DOI: 10.25338/b8ws8h
title: Input data for short-term water level forecasting at 3 stations near HWY 37, Sonoma/Marin County, California

description: Low-lying coastal highways are susceptible to flooding as the sea level
 rises.  Flooding events already impact some highways, like
 Highway 37 which runs across the lowlands at the northern end of San
 Francisco Bay and is crossed by several creeks/rivers. Short-term
 operational forecasts are required to enable planning for traffic
 disruption, evacuation, and protection of property and infrastructure.
 Traditional physically based numerical models have great predictive
 capability but require extensive datasets and are computationally
 expensive which limits their ability to do short-term forecasting. Here we
 develop a data-driven, site-specific method that can be implemented at
 multiple vulnerable sites throughout San Francisco Bay and other low-lying
 coastal areas across the State of California.  This method is
 based on direct observations of the water level at the site and is
 independent of large computer simulations.  For this study, we
 use a relatively simple statistical model (multiple-linear regression)
 combined with a forecast error correction inspired by an autoregressive
 moving average method (ARMA) commonly used in time-series forecasting. The
 model is then used to produce a 4-day water level forecast at 3 stations
 near HWY 37, Sonoma/Marin County, California. 
description: The input files for the model are grouped
 into three different datasets: a training dataset, a water level
 observations dataset, and a weather forecast dataset. All data within
 those files are sourced from public data servers.    <strong>Training
 Dataset</strong> <strong>Description</strong>: This
 dataset contains the time series of the four parameters that are used to
 train the model. It consists of hourly observed meteorological data such
 as wind, atmospheric pressure, and flow for the period of 2019-01-01 to
 2022-09-27. The dataset consists of 4 fields: Ocean Wind, Local Wind,
 Atmospheric Pressure and River flow. The raw data was collected from
 publicly available sources. The data was downloaded and resampled to
 hourly time intervals. Small data gaps were filled by linear
 interpolation. The wind data was transformed from a polar coordinate
 system of wind speed and direction to principal component x-y vectors. The
 principal components were oriented so that the alongshore (y-component) is
 oriented at 60 degrees North for the wind at Gnoss Field and 100 degrees
 north for the wind at the NDBC buoy. The listed onshore wind is the
 shorenormal (x-component) for the 2 locations.  <strong>Source</strong>:
 Column Name Location Data Type, Unit Agency Source Web link to raw data AtmPres Buoy 46026 Atmospheric Pressure, mBar NOAA NDBC https://www.ndbc.noaa.gov/station_page.php?station=46026 Gnoss_onshorewind Gnoss Field Airport Shore-normal component of the wind, m/s Sonoma County https://sonoma.onerain.com/site/?site_id=155&site=b4e33d63-e909-4ecd-bb2b-1ee2c587bb00 napa_flow_cfs Napa River River flow, cfs USGS NWIS https://waterdata.usgs.gov/ca/nwis/uv?site_no=11458000 ocean_onshorewind Buoy 46026 Shore-normal component of the wind, m/s NOAA NDBC https://www.ndbc.noaa.gov/station_page.php?station=46026   <strong>Water Level Datasets</strong> This dataset consists of three individual files each with 3 fields. The stage_m field is the raw data collected from the water level gauge station, the predicted_m field is the predicted tide as calculated below and the residual_m field is the difference between the two. <strong>Description</strong>: The raw water level data were collected from 3 stage stations for the period of 2019-01-01 to 2022-09-27 when available.  Field stage_m: The data was downloaded, detrended by removing the mean value, and resampled to hourly time intervals. Small data gaps were filled by linear interpolation.  Field predicted_m: The predicted tide was calculated using a publicly available Python routine based on a well-documented Matlab routine called Utide (http://www.po.gso.uri.edu/~codiga/utide/utide.htm).   Field residual: The residual is the stage-predicted time. It represents the variation of the water level due to non-tidal forcing. <strong>Source</strong>: The stage data was downloaded from the following sources:  File  Name Location Data Type, Unit Agency Source Web link to raw data novato_wl_1hr_up.csv Mouth of Novato Creek Stage, m Marin Co https://marin.onerain.com/site/?site_id=16808&site=a88e57c5-06b1-4855-a65c-92ef0063e6bb rowland_wl_1hr.csv Novato Creek at Rowland Bridge Stage, m Marin Co https://marin.onerain.com/site/?site_id=16809&site=82b05ca8-3c86-49cc-9660-63ca3abd3e35 petaluma_wl_1hr.csv Petaluma River at Horse Ranch Stage, m UC Davis, BML https://coastalocean.ucdavis.edu/ocean-observing/hwy37   <strong>W</strong><strong>eather Forecast Datasets</strong> This dataset is the weather forecast for the 4 parameters used by the model.  <strong>Description</strong>: This dataset contains forecasted meteorological data as obtained from NOAA data servers. The atmospheric pressure forecast was obtained from openweathermap, an open-source weather forecast app.   <strong>Source</strong>: Column Name Location Data Type, Unit Agency Source Web link to raw data AtmPres Buoy 46026 Atmospheric Pressure, mBar - https://openweathermap.org/  Gnoss_onshorewind Gnoss Field Airport Shore-normal component of the wind, m/s NOAA NWS https://www.weather.gov/documentation/services-web-api napa_flow_cfs Napa River River flow, cfs NOAA AHPS https://water.weather.gov/ahps2/hydrograph.php?gage=apcc1&wfo=mtr ocean_onshorewind Buoy 46026 Shore-normal component of the wind, m/s NOAA NWS https://www.weather.gov/documentation/services-web-api  
description: All data can be open with a simple text editor.