Ghaffari Zahra, Easson Greg, Yarbrough Lance D, Awawdeh Abdel Rahman, Jahan Md Nasrat, Ellepola Anupiya
Department of Geology & Geological Engineering, University of Mississippi, University, MS 38677, USA.
Mississippi Mineral Resources Institute, University of Mississippi, University, MS 38677, USA.
Sensors (Basel). 2023 Jul 15;23(14):6428. doi: 10.3390/s23146428.
The importance of high-resolution and continuous hydrologic data for monitoring and predicting water levels is crucial for sustainable water management. Monitoring Total Water Storage (TWS) over large areas by using satellite images such as Gravity Recovery and Climate Experiment (GRACE) data with coarse resolution (1°) is acceptable. However, using coarse satellite images for monitoring TWS and changes over a small area is challenging. In this study, we used the Random Forest model (RFM) to spatially downscale the GRACE mascon image of April 2020 from 0.5° to ~5 km. We initially used eight different physical and hydrological parameters in the model and finally used the four most significant of them for the final output. We executed the RFM for Mississippi Alluvial Plain. The validating data R for each model was 0.88. Large R and small RMSE and MAE are indicative of a good fit and accurate predictions by RFM. The result of this research aligns with the reported water depletion in the central Mississippi Delta area. Therefore, by using the Random Forest model and appropriate parameters as input of the model, we can downscale the GRACE mascon image to provide a more beneficial result that can be used for activities such as groundwater management at a sub-county-level scale in the Mississippi Delta.
高分辨率和连续的水文数据对于监测和预测水位至关重要，这对可持续水资源管理至关重要。使用诸如重力恢复与气候实验（GRACE）数据等粗分辨率（1°）的卫星图像来监测大面积的总蓄水量（TWS）是可行的。然而，使用粗分辨率卫星图像来监测小区域的TWS及其变化具有挑战性。在本研究中，我们使用随机森林模型（RFM）将2020年4月的GRACE质量块图像从0.5°空间降尺度到约5公里。我们最初在模型中使用了八个不同的物理和水文参数，最后使用其中四个最显著的参数作为最终输出。我们对密西西比冲积平原执行了RFM。每个模型的验证数据R为0.88。R值大以及RMSE和MAE值小表明RFM拟合良好且预测准确。本研究结果与密西西比三角洲中部地区报道的水资源枯竭情况相符。因此，通过使用随机森林模型和适当的参数作为模型输入，我们可以对GRACE质量块图像进行降尺度处理，以提供更有益的结果，可用于密西西比三角洲县级以下尺度的地下水管理等活动。
