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Journal of Arid Land
Journal of Arid Land  2024, Vol. 16 Issue (3): 331-354    DOI: 10.1007/s40333-024-0054-7     CSTR: 32276.14.s40333-024-0054-7
Research article     
Improving the accuracy of precipitation estimates in a typical inland arid area of China using a dynamic Bayesian model averaging approach
XU Wenjie1,2, DING Jianli1,2,*(), BAO Qingling1,2, WANG Jinjie1,2, XU Kun1,2
1College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830017, China
2Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, Xinjiang University, Urumqi 830017, China
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Abstract  

Xinjiang Uygur Autonomous Region is a typical inland arid area in China with a sparse and uneven distribution of meteorological stations, limited access to precipitation data, and significant water scarcity. Evaluating and integrating precipitation datasets from different sources to accurately characterize precipitation patterns has become a challenge to provide more accurate and alternative precipitation information for the region, which can even improve the performance of hydrological modelling. This study evaluated the applicability of widely used five satellite-based precipitation products (Climate Hazards Group InfraRed Precipitation with Station (CHIRPS), China Meteorological Forcing Dataset (CMFD), Climate Prediction Center morphing method (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA)) and a reanalysis precipitation dataset (ECMWF Reanalysis v5-Land Dataset (ERA5-Land)) in Xinjiang using ground-based observational precipitation data from a limited number of meteorological stations. Based on this assessment, we proposed a framework that integrated different precipitation datasets with varying spatial resolutions using a dynamic Bayesian model averaging (DBMA) approach, the expectation-maximization method, and the ordinary Kriging interpolation method. The daily precipitation data merged using the DBMA approach exhibited distinct spatiotemporal variability, with an outstanding performance, as indicated by low root mean square error (RMSE=1.40 mm/d) and high Person's correlation coefficient (CC=0.67). Compared with the traditional simple model averaging (SMA) and individual product data, although the DBMA-fused precipitation data were slightly lower than the best precipitation product (CMFD), the overall performance of DBMA was more robust. The error analysis between DBMA-fused precipitation dataset and the more advanced Integrated Multi-satellite Retrievals for Global Precipitation Measurement Final (IMERG-F) precipitation product, as well as hydrological simulations in the Ebinur Lake Basin, further demonstrated the superior performance of DBMA-fused precipitation dataset in the entire Xinjiang region. The proposed framework for solving the fusion problem of multi-source precipitation data with different spatial resolutions is feasible for application in inland arid areas, and aids in obtaining more accurate regional hydrological information and improving regional water resources management capabilities and meteorological research in these regions.

Key words： precipitation estimates      satellite-based and reanalysis precipitation      dynamic Bayesian model averaging      streamflow simulation      Ebinur Lake Basin      Xinjiang     
Received: 10 October 2023      Published: 31 March 2024
Corresponding Authors: *DING Jianli (E-mail: dingjl@xju.edu.cn)
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XU Wenjie
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XU Wenjie, DING Jianli, BAO Qingling, WANG Jinjie, XU Kun. Improving the accuracy of precipitation estimates in a typical inland arid area of China using a dynamic Bayesian model averaging approach. Journal of Arid Land, 2024, 16(3): 331-354.

URL:

http://jal.xjegi.com/10.1007/s40333-024-0054-7     OR     http://jal.xjegi.com/Y2024/V16/I3/331

Fig. 1 Overview of Xinjiang Uygur Autonomous Region (a) and the Ebinur Lake Basin (b) based on the digital elevation model (DEM). Note that the figures are based on the standard map (新S(2023)064) of the Map Service System (https://xinjiang.tianditu.gov.cn/main/bzdt.html) marked by the Xinjiang Uygur Autonomous Region Platform for Common Geospatial Information Services, and the standard map has not been modified.
Dataset Spatial
resolution		 Temporal resolution Coverage
range		 Period Website
CHIRPS 0.05° Daily 50°S-50°N 1981-present https://www.chc.ucsb.edu/data/chirps
CMFD 0.10° Daily China (land only) 1979-2018 https://data.tpdc.ac.cn/zh-hans/data
CMORPH 0.25° Daily 60°S-60°N 1998-present https://climatedataguide.ucar.edu/climate-data
TMPA 0.25° Daily 50°S-50°N 1998-2019 https://gpm.nasa.gov/data
PERSIANN-CDR 0.25° Daily 60°S-60°N 1983-present https://climatedataguide.ucar.edu/climate-data
ERA5-Land 0.10° Hourly Earth 1950-present https://cds.climate.copernicus.eu
IMERG-F 0.10° Hourly Earth 2000-present https://gpm.nasa.gov/data/imerg
Table 1 General information of the precipitation products used in this study
Fig. 2 Flowchart of the dynamic Bayesian model averaging (DBMA) approach for blending gridded precipitation data with different spatial resolutions. CHIRPS, Climate Hazards Group InfraRed Precipitation with Station; CMFD, China Meteorological Forcing Dataset; CMORPH, Climate Prediction Center morphing method; TMPA, Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis; PERSIANN-CDR, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record; ERA5-Land, ECMWF Reanalysis v5-Land; EM, Expectation-Maximization.
Fig. 3 Variations in the two indicators (RMSE and CC) between DBMA-based precipitation and ground-based observational precipitation data across different training period lengths. RMSE, root mean square error; CC, Pearson's correlation coefficient.
Evaluation indicator Equation Optimal value
Mean absolute error (MAE; mm/d) $\text{MAE}=\frac{1}{n}\sum\nolimits_{j=1}^{n}{\left| {{M}_{j}}-{{G}_{j}} \right|}$ 0
Pearson's correlation coefficient (CC) $\text{CC}=\frac{\sum\nolimits_{j=1}^{n}{\left( {{M}_{j}}-\overline{M} \right)}\left( {{G}_{j}}-\overline{G} \right)}{\sqrt{\sum\nolimits_{j=1}^{n}{{{\left( {{M}_{j}}-\overline{M} \right)}^{2}}}}\sqrt{\sum\nolimits_{j=1}^{n}{{{\left( {{G}_{j}}-\overline{G} \right)}^{2}}}}}$ 1
Root mean square error (RMSE; mm/d) $\text{RMSE}=\sqrt{\frac{1}{n}\sum\nolimits_{j=1}^{n}{{{\left( {{M}_{j}}-{{G}_{j}} \right)}^{2}}}}$ 0
Relative bias (RB; %) $\text{RB}=\frac{\sum\nolimits_{j=1}^{n}{\left( {{M}_{j}}-{{G}_{j}} \right)}}{\sum\nolimits_{j=1}^{n}{{{G}_{j}}}}\times 100%$ 0
Theil's U (μ) $\mu =\sqrt{\sum\nolimits_{j=1}^{n}{{{\left( {{M}_{j}}-{{G}_{j}} \right)}^{2}}/\sum\nolimits_{j=1}^{n}{M_{j}^{2}}}}$ 0
Kling-Gupta efficiency (KGE) $\text{KGE}=1-\sqrt{{{(\text{CC}-1)}^{2}}+{{(\beta -1)}^{2}}+{{(\gamma -1)}^{2}}}$
where$\beta =\overline{M}/\overline{G}\text{, }\gamma =\text{C}{{\text{V}}_{M}}/\text{C}{{\text{V}}_{G}}$		 1
Critical success index (CSI) $\text{CSI}=\frac{H}{H+T+F}$ 1
Probability of detection (POD) $\text{POD}=\frac{H}{H+T}$ 1
False alarm rate (FAR) $\text{FAR}=\frac{F}{H+F}$ 0
Nash-Sutcliffe efficiency (NSE) $\text{NSE}=1-\frac{\sum\nolimits_{j=1}^{n}{{{\left( {{S}_{sim}}-{{S}_{obs}} \right)}^{2}}}}{\sum\nolimits_{j=1}^{n}{{{\left( {{S}_{obs}}-\overline{{{S}_{obs}}} \right)}^{2}}}}$ 1
Table 2 Equations and optimal values of the evaluation indicators used in this study
Statistic CHIRPS CMORPH CMFD PERSIANN-CDR TMPA ERA5-Land
CC 0.27 0.25 0.62 0.28 0.28 0.55
RMSE (mm/d) 2.58 2.73 1.78 2.16 2.38 2.00
RB (%) -0.96 11.87 -0.11 16.07 -5.08 34.46
POD 0.33 0.45 0.83 0.71 0.40 0.92
FAR 0.67 0.62 0.58 0.70 0.61 0.72
CSI 0.20 0.26 0.39 0.26 0.25 0.27
Table 3 Statistical evaluation of the daily precipitation for the six satellite-based and reanalysis precipitation products compared with the ground-based observational precipitation data during 1999-2018
Fig. 4 Spatial distributions of the statistical indicators (CC, POD, and CSI) for CHIRPS (a1-a3), CMFD (b1-b3), CMORPH (c1-c3), PERSIANN-CDR (d1-d3), TMPA (e1-e3), and ERA5-Land (f1-f3) precipitation products at 105 meteorological stations in Xinjiang during 1999-2018. POD, probability of detection; CSI, critical success index; CHIRPS, Climate Hazards Group InfraRed Precipitation with Station; CMFD, China Meteorological Forcing Dataset; CMORPH, Climate Prediction Center morphing method; PERSIANN-CDR, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record; TMPA, Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis; ERA5-Land, ECMWF Reanalysis v5-Land.
Season Statistic CHIRPS CMORPH CMFD PERSIANN-CDR TMPA ERA5-Land
Spring CC 0.31 0.15 0.60 0.29 0.23 0.57
RMSE (mm/d) 2.36 3.18 1.80 2.12 2.46 2.03
RB (%) 4.50 25.13 -3.51 4.81 -17.21 48.36
POD 0.45 0.46 0.81 0.77 0.38 0.91
FAR 0.70 0.67 0.59 0.73 0.62 0.74
CSI 0.22 0.24 0.37 0.25 0.23 0.26
Summer CC 0.24 0.36 0.67 0.25 0.37 0.50
RMSE (mm/d) 3.82 3.27 2.27 3.04 2.97 2.85
RB (%) -5.59 20.09 -2.28 18.39 2.67 19.03
POD 0.21 0.72 0.87 0.76 0.63 0.93
FAR 0.45 0.55 0.57 0.63 0.54 0.66
CSI 0.18 0.38 0.40 0.33 0.36 0.33
Autumn CC 0.27 0.23 0.63 0.30 0.24 0.58
RMSE (mm/d) 2.25 2.33 1.54 1.87 2.11 1.74
RB (%) 2.68 10.58 2.83 18.57 -16.48 50.48
POD 0.31 0.44 0.80 0.71 0.34 0.89
FAR 0.63 0.67 0.61 0.71 0.65 0.75
CSI 0.20 0.23 0.36 0.26 0.21 0.24
Winter CC 0.31 0.01 0.40 0.27 0.13 0.64
RMSE (mm/d) 1.07 1.87 1.35 1.12 1.82 0.86
RB (%) -3.17 -44.47 9.94 28.55 14.64 29.90
POD 0.38 0.02 0.80 0.57 0.14 0.92
FAR 0.75 0.90 0.52 0.76 0.75 0.75
CSI 0.18 0.02 0.43 0.20 0.10 0.25
Table 4 Statistical evaluation of the six satellite-based and reanalysis precipitation products compared with observed precipitation data in different seasons during 1999-2018
Fig. 5 Distributions of the multi-year monthly average relative weights of the six satellited-based and reanalysis precipitation products (CHIRPS, CMFD, CMORPH, PERSIANN-CDR, TMPA, and ERA5-Land) during 1999-2018
Fig. 6 Average relative weights of the six satellited-based and reanalysis precipitation products (CHIRPS, CMFD, CMORPH, PERSIANN-CDR, TMPA, and ERA5-Land) used for DBMA calculations in different seasons during 1999-2018. (a), spring; (b), summer; (c), autumn; (d), winter. Dots indicate data points of average relative weights. Box boundaries indicate the 25th and 75th percentiles, and whiskers below and above the box indicate the 10th and 90th percentiles, respectively. The black horizontal line within each box indicates the median of data points.
Fig. 7 Spatial distributions of RMSE (a), CC (b), RB (c), and POD (d) of DBMA-fused precipitation data at 105 meteorological stations in Xinjiang during 1999-2018. RB, relative bias.
Ensemble/member MAE (mm/d) CC RMSE (mm/d) POD KGE score Theil's U
DBMA 0.45# 0.67# 1.40# 0.92 0.56 0.87#
SMA 0.59 0.55 1.80 0.97# 0.35 1.30
CHIRPS 0.72 0.27 2.58 0.33 0.27 1.19
CMORPH 0.77 0.25 2.73 0.45 0.24 1.16
CMFD 0.46 0.62 1.78 0.83 0.59# 0.93
PERSIANN-CDR 0.76 0.28 2.16 0.71 0.10 1.61
TMPA 0.70 0.28 2.38 0.40 0.27 1.29
ERA5-Land 0.62 0.55 2.00 0.92 0.36 0.93
Table 5 Evaluation indicators of daily precipitation from two ensembles (DBMA-fused precipitation and SMA-based precipitation) and six satellite-based and reanalysis precipitation products at 105 meteorological stations in Xinjiang
Fig. 8 Spatial distributions of multi-year average precipitation in Xinjiang during 1999-2018 based on CHIRPS (a), CMFD (b), CMORPH (c), PERSIANN-CDR (d), TMPA (e), ERA5-Land (f), SMA (g), and DBMA (h). SMA, simple model averaging.
Dataset MAE (mm/d) CC RMSE (mm/d) POD KGE score Theil's U
DBMA 0.55# 0.65# 1.79# 0.87# 0.54# 0.93#
IMERG-F 0.71 0.42 2.28 0.68 0.38 1.16
Table 6 Daily-scale evaluation indicators of DBMA-fused precipitation dataset and the latest IMERG-F product at 105 meteorological stations in Xinjiang during 2001-2014
Fig. 9 Comparison of simulated streamflow from the VIC model driven by DBMA-fused precipitation dataset (a) and IMERG-F precipitation product (b) with observed streamflow from the Wenquan hydrological station in the Ebinur Lake Basin from 2001 to 2014. VIC, Variable Infiltration Capacity; IMERG-F, Integrated Multi-satellitE Retrievals for Global Precipitation Measurement Final; NSE, Nash-Sutcliffe efficiency.
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