DMAPS

AcronymDefinition
DMAPSDepot Maintenance Accounting and Production System
References in periodicals archive ?
Because statistical and dynamical approaches come with specific strengths and limitations in their own rights, integration of both methods (or the hybrid statistical-dynamical method) can be included in the development of DMAPS for early warning.
In the past decades, the development of DMAPS has been achieved in many regions or countries (Heim and Brewer 2012), including the United States (Svoboda et al.
Table 1 lists parts of the regional DMAPS developed in recent years in different regions of the world, which monitor different aspects of drought, including the vegetation condition.
The USDM drought category has been used as a benchmark for developing categorical DMAPS in the United States, either through optimizing drought area percentage (Xia et al.
2015), the development of a comprehensive DMAPS of the global coverage can only be achieved by reconstructing long-term records through integrating remote sensing products with ground-based observations and land surface model simulations (Pozzi et al.
For illustrative purposes, Table 2 lists DMAPS at the global scale that are mostly updated consistently.
Merging in situ observations, remote sensing products, land surface model simulations, and climate forecasts with methods such as data assimilation for drought monitoring (and prediction) is an important advance in the development of DMAPS. These data products for drought characterization are not only on a monthly basis but may also be available on a weekly or daily scale with refined spatial resolution (e.g., 4 km).
The reliability and accuracy of DMAPS largely depend on the availability and quality of hydroclimatic observations (or simulations) and impact data.
The lack of an internationally accepted and agreed drought indicator for different types of drought is a sustained barrier in the development of operational DMAPS, especially for hydrological and agricultural droughts.
Most of the current DMAPS are based on physical indicators, while only few systems have been linked to socioeconomical or environmental impacts (Blauhut et al.
2015), would be particularly useful in improving DMAPS in the United States and other regions.
This is of particular importance for the research-to-operation (R20) transitions, which require not only effective distribution or delivery tools (e.g., user-friendly web interface, location-aware applications on smartphones and mobile devices) but also building of added values into DMAPS products with a deeper understanding of the needs of different user communities.