scholarly journals Using the Change Point Model (CPM) Framework to Identify Windows for Water Resource Management Action in the Lower Colorado River Basin of Texas, USA

Water ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 18
Author(s):  
Brendan L. Lavy ◽  
Russell C. Weaver ◽  
Ronald R. Hagelman
Keyword(s):  
Resource Management ◽  
Water Use ◽  
Change Point ◽  
Water Resource Management ◽  
Colorado River ◽  
Change Points ◽  
Point Model ◽  
Change Point Model ◽  
Lower Colorado River Basin ◽  
Lower Colorado River

In water-stressed river basins with growing urban populations, conflicts over water resources have emerged between urban and agricultural interests, as managerial interventions occur with little warning and tend to favor urban over agricultural water uses. This research documents changes in water use along an urban-to-agricultural gradient to examine whether it is possible to leverage temporal fluctuations in key quantitative data indicators to detect periods in which we could expect substantive managerial interventions in water resource management. We employ the change point model (CPM) framework to locate shifts in water use, climate-related indicators, lake and river characteristics, and agricultural trends across urban and agricultural counties in the lower Colorado River basin of Texas. Three distinctive groupings of change points appear. Increasing water use by urban counties and a shift in local climate conditions characterize the first period. Declines in agricultural counties’ water use and crop production define the second. Drops in lake levels, lower river discharge, and an extended drought mark the third. We interpret the results relative to documented managerial intervention events and show that managerial interventions occur during and after significant change points. We conclude that the CPM framework may be used to monitor the optimal timing of managerial interventions and their effects to avoid negative outcomes.

scholarly journals Bayesian Inference on Regression Model with an Unknown Change Point

2021 ◽  
pp. 48-55
Author(s):  
Oluwadare O Ojo
Keyword(s):  
Regression Model ◽  
Change Point ◽  
Change Points ◽  
Bayesian Procedure ◽  
Point Model ◽  
Simulation Experiments ◽  
True Parameter ◽  
Posterior Inference ◽  
Change Point Model ◽  
Parameter Values

In this work, we describe a Bayesian procedure for detection of change-point when we have an unknown change point in regression model. Bayesian approach with posterior inference for change points was provided to know the particular change point that is optimal while Gibbs sampler was used to estimate the parameters of the change point model. The simulation experiments show that all the posterior means are quite close to their true parameter values. The performance of this method is recommended for multiple change points.

scholarly journals Winter Inputs Buffer Streamflow Sensitivity to Snowpack Losses in the Salt River Watershed in the Lower Colorado River Basin

Water ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 3
Author(s):  
Marcos D. Robles ◽  
John C. Hammond ◽  
Stephanie K. Kampf ◽  
Joel A. Biederman ◽  
Eleonora M. C. Demaria
Keyword(s):  
River Basin ◽  
Colorado River ◽  
Snow Accumulation ◽  
Colorado River Basin ◽  
Scale Model ◽  
Annual Streamflow ◽  
Basin Scale ◽  
Salt River ◽  
Lower Colorado River Basin ◽  
Lower Colorado River

Recent streamflow declines in the Upper Colorado River Basin raise concerns about the sensitivity of water supply for 40 million people to rising temperatures. Yet, other studies in western US river basins present a paradox: streamflow has not consistently declined with warming and snow loss. A potential explanation for this lack of consistency is warming-induced production of winter runoff when potential evaporative losses are low. This mechanism is more likely in basins at lower elevations or latitudes with relatively warm winter temperatures and intermittent snowpacks. We test whether this accounts for streamflow patterns in nine gaged basins of the Salt River and its tributaries, which is a sub-basin in the Lower Colorado River Basin (LCRB). We develop a basin-scale model that separates snow and rainfall inputs and simulates snow accumulation and melt using temperature, precipitation, and relative humidity. Despite significant warming from 1968–2011 and snow loss in many of the basins, annual and seasonal streamflow did not decline. Between 25% and 50% of annual streamflow is generated in winter (NDJF) when runoff ratios are generally higher and potential evapotranspiration losses are one-third of potential losses in spring (MAMJ). Sub-annual streamflow responses to winter inputs were larger and more efficient than spring and summer responses and their frequencies and magnitudes increased in 1968–2011 compared to 1929–1967. In total, 75% of the largest winter events were associated with atmospheric rivers, which can produce large cool-season streamflow peaks. We conclude that temperature-induced snow loss in this LCRB sub-basin was moderated by enhanced winter hydrological inputs and streamflow production.

A Bayesian Change Point Model for Historical Time Series Analysis

2004 ◽  
Vol 12 (4) ◽  
pp. 354-374 ◽  
Author(s):  
Bruce Western ◽  
Meredith Kleykamp
Keyword(s):  
Time Series ◽  
Change Point ◽  
Regression Coefficients ◽  
Simulation Methods ◽  
Historical Time ◽  
Point Location ◽  
Point Model ◽  
Political Relationships ◽  
Standard Models ◽  
Change Point Model

Political relationships often vary over time, but standard models ignore temporal variation in regression relationships. We describe a Bayesian model that treats the change point in a time series as a parameter to be estimated. In this model, inference for the regression coefficients reflects prior uncertainty about the location of the change point. Inferences about regression coefficients, unconditional on the change-point location, can be obtained by simulation methods. The model is illustrated in an analysis of real wage growth in 18 OECD countries from 1965–1992.

scholarly journals Discriminating between Long Memory and Volatility Shifts

2016 ◽  
Vol 36 (4) ◽  
Author(s):  
Aonan Zhang ◽  
Robertas Gabrys ◽  
Piotr Kokoszka
Keyword(s):  
Long Memory ◽  
Change Point ◽  
Kernel Estimator ◽  
Practical Implementation ◽  
Point Model ◽  
Variance Estimators ◽  
Long Run ◽  
The Us ◽  
Change Point Model ◽  
Stock Indexes

We develop a practical implementation of the test proposed in Berkes, Horv´ath, Kokoszka, and Shao (2006) designed to distinguish between a change-point model and a long memory model. Our implementation is calibrated to distinguish between a shift in volatility of returns and long memory in squared returns. It uses a kernel estimator of the long-run variance of squared returns with the maximal lag selected by a data driven procedure which depends on the sample size, the location of the estimated change point and the direction of the apparent volatility shift (increase versus decrease). In a simulations study, we also consider other long-run variance estimators, including the VARHAC estimator, but we find that they lead to tests with inferior performance. Applied to returns on indexes and individual stocks, our test indicates that even for the same asset, a change-point model may be preferable for a certain period of time, whereas there is evidence of long memory in another period of time. Generally there is stronger evidence for long memory in the eight years ending June 2006 than in the eight years starting January 1992. This pattern is most pronounced for US stock indexes and shares in the US financial sector.

Changes in Water Use on the Lower Colorado River in the USA from 2000-2020

2021 ◽  
Author(s):  
Pamela Nagler ◽  
Armando Barreto-Muñoz ◽  
Sattar Chavoshi Borujeni ◽  
Hamideh Nouri ◽  
Christopher Jarchow ◽  
...  
Keyword(s):  
Water Use ◽  
Vegetation Index ◽  
Colorado River ◽  
Land Use Changes ◽  
Leaf Beetle ◽  
Wildlife Habitat ◽  
The Usa ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Lower Colorado River

<p>We studied the health and water use of seven riparian reaches of the Lower Colorado River from Hoover to Morelos Dam over the last 20-years, since 2000, to evaluate trends in the riparian ecosystem. This ecosystem has been in decline based on myriad pressures related to drought, water diversions and land use changes, such as defoliation events from the tamarisk leaf beetle, Diorhabda spp. We provide remotely sensed measurements of vegetation index (VI), daily evapotranspiration (ET, mmd<sup>-1</sup>) and annualized ET (mmyr<sup>-1</sup>). We used 250m Moderate Resolution Imaging Spectroradiometer (MODIS) and 30m Landsat EVI2 time-series. We selected EVI2 to parameterize our ET algorithm and tested the ET relationship between sensors by regression approaches and found a significant correlation between EVI2<sub>Landsat</sub> and EVI2<sub>MODIS</sub>. A key finding is that riparian health and its water use between Hoover and Morelos Dams has been in decline since 2000, as measured by Landsat with daily water use dropping from 4.79 mmd<sup>-1 </sup>to 3.18 mmd<sup>-1</sup>. Our results show that over the past two decades, the average greenness (EVI2<sub>Landsat</sub>) loss was 29% and total annual ET loss was 34% (-1.61 mmd<sup>-1 </sup>or -386 mmyr<sup>-1</sup>; a drop from 1163 mmyr<sup>-1 </sup>down to 777 mmyr<sup>-1</sup>). Greenness declined on average 29%, but certain reaches declined 42% or ca. -2.28 mmd<sup>-1</sup>, or -575 mmyr<sup>-1</sup> (Reach 6). Reach 3 showed an ET loss of 39% (-1.94 mmd<sup>-1</sup>, -410 mmyr<sup>-1</sup>). Our findings are significant because riparian plant species have declined so drastically, suggesting further deterioration of biodiversity, wildlife habitat and other key ecosystem services.    </p>

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