A Simplified Equation for Modeling Sediment Transport Capacity

Sediment transport capacity for shallow overland flow was represented as a quadratic function of downslope distance using the assumption of a linear increase in overland flow discharge with downslope distance and an approximation to the Yalin equation for sediment transport capacity. The simplified equation for sediment transport applies to complex topography having uniform soil and management characteristics. The simplified equation accurately approximated the Yalin equation when calibrated using the average of the hydraulic shear stresses at the end of a constant slope reference profile and the end of the actual profile. The simplified equation is useful in deriving closed-form solutions to the governing erosion equations for steady state conditions and reduces the computational time when numerical solutions are required

A Simplified Equation for Modeling Sediment Transport Capacity

Sediment transport capacity for shallow overland flow was represented as a quadratic function of downslope distance using the assumption of a linear increase in overland flow discharge with downslope distance and an approximation to the Yalin equation for sediment transport capacity. The simplified equation for sediment transport applies to complex topography having uniform soil and management characteristics. The simplified equation accurately approximated the Yalin equation when calibrated using the average of the hydraulic shear stresses at the end of a constant slope reference profile and the end of the actual profile. The simplified equation is useful in deriving closed-form solutions to the governing erosion equations for steady state conditions and reduces the computational time when numerical solutions are required

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

The water balance components of undisturbed tropical woodlands in the Brazilian cerrado

Deforestation of the Brazilian cerrado region has caused major changes in hydrological processes. These changes in water balance components are still poorly understood but are important for making land management decisions in this region. To better understand pre-deforestation conditions, we determined the main components of the water balance for an undisturbed tropical woodland classified as "cerrado sensu stricto denso". We developed an empirical model to estimate actual evapotranspiration (ET) by using flux tower measurements and vegetation conditions inferred from the enhanced vegetation index and reference evapotranspiration. Canopy interception, throughfall, stemflow, surface runoff, and water table level were assessed from ground measurements. We used data from two cerrado sites, Pé de Gigante (PDG) and Instituto Arruda Botelho (IAB). Flux tower data from the PDG site collected from 2001 to 2003 were used to develop the empirical model to estimate ET. The other hydrological processes were measured at the field scale between 2011 and 2014 at the IAB site. The empirical model showed significant agreement (<i>R</i>² = 0.73) with observed ET at the daily timescale. The average values of estimated ET at the IAB site ranged from 1.91 to 2.60 mm day⁻¹ for the dry and wet seasons, respectively. Canopy interception ranged from 4 to 20 % and stemflow values were approximately 1 % of the gross precipitation. The average runoff coefficient was less than 1 %, while cerrado deforestation has the potential to increase that amount up to 20-fold. As relatively little excess water runs off (either by surface water or groundwater), the water storage may be estimated by the difference between precipitation and evapotranspiration. Our results provide benchmark values of water balance dynamics in the undisturbed cerrado that will be useful to evaluate past and future land-cover and land-use changes for this region

Denoising the Denoisers: an independent evaluation of microbiome sequence error-correction approaches

High-depth sequencing of universal marker genes such as the 16S rRNA gene is a common strategy to profile microbial communities. Traditionally, sequence reads are clustered into operational taxonomic units (OTUs) at a defined identity threshold to avoid sequencing errors generating spurious taxonomic units. However, there have been numerous bioinformatic packages recently released that attempt to correct sequencing errors to determine real biological sequences at single nucleotide resolution by generating amplicon sequence variants (ASVs). As more researchers begin to use high resolution ASVs, there is a need for an in-depth and unbiased comparison of these novel “denoising” pipelines. In this study, we conduct a thorough comparison of three of the most widely-used denoising packages (DADA2, UNOISE3, and Deblur) as well as an open-reference 97% OTU clustering pipeline on mock, soil, and host-associated communities. We found from the mock community analyses that although they produced similar microbial compositions based on relative abundance, the approaches identified vastly different numbers of ASVs that significantly impact alpha diversity metrics. Our analysis on real datasets using recommended settings for each denoising pipeline also showed that the three packages were consistent in their per-sample compositions, resulting in only minor differences based on weighted UniFrac and Bray–Curtis dissimilarity. DADA2 tended to find more ASVs than the other two denoising pipelines when analyzing both the real soil data and two other host-associated datasets, suggesting that it could be better at finding rare organisms, but at the expense of possible false positives. The open-reference OTU clustering approach identified considerably more OTUs in comparison to the number of ASVs from the denoising pipelines in all datasets tested. The three denoising approaches were significantly different in their run times, with UNOISE3 running greater than 1,200 and 15 times faster than DADA2 and Deblur, respectively. Our findings indicate that, although all pipelines result in similar general community structure, the number of ASVs/OTUs and resulting alpha-diversity metrics varies considerably and should be considered when attempting to identify rare organisms from possible background noise

Impact of an Extreme Storm Event on River Corridor Bank Erosion and Phosphorus Mobilization in a Mountainous Watershed in the Northeastern United States

Movement of sediment, and associated phosphorus, from stream banks to freshwater lakes is predicted to increase with greater frequency of extreme precipitation events. This higher phosphorus load may accelerate harmful algal blooms in affected water bodies, such as Lake Champlain in Vermont, New York, and Québec. In the Mad River, a subwatershed in central Vermont\u27s Lake Champlain Basin, extreme flooding from Tropical Storm Irene in 2011 caused extensive erosion. We measured stream channel change along the main stem between 2008 and 2011 by digitizing available prestorm and poststorm aerial imagery. Soils were sampled post Irene at six active stream erosion sites, using an experimental design to measure differences in soil texture and phosphorus both with depth (90 cm) and distance from the stream. In addition to total phosphorus (TP), we determined bioavailable (soil test) phosphorus (STP) and the degree of phosphorus saturation (DPS). The six sites represented a 0.87-km length of stream bank that contributed an estimated 17.6 × 10 3 Mg of sediment and 15.8 Mg of TP, roughly the same as average annual watershed export estimates. At four sites, the STP and DPS were low and suggested little potential for short-term phosphorus release. At two agricultural sites where the lateral extent of erosion was high, imagery showed a clear loss of well-established riparian buffer. Present-day near-stream soils were elevated in STP and DPS. An increase in these extreme events will clearly increase sediment loads. There will also be increasing concentration of sediment phosphorus if stream banks continue to erode into actively managed agricultural fields

What Role Does Hydrological Science Play in the Age of Machine Learning?

ABSTRACT: This paper is derived from a keynote talk given at the Google's 2020 Flood Forecasting Meets Machine Learning Workshop. Recent experiments applying deep learning to rainfall‐runoff simulation indicate that there is significantly more information in large‐scale hydrological data sets than hydrologists have been able to translate into theory or models. While there is a growing interest in machine learning in the hydrological sciences community, in many ways, our community still holds deeply subjective and nonevidence‐based preferences for models based on a certain type of “process understanding” that has historically not translated into accurate theory, models, or predictions. This commentary is a call to action for the hydrology community to focus on developing a quantitative understanding of where and when hydrological process understanding is valuable in a modeling discipline increasingly dominated by machine learning. We offer some potential perspectives and preliminary examples about how this might be accomplished

Satellite cell myogenic capacity is maintained in aged human muscles

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Atrial septum fat deposition and atrial anatomy assessed by cardiac magnetic resonance: relationship to atrial electrophysiology

To assess the prevalence of fat deposition in the atrial septum with and its relationship with 12-lead electrocardiogram (ECG) atrial parameters (PR interval, P wave duration) and the presence of atrial fibrillation

Contour-based digital elevation modeling of watershed erosion and sedimentation: Erosion and sedimentation estimation tool (EROSET)

An erosion and sedimentation model, erosion and sedimentation estimation tool (EROSET), was developed and applied to a watershed in Happy Valley, South Australia. The model simulates the dynamics of event runoff, soil detachment, and transport processes. The erosion and sedimentation model is able to predict watershed erosion and deposition for storm events at an element as well as watershed scale. The model was developed and incorporated into an existing rainfall-runoff model based on a contour-based digital elevation framework. It combines the use of the USLE data source and extended erosion and transportation modeling into a distributed and intra storm erosion and deposition analysis. This results in storm-based, time-variant, distributed erosion and deposition modeling in the watershed for both storm-based and long-term sediment estimation. The modeling can better enable land managers to identify the areas in a watershed where erosion and deposition may occur. The modeled processes and results can be related to total storm erosion estimated by MUSLE, although they operate on different temporal and spatial frames. Satisfactory modeling results were obtained with very limited calibration which compares well with other studies.H. Sun, P. S. Cornish and T. M. Daniel