Abstract

More severe droughts in the United States will bring great challenges to irrigation water supply. Here, the authors assessed the potential adaptive effects of irrigation infrastructure under present and more extensive droughts. Based on data over 1985–2005, this study established a statistical model that suggests around 4.4% more irrigation was applied in response to a one-unit reduction in the Palmer drought severity index (PDSI), and approximately 5.0% of irrigation water application could be saved for each 10% decrease in the areas supplied by surface irrigation infrastructure. Based on the results, the model-projected irrigation infrastructure has played a greater role in changes in irrigation than drought in most areas under the current climate except some southwestern counties. However, under the predicted future more severe drought in 2080–99 under the representative concentration pathways 4.5 scenario, the model projected that the drought will require 0%–20% greater irrigation amounts assuming the current irrigation efficiency. Under the predicted drought scenario, irrigation depth can be maintained at or below the baseline level in the western United States only when better irrigation infrastructure replaced 40% of the current surface irrigation infrastructure areas. In the northeast United States, limited changes in irrigation depth were predicted under different irrigation infrastructure scenarios because the percentage of surface irrigation area is already low under the baseline climate, and thus there is limited opportunity to adapt to future drought with advanced irrigation infrastructure. These results indicate that other effective solutions are required to complement these measures and aid U.S. agriculture in the future, more extensive drought.

Abstract

El Niño–Southern Oscillation (ENSO) and Arctic Oscillation (AO) climatology (1980–2010) is developed and analyzed across the U.S. Corn Belt using state climate division weather and historic corn yield data using analysis of variance (ANOVA) and correlation analysis. Findings provide insight to agroclimatic conditions under different ENSO and AO episodes and are analyzed with a perspective for potential impacts to agricultural production and planning, with findings being developed into a web-based tool for the U.S. Corn Belt.

This study is unique in that it utilizes the oceanic Niño index and explores two teleconnection patterns that influence weather across different spatiotemporal scales. It is found that the AO has a more frequent weak to moderate correlation to historic yields than ENSO when correlated by average subgrowing season index values. Yield anomaly and ENSO and AO episode analysis affirms the overall positive impact of El Niño events on yields compared to La Niña events, with neutral ENSO events in between as found in previous studies. Yields when binned by the AO episode present more uncertainty. While significant temperature and precipitation impacts from ENSO and AO are felt outside of the primary growing season, correlation between threshold variables of episode-specific temperature and precipitation and historic yields suggests that relationships between ENSO and AO and yield are present during specific months of the growing season, particularly August. Overall, spatial climatic variability resulting from ENSO and AO episodes contributes to yield potential at regional to subregional scales, making generalization of impacts difficult and highlighting a continued need for finescale resolution analysis of ENSO and AO signal impacts on corn production.

Abstract

The Alaskan boreal forest is characterized by frequent extensive wildfires whose spatial extent has been mapped for the past 70 years. Simple predictions based on this record indicate that area burned will increase as a response to climate warming in Alaska. However, two additional factors have affected the area burned in this time record: the Pacific decadal oscillation (PDO) switched from cool and moist to warm and dry in the late 1970s and the Alaska Fire Service instituted a fire suppression policy in the late 1980s. In this paper a geographic information system (GIS) is used in combination with statistical analyses to reevaluate the changes in area burned through time in Alaska considering both the influence of the PDO and fire management. The authors found that the area burned has increased since the PDO switch and that fire management drastically decreased the area burned in highly suppressed zones. However, the temporal analysis of this study shows that the area burned is increasing more rapidly in suppressed zones than in the unsuppressed zone since the late 1980s. These results indicate that fire policies as well as regional climate patterns are important as large-scale controls on fires over time and across the Alaskan boreal forest.

Abstract

In this paper, monthly, maximum seasonal, and maximum annual hydrometeorological (i.e., evaporation, lake water levels, and rainfall) data series from the Kariba catchment area of the Zambezi River basin, Zimbabwe, have been analyzed in order to determine appropriate probability distribution models of the underlying climatology from which the data were generated. In total, 16 probability distributions were considered and the Kolmogorov–Sminorv (KS), Anderson–Darling (AD), and chi-square (χ²) goodness-of-fit (GoF) tests were used to evaluate the best-fit probability distribution model for each hydrometeorological data series. A ranking metric that uses the test statistic from the three GoF tests was formulated and used to select the most appropriate probability distribution model capable of reproducing the statistics of the hydrometeorological data series. Results showed that, for each hydrometeorological data series, the best-fit probability distribution models were different for the different time scales, corroborating those reported in the literature. The evaporation data series was best fit by the Pearson system, the Lake Kariba water levels series was best fit by the Weibull family of probability distributions, and the rainfall series was best fit by the Weibull and the generalized Pareto probability distributions. This contribution has potential applications in such areas as simulation of precipitation concentration and distribution and water resources management, particularly in the Kariba catchment area and the larger Zambezi River basin, which is characterized by (i) nonuniform distribution of a network of hydrometeorological stations, (ii) significant data gaps in the existing observations, and (iii) apparent inherent impacts caused by climatic extreme events and their corresponding variability.

Abstract

The authors examine five recent reanalysis products [NCEP Climate Forecast System Reanalysis (CFSR), Modern-Era Retrospective Analysis for Research and Applications (MERRA), Japanese 25-year Reanalysis Project (JRA-25), Interim ECMWF Re-Analysis (ERA-Interim), and Arctic System Reanalysis (ASR)] for 1) trends in near-surface radiation fluxes, air temperature, and humidity, which are important indicators of changes within the Arctic Ocean and also influence sea ice and ocean conditions, and 2) fidelity of these atmospheric fields and effects for an extreme event: namely, the 2007 ice retreat. An analysis of trends over the Arctic for the past decade (2000–09) shows that reanalysis solutions have large spreads, particularly for downwelling shortwave radiation. In many cases, the differences in significant trends between the five reanalysis products are comparable to the estimated trend within a particular product. These discrepancies make it difficult to establish a consensus on likely changes occurring in the Arctic solely based on results from reanalyses fields. Regarding the 2007 ice retreat event, comparisons with remotely sensed estimates of downwelling radiation observations against these reanalysis products present an ambiguity. Remotely sensed observations from a study cited herewith suggest a large increase in downwelling summertime shortwave radiation and decrease in downwelling summertime longwave radiation from 2006 and 2007. On the contrary, the reanalysis products show only small gains in summertime shortwave radiation, if any; however, all the products show increases in downwelling longwave radiation. Thus, agreement within reanalysis fields needs to be further checked against observations to assess possible biases common to all products.

Abstract

As is true of many tropical regions, northeastern Puerto Rico is an ecologically sensitive area with biological life that is highly elevation dependent on precipitation and temperature. Climate change has the potential to increase the risk of losing endemic species and habitats. Consequently, it is important to explore the pattern of trends in precipitation and temperature along an elevation gradient. Statistical derivatives of a frequently sampled dataset of precipitation and temperature at 20 sites along an elevation gradient of 1000 m in northeastern Puerto Rico were examined for trends from 2001 to 2013 with nonparametric methods accounting for annual periodic variations such as yearly weather cycles. Overall daily precipitation had an increasing trend of around 0.1 mm day⁻¹ yr⁻¹. The driest months of the annual dry, early, and late rainfall seasons showed a small increasing trend in the precipitation (around 0.1 mm day⁻¹ yr⁻¹). There was strong evidence that precipitation in the driest months of each rainfall season increased faster at higher elevations (0.02 mm day⁻¹ more increase for 100-m elevation gain) and some evidence for the same pattern in precipitation in all months of the year but at half the rate. Temperature had a positive trend in the daily minimum (around 0.02°C yr⁻¹) and a negative trend in the daily maximum whose size is likely an order of magnitude larger than the size of the daily minimum trend. Physical mechanisms behind the trends may be related to climate change; longer-term studies will need to be undertaken in order to assess the future climatic trajectory of tropical forests.

Abstract

To investigate topographic–thermal circulations and the associated moisture variability over western Puerto Rico, field data were collected from 15 to 31 March 2011. Surface meteorological instruments and ground-based GPS receivers measured the circulation and precipitable water with high spatial and temporal resolution, and the Weather Research and Forecasting (WRF) Model was used to simulate the mesoscale flow at 1-km resolution. A westerly onshore flow of ~4 m s⁻¹ over Mayaguez Bay was observed on many days, due to an interaction between thermally driven [3°C (10 km)⁻¹] sea-breeze circulation and an island wake comprised of twin gyres. The thermally driven sea breeze occurred only when easterly synoptic winds favorably oriented the gyres with respect to the coast. Moisture associated with onshore flow was characterized by GPS measured precipitable water (PW). There is diurnal cycling of PW > 3 cm over the west coast during periods of onshore flow. The WRF Model tends to overestimate PW on the west side of the island, suggesting evapotranspiration as a process needing further attention. Fluctuations of PW affect local rainfall in times of convective instability.

Abstract

This study develops a new conceptual tool to explore the potential societal consequences of climate change. The conceptual tool delineates three quasi-independent factors that contribute to the societal consequences of climate change: how climate changes; the sensitivity of physical systems, biological resources, and social institutions to climate change; and the degree of human dependence on those systems, resources, and institutions. This conceptual tool, as currently developed, is not predictive, but it enables the exploration of the dependence of climate change risks on key contributing factors. In exploring a range of plausible behaviors for these factors and methods for their synthesis, the authors show that plausible assumptions lead to a wide range in potential societal consequences of climate change. This illustrates that the societal consequences of climate change are currently difficult to constrain and that high-consequence climate change outcomes are not necessarily low probability, as suggested by leading economic analyses. With careful implementation, this new conceptual tool has potential to increase public understanding of climate change risks, to support risk management decision making, or to facilitate communication of climate risks across disciplinary boundaries.

Abstract

Several statistical downscaling models have been developed in the past couple of decades to assess the hydrologic impacts of climate change by projecting the station-scale hydrological variables from large-scale atmospheric variables simulated by general circulation models (GCMs). This paper presents and compares different statistical downscaling models that use multiple linear regression (MLR), positive coefficient regression (PCR), stepwise regression (SR), and support vector machine (SVM) techniques for estimating monthly rainfall amounts in the state of Florida. Mean sea level pressure, air temperature, geopotential height, specific humidity, U wind, and V wind are used as the explanatory variables/predictors in the downscaling models. Data for these variables are obtained from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis dataset and the Canadian Centre for Climate Modelling and Analysis (CCCma) Coupled Global Climate Model, version 3 (CGCM3) GCM simulations. The principal component analysis (PCA) and fuzzy c-means clustering method (FCM) are used as part of downscaling model to reduce the dimensionality of the dataset and identify the clusters in the data, respectively. Evaluation of the performances of the models using different error and statistical measures indicates that the SVM-based model performed better than all the other models in reproducing most monthly rainfall statistics at 18 sites. Output from the third-generation CGCM3 GCM for the A1B scenario was used for future projections. For the projection period 2001–10, MLR was used to relate variables at the GCM and NCEP grid scales. Use of MLR in linking the predictor variables at the GCM and NCEP grid scales yielded better reproduction of monthly rainfall statistics at most of the stations (12 out of 18) compared to those by spatial interpolation technique used in earlier studies.

Abstract

Riparian shallow groundwater and nutrient movement is important for aquatic and forest ecosystem health. Understanding stream water (SW)–shallow groundwater (GW) interactions is necessary for proper management of floodplain biodiversity, but it is particularly confounding in underrepresented semikarst hydrogeological systems. The Modular Three-Dimensional Finite-Difference Ground-Water Flow Model (MODFLOW) was used to simulate shallow groundwater flow and nutrient transport processes in a second-growth Ozark border forest for the 2011 water year. MODFLOW provided approximations of hydrologic head that were statistically comparable to observed data (Nash–Sutcliffe = 0.47, r ² = 0.77, root-mean-square error = 0.61 cm, and mean difference = 0.46 cm). Average annual flow estimates indicated that 82% of the reach length was a losing stream, while the remaining 18% was gaining. The reach lost more water to the GW during summer (2405 m³ day⁻¹) relative to fall (2184 m³ day⁻¹), spring (2102 m³ day⁻¹), and winter (1549 m³ day⁻¹) seasons. Model results showed that the shallow aquifer had the highest nitrate loading during the winter season (707 kg day⁻¹). A Particle-Tracking Model for MODFLOW (MODPATH) revealed significant spatial variations between piezometer sites (p = 0.089) in subsurface flow path and travel time, ranging from 213 m and 3.6 yr to 197 m and 11.6 yr. The current study approach is novel with regard to the use of transient flow conditions (as opposed to steady state conditions) in underrepresented semikarst geological systems of the U.S. Midwest. This study emphasizes the significance of semikarst geology in regulating SW–GW hydrologic and nutrient interactions and provides baseline information and modeling predictions that will facilitate future studies and management plans.