Daily Maximum Solar Radiation Research Articles

Imprints of evaporative conditions and vegetation type in diurnal temperature variations

Abstract. Diurnal temperature variations are strongly shaped by the absorption of solar radiation, but evaporation, or the latent heat flux, also plays an important role. Generally, evaporation cools. Its relation to diurnal temperature variations, however, is unclear. This study investigates the diurnal response of surface and air temperatures to evaporative conditions for different vegetation types. We use the warming rate, defined as the increase in temperature in response to absorbed solar radiation in the morning, and evaluate how it changes with evaporative fraction, which is an indicator of the evaporative conditions. Results for 51 FLUXNET sites show that the warming rate of air temperature carries very weak imprints of evaporative fraction across all vegetation types. However, the warming rate of surface temperature is highly sensitive to evaporative fraction with a value of ∼23×10-3 K (W m-2)-1, indicating stronger evaporative cooling for moister conditions. Contrarily, the warming rates of surface and air temperatures are similar at forest sites and carry literally no imprints of evaporative fraction. We explain these contrasting patterns with an analytical surface energy balance model. The derived expressions reproduce the observed warming rates and their sensitivity to evaporative fraction in all vegetation types. Multiplying the warming rate with daily maximum solar radiation gives an approximation for the diurnal surface temperature range (DTsR). We use our model to compare the individual contributions of solar radiation, evaporative conditions, and vegetation (by its aerodynamic conductance) in shaping DTsR and show that the high aerodynamic conductance of forests reduces DTsR substantially more (−56 %) than evaporative cooling (−22 %). We further show that the strong diurnal variation in aerodynamic conductance (∼2.5 times of the mean across vegetation types) reduces DTsR by ∼35 % in short vegetation and savanna but only by ∼22 % in forests. We conclude that diurnal temperature variations may be useful for predicting evaporation for short vegetation. In forests, however, the diurnal variations in temperatures are mainly governed by their high aerodynamic conductance, resulting in negligible imprints of evaporative conditions.

A practical algorithm for estimating surface soil moisture using combined optical and thermal infrared data

Surface soil moisture (SSM) is a critical variable for understanding the energy and water exchange between the land and atmosphere. A multi-linear model was recently developed to determine SSM using ellipse variables, namely, the center horizontal coordinate (x0), center vertical coordinate (y0), semi-major axis (a) and rotation angle (θ), derived from the elliptical relationship between diurnal cycles of land surface temperature (LST) and net surface shortwave radiation (NSSR). However, the multi-linear model has a major disadvantage. The model coefficients are calculated based on simulated data produced by a land surface model simulation that requires sufficient meteorological measurements. This study aims to determine the model coefficients directly using limited meteorological parameters rather than via the complicated simulation process, decreasing the dependence of the model coefficients on meteorological measurements. With the simulated data, a practical algorithm was developed to estimate SSM based on combined optical and thermal infrared data. The results suggest that the proposed approach can be used to determine the coefficients associated with all ellipse variables based on historical meteorological records, whereas the constant term varies daily and can only be determined using the daily maximum solar radiation in a prediction model. Simulated results from three FLUXNET sites over 30 cloud-free days revealed an average root mean square error (RMSE) of 0.042m3/m3 when historical meteorological records were used to synchronously determine the model coefficients. In addition, estimated SSM values exhibited generally moderate accuracies (coefficient of determination R2=0.395, RMSE=0.061m3/m3) compared to SSM measurements at the Yucheng Comprehensive Experimental Station.

THE SOLAR‐TO‐STREAM POWER RATIO: A DIMENSIONLESS NUMBER EXPLAINING DIEL FLUCTUATIONS OF TEMPERATURE IN MESOSCALE RIVERS

ABSTRACTThe diel variation of temperature in mesoscale river reaches (catchment area > 1000 km2) is analysed using concurrent measurements of water temperature and of those meteorological (incident short‐wave radiation, air temperature, relative humidity and wind speed variables) and hydraulic variables (streamflow, top width, channel slope and flow depth) controlling the thermal regime. Measurements were taken along two river reaches located in central Chile, on the Itata (11 290 km2, Strahler's order 6, reach length 30 km, Qbankfull = 400 m3 s−1) and Vergara (4340 km2, Strahler's order 5, reach length 20 km, Qbankfull = 85 m3 s−1) rivers. The measuring frequency was 15 min. The relevant energy fluxes at the air–water interface, that is, atmospheric long‐wave radiation, net short‐wave radiation, radiation emitted by the water body, evaporation (latent heat) and conduction heat are computed and analysed for four scenarios of 12 days duration each, representing typical conditions for the austral winter, spring, summer and autumn. We find large differences in the diel river temperature range between the two sites and across seasons (and thus, flows and meteorological conditions), as reported in previous studies, but no clear relationship with the controlling variables is overtly observed. Following a dimensional analysis, we obtain a dimensionless parameter corresponding to the ratio of solar‐to‐stream power, which adequately explains the diel variation of water temperature in mesoscale rivers. A number of our own measurements as well as literature data are used for preliminary testing of the proposed parameter. This easy‐to‐compute number is shown to predict quite well all of the cases, constituting a simple and useful criterion to estimate a priori the magnitude of temperature diel variations in a river reach, given prevailing meteorological (daily maximum solar radiation) and hydrologic–hydraulic (streamflow, mean top width) conditions. Copyright © 2012 John Wiley & Sons, Ltd.

Nonsurgical Septal Reduction Therapy for Obstructive Cardiomyopathy: One-Year Follow-up. Lakkis NM, Nagueh SF, Dunn K, Killip D, Spencer WH. J Am Coll Cardiol 2000;36:852–5

Surface soil moisture (SSM) is a critical variable for understanding the energy and water exchange between the land and atmosphere. A multi-linear model was recently developed to determine SSM using ellipse variables, namely, the center horizontal coordinate (x0), center vertical coordinate (y0), semi-major axis (a) and rotation angle (θ), derived from the elliptical relationship between diurnal cycles of land surface temperature (LST) and net surface shortwave radiation (NSSR). However, the multi-linear model has a major disadvantage. The model coefficients are calculated based on simulated data produced by a land surface model simulation that requires sufficient meteorological measurements. This study aims to determine the model coefficients directly using limited meteorological parameters rather than via the complicated simulation process, decreasing the dependence of the model coefficients on meteorological measurements. With the simulated data, a practical algorithm was developed to estimate SSM based on combined optical and thermal infrared data. The results suggest that the proposed approach can be used to determine the coefficients associated with all ellipse variables based on historical meteorological records, whereas the constant term varies daily and can only be determined using the daily maximum solar radiation in a prediction model. Simulated results from three FLUXNET sites over 30 cloud-free days revealed an average root mean square error (RMSE) of 0.042 m3/m3 when historical meteorological records were used to synchronously determine the model coefficients. In addition, estimated SSM values exhibited generally moderate accuracies (coefficient of determination R2 = 0.395, RMSE = 0.061 m3/m3) compared to SSM measurements at the Yucheng Comprehensive Experimental Station.