No abstract

Abstract. Distributed energy and water balance models require time-series surfaces of the climatological variables involved in hydrological processes. Among them, solar radiation constitutes a key variable to the circulation of water in the atmosphere. Most of the hydrological GIS-based models apply simple interpolation techniques to data measured at few weather stations disregarding topographic effects. Here, a topographic solar radiation algorithm has been included for the generation of detailed time-series solar radiation surfaces using limited data and simple methods in a mountainous watershed in southern Spain. The results show the major role of topography in local values and differences between the topographic approximation and the direct interpolation to measured data (IDW) of up to +42% and −1800% in the estimated daily values. Also, the comparison of the predicted values with experimental data proves the usefulness of the algorithm for the estimation of spatiallydistributed radiation values in a complex terrain, with a good fit for daily values (R 2 = 0.93) and the best fits under cloudless skies at hourly time steps. Finally, evapotranspiration fields estimated through the ASCE-Penman-Monteith equation using both corrected and non-corrected radiation values address the hydrologic importance of using topographicallycorrected solar radiation fields as inputs to the equation over uniform values with mean differences in the watershed of 61 mm/year and 142 mm/year of standard deviation. High speed computations in a 1300 km 2 watershed in the south of Spain with up to a one-hour time scale in 30 × 30 m 2 cells can be easily carried out on a desktop PC.

Abstract. Longwave radiation is an important component of the energy balance of the Earth's surface. The downward component, emitted by the clouds and aerosols in the atmosphere, is rarely measured, and is still not well understood. In mountainous areas, direct observations are even scarcer and the fitting of existing models is often subjected to local parameterization in order to surplus the particular physics of the atmospheric profiles. The influence of clouds makes it even harder to estimate for all sky conditions. This work presents a long-time continuous dataset of high-resolution longwave radiation measured in a weather station at a height of 2500 m a.s.l. in Sierra Nevada, Spain, together with the parameterization of the apparent atmospheric emissivity for clear and cloudy skies resulting from three different schemes. We evaluate the schemes of Brutsaert, and Crawford and Duchon with locally adjusted coefficients and compare them with a completely parametric expression adjusted for these data that takes into account three possible significant atmospheric states related to the cloud cover: clear, completely covered, and partly covered skies. All the parametric expressions are related to the screen-level values of temperature, relative humidity and solar radiation, which can be frequently found in standard weather stations. Unobserved cloudiness measurements needed for Brutsaert scheme for cloudy sky are also parameterized from screen-level measurements. The calibration performed for a 6-yr period at the study site resulted in satisfactory estimations of emissivity for all the analyzed schemes thanks to the local fitting of the parameterizations, with the best achievement found for the completely parametric expression. Further validation of the expressions in two alternative sites showed that the greater accuracy of the latter can also be found in very close sites, while a better performance of the Brutsaert scheme, with a more physical background and the successful parameterization of the clouds effect, is found in nearby sites outside the initial mountain range. The results show the feasibility for the local calibration of expressions to estimate instantaneous atmospheric emissivity for all sky conditions only using surface data, either with a completely parametric scheme if longwave data are available, or through obtaining of locally fitted coefficients for Brutsaert and derived schemes. Nevertheless, the best performance of the first approach would be at the expense of a reduced local applicability.

In this study we quantified the sensitivity of snow to climate warming in selected mountain sites having a Mediterranean climate, including the Pyrenees in Spain and Andorra, the Sierra Nevada in Spain and California (USA), the Atlas in Morocco, and the Andes in Chile. Meteorological observations from high elevations were used to simulate the snow energy and mass balance (SEMB) and calculate its sensitivity to climate. Very different climate sensitivities were evident amongst the various sites. For example, reductions of 9%-19% and 6-28 days in the mean snow water equivalent (SWE) and snow duration, respectively, were found per°C increase. Simulated changes in precipitation (±20%) did not affect the sensitivities. The Andes and Atlas Mountains have a shallow and cold snowpack, and net radiation dominates the SEMB; and explains their relatively low sensitivity to climate warming. The Pyrenees and USA Sierra Nevada have a deeper and warmer snowpack, and sensible heat flux is more important in the SEMB; this explains the much greater sensitivities of these regions. Differences in sensitivity help explain why, in regions where climate models project relatively greater temperature increases and drier conditions by 2050 (such as the Spanish Sierra Nevada and the Moroccan Atlas Mountains), the decline in snow accumulation and duration is similar to other sites (such as the Pyrenees and the USA Sierra Nevada), where models project stable precipitation and more attenuated warming. The snowpack in the Andes (Chile) exhibited the lowest sensitivity to warming, and is expected to undergo only moderate change (a decrease of <12% in mean SWE, and a reduction of < 7 days in snow duration under RCP 4.5). Snow accumulation and duration in the other regions are projected to decrease substantially (a minimum of 40% in mean SWE and 15 days in snow duration) by 2050.