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Thermal infrared sensing of evapotranspiration (E) through surface energy balance (SEB) models is challenging due to uncertainties in determining the aerodynamic conductance (g(A)) and due to inequalities between radiometric (T-R) and aerodynamic temperatures (T-0). We evaluated a novel analytical model, the Surface Temperature Initiated Closure (STIC1.2), that physically integrates T-R observations into a combined Penman-Monteith Shuttleworth-Wallace (PM-SW) framework for directly estimating E, and overcoming the uncertainties associated with T0 and gA determination. An evaluation of STIC1.2 against high temporal frequency SEB flux measurements across an aridity gradient in Australia revealed a systematic error of 10-52% in E from mesic to arid ecosystem, and low systematic error in sensible heat fluxes (H) (12-25%) in all ecosystems. Uncertainty in TR versus moisture availability relationship, stationarity assumption in surface emissivity, and SEB closure corrections in E were predominantly responsible for systematic E errors in arid and semi-arid ecosystems. A discrete correlation (r) of the model errors with observed soil moisture variance (r = 0.33-0.43), evaporative index (r = 0.77-0.90), and climatological dryness (r = 0.60-0.77) explained a strong association between ecohydrological extremes and T-R in determining the error structure of STIC1.2 predicted fluxes. Being independent of any leaf-scale biophysical parameterization, the model might be an important value addition in working group (WG2) of the Australian Energy and Water Exchange (OzEWEX) research initiative which focuses on observations to evaluate and compare biophysical models of energy and water cycle components. Plain Language Summary Evapotranspiration modeling and mapping in arid and semi-arid ecosystems are uncertain due to empirical approximation of surface and atmospheric conductances. Here we demonstrate the performance of a fully analytical model which is independent of any leaf-scale empirical parameterization of the conductances and can be potentially used for continental scale mapping of ecosystem water use as well as water stress using thermal remote sensing satellite data.d

Elevated suspended sediment (SS) loads released from peatlands after drainage operations and the resulting negative effect on the ecological status of the receiving water bodies have been widely recognized. Understanding the processes controlling erosion and sediment transport within the ditch network forms a prerequisite for adequate sediment control. While numerous experimental studies have been reported in this field, model based assessments are rare. This study presents a modeling approach to investigate sediment transport in a peatland ditch network. The transport model describes bed erosion, rain-induced bank erosion, floc deposition, and consolidation of the bed. Coupled to a distributed hydrological model, sediment transport was simulated in a 5.2 ha forestry-drained peatland catchment for two years after ditch cleaning. Comparing simulation results to measured SS concentrations suggested that the loose peat material, produced during excavation, contributed markedly to elevated SS concentrations immediately after ditch cleaning. Both snowmelt and summer rainstorms contributed critically to annual loads. Springtime peat erosion during snowmelt was driven by ditch flow whereas during summer rainfalls, bank erosion by raindrop impact was identified as an important process. Relating modeling results to observed spatial topographic changes in the ditch network was challenging and the results were difficult to verify. Nevertheless, the model has potential to identify risk areas for erosion. The results demonstrate that modeling is effective in separating the importance of different processes and complements pure experimental approaches. Modeling results can aid planning and designing efficient sediment control measures and guide the focus of experimental studies. This article is protected by copyright. All rights reserved.

Preferential flow dominates water movement and solute transport in boreal forest hillslopes. However, only a few model applications to date have accounted for preferential flow at forest sites. Here we present a parallel and coupled simulation of flow and transport processes in the preferential flow domain and soil matrix of a forested hillslope section in Kangaslampi, Finland, using a new, three-dimensional, physically based dual-permeability model. Our aim is to simulate lateral subsurface stormflow and solute transport at the slope during a chloride tracer experiment, and to investigate the role of preferential flow in the tracer transport. The model was able to mimic the observed tracer transport during tracer irrigation, but overestimated the dilution velocity of the tracer plume in the highly conductive soil horizons near the soil surface after changing the irrigation to tracer-free water. According to the model, 140 times more chloride was transported downslope in the preferential flow domain than in the soil matrix during the tracer irrigation. The simulations showed, together with reference simulations with a traditional one pore domain model, that a two pore domain approach was required to simulate the observed flow and transport event. The event was characterized by the transmissivity feedback phenomenon and controlled by preferential flow mechanisms, in particular by lateral by-pass flow. According to our results, accounting for the slow-flow and fast-flow domains of soil, as well as the water and solute exchange between the domains, is essential for a successful simulation of flow and solute transport in preferential flow dominated hillslopes.

[1] Bayesian inference is used to study the effect of precipitation and model structural uncertainty on estimates of model parameters and confidence limits of predictive variables in a conceptual rainfall-runoff model in the snow-fed Rudback catchment (142 ha) in southern Finland. The IHACRES model is coupled with a simple degree day model to account for snow accumulation and melt. The posterior probability distribution of the model parameters is sampled by using the Differential Evolution Adaptive Metropolis (DREAM(ZS)) algorithm and the generalized likelihood function. Precipitation uncertainty is taken into account by introducing additional latent variables that were used as multipliers for individual storm events. Results suggest that occasional snow water equivalent (SWE) observations together with daily streamflow observations do not contain enough information to simultaneously identify model parameters, precipitation uncertainty and model structural uncertainty in the Rudback catchment. The addition of an autoregressive component to account for model structure error and latent variables having uniform priors to account for input uncertainty lead to dubious posterior distributions of model parameters. Thus our hypothesis that informative priors for latent variables could be replaced by additional SWE data could not be confirmed. The model was found to work adequately in 1-day-ahead simulation mode, but the results were poor in the simulation batch mode. This was caused by the interaction of parameters that were used to describe different sources of uncertainty. The findings may have lessons for other cases where parameterizations are similarly high in relation to available prior information.