Geospatial Rainfall Modelling at Eastern Nepalese Highland from Ground Environmental Data

Abstract

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 km 2 . 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 . 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 N. Diodato (B) · G. Bellocchi N. Diodato et al.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.

Evaluating Satellite Products for Precipitation Estimation in Mountain Regions: A Case Study for Nepal

“…This suggests that there is still much work to be done in appropriately leveraging the high spatial resolution and temporal coverage of geostationary satellites to improve precipitation estimates at fine scales and short latency, particularly for mountainous areas, such as Nepal, where limited ground-based data is available for calibration and validation in near real time. Cokriging or related approaches could be tested for optimally merging station observations with remote sensing products having different resolutions to form an accurate high-resolution precipitation map [13,37].…”

“…Precipitation amount and timing impact key regional hazards, such as flooding and landslides [9][10][11]. Few rain gauge measurements from Nepal are publicly available, especially in near real time, and the national network is inadequate to capture precipitation variability in mountain regions [12,13]. Several studies have previously applied satellite precipitation products for the Indian subcontinent or for the Himalayas region, which includes Nepal.…”

Spatial GR4J conceptualization of the Tamor glaciated alpine catchment in Eastern Nepal: evaluation of GR4JSG against streamflow and MODIS snow extent

“…Set (3) represented a complicated pattern of precipitation variation as depicted by Diodato et al . ()—note the level of extrapolation is large because of the lack of reference stations.…”

“…TRMM described in Huffman et al ., ) against independent ground measurements using the normal observation network, high elevation stations (i.e. EV‐K2‐CNR stations examined by Diodato et al ., ) and short expeditions (Immerzeel et al ., ). In general, many consider APHRODITE the best of the freely available interpolated products (e.g.…”

The Determination of Reference Evapotranspiration for Spatial Distribution Mapping Using Geostatistics

“…To obtain a prediction for a location, Kriging uses the variogram model, the spatial correlation of the data, and the measured data around that location. Altogether, geostatistical methods can be used for producing spatial distribution maps of spatial data such as spatial variation analysis of major parameters which affect surface and groundwater quality (Masoud 2014), spatio-temporal maps of the water table depth in Italy (Barca et al 2013), interpolating the snow water equivalent using ordinary kriging technique in Iran (Marofi et al 2011), spatial analysis of rainfall in Nepal (Diodato et al 2010), spatial distributions of soil surface-layer saturated hydraulic conductivity in China (Zhang et al 2010), estimating regional groundwater recharge in USA (Manghi et al 2009), geostatistical assessment of Groundwater nitrate contamination in lebanon (Assaf and Saadeh 2009), spatial distribution of rainfall in India (Basistha et al 2008), ground water depth mapping in Iran (ahmadi and sedghamiz 2008), plotting the long-term mean daily ET0 for each month in Greece (Mardikis et al 2005), estimation of mean annual precipitation using geostatistics in Spain (Martinez-Cob 1996). New technologies like GIS allow us to use these methods and produce spatial distribution maps of spatial variables.…”