Search for: All records

Abstract. The orographic effects that influence rainfall fields in mountainous regions depend on elevation and the exposure of the topography to prevailing winds. Transitions between wet and dry areas can occur within a few kilometers, creating strong horizontal gradients of various rainfall statistics such as the frequency of occurrence, the distribution of intensity and the structure of spatial correlation. Most statistical models of daily rainfall assume spatial stationarity (i.e., the spatial homogeneity of rainfall statistics) and are therefore not well suited for studying the highly non-homogeneous characteristics of orographic rainfall. To overcome this limitation, we design a non-stationary trans-Gaussian geostatistical model for the analysis of daily rainfall fields over complex topography. The modeling framework presented in this paper infers rainfall statistics from sparse rain gauge observations, simulates realistic rainfall fields after calibration and stochastically interpolates rain gauge observations to create rainfall maps. The performance of the model is assessed with data from the Island of Hawai‘i where extreme spatial gradients in rainfall are observed. The results presented in this paper demonstrate that a non-stationary trans-Gaussian model can skillfully reproduce orographic rainfall statistics as well as their variations in space. 

The Cyberinfrastructure Training and Capacity Building in Climate and Environmental Sciences (CI-TRACS) program represents a pioneering initiative aimed at enhancing cyberinfrastructure proficiency within Hawaii’s academic community. This paper outlines the program’s comprehensive strategy, which integrates curriculum development, hands-on workshops, and professional growth opportunities to cultivate a robust foundation in CI practices. The initiative’s core objective is to elevate CI literacy, promote cross-disciplinary cooperation, and endorse the principles of open science. Significant contributions from the CI-TRACS program include a suite of educational materials and resources tailored for integration into higher education syllabi. Collaboration with the Hawaii Data Science Institute has been instrumental in nurturing a burgeoning network of data science professionals. The CI-TRACS program is instrumental in realizing the shared vision of equipping Hawaii’s emerging workforce with the sophisticated CI skills necessary to navigate and excel in the evolving landscape of climate and environmental sciences. 

Abstract The Hawai‘i Climate Data Portal (HCDP) is designed to facilitate streamlined access to a wide variety of climate data and information for the State of Hawai‘i. Prior to the development of the HCDP, gridded climate products and point datasets were fragmented, outdated, not easily accessible, and not available in near–real time. To address these limitations, HCDP researchers developed the cyberinfrastructure necessary to 1) operationalize data acquisition and product production in a near-real-time environment and 2) make data and products easily accessible to a wide range of users. The HCDP hosts several high-resolution (250 m) gridded products including monthly rainfall and daily temperature (maximum, minimum, and mean), station data, and gridded future projections of rainfall and temperature. HCDP users can visualize both gridded and point data, create and download custom maps, and query station and gridded data for export with relative ease. The “virtual station” feature allows users to create a climate time series at any grid point. The primary objective of the HCDP is to promote sharing and access to data and information to streamline research activities, improve awareness, and promote the development of tools and resources that can help to build adaptive capacities. The HCDP products have the potential to serve a wide range of users including researchers, resource managers, city planners, engineers, teachers, students, civil society organizations, and the broader community. 

Abstract The Hawaiian Islands have some of the most spatially diverse rainfall patterns on Earth, affected by prevailing trade winds, midlatitude disturbances, tropical cyclones, and complex island topography. However, it is the only state in the United States that does not have assigned climate divisions (boundaries defining climatically homogeneous areas), which excludes it from many national climate analyses. This study establishes, for the first time, official climate divisions for the state of Hawai‘i using cluster analysis applied to monthly gridded rainfall data from 1990 to 2019. Twelve climate divisions have been identified: two divisions were found each for the islands of Kaua‘i (Leeward Kaua‘i and Windward Kaua‘i), O‘ahu (Waianae and Ko‘olau), and Maui County (Leeward Maui Nui and Windward Maui Nui), and six divisions were identified for Hawai‘i Island (Leeward Kohala, Windward Kohala, Kona, Hawai‘i Mauka, Ka‘u, and Hilo). The climate divisions were validated using a statewide area-weighted division-average rainfall index which successfully captured the annual cycle and interannual rainfall variations in the statewide average rainfall series. Distinct rainfall seasonality features and interannual/decadal variability are found among the different divisions; Leeward Maui Nui, Leeward Kaua‘i, Kona, and Hawai‘i Mauka displayed the most significant rainfall seasonality. The western Hawai‘i Island divisions show the most significant long-term decreasing trends in annual rainfall during the past 100 years (ranging from −2.5% to −5.0% per decade). With these climate divisions now available, Hawai‘i will have access to numerous operational climate analyses that will greatly improve climatic research, monitoring, education, and outreach, as well as forecasting applications. Significance StatementThe Hawaiian Islands have some of the most spatially diverse climate patterns on Earth, but it is the only state in the United States that does not have assigned climate divisions, which excludes it from many national climate analyses. This paper establishes official climate divisions for the state of Hawai‘i, filling an incredibly important gap in the National Oceanic and Atmospheric Administration (NOAA)’s national coverage, moving toward better data equity and coverage outside the contiguous United States. Distinct rainfall seasonality features and interannual/decadal variability are revealed and compared among the different divisions. With these climate divisions now available, Hawai‘i will have access to numerous operational climate analyses that will greatly improve climatic research, monitoring, education, and outreach, as well as forecasting applications. 

Drought is a prominent feature of Hawaiʻi’s climate. However, it has been over 30 years since the last comprehensive meteorological drought analysis, and recent drying trends have emphasized the need to better understand drought dynamics and multi-sector effects in Hawaiʻi. Here, we provide 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. Results show that drought duration and magnitude have increased significantly, consistent with trends found in other Pacific Islands. We found that most droughts were associated with El Niño events, and the two worst droughts of the past century were multi-year events occurring in 1998–2002 and 2007–2014. The former event was most severe on the islands of O’ahu and Kaua’i while the latter event was most severe on Hawaiʻi Island. Within islands, we found different spatial patterns depending on leeward versus windward contrasts. Droughts have resulted in over $80 million in agricultural relief since 1996 and have increased wildfire risk, especially during El Niño years. In addition to providing the historical context needed to better understand future drought projections and to develop effective policies and management strategies to protect natural, cultural, hydrological, and agricultural resources, this work provides a framework for conducting drought analyses in other tropical island systems, especially those with a complex topography and strong climatic gradients. 

Abstract Gridded monthly rainfall estimates can be used for a number of research applications, including hydrologic modeling and weather forecasting. Automated interpolation algorithms, such as the “autoKrige” function in R, can produce gridded rainfall estimates that validate well but produce unrealistic spatial patterns. In this work, an optimized geostatistical kriging approach is used to interpolate relative rainfall anomalies, which are then combined with long-term means to develop the gridded estimates. The optimization consists of the following: 1) determining the most appropriate offset (constant) to use when log-transforming data; 2) eliminating poor quality data prior to interpolation; 3) detecting erroneous maps using a machine learning algorithm; and 4) selecting the most appropriate parameterization scheme for fitting the model used in the interpolation. Results of this effort include a 30-yr (1990–2019), high-resolution (250-m) gridded monthly rainfall time series for the state of Hawai‘i. Leave-one-out cross validation (LOOCV) is performed using an extensive network of 622 observation stations. LOOCV results are in good agreement with observations (R²= 0.78; MAE = 55 mm month⁻¹; 1.4%); however, predictions can underestimate high rainfall observations (bias = 34 mm month⁻¹; −1%) due to a well-known smoothing effect that occurs with kriging. This research highlights the fact that validation statistics should not be the sole source of error assessment and that default parameterizations for automated interpolation may need to be modified to produce realistic gridded rainfall surfaces. Data products can be accessed through the Hawai‘i Data Climate Portal (HCDP;http://www.hawaii.edu/climate-data-portal). Significance StatementA new method is developed to map rainfall in Hawai‘i using an optimized geostatistical kriging approach. A machine learning technique is used to detect erroneous rainfall maps and several conditions are implemented to select the optimal parameterization scheme for fitting the model used in the kriging interpolation. A key finding is that optimization of the interpolation approach is necessary because maps may validate well but have unrealistic spatial patterns. This approach demonstrates how, with a moderate amount of data, a low-level machine learning algorithm can be trained to evaluate and classify an unrealistic map output. 

Abstract Mycorrhizae alter global patterns of CO₂fertilization, carbon storage, and elemental cycling, yet knowledge of their global distributions is currently limited by the availability of forest inventory data. Here, we show that maps of tree‐mycorrhizal associations (hereafter “mycorrhizal maps”) can be improved by the novel technology of imaging spectroscopy because mycorrhizal signatures propagate up from plant roots to impact forest canopy chemistry. We analyzed measurements from 143 airborne imaging spectroscopy surveys over 112,975 individual trees collected across 13 years. Results show remarkable accuracy in capturing ground truth observations of mycorrhizal associations from canopy signals across disparate landscapes (R² = 0.92,p < 0.01). Upcoming imaging spectroscopy satellite missions can reveal new insights into landscape‐scale variations in water, nitrogen, phosphorus, carotenoid/anthocyanin, and cellulose/lignin composition. Applied globally, this approach could improve the spatial precision of mycorrhizal distributions by a factor of roughly 10⁴and facilitate the incorporation of dynamic shifts in forest composition into Earth system models.