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Impact of different sources of precipitation data on urban rainfall-runoff predictions: a comparison of rain gauges, commercial microwave links and radar
Disch, A., Scheidegger, A., Wani, O., & Rieckermann, J. (2019). Impact of different sources of precipitation data on urban rainfall-runoff predictions: a comparison of rain gauges, commercial microwave links and radar. In N. Peleg & P. Molnar (Eds.), Rainfall monitoring, modelling and forecasting in urban environments. Conference Proceedings UrbanRain18 (pp. 27-32). ETH Zurich.
Using a simple post-processor to predict residual uncertainty for multiple hydrological model outputs
Ehlers, L. B., Wani, O., Koch, J., Sonnenborg, T. O., & Refsgaard, J. C. (2019). Using a simple post-processor to predict residual uncertainty for multiple hydrological model outputs. Advances in Water Resources, 129, 16-30. https://doi.org/10.1016/j.advwatres.2019.05.003
Smart urban water systems: what could possibly go wrong?
Moy de Vitry, M., Schneider, M. Y., Wani, O., Manny, L., Leitão, J. P., & Eggimann, S. (2019). Smart urban water systems: what could possibly go wrong? Environmental Research Letters, 14(8), 081001 (4 pp.). https://doi.org/10.1088/1748-9326/ab3761
Exploring a copula-based alternative to additive error models—for non-negative and autocorrelated time series in hydrology
Wani, O., Scheidegger, A., Cecinati, F., Espadas, G., & Rieckermann, J. (2019). Exploring a copula-based alternative to additive error models—for non-negative and autocorrelated time series in hydrology. Journal of Hydrology, 575, 1031-1040. https://doi.org/10.1016/j.jhydrol.2019.06.006
Accounting for variation in rainfall intensity and surface slope in wash-off model calibration and prediction within the Bayesian framework
Muthusamy, M., Wani, O., Schellart, A., & Tait, S. (2018). Accounting for variation in rainfall intensity and surface slope in wash-off model calibration and prediction within the Bayesian framework. Water Research, 143, 561-569. https://doi.org/10.1016/j.watres.2018.06.022
Statistical methods for better hydrologic predictions. Improving parameter and uncertainty estimation
Wani, O. (2018). Statistical methods for better hydrologic predictions. Improving parameter and uncertainty estimation [Doctoral dissertation, ETH Zurich]. https://doi.org/10.3929/ethz-b-000315756
Comparing approaches to deal with non-Gaussianity of rainfall data in kriging-based radar-gauge rainfall merging
Cecinati, F., Wani, O., & Rico-Ramirez, M. A. (2017). Comparing approaches to deal with non-Gaussianity of rainfall data in kriging-based radar-gauge rainfall merging. Water Resources Research, 53(11), 8999-9018. https://doi.org/10.1002/2016WR020330
The potential of knowing more: a review of data-driven urban water management
Eggimann, S., Mutzner, L., Wani, O., Schneider, M. Y., Spuhler, D., Moy de Vitry, M., … Maurer, M. (2017). The potential of knowing more: a review of data-driven urban water management. Environmental Science and Technology, 51(5), 2538-2553. https://doi.org/10.1021/acs.est.6b04267
Parameter estimation of hydrologic models using a likelihood function for censored and binary observations
Wani, O., Scheidegger, A., Carbajal, J. P., Rieckermann, J., & Blumensaat, F. (2017). Parameter estimation of hydrologic models using a likelihood function for censored and binary observations. Water Research, 121, 290-301. https://doi.org/10.1016/j.watres.2017.05.038
Residual uncertainty estimation using instance-based learning with applications to hydrologic forecasting
Wani, O., Beckers, J. V. L., Weerts, A. H., & Solomatine, D. P. (2017). Residual uncertainty estimation using instance-based learning with applications to hydrologic forecasting. Hydrology and Earth System Sciences, 21(8), 4021-4036. https://doi.org/10.5194/hess-21-4021-2017