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G. J. P. Salazar, R. N. J. Aguirre, and M. G. A. Peñuela

Abstract

The behavior of manganese (Mn) at the sediment–water interface in bodies of water such as lakes and reservoirs is dependent on physicochemical factors such as pH, redox potential-Eh, organic matter, specific conductance, and the presence of organic and inorganic complexes. These allow the mobilization of Mn from the sediment to the water column and promote its precipitation as Mn oxyhydroxide. For the Riogrande II reservoir in Colombia (2550 m), it was found that redox potential-Eh below +350 mV is not appropriate for oxide stability. The availability and mobility of these oxides are more associated with organic complexation and desorption from sediments when the pH changes from neutral conditions to slightly acidic conditions (6.0). However, when the lower gates of the reservoir are opened during the dry season, the entry of oxygenated bottom currents most likely increases the dissolved oxygen (DO) and redox potential-Eh. Similarly, the increase in soluble Mn at the intake tower during the dry season is more associated with desorption than with reductive dissolution.

The primary objective of this study is to determine the main physicochemical factors favoring Mn remobilization from sediment to the water column and its relation to the operating mechanisms of the intake water tower of the Riogrande II reservoir.

One of the most notable results of this study is the observation that the operating mechanisms of the Riogrande II reservoir not only affect the type of water that is captured but also influence the geochemical processes at the bottom of the reservoir and in the sediment.

The results of this study highlight the influence of hydraulic processes on surface water bodies as regards the dynamics of metal remobilization, the generation of pollution into the water column, and the increasing costs of treatment and purification in reservoirs in high mountain areas in tropical countries.

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Pierre Valty, Olivier de Viron, Isabelle Panet, and Xavier Collilieux

Abstract

From space gravity and station position data over southern Europe from 2002 to 2010, this study investigates the interannual mass redistributions using principal component analysis. The dominant mode, which appears both in gravity and positioning, results from the North Atlantic Oscillation (NAO). This analysis allows us to isolate and characterize the NAO impact on the mass distribution, which appears centered over the Black Sea and its two main catchment basins, the Danube and Dnieper.

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Xing Liu, Jeff Andresen, Haishun Yang, and Dev Niyogi

Abstract

Detailed parameter sensitivity, model validation, and regional calibration of the Hybrid-Maize crop model were undertaken for the purpose of regional agroclimatic assessments. The model was run at both field scale and county scale. The county-scale study was based on 30-yr daily weather data and corn yield data from the National Agricultural Statistics Service survey for 24 locations across the Corn Belt of the United States. The field-scale study was based on AmeriFlux sites at Bondville, Illinois, and Mead, Nebraska. By using the one-at-a-time and interaction-explicit factorial design approaches for sensitivity analysis, the study found that the five most sensitive parameters of the model were potential number of kernels per ear, potential kernel filling rate, initial light use efficiency, upper temperature cutoff for growing degree-days’ accumulation, and the grain growth respiration coefficient. Model validation results show that the Hybrid-Maize model performed satisfactorily for field-scale simulations with a mean absolute error (MAE) of 10 bu acre−1 despite the difficulties of obtaining hybrid-specific information. At the county scale, the simulated results, assuming optimal crop management, overpredicted the yields but captured the variability well. A simple regional adjustment factor of 0.6 rescaled the potential yield to actual yield well. These results highlight the uncertainties that exist in applying crop models at regional scales because of the limitations in accessing crop-specific information. This study also provides confidence that uncertainties can potentially be eliminated via simple adjustment factor and that a simple crop model can be adequately useful for regional-scale agroclimatic studies.

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Amber L. Pearson, Jonathan D. Mayer, and David J. Bradley

Abstract

Even as millions live without reliable access to water, very little is known about how households cope with scarcity. The aims of this research were to 1) understand aspects of water scarcity in three rural villages in southwestern Uganda, 2) examine differences by demographics and type of source, 3) assess relationships between different factors related to water access, and 4) explore coping strategies used. Health implications and lessons learned that relate to future climate change are discussed.

Demographic data, water accessibility, and coping strategies used were recorded using a survey. Descriptive statistics were calculated, and Spearman’s rank correlations were calculated between self-reported level of access, walking minutes to source, ranked ownership of source, and source accessibility during the last two weeks of April (16–30 April). Changes in water source type across seasons and demographic and access measures by coping strategies were examined.

Over half of the households relied on seasonal water sources. Of those accessing “permanent” sources, ~30% experienced inaccessibility within the last two weeks of April. Self-reported better access to water was correlated with minutes spent walking to source and to some degree with the source being more public or shared. Those without access to public sources tended to migrate as the primary coping strategy. Water sharing and reciprocity appears crucial between wealthy and poor households; however, those from outside ethnic groups appear to be partially excluded. Middle income households followed by the poorest had the largest reliance on purchasing water to cope. These findings underscore how access to water resources, particularly in times of insecurity, involves social networks.

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Tianyi Zhang, Xiaomao Lin, Danny H. Rogers, and Freddie R. Lamm

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.

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Olivia Kellner and Dev Niyogi

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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.

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M. P. Calef, A. Varvak, A. D. McGuire, F. S. Chapin III, and K. B. Reinhold

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.

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S. Muchuru, C. M. Botai, J. O. Botai, and A. M. Adeola

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.

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Ayan H. Chaudhuri and Rui M. Ponte

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.

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Ashley E. Van Beusekom, Grizelle González, and Maria M. Rivera

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.

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