The ah ha moment of measurement: Introduction to the special section on Hydrologic Measurement Methods

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A glass always half full: Reconsideration of the Wales apparatus to apply constant head boundary conditions

A new apparatus is presented that is capable of applying a constant fluid pressure at inflow and outflow boundaries. The apparatus can be refilled during operation and does not rely on an overflow mechanism. The device is constructed of two vessels, one that contains the delivered fluid and the other that contains a less dense fluid. By matching the fluid densities and the areas of the vessels the absolute elevation of the delivered fluid is maintained as the fluid is added to or removed from the system. The history of the development of the device, the underlying physical principles, and two demonstrations of the operation of a prototype device are shown

Disruption of TBP-2 ameliorates insulin sensitivity and secretion without affecting obesity

Type 2 diabetes mellitus (T2DM) is characterized by defects in both insulin sensitivity and glucose-stimulated insulin secretion (GSIS) and is often accompanied by obesity. In this study, we show that disruption of thioredoxin binding protein-2 (TBP-2, also called Txnip) in obese mice (ob/ob) dramatically improves hyperglycaemia and glucose intolerance, without affecting obesity or adipocytokine concentrations. TBP-2-deficient ob/ob mice exhibited enhanced insulin sensitivity with activated insulin receptor substrate-1/Akt signalling in skeletal muscle and GSIS in islets compared with ob/ob mice. The elevation of uncoupling protein-2 (UCP-2) expression in ob/ob islets was downregulated by TBP-2 deficiency. TBP-2 overexpression suppressed glucose-induced adenosine triphosphate production, Ca2+ influx and GSIS. In β-cells, TBP-2 enhanced the expression level and transcriptional activity of UCP-2 by recruitment of peroxisome proliferator-activated receptor-γ co-activator-1α to the UCP-2 promoter. Thus, TBP-2 is a key regulatory molecule of both insulin sensitivity and GSIS in diabetes, raising the possibility that inhibition of TBP-2 may be a novel therapeutic approach for T2DM

Controlled Experiments of Hillslope Coevolution at the Biosphere 2 Landscape Evolution Observatory: Toward Prediction of Coupled Hydrological, Biogeochemical, and Ecological Change

Understanding the process interactions and feedbacks among water, porous geological media, microbes, and vascular plants is crucial for improving predictions of the response of Earth’s critical zone to future climatic conditions. However, the integrated coevolution of landscapes under change is notoriously difficult to investigate. Laboratory studies are limited in spatial and temporal scale, while field studies lack observational density and control. To bridge the gap between controlled laboratory and uncontrollable field studies, the University of Arizona built a macrocosm experiment of unprecedented scale: the Landscape Evolution Observatory (LEO). LEO comprises three replicated, heavily instrumented, hillslope-scale model landscapes within the environmentally controlled Biosphere 2 facility. The model landscapes were designed to initially be simple and purely abiotic, enabling scientists to observe each step in the landscapes’ evolution as they undergo physical, chemical, and biological changes over many years. This chapter describes the model systems and associated research facilities and illustrates how LEO allows for tracking of multiscale matter and energy fluxes at a level of detail impossible in field experiments. Initial sensor, sampler, and soil coring data are already providing insights into the tight linkages between water flow, weathering, and microbial community development. These interacting processes are anticipated to drive the model systems to increasingly complex states and will be impacted by the introduction of vascular plants and changes in climatic regimes over the years to come. By intensively monitoring the evolutionary trajectory, integrating data with mathematical models, and fostering community-wide collaborations, we envision that emergent landscape structures and functions can be linked, and significant progress can be made toward predicting the coupled hydro-biogeochemical and ecological responses to global change

Being Bayesian: Discussions from the Perspectives of Stakeholders and Hydrologists

Bayes' Theorem is gaining acceptance in hydrology, but it is still far from standard practice to cast hydrologic analyses in a Bayesian context especially in the realm of hydrologic practice. Three short discussions are presented to encourage more complete adoption of a Bayesian approach. The first, aimed at a stakeholder audience, seeks to explain that an informal Bayesian analysis is the default approach that we all take to any decision made under uncertainty. The second, aimed at a general hydrologist audience, seeks to establish multi-model approaches as the natural choice for Bayesian hydrologic analysis. The goal of this discussion is to provide a bridge from the stakeholder's natural approach to a more formal, quantitative Bayesian analysis. The third discussion is targeted to a more advanced hydrologist audience, suggesting that some elements of hydrologic practice do not yet reflect a Bayesian philosophy. In particular, an example is given that puts Bayes Theory to work to identify optimal observation sets before data are collected.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Automatic Training Sample Selection for a Multi-Evidence Based Crop Classification Approach

An approach to use the available agricultural parcel information to automatically select training samples for crop classification is investigated. Previous research addressed the multi-evidence crop classification approach using an ensemble classifier. This first produced confidence measures using three Multi-Layer Perceptron (MLP) neural networks trained separately with spectral, texture and vegetation indices; classification labels were then assigned based on Endorsement Theory. The present study proposes an approach to feed this ensemble classifier with automatically selected training samples. The available vector data representing crop boundaries with corresponding crop codes are used as a source for training samples. These vector data are created by farmers to support subsidy claims and are, therefore, prone to errors such as mislabeling of crop codes and boundary digitization errors. The proposed approach is named as ECRA (Ensemble based Cluster Refinement Approach). ECRA first automatically removes mislabeled samples and then selects the refined training samples in an iterative training-reclassification scheme. Mislabel removal is based on the expectation that mislabels in each class will be far from cluster centroid. However, this must be a soft constraint, especially when working with a hypothesis space that does not contain a good approximation of the targets classes. Difficulty in finding a good approximation often exists either due to less informative data or a large hypothesis space. Thus this approach uses the spectral, texture and indices domains in an ensemble framework to iteratively remove the mislabeled pixels from the crop clusters declared by the farmers. Once the clusters are refined, the selected border samples are used for final learning and the unknown samples are classified using the multi-evidence approach. The study is implemented with WorldView-2 multispectral imagery acquired for a study area containing 10 crop classes. The proposed approach is compared with the multi-evidence approach based on training samples selected randomly and border samples based on initial cluster centroids within agricultural parcels without any refinement. The results clarify the improvement in overall classification accuracy to 82.3 % based on the proposed approach from 74.9 % based on random selection and 71.4% on non-refined border samples

Event-Response Ellipses: A Method to Quantify and Compare the Role of Dynamic Storage at the Catchment Scale in Snowmelt-Dominated Systems

A method for quantifying the role of dynamic storage as a physical buffer between snowmelt and streamflow at the catchment scale is introduced in this paper. The method describes a quantitative relation between hydrologic events (e.g., snowmelt) and responses (e.g., streamflow) by generating event-response ellipses that can be used to (a) characterize and compare catchment-scale dynamic storage processes, and (b) assess the closure of the water balance. Event-response ellipses allow for the role of dynamic, short-term storage to be quantified and compared between seasons and between catchments. This method is presented as an idealization of the system: a time series of a snowmelt event as a portion of a sinusoidal wave function. The event function is then related to a response function, which is the original event function modified mathematically through phase and magnitude shifts to represent the streamflow response. The direct relation of these two functions creates an event-response ellipse with measurable characteristics (e.g., eccentricity, angle). The ellipse characteristics integrate the timing and magnitude difference between the hydrologic event and response to quantify physical buffering through dynamic storage. Next, method is applied to eleven snowmelt seasons in two well-instrumented headwater snowmelt-dominated catchments with known differences in storage capacities. Results show the time-period average daily values produce different event-response ellipse characteristics for the two catchments. Event-response ellipses were also generated for individual snowmelt seasons; however, these annual applications of the method show more scatter relative to the time period averaged values. The event-response ellipse method provides a method to compare and evaluate the connectivity between snowmelt and streamflow as well as assumptions of water balance