Deep Huber quantile regression networks

Typical machine learning regression applications aim to report the mean or the median of the predictive probability distribution, via training with a squared or an absolute error scoring function. The importance of issuing predictions of more functionals of the predictive probability distribution (quantiles and expectiles) has been recognized as a means to quantify the uncertainty of the prediction. In deep learning (DL) applications, that is possible through quantile and expectile regression neural networks (QRNN and ERNN respectively). Here we introduce deep Huber quantile regression networks (DHQRN) that nest QRNNs and ERNNs as edge cases. DHQRN can predict Huber quantiles, which are more general functionals in the sense that they nest quantiles and expectiles as limiting cases. The main idea is to train a deep learning algorithm with the Huber quantile regression function, which is consistent for the Huber quantile functional. As a proof of concept, DHQRN are applied to predict house prices in Australia. In this context, predictive performances of three DL architectures are discussed along with evidential interpretation of results from an economic case study.Comment: 31 pages, 9 figure

Who smells? Forecasting taste and odor in a drinking water reservoir

Taste and odor problems can impede public trust in drinking water and impose major costs on water utilities. The ability to forecast taste and odor events in source waters, in advance, is shown for the first time in this paper. This could allow water utilities to adapt treatment, and where effective treatment is not available, consumers could be warned. A unique 24-year time series, from an important drinking water reservoir in Saskatchewan, Canada, is used to develop forecasting models of odor using chlorophyll a, turbidity, total phosphorus, temperature, and the following odor producing algae taxa: Anabaena spp., Aphanizemenon spp., Oscillatoria spp., Chlorophyta, Cyclotella spp., and Asterionella spp. We demonstrate, using linear regression and random forest models, that odor events can be forecast at 0-26 week time lags, and that the models are able to capture a significant increase in threshold odor number in the mid-1990s. Models with a fortnight time-lag show a high predictive capacity (R2 = 0.71 for random forest; 0.52 for linear regression). Predictive skill declines for time lags from 0 to 15 weeks, then increases again, to R2 values of 0.61 (random forest) and 0.48 (linear regression) at a 26-week lag. The random forest model is also able to provide accurate forecasting of TON levels requiring treatment 12 weeks in advance-93% true positive rate with a 0% false positive rate. Results of the random forest model demonstrate that phytoplankton taxonomic data outperform chlorophyll a in terms of predictive importance

A stochastic reconstruction framework for analysis of water resource system vulnerability to climate-induced changes in river flow regime

Assessments of potential impacts of climate change on water resources systems are generally based on the use of downscaled climate scenarios to force hydrological and water resource systems models and hence quantify potential changes in system response. This approach, however, has several limitations. The uncertainties in current climate and hydrological models can be large, such analyses are rapidly outdated as new scenarios become available, and limited insight into system response is obtained. Here, we propose an alternative methodology in which system vulnerability is analyzed directly as a function of the potential variations in flow characteristics. We develop a stochastic reconstruction framework that generates a large ensemble of perturbed flow series at the local scale to represent a range of potential flow responses to climate change. From a theoretical perspective, the proposed reconstruction scheme can be considered as an extension of both the conventional resampling and the simple delta-methods. By the use of a two-parameter representation of regime change (i.e., the shift in the timing of the annual peak and the shift in the annual flow volume), system vulnerability can be visualized in a two-dimensional map. The methodology is applied to the current water resource system in southern Alberta, Canada, to explore the system's vulnerability to potential changes in the streamflow regime. Our study shows that the system is vulnerable to the expected decrease in annual flow volume, particularly when it is combined with an earlier annual peak. Under such conditions, adaptation will be required to return the system to the feasible operational mode. © 2013. American Geophysical Union. All Rights Reserved

What is the most efficient and effective method for long-term monitoring of alpine tundra vegetation?

Nondestructive estimations of plant community characteristics are essential to vegetation monitoring programs. However, there is no universally accepted method for this purpose in the Arctic, partly because not all programs share the same logistical constraints and monitoring goals. Our aim was to determine the most efficient and effective method for long-term monitoring of alpine tundra vegetation. To achieve this, we established 12 vegetation-monitoring plots on a south-facing slope in the alpine tundra of southern Yukon Territory, Canada. Four observers assessed these plots for vascular plant species abundance employing three methods: visual cover (VC) and subplot frequency (SF) estimation and modified point-intercept (PI) (includes rare species present but not intersected by a pin). SF performed best in terms of time required per plot and sensitivity to variations in species richness. All methods were similarly poor at estimating relative abundance for rare species, but PI and VC were substantially better at high abundances. Differences among methods were larger than among observers. Our results suggest that SF is best when the monitoring focus is on rare species or species richness across extensive areas. However, when the focus is on monitoring changes in relative abundance of common species, VC or PI should be preferred

Temporal dynamics of catchment transit times from stable isotope data

Time variant catchment transit time distributions are fundamental descriptors of catchment function but yet not fully understood, characterized, and modeled. Here we present a new approach for use with standard runoff and tracer data sets that is based on tracking of tracer and age information and time variant catchment mixing. Our new approach is able to deal with nonstationarity of flow paths and catchment mixing, and an irregular shape of the transit time distribution. The approach extracts information on catchment mixing from the stable isotope time series instead of prior assumptions of mixing or the shape of transit time distribution. We first demonstrate proof of concept of the approach with artificial data; the Nash-Sutcliffe efficiencies in tracer and instantaneous transit times were >0.9. The model provides very accurate estimates of time variant transit times when the boundary conditions and fluxes are fully known. We then tested the model with real rainfall-runoff flow and isotope tracer time series from the H.J. Andrews Watershed 10 (WS10) in Oregon. Model efficiencies were 0.37 for the 18O modeling for a 2 year time series; the efficiencies increased to 0.86 for the second year underlying the need of long time tracer time series with a long overlap of tracer input and output. The approach was able to determine time variant transit time of WS10 with field data and showed how it follows the storage dynamics and related changes in flow paths where wet periods with high flows resulted in clearly shorter transit times compared to dry low flow periods. Key Points: Approach for time variant catchment transit time Modeling irregular shape of transit time distributions by time variant mixing Modeling catchment transit time in WS10 of HJA Fores

Recent climatic, cryospheric, and hydrological changes over the interior of western Canada: A review and synthesis

It is well established that the Earth's climate system has warmed significantly over the past several decades, and in association there have been widespread changes in various other Earth system components. This has been especially prevalent in the cold regions of the northern mid- to high latitudes. Examples of these changes can be found within the western and northern interior of Canada, a region that exemplifies the scientific and societal issues faced in many other similar parts of the world, and where impacts have global-scale consequences. This region has been the geographic focus of a large amount of previous research on changing climatic, cryospheric, and hydrological regimes in recent decades, while current initiatives such as the Changing Cold Regions Network (CCRN) introduced in this review seek to further develop the understanding and diagnosis of this change and hence improve the capacity to predict future change. This paper provides a comprehensive review of the observed changes in various Earth system components and a concise and up-to-date regional picture of some of the temporal trends over the interior of western Canada since the mid- or late 20th century. The focus is on air temperature, precipitation, seasonal snow cover, mountain glaciers, permafrost, freshwater ice cover, and river discharge. Important long-term observational networks and data sets are described, and qualitative linkages among the changing components are highlighted. Increases in air temperature are the most notable changes within the domain, rising on average 2°C throughout the western interior since 1950. This increase in air temperature is associated with hydrologically important changes to precipitation regimes and unambiguous declines in snow cover depth, persistence, and spatial extent. Consequences of warming air temperatures have caused mountain glaciers to recede at all latitudes, permafrost to thaw at its southern limit, and active layers over permafrost to thicken. Despite these changes, integrated effects on stream flow are complex and often offsetting. Following a review of the current literature, we provide insight from a network of northern research catchments and other sites detailing how climate change confounds hydrological responses at smaller scales, and we recommend several priority research areas that will be a focus of continued work in CCRN. Given the complex interactions and process responses to climate change, it is argued that further conceptual understanding and quantitative diagnosis of the mechanisms of change over a range of scales is required before projections of future change can be made with confidence

Dynamic hydrological niche segregation: How plants compete for water in a semi-arid ecosystem

Hydrological niche segregation (HNS), specifically the variation in root water uptake depth among coexisting species, is an understudied area of research. This is especially the case in semi-arid ecosystems, such as China's Loess Plateau (CLP), where seasonal aridity necessitates adaptive water use strategies among plant species. In this study, we conducted a two-year investigation to understand the water sources and intrinsic water use efficiency (WUEi) of four coexisting plant species: Populus simonii (tree), Caragana korshinskii and Salix psammophila (shrubs), and Artemisia ordosica (semi-shrub). We analyzed the isotopic compositions of xylem and soil water (δ2H and δ18O) and leaf δ13C to identify the water sources and WUEi, respectively, of each species. We then used the nicheROVER model to quantify HNS based on the variations in xylem water δ2H and δ18O. Our results show that the four co-existing species occupied distinct positions on a hydrological niche axis, delineated by their respective water sources and disparate WUEi. The tree P. simonii exhibited a preference for deep soil water and demonstrated a high WUEi. Both shrubs, S. psammophila and C. korshinskii, utilized intermediate and deep soil water, respectively, and with comparable WUEi. Conversely, the semi-shrub A. ordosica relied on shallow soil water and displayed a low WUEi. These differences in water sources and WUEi led to HNS between A. ordosica and the other three species in a relatively wet year. However, in a relatively dry year, HNS between A. ordosica and the other three species contracted and WUEi increased as species increased the use of deep soil water. Overall, these results demonstrate that HNS is a dynamic phenomenon that varies on at least an annual basis. It expands and contracts as plants regulate their water uptake and loss in response to changing soil moisture conditions

DIY meteorology: Use of citizen science to monitor snow dynamics in a data-sparse city

Cities are under pressure to operate their services effectively and project costs of operations across various timeframes. In high-latitude and high-altitude urban centers, snow management is one of the larger unknowns and has both operational and budgetary limitations. Snowfall and snow depth observations within urban environments are important to plan snow clearing and prepare for the effects of spring runoff on cities' drainage systems. In-house research functions are expensive, but one way to overcome that expense and still produce effective data is through citizen science. In this paper, we examine the potential to use citizen science for snowfall data collection in urban environments. A group of volunteers measured daily snowfall and snow depth at an urban site in Saskatoon (Canada) during two winters. Reliability was assessed with a statistical consistency analysis and a comparison with other data sets collected around Saskatoon. We found that citizen-science-derived data were more reliable and relevant for many urban management stakeholders. Feedback from the participants demonstrated reflexivity about social learning and a renewed sense of community built around generating reliable and useful data. We conclude that citizen science holds great potential to improve data provision for effective and sustainable city planning and greater social learning benefits overall

DIY meteorology: Use of citizen science to monitor snow dynamics in a data-sparse city

Cities are under pressure to operate their services effectively and project costs of operations across various timeframes. In high-latitude and high-altitude urban centers, snow management is one of the larger unknowns and has both operational and budgetary limitations. Snowfall and snow depth observations within urban environments are important to plan snow clearing and prepare for the effects of spring runoff on cities' drainage systems. In-house research functions are expensive, but one way to overcome that expense and still produce effective data is through citizen science. In this paper, we examine the potential to use citizen science for snowfall data collection in urban environments. A group of volunteers measured daily snowfall and snow depth at an urban site in Saskatoon (Canada) during two winters. Reliability was assessed with a statistical consistency analysis and a comparison with other data sets collected around Saskatoon. We found that citizen-science-derived data were more reliable and relevant for many urban management stakeholders. Feedback from the participants demonstrated reflexivity about social learning and a renewed sense of community built around generating reliable and useful data. We conclude that citizen science holds great potential to improve data provision for effective and sustainable city planning and greater social learning benefits overall