PhD Thesis Defence: Advanced Remote Sensing Precipitation Input for Improved Runoff Simulation
On Thursday June 16, Hasan Hosseini is defending his PhD thesis in Water Resources Engineering, "Advanced Remote Sensing Precipitation Input for Improved Runoff Simulation – Local to Regional Scale Modelling". Welcome to attend!
Hasan Hosseini is defending his PhD thesis in Water Resources Engineering, Advanced Remote Sensing Precipitation Input for Improved Runoff Simulation – Local to Regional Scale Modelling. The faculty opponent is Associate Professor Søren Liedtke Thorndahl from Aalborg University.
Date: Thursday June 16, 2022
Place: V-huset, room V:C (John Ericssons väg 1, Lund)
Accurate precipitation data are crucial for hydrological modelling and rainwater runoff management. Precipitation variability exists through a wide range of spatial and temporal scales and cannot be captured well using sparse rain gauge networks. This limitation is further emphasised for urban and mountainous catchments, especially under global warming, causing an increased frequency of extreme events. Recent advances in remote sensing (RS) techniques make monitoring precipitation possible over larger areas at more regular resolutions than conventional rain gauge networks. The RS data can be biased mainly due to the indirect estimations prone to multiple error sources and temporally discrete observations. The wealth of spatiotemporal precipitation data by RS, however, calls for developing data-driven solutions for both the bias correction and hydrological modelling that, in turn, requires new procedures to assure generalization of the existing methods. The present dissertation comprises a comprehensive summary followed by five appended papers, attempting to evaluate quantitative precipitation estimations (QPE) by state-of-the-art instruments/products for local and regional hydrological applications. Accordingly, two recently installed dual polarimetric doppler X-band weather radars (X-WRs) in southern Sweden and multiple Global Precipitation Mission (GPM) products in Iran were studied at the relevant scales for urban hydrology (1–5-min and sub-km) and large water supply river–reservoir system operation (daily-monthly and 0.1°), respectively. The validation against rain gauge observations (Paper I and II) showed a significant dependency of the X-WR and GPM precipitation errors on the radial distance and regional precipitation pattern, respectively. Taking observations from local tipping bucket rain gauges at the 1–30-km ranges as a reference, the apparent problems with a single X-WR is related to the attenuation during heavy rains and overshooting (at higher elevation angle scans). An internationally bias-corrected GPM product called GPM-IMERG-Final shows a generally good correlation to synoptic observations of over 300 rain gauges in Iran except for extreme observations that are much better predicted by the GPM-IMERG Late product during spring, summer, and autumn seasons. To leverage the wealth of spatiotemporally complete and validated precipitation data for hydrological modelling, two novel data-driven procedures using artificial neural networks (ANNs) were developed. As in Paper III, the formulation of the new ANN input variables, namely, ECOVs and CCOVs, representing the event- and catchment-specific areal precipitation coverage ratios, improve monthly runoff estimations in all the studied sub-catchments of the Karkheh River basin (KRB) in the mountainous semi-arid climate of western Iran. Merging the doppler and dual-polarization data in the overlapping coverage of the two XWRs (Paper IV) via an ANN-based QPE improves rainfall detection and accuracy. ANN-assisted estimation of rainfall quantiles, compared to the merging with an empirically based regression model, also shows better results especially related to the extreme 5-min data. Finally, Paper V describes the impact of human activities such as agricultural developments that can equally affect the runoff variation. This fact is considered in Paper III by including MODIS Terra products as additional inputs.