Influence of Spatially Variable Instrument Networks on Climatic Averages

Copyright 1991 by the American Geophysical Union.Instrument networks for measuring surface air temperature (T) and precipitation (P) have varied considerably over the last century. Inadequate observing‐station locations have produced incomplete, uneven, and biased samples of the spatial variability in climate and, in turn, terrestrial and global scale averages of T and P have been biased. New high‐resolution climatologies [Legates and Willmott, 1990a; 1990b] are intensively sampled and integrated to illustrate the effects of these nontrivial sampling biases. Since station networks may not represent spatial climatic variability adequately, their ability to represent climate through time is suspect

Natural and Managed Watersheds Show Similar Responses to Recent Climate Change

Changes in climate are driving an intensification of the hydrologic cycle and leading to alterations of natural streamflow regimes. Human disturbances such as dams, land-cover change, and water diversions are thought to obscure climate signals in hydrologic systems. As a result, most studies of changing hydroclimatic conditions are limited to areas with natural streamflow. Here, we compare trends in observed streamflow from natural and human-modified watersheds in the United States and Canada for the 1981–2015 water years to evaluate whether comparable responses to climate change are present in both systems. We find that patterns and magnitudes of trends in median daily streamflow, daily streamflow variability, and daily extremes in human-modified watersheds are similar to those from nearby natural watersheds. Streamflow in both systems show negative trends throughout the southern and western United States and positive trends throughout the northeastern United States, the northern Great Plains, and southern prairies of Canada. The trends in both natural and human-modified watersheds are linked to local trends in precipitation and reference evapotranspiration, demonstrating that water management and land-cover change have not substantially altered the effects of climate change on human-modified watersheds compared with nearby natural watersheds

Analyzing the discharge regime of a large tropical river through remote sensing, ground-based climatic data, and modeling

This study demonstrates the potential for applying passive microwave satellite sensor data to infer the discharge dynamics of large river systems using the main stem Amazon as a test case. The methodology combines (1) interpolated ground-based meteorological station data, (2) horizontally and vertically polarized temperature differences (HVPTD) from the 37-GHz scanning multichannel microwave radiometer (SMMR) aboard the Nimbus 7 satellite, and (3) a calibrated water balance/water transport model (WBM/WTM). Monthly HVPTD values at 0.25° (latitude by longitude) resolution were resampled spatially and temporally to produce an enhanced HVPTD time series at 0.5° resolution for the period May 1979 through February 1985. Enhanced HVPTD values were regressed against monthly discharge derived from the WBM/WTM for each of 40 grid cells along the main stem over a calibration period from May 1979 to February 1983 to provide a spatially contiguous estimate of time-varying discharge. HVPTD-estimated flows generated for a validation period from March 1983 to February 1985 were found to be in good agreement with both observed arid modeled discharges over a 1400-km section of the main stem Amazon. This span of river is bounded downstream by a region of tidal influence and upstream by low sensor response associated with dense forest canopy. Both the WBM/WTM and HVPTD-derived flow rates reflect the significant impact of the 1982–1983 El Niño-;Southern Oscillation (ENSO) event on water balances within the drainage basin

Using knowledge brokers to facilitate the uptake of pediatric measurement tools into clinical practice: a before-after intervention study

<p>Abstract</p> <p>Background</p> <p>The use of measurement tools is an essential part of good evidence-based practice; however, physiotherapists (PTs) are not always confident when selecting, administering, and interpreting these tools. The purpose of this study was to evaluate the impact of a multifaceted knowledge translation intervention, using PTs as knowledge brokers (KBs) to facilitate the use in clinical practice of four evidence-based measurement tools designed to evaluate and understand motor function in children with cerebral palsy (CP). The KB model evaluated in this study was designed to overcome many of the barriers to research transfer identified in the literature.</p> <p>Methods</p> <p>A mixed methods before-after study design was used to evaluate the impact of a six-month KB intervention by 25 KBs on 122 practicing PTs' self-reported knowledge and use of the measurement tools in 28 children's rehabilitation organizations in two regions of Canada. The model was that of PT KBs situated in clinical sites supported by a network of KBs and the research team through a broker to the KBs. Modest financial remuneration to the organizations for the KB time (two hours/week for six months), ongoing resource materials, and personal and intranet support was provided to the KBs. Survey data were collected by questionnaire prior to, immediately following the intervention (six months), and at 12 and 18 months. A mixed effects multinomial logistic regression was used to examine the impact of the intervention over time and by region. The impact of organizational factors was also explored.</p> <p>Results</p> <p>PTs' self-reported knowledge of all four measurement tools increased significantly over the six-month intervention, and reported use of three of the four measurement tools also increased. Changes were sustained 12 months later. Organizational culture for research and supervisor expectations were significantly associated with uptake of only one of the four measurement tools.</p> <p>Conclusions</p> <p>KBs positively influenced PTs' self-reported knowledge and self-reported use of the targeted measurement tools. Further research is warranted to investigate whether this is a feasible, cost-effective model that could be used more broadly in a rehabilitation setting to facilitate the uptake of other measurement tools or evidence-based intervention approaches.</p

Climate change, water rights, and water supply: The case of irrigated agriculture in Idaho

We conduct a hedonic analysis to estimate the response of agricultural land use to water supply information under the Prior Appropriation Doctrine by using Idaho as a case study. Our analysis includes long-term climate (weather) trends and water supply conditions as well as seasonal water supply forecasts. A farm-level panel data set, which accounts for the priority effects of water rights and controls for diversified crop mixes and rotation practices, is used. Our results indicate that farmers respond to the long-term surface and ground water conditions as well as to the seasonal water supply variations. Climate change-induced variations in climate and water supply conditions could lead to substantial damages to irrigated agriculture. We project substantial losses (up to 32%) of the average crop revenue for major agricultural areas under future climate scenarios in Idaho. Finally, farmers demonstrate significantly varied responses given their water rights priorities, which imply that the distributional impact of climate change is sensitive to institutions such as the Prior Appropriation Doctrine. ? 2014. American Geophysical Union. All Rights Reserved

Projecting changes in regional temperature and precipitation extremes in the United States

Regional and local climate extremes, and their impacts, result from the multifaceted interplay between large-scale climate forcing, local environmental factors (physiography), and societal vulnerability. In this paper, we review historical and projected changes in temperature and precipitation extremes in the United States, with a focus on strengths and weaknesses of (1) commonly used definitions for extremes such as thresholds and percentiles, (2) statistical approaches to quantifying changes in extremes, such as extreme value theory, and (3) methods for post-processing (downscaling) global climate models (GCMs) to investigate regional and local climate. We additionally derive regional and local estimates of changes in temperature extremes by applying a quantile mapping approach to high-resolution gridded daily temperature data for 6 U.S. sub-regions. Consistent with the background warming in the parent GCMs, we project decreases in regional and local cold extremes and increases in regional and local warm extremes throughout the domain, but the downscaling approach removes bias and produces substantial spatial variability within the relatively small sub-regions. We finish with recommendations for future research on regional climate extremes, suggesting that focus be placed on improving understanding of extremes in the context of large-scale circulation and evaluating the corresponding cascade of scale interactions within GCMs

Decomposition of the mean absolute error (MAE) into systematic and unsystematic components.

When evaluating the performance of quantitative models, dimensioned errors often are characterized by sums-of-squares measures such as the mean squared error (MSE) or its square root, the root mean squared error (RMSE). In terms of quantifying average error, however, absolute-value-based measures such as the mean absolute error (MAE) are more interpretable than MSE or RMSE. Part of that historical preference for sums-of-squares measures is that they are mathematically amenable to decomposition and one can then form ratios, such as those based on separating MSE into its systematic and unsystematic components. Here, we develop and illustrate a decomposition of MAE into three useful submeasures: (1) bias error, (2) proportionality error, and (3) unsystematic error. This three-part decomposition of MAE is preferable to comparable decompositions of MSE because it provides more straightforward information on the nature of the model-error distribution. We illustrate the properties of our new three-part decomposition using a long-term reconstruction of streamflow for the Upper Colorado River