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Chang Liao and Qianlai Zhuang

Abstract

Droughts dramatically affect plant production of global terrestrial ecosystems. To date, quantification of this impact remains a challenge because of the complex plant physiological and biochemical processes associated with drought. Here, this study incorporates a drought index into an existing process-based terrestrial ecosystem model to estimate the drought impact on global plant production for the period 2001–10. Global Moderate Resolution Imaging Spectroradiometer (MODIS) gross primary production (GPP) data products are used to constrain model parameters and verify the model algorithms. The verified model is then applied to evaluate the drought impact. The study indicates that droughts will reduce GPP by 9.8 g C m−2 month−1 during the study period. On average, drought reduces GPP by 10% globally. As a result, the global GPP decreased from 106.4 to 95.9 Pg C yr−1 while the global net primary production (NPP) decreased from 54.9 to 49.9 Pg C yr−1. This study revises the estimation of the global NPP and suggests that the future quantification of the global carbon budget of terrestrial ecosystems should take the drought impact into account.

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Keith J. Harding, Tracy E. Twine, and Yaqiong Lu

Abstract

The rapid expansion of irrigation since the 1950s has significantly depleted the Ogallala Aquifer. This study examines the warm-season climate impacts of irrigation over the Ogallala using high-resolution (6.33 km) simulations of a version of the Weather Research and Forecasting (WRF) Model that has been coupled to the Community Land Model with dynamic crop growth (WRF-CLM4crop). To examine how dynamic crops influence the simulated impact of irrigation, the authors compare simulations with dynamic crops to simulations with a fixed annual cycle of crop leaf area index (static crops). For each crop scheme, simulations were completed with and without irrigation for 9 years that represent the range of observed precipitation. Reduced temperature and precipitation biases occur with dynamic versus static crops. Fundamental differences in the precipitation response to irrigation occur with dynamic crops, as enhanced surface roughness weakens low-level winds, enabling more water from irrigation to remain over the region. Greater simulated rainfall increases (12.42 mm) occur with dynamic crops compared to static crops (9.08 mm), with the greatest differences during drought years (+20.1 vs +5.9 mm). Water use for irrigation significantly impacts precipitation with dynamic crops (R 2 = 0.29), but no relationship exists with static crops. Dynamic crop growth has the largest effect on the simulated impact of irrigation on precipitation during drought years, with little impact during nondrought years, highlighting the need to simulate the dynamic response of crops to environmental variability within Earth system models to improve prediction of the agroecosystem response to variations in climate.

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A L. Hirsch, A. J. Pitman, J. Kala, R. Lorenz, and M. G. Donat

Abstract

The role of land–atmosphere coupling in modulating the impact of land-use change (LUC) on regional climate extremes remains uncertain. Using the Weather and Research Forecasting Model, this study combines the Global Land–Atmosphere Coupling Experiment with regional LUC to assess the combined impact of land–atmosphere coupling and LUC on simulated temperature extremes. The experiment is applied to an ensemble of planetary boundary layer (PBL) and cumulus parameterizations to determine the sensitivity of the results to model physics. Results show a consistent weakening in the soil moisture–maximum temperature coupling strength with LUC irrespective of the model physics. In contrast, temperature extremes show an asymmetric response to LUC dependent on the choice of PBL scheme, which is linked to differences in the parameterization of vertical transport. This influences convective precipitation, contributing a positive feedback on soil moisture and consequently on the partitioning of the surface turbulent fluxes. The results suggest that the impact of LUC on temperature extremes depends on the land–atmosphere coupling that in turn depends on the choice of PBL. Indeed, the sign of the temperature change in hot extremes resulting from LUC can be changed simply by altering the choice of PBL. The authors also note concerns over the metrics used to measure coupling strength that reflect changes in variance but may not respond to LUC-type perturbations.

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Mark R. Jury

Abstract

This study reconsiders the role of the Agulhas Current in South African climate variability. Here, the Agulhas Current is delimited by its anticyclonic looping flow and cluster analysis of detrended SST anomalies that lead to an area 28°–37°S, 18°–35°E, poleward of South Africa. Regression of detrended Agulhas SST with rainfall anomaly fields in the years 1950–2012 yields a surprising negative influence over the interior. In summer, the negative regression exhibits a northwest axis consistent with reduced cloud band activity. Positive influence is confined to the eastern escarpment in the September–November season when cutoff lows are prevalent. The overall negative influence of the Agulhas SST is confirmed by regression with the vegetation fraction and latent heat flux in the satellite era.

Mechanisms of South African rainfall suppression were investigated. The Agulhas SST index is positively related to the multivariate ENSO index at the 1–3-month lead time. Hence, warm years in the Agulhas Current follow Pacific El Niño. Composite ocean analysis shows enhanced westerly winds offshore and a westward extension of warm salty water from the anticyclonic south Indian Ocean gyre. Composite atmospheric analysis exhibits moist uplifted air over the Agulhas Current folding into an equatorward circulation that sinks over the interior plateau. Because Agulhas SST partially follows ENSO, its suppression of interior rainfall is concluded to be passive.

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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

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.

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