Temperature and precipitation change in Malawi: Evaluation of CORDEX-Africa climate simulations for climate change impact assessments and adaptation planning

Warnatzsch, Erika A.; Reay, Dave

doi:10.1016/j.scitotenv.2018.11.098

Cited by 72 publications

(58 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Otieno and Anyah 2013;Tierney et al 2015) as is the drying of Southern Africa (Sillmann et al 2013;Hoegh-Guldberg et al 2018). Although large uncertainties in CMIP5 results exist (e.g., Sillmann et al 2013;Seth et al 2013), works based on both GCMs and RCMs (Vizy et al 2013;Cook and Vizy 2015;Hoegh-Guldberg et al 2018;Klutse et al 2018) project an increase in rainfall intensity especially over the Sahel in July-September accompanied, however, by longer dry spells, qualitatively consistent with our results. Note that the intensification of hydrological extremes, with increasing mean intensity and higher frequency of heavy rainfall, has also been observed in the past decades (e.g., Taylor et al 2017;Panthou et al 2018).…”

Section: Rcms-based Projectionssupporting

confidence: 91%

“…Future climate projections have been analyzed in several studies (Laprise et al 2013;Haensler et al 2013;Teichmann et al 2013;Vizy et al 2013;Giorgi et al 2014;Buontempo et al 2014;Mariotti et al 2014;Vizy et al 2015;Dosio and Panitz 2016;Pinto et al 2016;Diallo et al 2016;Fotso-Nguemo et al 2017;Akinsanola and Zhou 2018;Endris et al 2018), although most of these are based on the results of a single RCMs downscaling an ensemble of GCMs, or on a small ensemble of RCMs downscaling a small ensemble of GCMs. Projections based on large ensembles of CORDEX-Africa RCMs are presented by e.g., Dosio (2017) for temperature extremes and heat waves at the end of the century, Abiodun et al (2017) for extreme precipitation over four coastal cities.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

What can we know about future precipitation in Africa? Robustness, significance and added value of projections from a large ensemble of regional climate models

et al. 2019

View full text Add to dashboard Cite

We employ a large ensemble of Regional Climate Models (RCMs) from the COordinated Regional-climate Downscaling EXperiment to explore two questions: (1) what can we know about the future precipitation characteristics over Africa? and (2) does this information differ from that derived from the driving Global Climate Models (GCMs)? By taking into account both the statistical significance of the change and the models' agreement on its sign, we identify regions where the projected climate change signal is robust, suggesting confidence that the precipitation characteristics will change, and those where changes in the precipitation statistics are non-significant. Results show that, when spatially averaged, the RCMs median change is usually in agreement with that of the GCMs ensemble: even though the change in seasonal mean precipitation may differ, in some cases, other precipitation characteristics (e.g., intensity, frequency, and duration of dry and wet spells) show the same tendency. When the robust change (i.e., the value of the change averaged only over the land points where it is robust) is compared between the GCMs and RCMs, similarities are striking, indicating that, although with some uncertainty on the geographical extent, GCMs and RCMs project a consistent future. Potential added value of downscaling future climate projections (i.e., non-negligible fine-scale information that is absent in the lower resolution simulations) is found for instance over the Ethiopian highlands, where the RCM ensemble shows a robust decrease in mean precipitation in contrast with the GCMs results. This discrepancy may be associated with the better representation of topographical details that are missing in the large scale GCMs. The impact of the heterogeneity of the GCM-RCM matrix on the results has been also investigated; we found that, for most regions and indices, where results are robust or non-significant, they are so independently on the choice of the RCM or GCM. However, there are cases, especially over Central Africa and parts of West Africa, where results are uncertain, i.e. most of the RCMs project a statistically significant change but they do not agree on its sign. In these cases, especially where results are clearly clustered according to the RCM, there is not a simple way of subsampling the model ensemble in order to reduce the uncertainty or to infer a more robust result.

show abstract

Section: Rcms-based Projectionssupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

What can we know about future precipitation in Africa? Robustness, significance and added value of projections from a large ensemble of regional climate models

et al. 2019

View full text Add to dashboard Cite

show abstract

“…The lake evaporative demand is implicitly a function of the temperature regime in an unlimited moisture supply environment like Lake Malawi. Many studies have reported on increasing temperatures in Malawi (Warnatzsch andReay 2015 andreferences within, Ngongondo et al 2015) and over Southern Africa (Engelbrecht et al 2015, Ngongondo et al 2015, Maúre et al 2018). In the "Temporal hydroclimatic pattern of Lake Malawi basin (1899 to 2017)" section, this study also reported on statistically significant increases in the temperature and the evaporation regimes of Lake Malawi.…”

Section: Covariate Modeling Of Both μ and σmentioning

confidence: 99%

Multivariate framework for the assessment of key forcing to Lake Malawi level variations in non-stationary frequency analysis

Ngongondo

Zhou

2020

Environ Monit Assess

View full text Add to dashboard Cite

Lake Malawi in south eastern Africa is a very important freshwater system for the socio-economic development of the riparian countries and communities. The lake has however experienced considerable recession in the levels in recent years. Consequently, frequency analyses of the lake levels premised on time-invariance (or stationarity) in the parameters of the underlying probability distribution functions (pdfs) can no longer be assumed. In this study, the role of hydroclimate forcing factors (rainfall, lake evaporation, and inflowing discharge) and low frequency climate variability indicators (e.g., El Nino Southern Oscillation-ENSO and the Indian Ocean Dipole Mode-IODM) on lake level variations is investigated using a monthly mean lake level dataset from 1899 to 2017. Non-stationarity in the lake levels was tested and confirmed using the Mann-Kendall trend test (α = 0.05 level) for the first moment and the F test for the second moment (α = 0.05 level). Change points in the series were identified using the Mann-Whitney-Pettit test. The study also compared stationary and non-stationary lake level frequency during 1961 to 2004, the common period where data were available for all the forcing factors considered. Annual maximum series (AMS) and peak over threshold (POT) analysis were conducted by fitting various candidate extreme value distributions (EVD) and parameter fitting methods. The Akaike information criteria (AIC), Bayesian information criteria (BIC), deviance information criteria (DIC), and likelihood ratios (RL) served as model evaluation criteria. Under stationary conditions, the AMS when fitted to the generalized extreme value (GEV) distribution with maximum likelihood estimation (MLE) was found to be superior to POT analysis. For the non-stationary models, open water evaporation as a covariate of the lake levels with the GEV and MLE was found to have the most influence on the lake level variations as compared with rainfall, discharge, and the low frequency climatic forcing. The results are very critical in flood zoning especially with various planned infrastructural developments around the lakeshore.

show abstract

“…Previous hydrological studies have assessed the trends and relationships between the water level in Lake Malawi [44,45], the discharge in the Shire River [46], and the observed and potential impacts of climate change on the local hydrology [47,48] and hydropower generation [49], as well as the perceived risks and potential adaptation options under climatic and socio-economic uncertainty in the Shire River Basin [50]. The literature has also highlighted that the lake level is highly sensitive to climate variability [46], with cyclic fluctuations in levels being largely subject to annual rainfall patterns and seasonal precipitation and temperature variables anticipating lake level changes by approximately two months.…”

Section: Introductionmentioning

confidence: 99%

Monitoring hydropower reliability in Malawi with satellite data and machine learning

Falchetta

Kasamba

Parkinson

2020

Environ. Res. Lett.

View full text Add to dashboard Cite

Hydro-climatic extremes can affect the reliability of electricity supply, in particular in countries that depend greatly on hydropower or cooling water and have a limited adaptive capacity. Assessments of the vulnerability of the power sector and of the impact of extreme events are thus crucial for decisionmakers, and yet often they are severely constrained by data scarcity. Here, we introduce and validate an energy-climate-water framework linking remotely-sensed data from multiple satellite missions and instruments (TOPEX/POSEIDON. OSTM/Jason, VIIRS, MODIS, TMPA, AMSR-E) and field observations. The platform exploits random forests regression algorithms to mitigate data scarcity and predict river discharge variability when ungauged. The validated predictions are used to assess the impact of hydroclimatic extremes on hydropower reliability and on the final use of electricity in urban areas proxied by nighttime light radiance variation. We apply the framework to the case of Malawi for the periods 2000-2018 and 2012-2018 for hydrology and power, respectively. Our results highlight the significant impact of hydro-climatic variability and dry extremes on both the supply of electricity and its final use. We thus show that a modelling framework based on open-access data from satellites, machine learning algorithms, and regression analysis can mitigate data scarcity and improve the understanding of vulnerabilities. The proposed approach can support long-term infrastructure development monitoring and identify vulnerable populations, in particular under a changing climate.

show abstract

Temperature and precipitation change in Malawi: Evaluation of CORDEX-Africa climate simulations for climate change impact assessments and adaptation planning

Cited by 72 publications

References 46 publications

What can we know about future precipitation in Africa? Robustness, significance and added value of projections from a large ensemble of regional climate models

What can we know about future precipitation in Africa? Robustness, significance and added value of projections from a large ensemble of regional climate models

Multivariate framework for the assessment of key forcing to Lake Malawi level variations in non-stationary frequency analysis

Monitoring hydropower reliability in Malawi with satellite data and machine learning

Contact Info

Product

Resources

About