ABSTRACT:

Due to changes in the climate and increased human consumptive uses, determining the availability of future water in the Colorado River Basin is critical. Availability of water can be estimated based on river discharge, precipitation, and air temperatures. Many research projects in the Colorado River Basin depend on these data. In order to simplify the data collection process for many projects, a single database with precipitation, temperature, and flow data from various sources was created. Discharge data were obtained from three independent sources while precipitation and temperature data were obtained from two independent sources. Using scripts in R, all the data was formatted and imported into a SQLite database named the Observation Data Model (ODM). The ODM provides researchers a vast amount of hydrologic data in a single database that can be easily downloaded. This product provides researchers the ability to spend less time gathering data and more time analysing the data.To determine the level of effort required to interact with the ODM, some minor analysis and plots were created in R. These plots revealed the disadvantage of using ODM rather than a gridded database. Extracting or querying the desired data is slightly more involved. To help mitigate this disadvantage, the process of extracting data is described in this report. The ODM and all associated input data and scripts were uploaded to public Hydroshare resource. This enables the ODM to be downloaded and used by anyone.

ABSTRACT:

This script was created in python that can access a specified USGS site and download discharge and stage data for a specified period. The python script can create a time series after downloading the data. The script is then transferred to jupyter notebook.

ABSTRACT:

This script was created in python that can access a specified USGS site and download discharge and stage data for a specified period. The python script can create a time series after downloading the data. The script is then transferred to jupyter notebook.
This script will download and create discharge and stage time series for USGS site – Missouri River at Sioux City, IA (USGS 06486000). User can change the site and date as he or she pleases.

ABSTRACT:

This script was created in python that can access a specified USGS site and download discharge and stage data for a specified period. The python script can create a time series after downloading the data. The script is then transferred to jupyter notebook.
This script will download and create discharge and stage time series for USGS site – Missouri River at Sioux City, IA (USGS 06486000). User can change the site and date as he or she pleases.

ABSTRACT:

This script was created in python that can access a specified USGS site and download discharge and stage data for a specified period. The python script can create a time series after downloading the data. The script is then transferred to jupyter notebook.
This script will download and create discharge and stage time series for USGS site – Missouri River at Sioux City, IA (USGS 06486000). User can change the site and date as he or she pleases.

ABSTRACT:

The Delta of the Colorado River is under extreme stress due to controlled flow, overallocation of water between different user groups in the upper and lower basins, and prolonged drought. Basin-wide approaches to restore the River and its Delta face many challenges. Through meetings with key stakeholders, extensive literature review, and critical analysis of the underlying issues preventing water from reaching the Delta was conducted. Numerous alternatives are presented to allow more water to reach the Delta in the years to come. A discussion of additional work that needed to make the restoration of the Delta more feasible is included.

ABSTRACT:

Some important reports on Blue Gold Program

ABSTRACT:

In recent years, most of the studies used the disaggregated smart meter data for demand modeling and identifying factors affecting residential water end-uses. Almost all of these studies tried to fit the data to known probability distributions while acknowledging that such distribution may not be applicable for a different dataset as water end-use varies with region, season, demography, climate, culture, etc. Few studies tried to utilize the high-frequency disaggregated end-use data as a feedback tool with the assumption that providing just the water-use data may help the users change their behavior. But behavioral transformation theories state that understanding users' intention towards environment, building conservation attitude, showing peers' conservation behaviors, and regular communication between the users and the water managers are essential factors for behavioral change which act in combination with the self-observation data supplied through the feedbacks (Ajzen 1985, Bandura 1991, Thibodeau et al., 1992, De Young, 1993). To fill the gap of prior studies, this research proposes to use a three-step approach for conservation practices. Steps include developing a set of baseline questions for understanding users' perception of conservation and environment, followed by segmentation of water-users based on users' separate and combined technological and behavioral potential by ranking water-use, technical efficiency (flowrate), and behavior (intensity and duration of use), and finally developing customized messages to encourage high water users to conserve by reviewing leading behavioral transformation theories.