Sevier River Research Articles

Haunted in Desolation: The Murder of Captain John Gunnison, Reconsidered

Deserts confuse, fogging memory and electrifying the imagination. In 1853, on Utah’s Sevier River, a ritualized killing spawned a folklore of deserts that lives on to this day. Captain John W. Gunnison, an engineer, had detoured into an ambush. Dismembered, decapitated, his heart torn from his chest, he had died, it was said, by order of the Mormon prophet and Utah’s Latter-day Saints. Fabulized over the decades, the tale was contorted with an evil king in a desert kingdom, with ghoulish assassins and restless corpses undead. Folklore saw what historians have been slow to perceive about hauntings in desolation. Memories of trauma run deep in disquieting strangeness. Places presumed to be empty set dark expectations for horror.

Maize Dependence among the Sevier Puebloans

Some archaeologists have argued that the Formative-era villagers of the Sevier River region in the eastern Great Basin were primarily foragers whose economy was dependent upon marsh resources, especially cattails. This paper compiles multiple lines of evidence from Backhoe Village and other Sevier villages to show that subsistence was based on maize agriculture, not foraging for wild resources.

Age of a Pliocene basin fill along the Sevier River, southwestern Utah, U.S.A., based on fossil rodents

Age of a Pliocene basin fill along the Sevier River, southwestern Utah, U.S.A., based on fossil rodents

A probabilistic assessment of agricultural water scarcity in a semi-arid and snowmelt-dominated river basin under climate change

Water resources planning and management is crucial and challenging in semi-arid regions to minimize water scarcity. Potential impacts due to climate change are a concern to water managers and stakeholders in semi-arid river basins with limited water availability. This study provides a probabilistic assessment of climate change impacts on water scarcity in the Sevier River Basin of Utah, which has a snowmelt-driven water supply and high agricultural water demands, using a decision-scaling framework. The methodology consists of a bottom-up approach that uses climate response functions, together with projections from 31 general circulation models (GCMs), to assess vulnerability to water scarcity for 2000–2099. Water scarcity is defined using an index comparing water availability to crop water demand predicted by the AquaCrop model from the Food and Agriculture Organization. Results showed that off-season precipitation is the most sensitive factor affecting water scarcity in the basin, followed by precipitation and temperature during the growing seasons. The GCM projections of temperature and precipitation suggest an increasing availability of water for agriculture in the basin. Still, a considerable risk probability of agricultural water shortage was found in years 2025 through 2049 with the emission scenario RCP4.5, suggesting the need for adaptation and mitigation strategies. The bottom-up decision scaling approach used here with a wide range of GCMs was practical to explore climate risk to agricultural water scarcity given the simplicity and minimal computational requirement.

Development of a Bayesian Belief Network Model Framework for Analyzing Farmers’ Irrigation Behavior

Canal operators need information to manage water deliveries to irrigators, especially in the case of on-demand irrigation supply systems. Short-term irrigation demand forecasts can provide potentially valuable information for a canal operator who must manage such a system, especially if these forecasts could be generated by using readily available information about bio-physical conditions of the irrigated area and the decision-making processes of irrigators. Additionally, Bayesian models of irrigation behavior can provide insight into the likely criteria which farmers use to make irrigation decisions. This paper develops a Bayesian belief network (BBN) to infer irrigation decision-making behavior of farmers based on factor interaction and posterior probabilities. The model discussed here was built from a combination of data about biotic, climatic, and edaphic conditions under which observed irrigation decisions were made. From all the possible initial trials, the model which was built from data comprising of conditions on days the irrigation decision was made, and a day before it, was found to be the best and is presented and discussed here. The paper includes a case study using data collected from the Canal B region of the Sevier River, near Delta, Utah. Alfalfa, barley and corn are the main crops in the Canal B area. The model has been tested with a portion of the data to affirm the model predictive capabilities. It was found that most of the farmers used consistent rules throughout all years and across different types of crops. Soil moisture stress, was found to be the most likely, significant predictive variable of the irrigation decision. Irrigation decisions appeared to be triggered by a farmer’s perception of soil stress (or a surrogate thereof), or by a perception of combined factors such as information about a neighbor irrigating or an apparent preference to irrigate on a weekend. Soil stress resulted in irrigation probabilities (chance that the farmer will irrigate) of 94.4% for alfalfa. Prediction accuracy of the timing for irrigations of alfalfa was observed to be 81.0%, and 61.0% for barley and corn. The study shows that BBNs can be a prospective tool to analyse likely decisions about irrigation in an on-demand system with good accuracy.

Exploring irrigation behavior at Delta, Utah using hidden Markov models

In on-demand irrigation systems, canal operators divert water from rivers to be delivered to the fields after receiving a water order from a farmer. These water orders are the result of a farmer's decision to irrigate. If farmers’ irrigation decisions could be better anticipated, it might be possible to improve canal operations using improved future short-term water demand estimates. The importance of how farmers make these irrigation decisions, however, is often overlooked because of their high variability and unpredictable nature. A hidden Markov model (HMM) was built to analyze irrigation decision behavior of farmers and make forecasts of their future decisions. The model inputs were relatively easily measured, or estimated, biophysical data, including such factors (i.e., those variables which are believed to affect irrigation decision-making) as cumulative evapotranspiration, depletion, soil stress coefficient, and canal flows. Irrigation decision series were the hidden states for the model. The paper evaluates data from the Canal B region of the Lower Sevier River Basin, near Delta, Utah. The main crops of the region are alfalfa, barley, and corn. A portion of the data was used to build and test the model capability to explore that factor and the level at which the farmer takes the decision to irrigate for future irrigation events. It was found that the farmers cannot be classified into certain classes based on their irrigation decisions, but varies in their behavior from irrigation-to-irrigation across all years and crops. The factors and the level selected can be adequately used to explore the future irrigation decisions in the short term. HMMs can be used as a tool to analyze what factor and, subsequently, what level of that factor the farmer most likely based the irrigation decision on. This was possible only when the maximum likelihood (ML) estimates of model parameters were known based on the historical evidence. The study shows that the HMM is a capable tool to study irrigation behavior which is not a memory-less process.

Recursive partitioning techniques for modeling irrigation behavior

Accurate forecasts of short-term irrigation demands can provide information useful for canal operators to manage water diversions and deliveries more efficiently. This can be accomplished by analyzing the actions of the farmers who make water use decisions. Readily available data on biophysical conditions in farmers' fields and the irrigation delivery system during the growing season can be utilized to anticipate irrigation water orders in the absence of any predictive socio-economic information that could be used to provide clues into future irrigation decisions. Decision classification and the common factors, form a basis for division of farmers into groups, which can be then used to make predictions of future decisions to irrigate. In this paper, we have implemented three tree algorithms, viz. classification and regression trees (CART), random forest (RF), and conditional inference trees (Ctree), to analyze farmers' irrigation decisions. These tools were then used to forecast future decisions. During the training process, the models inferred connections between input variables and the decision output. These variables were a time series of the biophysical conditions during the days prior to irrigation. Data from the Canal B region of the Lower Sevier River Basin, near the town of Delta, Utah were used. The main crops in the region are alfalfa, barley and corn. While irrigation practices for alfalfa are dependent on the timing of cuts, for barley and corn the critical crop growth stages are often used as indicators of farmer decisions to irrigate. Though all the models performed well in forecasting farmer decisions to irrigate, the best prediction accuracies by crop type were: 99.3% for alfalfa using all the three models; 98.7% for barley, using the CART model; and 97.6% for corn, with Ctree approximately. Crop water use, which is the amount of water lost through evapotranspiration, was the prime factor across all the crops to prompt irrigation, which complies with the irrigation principles. The analyses showed that the tree algorithms used here are able to handle large as well as small data sets, they can achieve high classification accuracy, and they offer potential tools to forecast future farmer actions. This can be subsequently useful in making short-term demand forecasts.

Real-time forecasting of short-term irrigation canal demands using a robust multivariate Bayesian learning model

In the lower Sevier River basin in Utah, the travel times between reservoir releases and arrival at irrigation canal diversions limit the reservoir operation in enabling delivery changes, which may not be compatible with the on demand schedule in the basin. This research presents a robust machine learning approach to forecast the short-term diversion demands for three irrigation canals. These real-time predictions can assist the operator to react promptly to short-term changes in demand and to properly release water from the reservoir. The models are developed in the form of a multivariate relevance vector machine (MVRVM) that is based on a Bayesian learning machine approach for regression. Predictive confidence intervals can also be obtained from the model with this Bayesian approach. Test results show that the MVRVM learns the input–output patterns with good accuracy. A bootstrap analysis is used to evaluate robustness of model parameter estimation. The MVRVM is compared in terms of performance and robustness with an Artificial Neural Network.

Multivariate Bayesian Regression Approach to Forecast Releases from a System of Multiple Reservoirs

This research presents a model that simultaneously forecasts required water releases 1 and 2 days ahead from two reservoirs that are in series. In practice, multiple reservoir system operation is a difficult process that involves many decisions for real-time water resources management. The operator of the reservoirs has to release water from more than one reservoir taking into consideration different water requirements (irrigation, environmental issues, hydropower, recreation, etc.) in a timely manner. A model that forecasts the required real-time releases in advance from a multiple reservoir system could be an important tool to allow the operator of the reservoir system to make better-informed decisions for releases needed downstream. The model is developed in the form of a multivariate relevance vector machine (MVRVM) that is based on a sparse Bayesian regression model approach. With this Bayesian approach, a predictive confidence interval is obtained from the model that captures the uncertainty of both the model and the data. The model is applied to the multiple reservoir system located in the Lower Sevier River Basin near Delta, Utah. The results show that the model learns the input–output patterns with high accuracy. Computing multiple-time-ahead predictions in real-time would require a model which guarantees not only good prediction accuracy but also robustness with respect to future changes in the nature of the inputs data. A bootstrap analysis is used to guarantee good generalization ability and robustness of the MVRVM. Test results demonstrate good performance of predictions and statistics that indicate robust model generalization abilities. The MVRVM is compared in terms of performance and robustness with another multiple output model such as Artificial Neural Network (ANN).

Rodents and lagomorphs (Mammalia) from the Hemphillian (late Miocene) of Utah

ABSTRACT Four species of rodents (two heteromyids and two cricetids) and one lagomorph are identified from the late Tertiary Sevier River Formation of Utah. The heteromyids include a new genus and species of heteromyine, Metaliomys sevierensis, which is intermediate in morphology between the Clarendonian and early Hemphillian Diprionomys Kellogg and the extant genera Liomys and Heteromys. A single specimen is referred to Diprionomys sp., cf. D. minimus (Kellogg). The cricetid Paronychomys lemredfieldi Jacobs is known from the Hemphillian of Arizona. The second cricetid is referred to a new genus Basirepomys. Peromyscus pliocenicus Wilson from the Hemphillian of California is designated as the type species of the new genus, to which the new species B. robertsi from Utah is referred. Basirepomys is viewed as intermediate between Peromyscus and the basal neotomyine Repomys May from the late Hemphillian and Blancan. The only lagomorph in the fauna is Hypolagus vetus (Kellogg). Four of the taxa recognized from the Sevier River Formation (Diprionomys, Paronychomys lemredfieldi, Basirepomys, and Hypolagus vetus) are elsewhere known from the Hemphillian of North America. However, it is not possible at this time to determine whether the fauna is early or late Hemphillian.

Application of the relevance vector machine to canal flow prediction in the Sevier River Basin

This work addresses management of water for irrigation in arid regions where significant delays between the time of order and the time of delivery present major difficulties. Motivated by improvements to water management that will be facilitated by an ability to predict water demand, it employs a data-driven approach to developing canal flow prediction models using the relevance vector machine (RVM), a probabilistic kernel-based learning machine. A search is performed across model attributes including input set, kernel scale parameter and model update scheme for models providing superior prediction capability using the RVM. Models are developed for two canals in the Sevier River Basin of southern Utah for prediction horizons of up to 5 days.

Downscaling and Forecasting of Evapotranspiration Using a Synthetic Model of Wavelets and Support Vector Machines

Providing reliable forecasts of evapotranspiration (ET) at farm level is a key element toward efficient water management in irrigated basins. This paper presents an algorithm that provides a means to downscale and forecast dependent variables such as ET images. Using the discrete wavelet transform (DWT) and support vector machines (SVMs), the algorithm finds multiple relationships between inputs and outputs at all different spatial scales and uses these relationships to predict the output at the finest resolution. Decomposing and reconstructing processes are done by using 2-D DWT with basis functions that suit the physics of the property in question. Two-dimensional DWT for one level will result in one datum image (low-low-pass filter image) and three detail images (low-high, high-low, and high-high). The underlying relationship between the input variables and the output are learned by training an SVM on the datum images at the resolution of the output. The SVM is then applied on the detailed images to produce the detailed images of the output, which are needed to help downscale the output image to a higher resolution. In addition to being downscaled, the output image can be shifted ahead in time, providing a means for the algorithm to be used for forecasting. The algorithm has been applied on two case studies, one in Bondville, IL, where the results have been validated against AmeriFlux observations, and another in the Sevier River Basin, UT.

BASIN SCALE WATER MANAGEMENT AND FORECASTING USING ARTIFICIAL NEURAL NETWORKS

ABSTRACT: Water scarcity in the Sevier River Basin in south-central Utah has led water managers to seek advanced techniques for identifying optimal forecasting and management measures. To more efficiently use the limited quantity of water in the basin, better methods for control and forecasting are imperative. Basin scale management requires advanced forecasts of the availability of water. Information about long term water availability is important for decision making in terms of how much land to plant and what crops to grow; advanced daily predictions of streamflows and hydraulic characteristics of irrigation canals are of importance for managing water delivery and reservoir releases; and hourly forecasts of flows in tributary streams to account for diurnal fluctuations are vital to more precisely meet the day-to-day expectations of downstream farmers. A priori streamflow information and exogenous climate data have been used to predict future streamflows and required reservoir releases at different timescales. Data on snow water equivalent, sea surface temperatures, temperature, total solar radiation, and precipitation are fused by applying artificial neural networks to enhance long term and real time basin scale water management information. This approach has not previously been used in water resources management at the basin-scale and could be valuable to water users in semi-arid areas to more efficiently utilize and manage scarce water resources.

Supergene destruction of a hydrothermal replacement alunite deposit at Big Rock Candy Mountain, Utah: mineralogy, spectroscopic remote sensing, stable-isotope, and argon-age evidences

Big Rock Candy Mountain is a prominent center of variegated altered volcanic rocks in west-central Utah. It consists of the eroded remnants of a hypogene alunite deposit that, at ∼21 Ma, replaced intermediate-composition lava flows. The alunite formed in steam-heated conditions above the upwelling limb of a convection cell that was one of at least six spaced at 3- to 4-km intervals around the margin of a monzonite stock. Big Rock Candy Mountain is horizontally zoned outward from an alunite core to respective kaolinite, dickite, and propylite envelopes. The altered rocks are also vertically zoned from a lower pyrite–propylite assemblage upward through assemblages successively dominated by hypogene alunite, jarosite, and hematite, to a flooded silica cap. This hydrothermal assemblage is undergoing natural destruction in a steep canyon downcut by the Sevier River in Marysvale Canyon. Integrated geological, mineralogical, spectroscopic remote sensing using AVIRIS data, Ar radiometric, and stable isotopic studies trace the hypogene origin and supergene destruction of the deposit and permit distinction of primary (hydrothermal) and secondary (weathering) processes. This destruction has led to the formation of widespread supergene gypsum in cross-cutting fractures and as surficial crusts, and to natrojarosite, that gives the mountain its buff coloration along ridges facing the canyon. A small spring, Lemonade Spring, with a pH of 2.6 and containing Ca, Mg, Si, Al, Fe, Mn, Cl, and SO 4, also occurs near the bottom of the canyon. The 40Ar/ 39Ar age (21.32±0.07 Ma) of the alunite is similar to that for other replacement alunites at Marysvale. However, the age spectrum contains evidence of a 6.6-Ma thermal event that can be related to the tectonic activity responsible for the uplift that led to the downcutting of Big Rock Candy Mountain by the Sevier River. This ∼6.6 Ma event also is present in the age spectrum of supergene natrojarosite forming today, and probably dates the beginning of supergene alteration at Big Rock Candy Mountain. The δ 34S value (11.9‰) of alunite is similar to those for replacement alunite from other deposits in the Marysvale volcanic field. The δ 34S values of natrojarosite (0.7‰ to −1.2‰) are similar to those for aqueous sulfate in Lemonade Spring, but are larger than those in pyrite (0.4‰ to −4.7‰). The δ 34S and δ 18O SO 4 values of gypsum show an excellent correlation, with values ranging from 15.2‰ to −5.2‰ and 7‰ to −8.2‰, respectively. The stable-isotope data indicate that the aqueous sulfate for gypsum is a mixture derived from the dissolution of hypogene gypsum and alunite, and from the supergene oxidation of pyrite. The aqueous sulfate for the natrojarosite, however, is derived largely from the supergene oxidation of pyrite, with a minor contribution from the dissolution of alunite and gypsum. The exceptional detailed spectral mapping capabilities of AVIRIS led to the recognition of a small amount of jarosite that is probably the top of the steam-heated system that produced the primary hypogene alteration at Big Rock Candy Mountain.

Stoneflies (Plecoptera) Of Southern Utah With An Updated Checklist Of Utah Species

Southern Utah comprises 4 major physiographic divisions: the Great Basin, Colorado Plateau, Central Rocky Mountains, and Southern Rocky Mountains, which have been partitioned into faunal regions. We discuss the uniqueness of southern Utah and the amount of land set aside for recreation and preservation, and we encourage the study and preservation of its water resources. The stonefly (Plecoptera) fauna of 13 counties in southern Utah was sur- veyed. We found 58 species representing 34 genera and 8 families. Three new state records for Utah and many new county records for southern Utah are presented, along with a discussion of distribution patterns and faunal affinities of each species. Distribution patterns indicate a historical connection between the Sevier River and the Colorado drainage and a separation of faunal areas within the Central Rocky Mountains at boundaries between the Wasatch Mountains, Wasatch Plateau, and the Southern High Plateau. The uniqueness of the isolated Abajo and LaSal Mountains, the Virgin River valley, and other faunal areas in southern Utah is presented.

Dynamic Equilibrium Between an Active Uplift and the Sevier River, Utah

In a zone of active tectonic uplift, one expects a river to change morphology simply because valley slope deforms. But by cutting through a transverse uplift, the Sevier River in southern Utah also adapts to the tectonics by eroding and depositing. These localized changes of transport are caused by varying channel geometry, pattern, sedimentology, and slope. In the uplift, the river is narrow, efficiently shaped, coarser‐grained, and expends a greater proportion of energy on bed particles. It deposits sediments downstream in exaggerated meandering reaches with wider, less efficient channels. Localized erosion and deposition create a dynamic equilibrium with the structural growth, but this equilibrium includes nonlinear responses like knickpoints and depositional lobes. In response to the required erosion and deposition, the river so dramatically alters channel grain size and channel geometry that the increase of sinuosity expected in the tectonically steepened valley does not occur.

Sequence stratigraphy of lacustrine deposits: A Quaternary example from the Bonneville basin, Utah

The late Quaternary lacustrine sedimentary record in the Bonneville lake basin in the eastern Great Basin provides an excellent example of sequence stratigraphy. Two sequences, referred to as the Little Valley and Bonneville Alloformations, are exposed in the bluffs of the Sevier River where it has entrenched its Pleistocene delta between Leamington and Delta, Utah. Both alloformations contain offshore marl units and fine-grained deltaic or underflow-fan deposits. They can be identified and mapped by tracing the unconformity separating them and employing a number of geochronometric tools, including amino acid epimerization in fossil gastropods, radiocarbon and thorium-230 ages, and basaltic tephrochronology. Thin transgressive sand of the Little Valley Alloformation is overlain by deeper-water marl and down-lapping regressive-phase deltaic silt. The Bonneville Alloformation lies unconformably above the Little Valley deposits. Fine-grained deltaic sediments deposited during the transgressive phase of Lake Bonneville fill the entrenched Sevier River valley that was eroded subsequent to the Little Valley lake cycle. Marl deposited during the deep-water phase is overlain by down-lapping deposits of the regressive phase below the Provo shoreline but is the uppermost unit in the altitudinal range where the lake was lowered catastrophically during the Bonneville Flood. The sequence stratigraphic interpretation leads to the conclusion that the Sevier River delta as a whole is probably made up of a number of sediment sequences, each composed of several facies. Recognition of this complexity could be important in potential applications of the stratigraphic model.

Late Pleistocene and Holocene lake fluctuations in the Sevier Lake basin, Utah, USA

Sevier Lake is the modern lake in the topographically closed Sevier Lake basin, and is fed primarily by the Sevier River. During the last 12000 years, the Beaver River also was a major tributary to the lake. Lake Bonneville occupied the Sevier Desert until late in its regressive phase when it dropped to the Old River Bed threshold, which is the low point on the drainage divide between the Sevier Lake basin and the Great Salt Lake basin. Lake Gunnison, a shallow freshwater lake at 1390 m in the Sevier Desert, overflowed continuously from about 12000 to 10000 yr B.P., into the saline lake in the Great Salt Lake basin, which continued to contract. This contrast in hydrologic histories between the two basins may have been caused by a northward shift of monsoon circulation into the Sevier Lake basin, but not as far north as the Great Salt Lake basin. Increased summer precipitation and cloudiness could have kept the Sevier Lake basin relatively wet.

Closure to “ Conjunctive Use in Sevier River System, Utah ” by Carl H. Carpenter (February, 1987, Vol. 113, No. 1)

Closure to “ <i>Conjunctive Use in Sevier River System, Utah</i> ” by Carl H. Carpenter (February, 1987, Vol. 113, No. 1)

Discussion of “ Conjunctive Use in Sevier River System, Utah ” by Carl H. Carpenter (February, 1987, Vol. 113, No. 1)

Discussion of “ <i>Conjunctive Use in Sevier River System, Utah</i> ” by Carl H. Carpenter (February, 1987, Vol. 113, No. 1)