Diurnal variation of land surface temperature (LST) is essential for land surface energy and water balance at regional or global scale. Diurnal temperature cycle (DTC) model with least parameters and high accuracy is the key issue in estimating the spatial-temporal variation of DTC. The alpine meadow is the main land cover in the Tibetan Plateau (TP). However, few studies have been reported on the performance of different DTC models over alpine meadows in the TP. Four semi-empirical types of DTC models were used to generate nine 4-parameter (4-para) models by fixing some of free parameters. The performance of the nine 4-para DTC models were evaluated with four in situ and MODIS observations. All models except GOT09-dT-t(s) (dT means the temperature residual between T-0 and T (t ->infinity); t(s) means the time when free attenuation begins) had higher correlation with in situ data (R-2 > 0.9), while the INA08-t(s) model performed best with NSE of 0.99 and RMSE of 2.04 K at all sites. The GOT09-t(s)-tau (tau is the total optical thickness), VAN06-t(s)-omega(1) (omega(1) means the half-width of the cosine term in the morning), and GOT01-t(s) models had better performance, followed by GOT09-dT-tau, GOT01-dT, and VAN06-t(s)-omega(2) (omega(2) means the half-width of the cosine term in the afternoon) models. All models had higher accuracy in summer than in other seasons, while poorer performance was produced in winter. The INA08-t(s) model showed best performance among all seasons. Models with fixing t(s) could produce higher accuracy results than that with fixing dT. The comparison of INA08-ts model driven by in situ and Moderate Resolution Imaging Spectroradiometer (MODIS) data indicated that the simulation accuracy mainly depended on the accuracy of MODIS LST. The daily maximum temperature generated by the nine models had high accuracy when compared with in situ data. The sensitivity analysis indicated that the INA08-dT and GOT09-dT-t(s) models were more sensitive to parameter dT, while all models were insensitive to parameter t(s), and all models had weak relationship with parameters omega and tau. This study provides a reference for exploring suitable DTC model in the TP.

Glaciers, as massive freshwater reservoirs, support the planet's living systems and have an impact on our daily lives, even for communities living far away. Ongoing and future climate change is predicted to have strong impacts on the mass balance of alpine glacier around the world. To understand the relationship between climate and glacier dynamics, a range of mass balance models are currently used. Most of these models however, ignore subsurface heat fluxes as a component of glacier mass balance. Here, we set out to investigate the importance of subsurface heat flux for the mass balance of an alpine glacier using a surface energy mass balance model (SEM) coupled with a multilayer subsurface heat conduction model (MSHCM) that resolves the subsurface glacier temperature. As a case study, we investigate the Urumqi Glacier No.1 in the Tianshan Mountains (NW China), which has a long and continuous time series of surface and subsurface glacier temperature measurements. We evaluate the results of both glacier temperature models (SEM and MSHCM) using these in situ observations and investigate the sensitivity of mass balance to five meteorological factors: air temperature, precipitation, incoming shortwave radiation, relative humidity, and wind speed. The mass balance of the glacier was simulated first by including the influence of subsurface heat flux, and second, the subsurface heat flux was neglected. Observed and simulated mass balance and the englacial temperature were found to be reasonably close in both cases. Furthermore, the mass balance was simulated with a zero surface temperature assumption, which resulted in a 6% overestimation of the summer ablation. We concluded that the mass balance of Urumqi Glacier No.1 was most sensitive to variations in temperature, followed by precipitation. Furthermore, our results show that subsurface heat flux in the ablation area can generally be neglected in estimating the mass balance of alpine glaciers during ablation season.

Hydrological models, with different levels of complexity, have become inherent tools in water resource management. Conceptual models with low input data requirements are preferred for streamflow modeling, particularly in poorly gauged watersheds. However, the inadequacy of model structures in the hydrologic regime of a given watershed can lead to uncertain parameter estimation. Therefore, an understanding of the model parameters' behavior with respect to the dominant hydrologic responses is of high necessity. In this study, we aim to investigate the parameterization of the HBV (Hydrologiska Byrans Vattenbalansavedelning) conceptual model and its influence on the model response in a semi-arid context. To this end, the capability of the model to simulate the daily streamflow was evaluated. Then, sensitivity and interdependency analyses were carried out to identify the most influential model parameters and emphasize how these parameters interact to fit the observed streamflow under contrasted hydroclimatic conditions. The results show that the HBV model can fairly reproduce the observed daily streamflow in the watershed of interest. However, the reliability of the model simulations varies from one year to another. The sensitivity analysis showed that each of the model parameters has a certain degree of influence on model behavior. The temperature correction factor (ETF) showed the lowest effect on the model response, while the sensitivity to the degree-day factor (DDF) highly depends on the availability of snow cover. Overall, the changes in hydroclimatic conditions were found to be mostly responsible for the annual variability of the optimal parameter values. Additionally, these changes seem to actuate the interdependency between the parameters of the soil moisture and the response routines, particularly Field Capacity (FC), the recession coefficient K0, the percolation coefficient (KPERC), and the upper reservoir threshold (UZL). The latter combines either to shrink the storage capacity of the model's reservoirs under extremely high peak flows or to enlarge them under overestimated water supply, mainly provoked by abundant snow cover.

Canopy resistance (rc) is a critical parameter for estimating vegetation transpiration. The site-specific rc can be calculated using the inversed Penman-Monteith (PM) equation with the effective leaf area index (LAI), which requires meteorological and turbulent flux data. The spatial distribution of rc is difficult to characterize due to the harsh environment of the Tibetan Plateau. The Jarvis-type model for modeling rc, described as a multiplicative function of environmental variables, has been widely used. However, the differences and optimization of different Jarvis-type models for alpine meadows have not been fully addressed. Consequently, our overall objective was to determine the appropriate functions for rc estimation and improve its accuracy for the alpine meadow ecosystem. Twelve Jarvis-type models composed of different stress functions were examined and compared with the observed rc calculated using PM equation at the Arou site in the northeastern Tibetan Plateau. The results suggest that the proper air temperature function and vapor pressure deficit function could improve model performance obviously. There was no obvious difference between the two different stress functions of downward shortwave radiation. The best model (M10), which was composed of an asymptotical function of downward shortwave radiation, a linear function of air temperature, an exponential function of vapor pressure deficit and a piecewise function of soil water content, had best performance with coefficient of determination of 0.93, root mean square error of 60.2 s m-1 and Nash-Sutcliffe efficiency coefficient of 0.92. The selection of proper stress functions is important for rc modeling. Models that considered the air temperature for rc calculations produced better results than those without temperature. The sensitivity analysis of rc to environmental variables indicated that rc was most sensitive to vapor pressure deficit, followed by LAI and downward shortwave radiation, whereas rc was less sensitive to soil water content. For all optimized parameters, rc was the most sensitive to kT (a fitting parameter for temperature), followed by kD (a fitting parameter for vapor pressure deficit) and rcmin (minimum rc under the optimal physiological condition). This study addresses the selection of proper stress functions in modeling rc for the alpine meadow site, which can also provide a reference for other ecosystems.

In the shortwave solar spectrum (0.25-5 mml:mspace width=3.33333ptmml:mspacem), radiation is affected by the change in various aerosol properties and also by water vapour and other gas molecules. The presence of a variety of aerosols over the Bay of Bengal (BoB) during different seasons results in a change in aerosol properties, including the aerosol layer height. The BoB is an integral part of the Indian monsoon, and hence it is essential to understand the radiation budget over the BoB. The sensitivity of the aerosol forcing due to the changes in aerosol properties and other parameters has been studied using the Santa Barbara discrete ordinates radiative transfer model. The aerosol forcing at the top of the atmosphere was found to depend on the aerosol loading (aerosol optical depth), aerosol type (single scattering albedo) and the angular distribution of the scattered radiation (asymmetry parameter). The analysis also shows the presence of a relationship between aerosol layer height and the total amount of water vapour present in the atmosphere. The present study highlights the need for better retrievals of vertical aerosol distribution and water vapour profiles for a better understanding of the role of aerosols in the climate.