On projected hydrological scenarios under the influence of bias-corrected climatic variables and LULC

Dey, Saumava; Dhar, Anirban; Debsarkar, Anupam; Pradhan, Biswajeet

doi:10.1016/j.ecolind.2019.105440

Cited by 11 publications

(3 citation statements)

References 35 publications

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“…To make assessments more robust, in this study we tested multiple bias correction methods with the objective to compare and better understand the methods by assessing the effects of error propagation of the bias-corrected rainfall to rainfall-runoff simulations. Several studies have tested multiple bias correction methods on rainfall inputs for rainfall-runoff modelling (Teutschbein and Seibert 2012, Fang et al 2015, Luo et al 2018, Sahoo et al 2019. For example, Luo et al (2018) found the PT and DM methods performed equally well in frequency-based statistics.…”

Section: Discussionmentioning

confidence: 99%

Error propagation of climate model rainfall to streamflow simulation in the Gidabo sub-basin, Ethiopian Rift Valley Lakes Basin

Worako

Haile

Rientjes³

et al. 2022

Hydrological Sciences Journal

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This study assesses bias error of rainfall from climate models and related error propagation effects to simulated streamflow in the Gidabo sub-basin, Ethiopia. Rainfall is obtained from a combination of four global and regional climate models (GCM-RCMs), and streamflow is simulated by means of the Hydrologiska Byråns Vattenbalansavdelning (HBV-96) rainfall-runoff model. Five bias correction methods were tested to reduce the rainfall bias. To assess the effects of rainfall bias error propagation, percent bias (PBIAS), difference in coefficient of variation (CV), and 10th and 90th percentile indicators were applied. Findings indicate that the bias of the uncorrected rainfall caused large errors in simulated streamflow. All five bias correction methods improved the HBV-96 model performance in terms of capturing the observed streamflow. Overall, the findings of this study indicate that the magnitude of the error propagation varies subject to the selected performance indicator, bias correction method and climate model.

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Section: Discussionmentioning

confidence: 99%

Error propagation of climate model rainfall to streamflow simulation in the Gidabo sub-basin, Ethiopian Rift Valley Lakes Basin

Worako

Haile

Rientjes³

et al. 2022

Hydrological Sciences Journal

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“…This method normally operates where the entire data are used to calibrate the models. The moving window technique can also be applied to correct daily and monthly climatic variables and has extensively been used by [44,60]. The technique focuses on a moving window centered on each day of the year to calibrate a correction.…”

Section: Moving Window Techniquementioning

confidence: 99%

Evaluation of Bias-Corrected GCM CMIP6 Simulation of Sea Surface Temperature over the Gulf of Guinea

Ideki,

Lupo

2024

Climate

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This study used an ERA5 reanalysis SST dataset re-gridded to a common grid with a 0.25° × 0.25° spatial resolution (latitude × longitude) for the historical (1940–2014) and projected (2015–2100) periods. The SST simulation under the SSP5-8.5 scenario was carried out with outputs from eight General Circulation Models (GCMs). The bias-corrected dataset was developed using Empirical Quantile Mapping (EQM) for the historical (1940–2015) and future (2030–2100) periods while the CMIP6 model simulation was evaluated against the ERA5 monthly observed reanalysis data for temperatures over the Gulf of Guinea. Overall, the CMIP6 models’ future simulations in 2030–20100 based on the SSP5-8.5 scenario indicate that SSTs are projected, for the Gulf of Guinea, to increase by 4.61 °C, from 31 °C in the coast in 2030 to 35 °C in 2100, and 2.6 °C in the Western GOG (Sahel). The Linux-based Ncview, Ferret, and the CDO (Climate Data Operator) software packages were used to perform further data re-gridding and assess statistical functions concerning the data. In addition, ArcGIS was used to develop output maps for visualizing the spatial trends of the historical and future outputs of the GCM. The correlation coefficient (r) was used to evaluate the performance of the CMIP6 models, and the analysis showed ACCESS 0.1, CAMS CSM 0.2, CAN ESM 0.3, CMCC 0.3, and MCM 0.4, indicating that all models performed well in capturing the climatological patterns of the SSTs. The CMIP6 bias-corrected model simulations showed that increased SST warming over the GOG will be higher in the far period than the near-term climate scenario. This study affirms that the CMIP6 projections can be used for multiple assessments related to climate and hydrological impact studies and for the development of mitigation measures under a warming climate.

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“…17,18,19,20,21,22,23,24,25,26,27,28 The ranks that every data point received were used in the test. The initial and final values of Mann-Kendall statistic's (S) is the result derived from deviations increases and decreases 29,30 Signum function (sgn) (x) = 1 for x > 0; Signum function (x) = 0 for x = 0; Signum function (x) = -1 for x < 0. The z-statistic = | S | /s 0.5.…”

Section: Trend Analysis Of Rainfallmentioning

confidence: 99%

Spatial and Temporal Rainfall Analysis of the Bidi Tobacco Growing Areas of India.

Reddy,

Rao,

Prasad

et al. 2023

Curr. World Environ

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The monsoon extremities, such as heavy rainfall, drought, and cyclones are becoming frequent across India and causing substantial damage to commercial crops. To predict/identify the trend in rainfall of Bidi tobacco growing areas were studied for variability, trend, and detection of any change in monthly rainfall over the years (1990-2020) using Mann-Kendall trend analysis and Sen’s slope tests. Monthly rainfall in three unique Bidi production zones has shown distinct trends. A positive trend was observed in March, April, August, September, November, and December while negative trends were in June and July months in Gujarat. During the crop growth period, the trend is positive except in October, the magnitude of change was negligible. However, supplemental irrigation is to be planned during water deficit periods of the nursery (June and July) and critical crop growth periods. In Karnataka, a positive trend from February to October was observed while a negative trend during November, December, and January. The magnitude was high in January (+) and September (-) and significant. The period of negative trend has coincided with the harvesting hence, the crop may not get affected. However, high rainfall during the crop growth period causes a detrimental effect. To mitigate the inundation proper drainage facility should be created through field channels to flush out excess water to avoid saturation at the root zone as this crop is especially extremely sensitive to poor aeration and anaerobic conditions. A positive trend was observed in January, February, March, June, August, and September months in Andhra Pradesh, whereas a negative trend was high in October & November which coincided with crop growth and might significantly affect Bidi tobacco. The positive trend in rainfall during June is high and the same can be utilized for nursery during July-August through proper moisture conservation measures.

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On projected hydrological scenarios under the influence of bias-corrected climatic variables and LULC

Cited by 11 publications

References 35 publications

Error propagation of climate model rainfall to streamflow simulation in the Gidabo sub-basin, Ethiopian Rift Valley Lakes Basin

Error propagation of climate model rainfall to streamflow simulation in the Gidabo sub-basin, Ethiopian Rift Valley Lakes Basin

Evaluation of Bias-Corrected GCM CMIP6 Simulation of Sea Surface Temperature over the Gulf of Guinea

Spatial and Temporal Rainfall Analysis of the Bidi Tobacco Growing Areas of India.

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