Standardized Precipitation Index: User Guide

Over the years, there has been much discussion on what drought indices should be used in a particular climate and for what application. Many drought definitions and indices have been developed and attempts have been made to provide some guidance on this issue. With this in mind, the Interregional Workshop on Indices and Early Warning Systems for Drought was organized and held at the University of Nebraska-Lincoln, United States of America, from 8 to 11 December 2009. It was jointly sponsored by the School of Natural Resources (SNR) of the University of Nebraska, the United States National Drought Mitigation Center (NDMC), the World Meteorological Organization (WMO), the United States National Oceanic and Atmospheric Administration (NOAA), the United States Department of Agriculture (USDA) and the United Nations Convention to Combat Desertification (UNCCD). The workshop brought together 54 participants representing 22 countries from all over the world. They reviewed the drought indices currently in use in different regions of the world to explain meteorological, agricultural and hydrological droughts; assessed the capacity for collecting information on the impacts of drought; reviewed the current and emerging technologies for drought monitoring, and discussed the need for consensus standard indices to describe different types of droughts. The experts at the meeting elaborated and approved the Lincoln Declaration on Drought Indices, which recommended that the Standardized Precipitation Index (SPI) be used by all National Meteorological and Hydrological Services (NMHSs) around the world to characterize meteorological droughts, in addition to other drought indices that were in use in their service. The Lincoln Declaration also recommended the development of a comprehensive SPI user manual. In June 2011, the Sixteenth World Meteorological Congress adopted a resolution that endorsed both of these recommendations. The Congress also requested that the SPI manual be published and distributed in all official languages of the United Nations. The full Lincoln Declaration on Drought Indices can be found on the WMO website at http:// www.wmo.int/pages/prog/wcp/agm/meetings/wies09/documents/Lincoln_Declaration_Drought_ Indices.pdf

Using climate information for drought planning

Historically, drought has been responded to rather than prepared for, yet studies have illustrated that proactive investment in drought risk management reduces impacts and overall response costs. One key element of preparedness is the use of sufficient climate information for monitoring, forecasting, and tracking long-term trends. In the face of a changing climate and increasing variability, these types of data are even more critical for planning and overall resiliency. The systematic use of these data to inform the drought planning component of drought risk management is a relatively recent development. Actionable science has direct applicability for planning and decision-making, and allows for an iterative process between scientists and end users that can build long-term drought resiliency. The article will describe how planners in Colorado are increasingly relying on climate data, ranging from paleoclimatological records to experimental seasonal forecasts, to guide their long-term drought preparedness and climate change adaptation efforts. This information can then be used to inform broader policy and planning efforts, unifying the scientific basis across multiple processes. In addition, the Integrated Drought Management Programme (IDMP), with the World Meteorological Organization (WMO) and the Global Water Partnership (GWP) as co-leads, promotes national policies encouraging proactive risk management, and provides a platform for sharing the lessons learned by the planners, policy makers, and scientists around the world. Data-driven decision-making using climate information can help depoliticize actions and increase overall resiliency and response in times of drought, which will be increasingly important as the world warms

Arctic Borderlands Ecological Knowledge Cooperative: can local knowledge inform caribou management?

While quantitative analyses have traditionally been used to measure overall caribou herd health, qualitative observational data can also provide timely information that reflects what people on the land are observing. The Arctic Borderlands Ecological Knowledge Co-op (ABEKC) monitors ecological change in the range of the Porcupine Caribou Herd (PCH). The community-based monitoring component of the Co-op’s mandate involves the gathering of local knowledge through interviews with local experts in a number of communities.We analyzed the responses to interviews collected during 2000–2007 related to caribou availability, harvest success, meeting needs and caribou health during fall and spring. Interviews revealed 1) caribou greater availability during the survey period, 2) an increasing trend in the proportion of harvesters that met their needs 3) no trend in animals harvested or proportion of successful hunters and 4) improving overall caribou health throughout the period.There was no population estimate for the herd between 2001 and 2010. In 2001, 123,000 caribou were estimated in the herd. Based on an estimated 178,000 in 1989, a declining trend of ~ 3% annually occurred at least until 2001. In the interim agencies and boards feared the herd continued to decline and worked towards and finalized a Harvest Management Plan for the herd. In contrast, from the Co-op interviews all indications suggested improving herd conditions throughout most of the decade. A successful survey in 2010 determined the herd had grown to 169,000 animals. We conclude that the community-based interviews provided a valid, unique information source to better understand caribou ecology and express community perceptions of overall herd status and could provide a valuable contribution to management decision making.  We recommend that ABEKC results become standard input into Porcupine Caribou harvest management decisions and serve as a model of integrating community based monitoring data into resource management decision making throughout the north

Vegetation Outlook (VegOut): Predicting Remote Sensing–Based Seasonal Greenness

Accurate and timely prediction of vegetation conditions enhances knowledge-based decision making for drought planning, mitigation, and response. This is very important in countries that are highly dependent on rainfed agriculture. For example, studies show that remote sensing–based observations and vegetation condition prediction have great potential for estimating crop yields (Verdin and Klaver, 2002; Ji and Peters, 2003; Seaquist et al., 2005; Tadesse et al., 2005a, 2008; Funk and Brown, 2006), which in turn may help to address agricultural development and food security issues, as well as improve early warning systems. Many studies have demonstrated the value of Vegetation Indices (VIs), such as the Normalized Difference Vegetation Index (NDVI), calculated from satellite observations for assessing vegetation cover and conditions (Tucker et al., 1985; Roerink et al., 2003; Anyamba and Tucker, 2005; Seaquist et al., 2005), and such data have become a common source of information for vegetation monitoring. The term vegetation condition in this chapter refers to vegetation greenness or vegetation health, as inferred from canopy reflectance values measured by satellite observations (Mennis, 2001; Anyamba and Tucker, 2005). The vegetation greenness metric is commonly calculated from time-series NDVI (Reed et al., 1994) and represents the seasonal, time-integrated NDVI at a specific date, which has been shown to be representative of indicators of general vegetation health including net primary production (NPP) and green biomass (Tucker et al., 1985; Reed et al., 1996; Yang et al., 1998; Eklundh and Olsson, 2003; Hill and Donald, 2003). As a result, VIs and VI derivatives such as time-integrated VI can be used to characterize the temporal and spatial relationships between climate and vegetation and improve our understanding of the lagged relationship between climate (e.g., precipitation and temperature) and vegetation response (Roerink et al., 2003; Anyamba and Tucker, 2005; Seaquist et al., 2005; Camberlin et al., 2007; Groeneveld and Baugh, 2007). Quantitative descriptions of climate-vegetation response lags can then be used to identify and predict vegetation stress during drought

Mercury removal from MSW incineration flue gas by mineral-based sorbents

Three samples of commercially available mineral-based sorbents (zeolite, bentonite and diatomaceous earth) were selected and evaluated for Hg capture under conditions of simulated dry flue gas atmosphere typical in Municipal Solid Waste Incineration (MSWI). The experiments were carried out in a lab-scale fixed-bed device at temperatures between 120 and 200°C. Two samples of activated carbons (AC) (raw-AC and sulphur impregnated AC) were tested under the same conditions. The mineral-based sorbents were chemically promoted by sulphur, FeCl3 and CaBr2, achieving an improvement in the overall reduction percentage of Hg0out (g) up to 85%, which was comparable to that obtained using a commercial activated carbon for Hg capture (sulphur impregnated AC). The study demonstrates that sorbents with a matrix relatively richer in TiO2, Fe2O3 and Al2O3, as bentonite, favour Hg heterogeneous oxidation. The best Hg capture capacity was achieved with a zeolite sorbent sample characterized by high specific surface (132 m2/g) and impregnated with elemental sulphur. The final form of mercury retained in this sorbent was HgS with proved long-term stability in disposal and landfilling. The higher the temperature, the lower the efficiency of Hg capture being the optimum temperature for Hg-capture in the range of 120-150°C. This study provides a basis for the development of new efficient non-carbon sorbents for mercury removal in the air pollution control lines of MSWI facilities considering the non-hazardous final form of mercury and its long-term landfilling/sequestration

Mitigation of gaseous mercury emissions from waste-to-energy facilities: Homogeneous and heterogeneous Hg-oxidation pathways in presence of fly ashes

This study describes the main mechanisms that take part in the mercury homogeneous oxidation pathway in presence of some of the main reactive compounds formed during waste incineration processes (O2, HCl, SO2 and NO). Series of model, synthetic dry flue gases were used to elucidate the effects of HCl, SO2, NO and their proportions in the gas on mercury behaviour. Three samples of fly ash collected from a MSWI facility were characterized and evaluated both for Hg heterogeneous oxidation and Hg removal in a laboratory scale device. The results obtained in this study showed that homogeneous mercury oxidation in the models MSWI and coal combustion flue gas atmospheres was 52 ± 5% and 25%, respectively. SO2, NO and HCl have a synergetic effect in Hg oxidation in presence of oxygen, but the main differences found are mainly caused by the strong influence of HCl and the likely inhibitory oxidation effects of SO2. Surface area together with carbon and chloride content of the fly ashes were correlated with their capacity for Hg-heterogeneous oxidation and adsorption. The sample of fly ash with relatively high content of unburnt carbon and chlorine, and with BET surface (2.42 m2/g) was able to remove up to 100% of Hg0 (g) during 300 min. The results obtained in this study provide a complete overview of the behaviour of mercury during MSWI processes and may help to clarify the fate/behaviour of mercury in a filter (e.g. electrostatic precipitator) providing a deeper knowledge about the impacts of fly ash properties on mercury fate in waste incineration

Development of a Long-Term (1884-2006) Serially Complete Dataset of U.S. Temperatures and Precipitation for Climate Services

Serially complete climate datasets with no missing data are necessary for a diverse group of users working in many economic sectors. In this article we describe the procedures used to create a Serially Complete Data set (SCD) for the U.S. We include the selection criterion applied to potential SCD stations, the various procedural steps and the details applied to each step. A few observations that were not previously digitized were obtained from observers official paper reports. The methods used to estimate missing data are the Spatial Regression Test and the Inverse Distance Weighting technique. Using the criterion for selecting stations we were able to include 2144 stations for the SCD that had at least 1 element (maximum/minimum temperature and/or precipitation) for a continuous period of at least 40 years. In addition, the quality control procedure assigned confidence intervals to all observations and many of the estimates. We continue to explore the options for estimating any missing data that remain after our 3 step approach and we look forward to changing the base data set form TD 3200 to GHCN

The Vegetation Outlook (VegOut): A New Method for Predicting Vegetation Seasonal Greenness

The vegetation outlook (VegOut) is a geospatial tool for predicting general vegetation condition patterns across large areas. VegOut predicts a standardized seasonal greenness (SSG) measure, which represents a general indicator of relative vegetation health. VegOut predicts SSG values at multiple time steps (two to six weeks into the future) based on the analysis of “historical patterns” (i.e., patterns at each 1 km grid cell and time of the year) of satellite, climate, and oceanic data over an 18-year period (1989 to 2006). The model underlying VegOut capitalizes on historical climate–vegetation interactions and ocean–climate teleconnections (such as El Niño and the Southern Oscillation, ENSO) expressed over the 18-year data record and also considers several environmental characteristics (e.g., land use/cover type and soils) that influence vegetation’s response to weather conditions to produce 1 km maps that depict future general vegetation conditions. VegOut provides regional level vegetation monitoring capabilities with local-scale information (e.g., county to sub-county level) that can complement more traditional remote sensing–based approaches that monitor “current” vegetation conditions. In this paper, the VegOut approach is discussed and a case study over the central United States for selected periods of the 2008 growing season is presented to demonstrate the potential of this new tool for assessing and predicting vegetation conditions