Parameterization of atmospheric longwave emissivity in a mountainous site for all sky conditions

135

144

Abstract. Estimating evaporation is important when managing water resources and cultivating crops. Evaporation can be estimated using land surface heat flux models and remotely sensed land surface temperatures (LST), which have recently become obtainable in very high resolution using lightweight thermal cameras and Unmanned Aerial Vehicles (UAVs). In this study a thermal camera was mounted on a UAV and applied into the field of heat fluxes and hydrology by concatenating thermal images into mosaics of LST and using these as input for the two-source energy balance (TSEB) modelling scheme. Thermal images are obtained with a fixed-wing UAV overflying a barley field in western Denmark during the growing season of 2014 and a spatial resolution of 0.20 m is obtained in final LST mosaics. Two models are used: the original TSEB model (TSEB-PT) and a dual-temperaturedifference (DTD) model. In contrast to the TSEB-PT model, the DTD model accounts for the bias that is likely present in remotely sensed LST. TSEB-PT and DTD have already been well tested, however only during sunny weather conditions and with satellite images serving as thermal input. The aim of this study is to assess whether a lightweight thermal camera mounted on a UAV is able to provide data of sufficient quality to constitute as model input and thus attain accurate and high spatial and temporal resolution surface energy heat fluxes, with special focus on latent heat flux (evaporation). Furthermore, this study evaluates the performance of the TSEB scheme during cloudy and overcast weather conditions, which is feasible due to the low data retrieval altitude (due to low UAV flying altitude) compared to satellite thermal data that are only available during clear-sky conditions. TSEB-PT and DTD fluxes are compared and validated against eddy covariance measurements and the comparison shows that both TSEB-PT and DTD simulations are in good agreement with eddy covariance measurements, with DTD obtaining the best results. The DTD model provides results comparable to studies estimating evaporation with similar experimental setups, but with LST retrieved from satellites instead of a UAV. Further, systematic irrigation patterns on the barley field provide confidence in the veracity of the spatially distributed evaporation revealed by model output maps. Lastly, this study outlines and discusses the thermal UAV image processing that results in mosaics suited for model input. This study shows that the UAV platform and the lightweight thermal camera provide high spatial and temporal resolution data valid for model input and for other potential applications requiring high-resolution and consistent LST.

Section: Methods Discussionmentioning

confidence: 99%

Estimating evaporation with thermal UAV data and two-source energy balance models

Hoffmann

Nieto

Jensen

et al. 2016

135

144

“…Nevertheless, forcing uncertainty merits more attention in some cases, such as in snow-affected watersheds where meteorological and energy balance measurements are scarce (Bales et al, 2006;Schmucki et al, 2014) and prone to errors due to environmental or instrumental factors (Huwald et al, 2009;Lundquist et al, 2015;Rasmussen et al, 2012). Forcing uncertainty is enhanced in complex terrain where meteorological variables exhibit high spatial variability Flint and Childs, 1987;Herrero and Polo, 2012;Lundquist and Cayan, 2007). As a result, the choice of forcing data can yield substantial differences in calibrated model parameters and in modeled hydrologic processes, such as snowmelt and evapotranspiration Wayand et al, 2013).…”

Section: S Raleigh Et Al: Physical Model Sensitivity To Forcing mentioning

confidence: 99%

“…Q si error ranges spanned errors in empirical methods (Bohn et al, 2013), radiative transfer models (Jing and Cess, 1998), satellite-derived products (Jepsen et al, 2012), and NWP models (Niemelä et al, 2001b). Q li error ranges spanned errors in empirical methods (Bohn et al, 2013;Flerchinger et al, 2009;Herrero and Polo, 2012) and NWP models (Niemelä et al, 2001a).…”

Section: Error Magnitudesmentioning

confidence: 99%

Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework

Raleigh

Lundquist

Clark

2015

142

128

Abstract. Physically based models provide insights into key hydrologic processes but are associated with uncertainties due to deficiencies in forcing data, model parameters, and model structure. Forcing uncertainty is enhanced in snowaffected catchments, where weather stations are scarce and prone to measurement errors, and meteorological variables exhibit high variability. Hence, there is limited understanding of how forcing error characteristics affect simulations of cold region hydrology and which error characteristics are most important. Here we employ global sensitivity analysis to explore how (1) different error types (i.e., bias, random errors), (2) different error probability distributions, and (3) different error magnitudes influence physically based simulations of four snow variables (snow water equivalent, ablation rates, snow disappearance, and sublimation). We use the Sobol' global sensitivity analysis, which is typically used for model parameters but adapted here for testing model sensitivity to coexisting errors in all forcings. We quantify the Utah Energy Balance model's sensitivity to forcing errors with 1 840 000 Monte Carlo simulations across four sites and five different scenarios. Model outputs were (1) consistently more sensitive to forcing biases than random errors, (2) generally less sensitive to forcing error distributions, and (3) critically sensitive to different forcings depending on the relative magnitude of errors. For typical error magnitudes found in areas with drifting snow, precipitation bias was the most important factor for snow water equivalent, ablation rates, and snow disappearance timing, but other forcings had a more dominant impact when precipitation uncertainty was due solely to gauge undercatch. Additionally, the relative importance of forcing errors depended on the model output of interest. Sensitivity analysis can reveal which forcing error characteristics matter most for hydrologic modeling.

“…The calculation of the shortwave radiation (K) was based on the measured downwelling short-radiation flux and the albedo (Aguilar et al, 2010;Pimentel et al, 2016). The calculation of the long-wave radiation was based on the formulation for atmospheric long-wave emissivity, developed in the Sierra Nevada area by Herrero and Polo (2012). Finally, the H term was modelled as a diffusion process (Dingman, 2002).…”

Section: Point Modelmentioning

confidence: 99%

“…For this purpose, a 4-year series of TP images of a 30 × 30 m scene at a snow monitoring site in the Sierra Nevada (southern Spain) was used to derive DC parameterizations representative of different snow accumulation-melting cycles. The resulting DCs were included in the snow model developed by Herrero et al (2009) and Herrero and Polo (2012), and the performance of this DC model was finally tested against field observations.…”

Section: Introductionmentioning

confidence: 99%

Subgrid parameterization of snow distribution at a Mediterranean site using terrestrial photography

Pimentel

Herrero

Polo

2017

Abstract. Subgrid variability introduces non-negligible scale effects on the grid-based representation of snow. This heterogeneity is even more evident in semiarid regions, where the high variability of the climate produces various accumulation melting cycles throughout the year and a large spatial heterogeneity of the snow cover. This variability in a watershed can often be represented by snow accumulation-depletion curves (ADCs). In this study, terrestrial photography (TP) of a cell-sized area (30 × 30 m) was used to define local snow ADCs at a Mediterranean site. Snow-cover fraction (SCF) and snow-depth (h) values obtained with this technique constituted the two datasets used to define ADCs. A flexible sigmoid function was selected to parameterize snow behaviour on this subgrid scale. It was then fitted to meet five different snow patterns in the control area: one for the accumulation phase and four for the melting phase in a cycle within the snow season. Each pattern was successfully associated with the snow conditions and previous evolution. The resulting ADCs were associated to certain physical features of the snow, which were used to incorporate them in the point snow model formulated by Herrero et al. (2009) by means of a decision tree. The final performance of this model was tested against field observations recorded over four hydrological years (2009)(2010)(2011)(2012)(2013). The calibration and validation of this ADC snow model was found to have a high level of accuracy, with global RMSE values of 105.8 mm for the average snow depth and 0.21 m 2 m −2 for the snow-cover fraction in the control area. The use of ADCs on the cell scale proposed in this research provided a sound basis for the extension of point snow models to larger areas by means of a gridded distributed calculation.