A large proportion of nutrients and sediment is mobilised in catchments during storm events. Therefore understanding a catchment's hydrological behaviour during storms and how this acts to mobilise ...and transport nutrients and sediment to nearby watercourses is extremely important for effective catchment management. The expansion of available in-situ sensors is allowing a wider range of water quality parameters to be monitored and at higher temporal resolution, meaning that the investigation of hydrochemical behaviours during storms is increasingly feasible. Studying the relationship between discharge and water quality parameters in storm events can provide a valuable research tool to infer the likely source areas and flow pathways contributing to nutrient and sediment transport. Therefore, this paper uses 2years of high temporal resolution (15/30min) discharge and water quality (nitrate-N, total phosphorus (TP) and turbidity) data to examine hysteretic behaviour during storm events in two contrasting catchments, in the Hampshire Avon catchment, UK. This paper provides one of the first examples of a study which comprehensively examines storm behaviours for up to 76 storm events and three water quality parameters. It also examines the observational uncertainties using a non-parametric approach. A range of metrics was used, such as loop direction, loop area and a hysteresis index (HI) to characterise and quantify the storm behaviour. With two years of high resolution information it was possible to see how transport mechanisms varied between parameters and through time. This study has also clearly shown the different transport regimes operating between a groundwater dominated chalk catchment versus a surface-water dominated clay catchment. This information, set within an uncertainty framework, means that confidence can be derived that the patterns and relationships thus identified are statistically robust. These insights can thus be used to provide information regarding transport processes and biogeochemical processing within river catchments.
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•Hysteretic storm behaviour was analysed in 3 water quality parameters at 2 sites•Storms were analysed within an observational uncertainty framework•Range of metrics were used, including a new index, to quantify storm hysteresis•Differences in transport mechanisms shown between nitrate and TP in chalk system.•Behaviour was complex but provides insight into catchment processes in landscapes.
The choice of hydrological model structure, that is, a model's selection of states and fluxes and the equations used to describe them, strongly controls model performance and realism. This work ...investigates differences in performance of 36 lumped conceptual model structures calibrated to and evaluated on daily streamflow data in 559 catchments across the United States. Model performance is compared against a benchmark that accounts for the seasonality of flows in each catchment. We find that our model ensemble struggles to beat the benchmark in snow‐dominated catchments. In most other catchments model structure equifinality (i.e., cases where different models achieve similar high efficiency scores) can be very high. We find no relation between the number of model parameters and performance during either calibration or evaluation periods nor evidence of increased risk of overfitting for models with more parameters. Instead, the choice of model parametrization (i.e., which equations are used and how parameters are used within them) dictates the model's strengths and weaknesses. Results suggest that certain model structures are inherently better suited for certain objective functions and thus for certain study purposes. We find no clear relationships between the catchments where any model performs well and descriptors of those catchments' geology, topography, soil, and vegetation characteristics. Instead, model suitability seems to relate strongest to the streamflow regime each catchment generates, and we have formulated several tentative hypotheses that relate commonalities in model structure to similarities in model performance. Modeling results are made publicly available for further investigation.
Key Points
Conceptual model structure uncertainty is high across different catchments and objective functions
There is no evidence of systematic overfitting for models with up to 15 calibrated parameters
Model performance relates more to streamflow signatures than to climate or catchment descriptors
Marine environments have increased in temperature by an average of 1°C since pre-industrial (1850) times 1. Given that species ranges are closely allied to physiological thermal tolerances in marine ...organisms 2, it may therefore be expected that ocean warming would lead to abundance increases at poleward side of ranges and abundance declines toward the equator 3. Here, we report a global analysis of abundance trends of 304 widely distributed marine species over the last century, across a range of taxonomic groups from phytoplankton to fish and marine mammals. Specifically, using a literature database, we investigate the extent that the direction and strength of long-term species abundance changes depend on the sampled location within the latitudinal range of species. Our results show that abundance increases have been most prominent where sampling has taken place at the poleward side of species ranges, and abundance declines have been most prominent where sampling has taken place at the equatorward side of species ranges. These data provide evidence of omnipresent large-scale changes in abundance of marine species consistent with warming over the last century and suggest that adaptation has not provided a buffer against the negative effects of warmer conditions at the equatorward extent of species ranges. On the basis of these results, we suggest that projected sea temperature increases of up to 1.5°C over pre-industrial levels by 2050 4 will continue to drive latitudinal abundance shifts in marine species, including those of importance for coastal livelihoods.
•Global-scale analysis of marine species shows abundance changes linked to warming•Increases at poleward sides of species ranges reflect new ecological opportunities•Declines at equatorward sides show failure to adapt to rapid climate change•Results imply future warming will impact further on abundance of marine species
Globally, the oceans have warmed by over 1°C during the last century. Hastings, Rutterford, et al. show how marine species are responding. At the poleward sides of species ranges, populations are increasing as warming makes habitat more favorable, but at equatorward sides, populations are shrinking as seas become too warm.
Introduction The objectives of this study were to determine the validity, reliability, and reproducibility of the iOC intraoral scanner (Cadent, Carlstadt, NJ) and its associated OrthoCAD software ...(Cadent) in measuring tooth widths and deriving Bolton ratios. Methods Thirty subjects had impressions taken of their teeth and rendered as stone casts. In addition, their mouths were scanned with the iOC and the scans were converted into digital models. Tooth widths were measured with a digital caliper from the physical models and with the OrthoCAD software from the virtual models. Bolton ratios were derived using the data from each method. Validity was assessed with a paired t test, reliability with the Pearson correlation coefficient, and reproducibility with the intraclass correlation coefficient. Results Although there were statistically significant differences between mean tooth widths ( P = 0.0083) and Bolton ratios ( P = 0.0354 and P <0.0001) with the 2 methods, the discrepancies were deemed to be clinically insignificant. The Pearson r for tooth-width replications was 0.99 for both techniques, and all intraclass correlation coefficient values exceeded 87%. Conclusions The iOC/OrthoCAD system can be used to measure tooth widths and calculate Bolton ratios with clinically acceptable accuracy and excellent reliability and reproducibility. It appears to be a sound orthodontic aid.
Phosphorus losses from land to water will be impacted by climate change and land management for food production, with detrimental impacts on aquatic ecosystems. Here we use a unique combination of ...methods to evaluate the impact of projected climate change on future phosphorus transfers, and to assess what scale of agricultural change would be needed to mitigate these transfers. We combine novel high-frequency phosphorus flux data from three representative catchments across the UK, a new high-spatial resolution climate model, uncertainty estimates from an ensemble of future climate simulations, two phosphorus transfer models of contrasting complexity and a simplified representation of the potential intensification of agriculture based on expert elicitation from land managers. We show that the effect of climate change on average winter phosphorus loads (predicted increase up to 30% by 2050s) will be limited only by large-scale agricultural changes (e.g., 20-80% reduction in phosphorus inputs).The impact of climate change on phosphorus (P) loss from land to water is unclear. Here, the authors use P flux data, climate simulations and P transfer models to show that only large scale agricultural change will limit the effect of climate change on average winter P loads in three catchments across the UK.
Benchmarking the quality of river discharge data and understanding its information content for hydrological analyses is an important task for hydrologic science. There is a wide variety of techniques ...to assess discharge uncertainty. However, few studies have developed generalized approaches to quantify discharge uncertainty. This study presents a generalized framework for estimating discharge uncertainty at many gauging stations with different errors in the stage‐discharge relationship. The methodology utilizes a nonparametric LOWESS regression within a novel framework that accounts for uncertainty in the stage‐discharge measurements, scatter in the stage‐discharge data and multisection rating curves. The framework was applied to 500 gauging stations in England and Wales and we evaluated the magnitude of discharge uncertainty at low, mean and high flow points on the rating curve. The framework was shown to be robust, versatile and able to capture place‐specific uncertainties for a number of different examples. Our study revealed a wide range of discharge uncertainties (10–397% discharge uncertainty interval widths), but the majority of the gauging stations (over 80%) had mean and high flow uncertainty intervals of less than 40%. We identified some regional differences in the stage‐discharge relationships, however the results show that local conditions dominated in determining the magnitude of discharge uncertainty at a gauging station. This highlights the importance of estimating discharge uncertainty for each gauging station prior to using those data in hydrological analyses.
Key Points:
A generalized framework for discharge uncertainty estimation is presented
Allows estimation of place‐specific discharge uncertainties for many catchments
Local conditions dominate in determining discharge uncertainty magnitudes
The calibration of hydrological models without streamflow observations is problematic, and the simultaneous, combined use of remotely sensed products for this purpose has not been exhaustively tested ...thus far. Our hypothesis is that the combined use of products can (1) reduce the parameter search space and (2) improve the representation of internal model dynamics and hydrological signatures. Five different conceptual hydrological models were applied to 27 catchments across Europe. A parameter selection process, similar to a likelihood weighting procedure, was applied for 1,023 possible combinations of 10 different data sources, ranging from using 1 to all 10 of these products. Distances between the two empirical distributions of model performance metrics with and without using a specific product were determined to assess the added value of a specific product. In a similar way, the performance of the models to reproduce 27 hydrological signatures was evaluated relative to the unconstrained model. Significant reductions in the parameter space were obtained when combinations included Advanced Microwave Scanning Radiometer ‐ Earth Observing System and Advanced Scatterometer soil moisture, Gravity Recovery and Climate Experiment total water storage anomalies, and, in snow‐dominated catchments, the Moderate Resolution Imaging Spectroradiometer snow cover products. The evaporation products of Land Surface Analysis ‐ Satellite Application Facility and MOD16 were less effective for deriving meaningful, well‐constrained posterior parameter distributions. The hydrological signature analysis indicated that most models profited from constraining with an increasing number of data sources. Concluding, constraining models with multiple data sources simultaneously was shown to be valuable for at least four of the five hydrological models to determine model parameters in absence of streamflow.
Key Points
1023 combinations of 9 remotely sensed products were explored for constraining parameters in the absence of streamflow data
Combining multiple (satellite) data sets for deriving posterior parameter ranges leads to a narrower parameter search space
Especially the soil moisture products of AMSR‐E, ASCAT, and the total water storage anomalies from GRACE helped in determining feasible parameter sets with good performance in streamflow prediction
Hydrological models frequently suffer from limited predictive power despite adequate calibration performances. This can indicate insufficient representations of the underlying processes. Thus, ways ...are sought to increase model consistency while satisfying the contrasting priorities of increased model complexity and limited equifinality. In this study, the value of a systematic use of hydrological signatures and expert knowledge for increasing model consistency was tested. It was found that a simple conceptual model, constrained by four calibration objective functions, was able to adequately reproduce the hydrograph in the calibration period. The model, however, could not reproduce a suite of hydrological signatures, indicating a lack of model consistency. Subsequently, testing 11 models, model complexity was increased in a stepwise way and counter‐balanced by “prior constraints,” inferred from expert knowledge to ensure a model which behaves well with respect to the modeler's perception of the system. We showed that, in spite of unchanged calibration performance, the most complex model setup exhibited increased performance in the independent test period and skill to better reproduce all tested signatures, indicating a better system representation. The results suggest that a model may be inadequate despite good performance with respect to multiple calibration objectives and that increasing model complexity, if counter‐balanced by prior constraints, can significantly increase predictive performance of a model and its skill to reproduce hydrological signatures. The results strongly illustrate the need to balance automated model calibration with a more expert‐knowledge‐driven strategy of constraining models.
Key Points
Expert‐knowledge‐based prior information has strong constraining power
Complex models with prior constraints are consistent and robust
Uncalibrated but constrained complex models outperform standard models
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