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Climate Change Impact on the Streamflow for the Nam Ou Catchment, North Laos in the Mekong Basin

Nguyen Nhu Y., Kha Dang Dinh, Hat Dang Thi Thuy

Journal of Ecological Engineering

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2023

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Vol. 24, nr 8

209--217

EN

Southeast Asia, in general, and the Mekong Basin (MB), in particular, with its typically warm and wet climate, face water resource challenges. A deep understanding of the future streamflow is needed to manage water resource successfully. Data scarcity and topographical differences have made it difficult to accurately reproduce the streamflow regime in the sub-catchment of the MB. The main goal of this study was to provide the first assessments of streamflow impacts due to climate change for the Nam Ou Basin, a primary Lao sub-catchment of the MB, employing the most updated Couple Model Intercomparison Project Phase 6 (CMIP6) climate scenarios. The MIKE-NAM (Nedbor Affstromnings Model), the observed hydro-meteorological data, and the Moderate Resolution Imaging Spectroradiometer (MODIS) evaporation were employed. The climate change scenarios showed increases in seasonal and annual river discharges with different magnitudes in the future. The annual streamflow was expected to rise by 0.31%, 16.75%, and 38.31% in the 2040s as well as 23.35%, 32.80%, and 74.82% in the 2080s under three scenarios, respectively. The wet season in the Nam Ou Basin occurs one month earlier. The wet season flows increased by 5.6–76.9%, and the dry season flow showed a contrasting directional change, decreased by 8.4%. The annual peak discharge also exhibited an increase of 3.2–14.6% for the SSP1-1.9 scenario in the mid-century (the 2040s), and end-century (2080s). Those figures are 8.9–19.7% for the SSP2-4.5, and 23.3–48.9% for the SSP5-8.5 scenario, respectively. The study revealed the streamflow variation under the effect of climate change in the Nam Ou Basin, a sub-catchment of the MB, highlighting the need for special consideration in disaster risk mitigation, especially under climate change.

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Value addition in coupled model intercomparison project phase 6 over phase 5: global perspectives of precipitation, temperature and soil moisture fields

Dutta Riya, Maity Rajib

Acta Geophysica

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2022

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Vol. 70, no 3

1401--1415

EN

This study establishes the improvements in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) simulations as compared to its previous version, CMIP5. First, the historical simulations are compared with the reanalysis products from the 5th generation European Centre for Medium-Range Weather Forecasts (ERA5). Quality improvement in CMIP6 is assured through its correspondence with ERA5 in terms of mean, standard deviation and mean bias. Global fields of three hydrometeorological variables, i.e. temperature, precipitation and soil moisture, are considered from multiple General Circulation Models. Among the three variables, maximum improvement is noticed in case of soil moisture followed by precipitation, especially in the tropical belt. In case of temperature, the mean bias has reduced by±3 °C across the parts of North America, Africa, and South Asia. Better reliance on the CMIP6 motivates for a trend analysis to peek into the future. The results indicate a significant increasing trend for precipitation in the temperate, polar and sub-polar regions, whereas a significant increase in temperature is noticed almost all across the world with highest slope in the polar and sub-polar regions. Furthermore, soil moisture shows a significant trend that can be grouped continent-wise, e.g. Africa, Central and South Asia exhibit an increasing trend, whereas North and Central America and Northern parts of South America exhibit an overall decreasing trend. Apart from underlining the better reliance on CMIP6, the findings of this study will also be useful across different parts of the world for many climate related studies using CMIP6.

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Performance of bias corrected monthly CMIP6 climate projections with diferent reference period data in Turkey

Oruc Sertac

Acta Geophysica

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2022

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Vol. 70, no 2

777--789

EN

Decisions that are based on the future climate data, and its consequences are significantly important for many sectors such as water, agriculture, built environment, however, the performance of model outputs have direct influence on the accuracy of these decisions. This study has focused on the performance of three bias correction methods, Delta, Quantile Mapping (QM) and Empirical Quantile Mapping (EQM) with two reference data sets (ERA and station-based observations) of precipitation for 5 single CMIP6 GCM models (ACCESS-CM2, CNRM-CM6-1-HR, GFDL-ESM4, MIROC6, MRI-ESM2-0) and ensemble mean approach over Turkey. Performance of model-bias correction method-reference data set combinations was assessed on monthly basis for every single station and regionally. It was shown that performance of GCM models mostly affected by the region and the reference data set. Bias correction methods were not detected as effective as the reference data set over the performance. Moreover, Delta method outperformed among the other bias correction techniques for the computation that used observation as reference data while the difference between bias correction methods was not significant for the ERA based computations. Besides ensemble approach, MIROC6 and MRI-ESM2-0 models were selected as the best performing models over the region. In addition, selection of the reference data sets also found to be a dominant factor for the prediction accuracy, 65% of the consistent performance at the stations achieved by the ERA reference used bias correction approaches.