Future climatic and hydrologic changes estimated by bias-adjusted regional climate model outputs of the Cordex-Africa project: case of the Tafna basin (North-Western Africa)

Mami, Amina; Raimonet, Mélanie; Yebdri, Djilali; Sauvage, Sabine; Zettam, Amin; Pérez, José Miguel Sánchez

doi:10.1504/ijgw.2021.112489

Cited by 8 publications

(6 citation statements)

References 62 publications

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“…This trend of climate variability justifies that the damage to agriculture and water scarcity has already been started and will be proliferated further over the next few years. Similar projections were reported on future climate which carried out using various climate models including multilevel mixed-effects model in the Moroccan context 74 , 106 – 111 , as well as Northern Africa 112 – 114 .…”

Section: Discussionsupporting

confidence: 74%

Wheat (Triticum aestivum) adaptability evaluation in a semi-arid region of Central Morocco using APSIM model

Briak

Kebede

2021

Sci Rep

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In this study, we evaluated the suitability of semi-arid region of Central Morocco for wheat production using Agricultural Production Systems sIMulator (APSIM) considering weather, soil properties and crop management production factors. Model calibration was carried out using data collected from field trials. A quantitative statistics, i.e., root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and index of agreement (d) were used in model performance evaluation. Furthermore, series of simulations were performed to simulate the future scenarios of wheat productivity based on climate projection; the optimum sowing date under water deficit condition and selection of appropriate wheat varieties. The study showed that the performance of the model was fairly accurate as judged by having RMSE = 0.13, NSE = 0.95, and d = 0.98. The realization of future climate data projection and their integration into the APSIM model allowed us to obtain future scenarios of wheat yield that vary between 0 and 2.33 t/ha throughout the study period. The simulated result confirmed that the yield obtained from plots seeded between 25 October and 25 November was higher than that of sown until 05 January. From the several varieties tested, Hartog, Sunstate, Wollaroi, Batten and Sapphire were yielded comparatively higher than the locale variety Marzak. In conclusion, APSIM-Wheat model could be used as a promising tool to identify the best management practices such as determining the sowing date and selection of crop variety based on the length of the crop cycle for adapting and mitigating climate change.

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Section: Discussionsupporting

confidence: 74%

Wheat (Triticum aestivum) adaptability evaluation in a semi-arid region of Central Morocco using APSIM model

Briak

Kebede

2021

Sci Rep

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“…Several previous studies investigated performance indices, weight computing techniques, multi criteria decision making techniques and group decision making in order to select the best suitable GCMs [23,[25][26][27][28][29][30][31] When compared to observed datasets, simulated precipitation and temperature outputs from regional climate models (RCMs) exhibit huge systematic biases [32] Bias correction methods are used to correct precipitation data from GCMs and satellite-based products [14,33,34]. Linear scaling, power transformation, precipitation local intensity scaling and distribution mapping are the methods used to correct biases in precipitation data using the CMHyd) tool [35]. Other bias correction methods include Linear Scaling (LS), Quantile Mapping (QM), Distribution Fitting (DF), Optimal Interpolation (OI) and Geographically Weighted Regression (GWR).…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Analysis of Climatic Variables in the Munneru River Basin, India, Using NEX-GDDP Data and the REA Approach

et al. 2022

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For effective management practices and decision-making, the uncertainty associated with Regional Climate Models (RCMs) and their scenarios need to be assessed in the context of climate change. The present study analyzes the various uncertainties in the precipitation and temperature datasets of NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) under Representative Concentrative Pathways (RCPs) 4.5 and 8.5 over the Munneru river basin, in India, using the Reliable Ensemble Averaging (REA) method. From the available 21 RCMs, the top five ranked are ensembled and bias-corrected at each grid using the non-parametric quantile mapping method for the precipitation and temperature datasets. The spatio-temporal variations in precipitation and temperature data for the future periods, i.e., 2021–2039 (near future), 2040–2069 (mid future) and 2070–2099 (far future) are analyzed. For the period 2021–2099, annual average precipitation increases by 233 mm and 287 mm, respectively, the in RCP 4.5 and RCP 8.5 scenarios when compared to the observed period (1951–2005). In both the RCP 4.5 and RCP 8.5 scenarios, the annual average maximum temperature rises by 1.8 °C and 1.9 °C, respectively. Similarly, the annual average minimum temperature rises by 1.8 °C and 2.5 °C for the RCP 4.5 and RCP 8.5 scenarios, respectively. The spatio-temporal climatic variations for future periods obtained from high-resolution climate model data aid in the preparation of water resource planning and management options in the study basin under the changing climate. The methodology developed in this study can be applied to any other basin to analyze the climatic variables suitable for climate change impact studies that require a finer scale, but the biases present in the historical simulations can be attributed to uncertainties in the estimation of climatic variable projections. The findings of the study indicate that NEX-GDDP datasets are in good agreement with IMD datasets on monthly scales but not on daily scales over the observed period, implying that these data should be scrutinized more closely on daily scales, especially when utilized in impact studies.

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“…Bias correction procedures performed a transformation algorithm for adjusting climate model output with the assumption that the correction algorithm and its parametrization for current climate conditions are to be valid for future conditions as well. The tool has been widely used for bias correction of precipitation and temperature for various applications (Worku et al 2020;Mami et al 2021;Yeboah et al 2022). The overall procedure in the bias correction as adopted in this article is described in Figure 3.…”

Section: Bias Correction Proceduresmentioning

confidence: 99%

“…Different scholars recommend the use of bias correction of RCM outputs since it is a practical and affordable approach for error reduction (Mami et al 2021). Correction of bias is essential to obtain realistic model data for future climate projection and to assess its impact (Teutschbein & Seibert 2012;Yeboah et al 2022).…”

Section: Introductionmentioning

confidence: 99%

Comparison of bias correction methods to regional climate model simulations for climate change projection in Muger Subbasin, Upper Blue Nile Basin, Ethiopia

Dinku,

Gibre

2024

Journal of Water and Climate Change

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The objective of this study was to evaluate the best performed bias correction methods to simulate the regional climate models for future climate change projections in Muger Subbasin. Delta change methods perform very good with a coefficient of correlation of 0.99 and a percent of bias –3. When we compare its corrected simulation result with observed data, delta change method seems to be with no biases for maximum temperature, but it increases by 1.67 °C from the mean for minimum temperature of 0.39 and 38.41 mm for monthly and annual precipitation, respectively. Delta change methods underestimate the model result for both temperature and precipitation. Linear scaling and variance scaling methods overestimate the maximum temperature of the simulation by 0.002 and 0.004 °C amount from the mean of the observed data, but it underestimates 1.59 and 1.56 °C the minimum temperature, respectively. The long-term temperature projection values (2060–2090) are higher than the near-term projections (2030–2060) for both RCP2.6 and RCP8.5 scenarios. Similarly, the change in annual precipitation for the long-term is higher than the near-term projections. As a conclusion, the results draw attention to the fact that bias-adjusted regional climate models data are crucial for the provision of local climate change impact studies in the Muger Subbasin.

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Future climatic and hydrologic changes estimated by bias-adjusted regional climate model outputs of the Cordex-Africa project: case of the Tafna basin (North-Western Africa)

Cited by 8 publications

References 62 publications

Wheat (Triticum aestivum) adaptability evaluation in a semi-arid region of Central Morocco using APSIM model

Wheat (Triticum aestivum) adaptability evaluation in a semi-arid region of Central Morocco using APSIM model

Spatio-Temporal Analysis of Climatic Variables in the Munneru River Basin, India, Using NEX-GDDP Data and the REA Approach

Comparison of bias correction methods to regional climate model simulations for climate change projection in Muger Subbasin, Upper Blue Nile Basin, Ethiopia

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