Morphological adjustments of the Yamuna River in the Himalayan foothills in response to natural and anthropogenic stresses

Abstract

Rivers can adjust to natural and anthropogenic stresses through changes in flow regime and sediment dynamics. Flow regulation due to dam construction can modify sediment transport processes, causing morphological adjustments and changes in conveyance capacity. In this study, we explored morphological adjustments along a 46 km segment of the Yamuna River in the Himalayan foothills from Dakpathar Barrage to Hathni Kund Barrage using a combination of remote sensing approaches. The cloud computing platform Google Earth Engine (GEE) was used to analyse multi‐temporal collections of Landsat satellite imagery acquired between 1989 and 2021. Active river channels (including water and exposed sediment) were classified using an MNDWI and NDVI thresholding approach from annually resolved temporal composite images. Declassified CORONA satellite imagery (acquired in 1965, 1973 and 1976) was manually digitized to provide longer‐term insights into channel change. Morphological adjustments were assessed using the RivMAP toolbox, providing quantitative information on the rates of bank erosion and accretion, bank line shifts and changes in active channel width. Results showed a substantial narrowing of the active channel after 2013, which coincided with the construction of the Lakhwar‐Vyasi Dam. We calculated a 67% reduction in mean active channel width, narrowing from ~800 m in 1989 to ~250 m in 2021. During the same period, evidence of sand mining in the active channel indicates a substantial increase in mining activities after 2015. The concept of stasis was explored under different flow regulation scenarios to suggest that the river has undergone a state of inactivity, where the river is doing nothing for much longer durations. We suggest that the combined effects of flow regulation and increased mining activities during the same period have altered river morphology, and further stresses due to a combination of human activities can be damaging for the river regime. This study sheds light on the potential implications of multiple stresses acting on the Yamuna River at the same time, which can inform future sustainable river management activities.

Characterization of Active Riverbed Spatiotemporal Dynamics through the Definition of a Framework for Remote Sensing Procedures

“…In the last decades, fluvial researchers have developed methods to characterize the main macro units-water, vegetation, and exposed sediments [9,48,49]-and to investigate changes in river morphology [50], such as vegetation encroachment [51,52] and meander sinuosity and migration rate [39,53]. In most cases, single images were used, with the notable exceptions of [28,32]. The continuous increase in the temporal resolution of satellite data, with weekly and sometimes daily images, allows the use of a temporal reducer to filter out small-scale variations and identify the different geomorphic macro units more accurately.…”

“…A common approach to mapping riverine macro units is based first on the use of GEE temporal reduction (Figure 2 Panel 1) on image bands and then on the computation of multi-spectral indexes from the reduced maps [28,32] (Figure 2 Panel 2, procedure 2A).…”

“…GEE offers the possibility to use several spatial and temporal statistical reducers. So far, the temporal median reducer, which gives each pixel its median value over the considered time step, has been used to extract fluvial morphological metrics [28,32]. A comparison between the median (50p) and the 90th percentile (90p) reducer is proposed here.…”

“…A comparison between the median (50p) and the 90th percentile (90p) reducer is proposed here. To do so, we classify potential active channels in every pixel with a 50p or 90p synthetic representative NDVI lower than 0.15 [32] and an MNDWI greater than −0.35, similarly to [8]. Thus, the potential active channel area is evaluated by comparing 50p and 90p, seasonal and annual extracted masks.…”

“…Moreover, all water and vegetation pixels detected with the synthetic bands (Figure 4, Panel b, light blue) are also masked with those detected by the indexes approach (yellow). As for the NDVI threshold, every pixel with an NDVI value higher than 0.15 is classified as vegetation [32,47]. Looking at the NDVI and MNDWI values within the masked water and vegetation pixels, it can be seen that the masks obtained from the indices have slightly higher values in their distributions.…”