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openAccessArticle: Truecover date: 2015-01-01pii: S2212-0963(15)00005-4Harvest Date: 2016-01-06 13:07:50issueName:Page Range: 11-11href scidir: http://www.sciencedirect.com/science/article/pii/S2212096315000054pubType

Accurate and current rainfall characterization is an important tool for waterrelated system design and management. Updated rainfall intensity-duration-frequency (IDF) relationships in peninsular India were developed; impacts on runoff and groundwater recharge due to changes in rainfall characteristics are discussed. Two datasets were used from gauges in Hyderabad city, the capital of Andhra Pradesh: hourly rainfall data for the 19 years from 1993-2011 and daily rainfall data for the 30 years from 1982-2011. Hourly data were used to develop updated rainfall IDF relationships; daily data were used for trend analysis of threshold-based rainfall events. IDF curves were developed for return periods of 2, 5, 10, 15, 25, 50, 75, and 100 years for 1, 2, 4, 8, and 24 hour durations. The updated IDF relationships showed a significant change in rainfall characteristics compared to older relationships for the region surrounding Hyderabad, India; they showed greater rainfall

Cotton and peanut grown under irrigation make up over 769,000 ha in the Southeast USA. The consumptive use of water for irrigation has significantly impacted groundwater resources, spring flows and streamflows in many parts of this region, particularly during severe droughts. This situation is further complicated with extreme weather events and climate variability. In this study, we compare yields and water use in a non-irrigated sod-based rotation system (SBR; bahiagrass–bahiagrass–peanut–cotton) to an irrigated conventional rotation system (ICR; peanut–cotton–cotton). Root mass of oat cover crop following peanut or cotton in a SBR and ICR system was also measured. A soil water assessment model (SWAT) was used to simulate irrigation water demands over a 34 yr period (1980–2013) under different soil types to quantify water saving potential of SBR. The average peanut yield in ICR from 2002 to 2013 was 4509 kg ha−1, while that in SBR was 4874 kg ha−1. Likewise the average cotton yield in ICR during the same period was 1237 kg ha−1, while that in SBR was 1339 kg ha−1. Oats had greater root mass in SBR than ICR. Simulation results indicate that crops in SBR consistently had substantially lower irrigation requirements (between 11 and 22 cm yr−1) than those in ICR in dry years. The water-saving potential of SBR varies positively with increasing sand content in soil.

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Measures of crop water use for mature blueberry plantings could offer improved irrigation management by growers, reducing irrigation diversions. The objective of this research was to provide crop coefficients for mature southern highbush blueberry plants.Measures of crop water requirements were made using a water balance enabled by suction lysimeters. Eight established, mature plants were instrumented for water balance. Irrigation was managed to ensure well-watered conditions. Monthly crop coefficients ranged from 0.59 to 1.10 with an annual mean of 0.84.

Spatial mapping of remote sensing data tends to be used less when valuing coastal ecosystem services than in other ecosystems. This research project aimed to understand obstacles to the use of remote sensing data in coastal ecosystem valuations, and to educate coastal stakeholders on potential remote sensing data sources and techniques. A workshop program identified important barriers to the adoption of remote sensing data: perceived gaps in spatial and temporal scale, uncertainty about confidence intervals and precision of remote sensing data, and linkages between coastal ecosystem services and values. Case studies that demonstrated the state of the science were used to show methods to overcome the barriers. The case studies demonstrate multiple approaches to valuation that have been used successfully in coastal projects, and validate that spatial mapping of remote sensing data may fill critical gaps, such as cost-effectively generating calibrated historical data.

This article introduces a web‐based climate information resource: an El Niño Southern Oscillation ENSO‐based monthly climatology for the United States, available on http://AgroClimate.org. Climate variability affects land managers and decision makers in a variety of ways. The ENSO is one of the leading drivers of seasonal and year‐to‐year climate variability. Understanding the historical rainfall and temperature anomalies associated with ENSO can be an important way for decision makers to learn how they might anticipate climate variability. We used the ENSO record from 1950 to 2013 to develop an ENSO‐specific climatology. This allows decision makers to pinpoint the combinations of location and month in which there are substantial ENSO‐associated rainfall or temperature anomalies. Learning about the timing of historical ENSO‐driven climate impacts can be an important first step for decision makers to develop management adjustments that could make the most positive outcomes based on the available climate information.