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The Hawai‘i Grass Atlas website features maps of every grass, native or not, now present across ka pae ‘āina o Hawai‘i. These maps were made from over 80,000 grass occurrence points derived from a diversity of sources including recent surveys, vegetation plots, and herbarium specimens. For species with sufficient occurrences, climate data from HCDP was used to train an ensemble of species distribution models to predict what areas across the island will be suitable for the growth of each grass under current climate conditions. Elevational histograms have also been made based on both the observed and modeled distribution. Dichotomous identification keys are also linked when available. The Atlas also has filters for both names as well as geography and traits of each species. For example you can set the filter to show native grasses on Kaua‘i which have rhizomes and a spike-like flower head. It also includes several photos of each grass in addition to descriptions and other info on the profile page for each grass. The goal of the atlas is to aid with grass identification and build more engagement with the most ecologically dominant, but often overlooked, group of plants now found across the islands.

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A new paper on mapping daily air temperature was recently published by Dr. Keri Kodama, an East-West Center Fellow and WRRC Affiliate Faculty, and other researchers at the University of Hawai’i. The paper introduces a piecewise linear regression model used to produce high-resolution near-surface air temperature maps for the State of Hawaiʻi for a 32-year period (1990–2021), making it the most up-to-date record of gridded temperature for Hawai’i. Creating daily air temperature maps for Hawai’i comes with numerous challenges due to steep changes in terrain, and the relatively limited availability of weather data measurements on which the maps are based. To account for these challenges, the daily air temperature maps were modeled based on other variables physically linked to temperature, such as elevation or rainfall, which were used as independent variables to predict the temperature values. It was found that the simplest model using only elevation as the predictor was the most efficient combination with respect to balancing lower prediction errors with model complexity. The maps produced by the methods in this paper will act as a valuable resource for scientific research, resource management, and environmental education and awareness. 

Kodama, K. M., Kourkchi, E., Longman, R. J., Lucas, M. P., Bateni, S. M., Huang, Y.-F., et al. (2024). Mapping daily air temperature over the Hawaiian Islands from 1990 to 2021 via an optimized piecewise linear regression technique. Earth and Space Science, 11, e2023EA002851.

Accurately estimating precipitation in Hawaiʻi presents a unique challenge, primarily due to the region’s distinctive environment characterized by steep rainfall gradients. The sensitivity of small watersheds, responding to rainfall within hours, adds another layer of complexity. Historically, the scarcity of high-temporal and fine-spatial resolution rainfall datasets has hindered our understanding of intricate weather patterns and their hydrological implications.

There are two main types of rainfall observations in Hawaiʻi: radar and rain gauge. Radar provides rain rate over a large spatial area with fine temporal resolution (~every 5 mins) but struggles with accuracy; while rain gauges provide “ground truth” values, but only measure at limited point locations. Building off of a previous effort that compiled hourly gauge and radar data throughout the state of Hawaiʻi (), this study “” advances rainfall estimates by merging both types of rainfall data.

By merging radar information with ground-level rain gauge data, a detailed hourly gridded rainfall dataset for Oʻahu was meticulously crafted. Employing the kriging with external drift (KED) method, our research refines rainfall values that were previously estimated by a single instrument. This innovative approach provides valuable insights into the method’s performance across diverse storm types, including tropical cyclones, cold fronts, upper-level troughs, and Kona lows. Furthermore, our findings delve into the accuracy of rainfall estimation concerning various storm types and rainfall structures (stratiform vs. convective) in the Hawaiian context. This research not only addresses existing challenges but also propels our understanding of rainfall dynamics in the unique setting of Hawaiʻi.

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The quality-controlled hourly gauge and radar rainfall data are published as “Hourly rainfall data from rain gauge networks and weather radar up to 2020 across the Hawaiian Islands” , and the data has been shared on .

The data includes:

Drought is a regular and natural component of the climate in Hawaiʻi with severe effects across many sectors statewide. This paper provides a comprehensive synthesis of past drought effects in Hawaiʻi that we integrate with geospatial analysis of drought characteristics using a newly developed 100-year (1920–2019) gridded Standardized Precipitation Index (SPI) dataset. The synthesis examines past droughts classified into five categories: Meteorological, agricultural, hydrological, ecological, and socioeconomic drought. The assessment of current drought literature revealed large gaps in our knowledge of socioeconomic and ecological drought effects in Hawaiʻi.

Spatiotemporal analysis of a new gridded drought index revealed that the two worst droughts for the State of Hawaiʻi in the past century were 2007–2014 and 1998–2002 (Figure 6). The island-level analysis identified that the 2007–2014 drought was the worst for Hawaiʻi Island, whereas the 1998–2002 drought was more severe for Kauaʻi, Oʻahu, and Maui Nui, with different spatial patterns (Figure 9). Significant trends were found in decadal drought duration and magnitude (droughts in Hawai‘i have gotten longer and more severe) (Figure 8), consistent with trends found in other Pacific Islands. Droughts have resulted in over $80 million in agricultural relief since 1996 and have increased wildfire risk, especially during El Niño years.

By coupling quantitative SPI analysis with a review of the economic and ecological effects of drought across different sectors, a more thorough understanding of historical drought trends can be used to better understand future projections in a given region. Although drought is experienced differently across landscapes, this combined analysis provides a framework that enables a holistic yet spatiotemporally relevant view that can contribute to more effective management.

Contributed by Mandeep Adhikari:  mandeep@hawaii.edu

Future projections indicated that air temperature would increase 1.3 to 1.8 °C by mid-century and 1.6 to 3.2 °C by the end-century (Zhang et al., 2016; Elison Timm, 2017) at the Dairy Farms (“OK Dairy” and “UP Dairy”) in Hawaii. The agriculture and livestock industries, particularly the dairy subsector in Hawai`i, is vulnerable to climate changes as higher temperatures and less rainfall will have adverse effects on cattle. This article highlights how additional heat stress and forage scarcity due to elevated temperature and reduced rainfall challenge animals’ production and health, forage growth, and ranch management. This work has been published in the journal of Translational Animal Science ( ).

To assess the risk of heat stress on cattle production, monthly Temperature Humidity Index (THIs) were calculated for both locations using the average monthly temperature and humidity data between 1920 and 2019. Results showed that the THI ranged from 64.6 to 70.1 at the “OK Dairy” site, while it ranged from 67.8 to 73.5 at the “UP Dairy” site. The four summer months (June to September) at the “OK Dairy” site were not conducive for high-producing dairy cattle (THI > 68). However, the THIs at the “OK Dairy” site never reached 72 (the critical threshold for low-producing cattle) and mostly remained within the range of 67 to 70, indicating favorable conditions for low-producing dairy cattle throughout the year. The high-producing dairy cows in the “UP Dairy” site were exposed to mild (THI > 68) to moderate (THI > 72) heat stress continuously (14 to 24 h) for several months (April to November). During these periods, THI hardly drops below 68, and therefore the dairy cows in the “UP Dairy” site experience more heat stress in absence of nighttime recovery than in the “OK Dairy” site. Therefore, High milk producing dairy cattle are vulnerable to heat stress at both locations particularly during hottest four months of the calendar year (Jun -Sep).

Rainfall at the “OK Dairy” site is expected to increase over time, while the “UP Dairy” site can be even dryer by the mid-century and the end-century. Empirical results for future forage production indicated that the monthly forage production in the “OK Dairy” site is projected to increase by 6% to 8% by mid-century and 13% to 19% by the end-century. Whereas, the forage production in the “UP Dairy” site is projected to decrease 5% to 8% by mid-century and 10% to 11% by the end-century. These projections revealed that the “UP Dairy” site suffers more from forage scarcity, making ranching activities even more difficult in the future unless irrigation is possible. In contrast, “OK Dairy” sites can be even more productive with abundant grass growth in the future.

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Hawai‘i’s recent drought is among the most severe on record. Wet-season (November-April) rainfall deficits during 2010–2019 rank second lowest among consecutive 10-yr periods since 1900. Various lines of empirical and model evidence indicate a principal natural atmospheric cause for the low rainfall, mostly unrelated to either internal oceanic variability or external forcing.

Empirical analysis reveals traditional factors favored wetness not drought in recent decades, including a cold phase of the Pacific Decadal Oscillation in sea surface temperatures (SSTs) and a weakened Aleutian low in atmospheric circulation. But correlations of Hawaiian rainfall with patterns of Pacific sea level pressure and SSTs that explained a majority of its variability during the 20th Century collapsed in the 21st Century. Atmospheric model simulations indicate a forced decadal signal (2010–2019 vs 1981–2000) of Aleutian low weakening, consistent with recent observed North Pacific circulation. However, model ensemble means do not generate reduced Hawaiian rainfall indicating that neither oceanic boundary forcing nor a weakened Aleutian low caused recent low Hawaiian rainfall.

Additional atmospheric model experiments explored the role of anthropogenic forcing. These reveal a strong sensitivity of Hawaiian rainfall to details of long-term SST change patterns. Under an assumption that anthropogenic forcing drives zonally uniform SST warming, Hawaiian rainfall declines, with a range 3%–9% among three models. Under an assumption that anthropogenic forcing also increases the equatorial Pacific zonal SST gradient, Hawaiian rainfall increases 2%–6%. Large spread among ensemble members indicates that neither forced signals are detectable.

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A paper describing these methods was recently published in the Journal of Hydrometeorology ().

These methods are currently being used to produce the monthly rainfall maps that are available for visualization and download in the Hawai‘i Climate Data Portal.

Tropical island ecosystems are highly vulnerable to the multiple threats of climate change (Nurse et al. 2014; Bonan 2008). In response, agencies and organizations are tasked with developing land-management strategies to help ecosystems adapt to changing environmental conditions (Swanston et al. 2016). Research has shown that proactive planning can reduce climate change impacts by facilitating more efficient and rapid responses (Bierbaum et al. 2013). Complex socio-ecological conditions, environmental change related stressors (e.g., wildfire, pests, disease, and drought), a lack of resources, and shifting public policy and agency mandates (Nagel et al. 2017) can all hinder response effectiveness (Crausbay et al. 2020). Despite these challenges, considerable progress has been made in assessing climate vulnerabilities of forest ecosystems and in developing adaptation options for land managers (Swanston and Janowiak 2012; Janowiak et al. 2014; Swanston et al. 2016; Halofsky et al. 2018; Schmitt et al. 2021). Adaptation planning in response to significant anticipated changes is becoming increasingly sophisticated, especially with respect to anticipated changes in forest wildfire regimes, species invasion, species composition, ecosystem health, and hydrological functioning due to climate change. Here we describe our conversion of a highly successful adaptation workshop process (Schmitt et al. 2021) to a virtual environment in response to COVID-19. We effectively delivered content to managers and created an experiential learning environment in which they developed adaptation tactics for their management projects, integrating Indigenous science and knowledge into the workshop format and content. This workshop was additionally novel because it used an adaptation process (Janowiak et al. 2014) that has been applied many times in the continental United States within primarily temperate and sub-boreal systems (https://forestadaptation.org/), and applied it for the first time to a tropical island system.

Avian malaria poses the threat of extinction to multiple unique Hawaiian forest bird species. With mosquito vector control as one of the likely tools needed to prevent such extinctions, a tool that helps to identify locations across the state where mosquito control can be prioritized can be critical in allocating scarce conservation resources. Combining daily temperature datasets provided by HCDP with mosquito development rate models calibrated to match field based mosquito occupancy across the landscape, we are generating such a tool. This work builds on a previous effort detailed at Berio Fortini, Lucas, Lauren R. Kaiser, and Dennis A. LaPointe. “Fostering Real-Time Climate Adaptation: Analyzing Past, Current, and Forecast Temperature to Understand the Dynamic Risk to Hawaiian Honeycreepers from Avian Malaria.” Global Ecology and Conservation 23 (September 1, 2020): e01069. .