
This April (2024), a paper in Water Resources Research caught the attention of our Blog Committee. Intrigued by its innovative findings using a mesoscale hydrology model, we couldn’t resist going deeper. We had the pleasure of discussing this research with Pallav Shrestha, the lead author, and a prominent researcher at the Helmholtz Center for Environmental Research in Germany. Here’s an inside look at our fascinating conversation and the insights we uncovered.
Please introduce yourself to our readers.
I am Pallav Kumar Shrestha. I come from Nepal and currently reside in Germany. I joined the lab of Luis Samaniego at UFZ in 2017 as a PhD researcher, focusing on locally relevant flood forecasting in managed river basins at a global scale. I am one of the active developers of the mesoscale hydrological model (mHM) and the lead developer of the SCC river network upscaling technique and mHM’s reservoir module. My PhD journey is finally coming together with the acceptance of two recent publications: one in Nature Communications and another in Water Resources Research. Today, I’ll delve into the latter.
What led you to integrate a random forest model with the mesoscale Hydrological Model in your study?
While developing the reservoir module in mHM, we were able to satisfactorily represent all aspects of reservoirs based on physics, except for one: the water demand. Demand is a complex human response, highly discontinuous, and is therefore less suited to being modeled as a continuous function like other hydrological processes. For this reason, we hypothesized and demonstrated that machine learning techniques such as random forest could be available option to estimate demand. It is important to note that the random forest modeling exercise was external to mHM, and the fitted demand functions as model input.
Could you explain the significance of the Kling-Gupta Efficiency improvement noted in your study?
Sure. The incorporation of reservoirs in large-scale modeling applications is not a new concept. However, the improvement in Kling-Gupta Efficiency (KGE) for streamflow observed in our study, compared to the naturalized flow, is significantly higher than those reported in previous literature. We succeeded in matching the sub-seasonal details of the observed hydrograph, whereas the majority of previous studies focused only on matching seasonality. The improved KGE implies that modelers can now use mHM to represent reservoir regulation more accurately in their large-scale streamflow simulations.
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