Agricultural Drought Information Cluster

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Project Submitted by: Chunming Peng

According to the National Oceanic and Atmospheric Administration, the year of 2012 is the hottest year ever recorded in the United States since 1895. And last year’s drought had affected 87% of the land dedicated to growing corn, 85% of land for soybeans, 63% of land for hay and 72% of land used for cattle (statistics by U. S. Drought Monitor). Due to the great impacts drought exerted upon agriculture, more and more scientists and researchers have their attention focused onto the cause and outcomes of agricultural drought. And they are yearning for a tool that would enable easy data downloading/accessing, calculation, analysis, and decision-making.
The traditional ways used for mapping and analyzing agricultural drought are not only time-consuming and also difficult to be applied to a large area at an everyday basis, largely because that the huge amount of satellite data, station-based observations, and drought related statistical information are stored separately in different servers (ftp, http or others) by the data providing agencies or groups. Researchers spend lots of time downloading data, and multiple Terabytes of space storing them. These data are often isolated from each other, and not always reusable (after being calculated or analyzed for a single index). Besides the big data challenge brought to drought researchers, technologies or methodologies that each agency used for calculating drought indices and analyzing drought are different, and most of them have not been known by the general public. There are hundreds of drought indicators in the field, yet not a platform exists for users to view how each of the existing indicators is being formed – we have to look into publications or individual user manuals for the detailed information of the data source, processing methods, calculation formula and other meta-data. Imagine how much time can be saved if we can use a platform that displays how each index is being made at each procedure from data collection to result analysis. A one-stop self-service drought information cluster can facilitate farmers and decision-makers, and the general public to have basic understanding of agricultural drought, and build up their own drought indicators from templates. And such a cluster will become an excellent model that carries out partnership and interoperability.
Ideally, the system will allow users to build up their own drought indicators in three steps:

  1. Choose Dimension. Users get to choose one or more dimensions from Vegetation Conditions, Soil Moisture Conditions, Surface Temperature, and Crop Phenology, etc. If users would like to upload any data not present in the following, they can choose to import their own dimension. Also, users can decide the relationships of each dimension – e.g. D1+D2+D3+ … + Dn, or, (D1+D2) / (D3+ … + Dn).
  2. Choose Indicators. For each dimension user has selected, he/she needs to specify one or more indicators. For example, you can choose Vegetation Condition Index (VCI), and Vegetation Health Index (VHI) for the first dimension. Then user is prompted to adjust the percentage (or, weight) of the indicator. Say that you may decide that 50% VCI + 50% VHI can represent the dimension of conditions of vegetation vigor. The indicator will be automatically calculated for you after the equation is being finalized, and the drought indicator is then mapped to drought severity level by the classification scheme that users pick. The product of this step is the drought severity map.
  3. Choose verification/validation methods. There are three ways that one can choose to validate the indicator: choose a point and check correlation between drought indicator and the weather data, choose a polygon and check correlation between drought indicators and the meteorological indices, and choose contrast mapping and look at the map from agencies like USDM, and the other made with the customized drought indicator. If the drought indicator survives the time-series validation, users can then go on to use the indicator for drought monitoring and forecasting at large scale.

The goal of agricultural drought information cluster is for users to define drought tailoring their own needs targeted for various applications, and to enable data, technology, and drought information sharing among different groups. It is to provide a comprehensive monitoring and forecasting system that incorporates all principal components necessary for the analysis of agricultural drought.

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