chapter  13
Precipitation Estimation from Remotely Sensed Information Using Arti–cial Neural Networks
Pages 24

Improving our understanding of weather and climate, along with the development of reliable and uninterrupted precipitation measurement techniques, is essential for the proper assessment of droughts. Precipitation plays a dominant role in the global hydrologic cycle and is one of the key variables in drought monitoring and analysis. In fact, drought is often de–ned as a prolonged period of de–cient precipitation with respect to the average expected values. The drought phenomenon is usually described using drought detection and monitoring indices. Typically, droughts are categorized into three major classes: (a) agricultural, (b) meteorological, and (c) hydrological. Agricultural drought is related to the total soil moisture de–cit, while meteorological drought is identi–ed by lack of precipitation as the main indicator. Hydrological drought, on the other hand, is characterized by a shortage of streamžow, as well as groundwater supplies. Droughts have signi–cant socioeconomic impacts that may vary for different sectors and at different spatiotemporal scales. For example, lack of precipitation over a 3 month period may be considered as a signi–cant agricultural drought, although it may not be considered as signi–cant from a hydrological viewpoint. Furthermore, the de–nition of drought may differ between various climate regions with different climatic properties.