Accessing the impact of Forest cover change on Water Yield in the Middle Hills of Nepal through the CCW-WaSSI Model. (2024)

Undergraduate: Jing Hu

Faculty Advisor: Conghe Song
Department: Geography and Environment

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This research project employs the CCW-WaSSI Model, an integrated eco-hydrological model, to
conduct scenario simulations to study the effects of forest cover change in the middle hills of
Nepal. Those water yield data would help provide insights for the study questions of
Dr. Song’s research includes how Community forestry affects people’s land use decisions and
influences the environmental system’s provision of goods and services. I use remote sensing
data from space that measures forest cover change through time, meteorological data as
input to CCW-WaSSI, and hydrological data from gauging stations for validation. The objective is
to provide references for water yield in the studied watersheds and to evaluate the influence of
Community Forestry on Socio-Environmental Systems in Nepal. Current findings from the CCW
component suggest that MODIS GPP product heavily underestimates GPP (Gross primary
production). The main reason accounting for the underestimation, especially during the growing
season, could largely caused by the sampling method and spatial resolution as the MODIS product of
500 meters would aggregate pixels at a larger scale, twice as much as the CCW model, without
the constraint of WUE (water use efficiency constraint). This finding is validated with other GPP
estimation products reviewed by papers in the current field. The key limitation of this study comes
from the original coarse dataset interpolated as Terr Climate’s output. Since the study area in the
middle of the hills in Nepal is mountainous, the constraints from the dataset and other
similar products of 250 m resolution make the output raster to present a girds-like pattern
unavoidably.