A ranking of hydrological signatures based on their predictability in space

Hydrological signatures are now used for a wide range of purposes, including catchment classification, process exploration and hydrological model calibration. The recent boost in the popularity and number of signatures has however not been accompanied by the development of clear guidance on signature selection. Here we propose that exploring the predictability of signatures in space provides important insights into their drivers, their sensitivity to data uncertainties, and is hence useful for signature selection. We use three complementary approaches to compare and rank 15 commonly‐used signatures, which we evaluate in 671 US catchments from the CAMELS data set (Catchment Attributes and MEteorology for Large‐sample Studies). Firstly, we employ machine learning (random forests) to explore how attributes characterizing the climatic conditions, topography, land cover, soil and geology influence (or not) the signatures. Secondly, we use simulations of a conceptual hydrological model (Sacramento) to benchmark the random forest predictions. Thirdly, we take advantage of the large sample of CAMELS catchments to characterize the spatial auto‐correlation (using Moran's I) of the signature field. These three approaches lead to remarkably similar rankings of the signatures. We show i) that signatures with the noisiest spatial pattern tend to be poorly captured by hydrological simulations, ii) that their relationship to catchments attributes are elusive (in particular they are not correlated to climatic indices) and iii) that they are particularly sensitive to discharge uncertainties. We suggest that a better understanding of their drivers and better characterization of their uncertainties would increase their value in hydrological studies

Risk Assessment of Erosion from Concentrated Flow on Rangelands Using Overland Flow Distribution and Shear Stress Partitioning

Erosion rates of overland flow on rangelands tend to be relatively low, but under certain conditions where flow is concentrated, soil loss can be significant. Therefore, a rangeland site can be highly vulnerable to soil erosion where overland flow is likely to concentrate and exert high shear stress on soil grains. This concept is commonly applied in cropland and wildland soil erosion modeling using predictions of flow effective shear stress (shear stress applied on soil grains). However, historical approaches to partition shear stress in erosion models are computationally complex and require extensive parameterization. Furthermore, most models are not capable of predicting the conditions in which concentrated flow occurs on rangelands. In this study, we investigated the rangelands conditions at which overland flow is more likely to become concentrated and developed equations for partitioning the shear stress of concentrated flow on rangelands. A logistic equation was developed to estimate the probability of overland flow to become concentrated. Total shear stress of rangeland overland flow was partitioned into components exerted on soil, vegetation, and rock cover using field experimental data. In addition, we investigated the vegetation cover limit at which the effective shear stress component is substantially reduced, limiting the erosion rate. The results from the partitioning equations show that shear stress exerted on soil grains was relatively small in sheet flow. Shear stress exerted on soil grains in concentrated flow was significantly higher when bare soil exceeded 60% of the total surface area but decreased significantly when the bare soil area was less than 25% or when the plant base cover exceeded 20%. These percentages could be used as relative measures of hydrologic recovery for disturbed rangelands or as triggers that indicate a site is crossing a threshold beyond which soil erosion might accelerate due to the high effective shear stress

Concentrated Flow Erodibility for Physically Based Erosion Models: Temporal Variability in Disturbed and Undisturbed Rangelands

Current physically based overland flow erosion models for rangeland application do not separate disturbed and undisturbed conditions in modeling concentrated flow erosion. In this study, concentrated flow simulations on disturbed and undisturbed rangelands were used to estimate the erodibility and to evaluate the performance of linear and power law equations that describe the relationship between erosion rate and several hydraulic parameters. None of the hydraulic parameters consistently predicted the detachment capacity well for all sites, however, stream power performed better than most of other hydraulic parameters. Using power law functions did not improve the detachment relation with respect to that of the linear function. Concentrated flow erodibility increased significantly when a site was exposed to a disturbance such as fire or tree encroachment into sagebrush steppe. This study showed that burning increases erosion by amplifying the erosive power of overland flow through removing obstacles and by changing the soil properties affecting erodibility itself. However, the magnitude of fire impact varied among sites due to inherent differences in site characteristics and variability in burn severity. In most cases we observed concentrated flow erodibility had a high value at overland flow initiation and then started to decline with time due to reduction of sediment availability. Thus we developed an empirical function to predict erodibility variation within a runoff event as a function of cumulative unit discharge. Empirical equations were also developed to predict erodibility variation with time postdisturbance as a function of readily available vegetation cover and surface soil texture data

Satellite cell myogenic capacity is maintained in aged human muscles

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Air Pollution and ST-Segment Depression in Elderly Subjects

Increased levels of daily ambient particle pollution have been associated with increased risk of cardiovascular morbidity. Black carbon (BC) is a measure of the traffic-related component of particles. We investigated associations between ambient pollution and ST-segment levels in a repeated-measures study including 269 observations on 24 active Boston residents 61–88 years of age, each observed up to 12 times from June through September 1999. The protocol involved continuous Holter electrocardiogram monitoring including 5 min of rest, 5 min of standing, 5 min of exercise outdoors, 5 min of recovery, and 20 cycles of paced breathing. Pollution-associated ST-depression was estimated for a 10th- to 90th-percentile change in BC. We calculated the average ST-segment level, referenced to the P-R isoelectric values, for each portion of the protocol. The mean BC level in the previous 12 hr, and the BC level 5 hr before testing, predicted ST-segment depression in most portions of the protocol, but the effect was strongest in the postexercise periods. During post-exercise rest, an elevated BC level was associated with −0.1 mm ST-segment depression (p = 0.02 for 12-hr mean BC; p = 0.001 for 5-hr BC) in continuous models. Elevated BC also predicted increased risk of ST-segment depression ≥0.5 mm among those with at least one episode of that level of ST-segment depression. Carbon monoxide was not a confounder of this association. ST-segment depression, possibly representing myocardial ischemia or inflammation, is associated with increased exposure to particles whose predominant source is traffic

Incorporating Hydrologic Data and Ecohydrologic Relationships into Ecological Site Descriptions

The purpose of this paper is to recommend a framework and methodology for incorporating hydrologic data and ecohydrologic relationships in Ecological Site Descriptions (ESDs) and thereby enhance the utility of ESDs for assessing rangelands and guiding resilience-based management strategies. Resilience-based strategies assess and manage ecological state dynamics that affect state vulnerability and, therefore, provide opportunities to adapt management. Many rangelands are spatially heterogeneous or sparsely vegetated where the vegetation structure strongly influences infiltration and soil retention. Infiltration and soil retention further influence soil water recharge, nutrient availability, and overall plant productivity. These key ecohydrologic relationships govern the ecologic resilience of the various states and community phases on many rangeland ecological sites (ESs) and are strongly affected by management practices, land use, and disturbances. However, ecohydrologic data and relationships are often missing in ESDs and state-and-transition models (STMs). To address this void, we used literature to determine the data required for inclusion of key ecohydrologic feedbacks into ESDs, developed a framework and methodology for data integration within the current ESD structure, and applied the framework to a select ES for demonstrative purposes. We also evaluated the utility of the Rangeland Hydrology and Erosion Model (RHEM) for assessment and enhancement of ESDs based in part on hydrologic function. We present the framework as a broadly applicable methodology for integrating ecohydrologic relationships and feedbacks into ESDs and resilience-based management strategies. Our proposed framework increases the utility of ESDs to assess rangelands, target conservation and restoration practices, and predict ecosystem responses to management. The integration of RHEM technology and our suggested framework on ecohydrologic relations expands the ecological foundation of the overall ESD concept for rangeland management and is well aligned with resilience-based, adaptive management of US rangelands. The proposed enhancement of ESDs will improve communication between private land owners and resource managers and researchers across multiple disciplines in the field of rangeland management

Estimating Conservation Needs for Rangelands Using USDA National Resources Inventory Assessments

This study presents (1) the overall concept of assessing non-federal western rangeland soil loss rates at a national scale for determining areas of vulnerability for accelerated soil loss using USDA Natural Resources Conservation Service (NRCS) National Resources Inventory (NRI) data and the Rangeland Hydrology and Erosion Model (RHEM) and (2) the evaluation of a risk-based vulnerability approach as an alternative to the conventional average annual soil loss tolerance (T) for assessment of rangeland sustainability. RHEM was used to estimate runoff and soil loss at the hillslope scale for over 10,000 NRCS NRI sample points in 17 western states on non-federal rangelands. The national average annual soil loss rate on non-federal rangeland is estimated to be 1.4 ton ha⁻¹ year⁻¹. Nationally, 20% of non-federal rangelands generate more than 50% of the average annual soil loss. Over 29.2 × 10⁶ ha (18%) of the non-federal rangelands might benefit from treatment to reduce 1559-1570 soil loss to below 2.2 ton ha⁻¹ year⁻¹. National average annual soil loss rates combine areas with low and accelerated soil loss. Evaluating data in this manner can misrepresent the magnitude of the soil loss problem on rangelands. Between 23% and 29% of U.S. non-federal rangelands are vulnerable to accelerated soil loss (≥ 2.2 ton ha⁻¹ event⁻¹) if assessed as a function of vulnerability to a runoff event with a return period of ≥ 25 years. The NRCS has not evaluated potential soil loss risk in national reports in the past, and adaptation of this technique will allow the USDA and its partners to be proactive in preventing accelerated soil loss on rangelands.Keywords: Soil erosion, Rangeland Hydrology and Erosion Model, Non-federal rangelands, Conservation Effects Assessment Project, Soil loss tolerance, Soil and water conservation, National resources inventor

Surface Energy Budgets of Arctic Tundra During Growing Season

This study analyzed summer observations of diurnal and seasonal surface energy budgets across several monitoring sites within the Arctic tundra underlain by permafrost. In these areas, latent and sensible heat fluxes have comparable magnitudes, and ground heat flux enters the subsurface during short summer intervals of the growing period, leading to seasonal thaw. The maximum entropy production (MEP) model was tested as an input and parameter parsimonious model of surface heat fluxes for the simulation of energy budgets of these permafrost‐underlain environments. Using net radiation, surface temperature, and a single parameter characterizing the thermal inertia of the heat exchanging surface, the MEP model estimates latent, sensible, and ground heat fluxes that agree closely with observations at five sites for which detailed flux data are available. The MEP potential evapotranspiration model reproduces estimates of the Penman‐Monteith potential evapotranspiration model that requires at least five input meteorological variables (net radiation, ground heat flux, air temperature, air humidity, and wind speed) and empirical parameters of surface resistance. The potential and challenges of MEP model application in sparsely monitored areas of the Arctic are discussed, highlighting the need for accurate measurements and constraints of ground heat flux.Plain Language SummaryGrowing season latent and sensible heat fluxes are nearly equal over the Arctic permafrost tundra regions. Persistent ground heat flux into the subsurface layer leads to seasonal thaw of the top permafrost layer. The maximum energy production model accurately estimates the latent, sensible, and ground heat flux of the surface energy budget of the Arctic permafrost regions.Key PointThe MEP model is parsimonious and well suited to modeling surface energy budget in data‐sparse permafrost environmentsPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/150560/1/jgrd55584.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/150560/2/jgrd55584_am.pd

Soil erosion modelling: A bibliometric analysis

Soil erosion can present a major threat to agriculture due to loss of soil, nutrients, and organic carbon. Therefore, soil erosion modelling is one of the steps used to plan suitable soil protection measures and detect erosion hotspots. A bibliometric analysis of this topic can reveal research patterns and soil erosion modelling characteristics that can help identify steps needed to enhance the research conducted in this field. Therefore, a detailed bibliometric analysis, including investigation of collaboration networks and citation patterns, should be conducted. The updated version of the Global Applications of Soil Erosion Modelling Tracker (GASEMT) database contains information about citation characteristics and publication type. Here, we investigated the impact of the number of authors, the publication type and the selected journal on the number of citations. Generalized boosted regression tree (BRT) modelling was used to evaluate the most relevant variables related to soil erosion modelling. Additionally, bibliometric networks were analysed and visualized. This study revealed that the selection of the soil erosion model has the largest impact on the number of publication citations, followed by the modelling scale and the publication\u27s CiteScore. Some of the other GASEMT database attributes such as model calibration and validation have negligible influence on the number of citations according to the BRT model. Although it is true that studies that conduct calibration, on average, received around 30% more citations, than studies where calibration was not performed. Moreover, the bibliographic coupling and citation networks show a clear continental pattern, although the co-authorship network does not show the same characteristics. Therefore, soil erosion modellers should conduct even more comprehensive review of past studies and focus not just on the research conducted in the same country or continent. Moreover, when evaluating soil erosion models, an additional focus should be given to field measurements, model calibration, performance assessment and uncertainty of modelling results. The results of this study indicate that these GASEMT database attributes had smaller impact on the number of citations, according to the BRT model, than anticipated, which could suggest that these attributes should be given additional attention by the soil erosion modelling community. This study provides a kind of bibliographic benchmark for soil erosion modelling research papers as modellers can estimate the influence of their paper

Soil erosion modelling: A global review and statistical analysis

To gain a better understanding of the global application of soil erosion prediction models, we comprehensivelyreviewed relevant peer-reviewed research literature on soil-erosion modelling published between 1994 and2017. We aimed to identify (i) the processes and models most frequently addressed in the literature, (ii) the re-gions within which models are primarily applied, (iii) the regions which remain unaddressed and why, and (iv)how frequently studies are conducted to validate/evaluate model outcomes relative to measured data. To per-form this task, we combined the collective knowledge of 67 soil-erosion scientists from 25 countries. Theresulting database, named‘Global Applications of Soil Erosion Modelling Tracker (GASEMT)’, includes 3030 indi-vidual modelling records from 126 countries, encompassing all continents (except Antarctica). Out of the 8471articles identified as potentially relevant, we reviewed 1697 appropriate articles and systematically evaluatedand transferred 42 relevant attributes into the database. This GASEMT database provides comprehensive insightsinto the state-of-the-art of soil- erosion models and model applications worldwide. This database intends to sup-port the upcoming country-based United Nations global soil-erosion assessment in addition to helping to informsoil erosion research priorities by building a foundation for future targeted, in-depth analyses. GASEMT is anopen-source database available to the entire user-community to develop research, rectify errors, andmakefutureexpansion