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Nádia Gilma Beserra de Lima and Emerson Galvani

Abstract

A mangrove is a transitional coastal ecosystem between marine and terrestrial environments that is characterized by salinity and constant tidal flooding. Mangroves contain plant communities that are adapted to several physical constraints, including the climate. The purpose of this study was to analyze the variations in climatic attributes (air temperature, relative air humidity, global solar radiation, wind, and rainfall) in the mangroves located in the municipality of Iguape, on the southern coast of the state of São Paulo, Brazil. In addition, it was determined whether the existing variation is related to the presence of the canopy environment. A microclimate tower was installed with two weather stations to obtain an analysis of the variation of the climatic attributes above and below the canopy. The results indicate that global solar radiation had an average transmissivity of 26.8%. The air temperature at 10 m was higher than that at the sensor at 2 m. The average rainfall interception for the mangrove environment was 19.6%. Both the maximum gust and average wind speed decreased by approximately 63.7% at 2 m. The mangrove canopy was found to be an important control on the variation of climatic attributes. On a microclimatic scale, the climate attributes had a direct influence on the spatial distribution of vegetation. Additionally, characteristics of the canopy are the main control for this variation, especially for the distribution of rainfall and the amount of solar radiation below the canopy, which influence the distribution of plant species in the environment.

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Mohammad H. Mokhtari, Ibrahim Busu, Hossein Mokhtari, Gholamreza Zahedi, Leila Sheikhattar, and Mohammad A. Movahed

Abstract

The current Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)-based broadband albedo model requires shortwave infrared bands 5 (2.145–2.185 nm), 6 (2.185–2.225 nm), 8 (2.295–2.365 nm), and 9 (2.360–2.430 nm) and visible/near-infrared bands 1 (0.52–0.60 nm) and 3 (0.78–0.86 nm). However, because of sensor irregularities at high temperatures, shortwave infrared wavelengths are not recorded in the ASTER data acquired after April 2008. Therefore, this study seeks to evaluate the performance of artificial neural networks (ANN) in estimating surface albedo using visible/near-infrared bands available in the data obtained after April 2008. It also compares the outcomes with the results of multiple linear regression (MLR) modeling. First, the most influential spectral bands used in the current model as well as band 2 (0.63–0.69 nm) (which is also available after April 2008 in the visible/near-infrared part) were determined by a primary analysis of the data acquired before April 2008. Then, multiple linear regression and ANN models were developed by using bands with a relatively high level of contribution. The results showed that bands 1 and 3 were the most important spectral ones for estimating albedo where land cover consisted of soil and vegetation. These two bands were used as the study input, and the albedo (estimated through a model that utilized bands 1, 3, 5, 6, 8, and 9) served as a target to remodel albedo. Because of its high collinearity with band 1, band 2 was identified less effectively by MLR as well as ANN. The study confirmed that a combination of bands 1 and 3, which are available in the current ASTER data, could be modeled through ANN and MLR to estimate surface albedo. However, because of its higher accuracy, ANN method was superior to MLR in developing objective functions.

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Pennan Chinnasamy, Jason A. Hubbart, and Govindasamy Agoramoorthy

Abstract

India is the greatest groundwater consumer in the world, with estimated annual withdrawals exceeding 230 km3. More than 60% of irrigated agriculture, 85% of drinking water supplies, and 50% of urban and industrial water needs are dependent on sustainable groundwater management. Regardless, groundwater overextraction is a growing problem in many regions. Predictions of groundwater resource availability in India are problematic in part because of a limited number of monitoring sites and insufficient data quality and quantity. Regional groundwater assessments are further complicated because of sporadic and low-frequency data. To help overcome these issues and more accurately quantify groundwater resource availability, scientists have begun using satellite-derived remote sensing data. In this study, the authors used seasonal and annual hydrologic signals obtained by NASA Gravity Recovery and Climate Experiment (GRACE) satellites and simulated soil moisture variations from land data assimilation systems to show groundwater depletion trends in the northwest state of Gujarat (surface area of 196 030 km2), India. Results were evaluated using direct measurement data from 935 wells. Remote sensing generated results compared favorably with well data (e.g., r 2 = 0.89 for Gandhinagar, a representative highly urbanized district in Gujarat: confidence interval (CI) = 0.05 and P = 0.002). Results show that remote sensing is an effective tool to compliment and interpolate observed regional groundwater well data and improve groundwater storage estimations in Gujarat, India. Properly implemented, the method will supply reliable science-based information to enhance management of groundwater resources in India and other geographic locations.

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Di Long, Bridget R. Scanlon, D. Nelun Fernando, Lei Meng, and Steven M. Quiring

Abstract

Large-scale environmental, social, and economic impacts of recent weather and climate extremes are raising questions about whether the frequency and intensity of these extremes have been increasing. Here, the authors evaluate trends in climate extremes during the past half century using the U.S. High Plains as a case study. A total of eight different extreme indices and the standardized precipitation index (SPI) were evaluated using daily maximum and minimum temperature and precipitation data from 207 stations and 0.25° gridded data. The 1958–2010 time period was selected to exclude the 1950s and 2011 droughts. Results show general consistency between the station data and gridded data. The annual extreme temperature range (ETR) decreased significantly (p < 0.05) in ~54% of the High Plains, with a spatial mean rate of −0.7°C decade−1. Decreases in ETR result primarily from increases in annual lowest temperature in ~63% of the stations at a mean rate of ~0.9°C decade−1, whereas increases in annual highest temperature were much less. Approximately 43% of the stations showed increasing warm nights (T min90) with a spatial mean rate of 0.5% decade−1. Precipitation intensity generally did not vary significantly in most grid cells and stations. Significant decreasing trends in consecutive dry days (CDD) were restricted to 21% of the stations in the northern High Plains with a spatial mean of −0.8 days decade−1. Areas experiencing severe dry periods (1-month SPI < −1.5) decreased over time from 8% to 4%. The number of dry months (SPI < 0) in each year also decreased. In summary, the ETR is decreasing and low temperatures are increasing. Precipitation extremes are generally not changing in the High Plains; however, high natural climatic variability in this semiarid region makes it difficult to assess climate extremes.

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Daniel J. McEvoy, Justin L. Huntington, John T. Abatzoglou, and Laura M. Edwards

Abstract

Nevada and eastern California are home to some of the driest and warmest climates, most mountainous regions, and fastest growing metropolitan areas of the United States. Throughout Nevada and eastern California, snow-dominated watersheds provide most of the water supply for both human and environmental demands. Increasing demands on finite water supplies have resulted in the need to better monitor drought and its associated hydrologic and agricultural impacts. Two multiscalar drought indices, the standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI), are evaluated over Nevada and eastern California regions of the Great Basin using standardized streamflow, lake, and reservoir water surface stages to quantify wet and dry periods. Results show that both metrics are significantly correlated to surface water availability, with SPEI showing slightly higher correlations over SPI, suggesting that the inclusion of a simple term for atmospheric demand in SPEI is useful for characterizing hydrologic drought in arid regions. These results also highlight the utility of multiscalar drought indices as a proxy for summer groundwater discharge and baseflow periods.

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Richard R. Heim Jr. and Michael J. Brewer

Abstract

The international scientific community has long recognized the need for coordinated drought monitoring and response, but many factors have prevented progress in the development of a Global Drought Early Warning System (GDEWS): some of which involve administrative issues (coordinated international action and policy) while others involve scientific, technological, and logistical issues. The creation of the National Integrated Drought Information System (NIDIS) Portal within the United States provided an opportunity to take the first steps toward building the informational foundation for a GDEWS: that is, a Global Drought Information System (GDIS). At a series of workshops sponsored by the World Meteorological Organization (WMO) and Group on Earth Observations (GEO) held in Asheville, North Carolina, in April 2010, it was recommended that a modular approach be taken in the creation of a GDIS and that the NIDIS Portal serve as the foundation for the GDIS structure. Once a NIDIS-based Global Drought Monitor (GDM) Portal (GDMP) established an international drought clearinghouse, the various components of a GDIS (drought monitoring, forecasting, impacts, history, research, and education) and later a GDEWS (drought relief, recovery, and planning) could be constructed atop it. The NIDIS Portal is a web-based information system created to address drought services and early warning in the United States, including drought monitoring, forecasting, impacts, mitigation, research, and education. This portal utilizes Open Geospatial Consortium (OGC) web mapping services (WMS) to incorporate continental drought monitors into the GDMP. As of early 2012, the GDM has incorporated continental drought information for North America (North American Drought Monitor), Europe (European Drought Observatory), and Africa (African Drought Monitor developed by Princeton University); interest has been expressed by groups representing Australia and South America; and coordination with appropriate parties in Asia is also expected. Because of the range of climates across the world and the diverse nature of drought and the sectors it impacts, the construction and functioning of each continental drought monitor needs to be appropriate for the continent in question. The GDMP includes a suite of global drought indicators identified by experts and adopted by the WMO as the necessary measures to examine drought from a meteorological standpoint; these global drought indicators provide a base to assist the global integration and interpretation of the continental drought monitors. The GDMP has been included in recent updates to the GEO Work Plan and has benefited from substantial coordination with WMO on both their Global Framework for Climate Services and the National Drought Policy efforts. The GDMP is recognized as having the potential to be a major contributor to both of these activities.

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David Shankman, Barry D. Keim, Tadanobu Nakayama, Rongfang Li, Dunyin Wu, and W. Craig Remington

Abstract

Poyang Lake in Jiangxi Province is the largest freshwater lake in China and is historically a region of significant floods. Maximum annual lake stage and the number of severe flood events have increased during the past few decades because of levee construction that reduced the area available for floodwater storage. The most severe floods since 1950 occurred during 1954, 1973, 1983, 1995, and 1998. Each of these floods followed El Niño events that influence the Asian monsoon and that are directly linked to rainfall in the Changjiang (Yangtze River) basin. The 1954 flood was the largest ever recorded until the 1990s. That year the peak Changjiang stage at Hukou was 21.6 m, which was 1.6 m above the previous record high. The last major flood on the Changjiang was during 1998, when the peak Changjiang stage reached 22.5 m, higher than during 1954, even though peak discharge was lower. The most severe floods, including those in 1954 and 1998, require both 1) high rainfall and tributary discharge into Poyang Lake and 2) high Changjiang discharge and stage at Hukou that backflows into the lake or slows Poyang Lake drainage. Since gauging stations were established on the Changjiang, these conditions always occurred following an El Niño.

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Ashok K. Mishra and Vijay P. Singh

Abstract

Because of their stochastic nature, droughts vary in space and time, and therefore quantifying droughts at different time units is important for water resources planning. The authors investigated the relationship between meteorological variables and hydrological drought properties using the Palmer hydrological drought index (PHDI). Twenty different spatial units were chosen from the unit of a climatic division to a regional unit across the United States. The relationship between meteorological variables and PHDI was investigated using a wavelet–Bayesian regression model, which enhances the modeling strength of a simple Bayesian regression model. Further, the wavelet–Bayesian regression model was tested for the predictability of global climate models (GCMs) to simulate PHDI, which will also help understand their role for downscaling purposes.

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John W. Recha, Johannes Lehmann, M. Todd Walter, Alice Pell, Louis Verchot, and Mark Johnson

Abstract

Tropical Africa is affected by intense land-use change, particularly forest conversion to agricultural land. In this study, the stream discharge of four small headwater catchments located within an area of 6 km2 in western Kenya was examined for 2 years (2007 and 2008). The four catchments cover a degradation gradient ranging from intact forest to agricultural land under maize cultivation for 5, 10, and 50 years. The runoff ratio (e.g., annual catchment discharge expressed as a percentage of rainfall) increased with increasing duration of cultivation from an average of 16.0% in the forest to 32.4% in the 50-yr-old agricultural catchment. Similarly, the average runoff ratio due to the stormflow component was 0.033 in the forest and increased gradually to 0.095 with increasing duration of cultivation. The conversion from forest to agricultural land in the first 5 years caused about half of the total observed increases in runoff ratio (46.3%) and discharge in relation to rainfall (50.6%). The other half of the changes in discharge occurred later during soil degradation after forest clearing. With increasing duration of cultivation, soil bulk density ρb at a depth of 0–0.1 m increased by 46%, while soil organic carbon (SOC) concentrations and total porosity decreased by 75% and 20%, respectively. The changes in hydrological responses that occurred in the initial years after forest clearing may suggest a significant potential for improved land management in alleviating runoff and enhanced storm flow and moisture retention in agricultural watersheds.

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Geovane Webler, Débora Regina Roberti, Santiago Vianna Cuadra, Virnei Silva Moreira, and Marcos Heil Costa

Abstract

With the growing demands for food and biofuel, new technologies and crop management systems are being used to increase productivity and minimize land-use impacts. In this context, estimates of productivity and the impacts of agriculture management practices are becoming increasingly important. Numerical models that describe the soil–surface–atmosphere interactions for natural and agricultural ecosystems are important tools to explore the impacts of these agronomical technologies and their environmental impacts. However, these models need to be validated by considering the different soil and environmental conditions before they can be widely applied. The process-based terrestrial agricultural version of the Integrated Biosphere Simulator (IBIS) model (Agro-IBIS) has only been calibrated and validated for North American sites. Here, the authors validate the Agro-IBIS results for an experimental soybean site in southern Brazil. At this site, soybean was grown under two different management systems: no tillage (NT) and conventional tillage (CT). The model results were evaluated against micrometeorological, soil condition, and biomass observations made during the soybean growing season. The leaf area index (LAI) was underestimated, approaching the values obtained in the CT crop system, with higher error in the leaf senescence period. The model shows higher skill for daily averages and the diurnal cycle of the energy balance components in the period of high LAI. The soil temperature and moisture were robustly simulated, although the latter is best correlated with the observations made at the CT field. The ecosystem respiration is highly underestimated, causing an overestimation of the cumulative net ecosystem exchange (NEE), particularly at the end of the crop cycle.

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