vol. 20, No. 13 ( 2016-04-01), p. 1-2
    In: Earth Interactions, American Meteorological Society, Vol. 20, No. 13 ( 2016-04-01), p. 1-2
    Type of Medium: Online Resource
    ISSN: 1087-3562
    Language: English
    Publisher: American Meteorological Society
    Publication Date: 2016
    detail.hit.zdb_id: 2025258-4
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  • 3
    In: Remote Sensing, MDPI AG, Vol. 10, No. 9 ( 2018-08-31), p. 1386-
    Abstract: The impact of Hurricane Maria on the U.S. Caribbean was used to study the causes of remotely-sensed spatial variation in the effects of (1) vegetation index loss and (2) landslide occurrence. The vegetation index is a measure of canopy ‘greenness’, a combination of leaf chlorophyll, leaf area, canopy cover and structure. A generalized linear model was made for each kind of effect, using idealized maps of the hurricane forces, along with three landscape characteristics that were significantly associated. In each model, one of these characteristics was forest fragmentation, and another was a measure of disturbance-propensity. For the greenness loss model, the hurricane force was wind, the disturbance-propensity measure was initial greenness, and the third landscape characteristic was fraction forest cover. For the landslide occurrence model, the hurricane force was rain, the disturbance-propensity measure was amount of land slope, and the third landscape characteristic was soil clay content. The model of greenness loss had a pseudo R2 of 0.73 and showed the U.S. Caribbean lost 31% of its initial greenness from the hurricane, with 51% lost from the initial in the Luquillo Experimental Forest (LEF) from Hurricane Maria along with Hurricane Irma. More greenness disturbance was seen in areas with less wind sheltering, higher elevation and topographic sides. The model of landslide occurrence had a pseudo R2 of 0.53 and showed the U.S. Caribbean had 34% of its area and 52% of the LEF area with a landslide density of at least one in 1 km2 from Hurricane Maria. Four experiments with parameters from previous storms of wind speed, storm duration, rainfall, and forest structure over the same storm path and topographic landscape were run as examples of possible future scenarios. While intensity of the storm makes by far the largest scenario difference, forest fragmentation makes a sizable difference especially in vulnerable areas of high clay content or high wind susceptibility. This study showed the utility of simple hurricane force calculations connected with landscape characteristics and remote-sensing data to determine forest susceptibility to hurricane effects.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    Language: English
    Publisher: MDPI AG
    Publication Date: 2018
    detail.hit.zdb_id: 2513863-7
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  • 4
    In: Climatic Change, Springer Science and Business Media LLC, Vol. 146, No. 1-2 ( 2018-1), p. 117-131
    Type of Medium: Online Resource
    ISSN: 0165-0009 , 1573-1480
    RVK:
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2018
    detail.hit.zdb_id: 751086-X
    detail.hit.zdb_id: 1477652-2
    SSG: 14
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  • 5
    In: International Journal of Climatology, Wiley, Vol. 36, No. 9 ( 2016-07), p. 3370-3383
    Abstract: Many tropical islands have limited water resources with historically increasing demand, all potentially affected by a changing climate. The effects of climate change on island hydrology are difficult to model due to steep local precipitation gradients and sparse data. This work uses 10 statistically downscaled general circulation models ( GCMs ) under two greenhouse gas emission scenarios to evaluate the uncertainty propagated from GCMs in projecting the effects of climate change on water resources in a tropical island system. The assessment is conducted using a previously configured hydrologic model, the Precipitation Runoff Modelling System ( PRMS ) for Puerto Rico. Projected climate data and their modelled hydrologic variables versus historical measurements and their modelled hydrologic variables are found to have empirical distribution functions that are statistically different with less than 1 year of daily data aggregation. Thus, only annual averages of the projected hydrologic variables are employed as completely bias‐corrected model outputs. The magnitude of the projected total flow decreases in the four regions covering Puerto Rico, but with a large range of uncertainty depending on the makeup of the GCM ensemble. The multi‐model mean projected total flow decreases by 49–88% of historical amounts from the 1960s to the 2090s for the high emissions scenarios and by 39–79% for the low emissions scenarios. Subsurface flow contributions decreased the least and groundwater flow contributions decreased the most across the island. At locations critical to water supply for human use, projected streamflow is shown to decrease substantially below projected withdrawals by 2099.
    Type of Medium: Online Resource
    ISSN: 0899-8418 , 1097-0088
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2016
    detail.hit.zdb_id: 1491204-1
    SSG: 14
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  • 6
    Online Resource
    Online Resource
    Copernicus GmbH ; 2017
    In:  Atmospheric Chemistry and Physics Vol. 17, No. 11 ( 2017-06-16), p. 7245-7259
    In: Atmospheric Chemistry and Physics, Copernicus GmbH, Vol. 17, No. 11 ( 2017-06-16), p. 7245-7259
    Abstract: Abstract. The degree to which cloud immersion provides water in addition to rainfall, suppresses transpiration, and sustains tropical montane cloud forests (TMCFs) during rainless periods is not well understood. Climate and land use changes represent a threat to these forests if cloud base altitude rises as a result of regional warming or deforestation. To establish a baseline for quantifying future changes in cloud base, we installed a ceilometer at 100 m altitude in the forest upwind of the TMCF that occupies an altitude range from ∼ 600 m to the peaks at 1100 m in the Luquillo Mountains of eastern Puerto Rico. Airport Automated Surface Observing System (ASOS) ceilometer data, radiosonde data, and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite data were obtained to investigate seasonal cloud base dynamics, altitude of the trade-wind inversion (TWI), and typical cloud thickness for the surrounding Caribbean region. Cloud base is rarely quantified near mountains, so these results represent a first look at seasonal and diurnal cloud base dynamics for the TMCF. From May 2013 to August 2016, cloud base was lowest during the midsummer dry season, and cloud bases were lower than the mountaintops as often in the winter dry season as in the wet seasons. The lowest cloud bases most frequently occurred at higher elevation than 600 m, from 740 to 964 m. The Luquillo forest low cloud base altitudes were higher than six other sites in the Caribbean by ∼ 200–600 m, highlighting the importance of site selection to measure topographic influence on cloud height. Proximity to the oceanic cloud system where shallow cumulus clouds are seasonally invariant in altitude and cover, along with local trade-wind orographic lifting and cloud formation, may explain the dry season low clouds. The results indicate that climate change threats to low-elevation TMCFs are not limited to the dry season; changes in synoptic-scale weather patterns that increase frequency of drought periods during the wet seasons (periods of higher cloud base) may also impact ecosystem health.
    Type of Medium: Online Resource
    ISSN: 1680-7324
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2017
    detail.hit.zdb_id: 2092549-9
    detail.hit.zdb_id: 2069847-1
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  • 7
    Online Resource
    Online Resource
    American Meteorological Society ; 2017
    In:  Earth Interactions Vol. 21, No. 6 ( 2017-08-01), p. 1-3
    In: Earth Interactions, American Meteorological Society, Vol. 21, No. 6 ( 2017-08-01), p. 1-3
    Type of Medium: Online Resource
    ISSN: 1087-3562
    Language: English
    Publisher: American Meteorological Society
    Publication Date: 2017
    detail.hit.zdb_id: 2025258-4
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  • 8
    Online Resource
    Online Resource
    American Geophysical Union (AGU) ; 2018
    In:  Water Resources Research Vol. 54, No. 7 ( 2018-07), p. 4983-5000
    In: Water Resources Research, American Geophysical Union (AGU), Vol. 54, No. 7 ( 2018-07), p. 4983-5000
    Abstract: From 1960–1987 and 1988–2015, 29% and 24% of the streamflow in the Copper River basin was simulated from glacier ice melt Trends of increasing temperature and snowfall drove simulated temporal differences in most, but not all, areas of the basin All subbasins lost simulated glacier storage over 1959–2015; only subbasins with small steep glaciers lost ice melt volume into streamflow
    Type of Medium: Online Resource
    ISSN: 0043-1397 , 1944-7973
    Language: English
    Publisher: American Geophysical Union (AGU)
    Publication Date: 2018
    detail.hit.zdb_id: 2029553-4
    detail.hit.zdb_id: 5564-5
    SSG: 13
    SSG: 14
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  • 9
    Online Resource
    Online Resource
    American Geophysical Union (AGU) ; 2018
    In:  Journal of Geophysical Research: Earth Surface Vol. 123, No. 5 ( 2018-05), p. 1116-1132
    In: Journal of Geophysical Research: Earth Surface, American Geophysical Union (AGU), Vol. 123, No. 5 ( 2018-05), p. 1116-1132
    Abstract: The addition of glaciers to a monthly water balance model provides a broadly usable tool for more complete simulations of cold region hydrology Remote sensing data enhances parameter calibration and model evaluation in remote regions with sparse monitoring data Simulation of glacier contributions to streamflow in the heavily glacierized Copper River in Southcentral Alaska provides important insights into the terrestrial water cycle in an ecologically important region
    Type of Medium: Online Resource
    ISSN: 2169-9003 , 2169-9011
    Language: English
    Publisher: American Geophysical Union (AGU)
    Publication Date: 2018
    detail.hit.zdb_id: 3094104-0
    detail.hit.zdb_id: 2130824-X
    detail.hit.zdb_id: 2138320-0
    SSG: 16,13
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