High-resolution Arctic River Hydrography Mapping And Analysis

In recent decades,the Arctic region has rapidly warmed up,glaciers and permafrost have rapidly melted,and precipitation has changed significantly.This has led to significant changes in the Arctic hydrological cycle and has become an indicator of global climate change.The Arctic river network is an important part of the Arctic hydrological cycle.In recent years,the Arctic river network has expanded significantly,forming many small rivers(river width<30 m).Small rivers are an important part of the terrestrial river network and play a key role in the biochemical cycle of the earth.Studying Arctic rivers can thus provide key insights into global hydrologic and biogeochemical processes.Small river ecosystems tend to be particularly variable over time and have frequent land-atmosphere interactions.At present,research on terrestrial river networks mainly focuses on large rivers(river width>30 m)or headwaters(river width<10 m).Yet our understanding of Arctic river hydrography–especially of small rivers–remains limited,due to challenges with both carrying out field surveys across such a remote region and in precisely studying rivers from space with coarse-resolution of satellite imagery.To sum up,it is very important to carry out high-resolution temporal and spatial distribution characteristics of river hydrography in typical Arctic basins in order to understand the hydrological process of Arctic rivers.Thanks to advances in earth observation technologies,it is now possible to measure actual river distribution and additional river planform properties with high-resolution satellite imagery.We present a new automated methodology to map the Arctic actively-flowing river networks,including small rivers as narrow as 10 m from Sentinel-2 multispectral satellite imagery and high-resolution Arctic DEM digital elevation data,which significantly improves river connectivity.We quantified the river hydrography(stream order,river width,river length,river surface area,river velocity,slope,sinuosity,and catchment area)of the Colville river network in detail,and then used the RAPID river routing model to simulate the spatial and temporal distribution of river discharge in the Arctic river network,revealing the dominant role of small rivers.These findings expand our understanding of Arctic river hydrography to a 10-m spatial resolution and raise prospects for tracking dynamic surface water processes with high-resolution satellite observations.The main research contents and conclusions of this article include:(1)A method of extracting remote sensing information from a continuous river network integrating multi-source remote sensing data.The river network extracted by remote sensing can correctly reflect the spatial distribution of rivers but there are fractures.The river network modeled by DEM is continuous but difficult to correctly reflect the starting point of the river.This research integrates 10 m Sentinel-2 remote sensing image and 2 m Arctic DEM terrain data,solves the technical problem of poor river connectivity in the remote sensing extraction of terrestrial river network,realizes the automatic remote sensing extraction of continuous and accurate river network,and produces high spatial resolution.Rate the remote sensing data products of rivers in typical Arctic basins.We conclude:SARNs have similar quality to the>1st order NHD(>70%of NHD streams appear in SARNs).At a reach scale,SARNs include more small rivers with the drainage densities at least?4.5 times larger than Landsat-derived hydrography data products.Similarly,coupling Arctic DEM data with 30 m Landsat satellite images increases resultant drainage densities approximately three-fold.At a catchment scale,merging remotely sensed river networks with Arctic DEM-modeled drainage networks significantly improves river connectivity(defined as the longest connected river length)with at least a ten-fold increase over the other hydrography data products examined here.In sum,merging high-resolution Sentinel-2 imagery with Arctic DEM data enable effective,continuous mapping of complex fluvial drainage patterns of Arctic rivers.(2)Adaptive analysis of the Arctic river hydrography at the river network scale of the basin.The river network modeled by remote sensing can provide river cross-section information,and the river network modeled by DEM can provide river longitudinal section information.This research uses the proposed river remote sensing information extraction method,and integrates multi-source remote sensing data to produce a 10 m river network product in a typical Arctic basin,and quantifies the river hydrography of the river network in a typical Arctic basin(river order,width,length,surface area,river velocity,slope,sinuosity and catchment area).Our results show that:Small rivers occupy>80%of total river length and surface area of the Colville river network and drain>90%of the catchment area;including numerous small rivers increases the peak of hillslope-channel travel time distribution by?4 times,and shortens the mean hillslope-channel travel time by at least an order of magnitude compared to coarser-resolution river hydrography products;and 87%of the Colville River Basin's carbon dioxide is emitted from small rivers.In sum,we show that small Arctic rivers to greatly influence streamflow travel time distribution and carbon cycle.These findings expand our understanding of Arctic river hydrography to a 10-m spatial resolution and raise prospects for tracking dynamic surface water processes with high-resolution satellite observations.(3)The discharge simulation of the Arctic river network integrating multi-source remote sensing data.River hydrography is the external manifestation of river discharge,and changes in river hydrography respond to changes in river discharge.This study combines multi-source remote sensing data and reconstructs a river network with a spatial resolution of 10 m.The river flow rate observed by remote sensing is used as the input parameter of the river network confluence model to simulate river discharge based on the reach of the Arctic river network.Results show that:Fusion of remotely sensed data can better model the peak river discharge(RRMSE=15.4%)and the time to peak river discharge(RRMSE=3.0%)of the basin outlet.Compared with the modeling results without fusion of remotely sensed data,fusion of remotely sensed data can increase the modeling accuracy by 38.2%and8.9%;River discharge increases exponentially with stream order,and the river discharge of small Arctic rivers is about 10^-2?10¹ m³/s,and the river discharge range of medium rivers is about 10¹?10² m³/s,the river discharge range of large rivers is about10²?10³ m³/s.Fusion of remotely sensed satellite imagery and digital terrain model data reveals the spatial distribution characteristics of the discharge distribution of small rivers in the Arctic,and provides high-precision and low-cost modeling data for hydrological observations in extremely remote areas such as the Arctic.