A spatiotemporal framework to calibrate high‐resolution global monthly precipitation products: An application to the Urmia Lake Watershed in Iran

Nasseri, Mohsen; Schoups, Gerrit; Taheri, Mercedeh

doi:10.1002/joc.7358

Cited by 12 publications

(4 citation statements)

References 82 publications

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“…These correction factor grids can be used for realtime practices of generating precipitation maps using PPs. For more information, the readers are referred to the works of Hong et al (2021), Ma et al (2018), andNasseri et al (2021). Lastly, exploring the effect of NDVI and LST on spatial patterns of precipitation in terms of landatmosphere interactions should be explored in future studies.…”

Section: Discussionmentioning

confidence: 99%

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A locally weighted linear ridge regression framework for spatial interpolation of monthly precipitation over an orographically complex area

Zandi

Nasseri

Zahraie

2023

Intl Journal of Climatology

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This article aimed to investigate the potential of using three large‐scale precipitation products (PPs), including TRMM, ERA5‐Land, and MSWEP, and two land surface characteristics, including NDVI and Land Surface Temperature (LST), to improve the accuracy of an elevation‐based spatial non‐stationary, Locally Weighted Linear Regression (LWLR) method. A two‐step approach consisting of downscaling and merging was proposed and assessed across an orographically complex region in Iran to construct monthly precipitation maps for the 2001–2015 period. In the first step, three large‐scale PPs were downscaled to 1 km spatial resolution via the Area To Point Kriging (ATPK) method to address the spatial resolution discrepancy between the coarse pixels of PPs and pointwise gauge data. In the second step, five regression models with different combinations of predictors were developed and compared with the benchmark precipitation‐elevation regression model. The L2 regularization method was adopted to overcome the potential multicollinearity issue. The proposed framework could successfully diagnose the multicollinearity issues and dynamically estimate the regularization parameter in space and time. We utilized the holdout method to validate and compare the developed models, in which 50% of gauges were used for fitting the regression functions, and the remaining gauges were assigned to the validation dataset. The validation results showed that the overall monthly accuracy of the benchmark univariate elevation‐based model was improved where the MAE metric was decreased by 16.5%, and CC and KGE were increased by 3.4 and 5.8%, respectively. Also, the rBias was enhanced from −5.72 to 1.24%. Mean monthly precipitation maps of the 2001–2015 period generated by the model with the best combination of predictors and the benchmark model displayed the same spatial pattern consistent with the topography. Further analysis under different validation scenarios revealed that the contribution of PPs and auxiliary variables is the greatest when the training gauge network is sparse. Also, the developed multivariate model has a higher extrapolation ability and is more robust than the benchmark model.

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Section: Discussionmentioning

confidence: 99%

“…(2018), and Nasseri et al . (2021). Lastly, exploring the effect of NDVI and LST on spatial patterns of precipitation in terms of land‐atmosphere interactions should be explored in future studies.…”

Section: Discussionmentioning

confidence: 99%

A locally weighted linear ridge regression framework for spatial interpolation of monthly precipitation over an orographically complex area

Zandi

Nasseri

Zahraie

2023

Intl Journal of Climatology

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“…Linear Scaling (LS) is a statistical method to match the mean of downscaled data with the observed data [35] and has been used in various climate change impact studies [36][37][38][39]. Using the differences between the observed and simulated data obtained directly from GCMs and RCMs, LS operates with monthly downscaling values.…”

Section: Downscaling Methodsmentioning

confidence: 99%

Investigating the Role of Bias Correction Methods and Climate Models on Water Budget of Büyük Menderes Basin

ERKOL,

DALOGLU ÇETİNKAYA

2023

Sakarya University Journal of Science

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Büyük Menderes Basin is one of the largest basins in Turkey, with almost half of the basin area utilized for agricultural purposes. The amount of water allocated to the agricultural areas in the basin corresponds to 80% of water use in the watershed. Hence, the impact of climate change on the water supply in the Büyük Menderes Basin will be significant for the basin. In this study, we model the effects of climate change on the water budget (water supply and demand balance) of the Büyük Menderes Basin using the Water and Evaluation and Planning (WEAP) tool. Future precipitation, temperature, and evaporation data for the basin are attained from outputs of the HadGEM2-ES global circulation model (GCM), along with CNRM-CM5.1 and GFDL-ESM2M regional circulation models (RCM) for RCP 4.5 and RCP 8.5 scenarios. Subsequently, the study applies different statistical bias correction methods (Linear Scaling (LS), Distribution Mapping (DM), Local Precipitation Scaling (PLIS), and Power Transformation of Precipitation (PTP) for raw outputs of GCMs and RCMs and analyzes the changes in outcomes of projected climate data and the impact of changes on the hydrology of the basin using the WEAP model. For this analysis, calibrated and validated WEAP model for the 12 reservoirs of Büyük Menderes Basin is used to understand the impact of different bias correction methods on reservoir levels.

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“…They include studies performed in Central and Western Europe (Lockhoff et al 2019), China (An et al 2020;Bai et al 2020;Li et al 2018), Central Asia (Lu et al 2021), Australia and Africa (Awange et al 2019), India (Bhattacharyya et al 2022;Kolluru et al 2020), Thailand (Gunathilake et al 2021), Iraq and Eastern Africa (Salman et al 2019), Kenya, Uganda, and Tanzania (Garibay et al 2021). In addition, various research works in Iran have addressed global precipitation datasets for arid and semiarid regions (Darand and Khandu 2020;Eini et al 2019;Fallah et al 2020;Ghajarnia et al 2015;Hosseini-Moghari et al 2018;Izadi et al 2021;Keikhosravi-Kiany et al 2021;Nasseri et al 2021;Saemian et al 2021;Shayeghi et al 2020;Taghizadeh et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Improving the accuracy of satellite and reanalysis precipitation data by their ensemble usage

2022

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This study evaluated the outputs of five precipitation (PCP) datasets. These models are ECMWF reanalysis 5th generation (ERA5), precipitation estimation from remotely sensed information using artificial neural networks-climate data record (PERSIANN-CDR), Asian precipitation-highly resolved observational data integration toward evaluation (APHRODITE), The national centers for environmental prediction climate forecast system reanalysis (NCEP CFSR) and climatic research unit (CRU). The PCP outputs of these models were compared with data of nine synoptic stations in the Khuzestan province. The results indicated a better match between the APHRODITE outputs and the PCP data at most stations (R2 > 0.85, root-mean-square error (RMSE) < 17.049 mm and − 4.25 < Bias < 2.633 mm). However, CRU model has the highest critical success index (more than 0.711) and the lowest false alarm ratio (less than 0.2) and ERA5 has the highest probability of detection (more than 0.967) at most stations. Then, PCP outputs of five reanalysis (ERA5), interpolated (APHRODITE, NCEP CFSR and CRU) and satellite (PERSIANN-CDR) PCP datasets were combined to reduce the PCP estimation error. The multivariate adaptive regression splines models were employed for this purpose. The results show that the RMSE of all the stations, except Ahvaz station, decreased and the BIAS decreased too. Given the results, using ensemble data methods is a suitable way for reducing the error and increasing the accuracy of these models.

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A spatiotemporal framework to calibrate high‐resolution global monthly precipitation products: An application to the Urmia Lake Watershed in Iran

Cited by 12 publications

References 82 publications

A locally weighted linear ridge regression framework for spatial interpolation of monthly precipitation over an orographically complex area

A locally weighted linear ridge regression framework for spatial interpolation of monthly precipitation over an orographically complex area

Investigating the Role of Bias Correction Methods and Climate Models on Water Budget of Büyük Menderes Basin

Improving the accuracy of satellite and reanalysis precipitation data by their ensemble usage

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