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Against the backdrop of global warming, extreme precipitation events have become more frequent. In complex terrain regions, due to the vulnerability of their ecosystems, extreme precipitation events can lead to significant secondary disasters. Utilizing daily rainfall data from the National Meteorological Information Center of China and statistical analysis, this study explores the spatial and temporal distribution of extreme precipitation in the Central Southwest China (CSC) region. The temporal pattern of extreme precipitation in CSC shows a consistent trend, while the spatial distribution reveals an opposite phase between the northern and southern parts of CSC. Based on this, we propose a new method for constructing extreme precipitation prediction models for complex terrain regions based on physical mechanisms, and take CSC area as a study case. Instead of anonymous feature selection, this method improves the accuracy and stability of the model by studying the impact of sea–air interactions on extreme precipitation and then introducing it into deep learning. It was found that the sea surface temperature (SST) anomaly in the South Indian Ocean affects extreme precipitation in the CSC by influencing uplift, atmospheric instability, and moisture. The SST anomaly also affects the intensity of cross-equatorial airflow, which changes the trajectory of the Pacific–Japan teleconnection wave and impacts extreme precipitation. These findings provide a comprehensive and reliable approach for forecasting extreme precipitation in CSC and are further integrated into the extreme precipitation prediction models. [ABSTRACT FROM AUTHOR]

• A novel blended algorithm was proposed to remotely observe lake POC across China. • POC concentrations in 450 large lakes were first mapped using OLCI/Sentinel-3 data. • Lake POC concentration across China was low in the west and high in the east. Remote sensing monitoring of particulate organic carbon (POC) concentration is essential for understanding phytoplankton productivity, carbon storage, and water quality in global lakes. Some algorithms have been proposed, but only for regional eutrophic lakes. Based on in-situ data (N = 1269) in 49 lakes across China, we developed a blended POC algorithm by distinguishing Type-I and Type-II waters. Compared to Type-I, Type-II waters had higher reflectance peak around 560 nm (>0.0125 sr−1) and mean POC (4.65 ± 4.11 vs. 2.66 ± 3.37 mg/L). Furthermore, because POC was highly related to algal production (r = 0.85), a three-band index (R 2 = 0.65) and the phytoplankton fluorescence peak height (R 2 = 0.63) were adopted to estimate POC in Type-I and Type-II waters, respectively. The novel algorithm got a mean absolute percent difference (MAPD) of 35.93 % and outperformed three state-of-the-art formulas with MAPD values of 40.56–76.42 %. Then, the novel algorithm was applied to OLCI/Sentinel-3 imagery, and we first obtained a national map of POC in 450 Chinese lakes (> 20 km2), which presented an apparent spatial pattern of "low in the west and high in the east". In brief, water classification should be considered when remotely monitoring lake POC concentration over a large area. Moreover, a process-oriented method is required when calculating water column POC storage from satellite-derived POC concentrations in type-II waters. Our results contribute substantially to advancing the dynamic observation of the lake carbon cycle using satellite data. [Display omitted] [ABSTRACT FROM AUTHOR]

Dissolved organic carbon (DOC) in lakes serves as a substrate for heterotrophic bacterial growth, a regulator of the global carbon cycle, and a light absorption agent. DOC in eutrophic lakes is greatly influenced by phytoplankton phenology and terrigenous input by rivers. Therefore, it is necessary and significant to dynamically monitor the concentration, storage, and riverine exchange flux of DOC. By using in-situ DOC measurements from 2004 until 2018 (N = 2019), a machine learning algorithm, namely, a multilayer back-propagation neural network (MBPNN) model, was developed in this work to improve the remote sensing estimation of DOC concentrations in eutrophic Lake Taihu. The model yielded a mean estimation error of 15.14% for the testing dataset. The monthly mean DOC concentration significantly increased from 2003 to 2018 (N = 192, p < 0.01). High DOCs were observed in lake bays with high chlorophyll a (Chl-a) levels, and phytoplankton growth explained more than 50% of the monthly DOC variations. Then, given the evenly mixed DOC in the water column on the monthly and annual scales, we further estimated the monthly mean DOC storage in the lake from 2003 until 2018 and DOC fluxes (input and output) due to rivers during 2008–2018. Although the mean net riverine DOC input (50.56 ± 32.22 × 103 t C) was approximately 5.2 times the average DOC storage (9.73 ± 1.23 × 103 t C), phytoplankton growth controlled DOC variations, which indicated that much terrigenous DOC was transformed into other carbon forms after entering Lake Taihu. This study verified the feasibility of remote sensing of DOC (surface concentration, storage, and riverine exchange flux) in eutrophic lakes. • Machine learning improved the remote sensing of DOC in eutrophic Lake Taihu. • DOC variations in the Lake Taihu were controlled by phytoplankton growth. • Net DOC input from rivers was ~5.2 times the DOC storage during 2008–2018. [ABSTRACT FROM AUTHOR]

• A novel algorithm was proposed to remotely observe POC storage in eutrophic lakes. • POC profile could be identified and parameterized by surface Chl-a, POC, and wind. • Spatiotemporal variations in POC storage were determined by temperature and depth. Particulate organic carbon (POC) storage play an essential role in determining carbon sinks and water quality in shallow eutrophic lakes. Although satellite data have been adopted to observe surface POC content, the previous remote sensing results cannot indicate water column-integrated POC storage in shallow eutrophic lakes with vertically nonuniform POC profiles. Based on 272 in-situ POC profiles collected from 19 shallow eutrophic lakes in China's Yangtze-Huai Plain (YHP), this study developed a novel process-oriented method to derive water column-integrated POC storage from OLCI/Sentinel-3A satellite data. The method included three critical steps: remotely estimating surface POC contents, identifying POC profile types (uniform, exponential decay, or power decay) through a binary decision tree, and parameterizing POC profiles using surface POC concentrations. The developed algorithms showed a bias of -25.79% for in-situ match-ups from 15 lakes (N = 88). The POC in the eutrophic lakes was mainly produced by phytoplankton (R 2 = 0.87). As a result, the surface POC content presented a unimodal peak in summer. However, in addition to air temperature regulations (0.52 – 68.53%), the water level also showed apparent effects on increasing POC storage, with relative contributions of 29.29 – 95.51% for the six largest lakes. Therefore, multisource satellite data are needed to derive POC storage in global eutrophic lakes remotely. This study is the first attempt to three-dimensionally observe lake POC contents from space and is important for understanding the carbon cycle in shallow eutrophic lakes. [Display omitted] [ABSTRACT FROM AUTHOR]

• OC transport variations in 41 rivers across China during 2004-2018 were studied. • Human activities were the main driving forces decreasing the riverine OC content. • Chinese rivers are losing their Asian features of low DOC/POC ratios. Rivers serve as regulators of global climate by releasing greenhouse gases, burying particulate carbon, and connecting different ecosystem carbon pools. However, long-term organic carbon (OC) transport features across different Asian rivers are not well known due to unavailable data. Based on routinely monitored environmental and hydrological data during 2004-2018, this study investigated the spatiotemporal variations in dissolved (DOC) and particulate OC (POC) transport across 41 rivers in China. Across different rivers, both DOC (1.35 – 16.8 mg/L) and POC (0.27 – 4.48 mg/L) concentrations covered wide ranges. The DOC content was high in the north and low in the south, with significantly higher (t test, p < 0.01) values for rivers north of 30°N (5.39 ± 3.66 mg/L vs. 2.39 ± 1.14 mg/L). Human activities greatly influenced the riverine DOC and POC distributions. The riverine ammonia nitrogen (NH+ 4-N) content was positively correlated with DOC (r = 0.81 and p < 0.01) and explained 85.59% of its spatial variation. High vegetation coverage had significant effects on decreasing the riverine POC content, with r = -0.55 and p < 0.05. During 2004-2018, water pollution prevention and control strategies decreased DOC concentrations in 60.98% of rivers; meanwhile, anthropogenic vegetation restoration and dam construction led to POC content decreases in 90.48% of rivers. Importantly, along with DOC and POC changes, increasing DOC/POC ratios were found in 90.48% of the rivers, with 42.86% being significant, which indicated that Chinese rivers are losing their Asian features of low DOC/POC ratios due to artificial disturbance. This study is significant for accurately quantifying greenhouse gas emissions, carbon burial, and OC export to estuaries by Chinese rivers. [Display omitted] [ABSTRACT FROM AUTHOR]