Bias correction framework for satellite precipitation products using a rain/no rain discriminative model

High spatial variability of precipitation over Nepal demands dense network of rain-gauge stations. But to set-up a dense rain gauge network is almost impossible due to mountainous topography of Nepal. Also the dense rain gauge network will be very expensive and some time impossible for timely maintenance. Satellite precipitation products are an alternative way to collect precipitation data with high temporal and spatial resolution over Nepal. In this study, the satellite precipitation products TRMM and GSMaP were analyzed. Precipitation was compared with ground based gauge precipitation in the Narayani basin, while the applicability of these rainfall products for runoff simulation were tested using the LANDPINE model for Trishuli basin which is a sub-basin within Narayani catchment. The Nash-Sutcliffe efficiency calculated for TRMM and GSMaP from point to pixel comparison is negative for most of stations. Also the estimation bias for both the products is negative indicating under estimation of precipitation by satellite products, with least under estimation for the GSMaP precipitation product. After point to pixel comparison, satellite precipitation estimates were used for runoff simulation in the Trishuli catchment with and without bias correction for each product. Among the two products, TRMM shows good simulation result without any bias correction for calibration and validation period with scaling factor of 2.24 for precipitation which is higher than that for gauge precipitation. This suggests, it could be used for runoff simulation to the catchments where there is no precipitation station. But it is too early to conclude by just looking into one catchment. So extensive study need to be done to make such conclusion.Journal of Hydrology and Meteorology, Vol. 8(1) p.22-31

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Evaluation of bias-adjusted satellite precipitation estimations for extreme flood events in Langat river basin, Malaysia

Hydrology Research ◽

10.2166/nh.2019.071 ◽

2019 ◽

Vol 51 (1) ◽

pp. 105-126 ◽

Cited By ~ 1

Author(s):

Eugene Zhen Xiang Soo ◽

Wan Zurina Wan Jaafar ◽

Sai Hin Lai ◽

Faridah Othman ◽

Ahmed Elshafie ◽

...

Keyword(s):

River Basin ◽

Bias Correction ◽

Monsoon Season ◽

Rainfall Threshold ◽

Careful Consideration ◽

Northeast Monsoon ◽

Satellite Precipitation ◽

Extreme Floods ◽

Extreme Flood ◽

Langat River

Abstract Even though satellite precipitation products have received an increasing amount of attention in hydrology and meteorology, their estimations are prone to bias. This study investigates the three approaches of bias correction, i.e., linear scaling (LS), local intensity scaling (LOCI) and power transformation (PT), on the three advanced satellite precipitation products (SPPs), i.e., CMORPH, TRMM and PERSIANN over the Langat river basin, Malaysia by focusing on five selected extreme floods due to northeast monsoon season. Results found the LS scheme was able to match the mean precipitation of every SPP but does not correct standard deviation (SD) or coefficient of variation (CV) of the estimations regardless of extreme floods selected. For LOCI scheme, only TRMM and CMORPH estimations in certain floods have showed some improvement in their results. This might be due to the rainfall threshold set in correcting process. PT scheme was found to be the best method as it improved most of the statistical performances as well as the rainfall distribution of the floods. Sensitivity of the parameters used in the bias correction is also investigated. PT scheme is found to be least sensitive in correcting the daily SPPs compared to the other two schemes. However, careful consideration should be given for correcting the CMORPH and PERSIANN estimations.

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A Censored Shifted Mixture Distribution Mapping Method to Correct the Bias of Daily IMERG Satellite Precipitation Estimates

Remote Sensing ◽

10.3390/rs11111345 ◽

2019 ◽

Vol 11 (11) ◽

pp. 1345 ◽

Cited By ~ 2

Author(s):

Qiumei Ma ◽

Lihua Xiong ◽

Jun Xia ◽

Bin Xiong ◽

Han Yang ◽

...

Keyword(s):

River Basin ◽

Bias Correction ◽

Hydrological Modeling ◽

Extreme Values ◽

Mixture Distribution ◽

Satellite Precipitation ◽

Gamma Distributions ◽

Mapping Bias ◽

Precipitation Estimates ◽

Distribution Mapping

Satellite precipitation estimates (SPE) provide useful input for hydrological modeling. However, hydrological modeling is frequently hindered by large bias and errors in SPE, inducing the necessity for bias corrections. Traditional distribution mapping bias correction of daily precipitation commonly uses Bernoulli and gamma distributions to separately model the probability and intensities of precipitation and is insufficient towards extremes. This study developed an improved distribution mapping bias correction method, which established a censored shifted mixture distribution (CSMD) as a transfer function when mapping raw precipitation to the reference data. CSMD coupled the censored shifted statistical distribution to jointly model both the precipitation occurrence probability and intensity with a mixture of gamma and generalized Pareto distributions to enhance extreme-value modeling. The CSMD approach was applied to correct the up-to-date SPE of Integrated Multi-satelliE Retrievals for Global Precipitation Measurement (GPM) with near-real-time “Early” run (IMERG-E) over the Yangtze River basin. To verify the hydrological response of bias-corrected IMERG-E, the streamflow of the Wujiang River basin was simulated using Ge´nie Rural with 6 parameters (GR6J) and Coupled Routing Excess Storage (CREST) models. The results showed that the bias correction using both BerGam (traditional bias correction combining Bernoulli with gamma distributions) and the improved CSMD could reduce the systematic errors of IMERG-E. Furthermore, CSMD outperformed BerGam in correcting overall precipitation (with the median of mean absolute errors of 2.46 mm versus 2.81 mm for CSMD and BerGam respectively, and the median of modified Nash–Sutcliffe efficiency of 0.39 versus 0.29) and especially in extreme values for uniform format and particular attention paid to extremes. In addition, the hydrological effect that CSMD correction exerted on IMERG-E, driving GR6J and CREST rainfall-runoff modeling, outperformed that of the BerGam correction. This study provides a promising integrated distribution mapping framework to correct the biased daily SPE, contributing to more reliable hydrological forecasts by informing accurate precipitation forcing.

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Correcting Satellite Precipitation Data and Assimilating Satellite-Derived Soil Moisture Data to Generate Ensemble Hydrological Forecasts within the HBV Rainfall-Runoff Model

Water ◽

10.3390/w11102138 ◽

2019 ◽

Vol 11 (10) ◽

pp. 2138

Author(s):

Maurycy Ciupak ◽

Bogdan Ozga-Zielinski ◽

Jan Adamowski ◽

Ravinesh C Deo ◽

Krzysztof Kochanek

Keyword(s):

Soil Moisture ◽

Kalman Filter ◽

Ensemble Kalman Filter ◽

Bias Correction ◽

Hydrological Modeling ◽

Snow Water Equivalent ◽

Model Updating ◽

Rainfall Runoff ◽

Satellite Precipitation ◽

Hbv Model

An implementation of bias correction and data assimilation using the ensemble Kalman filter (EnKF) as a procedure, dynamically coupled with the conceptual rainfall-runoff Hydrologiska Byråns Vattenbalansavdelning (HBV) model, was assessed for the hydrological modeling of seasonal hydrographs. The enhanced HBV model generated ensemble hydrographs and an average stream-flow simulation. The proposed approach was developed to examine the possibility of using data (e.g., precipitation and soil moisture) from the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility for Support to Operational Hydrology and Water Management (H-SAF), and to explore its usefulness in improving model updating and forecasting. Data from the Sola mountain catchment in southern Poland between 1 January 2008 and 31 July 2014 were used to calibrate the HBV model, while data from 1 August 2014 to 30 April 2015 were used for validation. A bias correction algorithm for a distribution-derived transformation method was developed by exploring generalized exponential (GE) theoretical distributions, along with gamma (GA) and Weibull (WE) distributions for the different data used in this study. When using the ensemble Kalman filter, the stochastically-generated ensemble of the model states generally induced bias in the estimation of non-linear hydrologic processes, thus influencing the accuracy of the Kalman analysis. In order to reduce the bias produced by the assimilation procedure, a post-processing bias correction (BC) procedure was coupled with the ensemble Kalman filter (EnKF), resulting in an ensemble Kalman filter with bias correction (EnKF-BC). The EnKF-BC, dynamically coupled with the HBV model for the assimilation of the satellite soil moisture observations, improved the accuracy of the simulated hydrographs significantly in the summer season, whereas, a positive effect from bias corrected (BC) satellite precipitation, as forcing data, was observed in the winter. Ensemble forecasts generated from the assimilation procedure are shown to be less uncertain. In future studies, the EnKF-BC algorithm proposed in the current study could be applied to a diverse array of practical forecasting problems (e.g., an operational assimilation of snowpack and snow water equivalent in forecasting models).

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Bias correction of daily satellite precipitation data using genetic algorithm

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/149/1/012071 ◽

2018 ◽

Vol 149 ◽

pp. 012071 ◽

Cited By ~ 3

Author(s):

A W Pratama ◽

A Buono ◽

R Hidayat ◽

H Harsa

Keyword(s):

Genetic Algorithm ◽

Bias Correction ◽

Precipitation Data ◽

Satellite Precipitation

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Adequacy of Near Real-Time Satellite Precipitation Products in Driving Flood Discharge Simulation in the Fuji River Basin, Japan

Applied Sciences ◽

10.3390/app11031087 ◽

2021 ◽

Vol 11 (3) ◽

pp. 1087

Author(s):

Li Zhou ◽

Mohamed Rasmy ◽

Kuniyoshi Takeuchi ◽

Toshio Koike ◽

Hemakanth Selvarajah ◽

...

Keyword(s):

Real Time ◽

River Basin ◽

Bias Correction ◽

Large Scale ◽

River Flow ◽

Hydrologic Model ◽

Satellite Precipitation ◽

Flood Discharge ◽

Discharge Simulation ◽

The Impact

Flood management is an important topic worldwide. Precipitation is the most crucial factor in reducing flood-related risks and damages. However, its adequate quality and sufficient quantity are not met in many parts of the world. Currently, near real-time satellite precipitation products (NRT SPPs) have great potential to supplement the gauge rainfall. However, NRT SPPs have several biases that require corrections before application. As a result, this study investigated two statistical bias correction methods with different parameters for the NRT SPPs and evaluated the adequacy of its application in the Fuji River basin. We employed Global Satellite Mapping of Precipitation (GSMaP)-NRT and Integrated Multi-satellitE Retrievals for GPM (IMERG)-Early for NRT SPPs as well as BTOP model (Block-wise use of the TOPMODEL (Topographic-based hydrologic model)) for flood runoff simulation. The results showed that the corrected SPPs by the 10-day ratio based bias correction method are consistent with the gauge data at the watershed scale. Compared with the original SPPs, the corrected SPPs improved the flood discharge simulation considerably. GSMaP-NRT and IMERG-Early have the potential for hourly river-flow simulation on a basin or large scale after bias correction. These findings can provide references for the applications of NRT SPPs in other basins for flood monitoring and early warning applications. It is necessary to investigate the impact of number of ground observation and their distribution patterns on bias correction and hydrological simulation efficiency, which is the future direction of this study.

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Decision tree-based reduction of bias in monthly IMERG satellite precipitation dataset over India

H2Open Journal ◽

10.2166/h2oj.2020.124 ◽

2020 ◽

Vol 3 (1) ◽

pp. 236-255 ◽

Cited By ~ 1

Author(s):

Shushobhit Chaudhary ◽

C. T. Dhanya

Keyword(s):

Bias Correction ◽

Performance Metrics ◽

Regression Tree ◽

India Meteorological Department ◽

Classification And Regression Tree ◽

Minimum Size ◽

Training Dataset ◽

Satellite Precipitation ◽

Non Linear ◽

Cart Model

Abstract Decision trees are ideally suited for handling huge datasets and modelling non-linear relationships between different variables. Given the relationship between precipitation and bias may be very complex and non-linear, bias-correction of satellite precipitation is a challenge. We examine the applicability of Classification and Regression tree (CART) for bias-correction of the Integrated Multi-satellite Retrievals for Global Precipitation Mission (IMERG) precipitation dataset over India. The gauge-based 0.25° gridded precipitation dataset from India Meteorological Department is considered as the reference. The CART model is trained (2001–2011) and tested (2012–2016) over each 0.25° grids. The training dataset is subjected to 10-fold cross-validation and optimization of the minimum size of leaf node (one of the hyper-parameter). Efficiency of the CART model is evaluated using performance metrics like R2, RMSE and MAB over the whole of India and different climate and elevation zones in India. CART model is observed to be highly effective in capturing the bias during the training (average R2= 0.77) and testing (average R2 = 0.66) period. Significant improvement in average monthly MAB (−6.3 to 29.2%) and RMSE (8.7–37.3%) was obtained post bias-correction by CART. Better performance of CART model was observed when compared to two widely adopted bias-correction techniques.

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Evaluating the Benefits of Merging Near-Real-Time Satellite Precipitation Products: A Case Study in the Kinu Basin Region, Japan

Journal of Hydrometeorology ◽

10.1175/jhm-d-18-0190.1 ◽

2019 ◽

Vol 20 (6) ◽

pp. 1213-1233 ◽

Cited By ~ 8

Author(s):

Nikolaos Mastrantonas ◽

Biswa Bhattacharya ◽

Yoshihiro Shibuo ◽

Mohamed Rasmy ◽

Gonzalo Espinoza-Dávalos ◽

...

Keyword(s):

Real Time ◽

Bias Correction ◽

Error Variance ◽

Early Warning Systems ◽

Cumulative Distribution ◽

Spatiotemporal Resolution ◽

Temporal Scales ◽

Satellite Precipitation ◽

The Individual ◽

Basin Region

Abstract After the launch of the Global Precipitation Measurement (GPM) mission in 2014, many satellite precipitation products (SPPs) are available at finer spatiotemporal resolution and/or with reduced latency, potentially increasing the applicability of SPPs for near-real-time (NRT) applications. Therefore, there is a need to evaluate the NRT SPPs in the GPM era and investigate whether bias-correction techniques or merging of the individual products can increase the accuracy of these SPPs for NRT applications. This study utilizes five commonly used NRT SPPs, namely, CMOPRH RT, GSMaP NRT, IMERG EARLY, IMERG LATE, and PERSIANN-CCS. The evaluation is done for the Kinu basin region in Japan, an area that provides observed rainfall data with high accuracy in space and time. The selected bias correction techniques are the ratio bias correction and cumulative distribution function matching, while the merged products are derived with the error variance, inverse error variance weighting, and simple average merging techniques. Based on the results, all SPPs perform best for lower-intensity rainfall events and have challenges in providing accurate estimates for typhoon-induced rainfall (generally more than 50% underestimation) and at very fine temporal scales. Although the bias correction techniques successfully reduce the bias and improve the performance of the SPPs for coarse temporal scales, it is found that for shorter than 6-hourly temporal resolutions, both techniques are in general unable to bring improvements. Finally, the merging results in increased accuracy for all temporal scales, giving new perspectives in utilizing SPPs for NRT applications, such as flood and drought monitoring and early warning systems.

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