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Runoff modelling using radar data and flow measurements in a stochastic state space approach.
S Krämer1, M Grum, H R Verworn
1Inst. of Water Resources Management, University of Hannover, Appelstr. 9a, 30167 Hannover, Germany. kraemer@wawi.uni-hannover.de
Summary
This study introduces a new method to improve urban drainage modeling by accounting for uncertainties in rainfall and flow measurements. The approach enhances model accuracy for better water resource management.
Area of Science:
- Environmental science
- Hydrology
- Water resource management
Background:
- Urban drainage modeling is complex due to uncertain rainfall and flow measurements.
- These uncertainties significantly impact model parameters and results.
- Accurate flow measurements are crucial for calibrating and evaluating deterministic models.
Purpose of the Study:
- To develop a novel methodology for urban drainage modeling that addresses uncertainties in rainfall and flow data.
- To improve the accuracy of runoff estimation in urban catchments.
- To present results from a case study in the Emscher river basin, Germany.
Main Methods:
- A stochastic state-space model approach integrating simple rain plane and runoff models.
- State estimation using the extended Kalman filter.
- Combined consideration of uncertainties in distributed radar rainfall and measured flows, utilizing maximum likelihood criterion and off-line optimization.
Main Results:
- The new methodology effectively incorporates uncertainties from both rainfall and flow data.
- Improved accuracy in urban runoff estimation was achieved.
- Demonstrated applicability in a real-world urban catchment within the Emscher river basin.
Conclusions:
- The developed methodology offers a robust solution for urban drainage modeling under data uncertainty.
- It provides a more reliable basis for water resource management and infrastructure planning.
- The approach highlights the importance of combined uncertainty consideration for accurate hydrological predictions.