Drainage congestion due to road network on the Kosi alluvial Fan, Himalayan Foreland

We use surface soil moisture content as a proxy to assess the effect of drainage congestion due to structural barriers on the alluvial Fan of the Kosi River on the Himalayan Foreland. We used Sentinel-1 satellite images to evaluate the spatial distribution of soil moisture in the proximity of structural barriers (i.e., road network). We applied modified Dubois and a fully connected feed-forward artificial neural network (FC-FF-ANN) models to estimate soil moisture. We observed that the FC-FF-ANN predicts soil moisture more accurately (R = 0.85, RMSE = 0.05 m(3)/m(3), and bias = 0) as compared to the modified Dubois model. Therefore, we have used the soil moisture from the FC-FF-ANN model for further analysis. We identified the road network that traverses on the Kosi Fan horizontally, vertically, and with inclination. We create a buffer of 1 km along either side of the road. Within this, we assessed the spatial distribution of soil moisture. We observed a high concentration of soil moisture near the structural barrier, and decreases gradually as we move farther in either direction across the orientation of the road. The impact of structural barriers on the spatial distribution of soil moisture is prominent in a range between 300 to 750 m within the road buffer. This study is a step towards assessing the effect of structural interventions on drainage congestion and flood inundation.

Automated summary

This study uses satellite images and artificial neural network models to assess the effect of structural barriers on drainage congestion and flood inundation in the alluvial Fan of the Kosi River. The results show that soil moisture concentration is higher near the road network and decreases gradually as we move away from the road. This study provides important insights into the impact of structural interventions on drainage congestion and flood inundation.