Performance of SEACLID/CORDEX-SEA multi-model experiments in simulating temperature and rainfall in Vietnam

Tuyết, Nguyễn Thị; Thanh, Ngo Duc; Tan, Phan Van

doi:10.15625/0866-7187/41/4/14259

Cited by 15 publications

(8 citation statements)

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“…Coordinated sets of experiments were recently performed or are still ongoing for CORDEX regions such as South America (Solman et al, 2013), Central America (Fuentes-Franco et al, 2015), North America (Wang and Kotamarthi, 2015;Diaconescu et al, 2016;Whan and Zwiers, 2017), South Asia (Ghimire et al, 2018), East Asia (Zou et al, 2016), Southeast Asia (Tangang et al, 2018;Tangang et al, 2019;Tuyet et al, 2019), Australasia (Di Virgilio et al, 2019), Arctic (Koenigk et al, 2015), Antarctic (Souverijns et al, 2019) and Middle East North Africa (Almazroui et al, 2016;Bucchignani et al, 2018). In this paper we discuss the results of the simulations over the CAS-CORDEX domain.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of regional climate models ALARO-0 and REMO2015 at 0.22° resolution over the CORDEX Central Asia domain

Top¹,

Kotova²,

Cruz³

et al. 2020

Preprint

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Abstract. To allow for climate impact studies on human and natural systems high-resolution climate information is needed. Over some parts of the world plenty of regional climate simulations have been carried out, while in other regions hardly any high-resolution climate information is available. This publication aims at addressing one of these regional gaps by presenting an evaluation study for two regional climate models (RCMs) (REMO and ALARO-0) at a horizontal resolution of 0.22° (25 km) over Central Asia. The output of the ERA-Interim driven RCMs is compared with different observational datasets over the 1980–2017 period. The choice of the observational dataset has an impact on the scores but in general one can conclude that both models reproduce reasonably well the spatial patterns for temperature and precipitation. The evaluation of minimum and maximum temperature demonstrates that both models underestimate the daily temperature range. More detailed studies of the annual cycle over subregions should be carried out to reveal whether this is due to an incorrect simulation in cloud cover, atmospheric circulation or heat and moisture fluxes. In general, the REMO model scores better for temperature whereas the ALARO-0 model prevails for precipitation. This publication demonstrates that the REMO and ALARO-0 RCMs can be used to perform climate projections over Central Asia and that the produced climate data can be applied in impact modelling.

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Section: Methodsmentioning

confidence: 99%

Evaluation of regional climate models ALARO-0 and REMO2015 at 0.22° resolution over the CORDEX Central Asia domain

Top¹,

Kotova²,

Cruz³

et al. 2020

Preprint

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show abstract

“…other RCMs over the CAS-CORDEX region (Russo et al, 2019) and Central Asian subregions (Wang et al, 2020;Zhu et al, 2020). Zhu et al (2020) conducted model runs with different land cover schemes in the WRF model over a smaller domain than CAS-CORDEX containing Kazakhstan, Uzbekistan, Kyrgyzstan, Turkmenistan, and Tajikistan.…”

Section: Precipitationmentioning

confidence: 99%

“…These large ensembles consist of more than 10 different global-regional climate model (GCM-RCM) combinations. In order to provide such ensembles over all CORDEX regions, coordinated sets of experiments were recently performed or are still ongoing for CORDEX regions such as South America (Solman et al, 2013), Central America (Fuentes-Franco et al, 2015;Cabos et al, 2019), South Asia (Ghimire et al, 2018), East Asia (Zou et al, 2016), South-East Asia (Tangang et al, 2018(Tangang et al, , 2019Tuyet et al, 2019), Australasia (Di Virgilio et al, 2019), the Arctic (Koenigk et al, 2015;Akperov et al, 2018), Antarctic (Souverijns et al, 2019), and the Middle East-North Africa (Almazroui et al, 2016;Bucchignani et al, 2018). In addition, a new ensemble of climate change simulations covering all major inhabited regions with a spatial resolution of about 25 km has been established within the WCRP CORDEX COmmon Regional Experiment (CORE) framework to support the growing demands for climate services (Remedio et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of regional climate models ALARO-0 and REMO2015 at 0.22° resolution over the CORDEX Central Asia domain

et al. 2021

View full text Add to dashboard Cite

Abstract. To allow for climate impact studies on human and natural systems, high-resolution climate information is needed. Over some parts of the world plenty of regional climate simulations have been carried out, while in other regions hardly any high-resolution climate information is available. The CORDEX Central Asia domain is one of these regions, and this article describes the evaluation for two regional climate models (RCMs), REMO and ALARO-0, that were run for the first time at a horizontal resolution of 0.22∘ (25 km) over this region. The output of the ERA-Interim-driven RCMs is compared with different observational datasets over the 1980–2017 period. REMO scores better for temperature, whereas the ALARO-0 model prevails for precipitation. Studying specific subregions provides deeper insight into the strengths and weaknesses of both RCMs over the CAS-CORDEX domain. For example, ALARO-0 has difficulties in simulating the temperature over the northern part of the domain, particularly when snow cover is present, while REMO poorly simulates the annual cycle of precipitation over the Tibetan Plateau. The evaluation of minimum and maximum temperature demonstrates that both models underestimate the daily temperature range. This study aims to evaluate whether REMO and ALARO-0 provide reliable climate information over the CAS-CORDEX domain for impact modeling and environmental assessment applications. Depending on the evaluated season and variable, it is demonstrated that the produced climate data can be used in several subregions, e.g., temperature and precipitation over western Central Asia in autumn. At the same time, a bias adjustment is required for regions where significant biases have been identified.

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“…In this study, flow accumulation was generated using ArcGIS software and classified using the natural break method (Figure 2e). Rainfall (Minh et al 2018; Tuyet et al 2019) is also a main source of groundwater. In this study, rainfall map (mm) was constructed using mean annual rainfall data (mm) of rain gauges located in part of the catchment (Figure 2f).…”

Section: Data Usedmentioning

confidence: 99%

Groundwater Potential Mapping Using GIS‐Based Hybrid Artificial Intelligence Methods

Phong

Pham

et al. 2021

Groundwater

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Groundwater is one of the major valuable water resources for the use of communities, agriculture, and industries. In the present study, we have developed three novel hybrid artificial intelligence (AI) models which is a combination of modified RealAdaBoost (MRAB), bagging (BA), and rotation forest (RF) ensembles with functional tree (FT) base classifier for the groundwater potential mapping (GPM) in the basaltic terrain at DakLak province, Highland Centre, Vietnam. Based on the literature survey, these proposed hybrid AI models are new and have not been used in the GPM of an area. Geospatial techniques were used and geo‐hydrological data of 130 groundwater wells and 12 topographical and geo‐environmental factors were used in the model studies. One‐R Attribute Evaluation feature selection method was used for the selection of relevant input parameters for the development of AI models. The performance of these models was evaluated using various statistical measures including area under the receiver operation curve (AUC). Results indicated that though all the hybrid models developed in this study enhanced the goodness‐of‐fit and prediction accuracy, but MRAB‐FT (AUC = 0.742) model outperformed RF‐FT (AUC = 0.736), BA‐FT (AUC = 0.714), and single FT (AUC = 0.674) models. Therefore, the MRAB‐FT model can be considered as a promising AI hybrid technique for the accurate GPM. Accurate mapping of the groundwater potential zones will help in adequately recharging the aquifer for optimum use of groundwater resources by maintaining the balance between consumption and exploitation.

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Performance of SEACLID/CORDEX-SEA multi-model experiments in simulating temperature and rainfall in Vietnam

Cited by 15 publications

References 0 publications

Evaluation of regional climate models ALARO-0 and REMO2015 at 0.22° resolution over the CORDEX Central Asia domain

Evaluation of regional climate models ALARO-0 and REMO2015 at 0.22° resolution over the CORDEX Central Asia domain

Evaluation of regional climate models ALARO-0 and REMO2015 at 0.22° resolution over the CORDEX Central Asia domain

Groundwater Potential Mapping Using GIS‐Based Hybrid Artificial Intelligence Methods

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