Three satellite rainfall products (3B42V7, IMERGV05/V04/V03, and China hourly Merged Precipitation Analysis product) are evaluated using measurements from a dense rain gauge network as reference in Guangdong Province, China from April 2014 to December 2016. The three products are compared with gauges in annual, monthly, daily and hourly accumulation, and at gridded, sub‐regional and regional scales. An error decomposition approach is employed to separate the total bias into Hit, Miss and False components. Overall, the CMPA estimate is the best in agreement with gauge observations. The improvement of IMERGV05 over 3B42V7 is notable, especially in reducing the hit bias and missed precipitation, resulting in better detection of the light rain and heavy rain. The enhancement of IMERG is more significant at the 3‐hr scale than daily scale. IMERGV05 shows better performance than its previous versions especially for IMERGV03, which had an abnormal blocky pattern mainly from July to September over the Pearl River Delta area. The three products have different error characteristics and show large spatial variations. 3B42V7 and IMERG have large areas of overestimation in the mountainous areas and underestimation in the coastal areas, while CMPA is characterized by an alternate distribution of small positive and negative values. The overestimation of precipitation is partially attributed to the positive hit biases that falsely estimate the precipitation from moderate intensity to heavy rainfall by the three products.
The retrieval of global significant wave height (SWH) data is crucial for maritime navigation, aquaculture safety, and oceanographic research. Leveraging the high temporal resolution and spatial coverage of Cyclone Global Navigation Satellite System (CYGNSS) data, machine learning models have shown promise in SWH retrieval. However, existing models struggle with accuracy under high-SWH conditions and discard a significant number of such observations due to low quality, which limits their effectiveness in global SWH retrieval, particularly for monitoring tropical cyclone (TC) events. To address this, this study proposes a daily global SWH retrieval framework through the enhanced eXtreme Gradient Boosting model (XGBoost-SC), which incorporates Cumulative Distribution Function (CDF) matching to introduce prior distribution information and reduce errors for SWH values exceeding 3 m. An enhanced loss function is employed to improve accuracy and mitigate the distribution bias in low-SWH retrieval induced by CDF matching. The results were tested over one million sample points and validated against the European Centre for Medium-Range Weather Forecasts (ECMWF) SWH product. With the help of CDF matching, XGBoost-SC outperformed all models, significantly reducing RMSE and bias while improving the retrieval capability for high SWHs. For SWH values between 3–6 m, the RMSE and bias were 0.94 m and −0.44 m, and for values above 6 m, they were 2.79 m and −2.0 m. The enhanced performance of XGBoost-SC for large SWHs was further confirmed in TC conditions over the Western North Pacific and in the Western Atlantic Ocean. This study provides a reference for large-scale SWH retrieval, particularly under TC conditions.
Water impoundment in the Three Gorges Reservoir (TGR) of China caused a large mass redistribution from the oceans to a concentrated land area in a short time period. We show that this mass shift is captured by the Gravity Recovery and Climate Experiment (GRACE) unconstrained global solutions at a 400 km spatial resolution after removing correlated errors. The WaterGAP Global Hydrology Model (WGHM) is selected to isolate the TGR contribution from regional water storage changes. For the first time, this study compares the GRACE (minus WGHM) estimated TGR volume changes with in situ measurements from April 2002 to May 2010 at a monthly time scale. During the 8 year study period, GRACE‐WGHM estimated TGR volume changes show an increasing trend consistent with the TGR in situ measurements and lead to similar estimates of impounded water volume. GRACE‐WGHM estimated total volume increase agrees to within 14% (3.2 km 3 ) of the in situ measurements. This indicates that GRACE can retrieve the true amplitudes of large surface water storage changes in a concentrated area that is much smaller than the spatial resolution of its global harmonic solutions. The GRACE‐WGHM estimated TGR monthly volume changes explain 76% ( r 2 = 0.76) of in situ measurement monthly variability and have an uncertainty of 4.62 km 3 . Our results also indicate reservoir leakage and groundwater recharge due to TGR filling and contamination from neighboring lakes are nonnegligible in the GRACE total water storage changes. Moreover, GRACE observations could provide a relatively accurate estimate of global water volume withheld by newly constructed large reservoirs and their impacts on global sea level rise since 2002.
Most glaciers in China lie in high mountainous environments and have relatively large surface slopes. Common analyses consider glaciers’ projected areas (2D Area) in a two-dimensional plane, which are much smaller than glacier’s topographic surface extents (3D Area). The areal difference between 2D planar areas and 3D surface extents exceeds −5% when the glacier’s surface slope is larger than 18°. In this study, we establish a 3D model in the Muzart Glacier catchment using ASTER GDEM data. This model is used to quantify the areal difference between glaciers’ 2D planar areas and their 3D surface extents in various slope zones and elevation bands by using the second Chinese Glacier Inventory (CGI2). Finally, we analyze the 2D and 3D area shrinking rate between 2007 and 2013 in Central Tianshan using glaciers derived from Landsat images by an object-based classification approach. This approach shows an accuracy of 89% when it validates by comparison of glaciers derived from Landsat and high spatial resolution GeoEye images. The extracted glaciers in 2007 also have an agreement of 89% with CGI2 data in the Muzart Glacier catchment. The glaciers’ 3D area is 34.2% larger than their 2D area from CGI2 in the Muzart Glacier catchment and by 27.9% in the entire Central Tianshan. Most underestimation occurs in the elevation bands of 4000–5000 m above sea level (a.s.l.). The 3D glacier areas reduced by 30 and 115 km2 between 2007 and 2013 in the Muzart Glacier catchment and Central Tianshan, being 37.0% and 27.6% larger than their 2D areas reduction, respectively. The shrinking rates decrease with elevation increase.
Abstract Permafrost peatland plays an important role in the global carbon (C) cycle, and dissolved organic carbon (DOC) is one of the most important components to C budgets in this system. Through the Chinese–United States EcoPartnership program, Dr Xianwei Wang, Li Sun, and Prof Changchun Song et al. from the Northeast Institute of Geography and Agroecology of Chinese Academy of Sciences and Prof Aixin Hou from the Louisiana State University observed dynamics of DOC concentrations and specific ultraviolet absorbance at 254 nm (SUVA 254 ) obtained from soil porewater of permafrost peatlands for two growing seasons in the Great Hing'an Mountains, Northeast China. Soil porewater DOC concentrations varied greatly with depths during the growing season, ranging between 22.08 and 65.02 mg L −1 . There was no significant relationship between DOC concentrations and SUVA 254 . DOC concentrations were higher in autumn and increased as the seasonal thaw depth increased. DOC concentrations of supra‐permafrost water at freeze–thaw boundary were also higher than those at the other soil depths. Temperature and thawing depth had been shown to affect DOC concentrations at different soil depths. Our finding suggests that warming and deepening of the active layer likely increase the DOC productions in the permafrost peatland, which may influence the C balance in these C‐enriched ecosystems.
Snow cover is an important fact for regional climate change studies, agriculture, and water source management. Satellite-based snow measurements have revolutionized the monitoring of spatiotemporal variation of snow cover in complex natural conditions at regional and global scales. This chaper introduces the principle of optical remote sensing snow cover detection and the MODIS standard snow cover products, summarizes the recent efforts and improvements on reducing cloud contamination and increasing snow mapping accuracy in all-sky conditions, and finally, illustrates the spatiotemporal variatons of snow cover and its applications in the Tibetan Plateau and a few other places.
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