A Spatial Downscaling Methodology for GRACE Total Water Storage Anomalies Using GPM IMERG Precipitation Estimates

A downscaling framework for coarse resolution Gravity Recovery and Climate Experiment (GRACE) Total Water Storage Anomaly (TWSA) data is described, exploiting the observations of precipitation from the Global Precipitation Measurement (GPM) mission, using the Integrated Multi-satellite Retrievals for GPM (IMERG). Considering that the major driving force for changes in TWS is precipitation, we tested our hypothesis that coarse resolution, i.e., 1°, GRACE TWSA can be effectively downscaled to 0.1° using GPM IMERG data. The algorithm for the downscaling process comprises the development of a regression equation at the coarse resolution between the GRACE and GPM IMERG data, which is then applied at the finer resolution with a subsequent residual correction procedure. An ensemble of GRACE data from three processing centers, i.e., GFZ, JPL and CSR, was used for the time period from June 2018 until March 2021. To verify our downscaling methodology, we applied it with GRACE data from 2005 to 2015, and we compared it against modeled TWSA from two independent datasets in the Thrace and Thessaly regions in Greece for the same period and found a high performance in all examined metrics. Our research indicates that the downscaled GRACE observations are comparable to the TWSA estimated with hydrological modeling, thus highlighting the potential of GRACE data to contribute to the improvement of hydrological model performance, especially in ungauged basins.

Improving the Spatial Resolution of GRACE-Derived Terrestrial Water Storage Changes in Small Areas Using the Machine Learning Spatial Downscaling Method

Gravity Recovery and Climate Experiment (GRACE) satellites can effectively monitor terrestrial water storage (TWS) changes in large-scale areas. However, due to the coarse resolution of GRACE products, there is still a large number of deficiencies that need to be considered when investigating TWS changes in small-scale areas. Hence, it is necessary to downscale the GRACE products with a coarse resolution. First, in order to solve this problem, the present study employs modeling windows of different sizes (Window Size, WS) combined with multiple machine learning algorithms to develop a new machine learning spatial downscaling method (MLSDM) in the spatial dimension. Second, The MLSDM is used to improve the spatial resolution of GRACE observations from 0.5° to 0.25°, which is applied to Guantao County. The present study has verified the downscaling accuracy of the model developed through the combination of WS3, WS5, WS7, and WS9 and jointed with Random Forest (RF), Extra Tree Regressor (ETR), Adaptive Boosting Regressor (ABR), and Gradient Boosting Regressor (GBR) algorithms. The analysis shows that the accuracy of each combined model is improved after adding the residuals to the high-resolution downscaled results. In each modeling window, the accuracy of RF is better than that of ETR, ABR, and GBR. Additionally, compared to the changes in the TWS time series that are derived by the model before and after downscaling, the results indicate that the downscaling accuracy of WS5 is slightly more superior compared to that of WS3, WS7, and WS9. Third, the spatial resolution of the GRACE data was increased from 0.5° to 0.05° by integrating the WS5 and RF algorithm. The results are as follows: (1) The TWS (GWS) changes before and after downscaling are consistent, decreasing at −20.86 mm/yr and −21.79 mm/yr (−14.53 mm/yr and −15.46 mm/yr), respectively, and the Nash–Sutcliffe efficiency coefficient (NSE) and correlation coefficient (CC) values of both are above 0.99 (0.98). (2) The CC between the 80% deep groundwater well data and the downscaled GWS changes are above 0.70. Overall, the MLSDM can not only effectively improve the spatial resolution of GRACE products but also can preserve the spatial distribution of the original signal, which can provide a reference scheme for research focusing on the downscaling of GRACE products.

Prediction of Regional Forest Soil Nutrients Based on Gaofen-1 Remote Sensing Data

The study on the spatial distribution of forest soil nutrients is important not only as a reference for understanding the factors affecting soil variability, but also for the rational use of soil resources and the establishment of a virtuous cycle of forest ecosystems. The rapid development of remote sensing satellites provides an excellent opportunity to improve the accuracy of forest soil prediction models. This study aimed to explore the utility of the Gaofen-1 (GF-1) satellite in the forest soil mapping model in Luoding City, Yunfu City, Guangdong Province, Southeast China. We used 1000 m resolution coarse-resolution soil map to represent the overall regional soil nutrient status, 12.5 m resolution terrain-hydrology variables to reflect the detailed spatial distribution of soil nutrients, and 8 m resolution remote sensing variables to reflect the surface vegetation status to build terrain-hydrology artificial neural network (ANN) models and full variable ANNs, respectively. The prediction objects were alkali-hydro-nitrogen (AN), available phosphorus (AP), available potassium (AK), and organic matter (OM) at five soil depths (0–20, 20–40, 40–60, 60–80, and 80–100 cm). The results showed that the full-variable ANN accuracy at five soil depths was better than the terrain-hydrology ANNs, indicating that remote sensing variables reflecting vegetation status can improve the prediction of forest soil nutrients. The remote sensing variables had different effectiveness for different soil nutrients and different depths. In upper soil layers (0–20 and 20–40 cm), remote sensing variables were more useful for AN, AP, and OM, and were between 10%–14% (R2), and less effective for AK at only 8% and 6% (R2). In deep soil layers (40–60, 60–80, and 80–100 cm), the improvement of all soil nutrient models was not significant, between 3 and 6% (R2). RMSE and ROA ± 5% also decreased with the depth of soil. Remote sensing ANNs (coarse resolution soil maps + remote sensing variables) further demonstrated that the predictive power of remote sensing data decreases with soil depth. Compared to terrain-hydrological variables, remote sensing variables perform better at 0–20 cm, but the predictive power decreased rapidly with depth. In conclusion, the results of the study showed that the integration of remote sensing with coarse-resolution soil maps and terrain-hydrology variables could strongly improve upper forest soil (0–40 cm) nutrients prediction and NDVI, green band, and forest types were the best remote sensing predictors. In addition, the study area is rich in AN and OM, while AP and AK are scarce. Therefore, to improve forest health, attention should be paid to monitoring and managing AN, AP, AK, and OM levels.

Evaluation of synoptic eddy activities and their feedback onto the midlatitude jet in five atmospheric reanalyses with coarse versus fine model resolutions

Interaction between synoptic eddy and mean flow plays a crucial role in maintaining midlatitude westerly jet. In this study, climatologies of synoptic eddy activities and their feedback onto midlatitude jet for 1980–2016 are evaluated and compared through analyzing daily data from five atmospheric reanalyses with different resolutions including one coarse-resolution reanalysis (NCEP2) and four fine-resolution reanalyses (ERA-Interim, JRA-55, MERRA-2, and CFSR). Horizontal resolutions of the atmospheric models generating those reanalyses are approximately equivalent to 210, 79, 60, 50, and 38 km, respectively. Results show that the eddy activities and their feedback onto the midlatitude jet in those fine-resolution reanalyses are consistently and significantly stronger than those in the coarse-resolution reanalysis (NCEP2). The maximal relative increases that are found to occur primarily in the midlatitudes of the Southern Hemisphere are estimated to be up to 55% for the baroclinicity, 53% for the eddy energetics, 59% for the eddy forcing, and even 85% for the eddy feedback onto the mean flow. Those increases are reasonably conjectured to be related to increased model resolutions, since the synoptic eddy genesis is proportional to the low-level atmospheric meridional temperature gradient which is sensitive to the meridional resolution of atmospheric models. Although the coarse-resolution reanalysis resolves synoptic eddies insufficiently and thus underestimates their feedback onto the mean flow, the magnitudes of eddy-driven jets are almost the same among five reanalyses, implying a mismatch between the eddy feedback and the eddy-driven jet in the coarse-resolution reanalysis. Therefore, the results of this study imply the importance of using fine-resolution reanalyses in accurately understanding the midlatitude synoptic eddy–mean flow interaction.

Understanding model diversity in future precipitation projections for South America

Precipitation patterns are expected to change in the future climate, affecting humans through a number of factors. Global climate models (GCM) are our best tools for projecting large-scale changes in climate, but they cannot make reliable projections locally. To abate this problem, we have downscaled three GCMs with the Weather Research and Forecasting (WRF) model to 50 km horizontal resolution over South America, and 10 km resolution for central Chile, Peru and southern Brazil. Historical simulations for years 1996–2005 generally compare well to precipitation observations and reanalyses. Future simulations for central Chile show reductions in annual precipitation and increases in the number of dry days at the end-of-the-century for a high greenhouse gas emission scenario, regardless of resolution and GCM boundary conditions used. However, future projections for Peru and southern Brazil are more uncertain, and simulations show that increasing the model resolution can switch the sign of precipitation projections. Differences in future precipitation changes between global/regional and high resolution (10 km) are only mildly influenced by the orography resolution, but linked to the convection parameterization, reflected in very different changes in dry static energy flux divergence, vertical velocity and boundary layer height. Our findings imply that using results directly from GCMs, and even from coarse-resolution (50 km) regional models, may give incorrect conclusions about regional-scale precipitation projections. While climate modelling at convection-permitting scales is computationally costly, we show that coarse-resolution regional simulations using a scale-aware convection parameterization, instead of a more conventional scheme, better mirror fine-resolution precipitation projections.

ValLAI_Crop, a validation dataset for coarse-resolution satellite LAI products over Chinese cropland

Numerous validation efforts have been conducted over the last decade to assess the accuracy of global leaf area index (LAI) products. However, such efforts continue to face obstacles due to the lack of sufficient high-quality field measurements. In this study, a fine-resolution LAI dataset consisting of 80 reference maps was generated during 2003–2017. The direct destructive method was used to measure the field LAI, and fine-resolution LAI images were derived from Landsat images using semiempirical inversion models. Eighty reference LAI maps, each with an area of 3 km × 3 km and a percentage of cropland larger than 75%, were selected as the fine-resolution validation dataset. The uncertainty associated with the spatial scale effect was also provided. Ultimately, the fine-resolution reference LAI dataset was used to validate the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product. The results indicate that the fine-resolution reference LAI dataset builds a bridge to link small sampling plots and coarse-resolution pixels, which is extremely important in validating coarse-resolution LAI products.