Downscaling Precipitation in the Data-Scarce Inland River Basin of Northwest China Based on Earth System Data Products

Zuo,; Xu, Jiang; Chen, Guihai; Wang, Hong

doi:10.3390/atmos10100613

Cited by 18 publications

(8 citation statements)

References 74 publications

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“…Statistical indexes were used to conduct the quantitative analysis of the original SM and the estimated SM, and to compare the different downscaling methods. This study used the correlation coefficient (R) [49], root mean squared error (RMSE) [50], mean absolute error (MAE) [51], and unbiased root mean square error (ubRMSE) in the paper. The calculation formulas are as follows:…”

Section: Discussionmentioning

confidence: 99%

Downscaling of AMSR-E Soil Moisture over North China Using Random Forest Regression

Zhang

Wang

Liu

et al. 2022

IJGI

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Satellite retrieval can offer global soil moisture information, such as Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) data. AMSR-E has been used to provide soil moisture all over the world, with a coarse resolution of 25 km × 25 km. The coarse resolution of the soil moisture dataset often hinders its use in local or regional research. This work proposes a new framework based on the random forest (RF) model while using five auxiliary data to downscale the AMSR-E soil moisture data over North China. The downscaled results with a 1 km spatial resolution are verified against in situ measurements. Compared with AMSR-E data, the correlation coefficient of the downscaled data is increased by 0.17, and the root mean squared error, mean absolute error, and unbiased root mean square error are reduced by 0.02, 0.01, and 0.03 m3/m3, respectively. In addition, the comparison results with Multiple Linear Regression and Support Vector Regression downscaled data show that the proposed method significantly outperforms the other two methods. The feasibility of our model is well supported by the importance analysis and leave-one-out analysis. Our study, which combines RF with spatiotemporal search algorithms and efficient auxiliary data, may provide insights into soil moisture downscaling in large areas with various surface characteristics and climatic conditions.

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Section: Discussionmentioning

confidence: 99%

Downscaling of AMSR-E Soil Moisture over North China Using Random Forest Regression

Zhang

Wang

Liu

et al. 2022

IJGI

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“…On the other hand, the spatial resolution of downscaled remote sensing data still has limitations [67]. For example, local terrain variations can cause differences in temperature, wind speed, and radiation at small scales [68][69][70], but these differences are difficult to reflect in existing meteorological driving data. The missing meteorological stations also make it difficult for downscaling algorithms to improve local accuracy [71].…”

Section: The Differences In Dominant Factors Affecting Snow Melting A...mentioning

confidence: 99%

Investigating the Spatial, Proximity, and Multiscale Effects of Influencing Factors in the Snowmelt Process in the Manas River Basin Using a Novel Zonal Spatial Panel Model

Li,

Liu,

Xiao

et al. 2023

Remote Sensing

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It is essential to investigate the influences of environmental elements on snow cover to understand the mechanism of the snowmelt process. These elements, as influencing factors, have spatial heterogeneity, which results in significant differences and uncertainties in the extent and range of their effects at different scales. However, little research has been conducted on the spatial interaction and mechanisms of these factors at multiple scales. This study selected the Manas River basin in the Tianshan Mountains as the study area. The study period is 2015–2020. The snow cover status index is calculated based on available Landsat8-OLS/TIRS data; influencing factors are collected from multiple datasets. Their relationships are explored using a novel zonal spatial panel model, fully considering the spatial, proximity, and scale effects. The findings are as follows: (1) There is a robust spatial interaction and proximity effect between snowmelt and various factors, and such effects display distinct spatial heterogeneity. The elevation (ELE), slope (SLP), land surface temperature (LST), and normalized digital vegetation index (NDVI) showed significant overall dominant effects on the snow melting process. The influencing factors with apparent proximity effects are LST, ELE, SLP, NDVI, and air temperature (TEMP), and their influence ranges are different. (2) The relative importance and significance rank of dominant influencing factors vary under different partition schemes and scales. As the scale decreases, the significance of terrain- and vegetation-related factors increases, whereas the significance of temperature- and elevation-related factors decreases, and the number of dominant factors also decreases. (3) The influencing factors represent distinct characteristics among each zone at the optimally partitioned scale we defined. The overall influencing pattern demonstrates a characteristic of being globally dictated by elevation and temperature, with local terrain factors, vegetation, and wind speed modifying this pattern. Our study provides practical data support and a theoretical basis for deepening our understanding of the influence mechanism of the snow melting process.

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“…The advances in satellite remote sensing provide a valuable tool for monitoring water resources changes [ 22 , 23 , 24 , 25 ], such as precipitation [ 26 , 27 ], evaporation [ 28 , 29 ], lakes water [ 30 , 31 ], soil moisture [ 32 , 33 , 34 ], groundwater [ 35 , 36 ] and total terrestrial water storage (TWS) [ 37 , 38 ]. Guo et al [ 39 ] evaluated the temporal and spatial changes of TWS in Inner Mongolia during 2003–2021 based on Gravity Recovery and Climate Experiment (GRACE) and GRACE-Follow on (GFO), which revealed the major driving factors.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Hydrological Components Contributions to Terrestrial Water Storage Changes in Inner Mongolia with Multiple Datasets

Guo

Xing

Gan

et al. 2023

Sensors

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In this study, multiple remote sensing data were used to quantitatively evaluate the contributions of surface water, soil moisture and groundwater to terrestrial water storage (TWS) changes in five groundwater resources zones of Inner Mongolia (GW_I, GW_II, GW_III, GW_IV and GW_V), China. The results showed that TWS increased at the rate of 2.14 mm/a for GW_I, while it decreased at the rate of 4.62 mm/a, 5.89 mm/a, 2.79 mm/a and 2.62 mm/a for GW_II, GW_III, GW_IV and GW_V during 2003–2021. Inner Mongolia experienced a widespread soil moisture increase with the rate of 4.17 mm/a, 2.13 mm/a, 1.20 mm/a, 0.25 mm/a and 1.36 mm/a for the five regions, respectively. Significant decreases were detected for regional groundwater storage (GWS) with the rate of 2.21 mm/a, 6.76 mm/a, 6.87 mm/a, 3.01 mm/a, and 4.14 mm/a, respectively. Soil moisture was the major contributor to TWS changes in GW_I, which accounted 58% of the total TWS changes. Groundwater was the greatest contributor to TWS changes in other four regions, especially GWS changes, which accounted for 76% TWS changes in GW_IV. In addition, this study found that the role of surface water was notable for calculating regional GWS changes.

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Downscaling Precipitation in the Data-Scarce Inland River Basin of Northwest China Based on Earth System Data Products

Cited by 18 publications

References 74 publications

Downscaling of AMSR-E Soil Moisture over North China Using Random Forest Regression

Downscaling of AMSR-E Soil Moisture over North China Using Random Forest Regression

Investigating the Spatial, Proximity, and Multiscale Effects of Influencing Factors in the Snowmelt Process in the Manas River Basin Using a Novel Zonal Spatial Panel Model

Evaluating the Hydrological Components Contributions to Terrestrial Water Storage Changes in Inner Mongolia with Multiple Datasets

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