Estimating Rainfall Interception Loss Sentinel-2 in the Veluwe area, the Netherland

Abstract

The process of rainfall interception plays a big role in the hydrological cycle. Interception loss from forested areas is significant and can have a serious impact on water balance. The amount of rainfall which is intercepted basically depends on vegetation properties, such as canopy structure, leaf phenology, and density. Therefore, the description of the spatial and temporal variation of vegetation is very important in the estimation of interception loss. This research is aimed at quantification of rainfall interception loss of different land cover classes on the large area using remote sensing method. The study area is the Veluwe forested area central part of the Netherlands in the western part of Gelderland province. In this study, leaf area index (LAI) and fractional vegetation cover (FVC) maps of the study area were derived from Sentinel-2 time series images of June to December 2016 (excluding November, because all the images of this month were affected by clouds). Five land cover classes (broad-leaved forest, coniferous forest, mixed forest, pastures, and natural grassland) were selected to assess the spatial and temporal variation of leaf area index and fractional vegetation cover. The mean LAI values range from 0.3 to 2.8. The mean FVC values range from 11% to 67%. The monthly canopy storage capacity of each land cover was estimated. Therefore, knowing the canopy storage capacity and other vegetation properties of different land cover classes, different interception losses can be estimated using remote sensing method. In the present study, to estimate rainfall interception loss per land cover class, remote sensing (RS) Gash model was used, and event-based rainfall analysis was carried out in each month. The rainfall interception was estimated based on wet vegetation canopy (gross rainfall greater than 0.5 mm). The mean interception loss per landcover class in the whole season (June to December 2016) for broadleaf forest (BLF), coniferous forest (CF), mixed forest (MF), pastures (PS) and grassland (NGL) are 28.7% , 27.8%, 27.6% , 20.4% , and 16.2% of total rainfall respectively . Broad-leaved forest has high interception loss due to their maximum leaf area index which leads to high canopy storage capacity. In general, forests have large interception loss than other land cover types such as pastures and grassland. Temporal variability of interception loss in the whole season the mean monthly interception loss is 25.3%, 31.6%, 28.3%, 32.1%, 15.1%, and 12.3% of gross rainfall in June, July, August, September, October and December 2016 respectively. The high interception found in the months of summer season when the vegetations are at their peak productivity and the low interception found in December (winter season ) when the most vegetations shade off their leaves, this reduction in leaf amounts cause decrease in intercepted rainfall, there is significant relationship between interception loss and leaf area index. From sensitivity analysis of five main parameters to the interception loss simulated by remote sensing Gash model shows that the fractional vegetation cover is the most sensitive parameter to the simulated interception loss. As the fractional vegetation cover increases the interception loss increases rapidly. The comparison between measured and estimated interception loss at the coniferous plot. From the previous study by Cisneros et al. (2018) reported the measured interception loss of 39% of gross rainfall whereas the estimated interception loss of the present study is 30% of gross rainfall. The model of the present study underestimates the interception loss by 9% of gross rainfall. Therefore it is very important to measure and monitor LAI and Smax of different land cover classes in order to quantify rainfall interception losses.