Pallav Kumar SHRESTHA

PhD Research Theme

My PhD aims to contribute towards locally relevant flood forecasting in managed river basins, at global scale. The first chapter of my PhD is an application paper (published in Nature Communications) where we developed and tested a high-resolution (1 km) flood early warning system, FEWS, with mHM, on a real flood event (The 2021 summer flood in Germany), retrospectively. This proof of concept produced impact forecast as well as early notice time till 100 years return period water level at each model grid.

Integrating such local level FEWS in regional/continental domains in global scale poses challenges. Gridded hydrological models, such as mHM, incur simulation errors at local level using the existing (classic or state-of-the-art) stream network upscaling methods. While hyperresolution modeling theoretically addresses this issue, its high computational cost bars its use in large-scale modeling, prompting the search for alternatives. In the second chapter of my PhD (submission preparation), we augment global hydrological models with the missing "eagle vision". We achieve this by developing a new stream network upscaling technique called subgrid catchment conservation. SCC offers three distinct advantages: 1) generates locally relevant streamflow i.e., ensures consistency of streamflow performance across catchment sizes (1 km² to 4,680,000 km²), 2) improves consistency of streamflow across model resolution, and 3) resolves multiple gauges within a grid.

Reservoirs stand as the bastions of humanity's defence against floods. Preparing FEWS for managed basins in large-scale modeling was another challenge we tackled in the final chapter of my PhD. We developed a new reservoir module for mHM (published in WRR), an improvement over the state-of-the-art representation in large-scale modeling. The research delves into three key aspects: 1) employing machine learning methods to reverse estimate non-consumptive demands (e.g., hydropower), 2) sensitivity of simulations to reservoir bathymetry, and 3) possible thresholds to identify and exclude non-disruptive reservoirs from the model simulation.

Scientific Career

Project Contributions

Ph.D.

Development of a novel steam network upscaling method (SCC) for mHM (PhD chapter 2). This development allows to accurately represent small catchments in large single domain runs. Development of a new reservoir module for mHM (PhD chapter 3). This development enables mHM to internally delineate reservoirs and correctly collect reservoir inflow based on SCC. These two developments led to two publications, one published and another one under review, both in Water Resources Research (WRR).

mHM development

One of the active members of the mHM development team since 2017. Support in bug hunting, debugging, and issue resolving at the GitLab repository. Active participation in user support and outreach via mHM user forums, GitHub discussion page, trainings (Nepal), and supporting university students in their undergrad/grad projects in Nepal and guest researchers at UFZ.

2021 Ahr Flood

mHM development support for sub-daily streamflow simulations. Damage analysis and lead time analysis of flood inundation simulations. Visualisations for the Nature Communications paper (PhD chapter 1).

State of Global Water Resources (WMO)

Extraction and compilation of global streamflow simulations for WMO's annual state of global water resources report. mHM participates alongside 8-10 global hydrological models for the report analyses.

ULYSSES (1y, Copernicus funded)

Production support (operation, debugging, local restarts) for operational, global seasonal forecasts using the project ecFlow suite. Development of a Fortran program for forecast skill assessment (SkillAs). Analysis of skill assessment, result visualization and compilation (27 years long hindcast, 51 ensemble members, 4 hydrological and land surface models, 6 lead months, 5+ skill measures) using SkillAs for final project report.

SaWaM (3y, BMBF funded)

WP3 on seasonal hydrological forecasts. Contributed to the kickoff meetings in Iran and Sudan and project conferences in Germany. Disseminated progress and results at 5x international conferences (IUGG, AGU, EGU). Development and operation of the seasonal hydrological forecasting system code base. Analyses of hindcast simulations and indicators produced by the forecasting system. The forecasting system code forms the basis of the HS2S forecasting system.

Publications