Lucas Berio Fortini
Lauren R. Kaiser
20230420
Hawaiian Islands excess rainfall conditions under current (2002-2012) and future (2090-2099) climate scenarios
raster digital data
https://doi.org/10.5066/P9VOTDH3
Lucas Berio Fortini
Lauren R. Kaiser
Kim S. Perkins
Lulin Xue
Yaping Wang
20230423
Estimating the Impact of Climate and Vegetation Changes on Runoff Risk across the Hawaiian Landscape
publication
Conservation
vol. 3, issue 2
n/a
MDPI AG
ppg. 291-302
https://doi.org/10.3390/conservation3020020
One of the determinants of runoff is the occurrence of excess rainfall events where rainfall rates exceed the infiltration capacity of soils. To help understand runoff risks, we calculated the probability of excess rainfall events across the Hawaiian landscape by comparing the probability distributions of projected rainfall frequency and land cover-specific infiltration capacity. We characterized soil infiltration capacity based on different land cover types (bare soil, grasses, and woody vegetation) and compared them to the frequency of large rainfall events under current and future (pseudo-global warming) climate scenarios. This simple analysis allowed us to map the potential risk of excess rainfall across the main Hawaiian Islands. Here we provide rasters that contain the probability of rainfall exceeding infiltration capacity in each grid cell at 90 m. We have included rasters of excess rainfall probabilities for current (2002-2012) and future (2090-2099) scenarios as well as by each individual land cover class considered.
While runoff analyses have been done for specific watersheds, no previous work has taken this landscape level approach. This information is pertinent to federal, state, and non-governmental land managers and city planners alike to understand how changes in land cover can influence runoff and erosion, which can be ecologically and economically costly, resulting in both ecosystem and infrastructure damage.
2002
2099
observed
None planned
-159.7897
-154.6691
22.2952
18.8510
USGS
Infiltration
Rainfall
Ecohydrology
Runoff
Landcover
Climate Shift
USGS Metadata Identifier
USGS:63c07be9d34e92aad3ce663d
None
Hawai’i
Main Hawaiian Islands
None. Please see 'Distribution Info' for details.
None. Users are advised to read the dataset's metadata thoroughly to understand appropriate use and data limitations.
Lucas Berio Fortini
U.S. Geological Survey
Research Ecologist
mailing address
1845 Wasp Blvd, Bldg 176
Honolulu
HI
96818
United States
808-230-3669
lfortini@usgs.gov
Funding and support for this project was made possible by funding by the USGS Pacific Island Climate Adaptation Science Center (PICASC), project award #COA.16DIR.USGS.LFO.01
R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
URL https://www.R-project.org/.; RStudio Version 1.3.959; Data are <1 GB.
No formal attribute accuracy tests were conducted.
No formal logical consistency tests were conducted.
Data set is considered complete for the information presented, as described in the abstract. Users are advised to read the rest of the metadata record carefully and the referenced publication for additional details.
Spatial data have been reviewed by multiple authors for accuracy.
No formal vertical accuracy test has been conducted, as data do not contain Z coordinates.
Lulin Xue
Yaping Wang
Andrew J. Newman
Kyoko Ikeda
Roy M. Rasmussen
Thomas W. Giambelluca
Ryan J. Longman
Andrew J. Monaghan
Martyn P. Clark
Jeffrey R. Arnold
20201219
How will rainfall change over Hawai‘i in the future? High-resolution regional climate simulation of the Hawaiian Islands
publication
Bulletin of Atmospheric Science and Technology
vol. 1, issue 3-4
n/a
Springer Science and Business Media LLC
ppg. 459-490
https://doi.org/10.1007/s42865-020-00022-5
Digital and/or Hardcopy
2020
publication date
Xue et al., 2020
Fine-scale regional climate simulations for the main Hawaiian Islands used to develop estimates of hourly rainfall intensity necessary for our research
Lucas Berio Fortini
Christina R. Leopold
Kim S. Perkins
Oliver A. Chadwick
Stephanie G. Yelenik
James D. Jacobi
Kai’ena Bishaw
Makani Gregg
20210313
Landscape level effects of invasive plants and animals on water infiltration through Hawaiian tropical forests
publication
Biological Invasions
vol. 23, issue 7
n/a
Springer Science and Business Media LLC
ppg. 2155-2172
https://doi.org/10.1007/s10530-021-02494-8
Digital and/or Hardcopy
2021
publication date
Berio Fortini et al., 2021
Infiltration measurements (saturated hydraulic conductivity, Kfs) used to create infiltration rate estimates for three general land cover types: bare soil, grasses, and woody vegetation (shrubs and forests combined)
Joseph J Kennedy
Alan Mair
Kimberlie Perkins
2019
Summary of soil field-saturated hydraulic conductivity, hydrophobicity, preferential-flow, and particle-size measurements collected at four research sites on the island of Maui, Hawaii, September 2017-August 2018
dataset
https://www.sciencebase.gov
U.S. Geological Survey
https://doi.org/10.5066/p9op3bhm
Digital and/or Hardcopy
2019
publication date
Kennedy et al., 2019
Infiltration measurements (saturated hydraulic conductivity, Kfs) used to create infiltration rate estimates for three general land cover types: bare soil, grasses, and woody vegetation (shrubs and forests combined)
Kim S. Perkins
Jonathan D. Stock
John R. Nimmo
20180330
Vegetation influences on infiltration in Hawaiian soils
publication
Ecohydrology
vol. 11, issue 5
n/a
Wiley
https://doi.org/10.1002/eco.1973
Digital and/or Hardcopy
2018
publication date
Perkins et al., 2018
Infiltration measurements (saturated hydraulic conductivity, Kfs) used to create infiltration rate estimates for three general land cover types: bare soil, grasses, and woody vegetation (shrubs and forests combined)
Kim S. Perkins
John R. Nimmo
Arthur C. Medeiros
Daphne J. Szutu
Erica von Allmen
20140123
Assessing effects of native forest restoration on soil moisture dynamics and potential aquifer recharge, Auwahi, Maui
publication
n/a
Wiley
ppg. n/a-n/a
https://doi.org/10.1002/eco.1469
Digital and/or Hardcopy
2014
publication date
Perkins et al., 2014
Infiltration measurements (saturated hydraulic conductivity, Kfs) used to create infiltration rate estimates for three general land cover types: bare soil, grasses, and woody vegetation (shrubs and forests combined)
K. S. Perkins
J. R. Nimmo
A. C. Medeiros
20120314
Effects of native forest restoration on soil hydraulic properties, Auwahi, Maui, Hawaiian Islands
publication
Geophysical Research Letters
vol. 39, issue 5
n/a
American Geophysical Union (AGU)
ppg. n/a-n/a
https://doi.org/10.1029/2012GL051120
Digital and/or Hardcopy
2012
publication date
Perkins et al., 2012
Infiltration measurements (saturated hydraulic conductivity, Kfs) used to create infiltration rate estimates for three general land cover types: bare soil, grasses, and woody vegetation (shrubs and forests combined)
We used a landscape-scale approach to estimate the probability of excess rainfall as an indicator of runoff risk based on land cover type under current and future rainfall regimes. This was done by combining regionally downscaled estimates of hourly rainfall with infiltration measurements collected across three different cover classes (bare soils, grasslands, and woody vegetation).
To develop estimates of hourly rainfall intensity necessary for our research, we utilized fine-scale regional climate simulations for the main Hawaiian Islands recently developed by the National Center for Atmospheric Research (NCAR, (Xue et al., 2020). These estimates of hourly rainfall intensity and frequency were derived using the Weather Research and Forecasting (WRF) model and are based on a 10-year simulation period for both a current (2002-2012) and future (2090-2099) representative concentration pathway (RCP) 8.5 scenario.
Xue et al., 2020
2022
We combined infiltration measurements (saturated hydraulic conductivity, Kfs) from previous research (Berio Fortini et al., 2021, Kennedy et al., 2019, Perkins et al., 2018, 2014, 2012) and classified them based on three general land cover types: bare soil, grasses, and woody vegetation (shrubs and forests combined) to create infiltration rate estimates. Using these estimates, we calculated when rainfall events are expected to exceed local cover-specific infiltration rates to determine the probability of excess rainfall.
Berio Fortini et al., 2021
Kennedy et al., 2019
Perkins et al., 2018
Perkins et al., 2014
Perkins et al., 2012
2022
With these two datasets, we then calculated the probability that rainfall events may exceed the infiltration capacity at each pixel across the landscape to estimate runoff. The probability of excess rainfall was calculated based on the comparison of the probability distribution of rainfall intensity versus the cumulative probability distribution of infiltration capacity. More specifically, the probability of excess rainfall occurring for a specific rainfall intensity (e.g., 50mm/hr) is the probability of occurrence of such rainfall event multiplied by the probability that the infiltration rate is smaller than the given rainfall intensity (50mm/hr). By calculating this probability over all location-specific rainfall intensity intervals, we can estimate the total probability of excess rainfall events for each point across the landscape. Using the future projections for rainfall intensities, we calculated equivalent excess rainfall probabilities for the future (2090-2099) RCP 8.5 scenario.
2022
Raster
Grid Cell
4176
5866
1
Universal Transverse Mercator
4
0.9996
-159.0
0.0
500000.0
0.0
row and column
90.0
90.0
meters
North_American_Datum_1983
GRS 1980
6378137.0
298.257222101
The probability that excess rainfall exceeds the infiltration capacity was calculated for the main Hawaiian Islands under current (2002-2012) and future (2090-2099) scenarios. These probabilities were calculated for the entire state of Hawaiʻi and for select individual land cover class types (bare soil, grasses, and woody vegetation) all at 90 m.
Estimating the impact of climate and vegetation changes on runoff risk across the Hawaiian landscape (Berio Fortini et. al., in publication)
GS ScienceBase
U.S. Geological Survey
mailing address
Denver Federal Center, Building 810, Mail Stop 302
Denver
CO
80225
United States
1-888-275-8747
sciencebase@usgs.gov
Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
Digital Data
https://doi.org/10.5066/P9VOTDH3
None
20230503
U.S. Geological Survey - PIERC
Data Steward
physical
Bldg 344 Crater Rim Drive
Hawaii National Park
HI
96718
USA
808-985-6420
pierc-datasteward@usgs.gov
FGDC Content Standard for Digital Geospatial Metadata
FGDC-STD-001-1998