Nuestro grupo organiza más de 3000 Series de conferencias Eventos cada año en EE. UU., Europa y América. Asia con el apoyo de 1.000 sociedades científicas más y publica más de 700 Acceso abierto Revistas que contienen más de 50.000 personalidades eminentes, científicos de renombre como miembros del consejo editorial.
Revistas de acceso abierto que ganan más lectores y citas
700 revistas y 15 000 000 de lectores Cada revista obtiene más de 25 000 lectores
Rahul Mahanta, Prolay Saha, P V Rajesh, Sudipta Nandy, Yasmin Zahan, Anupam Mahanta
The North East India (NEI) is an IUCN (International Union for Conservation of Nature) biodiversity hot spot. A region known for its highest annual rainfall in the world together with the unique topography and mighty Brahmaputra, makes the region vulnerable to climate change induced hydrological disasters and biodiversity loss. For building resilience to extreme rainfall events, food security and biodiversity management, dependable and consistent estimates of trend and modes of variability based on over 100 years of daily rainfall are critical. However, the region is poorly sampled by continuous rain gauges and in a region of large spatial variability of the mean rainfall, approximately 10 stations with such data are highly inadequate for estimating extreme event statistics. We were successful in developing a quality controlled daily rainfall data collection on a set of 24 well-distributed fixed stations around the region in order to improve this condition. This technical note describes combining conventional weather station records with rain-gauge records from a number of sources like privately owned tea estates to create a continuous daily rainfall record from 1 January 1920 to 31st December 2009 for the north-eastern region of India. Remaining data gaps are less than 3% of the total data in each station. With the goal of improving estimates of long-term changes in climate variability over NEI, every attempt has been made to reconstruct data gaps. The NEI final rebuilt data set is ideally adapted to estimating both long-term trend and multi-decadal variability of rainfall over the region.