Many existing hydrological modelling procedures do not make best use of available information, resulting in non-minimal uncertainties in model structure and parameters, and a lack of detailed information regarding model behaviour. A framework is required that balances the level of model complexity supported by the available data with the level of performance suitable for the desired application. Tools are needed that make optimal use of the information available in the data to identify model structure and parameters, and that allow a detailed analysis of model behaviour. This should result in appropriate levels of model complexity as a function of available data, hydrological system characteristics and modelling purpose. This paper introduces an analytical framework to achieve this, and tools to use within it, based on a multi-objective approach to model calibration and analysis. The utility of the framework is demonstrated with an example from the field of rainfall-runoff modelling.

Abstract:Conceptual modelling requires the identification of a suitable model structure and the estimation of parameter values through calibration against observed data. A lack of objective approaches to evaluate model structures and the inability of calibration procedures to distinguish between the suitability of different parameter sets are major sources of uncertainty in current modelling procedures. This paper presents an approach analysing the performance of the model in a dynamic fashion resulting in an improved use of available information. Model structures can be evaluated with respect to the failure of individual components, and periods of high information content for specific parameters can be identified. The procedure is termed dynamic identifiability analysis (DYNIA) and is applied to a model structure built from typical conceptual components.

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Abstract. The relationship between the distributed transient storage (TS) and lumped aggregate dead zone (ADZ) models of longitudinal solute transport in rivers and streams is examined by a parallel application to tracer data and through an investigation of parameter relationships. Both models accurately describe observed solute transport in a stream where the effects of storage or dead zones significantly affect longitudinal dispersion. A moment matching technique, based on theoretical temporal moments, is used to develop parameter relationships. Tests using the previously calibrated parameters, in addition to simulation experiments, show that the moment matching procedure allows ADZ model parameters to be reliably derived from TS model parameters and vice versa. An investigation of these parameter relationships reveals an important difference between the effective solute transport velocity and the average reach flow velocity in streams subject to transient storage or dead zone processes. A number of practical uses for the derived relationships are suggested, including the ability to utilize powerful methods of system identification in the estimation of TS model parameters.

The purpose of this study was to investigate longitudinal body composition of professional rugby 2 union players over one competitive season. Given the potential for variability in changes, and as the 3 first to do so, we conducted individual analysis in addition to analysis of group means. Thirty-five 4 professional rugby union players from one English Premiership team (forwards: n=20, age: 25.5±4.7 5 years; backs: n=15, age: 26.1±4.5 years) received one total-body dual-energy X-ray absorptiometry 6 (DXA) scan at preseason (August), midseason (January) and endseason (May), enabling 7 quantification of body mass, total and regional fat mass, lean mass, percentage tissue fat mass 8 (%TFM) and bone mineral content (BMC). Individual analysis was conducted by applying least 9 significant change (LSC), derived from our previously published precision data and in accordance 10 with International Society for Clinical Densitometry (ISCD) guidelines. Mean body mass remained 11 stable throughout the season (p>0.05), but total fat mass and %TFM increased from pre to endseason, 12 and mid to endseason (p<0.05). There were also statistically significant increases in total-body BMC 13 across the season (p<0.05). In both groups, there was a loss of lean mass between mid and endseason 14

Super League (SL) and Championship (RLC) rugby league players will compete against each other in 2015 and beyond. To identify possible discrepancies, this study compared the anthropometric profile and body composition of current SL (full-time professional) and RLC (part-time semi-professional) players using dual-energy X-ray absorptiometry (DXA). A cross-sectional design involved DXA scans on 67 SL (n=29 backs, n=38 forwards) and 46 RLC (n=20 backs, n=26 forwards) players during preseason. A one-way ANOVA was used to compare age, stature, body mass, soft tissue fat percentage, bone mineral content (BMC), total and regional (i.e., arms, legs and trunk) fat and lean mass between SL forwards, SL backs, RLC forwards and RLC backs. No significant differences in age, stature or body mass were observed. SL forwards and backs had relatively less soft tissue fat (17.5 ± 3.7 and 14.8 ± 3.6 vs. 21.4 ± 4.3 and 20.8 ± 3.8%), greater BMC (4,528 ± 443 and 4,230 ± 447 vs. 4,302 ± 393 and 3,971 ± 280 g), greater trunk lean mass (37.3 ± 3.0 and 35.3 ± 3.8 vs. 34.9 ± 32.3 and 32.3 ± 2.6 kg) and less trunk fat mass (8.5 ± 2.7 and 6.2 ± 2.1 vs. 10.7 ± 2.8 and 9.5 ± 2.9 kg) than RLC forwards and backs. Observed differences may reflect selection based on favourable physical attributes, or training adaptations. To reduce this discrepancy, some RLC players should reduce fat mass and increase lean mass, which may be of benefit for the 2015 season and beyond.

Dual-energy X-ray absorptiometry (DXA) is a medical imaging device which has become the method of choice for the measurement of body composition in athletes. The objectives of this review were to evaluate published longitudinal DXA body composition studies in athletic populations for interpretation of "meaningful" change, and to propose a best practice measurement protocol. An online search of PubMed and CINAHL via EBSCO Host and Web of Science enabled the identification of studies published until November 2016. Those that met the inclusion criteria were reviewed independently by 2 authors according to their methodological quality and interpretation of body composition change. Twenty-five studies published between 1996 and November 2016 were reviewed (male athletes: 13, female athletes: 3, mixed: 9) and sample sizes ranged from n = 1 to 212. The same number of eligible studies was published between 2013 and 2016, as over the 16 yr prior (between 1996 and 2012). Seven did not include precision error, and fewer than half provided athlete-specific precision error. There were shortfalls in the sample sizes on which precision estimates were based and inconsistencies in the level of pre-scan standardization, with some reporting full standardization protocols and others reporting only single (e.g., overnight fast) or no control measures. There is a need for standardized practice and reporting in athletic populations for the longitudinal measurement of body composition using DXA. Based on this review and those of others, plus the official position of the International Society for Clinical Densitometry, our recommendations and protocol are proposed as a guide to support best practice.

The paper presents a data-driven approach to the modelling and forecasting of hydrological systems based on nonlinear time-series analysis. Time varying parameters are estimated using a combined Kalman filter and fixed-interval-smoother, and state-dependent parameter relations are identified leading to nonlinear extensions to common time-series models such as the autoregressive exogenous (ARX) and general transfer function (TF). This nonlinear time-series technique is used as part of a data-based mechanistic modelling methodology where models are objectively identified from the data, but are only accepted as a reasonable representation of the system if they have a valid mechanistic interpretation. To this end it is shown that the TF model can represent a general linear storage model that subsumes many common hydrological flow forecasting models, and that the rainfall-runoff process can be represented using a nonlinear input transformation in combination with a TF model. One advantage of the forecasting models produced is that the Kalman filter can be used for real-time state updating leading to improved forecasts and an estimate of associated forecast uncertainty. Rainfall-runoff and flood routing case studies are included to demonstrate the power of the modelling and forecasting methods. One important conclusion is that optimal system identification techniques are required to objectively identify parallel flow pathways.