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.

The high-elevation northeastern slopes of Haleakalā Volcano are home to some of the most remote and undisturbed forest ecosystems in Hawai‘i. These ecosystems are dominated with native vegetation, insects and bird species. Early Hawaiians did not use these high- elevation lands extensively (Burney et al., 1995) and current conservation efforts put forth by Haleakalā National Park and the Hawai‘i State Natural Area Reserve (NAR) have focused on reducing the spread of invasive species and protecting rare and endangered species in these areas (Loope et al., 1992). In August of 2005 a network of 12 climate stations was established to extend westward from HaleNet and to integrate with 134 permanent vegetation plots established from 2003-2006 on the northeast slope of Haleakalā Volcano (Figure 49; Crausbay and Hotchkiss, 2010). The climate stations (referred to as “Little HaleNet”) range from ~1980 m to ~ 2315 m in elevation and create three elevational transects from the Northeast Rift to Pu‘u Alaea. Each elevational transect consists of one station in the alpine grassland and three stations that bracket the upper cloud forest limit. The climate stations are equipped with instrumentation to measure precipitation (established 2005), soil moisture (established 2007), air temperature (established 2008), relative humidity (established 2008), photosynthetically active radiation (established 2011) and soil temperature (2005-2008). Vegetation plots are arranged along nine elevational transects and represent points within the upper ~300 m of cloud forest and ~ 300 m immediately above the forest line.

Little HaleNet climate network on the eastern slope of Haleakalā Volcano.

The Little HaleNet climate network has been used to: 1) create climate maps that describe spatial pattern of climate during strong El Niño and La Niña events relative to neutral climate (Crausbay et al., 2014), 2) identify the changes in plant species assemblage and structure due to changes in moisture (Crausbay and Hotchkiss, 2010), and 3) model the strong relationship between the cloud forest’s upper limit and humidity during strong El Niño events and to determine the relationships between cloud forest species assemblage and mean rainfall (Crausbay et al., 2014). The close spatial proximity of the climate stations along their respective transects provides essential information regarding variations in microclimate in relation to the cloud forest boundary. A recent study Gotsch et al. (2014) concluded that the microclimates along these transects affect stomatal conductance and transpiration of the dominant native species (ōhi‘a lehua, Metrosideros polymorpha) found there. The data obtained from Little HaleNet was integrated with data obtained from HaleNet station HN-162 (2260 m), which is located within the Little HaleNet domain, and HN-161 (2460 m) and HN-164 (1650 m) which are above and below Little HaleNet, respectively.

Cloud water interception (CWI), the passive capturing of fog water by plants, is a unique ecohydrological process in tropical montane cloud forests that have long been believed to increase water supply. By gaining extra water from the passing clouds, vegetation in the cloud zone on Hawaiian mountains may play an important role in the islands’ hydrological processes and water resources. However, the lack of information about large-scale CWI quantity, distribution, and variability has made evaluating the hydrological benefits of tropical montane cloud forests difficult. The root of the problem lies in the high heterogeneity of CWI patterns and the technical challenges to make measurements and compare between sites. This study aims to enable the mapping of CWI over the Hawaiian Islands. First, fog and CWI were measured at five sites using standardized, precise methods developed to allow for quantitative comparisons. Second, comparisons of CWI with the fog quantity, climatic variables, and vegetation characteristics were compared across sites to identify the main factors of CWI. Finally, a model for CWI prediction for Hawaiian cloud zone ecosystems was developed based on the observations and previous modeling studies. The CWI model performed reasonably well in reproducing the annual CWI at each site and the monthly fluctuations of CWI and can be a useful tool for mapping CWI and making projections of CWI under future climate and land cover changes to support future research and decision-making.

Long-term, accurate observations of atmospheric phenomena are essential for a myriad of applications, including historic and future climate assessments, resource management, and infrastructure planning. In Hawai‘i, climate data are available from individual researchers, local, State, and Federal agencies, and from large electronic repositories such as the National Centers for Environmental Information (NCEI). Each data source is unique because a different variables are measured within each network and measurements are taken at different time steps (i.e. sub-hourly, hourly or daily)

Climate networks in Hawai‘i. Where; Network is the measurement network; Spatial-Extent is the geographical extent of the measurement network; All Sta. is the number of stations identified within a given network; Active Sta. is the number of stations active as of 1 January 2017, Observations is the meteorological observations taken within a given network (RF = rainfall; Ta = Surface air temperature; RH = Relative Humidity; WS = Wind speed; Sw = incoming short wave solar radiation; Lw = downwelling longwave radiation): Meas. Interval is the measurement Interval used by each network; First Yr. is the first year of measurements; Last Yr. is the last year of measurements.

Researchers attempting to make use of available data are faced with a series of challenges that include: (1) identifying potential data sources; (2) acquiring data; (3) establishing data quality assurance and quality control (QA/QC) protocols; and (4) implementing robust gap filling techniques.

Spatial distribution of measured climate variables in Hawai‘i. Showing (red) stations included in the datasets accompanying this manuscript; and (gray) climate stations not included in the accompanying datasets.

This research was published in 2018 in the Nature journal “Scientific Data”. The paper addresses these challenges by providing: (1) a summary of the available climate data in Hawai‘i including a detailed description of the various meteorological observation networks and data accessibility, and (2) a quality controlled meteorological dataset across the Hawaiian Islands for the 25-year period 1990-2014. The dataset draws on observations from 471 climate stations and includes rainfall, maximum and minimum surface air temperature, relative humidity, wind speed, downward shortwave and longwave radiation data.

At present efforts are underway to update these data sets to present day and to continue the data collection and publication in near-real-time moving forward. 

MLO is located on the north flank of Mauna Loa Volcano, on the Big Island of Hawaii. Due to its remote location in the Pacific Ocean, high altitude (3397 meters, or 11,135 feet above sea level), and great distance from major pollution sources, MLO is a prime spot for sampling the Earth’s background air in the well mixed free troposphere. The observatory protrudes through the strong marine temperature inversion layer present in the region, which separates the more polluted lower portions of the atmosphere from the much cleaner free troposphere. 

MLO began continuously monitoring and collecting data related to climate change, atmospheric composition, and air quality in the 1950’s. Today, the observatory is best known for its measurements of rising anthropogenic carbon dioxide (CO₂) concentrations in the atmosphere. This trend is sometimes referred to as the “Keeling Curve”. MLO is also known for its measurements of ozone, chemicals that destroy ozone (such as CFCs and HCFCs), solar radiation, and both tropospheric and stratospheric aerosols. Data from MLO is also used to calibrate and verify data from satellites and stations around the world. 

The Observatory at Mauna Loa has been a model for several recently instituted atmospheric research stations. In its 50 years of operation, MLO has supported hundreds of cooperative research programs with national and international universities, government organizations, and foreign agencies. Hundreds of papers have been published based on data collected at MLO. 

Mauna Loa Observatory is also affiliated with the , , . Several MLO employees work for JIMAR ().

MLO is also a primary observing site for the . The NDACC is a set of high-quality remote-sounding research stations for observing and understanding the physical and chemical state of the stratosphere. Ozone and key ozone-related chemical compounds and parameters are targeted for measurement. The NDACC is a major component of the international upper atmosphere research effort and has been endorsed by national and international scientific agencies, including the , the , and the .

Remote Automated Weather Stations () are weather stations set up on tripods, and they look like little “Lunar Landers.” The data collected from these stations are used in numerous applications, including fire weather, climatology, resource management, flood warning, noxious weed control, all-risk management, and air quality management. These solar-powered units gather important weather information on an hourly basis. 

RAWS sensors monitor: • Wind speed and direction 

• Wind gusts 

• Precipitation 

• Air temperature 

• Solar radiation 

• Relative humidity 

• Fuel moisture 

• Soil moisture and temperature

About 1,850 RAWS are strategically positioned throughout the United States. RAWS units collect, store, and forward data hourly (via satellite 22,300 miles above the equator) to a computer system located at the National Interagency Fire Center, Boise, Idaho. Weather information travels from the RAWS units to a satellite and then back to earth in one-quarter of a second. Each RAWS unit operates on eight to 10 watts of power, which is nearly equivalent to the power needed to operate a hand-held radio. The battery lasts about three years.

Of the eleven stations that once operated in the HaleNet climate network only eight are still in full operation (blue circles in the firgure above).  The Waikamoi station (HN-142) which was only erected on a temporary basis was removed in August of 2003. The Horseshoe Pu`u station (HN-163) at 1960 m elevation was badly damaged in 1996 by lightning and high winds and is no longer recording data.  The Pu`u Pahu station (HN-106) at 1650 m elevation was vandalized in 2003 and now only collects rainfall data. The entire Network has a vertical coverage 810 m (1650 – 2460 m) and 2030 m (960 – 2990 m) along the windward and leeward exposures respectively

The complex topography of Haleakalā interacts with atmospheric circulation to produce some of the most spatially complex rainfall patterns in the world.  Desert-like precipitation minimum zones and extreme wet conditions can be found within a few tens of kilometers distance

The 11 HaleNet stations still in operation are equipped with instrumentation that monitor: the upward and downward components of short and long wave radiation, net radiation, surface air, surface temperature, soil temperature,  wind speed, wind direction, relative humidity, soil moisture and precipitation. These variables can also be used to calculate, soil heat flux, vapor pressure deficit and potential evapotranspiration.