Comparison and assessment of spatial downscaling methods for enhancing the accuracy of satellite-based precipitation over Lake Urmia Basin

Ghorbanpour, Ali Karbalaye; Hessels, Tim; Moghim, Sanaz; Afshar, Abbas

doi:10.1016/j.jhydrol.2021.126055

Cited by 42 publications

(39 citation statements)

References 53 publications

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“…In contrast, the ML methods including RF and SRF are always more accurate than GWR due to their merits for handling the complex nonlinear predictors-precipitation relationship. This conclusion agrees well with previous studies (Karbalaye Ghorbanpour et al, 2021;Sachindra et al, 2018). In addition, the ML methods do not require residual correction (Jing et al, 2016;Shi et al, 2015).…”

Section: Modelsupporting

confidence: 93%

“…However, there are at least two limitations: (i) the ML algorithms were simply taken as a statistical tool without considering the spatial autocorrelation between precipitation measurements; and (ii) the ML algorithms were adopted in either downscaling or calibration, without being used in both downscaling and calibration. More specifically, some (Jing et al, 2016;Karbalaye Ghorbanpour et al, 2021;Yan et al, 2021) attempted to use the ML methods for downscaling and then use the classical method (e.g. GDA and cokriging) for calibration, while some (Zhang et al, 2021) employed the classical interpolation methods (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Easy-to-use spatial Random Forest-based downscaling-calibration method for producing high resolution and accurate precipitation data

Chen

2021

Preprint

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Abstract. High resolution and accurate precipitation data is significantly important for numerous hydrological applications. To enhance the spatial resolution and accuracy of satellite-based precipitation products, an easy-to-use downscaling-calibration method based on spatial Random Forest (SRF) is proposed in this paper, where the spatial autocorrelation between precipitation measurements is taken into account. The proposed method consists of two main stages. Firstly, the satellite-based precipitation was downscaled by SRF with the incorporation of some high-resolution covariates including latitude, longitude, DEM, NDVI, terrain slope, aspect, relief, and land surface temperatures. Then, the downscaled precipitation was calibrated by SRF with rain gauge observations and the aforementioned high-resolution variables. The monthly Integrated MultisatellitE Retrievals for Global Precipitation Measurement (IMERG) located in Sichuan province, China from 2015 to 2019 was processed using our method and its results were compared with those of some classical methods including geographically weighted regression (GWR), artificial neural network (ANN), random forest (RF), kriging interpolation only on gauge measurements, bilinear interpolation-based downscaling and then SRF-based calibration (Bi-SRF), and SRF-based downscaling and then geographical difference analysis (GDA)-based calibration (SRF-GDA). Results show that: (1) the proposed method outperforms the other methods as well as the original IMERG; (2) the monthly-based SRF estimation is slightly more accurate than the annual-based SRF fraction disaggregation method; (3) SRF-based downscaling and calibration preforms better than bilinear downscaling (Bi-SRF) and GDA-based calibration (SRF-GDA); (4) kriging seems more accurate than GWR and ANN in terms of quantitative accuracy measures, whereas its precipitation map cannot capture the detailed spatial precipitation patterns; and (5) among the predictors for calibration, the precipitation interpolated by kriging on the gauge measurements is the most important variable, indicating the significance for the inclusion of spatial autocorrelation information in gauge measurements.

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Section: Modelsupporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Easy-to-use spatial Random Forest-based downscaling-calibration method for producing high resolution and accurate precipitation data

Chen

2021

Preprint

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“…Moreover, they were adopted in either downscaling or calibration of precipitation. Specifically, some (Karbalaye Ghorbanpour et al, 2021;Yan et al, 2021;Jing et al, 2016) attempted to use the ML methods for downscaling and then use the classical method (e.g., GDA) for calibration, while some (Zhang et al, 2021) employed the classical interpolation methods (e.g., bilinear interpolation) for downscaling and then used the ML methods for calibration. However, we believe that the use of ML methods in both downscaling and calibration could improve the accuracy of precipitation.…”

mentioning

confidence: 99%

“…However, we believe that the use of ML methods in both downscaling and calibration could improve the accuracy of precipitation. To the best of our knowledge, no previous studies have used the ML technique in both downscaling and calibration (Karbalaye Ghorbanpour et al, 2021;Yan et al, 2021).…”

mentioning

confidence: 99%

Easy-to-use spatial random-forest-based downscaling-calibration method for producing precipitation data with high resolution and high accuracy

Chen

2021

Hydrol. Earth Syst. Sci.

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Abstract. Precipitation data with high resolution and high accuracy are significantly important in numerous hydrological applications. To enhance the spatial resolution and accuracy of satellite-based precipitation products, an easy-to-use downscaling-calibration method based on a spatial random forest (SRF-DC) is proposed in this study, where the spatial autocorrelation of precipitation measurements between neighboring locations is considered. SRF-DC consists of two main stages. First, the satellite-based precipitation is downscaled by the SRF with the incorporation of high-resolution variables including latitude, longitude, normalized difference vegetation index (NDVI), digital elevation model (DEM), terrain slope, aspect, relief and land surface temperatures. Then, the downscaled precipitation is calibrated by the SRF with rain gauge observations and the aforementioned high-resolution variables. The monthly Integrated MultisatellitE Retrievals for Global Precipitation Measurement (IMERG) over Sichuan Province, China, from 2015 to 2019 was processed using SRF-DC, and its results were compared with those of classical methods including geographically weighted regression (GWR), artificial neural network (ANN), random forest (RF), kriging interpolation only on gauge measurements, bilinear interpolation-based downscaling and then SRF-based calibration (Bi-SRF), and SRF-based downscaling and then geographical difference analysis (GDA)-based calibration (SRF-GDA). Comparative analyses with respect to root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (CC) demonstrate that (1) SRF-DC outperforms the classical methods as well as the original IMERG; (2) the monthly based SRF estimation is slightly more accurate than the annually based SRF fraction disaggregation method; (3) SRF-based downscaling and calibration perform better than bilinear downscaling (Bi-SRF) and GDA-based calibration (SRF-GDA); (4) kriging is more accurate than GWR and ANN, whereas its precipitation map loses detailed spatial precipitation patterns; and (5) based on the variable-importance rank of the RF, the precipitation interpolated by kriging on the rain gauge measurements is the most important variable, indicating the significance of incorporating spatial autocorrelation for precipitation estimation.

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“…The consistency of different PPs has been carried out by several authors in regional and global scales around the world [21][22][23]. However, the reliability of PPs over a specific area is not applicable for another region, and an individual assessment is needed to address the stability of PPs.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Satellite and Reanalysis Precipitation Products for Rainfall–Runoff Modelling in a Mountainous Basin

Hafizi

Şorman

2021

The 4th International Electronic Conference on Atmospheric Sciences

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Precipitation measurement over a complex topography and highly elevated regions has always been a great challenge in recent decades. On the other hand, satellite-based and numerical weather prediction model outputs can be an alternative to fill this gap. Hence, the goal of this study is to evaluate the spatiotemporal stability and hydrologic utility of four precipitation products (TMPA-3B42v7, IMERGHHFv06, ERA5, and PERSIANN) over a mountainous basin (Karasu basin) located in the eastern part of Turkey. Moreover, the Kling–Gupta efficiency (KGE), including its correlation, bias, and variability ratio components, are used for a direct comparison of precipitation products (PPs) with observed gauge data, and the Hansen–Kuiper (HK) score is utilized to assess the detectability strength of PPs for different precipitation events. In the same way, the hydrologic utility of PPs is tested by exploiting a conceptual rainfall–runoff model under Kling–Gupta efficiency (KGE) and Nash–Sutcliffe efficiency (NSE) metrics. Generally, all PPs show low performance for a direct comparison with observed data while their performance considerably increases for streamflow simulation. TMPA-3B42v7 has high reproducibility in streamflow (KGE = 0.84), followed by IMERGHHFv06 (KGE = 0.76), ERA5 (KGE = 0.75), and PERSIANN (KGE = 0.70), for the entire period (2015–2019) of this study.

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Comparison and assessment of spatial downscaling methods for enhancing the accuracy of satellite-based precipitation over Lake Urmia Basin

Cited by 42 publications

References 53 publications

Easy-to-use spatial Random Forest-based downscaling-calibration method for producing high resolution and accurate precipitation data

Easy-to-use spatial Random Forest-based downscaling-calibration method for producing high resolution and accurate precipitation data

Easy-to-use spatial random-forest-based downscaling-calibration method for producing precipitation data with high resolution and high accuracy

Assessment of Satellite and Reanalysis Precipitation Products for Rainfall–Runoff Modelling in a Mountainous Basin

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