Precipitation estimates with fine quality and spatio-temporal resolutions play significant roles in understanding the global and regional cycles of water, carbon, and energy. Satellite-based precipitation products are capable of detecting spatial patterns and temporal variations of precipitation at fine resolutions, which is particularly useful over poorly gauged regions. However, satellite-based precipitation products are the indirect estimates of precipitation, inherently containing regional and seasonal systematic biases and random errors. In this study, focusing on the potential drawbacks in generating Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) and its recently updated retrospective IMERG in the Tropical Rainfall Measuring Mission (TRMM) era (finished in July 2019), which were only calibrated at a monthly scale using ground observations, Global Precipitation Climatology Centre (GPCC, 1.0°/monthly), we aim to propose a new calibration algorithm for IMERG at a daily scale and to provide a new AIMERG precipitation dataset (0.1°/half-hourly, 2000–2015, Asia) with better quality, calibrated by Asian Precipitation – Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE, 0.25°/daily) at the daily scale for the Asian applications. The main conclusions include but are not limited to the following: (1) the proposed daily calibration algorithm (Daily Spatio-Temporal Disaggregation Calibration Algorithm, DSTDCA) is effective in considering the advantages from both satellite-based precipitation estimates and the ground observations; (2) AIMERG performs better than IMERG at different spatio-temporal scales, in terms of both systematic biases and random errors, over mainland China; and (3) APHRODITE demonstrates significant advantages compared to GPCC in calibrating IMERG, especially over mountainous regions with complex terrain, e.g. the Tibetan Plateau. Additionally, results of this study suggest that it is a promising and applicable daily calibration algorithm for GPM in generating the future IMERG in either an operational scheme or a retrospective manner. The AIMERG data are freely accessible at https://doi.org/10.5281/zenodo.3609352 (for the period from 2000 to 2008) (Ma et al., 2020a) and https://doi.org/10.5281/zenodo.3609507 (for the period from 2009 to 2015) (Ma et al., 2020b). Highlights. A new effective daily calibration approach, DSTDCA, for improving the GPM-era IMERG is provided. New AIMERG precipitation data (0.1°/half-hourly, 2000–2015, Asia) are provided. Bias of AIMERG is significantly improved compared with that of IMERG. APHRODITE is more suitable than GPCC in anchoring IMERG over Asia.

Man-made reservoirs are key components of terrestrial hydrological systems. Identifying the location and number of reservoirs is the premise for studying the impact of human activities on water resources and environmental changes. While complete bottom-up censuses can provide a comprehensive view of the reservoir landscape, they are time-consuming and laborious and are thus infeasible on a global scale. Moreover, it is challenging to distinguish man-made reservoirs from natural lakes in remote sensing images. This study proposes a convolutional neural network (CNN)-based framework to recognize global reservoirs from Landsat 8 imageries. On the basis of the HydroLAKES dataset, a Landsat 8 cloud-free mosaic of 2017 was clipped for each feature (reservoir or lake) and was resized into 224 × 224 patches, which were collected as training and testing samples. Compared to other deep learning methods (Alexnet and VGG) and state-of-the-art traditional machine learning methods (support vector machine, random forest, gradient boosting, and bag-of-visual-words), we found that fine-tuning the pretrained CNN model, ResNet-50, could reach the highest accuracy (91.45%). Application cases in Kansas (USA, North America), Mpumalanga (South Africa, Africa), and Kostanay (Kazakhstan, Asia) resulted in classification accuracies of better than 99%, which showed the applicability of the proposed ResNet-50 model to the extraction of reservoirs from a vast amount of moderate resolution images. The framework that was developed in this paper is the first attempt to combine remote sensing big data and the deep learning technique to the recognition of reservoirs at a global scale.

Accurate long-term precipitation information is critical for understanding the mechanisms behind how precipitation couples with Earth’s water fluxes, energy balances, and biogeochemical cycles across space–time scales under the changing climate. This study proposes a novel approach [daily total volume controlled merging and disaggregation algorithm (DTVCMDA)] for generating a new long-term precipitation dataset, AERA5-Asia (0.1°, 1-hourly, 1951–2015, Asia; “AERA5” is a combination of the “A” from APHRODITE and the “ERA5” from ERA5-Land), by comprehensively considering the characteristics of the high spatiotemporal resolutions and continuity of the ERA5-Land dataset and the high quality of the APHRODITE dataset. The main conclusions include, but are not limited to, the following: 1) AERA5-Asia provides time series of precipitation of sufficient resolutions, length, consistency, continuity, and quality over Asia. 2) AERA5-Asia substantially outperforms ERA5-Land and IMERG-Final in terms of both magnitudes and occurrences of precipitation events in Mainland China, especially the systematic biases. For instance, the bias of ERA5-Land, IMERG-Final, and AERA5-Asia against ground gauge-based observations in Mainland China are ∼20%, ∼11%, and ∼5%, respectively. 3) AERA5-Asia performs notably better than ERA5-Land and IMERG-Final against ground gauge-based observations in regional extreme rainfall systems (e.g., two typhoon events, Trami and Usagi). 4) AERA5-Asia should prove to be a useful precipitation dataset for addressing various key climatological and hydrological research questions that require precipitation data with longer spans and finer resolutions (0.1°, 1-hourly).

The availability of lake bathymetry maps is imperative for estimating lake water volumes and their variability, which is a sensitive indicator of climate. It is difficult, if not impossible, to obtain bathymetric measurements from all of the thousands of lakes across the globe due to costly labor and/or harsh topographic regions. In this study, we develop a new digital lake bathymetry model (DLBM) using the step-wise water recession method (WRM) to generate 3-dimensional lake bathymetric maps based on the digital elevation model (DEM) alone, with two assumptions: (1) typically, the lake’s bathymetry is formed and shaped by geological processes similar to those that shaped the surrounding landmasses, and (2) the agent rate of water (the thickness of the sedimentary deposit proportional to the lake water depth) is uniform. Lake Ontario and Lake Namco are used as examples to demonstrate the development, calibration, and refinement of the model. Compared to some other methods, the estimated 3D bathymetric maps using the proposed DLBM could overcome the discontinuity problem to adopt the complex topography of lake boundaries. This study provides a mathematically robust yet cost-effective approach for estimating lake volumes and their changes in regions lacking field measurements of bathymetry, for example, the remote Tibetan Plateau, which contains thousands of lakes.

Lakes and reservoirs are essential elements of the hydrological and biochemical cycles, considered sentinels of global climate change. However, comprehensive quantifications of their water storage changes (∆V) at a large spatiotemporal scale are still rare. Here, we integrated a global surface water dataset and SRTM digital elevation models, both available from Google Earth Engine platform at a spatial resolution of 30 m, to evaluate ∆V for a total of 760 lakes and reservoirs across China at an annual timescale since 1984. The results indicated that (1) the aggregated water storage went through a slight increase of 41.5 Gt (1.7 Gt/yr) during 1985–2005, a significant decrease of 100 Gt (−20.6 Gt/yr) during 2005–2009, and then increased by 136.3 Gt (21.3 Gt/yr) during 2009–2015. (2) The increasing trend was largely attributed to lakes and reservoirs in the Tibetan Plateau Lake Zone, and the decreasing trend was mainly due to the North and Northwest Lake Zone, with little variations observed for the Northeast and Southwest Lake Zones. (3) Qinghai lake was associated with the largest increase (18.3 Gt) and Poyang lake presented the largest decline (−9.2 Gt). The results can help advance our understanding of the impact of climate change and improve future projection.

Evaluation is necessary and informative for processing and applying the precipitation products. However, researchers mainly focus on the statistical indicators of satellite‐based precipitation evaluation over manually determined regions without further considering the geographical information. To reveal the potential connections between geographical features and the error patterns of satellite precipitation estimates, this study focuses on proposing a new perspective for charactering the spatiotemporal patterns of errors in the Integrated Multi‐satellitE Retrievals for global precipitation measurement (GPM; IMERG) based on China Merged Precipitation Analysis over mainland China in the period from 2012 to 2018. We applied a quantitative cluster analysis method to analyze the characteristics of the errors in GPM IMERG based on three geographical features including elevation (Ele), latitude (Lat), and the distance from seashore (DFS). Additionally, the analysis was conducted in both warm and cold seasons to capture the temporal error patterns in IMERG. The results show that: (1) IMERG has the ability to capture spatiotemporal precipitation patterns over mainland China, with relative overestimations mainly over southeastern region; (2) geographical features (Ele, Lat, and DFS) have strong linear relationships with the statistical error indexes (e.g., correlation coefficient) of IMERG; and (3) the spatial patterns of errors in IMERG were automatically determined and demonstrated significant geographical characteristics. Finally, results of this study would provide valuable references for improving the quality of the IMERG over land.