Remote Sensing and Clustering Applications in Landscape Hydrology: Characterizing a Subarctic Watershed in Nunavik (Canada)

Hydrological data are often sparse and incomplete for large northern watershed with difficult access. Landscape hydrology approaches are useful for the indirect assessment of their hydrological characteristics by analysing the landscape properties of the watersheds. In this study, we use unsupervised Geographic Object-Based Image Analysis (GeOBIA) paired with the Fuzzy C-Means (FCM) clustering algorithm to produce a total of seven high-resolution territorial classifications for the 1985-2019 time-period. Each classification spans 5-year period and is based on key hydro-geomorphic metrics. Our application site is the George River watershed (GRW), draining a 42 000 km² area and is located in Nunavik, northern Québec (Canada). The retrieved subwatersheds within the GRW are used as the objects of the GeOBIA and are classified in function of their hydrological similarities.

First, classification results for the time-period 2015-2019 show that the GRW is composed of two main types of subwatersheds distributed along a latitudinal gradient. This indicates differences in water balance, and hydrological regime and response. Second, six other classifications are then computed for the period 1985-2014 to investigate past changes in hydrological behavior. The seven-classification time series present an expansion of the southern-type subwatersheds northwards, principally along the George River’s main channel. This expansion is due to increases of (i) vegetation production and (ii) moisture content in soil and canopy. These are the major changes occurring in the land cover metrics of the GRW. We speculate that a rise in vegetation production contributes to evapotranspiration increase and therefore induces changes in water balance, which could explain the measured decrease of about 1% in the George River’s discharge since the mid-1970s.