RESUMEN
In the Amazon region, the estimation of radiation fluxes through remote sensing techniques is hindered by the lack of ground measurements required as input in the models, as well as the difficulty to obtain cloud-free images. Here, we assess an approach to estimate net radiation (Rn) and its components under all-sky conditions for the Amazon region through the Surface Energy Balance Algorithm for Land (SEBAL) model utilizing only remote sensing and reanalysis data. The study period comprised six years, between January 2001-December 2006, and images from MODIS sensor aboard the Terra satellite and GLDAS reanalysis products were utilized. The estimates were evaluated with flux tower measurements within the Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) project. Comparison between estimates obtained by the proposed method and observations from LBA towers showed errors between 12.5% and 16.4% and 11.3% and 15.9% for instantaneous and daily Rn, respectively. Our approach was adequate to minimize the problem related to strong cloudiness over the region and allowed to map consistently the spatial distribution of net radiation components in Amazonia. We conclude that the integration of reanalysis products and satellite data, eliminating the need for surface measurements as input model, was a useful proposition for the spatialization of the radiation fluxes in the Amazon region, which may serve as input information needed by algorithms that aim to determine evapotranspiration, the most important component of the Amazon hydrological balance.
RESUMEN
The process of the land-surface water cycle has undergone substantial changes as a result of climate change and human activities. Disclosing the evolution of the water cycle and its mechanisms in a changing environment is a challenging and hot issue in hydrological science research, which is essential for regional ecological protection and sustainable development. Based on the MIKE SHE/MIKE 11 model, multi-source data are used to simulate the water cycle change process in the Songnen Plain from 1980 to 2020. The study indicates that groundwater data inverted by GRACE and GLDAS data is relatively accurate, which effectively reflects the process of groundwater storage change in particular regions. Moreover, the surface-groundwater coupling model employs strongly correlated inverse groundwater data to simulate the water cycle change process in the Songnen Plain, yielding highly accurate simulation results. In terms of the impact of climate change and human activities on the water cycle process, climate change is the primary cause of changes in the regional water cycle, with contributions to actual evapotranspiration, surface runoff, and groundwater level of 77.04 %, 70.88 %, and 67.86 %, respectively. Nonetheless, as human activities intensify, their impact on the water cycle process progressively increases. From the perspective of the mechanism of water cycle change, the decrease in wetland area, the expansion of urban areas, and the increase in urban water demand are the primary causes of regional water cycle change between 1995 and 2010. The establishment of water conservation facilities and the dramatic increase in paddy field area are the primary causes of the water cycle change between 2011 and 2020. This study combines multi-source remote sensing data with hydrological models to simulate medium- and large-scale water cycle processes, providing new concepts and methods for examining water cycle processes in water-scarce areas.