skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Title: Independent validation of downscaled climate estimates from a coastal Alaska watershed using local historical weather journals
Downscaling coarse global and regional climate models allows researchers to access weather and climate data at finer temporal and spatial resolution, but there remains a need to compare these models with empirical data sources to assess model accuracy. Here, we validate a widely used software for generating North American downscaled climate data, ClimateNA, with a novel empirical data source, 20th century weather journals kept by Admiralty Island, Alaska homesteader, Allen Hasselborg. Using Hasselborg’s journals, we calculated monthly precipitation and monthly mean of the maximum daily air temperature across the years 1926 to 1954 and compared these to ClimateNA data generated from the Hasselborg homestead location and adjacent areas. To demonstrate the utility and potential implications of this validation for other disciplines such as hydrology, we used an established regression equation to generate time series of 95% low duration flow estimates for the month of August using mean annual precipitation from ClimateNA predictions and Hasselborg data. Across 279 months, we found strong correlation between modeled and observed measurements of monthly precipitation ( ρ  = 0.74) and monthly mean of the maximum daily air temperature ( ρ  = 0.98). Monthly precipitation residuals (calculated as ClimateNA data - Hasselborg data) generally demonstrated heteroscedasticity around zero, but a negative trend in residual values starting during the last decade of observations may have been due to a shift to the cold-phase Pacific Decadal Oscillation. Air temperature residuals demonstrated a consistent but small positive bias, with ClimateNA tending to overestimate air temperature relative to Hasselborg’s journals. The degree of correlation between weather patterns observed at the Hasselborg homestead site and ClimateNA data extracted from spatial grid cells across the region varied by wet and dry climate years. Monthly precipitation from both data sources tended to be more similar across a larger area during wet years (mean ρ across grid cells = 0.73) compared to dry years (mean ρ across grid cells = 0.65). The time series of annual 95% low duration flow estimates for the month of August generated using ClimateNA and Hasselborg data were moderately correlated ( ρ  = 0.55). Our analysis supports previous research in other regions which also found ClimateNA to be a robust source for past climate data estimates.  more » « less
Award ID(s):
1757348
PAR ID:
10336378
Author(s) / Creator(s):
;
Date Published:
Journal Name:
PeerJ
Volume:
9
ISSN:
2167-8359
Page Range / eLocation ID:
e12055
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. null (Ed.)
    A constrained stochastic weather generator (CSWG) for producing daily mean air temperature and precipitation based on annual mean air temperature and precipitation from tree-ring records is developed and tested in this paper. The principle for stochastically generating daily mean air temperature assumes that temperatures in any year can be approximated by a sinusoidal wave function plus a perturbation from the baseline. The CSWG for stochastically producing daily precipitation is based on three additional assumptions: (1) In each month, the total precipitation can be estimated from annual precipitation if there exists a relationship between the annual and monthly precipitations. If that relationship exists, then (2) for each month, the number of dry days and the maximum daily precipitation can be estimated from the total precipitation in that month. Finally, (3) in each month, there exists a probability distribution of daily precipitation amount for each wet day. These assumptions allow the development of a weather generator that constrains statistically relevant daily temperature and precipitation predictions based on a specified annual value, and thus this study presents a unique method that can be used to explore historic (e.g., archeological questions) or future (e.g., climate change) daily weather conditions based upon specified annual values. 
    more » « less
  2. Following potential reforestation in the Amazon Basin, changes in the biophysical characteristics of the land surface may affect the fluxes of heat and moisture behavior. This research examines the impacts of potential tropical reforestation on surface energy and moisture budgets, including precipitation and temperature. The study is novel in that while most studies look at the opposite driver (deforestation), this one examines the impact of potential forest rehabilitation on atmospheric behavior using WRF.V3.9 (weather research and forecast model). We found that forest rehabilitation across the Amazon Basin can make the atmosphere cooler with more moisture and latent heat (LH), especially during May-November. For instance, the mean seasonal temperature decreased significantly by about 1.2 °C, indicating the cooling effects of reforestation. Also, the seasonal precipitation increased by 5 mm/day in reforested areas. By reforestation, the mean monthly LH also increased as much as 50 W m−2 in August in certain areas, while available moisture to the atmosphere increased by 27%, indicating possible causal mechanisms between increased LH and precipitation and emphasizing the mechanisms that were identified between the onset of the wet season and forest cover. Therefore, it is likely that forest regrowth across the basin leads to, if not reverses regional climate change, at least slowing down the rate of changes in the climate. 
    more » « less
  3. Introduction: The incidence of diarrhea, a leading cause of morbidity and mortality in low-income countries such as Nepal, is temperature-sensitive, suggesting it could be associated with climate change. With climate change fueled increases in the mean and variability of temperature and precipitation, the incidence of water and food-borne diseases are increasing, particularly in sub-Saharan Africa and South Asia. This national-level ecological study was undertaken to provide evidence linking weather and climate with diarrhea incidence in Nepal. Method: We analyzed monthly diarrheal disease count and meteorological data from all districts, spanning 15 eco-development regions of Nepal. Meteorological data and monthly data on diarrheal disease were sourced, respectively, from the Department of Hydrology and Meteorology and Health Management Information System (HMIS) of the Government of Nepal for the period from 2002 to 2014. Time-series log-linear regression models assessed the relationship between maximum temperature, minimum temperature, rainfall, relative humidity, and diarrhea burden. Predictors with p-values < 0.25 were retained in the fitted models. Results: Overall, diarrheal disease incidence in Nepal significantly increased with 1 °C increase in mean temperature (4.4%; 95% CI: 3.95, 4.85) and 1 cm increase in rainfall (0.28%; 95% CI: 0.15, 0.41). Seasonal variation of diarrheal incidence was prominent at the national level (11.63% rise in diarrheal cases in summer (95% CI: 4.17, 19.61) and 14.5% decrease in spring (95% CI: −18.81, −10.02) compared to winter season). Moreover, the effects of temperature and rainfall were highest in the mountain region compared to other ecological regions of Nepal. Conclusion: Our study provides empirical evidence linking weather factors and diarrheal disease burden in Nepal. This evidence suggests that additional climate change could increase diarrheal disease incidence across the nation. Mountainous regions are more sensitive to climate variability and consequently the burden of diarrheal diseases. These findings can be utilized to allocate necessary resources and envision a weather-based early warning system for the prevention and control of diarrheal diseases in Nepal. 
    more » « less
  4. The evaluation of historical water use in the Upper Rio Grande Basin (URGB), United States and Mexico, using Landsat-derived actual evapotranspiration (ETa) from 1986 to 2015 is presented here as the first study of its kind to apply satellite observations to quantify long-term, basin-wide crop consumptive use in a large basin. The rich archive of Landsat imagery combined with the Operational Simplified Surface Energy Balance (SSEBop) model was used to estimate and map ETa across the basin and over irrigated fields for historical characterization of water-use dynamics. Monthly ETa estimates were evaluated using six eddy-covariance (EC) flux towers showing strong correspondence (r2 > 0.80) with reasonable error rates (root mean square error between 6 and 19 mm/month). Detailed spatiotemporal analysis using peak growing season (June–August) ETa over irrigated areas revealed declining regional crop water-use patterns throughout the basin, a trend reinforced through comparisons with gridded ETa from the Max Planck Institute (MPI). The interrelationships among seven agro-hydroclimatic variables (ETa, Normalized Difference Vegetation Index (NDVI), land surface temperature (LST), maximum air temperature (Ta), potential ET (ETo), precipitation, and runoff) are all summarized to support the assessment and context of historical water-use dynamics over 30 years in the URGB. 
    more » « less
  5. Objectives: Diarrheal disease continues to be a significant cause of morbidity and mortality. We investigated how anomalies in monthly average temperature, precipitation, and surface water storage (SWS) impacted bacterial, and viral diarrhea morbidity in Taiwan between 2004 and 2015. Methods: A multivariate analysis using negative binomial generalized estimating equations was employed to quantify age- and cause-specific cases of diarrhea associated with anomalies in temperature, precipitation, and SWS. Results: Temperature anomalies were associated with an elevated rate of all-cause infectious diarrhea at a lag of 2 months, with the highest risk observed in the under-5 age group (incidence rate ratio [IRR]=1.03, 95% CI, 1.01-1.07). Anomalies in SWS were associated with increased viral diarrhea rates, with the highest risk observed in the under-5 age group at a 2-month lag (IRR= 1.27; 95% CI: 1.14, 1.42) and a lesser effect at a 1-month lag (IRR=1.18; 95% CI, 1.06-1.31). Furthermore, cause-specific diarrheal diseases were significantly affected by extreme weather events in Taiwan. Both extremely cold and hot conditions were associated with an increased risk of all-cause infectious diarrhea regardless of age, with IRRs ranging from 1.03 (95% CI, 1.02-1.12) to 1.18 (95% CI, 1.16-1.40).Conclusions: The risk of all-cause infectious diarrhea was significantly associated with average temperature anomalies in the population aged under 5 years. Viral diarrhea was significantly associated with anomalies in SWS. Therefore, we recommend strategic planning and early warning systems as major solutions to improve resilience against climate change. 
    more » « less