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To improve the accuracy and reliability of precipitation estimation, numerous models based on machine learning technology have been developed for integrating data from multiple sources. However, little attention has been paid to extracting the spatiotemporal correlation patterns between satellite products and rain gauge observations during the merging process. This paper focuses on this issue by proposing an integrated framework to generate an accurate and reliable spatiotemporal estimation of precipitation. The proposed framework integrates Funk-Singular Value Decomposition (F-SVD) in the recommender system to achieve the accurate spatial distribution of precipitation based on the spatiotemporal interpolation of rain gauge observations and Convolutional Long Short-Term Memory (ConvLSTM) to merge precipitation data from interpolation results and satellite observation through exploiting the spatiotemporal correlation pattern between them. The framework (FS-ConvLSTM) is utilized to obtain hourly precipitation merging data with a resolution of 0.1° in Jianxi Basin, southeast of China, from both rain gauge data and Global Precipitation Measurement (GPM) from 2006 to 2018. The LSTM and Inverse Distance Weighting (IDW) are constructed for comparison purposes. The results demonstrate that the framework could not only provide more accurate precipitation distribution but also achieve better stability and reliability. Compared with other models, it performs better in variation process description and rainfall capture capability, and the root mean square error (RSME) and probability of detection (POD) are improved by 63.6% and 22.9% from the original GPM, respectively. In addition, the merged precipitation combines the strength of different data while mitigating their weaknesses and has good agreement with observed precipitation in terms of magnitude and spatial distribution. Consequently, the proposed framework provides a valuable tool to improve the accuracy of precipitation estimation, which can have important implications for water resource management and natural disaster preparedness. [ABSTRACT FROM AUTHOR]

Whether or not large-scale vegetation restoration will lead to a decrease in regional terrestrial water storage is a controversial topic. This study employed the Geodetector model, in conjunction with observed and satellite hydro-meteorological data, to detect the changes in terrestrial water storage anomaly (TWSA) and to identify the contributions of climate change and vegetation greening across China during the years 1982–2019. The results revealed that: (1) during the period of 1982–2019, TWSA showed a downward trend in about two thirds of the country, with significant declines in North China, southeast Tibet, and northwest Xinjiang, and an upward trend in the remaining third of the country, with significant increases mainly in the Qaidam Basin, the Yangtze River, and the Songhua River; (2) the positive correlation between normalized vegetation index (NDVI) and TWSA accounts for 48.64% of the total vegetation area across China. In addition, the response of vegetation greenness lags behind the TWSA and precipitation, and the lag time was shorter in arid and semi-arid regions dominated by grasslands, and longer in relatively humid regions dominated by forests and savannas; (3) furthermore, TWSAs decreased with the increase in NDVI and evapotranspiration (ET) in arid and semi-arid areas, and increased with the rise in NDVI and ET in the humid regions. The Geodetector model was used to detect the effects of climate, vegetation, and human factors on TWSA. It is worth mentioning that NDVI, precipitation, and ET were some of the main factors affecting TWSA. Therefore, it is essential to implement rational ecological engineering to mitigate climate change's negative effects and maintain water resources' sustainability in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

Understanding the evolution of river morphology is crucial for comprehending changes in water resources and implementing development projects along rivers. This study proposes an integrated approach utilizing remote sensing image data combined with deep learning and visual interpretation algorithms to analyze continuous-type changes in river morphology. This research focuses on the lower reaches of the Minjiang River in China and comprehensively analyzes the river's morphological evolution from 1986 to 2021. The results show that the proposed method of river water identification in this study demonstrates high accuracy and effectiveness, with an F1 score and Kappa coefficient greater than 0.96 and 0.91, respectively. The morphology of the river channel remains stable in the upstream and estuarine sections of the study region while undergoing substantial alterations in the middle section. Additionally, this study also identifies several factors that significantly impact the evolution of river morphology, including reservoir construction, river sediment mining, river training measures, geological conditions, and large flood events. The findings of this study can provide some insights into the management and conservation of water resources. [ABSTRACT FROM AUTHOR]

Rubber (Hevea brasiliensis) plantations have high water consumption through evapotranspiration, which can contribute to water scarcity. In addition, there is a lack of spatial observation data and estimation methods for evapotranspiration (ET) for rubber plantations. To alleviate the water stress of expanding rubber plantations caused by seasonal drought in Xishuangbanna, Southwest China, an up to 7 days crop evapotranspiration (ETc) forecast method, "Kc-ET0" for rubber plantations with limited meteorological data, was proposed by using rubber crop coefficient Kc and public weather forecasts. The results showed that the average absolute error (MAE) of forecasted ETc was 0.68 mm d−1, the root mean square error (RMSE) was 0.85 mm d−1, and the average correlation coefficient (R) was 0.69 during the rainy season, while during the dry season these metrics were 0.52 mm d−1, 0.68 mm d−1, and 0.85, respectively. The accuracy of ETc forecast in the dry season was higher. Additionally, Kc was the main factor influencing the accuracy of rubber plantations ETc forecast, while the accuracy of the temperature forecast and the chosen Hargreaves-Samani (HS) model were also considerable. Our results suggested that the "Kc-ET0" short-term rubber plantation ETc forecasting method shows good performance and acceptable accuracy, especially in the dry season. The study provides an important basis for creating ET-based irrigation scheduling for improving regional-scale water management in high water consumption rubber plantations. [ABSTRACT FROM AUTHOR]

This study comprehensively evaluates eight satellite-based precipitation datasets in streamflow simulations on a monsoon-climate watershed in China. Two mutually independent datasets—one dense-gauge and one gauge-interpolated dataset—are used as references because commonly used gauge-interpolated datasets may be biased and unable to reflect the real performance of satellite-based precipitation due to sparse networks. The dense-gauge dataset includes a substantial number of gauges, which can better represent the spatial variability of precipitation. Eight satellite-based precipitation datasets include two raw satellite datasets, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) and Climate Prediction Center MORPHing raw satellite dataset (CMORPH RAW); four satellite-gauge datasets, Tropical Rainfall Measuring Mission 3B42 (TRMM), PERSIANN Climate Data Record (PERSIANN CDR), CMORPH bias-corrected (CMORPH CRT), and gauge blended datasets (CMORPH BLD); and two satellite-reanalysis-gauge datasets, Multi-Source Weighted-Ensemble Precipitation (MSWEP) and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS). The uncertainty related to hydrologic model physics is investigated using two different hydrological models. A set of statistical indices is utilized to comprehensively evaluate the precipitation datasets from different perspectives, including detection, systematic, random errors, and precision for simulating extreme precipitation. Results show that CMORPH BLD and MSWEP generally perform better than other datasets. In terms of hydrological simulations, all satellite-based datasets show significant dampening effects for the random error during the transformation process from precipitation to runoff; however, these effects cannot hold for the systematic error. Even though different hydrological models indeed introduce uncertainties to the simulated hydrological processes, the relative hydrological performance of the satellite-based datasets is consistent in both models. Namely, CMORPH BLD performs the best, which is followed by MSWEP, CMORPH CRT, and TRMM. PERSIANN CDR and CHIRPS perform moderately well, and two raw satellite datasets are not recommended as proxies of gauged observations for their worse performances. [ABSTRACT FROM AUTHOR]

Bias correction methods are routinely used to correct climate model outputs for hydrological and agricultural impact studies. Even though superior bias correction methods can correct the distribution of daily precipitation amounts, as well as the wet-day frequency, they usually fail to correct the temporal sequence or structure of precipitation occurrence. To solve this problem, we presented a hybrid bias correction method for simulating the temporal sequence of daily precipitation occurrence. We did this by combining a first-order two-state Markov chain with a quantile-mapping (QM) based bias correction method. Specifically, a QM-based method was used to correct the distributional attributes of daily precipitation amounts and the wet-day frequency simulated by climate models. Then, the sequence of precipitation occurrence was simulated using the first-order two-state Markov chain with its parameters adjusted based on linear relationships between QM-corrected mean monthly precipitation and the transition probabilities of precipitation occurrence. The proposed Markov chain-based bias correction (MCBC) method was compared with the QM-based method with respect to reproducing the temporal structure of precipitation occurrence over 10 meteorological stations across China. The results showed that the QM-based method was unable to correct the temporal sequence, with the cumulative frequency of wet- and dry-spell length being considerably underestimated for most stations. The MCBC method can could reproduce the temporal sequence of precipitation occurrence, with the generated cumulative frequency of wet- and dry-spell lengths fitting that of the observation well. The proposed method also performed reasonably well with respect to reproducing the mean, standard deviation, and the longest length of observed wet- and dry-spells. Overall, the MCBC method can simulate the temporal sequence of precipitation occurrence, along with correcting the distributional attributes of precipitation amounts. This method can be used with crop and hydrological models in climate change impact studies at the field and small watershed scales. [ABSTRACT FROM AUTHOR]