1. Theme III: Water Predictions and Ecosystem Services
Project 1: Floods, Risks and the whole “dam” thing
Cesar Hincapie
Nasser Najibi
Naresh Devineni

2. Creating a Hybrid Statistical-Physical Flood Estimation Model for New York City
Cesar Hincapie1 and Naresh Devineni2
1NOAA Center for Earth System Sciences and Remote Sensing Technologies Fellow; Earth System Science and Environmental Engineering; The City College of New York, NY
2 NOAA Center for Earth System Sciences and Remote Sensing Technologies; Civil Engineering; The City College of New York, NY
Research Goal
Data
Results
The increased likelihood of the New York City metropolitan area to flooding is a pressing matter. A comprehensive flood estimation model that can inform regions at risk of floods (current and future) is explored and presented. The hybrid model (statistical and physical) incorporates rainfall intensity and duration along with land-use characters. We did this using radar rainfall data (NEXRAD Stage IV). The radar data is used as a reference point from which inference is made on the homogeneity of the precipitation throughout the city. The rainfall events are used to model the surface runoff flow and distribution to the sewer conveyance system. A comprehensive sewer network is design which takes into account, the topography of the metropolitan area which plays an important role on the way that runoff behaves; ultimately affecting the way water is channeled and transported by the sewer system. The sewer network is created in ArcGIS using publically available data to map out the main interceptors, the catch basins and other infrastructure that will likely influence the flood volume. In addition, a conveyance capacity is giving to the sewer network depending on the characteristics (such as diameter) and other parameters affecting the amount of flow. The study also accounts for the water treatment plants’ capacity and the effects of environmental factors such as high tides and explores its relation to increase flooding. The model includes links to drainage areas, peak flows and directions of flow together with the sewer interceptors and its capacity to identify areas of potential flooding. Relevant results on spatial rainfall risk assessment from this project are presented here.
Precipitation data acquired from NOAA- (NEXRAD Stage IV)
NYC Sewer basins Layer acquired from ArcGIS only, modified to meet Department of Environmental Protection (DEP) presented maps
Elevation data acquired from NHDPlus V2
Methodology
Rain probability computation
Estimates of
No Risk, Partial Risk and Full Risk
Acquisition of (NEXRAD Stage IV) Daily Gridded Precipitation data1
Acquisition of Sewer Shed Layers
Creation of NYC’s sewer shed map to closely reflect the DEP sewer basin maps
Binary operation on rainfall
Spatially join and correlate gridded precipitation with sewer basins
Overview
Owls Head
Sewer Basin binary rainfall matrix
The project aims at building a hybrid model that will incorporate physical and statistical methods.
We are currently in the beginning phase of the project and preliminary work has been conducted on the statistical part.
In addition, experimental procedures have been conducted with the topology of NYC. This is done via a Digital elevation model (DEM) with a 30m by 30m resolution. It was used to perform preliminary work on flow direction analysis. Further work will be done using an 8-Directional model and incorporated.
The New York sewer system is also a major component of the model.
Spatial rainfall risk as seen from radar data is estimated and presented. These risk estimates will inform future sewer design.
Grid location
40.65
40.63
40.58
-73.94
-73.90
-73.88 1
NYC main sewer lines
Statistics of Spatial Partial Risk
Per sewer shed
Components
Risk Assessment
Empirical Flow Model Design
Spatial Risk Maps
Conclusion
Rainfall Data is used to derive Statistical risk
Preliminary assessment is done on the distribution of rainfall event to obtain spatial risk estimates
Incorporation of factors such as
Storm water flow direction based on elevation
Sewer system distribution and capacity
Land use characteristics- runoff coefficients
Development of the flow model.
The analysis was conducted with daily precipitation data from year 2006 to 2011
The data acquired was spatially joined to the sewer shed, and the precipitation belonging to the grid was attributed to the sewer basin. This was done on a centroid basis. Of the 14 sewer basins, 10 sewer basins were assigned cell precipitation values. The remainder were not consider due to the experimental setup and grid centroid distribution. A binary operator is used to determine daily grid rainfall. It is then normalized to provide individual sewer basin rainfall occurrence probability. From here several estimates are derived.
The statistical analysis performed reveals the following. There is a 38% probability of precipitation over the city on any given day. This probability is different for each of the sewer basins as some basins experience more precipitation days. This has been attributed as differential spatial risk of that basin. The east side part of the city (Jamaica) has higher mean and tail risk. The north river basin experiences higher mean risk but low extreme risk. This spatial risk has implication for future sewer design as well as planning for the implementation of green infrastructure. We are currently investigating the causality of this spatial risk based on storm transposition.
ACKNOWLEDGEMENT This study is supported and monitored by The National Oceanic and Atmospheric Administration – Cooperative Science Center for Earth System Sciences and Remote Sensing Technologies under the Cooperative Agreement Grant #: NA16SEC The authors would like to thank The City College of New York and NOAA Office of Education, Educational Partnership Program with Minority Serving Institutions (EPP/MSI) for full fellowship support for Cesar Hincapie. The statements contained within the manuscript/research article are not the opinions of the funding agency or the U.S. government, but reflect the author’s opinions.
1 Hamidi, A., Devineni, N., Booth, J., Hosten, A., Ferraro, R., & Khanbilvardi, R. (2017). Classifying urban rainfall extremes using weather radar data: An application to the Greater New York Area. Journal of Hydrometeorology, 18, 611–623, doi: /JHM-D

3. Study Area: Missouri or pick your favorite place!

4. So what are we up to? – Storm transposition

6. The Three Tasks:
Create storms and their orientations – the storm is coming.
Overlay or superimpose on the river basin/dams – Your GIS skills to the help.
Dam risk based on phase differences of the storms and the basin.