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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 Statement A 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.

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.

Summary Novel forms of drought are emerging globally, due to climate change, shifting teleconnection patterns, expanding human water use, and a history of human influence on the environment that increases the probability of transformational ecological impacts. These costly ecological impacts cascade to human communities, and understanding this changing drought landscape is one of today’s grand challenges. By using a modified horizon-scanning approach that integrated scientists, managers, and decision-makers, we identified the emerging issues in ecological drought that represent key challenges to timely and effective responses. Here we review the themes that most urgently need attention, including novel drought conditions, the potential for transformational drought impacts, and the need for anticipatory drought management. This horizon scan and review provides a roadmap to facilitate the research and management innovations that will support forward-looking, co-developed approaches to reduce the risk of drought to our socio-ecological systems during the 21st century.

Spatially continuous data products are essential for a number of applications including climate and hydrologic modeling, weather prediction, and water resource management. In this work, a distance-weighted interpolation method used to map daily rainfall and temperature in Hawaii is described and assessed. New high-resolution (250 m) maps were developed for daily rainfall and daily maximum (Tmax) and minimum (Tmin) near-surface air temperature for the period 1990–2014. Maps were produced using climatologically aided interpolation, in which station anomalies were interpolated using an optimized inverse distance weighting approach and then combined with long-term means to produce daily gridded estimates. Leave-one-out cross validation was performed to assess the quality of the final daily grids. The median absolute prediction error for rainfall was 0.1 mm with an average overprediction (+0.6 mm) on days when total rainfall was less than 1 mm. On days with total rainfall greater than 1 mm, median absolute prediction errors were 2 mm and rainfall was typically underpredicted above the 10-mm threshold. For daily temperature, median absolute prediction errors were 3.1° and 2.8°C for Tmax and Tmin, respectively. On average, this method overpredicted Tmax (+1.1°C) and Tmin (+1.5°C), and errors varied considerably among stations. Errors for all variables exhibited significant seasonal variations. However, the annual range of errors was small. The methods presented here provide an effective approach for mapping daily weather fields in a topographically diverse region and improve on previous products in their spatial resolution, time period of coverage, and use of data.

Climate change is expected to alter precipitation patterns worldwide, which will affect streamflow in riverine ecosystems. It is vital to understand the impacts of projected flow variations, especially in tropical regions where the effects of climate change are expected to be one of the earliest to emerge. Space-for-time substitutions have been successful at predicting effects of climate change in terrestrial systems by using a spatial gradient to mimic the projected temporal change. However, concerns have been raised that the spatial variability in these models might not reflect the temporal variability. We utilized a well-constrained rainfall gradient on Hawaii Island to determine (a) how predicted decreases in flow and increases in flow variability affect stream food resources and consumers and (b) if using a high temporal (monthly, four streams) or a high spatial (annual, eight streams) resolution sampling scheme would alter the results of a space-for-time substitution. Declines in benthic and suspended resource quantity (10- to 40-fold) and quality (shift from macrophyte to leaf litter dominated) contributed to 35-fold decreases in macroinvertebrate biomass with predicted changes in the magnitude and variability in the flow. Invertebrate composition switched from caddisflies and damselflies to taxa with faster turnover rates (mosquitoes, copepods). Changes in resource and consumer composition patterns were stronger with high temporal resolution sampling. However, trends and ranges of results did not differ between the two sampling regimes, indicating that a suitable, well-constrained spatial gradient is an appropriate tool for examining temporal change. Our study is the first to investigate resource to community wide effects of climate change on tropical streams on a spatial and temporal scale. We determined that predicted flow alterations would decrease stream resource and consumer quantity and quality, which can alter stream function, as well as biomass and habitat for freshwater, marine, and terrestrial consumers dependent on these resources.

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.

Drought is a growing threat to hydrological, ecological, agricultural, and socio-cultural systems of the tropics, especially tropical islands of the Pacific where severe droughts can compromise food and water security. Overcoming barriers to knowledge sharing between land managers and researchers is a critical cross-sector strategy for engaging and mitigating or adapting to drought. Here we describe the establishment and functioning of the Pacific Drought Knowledge Exchange (PDKE), which provides users with easier access to: (1) sector- and geography-specific climate information; (2) better and more comprehensive information; (3) improved technical assistance; and (4) a more collaborative information-transfer environment through participation in knowledge co-production. We focus on our collaborative work with managers of important tropical dryland ecosystems from three distinct geographies to pilot the collaborative development of climate change, climate variability, and drought “portfolios” featuring site-specific historical and forecasted future information. This information was then used to collaboratively produce factsheets that partners used to: (i) better understand past and projected climate for specific management units; (ii) integrate new climate knowledge into management planning; and (iii) support climate-focused educational and outreach efforts. This pilot effort demonstrates the successful application of climate-focused co-production in dry tropical landscapes.

Drought is a prominent feature of Hawaii's climate, however, the biological, ecological, cultural, and socioeconomic impacts of drought in Hawaii are not well understood. This paper provides a comp...

The ongoing emission of greenhouse gases (GHGs) is triggering changes in many climate hazards that can impact humanity. We found traceable evidence for 467 pathways by which human health, water, food, economy, infrastructure and security have been recently impacted by climate hazards such as warming, heatwaves, precipitation, drought, floods, fires, storms, sea-level rise and changes in natural land cover and ocean chemistry. By 2100, the world’s population will be exposed concurrently to the equivalent of the largest magnitude in one of these hazards if emmisions are aggressively reduced, or three if they are not, with some tropical coastal areas facing up to six simultaneous hazards. These findings highlight the fact that GHG emissions pose a broad threat to humanity by intensifying multiple hazards to which humanity is vulnerable.

Over the last century, significant declines in rainfall across the state of Hawai‘i have been observed, and it is unknown whether these declines are due to natural variations in climate, or manifestations of human-induced climate change. Here, a statistical analysis of the observed rainfall variability was applied as first step towards better understanding causes for these long-term trends. Gridded seasonal rainfall from 1920 to 2012 is used to perform an empirical orthogonal function (EOF) analysis. The leading EOF components are correlated with three indices of natural climate variations (El Nino-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and Pacific North American (PNA)), and multiple linear regression (MLR) is used to model the leading components with climate indices. PNA is the dominant mode of wet season (November–April) variability, while ENSO is most significant in the dry season (May–October). To assess whether there is an anthropogenic influence on rainfall, two methods are used: a linear trend term is included in the MLR, and pattern correlation coefficients (PCC) are calculated between recent rainfall trends and future changes in rainfall projected by downscaling methods. PCC results indicate that recent observed rainfall trends in the wet season are positively correlated with future expected changes in rainfall, while dry season PCC results do not show a clear pattern. The MLR results, however, show that the trend term adds significantly to model skill only in the dry season. Overall, MLR and PCC results give weak and inconclusive evidence for detection of anthropogenic signals in the observed rainfall trends.

Spatially explicit, wall-to-wall rainfall data provide foundational climatic information but alone are inadequate for characterizing meteorological, hydrological, agricultural, or ecological drought. The Standardized Precipitation Index (SPI) is one of the most widely used indicators of drought and defines localized conditions of both drought and excess rainfall based on period-specific (e.g., 1-month, 6-month, 12-month) accumulated precipitation relative to multi-year averages. A 93-year (1920–2012), high-resolution (250 m) gridded dataset of monthly rainfall available for the State of Hawai‘i was used to derive gridded, monthly SPI values for 1-, 3-, 6-, 9-, 12-, 24-, 36-, 48-, and 60-month intervals. Gridded SPI data were validated against independent, station-based calculations of SPI provided by the National Weather Service. The gridded SPI product was also compared with the U.S. Drought Monitor during the overlapping period. This SPI product provides several advantages over currently available drought indices for Hawai‘i in that it has statewide coverage over a long historical period at high spatial resolution to capture fine-scale climatic gradients and monitor changes in local drought severity.

The Hawaiian Islands have one of the most spatially diverse rainfall patterns on earth. Knowledge of these patterns is critical for a variety of resource management issues and, until now, only long-term mean monthly and annual rainfall maps have been available for Hawai‘i. In this study, month-year rainfall maps from January 1920 to December 2012 were developed for the major Hawaiian Islands. The maps were produced using climatologically aided interpolation (CAI), where the station anomalies were interpolated first, and then combined with the mean maps. A geostatistical method comparison was performed to choose the best interpolation method. The comparison focuses on three kriging algorithms: ordinary kriging (OK), ordinary cokriging (OCK), and kriging with an external drift (KED). Two covariates, elevation and mean rainfall, were tested with OCK and KED. The combinations of methods and covariates were compared using cross-validation statistics, where OK produced the lowest error statistics. Station anomalies for each month were interpolated using OK and combined with the mean monthly maps to produce the final month-year rainfall maps.

Ecological and societal disruptions by modern climate change are critically determined by the time frame over which climates shift beyond historical analogues. Here we present a new index of the year when the projected mean climate of a given location moves to a state continuously outside the bounds of historical variability under alternative greenhouse gas emissions scenarios. Using 1860 to 2005 as the historical period, this index has a global mean of 2069 (±18 years s.d.) for near-surface air temperature under an emissions stabilization scenario and 2047 (±14 years s.d.) under a 'business-as-usual' scenario. Unprecedented climates will occur earliest in the tropics and among low-income countries, highlighting the vulnerability of global biodiversity and the limited governmental capacity to respond to the impacts of climate change. Our findings shed light on the urgency of mitigating greenhouse gas emissions if climates potentially harmful to biodiversity and society are to be prevented.

nteraction among trade winds,terrain, land thermal effects, andthe trade-wind inversion give theHawaiian Islands one of the mostvaried rainfall patterns on Earth.Distinct, persistent patterns of upliftlead to dramatic rainfall gradientsand, together with elevation-relatedtemperature differences, producenearly the full range of climate types.This microcosm of global environ-mental diversity provides a uniquenatural laboratory for world-classresearch on topics such as terres-trial ecosystem carbon dynamics, soilgeochemistry, and the mechanics ofspecies invasion. Knowledge of meanrainfall patterns in Hawai'i is criticallyimportant in support of these researchendeavors as well as for managing andprotecting groundwater and surfacewater resources, controlling and eradicating invasivespecies, protecting and restoring native ecosystems,and planning for the effects of global climate change.The Rainfall Atlas of Hawai'i is a set of digitalmaps of the spatial patterns of 1978-2007 meanmonthly and annual rainfall for the major Hawaiian

[1] Estimates of clear sky global solar irradiance using the parametric model SPCTRAL2 were tested against clear sky radiation observations at four sites in Hawai‘i using daily, mean monthly, and 1 year mean model parameter settings. Atmospheric parameters in SPCTRAL2 and similar models are usually set at site-specific values and are not varied to represent the effects of fluctuating humidity, aerosol amount and type, or ozone concentration, because time-dependent atmospheric parameter estimates are not available at most sites of interest. In this study, we sought to determine the added value of using time dependent as opposed to fixed model input parameter settings. At the AERONET site, Mauna Loa Observatory (MLO) on the island of Hawai‘i, where daily measurements of atmospheric optical properties and hourly solar radiation observations are available, use of daily rather than 1 year mean aerosol parameter values reduced mean bias error (MBE) from 18 to 10 W m−2 and root mean square error from 25 to 17 W m−2. At three stations in the HaleNet climate network, located at elevations of 960, 1640, and 2590 m on the island of Maui, where aerosol-related parameter settings were interpolated from observed values for AERONET sites at MLO (3397 m) and Lāna‘i (20 m), and precipitable water was estimated using radiosonde-derived humidity profiles from nearby Hilo, the model performed best when using constant 1 year mean parameter values. At HaleNet Station 152, for example, MBE was 18, 10, and 8 W m−2 for daily, monthly, and 1 year mean parameters, respectively.

Replying to E. Hawkins et al. , 10.1038/nature13523 (2014) In the accompanying Comment, Hawkins et al.1 suggest that our index2 of the projected timing of climate departure from recent variability is biased to occur too early and is given with overestimated confidence. We contest their assertions and maintain that our findings are conservative and remain unaltered in light of their analysis.

Detailed assessments of species responses to climate change are uncommon, owing to the limited nature of most ecological and local climate data sets. Exceptions, such as the case of the Haleakalā silversword, can provide important insights into the complexity of biological responses to changing climate conditions. We present a time series of decadal population censuses, combined with a pair of early population projections, which together span the past 80 years of demographic history for this alpine plant. The time series suggests a strong population recovery from the 1930s through the 1980s, likely owing at least in part to management actions taken on its behalf. In contrast, the population is estimated to have suffered a decline of approximately 60 % since the early 1990s. Fine-scale estimates of rainfall within silversword habitat are strongly correlated with these decadal-scale population changes over the past 50 years, with rainfall estimated to be substantially lower on average during the two most recent inter-census periods (after 1991). The reversal in the silversword population trajectory, and declines in rainfall in silversword habitat, coincide with an abrupt increase in the frequency of occurrence of the trade wind inversion (TWI) in Hawaiʻi around 1990. The shift in TWI incidence, which is linked to stronger subsidence in the Hadley circulation, has led to drier conditions in high elevation ecosystems in Hawaiʻi and appears to be eliciting ecological responses. Other regions influenced by the TWI could be similarly affected. The silversword case study reveals additional unexpected outcomes, such as the likely initial retraction from wetter, rather than drier, portions of the range in response to drying conditions. This pattern may stem in part from variation in drought tolerance across the range, highlighting the importance of detailed ecological and climatic information for making accurate predictions about climate change responses.