Spatial Downscaling of Remote Sensing Precipitation Data in the Beijing-Tianjin-Hebei Region

Precipitation is an important part of the global hydrological cycle. The large- scale, high-precision continuous precipitation data obtained by satellite remote sensing detection technology has become an important source of spatial precipitation data. However, because the spatial resolution of remote sensing precipitation data is still low, it is difficult to meet the needs of hydrological research, which restricts their application in drought and flood analysis, hydrological simulation, etc. In response to this problem, this paper takes the Beijing-Tianjin-Hebei region as the research area, downscaling the TRMM data and the GPM data space of the continuation plan, and increasing the spatial resolution of the data to 1 km. Compared with the original data, spatial downscaling data not only greatly improves the spatial resolution, but also increases the accuracy of the data, which has better applicability.

A systematic review of spatial disaggregation methods for climate action planning

National-level climate action plans are often formulated broadly. Spatially disaggregating these plans to individual municipalities can offer substantial benefits, such as enabling regional climate action strategies and for assessing the feasibility of national objectives. Numerous spatial disaggregation approaches can be found in the literature. This study reviews and categorizes these. The review is followed by a discussion of the relevant methods for the disaggregation of climate action plans. It is seen that methods employing proxy data, machine learning models, and geostatistical ones are the most relevant methods for the spatial disaggregation of national energy and climate plans. The analysis offers guidance for selecting appropriate methods based on factors such as data availability at the municipal level and the presence of spatial autocorrelation in the data.As the urgency of addressing climate change escalates, understanding the spatial aspects of national energy and climate strategies becomes increasingly important. This review will serve as a valuable guide for researchers and practitioners applying spatial disaggregation in this crucial field.

A cellular automata downscaling based 1 km global land use datasets (2010–2100)

Global climate and environmental change studies require detailed land-use and land-cover （LULC） information about the past, present, and future. In this paper, we discuss a methodology for downscaling coarse-resolution （i.e., half-degree） future land use scenarios to finer （i.e., 1 km） resolutions at the global scale using a grid-based spatially explicit cellular automata （CA） model. We account for spatial heterogeneity from topography, climate, soils, and socioeconomic variables. The model uses a global 30 m land cover map （2010） as the base input, a variety of biogeographic and socioeconomic variables, and an empirical analysis to downscale coarse-resolution land use information （specifically urban, crop and pasture）. The output of this model offers the most current and finest-scale future LULC dynamics from 2010 to 2100 （with four representative concentration pathway （RCP） scenarios--RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5） at a 1 km resolution within a globally consistent framework. The data are freely available for download, and will enable researchers to study the impacts of LULC change at the local scale.

A fundamental theorem for eco-environmental surface modelling and its applications

We propose a fundamental theorem for eco-environmental surface modelling(FTEEM) in order to apply it into the fields of ecology and environmental science more easily after the fundamental theorem for Earth’s surface system modeling(FTESM). The Beijing-Tianjin-Hebei(BTH) region is taken as a case area to conduct empirical studies of algorithms for spatial upscaling, spatial downscaling, spatial interpolation, data fusion and model-data assimilation, which are based on high accuracy surface modelling(HASM), corresponding with corollaries of FTEEM. The case studies demonstrate how eco-environmental surface modelling is substantially improved when both extrinsic and intrinsic information are used along with an appropriate method of HASM. Compared with classic algorithms, the HASM-based algorithm for spatial upscaling reduced the root-meansquare error of the BTH elevation surface by 9 m. The HASM-based algorithm for spatial downscaling reduced the relative error of future scenarios of annual mean temperature by 16%. The HASM-based algorithm for spatial interpolation reduced the relative error of change trend of annual mean precipitation by 0.2%. The HASM-based algorithm for data fusion reduced the relative error of change trend of annual mean temperature by 70%. The HASM-based algorithm for model-data assimilation reduced the relative error of carbon stocks by 40%. We propose five theoretical challenges and three application problems of HASM that need to be addressed to improve FTEEM.

中国天山山体效应评估及空间分异归因

The mass elevation effect(MEE)is a thermal effect,in which heating produced by long wave radiation on a mountain surface generates atmospheric uplift,which has a profound impact on the hydrothermal conditions and natural geographical processes in mountainous areas.Based on multi-source remote sensing data and field observations,a spatial downscaling inversion of temperature in the Tianshan Mountains in China was conducted,and the MEE was estimated and a spatio-temporal analysis was conducted.The Geo Detector model(GDM)and a geographically weighted regression(GWR)model were applied to explore the spatial and temporal heterogeneity of the study area.Four key results can be obtained.(1)The temperature pattern is complex and diverse,and the overall temperature presented a pattern of high in the south and east,but low in the north and west.There were clear zonal features of temperature that were negatively correlated with altitude,and the temperature difference between the internal and external areas of the mountains.(2)The warming effect of mountains was prominent,and the temperature at the same altitude increased in steps from west to east and north to south.Geomorphological units,such as large valleys and intermontane basins,weakened the latitudinal zonality and altitudinal dependence of temperature at the same altitude,with the warming effect of mountains in the southern Tianshan Mountains.(3)The dominant factors affecting the overall pattern of the MEE were topography and location,among which the difference between the internal and external areas of the mountains,and the absolute elevation played a prominent role.The interaction between factors had a greater influence on the spatial differentiation of mountain effects than single factors,and there was a strong interaction between terrain and climate,precipitation,nthe normalized difference vegetation index(NDVI),and other factors.(4)There was a spatial heterogeneity in the direction and intensity of the spatial variation of the MEE.Absolute elevation was significantly positively correlated with the change of MEE,while precipitation and the NDVI were dominated by negative feedback.In general,topography had the largest effect on the macroscopic control of MEE,and coupled with precipitation,the underlying surface,and other factors to form a unique mountain circulation system and climate characteristics,which in turn enhanced the spatial and temporal heterogeneity of the MEE.The results of this study will be useful in the further analysis of the causes of MEE and its ecological effects.