Colorado State Decision Tools

This blog post details the available information from Colorado state’s decision tools that can be used in different research areas. These tools have extensive information on water administration, climate data, reservoir details, and groundwater data. Snapshots of each tool are provided to highlight the features.

Water administration

Prior Appropriation Doctrine is the priority system followed. Water source, location structure, call priority, and priority date details are accessible. Administrative call data is also available (active and historical). The following is the snapshot of viewing active call data. The additional resources tab in the lower bottom has more information linking to call analysis for each structure and water source. Acquisition and new appropriation data for lakes and streams is also available. Decree details, court docs and net amounts for different structure types (acquifer reservation, augmentation plan, basin determination, ditch system, entity, erosion control dam, exchange plan, livestock water tank, measuring point, Mine, minimum flow, pipeline, power plant, pump, reach and recharge area) can be accessed in https://dwr.state.co.us/Tools/WaterRights/NetAmounts

Climate Stations

Climate data is available for 12157 stations from different sources such as NRCS, NOAA, CoCoRaHS, and CoAgMet. The data can be viewed as a table, or graph and downloaded as a CSV file for each site. Climate parameters include evaporation, temperature, precipitation, snow, vapor pressure and wind speed.

Dams

Information on the structural details of the dam is provided under the dam safety tool. The data can be downloaded from this link. Data includes 53 columns for each dam with details on dam type, length, height, crest elevation, storage, federal regulations, surface area, drainage area, spillway capacity, outlet description, last inspection date, safe storage and others.

Groundwater

This page has well-documented groundwater level reports for the years 1989-2023 for aquifers in different basins of the Colorado state along with rules for water well construction and inspection details. The groundwater – water levels tool has data for around 10000 wells with details on well name, water level depth, water level elevation, period of record count, measured agency, aquifer name, county, basin and others. Well permit data is available in this page.

Surface water data

Current and historical gage data is available for 2554 stations with tabled data on station name, station type, data source, river source, record start date and end date. This page has the details on stream gage station, diversion structures and storage structures. Along with flow data, different environmental variables data is available as water temperature, air temperature, conductivity, pH.

GIS and Mapping

GIS data is available for download for all river basins that includes different layers as diversion, wells, streams/rivers, canals, irrigated acreage and climate stations. Other tools include location converter and distance calculator (https://dwr.state.co.us/Tools/Home). They also provide an extensive tool called Map Viewer that can be used to view GIS data online. Different map viewers are as follows

Map Description	Link
DWR Map Viewer: All inclusive map viewer with over 170 map layers of DWR and CWCB data. (Use the Layer Catalog to add layers that are not pre-populated.)	Launch DWR Map Viewer
DWR Well Permit Viewer: A map viewer with data used to research well permits. (Use the Layer Catalog to add layers that are not pre-populated.)	Launch DWR Well Permit Map Viewer
CWCB Data Viewer: View data specific to CWCB instream flows, Construction Fund loans, recreational in-channel diversions (RICDs), WSRF grants and more.	Launch CWCB Data Viewer
CDSS SNODAS Tools: The SNODAS tools page provides interactive analysis tools to explore historic snowpack from 2003 forward.	Launch SNODAS Tools

Table taken from https://cdss.colorado.gov/map-viewers

Environmental Flow Tool

This flow tool is built for ecological risk assessment in the basin. This tool is developed in excel and has embedded historical and future datasets of colorado river basin. Future data is available for five planning scenarios defined as business as casual, weak economy, cooperative growth, adaptive innovation and hot growth. Environmental flow risk categories are low, moderate, less moderate, high and very high ecological risk. Ecological risk maps are generated for each basin.

Cost estimating tool

The structure of cost estimating tool is shown in the following figure

Technical Update to the Water Plan

CWCB released update to water plan for use by researchers, decision makers, water users and stakeholders with data on historical and future availability covering different sectors utilizing the above defined tools. In my opinion, this report is worth reading to understand the Colorado’s water assessment.

All the heading are linked to web pages where the tool data is available which are accessible as of May 6, 2024.

Python Profiling with line_profiler

The line_profiler can be used to see the amount of time taken to execute each line in a function of your code. I think this is an important tool that can be used to reduce the runtime of a code. Simple command “pip install line_profiler” shall install the package or “conda install line_profiler” to install into an existing conda environment.

I shall present the usage of this line_profiler tool for a randomly generated data to calculate supply to demand ratio for releases from a reservoir. Demand or target supply is defined for day of the water year. The following code first defines the calculation for day of water year, generates random data for demand and supply, and two functions are defined for different methods of calculation of ratio of supply to demand. Include the line @profile before the function definition line to get the profile of execution of each line in the function.

import pandas as pd
import numpy as np
from line_profiler import profile

#function to caluclate day of water year
def get_dowy(date):
    water_year_start = pd.Timestamp(year=date.year, month=10, day=1)
    if date < water_year_start:
        water_year_start = pd.Timestamp(year=date.year - 1, month=10, day=1)
    return (date - water_year_start).days + 1

# Generate random data for demand for each day of water year
np.random.seed(0)
data = {
    'Median_Demand': np.random.randint(0, 1000, 367),
}

# Create dataframe
df_demand = pd.DataFrame(data)

## Generate random data for supply from years 2001 to 2010 and also define corresponding day of water year
date_range = pd.date_range(start='2001-10-01', end='2091-09-30', freq='D')
data = {
    'dowy': [get_dowy(date) for date in date_range],
    'Supply': np.random.uniform(0, 2500, len(date_range))
}
# Create dataframe
df_supply = pd.DataFrame(data, index=date_range)

@profile #define before the function for profiling
def calc_supply_demand_1(df,df_median):
    ratio = pd.DataFrame()
    medians_dict = df_demand['Median_Demand'].to_dict()
    demand = df_supply['dowy'].map(medians_dict)
    supply = df_supply['Supply']
    ratio = supply.resample('AS-OCT').sum() / demand.resample('AS-OCT').sum()
    return ratio

@profile
def calc_supply_demand_2(df,df_median):
    ratio = pd.DataFrame()
    medians_dict = df_demand['Median_Demand'].to_dict()
    demand = pd.Series([df_demand['Median_Demand'][i] for i in df.dowy], index=df.index)
    supply = df_supply['Supply']
    ratio = supply.resample('AS-OCT').sum() / demand.resample('AS-OCT').sum()
    return ratio 

ratio1 = calc_supply_demand_1(df_supply, df_demand)
ratio2 = calc_supply_demand_2(df_supply,df_demand)

Running just the code wouldn’t output anything related to line_profiler. To enable profiling, run the script as follows (this sets the environment variable LINE_PROFILE=1)

LINE_PROFILE=1 python Blog_Post.py

The above line generates three output files as profile_output.txt, profile_output_.txt, and profile_output.lprof and stdout is as follows:

Timer unit: 1e-09 s

  0.04 seconds - /directory/Blog_Post.py:30 - calc_supply_demand_1
  2.43 seconds - /directory/Blog_Post.py:39 - calc_supply_demand_2
Wrote profile results to profile_output.txt
Wrote profile results to profile_output_2024-03-29T192919.txt
Wrote profile results to profile_output.lprof
To view details run:
python -m line_profiler -rtmz profile_output.lprof

On executing the line “python -m line_profiler -rtmz profile_output.lprof”, the following is printed.

Timer unit: 1e-06 s

Total time: 0.0393394 s
File: /directory/Blog_Post.py
Function: calc_supply_demand_1 at line 30

Line #      Hits         Time  Per Hit   % Time  Line Contents
==============================================================
    30                                           @profile
    31                                           def calc_supply_demand_1(df,df_median):
    32         1       2716.4   2716.4      6.9      ratio = pd.DataFrame()
    33         1       1365.2   1365.2      3.5      medians_dict = df_demand['Median_Demand'].to_dict()
    34         1       3795.6   3795.6      9.6      demand = df_supply['dowy'].map(medians_dict)
    35         1        209.7    209.7      0.5      supply = df_supply['Supply']
    36         1      31252.0  31252.0     79.4      ratio = supply.resample('AS-OCT').sum() / demand.resample('AS-OCT').sum()
    37         1          0.5      0.5      0.0      return ratio

Total time: 2.43446 s
File: /directory/Blog_Post.py
Function: calc_supply_demand_2 at line 39

Line #      Hits         Time  Per Hit   % Time  Line Contents
==============================================================
    39                                           @profile
    40                                           def calc_supply_demand_2(df,df_median):
    41         1       1365.1   1365.1      0.1      ratio = pd.DataFrame()
    42         1        697.5    697.5      0.0      medians_dict = df_demand['Median_Demand'].to_dict()
    43         1    2411800.5    2e+06     99.1      demand = pd.Series([df_demand['Median_Demand'][i] for i in df.dowy], index=df.index)
    44         1         53.9     53.9      0.0      supply = df_supply['Supply']
    45         1      20547.0  20547.0      0.8      ratio = supply.resample('AS-OCT').sum() / demand.resample('AS-OCT').sum()
    46         1          0.6      0.6      0.0      return ratio

  0.04 seconds - /directory/Blog_Post.py:30 - calc_supply_demand_1
  2.43 seconds - /directory/Blog_Post.py:39 - calc_supply_demand_2

The result shows line number, number of hits (number of times the line is executed; hits increase when executed in a for loop), total time, time per hit, percentage of time and the line contents. The above result implies that for the first function, 79.4% of time was used to execute ratio, whereas for the second function 99.1% is used in execution of demand. ratio1 and ratio2 are the two exact same outputs where demand is defined in different ways in both the functions. We also see that time taken to execute calc_supply_demand_1 function is 0.04 seconds and calc_supply_demand_2 is 2.43 seconds. Using this I could reduce the runtime by 61 times (2.43/0.04) identifying that demand calculation takes 99.1% of time in calc_supply_demand_2 function using line_profiler. Another method is using cprofile (details are in this blog post). cprofile gives more detailed information.

References:

https://kernprof.readthedocs.io/en/latest

https://researchcomputing.princeton.edu/python-profiling