Streamflow Predictions in Ungauged Catchments
The overwhelming majority of the world's catchments are ungauged. Predicting streamflow signatures in these basins for the management of risk, water and environmental resources lies at the heart of hydrology as an engineering science. The challenge is particularly difficult and urgent in rapidly changing mountainous watersheds in developing countries, which are especially poorly gauged. Our efforts have focused on predicting flow duration curves in ungauged Himalayan catchments. Flow duration curves are critical infrastructure design inputs and provide important information on the availability and reliability of surface water to supply ecosystem services and satisfy anthropogenic needs. We focus on developing simple probabilistic models, based on a stochastic-dynamic soil moisture framework. These models are causal, analytically tractable and can be driven by remote sensing observations, which makes them ideally applicable to non-stationary settings of extreme data-scarcity. We also develop approaches to adjust and validate remote sensing data, perform geostatistical regionalization on stream networks, and evaluate and select the appropriate prediction model for the considered application.
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- Muller, M.F., Gorelick S.M. and Thompson, S.E. (In Prep.) "A pragmatic model selection framework for environmental random variables"
- Dralle, D., Karst, N., Muller, M.F., Vico, G. and Thompson, S. (2017) "Stochastic modelling of inter-annual variation of hydrologic variables", Geophysical Research Letters
- Muller, M.F. and Thompson, S.E (2016) "Comparing statistical and process-based flow duration curve models in ungauged basins and changing rain regimes." Hydrology and Earth System Science (20)
- Muller, M.F. and Thompson, S.E. (2015). A topological restricted maximum likelihood (TopREML) approach to regionalize trended runoff signatures in stream networks. Hydrology and Earth System Science (19).
- Muller, M.F., Dralle D.N., and Thompson, S.E. (2014). Analytical model for Flow Duration Curves in seasonally dry climates. Water Resources Research (50).
- Muller, M.F. and Thompson, S.E. (2013) Bias adjustment of satellite rainfall data through stochastic modeling: Methods development and application to Nepal. Advances in Water Resources (60).