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

Parameter Sensitivity of the Noah-MP Land Surface Model with Dynamic Vegetation

The Noah land surface model with multiple parameterization options (Noah-MP) includes a routine for dynamic simulation of vegetation carbon assimilation and soil carbon decomposition processes. To use remote sensing observations of vegetation to constrain simulations from this model, it is necessary first to understand the sensitivity of the model to its parameters. This is required for efficient parameter estimation, which is both a valuable way to use observations and also a first or concurrent step in many state-updating data assimilation procedures. We use variance decomposition to assess the sensitivity of estimates of sensible heat, latent heat, soil moisture, and net ecosystem exchange made by certain standard Noah-MP configurations that include dynamic simulation of vegetation and carbon to forty-three primary user-specified parameters. This is done using thirty-two years' worth of data from ten international FluxNet sites. Findings indicate that there are five soil parameters and six (or more) vegetation parameters (depending on the model configuration) that act as primary controls on these states and fluxes

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

Assimilating Remote Sensing Observations of Leaf Area Index and Soil Moisture for Wheat Yield Estimates: An Observing System Simulation Experiment

Observing system simulation experiments were used to investigate ensemble Bayesian state updating data assimilation of observations of leaf area index (LAI) and soil moisture (theta) for the purpose of improving single-season wheat yield estimates with the Decision Support System for Agrotechnology Transfer (DSSAT) CropSim-Ceres model. Assimilation was conducted in an energy-limited environment and a water-limited environment. Modeling uncertainty was prescribed to weather inputs, soil parameters and initial conditions, and cultivar parameters and through perturbations to model state transition equations. The ensemble Kalman filter and the sequential importance resampling filter were tested for the ability to attenuate effects of these types of uncertainty on yield estimates. LAI and theta observations were synthesized according to characteristics of existing remote sensing data, and effects of observation error were tested. Results indicate that the potential for assimilation to improve end-of-season yield estimates is low. Limitations are due to a lack of root zone soil moisture information, error in LAI observations, and a lack of correlation between leaf and grain growth

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

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

Prediction of sudden cardiac death with automated high-throughput analysis of heterogeneity in standard resting 12-lead electrocardiograms

BACKGROUND Heterogeneity of depolarization and repolarization underlies the development of lethal arrhythmias. OBJECTIVE We investigated whether quantification of spatial depolarization and repolarization heterogeneity identifies individuals at risk for sudden cardiac death (SCD). METHODS Spatial R-, J-, and T-wave heterogeneity (RWH, JWH, and TWH, respectively) was analyzed using automated second central moment analysis of standard digital 12-lead electrocardiograms in 5618 adults (2588, 46% men; mean +/- SEM age 50.9 +/- 0.2 years), who took part in the epidemiological Health 2000 Survey as representative of the entire Finnish adult population. RESULTS During the follow-up period of 7.7 +/- 0.2 years, a total of 72 SCDs occurred (1.3%), with an average yearly incidence rate of 0.17% per year. Increased RWH, JWH, and TWH in left precordial leads (V-4-V-6) were univariately associated with SCD (P = 102 mu V) was associated with a 1.7-fold adjusted relative risk for SCD (95% confidence interval [CI] 1.0-2.9; P = .048) and increased JWH (>= 123 mu V) with a 2.0-fold adjusted relative risk for SCD (95% CI 1.2-3.3; P = .006). When both TWH and JWH were above the threshold, the adjusted relative risk for SCD was 2.9-fold (95% CI 1.5-5.7; P = .002). When RWH (>= 470 mu V), JWH, and TWH were all above the threshold, the adjusted relative risk for SCD was 3.2-fold (95% CI 1.4-7.1; P = .009). CONCLUSION Second central moment analysis of standard resting 12-lead electrocardiographic morphology provides an ultrarapid means for the automated measurement of spatial RWH, JWH, and TWH, enabling analysis of high subject volumes and screening for SCD risk in the general population.Peer reviewe

Models of everywhere revisited: a technological perspective

The concept ‘models of everywhere’ was first introduced in the mid 2000s as a means of reasoning about the environmental science of a place, changing the nature of the underlying modelling process, from one in which general model structures are used to one in which modelling becomes a learning process about specific places, in particular capturing the idiosyncrasies of that place. At one level, this is a straightforward concept, but at another it is a rich multi-dimensional conceptual framework involving the following key dimensions: models of everywhere, models of everything and models at all times, being constantly re-evaluated against the most current evidence. This is a compelling approach with the potential to deal with epistemic uncertainties and nonlinearities. However, the approach has, as yet, not been fully utilised or explored. This paper examines the concept of models of everywhere in the light of recent advances in technology. The paper argues that, when first proposed, technology was a limiting factor but now, with advances in areas such as Internet of Things, cloud computing and data analytics, many of the barriers have been alleviated. Consequently, it is timely to look again at the concept of models of everywhere in practical conditions as part of a trans-disciplinary effort to tackle the remaining research questions. The paper concludes by identifying the key elements of a research agenda that should underpin such experimentation and deployment