bottom-up vulnerability assessment and adaptive planning
TRANSCRIPT
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‐ Uncertainty ‐ Reservoir vulnerability‐ Identification of Adaptation measures (adaptive
planning process)
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The project can be roughly broken into three major components: a vulnerability assessment, adaptation planning and a reports and presentations phase.
‐ In the vulnerability assessment phase we spend a considerable amount of time creating a range of plausible future scenarios. This will take a while in part because its not really been done for flood studies like this before.
‐ Under each of those scenarios we evaluate project performance. The output looks something like this pretty picture Andrew will show you more of later.
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It describes for the entire range of future scenarios the response of the project in terms of its performance. In this case larger SWP deliveries under wetter, less hot scenarios and declining SWP deliveries for hotter, drier futures.
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This vulnerability assessment informs our plan formulation:
Flexible plans and decision points are formulated for less certain or less consequential scenarios. Such as:
(1) Distant futures., where action can be deferred as more information is collected about our fate AND(2) Uncertain but lower consequence near term possibilities. E.g. more marginal projects that only become justified when a loss of performance become sure.
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Usually a adaptive plan will contain a flexible series of actions that are robust to nearer term (more certain) futures.
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As we are fond of saying at HEC we are moving the STATE‐OF‐ART into the STATE‐OF‐PRACTICE.
So, along the way we’ll show our work with a short (50 page or less) pilot report. The goal here is to provide a read‐along study guide not to document every single decision, perfectly describe the system or document ever decision.
We’ll also document how our process fits and does NOT fit within current USACE policy. If any policy changes are required we’ll be careful to detail those challenges. If none are required we’ll document
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carefully why they are not required (e.g. what policies they are in compliance with) and finally we provide a discussion about what implementation guidance my include.
These two documents will be summarized in a compelling narrative that can be presented to leaders in both organizations.
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Probability Density Function ‐
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Weather Generator – Produce a time‐series of meteorological variables
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‐ Weather generators allow us to rapidly develop and explore a wide range of climate scenarios. Thereby, enabling bottom‐up analysis.‐ At its core the weather generator is relatively simple. It accepts historic time series gridded metrological records (such as precipitation and temperature observations) and reorganizes them to produces synthetic time series of weather data which drive our hydrologic model.‐ With the exception of large flood events this is done non‐parametrically, so our synthetic time series is made up of observed storms and dry
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spells ‐ yielding physically realistic and relatable droughts and floods. ‐ 'Large' flood events, e.g. those which are deeper (in terms of precipitation) than those found in our observed record ‐ are built by parametrically, by scaling real storms up to lower exceedance frequency depths (using a Partial Duration Series). Since even these 'extreme events' are based on actual storms, the spatial and temporal distribution of the scaled precipitation ‐ reflect relatable, observed physical realities.
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‐ The weather generator is modular in nature, this means depending on the types of events that are of concern, time and money available for the analysis ‐ multiple options exist for producing droughts, flood and climate scenarios.
‐ In our study, climate is linked to flood through the 'weather regimes'. These regimes represent the large scale pattern or high and low pressure systems which are the causal mechanism behind synoptic scale storms ‐which fully encompasses the types of storms
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which produce flooding in CA.
Annual Module:Input: Observed or historical time series of weather data (Dendrochronology)Output: many simulated time series of weather data• Models low frequency inter‐annual
variation• Produces multi‐year drought with the
expected frequency and depth
Annual Module:Input: (1) a time series of simulated annual
precipitation data(2) a time series of basin averaged daily
gridded metrological data,
• Produces statistically accurate
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precipitation and weather data values• Maintains accurate spatial and temporal
correlations
Daily Module
Input:
(1) a time series of basin averaged daily gridded metrological data,
(2) time series of simulated annual precipitation data
Output: many simulated time series of gridded daily precipitation values
Flood Scaling Module(1) a partial duration series taken from
historical data,(2) a time series of simulated basin
averaged daily precipitation data
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• Produces flood events of concern with expected frequency of occurrence
• Maintains realistic spatial correlations
Hourly Module(1) a simulated time series of daily gridded
precipitation data with scaled flood events,
(2) a time series of hourly gridded metrological data
• Produces flood events of concern with expected frequency of occurrence
• Maintains realistic spatial correlations
Atmospheric Module (output)(1) categorized weather regimes,
(2) a clustered set of categorized daily gridded atmospheric data,
(3) a simulated time series of weather regime states.
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• Connection between climate and extreme floods requires connecting climate to daily weather
• Mid‐latitude intra‐seasonal variation is characterized by large‐scale atmospheric flow patterns (regimes)
o regimes reoccur at fixed locations
o regimes persist beyond
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the lifetime of a synoptic‐scale storm
• Transition rapidly based on nonlinear atmospheric dynamics
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HEC‐WAT Integrates different HEC modeling software into one modeling tool, streamlining the modeling process.
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Meteorological Model Inputs
Net Radiation (Short + Longwave) from LIVNEH (daily)
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Precipitation & Temperature from NLDAS‐2 (hourly)ET by Priestley‐Taylor
Snowmelt: Temperature Index
Elevation Bands: Roughly every 500’ above 4500’, but
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some adjustment to have band area > 10%
Dan Steiner FERC Model
Runtime Improvements
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Original Model setup for flood operationsUsing the model to model thousands of years of continuous hydrology.Want to model non‐flood operations for carryover storageRuntime improvements
Historical data used to calibrate and validate the model
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