Large‐scale hydro‐climatology of the terrestrial Arctic drainage system

The large‐scale hydro‐climatology of the terrestrial Arctic drainage system is examined, focusing on the period 1960 onward. Special attention is paid to the Ob, Yenisey, Lena, and Mackenzie watersheds, which provide the bulk of freshwater discharge to the Arctic Ocean. Station data are used to compile monthly gridded time series of gauge‐corrected precipitation (P). Gridded time series of precipitation minus evapotranspiration (P−ET) are calculated from the moisture flux convergence using NCEP reanalysis data. Estimates of ET are obtained as a residual. Runoff (R) is obtained from available discharge records. For long‐term water‐year means, P−ET for the Yenisey, Lena, and Mackenzie watersheds is 16–20% lower than the observed runoff. In the Ob watershed, the two values agree within 9%. Given the uncertainties in P−ET, we consider the atmospheric and surface water budgets to be reasonably closed. Compared to the other three basins, the mean runoff ratio (R/P) is lower in the Ob watershed, consistent with the high fraction of annual precipitation lost through ET. All basins exhibit summer maxima in P and minima in P−ET. Summer P−ET in the Ob watershed is negative due to high ET rates. For large domains in northern Eurasia, about 25% of July precipitation is associated with the recycling of water vapor evapotranspirated within each domain. This points to a significant effect of the land surface on the hydrologic regime. Variability in P and P−ET has generally clear associations with the regional atmospheric circulation. A strong link with the Urals trough is documented for the Ob. Relationships with indices of the Arctic Oscillation and other teleconnections are generally weak. Water‐year time series of runoff and P−ET are strongly correlated in the Lena watershed only, reflecting extensive permafrost. Cold‐season runoff has increased in the Yenisey and Lena watersheds. This is most pronounced in the Yenisey watershed, where runoff has also increased sharply in spring, decreased in summer, but has increased for the year as a whole. The mechanisms for these changes are not entirely clear. While they fundamentally relate to higher air temperatures, increased winter precipitation, and strong summer drying, we speculate links with changes in active layer thickness and thawing permafrost

The Virtual Water Gallery: Changing Attitudes through Art

EGU23-8658, updated on 26 Feb 2023 https://doi.org/10.5194/egusphere-egu23-8658 EGU General Assembly 2023 © Author(s) 2023. This work is distributed under the Creative Commons Attribution 4.0 License.Peer ReviewedWater is life. Water-related challenges, such as droughts, floods, wildfires, water quality degradation, permafrost thaw and glacier melt, exacerbated by climate change, affect everyone. Yet, it is challenging to communicate science on difficult, highly volatile topics such as water and climate change. Conceptualizing water-related environmental and social issues in novel ways, with engagement between diverse audiences may lead to comprehensive solutions to these complex challenges. Art can be a catalyst in the co-creation of new knowledge for the benefit of society. The Virtual Water Gallery (VWG) is a transdisciplinary science and art project of the Global Water Futures (GWF) program. Launched in 2020, the VWG aims to provide a collaborative space for dialogues between water experts, artists, and the wider public, to explore water challenges. As part of this project, 13 artists representing women’s, men’s and Indigenous voices across Canada were paired with teams of GWF scientists to co-explore specific water challenges in various Canadian ecoregions and communities. These collaborations led to the co-creation of artworks exhibited online on the VWG (www.virtualwatergallery.ca) in 2021. The VWG recently came to life in 2022 with an in-person exhibition in Canmore, Alberta, Canada. Surveys were developed to capture changes in perspectives regarding climate change and water challenges through this art-science exhibit. Participants of the VWG (artists and scientists), visitors to the online gallery, and visitors to the in-person exhibition in Canmore were all invited to take part in those surveys. The preliminary results from the surveys suggest that participants experienced changes in behaviour regarding water-related climate change mitigation, and that the degree of change depends on factors such as age, income and lived experience (i.e., floods and droughts). The results help elucidate how art viewers engage with art based on science and how science messages can be more effectively communicated through art

Development of a multivariable risk model integrating urinary cell DNA methylation and cell-free RNA data for the detection of significant prostate cancer

Background: Prostate cancer exhibits severe clinical heterogeneity and there is a critical need for clinically implementable tools able to precisely and noninvasively identify patients that can either be safely removed from treatment pathways or those requiring further follow up. Our objectives were to develop a multivariable risk prediction model through the integration of clinical, urine-derived cell-free messenger RNA (cf-RNA) and urine cell DNA methylation data capable of noninvasively detecting significant prostate cancer in biopsy naïve patients. Methods: Post-digital rectal examination urine samples previously analyzed separately for both cellular methylation and cf-RNA expression within the Movember GAP1 urine biomarker cohort were selected for a fully integrated analysis (n = 207). A robust feature selection framework, based on bootstrap resampling and permutation, was utilized to find the optimal combination of clinical and urinary markers in a random forest model, deemed ExoMeth. Out-of-bag predictions from ExoMeth were used for diagnostic evaluation in men with a clinical suspicion of prostate cancer (PSA ≥ 4 ng/mL, adverse digital rectal examination, age, or lower urinary tract symptoms). Results: As ExoMeth risk score (range, 0-1) increased, the likelihood of high-grade disease being detected on biopsy was significantly greater (odds ratio = 2.04 per 0.1 ExoMeth increase, 95% confidence interval [CI]: 1.78-2.35). On an initial TRUS biopsy, ExoMeth accurately predicted the presence of Gleason score ≥3 + 4, area under the receiver-operator characteristic curve (AUC) = 0.89 (95% CI: 0.84-0.93) and was additionally capable of detecting any cancer on biopsy, AUC = 0.91 (95% CI: 0.87-0.95). Application of ExoMeth provided a net benefit over current standards of care and has the potential to reduce unnecessary biopsies by 66% when a risk threshold of 0.25 is accepted. Conclusion: Integration of urinary biomarkers across multiple assay methods has greater diagnostic ability than either method in isolation, providing superior predictive ability of biopsy outcomes. ExoMeth represents a more holistic view of urinary biomarkers and has the potential to result in substantial changes to how patients suspected of harboring prostate cancer are diagnosed

On the choice of calibration metrics for “high-flow” estimation using hydrologic models

Calibration is an essential step for improving the accuracy of simulations generated using hydrologic models. A key modeling decision is selecting the performance metric to be optimized It has been common to use squared error performance metrics, or normalized variants such as Nash-Sutcliffe efficiency (NSE), based on the idea that their squared-error nature will emphasize the estimates of high flows. However, we conclude that NSE-based model calibrations actually result in poor reproduction of high-flow events, such as the annual peak flows that are used for flood frequency estimation. Using three different types of performance metrics, we calibrate two hydrological models at a daily step, the Variable Infiltration Capacity (VIC) model and the mesoscale Hydrologic Model (mHM), and evaluate their ability to simulate high-flow events for 492 basins throughout the contiguous United States. The metrics investigated are (1) NSE, (2) Kling-Gupta efficiency (KGE) and its variants, and (3) annual peak flow bias (APFB), where the latter is an application-specific metric that focuses on annual peak flows. As expected, the APFB metric produces the best annual peak flow estimates; however, performance on other high-flow-related metrics is poor. In contrast, the use of NSE results in annual peak flow estimates that are more than 20 % worse, primarily due to the tendency of NSE to underestimate observed flow variability. On the other hand, the use of KGE results in annual peak flow estimates that are better than from NSE, owing to improved flow time series metrics (mean and variance), with only a slight degradation in performance with respect to other related metrics, particularly when a non-standard weighting of the components of KGE is used. Stochastically generated ensemble simulations based on model residuals show the ability to improve the high-flow metrics, regardless of the deterministic performances. However, we emphasize that improving the fidelity of streamflow dynamics from deterministically calibrated models is still important, as it may improve high-flow metrics (for the right reasons). Overall, this work highlights the need for a deeper understanding of performance metric behavior and design in relation to the desired goals of model calibration.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]

Probabilistic Evaluation of Drought in CMIP6 Simulations

As droughts have widespread social and ecological impacts, it is critical to develop long-term adaptation and mitigation strategies to reduce drought vulnerability. Climate models are important in quantifying drought changes. Here, we assess the ability of 285 CMIP6 historical simulations, from 17 models, to reproduce drought duration and severity in three observational data sets using the Standardized Precipitation Index (SPI). We used summary statistics beyond the mean and standard deviation, and devised a novel probabilistic framework, based on the Hellinger distance, to quantify the difference between observed and simulated drought characteristics. Results show that many simulations have less than error in reproducing the observed drought summary statistics. The hypothesis that simulations and observations are described by the same distribution cannot be rejected for more than of the grids based on our distance framework. No single model stood out as demonstrating consistently better performance over large regions of the globe. The variance in drought statistics among the simulations is higher in the tropics compared to other latitudinal zones. Though the models capture the characteristics of dry spells well, there is considerable bias in low precipitation values. Good model performance in terms of SPI does not imply good performance in simulating low precipitation. Our study emphasizes the need to probabilistically evaluate climate model simulations in order to both pinpoint model weaknesses and identify a subset of best-performing models that are useful for impact assessments

The Perils of Regridding: Examples Using a Global Precipitation Dataset

Canada First Research Excellence Fund’s Global Water Futures program, the Natural Sciences and Engineering Research Council of Canada, the Canada Research Chairs program, and the Pacific Institute for Mathematical StudiesPeer ReviewedGridded precipitation datasets are used in many applications such as the analysis of climate variability/change and hydrological modeling. Regridding precipitation datasets is common for model coupling (e.g., coupling atmospheric and hydrological models) or comparing different models and datasets. However, regridding can considerably alter precipitation statistics. In this global analysis, the effects of regridding a precipitation dataset are emphasized using three regridding methods (first-order conservative, bilinear, and distance-weighted averaging). The differences between the original and regridded dataset are substantial and greatest at high quantiles. Differences of 46 and 0.13 mm are noted in high (0.95) and low (0.05) quantiles, respectively. The impacts of regridding vary spatially for land and oceanic regions; there are substantial differences at high quantiles in tropical land regions, and at low quantiles in polar regions. These impacts are approximately the same for different regridding methods. The differences increase with the size of the grid at higher quantiles and vice versa for low quantiles. As the grid resolution increases, the difference between original and regridded data declines, yet the shift size dominates for high quantiles for which the differences are higher. While regridding is often necessary to use gridded precipitation datasets, it should be used with great caution for fine resolutions (e.g., daily and subdaily), because it can severely alter the statistical properties of precipitation, specifically at high and low quantiles

Declining mountain snowpack in western North America

In western North America, snow provides crucial storage of winter precipitation, effectively transferring water from the relatively wet winter season to the typically dry summers. Manual and telemetered measurements of spring snow-pack, corroborated by a physically based hydrologic model, are examined here for climate-driven fluctuations and trends during the period of 1916–2002. Much of the mountain West has experienced declines in spring snowpack, especially since midcentury, despite increases in winter precipitation in many places. Analysis and modeling show that climatic trends are the dominant factor, not changes in land use, forest canopy, or other factors. The largest decreases have occurred where winter temperatures are mild, especially in the Cascade Mountains and northern California. In most mountain ranges, relative declines grow from minimal at ridgetop to substantial at snow line. Taken together, these results emphasize that the West's snow resources are already declining as earth's climate warms

A unified approach for process-based hydrologic modeling: 1. Modeling concept

This work advances a unified approach to process-based hydrologic modeling to enable controlled and systematic evaluation of multiple model representations (hypotheses) of hydrologic processes and scaling behavior. Our approach, which we term the Structure for Unifying Multiple Modeling Alternatives (SUMMA), formulates a general set of conservation equations, providing the flexibility to experiment with different spatial representations, different flux parameterizations, different model parameter values, and different time stepping schemes. In this paper, we introduce the general approach used in SUMMA, detailing the spatial organization and model simplifications, and how different representations of multiple physical processes can be combined within a single modeling framework. We discuss how SUMMA can be used to systematically pursue the method of multiple working hypotheses in hydrology. In particular, we discuss how SUMMA can help tackle major hydrologic modeling challenges, including defining the appropriate complexity of a model, selecting among competing flux parameterizations, representing spatial variability across a hierarchy of scales, identifying potential improvements in computational efficiency and numerical accuracy as part of the numerical solver, and improving understanding of the various sources of model uncertainty.Keywords: unified model, hydrometeorology, scaling behavio