Abstract:The nighttime light data records artificial light on the Earth's surface and can be used to estimate the spatial distribution of the gross domestic product (GDP) and the electric power consumption (EPC). In early 2013, the first global NPP-VIIRS nighttime light data were released by the Earth Observation Group of National Oceanic and Atmospheric Administration's National Geophysical Data Center (NOAA/NGDC). As new-generation data, NPP-VIIRS data have a higher spatial resolution and a wider radiometric detection range than the traditional DMSP-OLS nighttime light data. This study aims to investigate the potential of NPP-VIIRS data in modeling GDP and EPC at multiple scales through a case study of China. A series of preprocessing procedures are proposed to reduce the background noise of original data and to generate corrected NPP-VIIRS nighttime light images. Subsequently, linear regression is used to fit the correlation between the total nighttime light (TNL) (which is extracted from corrected NPP-VIIRS data and DMSP-OLS data) and the OPEN ACCESSRemote Sens. 2014, 6 1706 GDP and EPC (which is from the country's statistical data) at provincial-and prefectural-level divisions of mainland China. The result of the linear regression shows that R 2 values of TNL from NPP-VIIRS with GDP and EPC at multiple scales are all higher than those from DMSP-OLS data. This study reveals that the NPP-VIIRS data can be a powerful tool for modeling socioeconomic indicators; such as GDP and EPC.

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Quantifying the human effects on water resources plays an important role in river basin management. In this study, we proposed a framework, which integrates the Gravity Recovery and Climate Experiment (GRACE) satellite estimation with macroscale hydrological model simulation, for detection and attribution of spatial terrestrial water storage (TWS) changes. In particular, it provides valuable insights for regions where ground‐based measurements are inaccessible. Moreover, this framework takes into account the feedback between land and atmosphere and innovatively put forward several suggestions (e.g., study period selection, hydrological model selection based on soil moisture‐climate interactions) to minimize the uncertainties brought by the interaction of human water use with terrestrial water fluxes. We demonstrate the use of the proposed framework in the Yangtze River basin of China. Our results show that, during the period 2003–2010, the TWS was continually increasing in the middle and south eastern reaches of the basin, at a mean rate of about 3 cm yr−1. This increment in TWS was attributed to anthropogenic modification of the hydrological cycle, rather than natural climate variability. The dominant contributor to the TWS excess was found to be intensive surface water irrigation, which recharged the water table in the middle and south eastern parts of the basin. Water impoundment in the Three Gorges Reservoir (TGR) is found to account for nearly 20% of the human‐induced TWS increment in the region where the TGR is located. The proposed framework gives water managers/researchers a useful tool to investigate the spatial human effects on TWS changes.

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