Development and Application of Meteorological Disaster Monitoring and Early Warning Platform for Characteristic Agriculture in Huzhou City Based on GIS

Based on the needs of characteristic agricultural production for meteorological services in Huzhou City,we use C# programming language to develop the meteorological disaster monitoring and early warning platform for characteristic agriculture in Huzhou City. This platform integrates the functions of meteorological and agricultural information monitoring,disaster identification and early warning,fine weather forecast product display,and data query and management,which effectively enhances the capacity of meteorological disaster monitoring and early warning for characteristic agriculture in Huzhou City,and provides strong technical support for the meteorological and agricultural departments in the agricultural meteorological services.

Estimation of meteorological drought indices based on AgMERRA precipitation data and station-observed precipitation data

Meteorological drought is a natural hazard that can occur under all climatic regimes. Monitoring the drought is a vital and important part of predicting and analyzing drought impacts. Because no single index can represent all facets of meteorological drought, we took a multi-index approach for drought monitoring in this study. We assessed the ability of eight precipitation-based drought indices（SPI（Standardized Precipitation Index）, PNI（Percent of Normal Index）, DI（Deciles index）, EDI（Effective drought index）, CZI（China-Z index）, MCZI（Modified CZI）, RAI（Rainfall Anomaly Index）, and ZSI（Z-score Index）） calculated from the station-observed precipitation data and the Ag MERRA gridded precipitation data to assess historical drought events during the period 1987–2010 for the Kashafrood Basin of Iran. We also presented the Degree of Dryness Index（DDI） for comparing the intensities of different drought categories in each year of the study period（1987–2010）. In general, the correlations among drought indices calculated from the Ag MERRA precipitation data were higher than those derived from the station-observed precipitation data. All indices indicated the most severe droughts for the study period occurred in 2001 and 2008. Regardless of data input source, SPI, PNI, and DI were highly inter-correlated（R^2=0.99）. Furthermore, the higher correlations（R^2=0.99） were also found between CZI and MCZI, and between ZSI and RAI. All indices were able to track drought intensity, but EDI and RAI showed higher DDI values compared with the other indices. Based on the strong correlation among drought indices derived from the Ag MERRA precipitation data and from the station-observed precipitation data, we suggest that the Ag MERRA precipitation data can be accepted to fill the gaps existed in the station-observed precipitation data in future studies in Iran. In addition, if tested by station-observed precipitation data, the Ag MERRA precipitation data may be used for the data-lacking areas.