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Improving Flood Prediction Assimilating Uncertain Crowdsourced Data
Into Hydrologic and Hydraulic Model

Inglese · Copertina rigida

Spedizione di solito entro 1 a 3 settimane

Descrizione

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Sommario

Summary1 Introduction2 Case studies and models3 Data assimilation methods4 Assimilation of synchronous data in hydrological models5 Assimilation of asynchronous data in hydrological models6 Assimilation of synchronous data in hydraulic models7 Assimilation of synchronous data in a cascade of models8 Conclusions and recommendationsReferences

Info autore

Maurizio Mazzoleni was born in Brescia in November 1986. Mr. Mazzoleni graduated from University of Brescia, in Brescia, Italy, in May 2011. During his university studies he continued to pursue his interest in the flood protection by moving to UNESCO-IHE with the support of a scholarship awarded by University of Brescia to carry out his Master Thesis. Afterwards, he cooperate for 1 year within the KULTURisk Project as research fellow of the University of Brescia. Currently, Mr. Mazzoleni is a PhD candidate at UNESCO-IHE Institute for Water Education under the Department of Integrated Water Systems and Governance, Delft, The Netherlands. His research interest include hydrologic and hydrodynamic modelling, in particular he dealt with issue related to flood forecasting, data assimilation, flood inundation mapping, flood risk and uncertainty analysis, flood defence systems design and reliability analysis, statistical hydrology.

Riassunto

On a global scale, sewage represents the main point-source of water pollution and is also the predominant source of nitrogen contamination in urban regions. The present research is focused on the study of the main challenges that need to be addressed in order to achieve a successful inorganic nitrogen post-treatment of anaerobic effluents in the mainstream. The post-treatment is based on autotrophic nitrogen removal. The challenges are classified in terms of operational features and system configuration, namely: (i) the short-term effects of organic carbon source, the COD/N ratio and the temperature on the autotrophic nitrogen removal; the results from this study confirms that the Anammox activity is strongly influenced by temperature, in spite of the COD source and COD/N ratios applied. (ii) The long-term performance of the Anammox process under low nitrogen sludge loading rate (NSLR) and moderate to low temperatures; it demonstrates that NSLR affects nitrogen removal efficiency, granular size and biomass concentration of the bioreactor. (iii) The Anammox cultivation in a closed sponge-bed trickling filter (CSTF) and (iv) the autotrophic nitrogen removal over nitrite in a sponge-bed trickling filter (STF). Both types of Anammox sponge-bed trickling filters offer a plane technology with good nitrogen removal efficiency.

Dettagli sul prodotto

Autori Mazzoleni, Maurizio (UNESCO-IHE Institute for Water Education Mazzoleni, Maurizio (Unesco-Ihe Institute for Wate Mazzoleni, Maurizio Mazzoleni, Mazzoleni Maurizio
Editore Taylor & Francis Ltd.
 
Contenuto Libro
Forma del prodotto Copertina rigida
Data pubblicazione 31.12.2018
Categoria Scienze sociali, diritto, economia > Economia > Tematiche generali, enciclopedie
 
EAN 9781138474420
ISBN 978-1-138-47442-0
Numero di pagine 240
 
Serie IHE Delft PhD Thesis Series
Categorie BUSINESS & ECONOMICS / Green Business, BUSINESS & ECONOMICS / Environmental Economics, Environmental Economics, Business and the environment; ‘green’ approaches to business, Business & the environment, 'Green' approaches to business, TECHNOLOGY & ENGINEERING / Civil / Flood Control, Flood control, Kalman Filter, Flood Forecasting, sustainable approaches to business, environmental monitoring sensors, hydrological data assimilation, uncertainty quantification methods, hydraulic modelling techniques, heat flux sensors, participatory hydrological modelling applications, real-time flood forecasting, citizen science water management, EnKF Algorithm, Outflow Hydrograph, Improving Flood Prediction, Optimal Sensor Location, Flood Events, Arrival Frequency, Assimilation Point, Ensemble Transform Kalman Filter, Snow Depth Sensors, Larger Ensemble Size, Semi-distributed Hydrological Model, Streamflow Observations, High NSE, River Reach, EnKF, Muskingum Model, Lead Time Values, Model Time Step
 

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