Multi-variate infilling of missing daily discharge data on the Niger basin

Oyerinde, Ganiyu Titilope; Lawin, Agnidé Emmanuel; Adeyeri, Oluwafemi E.

doi:10.2166/wpt.2021.048

Cited by 7 publications

(5 citation statements)

References 49 publications

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“…Statistically significant trends and climate change signals were examined using the Student's t-test at a 95% confidence level 35 . The trend in the climate extreme events was calculated based on the Mann-Kendall trend test, while the trend's magnitude was estimated using Sen's slope 35,[44][45][46] .…”

Section: Climate Change Signals In Extreme Eventsmentioning

confidence: 99%

The trend and spatial spread of multisectoral climate extremes in CMIP6 models

Adeyeri

Zhou

Wang

et al. 2022

Sci Rep

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Climate change could exacerbate extreme climate events. This study investigated the global and continental representations of fourteen multisectoral climate indices during the historical (1979–2014), near future (2025–2060) and far future (2065–2100) periods under two emission scenarios, in eleven Coupled Model Intercomparison Project (CMIP) General Circulation Models (GCM). We ranked the GCMs based on five metrics centred on their temporal and spatial performances. Most models followed the reference pattern during the historical period. MPI-ESM ranked best in replicating the daily precipitation intensity (DPI) in Africa, while CANESM5 GCM ranked first in heatwave index (HI), maximum consecutive dry days (MCCD). Across the different continents, MPI-LR GCM performed best in replicating the DPI, except in Africa. The model ranks could provide valuable information when selecting appropriate GCM ensembles when focusing on climate extremes. A global evaluation of the multi-index causal effects for the various indices shows that the dry spell total length (DSTL) was the most crucial index modulating the MCCD for all continents. Also, most indices exhibited a positive climate change signal from the historical to the future. Therefore, it is crucial to design appropriate strategies to strengthen resilience to extreme climatic events while mitigating greenhouse gas emissions.

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Section: Climate Change Signals In Extreme Eventsmentioning

confidence: 99%

The trend and spatial spread of multisectoral climate extremes in CMIP6 models

Adeyeri

Zhou

Wang

et al. 2022

Sci Rep

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“…Ekeu-wei et al [13] introduced the concept of multiple imputation to impute annual peak river discharge, which is vital for flood frequency estimation. Oyerinde et al [42] used the PMM method to impute missing data from 22 water discharge stations with different missing data percentages. However, these two studies failed to consider the missing data mechanism/pattern, which is important for handling missing data.…”

Section: Discussionmentioning

confidence: 99%

“…However, Madley-Dowd et al [70] found that, for data where values are missing according to the missing at random mechanism, imputation can provide unbiased results even with a large percentage of missing data (up to 90% missing). The study in [42] also considered missing data from 2% up to 70% of the dataset size for water discharge data. Therefore, it would be worth examining this further in the context of water level imputation, especially as even in one of the raw datasets (from the Makurdi water station) used to construct the multivariate data 68% of the water levels were missing.…”

Section: Discussionmentioning

confidence: 99%

“…However, for data with more than 3 years missing, radar altimetry was better. Oyerinde et al [42] used the PMM method to impute missing data from 22 water discharge stations with different missing percentages from 2-70% and recommended PMM for imputing data gaps in data from the Niger basin. However, these two studies failed to consider the missing data mechanism/pattern, which is important for imputing missing data.…”

Section: Introductionmentioning

confidence: 99%

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Comparing Single and Multiple Imputation Approaches for Missing Values in Univariate and Multivariate Water Level Data

Umar

Gray

2023

Water

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Missing values in water level data is a persistent problem in data modelling and especially common in developing countries. Data imputation has received considerable research attention, to raise the quality of data in the study of extreme events such as flooding and droughts. This article evaluates single and multiple imputation methods used on monthly univariate and multivariate water level data from four water stations on the rivers Benue and Niger in Nigeria. The missing completely at random, missing at random and missing not at random data mechanisms were each considered. The best imputation method is identified using two error metrics: root mean square error and mean absolute percentage error. For the univariate case, the seasonal decomposition method is best for imputing missing values at various missingness levels for all three missing mechanisms, followed by Kalman smoothing, while random imputation is much poorer. For instance, for 5% missing data for the Kainji water station, missing completely at random, the Kalman smoothing, random and seasonal decomposition methods had average root mean square errors of 13.61, 102.60 and 10.46, respectively. For the multivariate case, missForest is best, closely followed by k nearest neighbour for the missing completely at random and missing at random mechanisms, and k nearest neighbour is best, followed by missForest, for the missing not at random mechanism. The random forest and predictive mean matching methods perform poorly in terms of the two metrics considered. For example, for 10% missing data missing completely at random for the Ibi water station, the average root mean square errors for random forest, k nearest neighbour, missForest and predictive mean matching were 22.51, 17.17, 14.60 and 25.98, respectively. The results indicate that the seasonal decomposition method, and missForest or k nearest neighbour methods, can impute univariate and multivariate water level missing data, respectively, with higher accuracy than the other methods considered.

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“…Subsequently, the Mann-Kendall test [68][69][70] quantified the spatial trend, while the Student's t-test at a 95% confidence level [5,71] enumerated the significant plot.…”

Section: Utci Response To Lulc Categoriesmentioning

confidence: 99%

Land use and land cover dynamics influence the thermal stress spread and energy demands

Adeyeri,

Zhou,

Laux

et al. 2023

Preprint

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This study examined the interaction between land use land cover (LULC) dynamics and thermal stress distribution using the universal thermal comfort index (UTCI) and different LULC classifications under two Coupled Model Intercomparison Project Phase 6 (CMIP6) Shared Socioeconomic Pathways (i.e., SSP 370 and 585) climate and land use scenarios for the historical (1959–2014) and future period (2045–2100). The moderate to strong cold stress in the annual and winter climatology in the midlatitudes was replaced by no thermal stress in the summer, while the summertime ranged from moderate to strong heat stress. A negative correlation was observed between thermal stress and southern hemispheric primary forests. Perennial croplands had the most dynamic active change intensities during the historical period. Primary nonforest and secondary forests actively influenced global thermal stress. Areas in the tropics recording moderate heat stress coincided with secondary nonforest, pastureland and annual cropland expansions. The conversion of forest to range land and croplands and the subsequent negative forest trends increased the severity of thermal stress. The future projection showed more intense thermal stress; however, the SSP 585 signals were more potent. As a result, cooling demands will rise, and heating demands will decline; yet, improved thermal comfort necessitates a higher cooling capacity, especially in the summer. Thermal stress may make it difficult for many cooling systems to meet people’s energy demands. These could be a driving factor in shaping better land use policies, improved energy demand preparedness and elucidating the potentially severe impacts of thermal stress.

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Multi-variate infilling of missing daily discharge data on the Niger basin

Cited by 7 publications

References 49 publications

The trend and spatial spread of multisectoral climate extremes in CMIP6 models

The trend and spatial spread of multisectoral climate extremes in CMIP6 models

Comparing Single and Multiple Imputation Approaches for Missing Values in Univariate and Multivariate Water Level Data

Land use and land cover dynamics influence the thermal stress spread and energy demands

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