Geospatial Rainfall Modelling at Eastern Nepalese Highland from Ground Environmental Data
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- Diodato, N., Tartari, G. & Bellocchi, G. Water Resour Manage (2010) 24: 2703. doi:10.1007/s11269-009-9575-2
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The study presents a geospatial knowledge transfer framework by accommodating precipitation maps for the Eastern Nepalese Highland (ENH) across an area of about 100,000 km2. For this remote area, precipitation–elevation relationships are not homogeneously distributed, but present a chaotic gradient of correlations at altitude ranges. This is mainly due to impervious orography, extreme climate, and data scarcity (most of the rain gauges in Himalaya are located at valley bottoms). Applying geostatistical models (e.g. multivariate geospatial approaches) is difficult in these zones. This makes the ENH an interesting test area where we obtained monthly precipitation spatial patterns for a 30-year period (1961–1990). The aim was to both capture orographic meso-α spatial regimen (~30 km) and local pattern variability (~10 km). Data from 58 FAO raingauges were used plus data from an atmospheric weather station (AWS Pyramid) operating at 5,050 m a.s.l., used to compensate the gap of precipitation pattern presents in the area surrounding the Mount Everest. In these complex orographically remote areas of the Himalayas, monsoon precipitation systems exhibit important topographical interactions and spatial correlations, depending on the scale at which the primary variable (e.g., precipitation) and co-variables (e.g., elevation) are recorded and analysed. Precipitations were assessed for months—May, July and September—representative of the monsoon season. For the rainiest month (July), cokriging indicated a range of precipitation values from ~100 mm over the Tibetan Plateau to ~500 mm in the southern part of Nepal, up to ~900 mm towards the pre-Himalayan range. For July, cokriging precipitation map also showed correspondence with the map of vegetation pattern, and therein lies the clue to using multivariate geostatistical models as flexible approaches for estimating precipitation spatial patterns in remote areas.