Distributed lag models for hydrological data

The distributed lag model (DLM), used most prominently in air pollution studies, finds application wherever the effect of a covariate is delayed and distributed through time. We explore the use of modified formulations of DLMs to provide flexible varying-coeficient models with smoothness constraints, applicable in any setting in which lagged covariates are regressed on a time-dependent response. The models are applied to simulated flow and rainfall data and to flow data from a Scottish mountain river, with particular emphasis on approximating the relationship between environmental covariates and flow regimes in order to detect the influence of unobserved processes. It was found that under certain rainfall conditions some of the variability in the influence of rainfall on flow arises through a complex interaction between antecedent ground wetness and the time-delay in rainfall. The models are able to identify subtle changes in rainfall response, particularly in the location of peak influence in the lag structure and offer a computationally attractive approach for fitting DLMs

Analysis and use of VAS satellite data

A series of interrelated investigations has examined the analysis and use of VAS (VISSR Atmospheric Sounder) satellite data. A case study of VAS-derived mesoscale stability parameters suggested that they would have been a useful supplement to conventional data in the forecasting of thunderstorms on the day of interest. However, the meteorological significance of small or short lived stability features was uncertain. A second investigation examined the roles of first guess and VAS radiometric data in producing sounding retrievals. The radiance data often did not have a decisive influence on the final satellite soundings. Broad-scale patterns of the first guess, radiances, and retrievals frequently were similar, whereas small scale retrieval features, especially in the dew points, were often of uncertain origin

Downstream changes in DOC:inferring contributions in the face of model uncertainties

Dissolved organic carbon (DOC) is a central constituent of surface waters which control its characteristic color and chemistry. While the sources and controls of headwater stream DOC can be mechanistically linked to the dominant landscape types being drained, much remains unknown about the downstream controls at larger spatial scales. As DOC is transported from the headwaters to catchment outlets, the fate of stream DOC is largely dependent on the interaction of varying catchment processes. In this study, we investigated the main mechanisms regulating stream DOC in a mesoscale catchment. A landscape-mixing model was used to test the role of landscapes in determining stream concentrations. The quantity of DOC lost to in-stream processes was calculated using bacterial respiration and photooxidation rates. We investigated whether there was a change in water pathways using a mass balance model and comparison of hydrology between a headwater catchment and the entire catchment. A Monte Carlo approach was used to test robustness of the model assumptions and results to uncertainty in the process parameterizations. The results indicated that during high- and intermediate-flow conditions, DOC concentrations were regulated by the contributing upstream landscape types. During base flow, the connectivity between the mesoscale river and the upstream landscape reduced resulting in large residuals in the landscape model which could not be explained by the in-stream processes. Both the mass balance model and a specific runoff comparison between upstream/downstream sites independently indicated large input of deep groundwater during base flow. Deep groundwater was important for diluting stream DOC concentrations during base flow. Key Points Landscape types determine stream chemistry during high and intermediate flows Deep groundwater has large influences on stream chemistry during baseflow DOC lost to instream processes were smal

Analysis and use of VAS satellite data

Four interrelated investigations have examined the analysis and use of VAS satellite data. A case study of VAS-derived mesoscale stability parameters suggested that they would have been a useful supplement to conventional data in the forecasting of thunderstorms on the day of interest. A second investigation examined the roles of first guess and VAS radiometric data in producing sounding retrievals. Broad-scale patterns of the first guess, radiances, and retrievals frequently were similar, whereas small-scale retrieval features, especially in the dew points, were often of uncertain origin. Two research tasks considered 6.7 micron middle tropospheric water vapor imagery. The first utilized radiosonde data to examine causes for two areas of warm brightness temperature. Subsidence associated with a translating jet streak was important. The second task involving water vapor imagery investigated simulated imagery created from LAMPS output and a radiative transfer algorithm. Simulated image patterns were found to compare favorably with those actually observed by VAS. Furthermore, the mass/momentum fields from LAMPS were powerful tools for understanding causes for the image configurations

Ensemble evaluation of hydrological model hypotheses

It is demonstrated for the first time how model parameter, structural and data uncertainties can be accounted for explicitly and simultaneously within the Generalized Likelihood Uncertainty Estimation (GLUE) methodology. As an example application, 72 variants of a single soil moisture accounting store are tested as simplified hypotheses of runoff generation at six experimental grassland field-scale lysimeters through model rejection and a novel diagnostic scheme. The fields, designed as replicates, exhibit different hydrological behaviors which yield different model performances. For fields with low initial discharge levels at the beginning of events, the conceptual stores considered reach their limit of applicability. Conversely, one of the fields yielding more discharge than the others, but having larger data gaps, allows for greater flexibility in the choice of model structures. As a model learning exercise, the study points to a “leaking” of the fields not evident from previous field experiments. It is discussed how understanding observational uncertainties and incorporating these into model diagnostics can help appreciate the scale of model structural error

Hyperresolution information and hyperresolution ignorance in modelling the hydrology of the land surface

There is a strong drive towards hyperresolution earth system models in order to resolve finer scales of motion in the atmosphere. The problem of obtaining more realistic representation of terrestrial fluxes of heat and water, however, is not just a problem of moving to hyperresolution grid scales. It is much more a question of a lack of knowledge about the parameterisation of processes at whatever grid scale is being used for a wider modelling problem. Hyperresolution grid scales cannot alone solve the problem of this hyperresolution ignorance. This paper discusses these issues in more detail with specific reference to land surface parameterisations and flood inundation models. The importance of making local hyperresolution model predictions available for evaluation by local stakeholders is stressed. It is expected that this will be a major driving force for improving model performance in the future. Keith BEVEN, Hannah CLOKE, Florian PAPPENBERGER, Rob LAMB, Neil HUNTE

Dry-season length and runoff control annual variability in stream DOC dynamics in a small, shallowgroundwater-dominated agricultural watershed

International audienceAs a phenomenon integrating climate conditions and hydrological control of the connection betweenstreams and terrestrial dissolved organic carbon (DOC) sources, groundwater dynamics controlpatterns of stream DOC characteristics (concentrations and fluxes). Influence of intra-annualvariations in groundwater level, discharge and climatic factors on DOC concentrations and fluxeswere assessed over 13 years at the headwater watershed of Kervidy-Naizin (5 km²) in westernFrance. Four seasonal periods were delineated within each year according to groundwaterfluctuations (A: rewetting, B: high flow, C: recession, and D: drought). Annual and seasonal baseflow vs stormflow DOC concentrations were defined based on daily hydrograph readings. Highinter-annual variability of annual DOC fluxes (5.4-39.5 kg.ha-1.yr-1) indicates that several years ofdata are required to encompass variations in water flux to evaluate the actual DOC export capacity ofa watershed. Inter-annual variability of mean annual DOC concentrations was much lower (4.9-7.5mg C.l-1), with concentrations decreasing within each year from ca. 9.2 mg C.l-1 in A to ca. 3.0 mgC.l-1 in C. This indicates an intra-annual pattern of stream DOC concentrations controlled by DOCsource characteristics and groundwater dynamics very similar across years. Partial least squareregressions combined with multiple linear regressions showed that the dry season characteristics(length and drawdown) determine the mean annual DOC concentration while annual runoffdetermines the annual flux. Antagonistic mechanisms of production-accumulation and dilution depletioncombined with an unlimited DOC supply from riparian wetland soils can mitigate theresponse of stream concentrations to global changes and climatic variations

Parameter identification of the STICS crop model, using an accelerated formal MCMC approach

This study presents a Bayesian approach for the parameters’ identification of the STICS crop model based on the recently developed Differential Evolution Adaptive Metropolis (DREAM) algorithm. The posterior distributions of nine specific crop parameters of the STICS model were sampled with the aim to improve the growth simulations of a winter wheat (Triticum aestivum L.) culture. The results obtained with the DREAM algorithm were initially compared to those obtained with a Nelder-Mead Simplex algorithm embedded within the OptimiSTICS package. Then, three types of likelihood functions implemented within the DREAM algorithm were compared, namely the standard least square, the weighted least square, and a transformed likelihood function that makes explicit use of the coefficient of variation (CV). The results showed that the proposed CV likelihood function allowed taking into account both noise on measurements and heteroscedasticity which are regularly encountered in crop modellingPeer reviewe