On the use of climate models for estimating the non-stationary characteristic values of climatic actions in civil engineering practice

Abrahamczyk, Lars; Uzair, Aanis

doi:10.3389/fbuil.2023.1108328

Cited by 3 publications

(1 citation statement)

References 31 publications

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“…However, climate data are complex, nonlinear, and noisy, and it is notoriously difficult to make predictions in climate systems. As well, climate extremes exhibit non-stationary behavior which are caused by temporal variation in the statistical properties in climatic factors (Abrahamczyk and Uzair, 2023). Climate systems also connect local short-term weather patterns with long-term global climate change, which necessitates the use of methods that can capture long and short-range dependencies.…”

Section: Discussionmentioning

confidence: 99%

A survey on Bayesian nonparametric learning for time series analysis

Vélez-Cruz

2024

Front. Signal Process.

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Time series analysis aims to understand underlying patterns and relationships in data to inform decision-making. As time series data are becoming more widely available across a variety of academic disciplines, time series analysis has become a rapidly growing field. In particular, Bayesian nonparametric (BNP) methods are gaining traction for their power and flexibility in modeling, predicting, and extracting meaningful information from time series data. The utility of BNP methods lies in their ability to encode prior information and represent complex patterns in the data without imposing strong assumptions about the underlying distribution or functional form. BNP methods for time series analysis can be applied to a breadth of problems, including anomaly detection, noise density estimation, and time series clustering. This work presents a comprehensive survey of the existing literature on BNP methods for time series analysis. Various temporal BNP models are discussed along with notable applications and possible approaches for inference. This work also highlights current research trends in the field and potential avenues for further development and exploration.

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Section: Discussionmentioning

confidence: 99%

A survey on Bayesian nonparametric learning for time series analysis

Vélez-Cruz

2024

Front. Signal Process.

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Assessing the Climatological Characteristics of Observed and Simulated Seasonal Onset of Precipitation Over Southern and Eastern Africa

Lawal,

Hewitson,

Lennard

2024

Intl Journal of Climatology

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Prediction of seasonal onset is crucial to agriculture in southern and eastern Africa. Here, we applied two definitions of onset, namely meteorological and agricultural (crop‐germination), to evaluate CMIP6 models through the lens of rainfall onset over representative maize agricultural regions of South Africa, Tanzania, Malawi and Zambia. We use the ERA5 reanalysis as a proxy for observations, and robust regression to calculate a statistical comparison of the onset definitions for the period 1979–2021. Evaluation of ERA5 reanalysis shows similar magnitude and pattern as gauge based MSWEP. Our results show that, for meteorological onset, Johannesburg, with a subtropical highland climate, experienced earliest onset after 23 December; and an increasing trend (later onset) but not statistically significant (p = 0.2). Over Bethlehem, which has continental climate, the earliest onset date was after October 9 and an increasing interannual variability since 2000 is noted. The standard deviation of onset dates across the regions shows an East‐Central‐South gradient. We also found that the crop‐germination onset definition shows earlier onset of seasonal rains, it differs considerably across regions, and has higher interannual variability, in comparison with the meteorological definition. Over Lilongwe, Mbeya and Lusaka, late meteorological onset with a weak positive and insignificant trend is observed. The CMIP6 model's representation of onset trend differs from reanalysis data, with inter‐model differences. Late meteorological onset is underestimated by GFDL‐CM4 and MPI while INM5, MPI and NorESM overestimate the observed earliest onset. The largest bias is shown by INM and MPI which simulate earliest and latest onset as 190 (07 January) and 206 (23 January) respectively. In addition, models often fail to simulate sufficient precipitation to produce onset for seed germination and crop development. The ACCESS model showed an insignificant trend (p value = 2) and later onset over Lilongwe, an insignificant trend (p value = 0.9) over Lusaka, and an earlier onset over Mbeya. Using the agricultural onset definition, over Bethlehem, all the models and the ERA5 reanalysis did not produce enough precipitation to meet onset conditions. We suggest that rainfall onset studies use several definitions or metrics of onset and that the choice of metric be informed by the research question. Using such an ensemble of onset metrics contributes to a better understanding of variability and uncertainties in agricultural productivity.

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Assessment on future rainfall variability for adaptive water resource management in Sabah

Leong,

Ngui,

Ayog

2024

BIO Web Conf.

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Climate is changing at an unprecedented rate, and the increasing demands of the growing population will place further pressures on the climate and environment. Water infrastructures such as water treatment plants often face increased stress during extreme weather events such as heavy rainfall or prolonged drought, disrupting societal needs and prompt urgent upgrades to adapt to the changing climate. The use of climate model projections is increasingly adopted in engineering practices for assessing climate risks and impacts to inform adaptive design and strategy of new infrastructures or upgrades. Using a bias-corrected ensemble member of the fifth phase of the Coupled Model Intercomparison Project (CMIP5) tailored for applications in the water sector, we assessed the variability of projected rainfall from 2021 to 2050 under the moderate warming scenario (RCP 4.5). The findings from this study aim to inform adaptive strategies for upgrading 22 water treatment plants across 18 sites in Sabah, Malaysia, where these plants have experienced numerous disruptions due to high turbidity resulting from heavy rainfall in recent years. The average annual rainfall amount ranges from 1630 mm to 4415 mm, with average total monthly rainfall peaks (>460 mm) in June and August at most sites. The observed shift in June and August is a deviation from historical patterns and is also reflected in the maximum consecutive 1- to 5-day rainfall, ranging from 22 mm to 760 mm across all sites, particularly in the interior and west coast districts. This highlights the need for resilient infrastructure upgrade strategy and adaptive water resource management, such as the introduction of off-site river storage to accommodate the anticipated future rainfall.

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On the use of climate models for estimating the non-stationary characteristic values of climatic actions in civil engineering practice

Cited by 3 publications

References 31 publications

A survey on Bayesian nonparametric learning for time series analysis

A survey on Bayesian nonparametric learning for time series analysis

Assessing the Climatological Characteristics of Observed and Simulated Seasonal Onset of Precipitation Over Southern and Eastern Africa

Assessment on future rainfall variability for adaptive water resource management in Sabah

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