analysis and synthesis of data about floods in durban

Articles on Durban floods

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Floods create health risks: what to look out for and how to avoid them

Juno Thomas , National Institute for Communicable Diseases and Linda Erasmus , National Institute for Communicable Diseases

How geology put a South African city at risk of landslides

Charles MacRobert , Stellenbosch University

Early warnings for floods in South Africa: engineering for future climate change

Justin Pringle , University of KwaZulu-Natal

South African floods wreaked havoc because people are forced to live in disaster prone areas

Hope Magidimisha-Chipungu , University of KwaZulu-Natal

Floods in South Africa: protecting people must include a focus on women and girls

Fidelis Udo , University of KwaZulu-Natal and Maheshvari Naidu , University of KwaZulu-Natal

Local knowledge adds value to mapping flood risk in South Africa’s informal settlements

Garikai Martin Membele , University of KwaZulu-Natal ; Maheshvari Naidu , University of KwaZulu-Natal , and Onisimo Mutanga , University of KwaZulu-Natal

How cities can approach redesigning informal settlements after disasters

Fiona Anciano , University of the Western Cape and Laurence Piper , University of the Western Cape

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Landslide and flash floods impact analysis West of Durban, eThekwini Metropolitan Municipality, KwaZulu-Natal Provincce,

UNOSAT code: FL20220418ZAF This map illustrates satellite-detected landslides/mudflow West of Durban City, eThekwini Metropolitan Municipality, KwaZulu-Natal Province, South Africa as observed from a WorldView-3 imagery acquired on 14 April 2022. Within the analyzed area, at least 62 structures and 2 bridges appear to be affected by floods and/or landslides. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

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Related links, on-line version  issn 1816-7950 print version  issn 0378-4738, water sa vol.41 n.3 pretoria apr. 2015, http://dx.doi.org/10.4314/wsa.v41i3.11 .

Performance of regional flood frequency analysis methods in KwaZulu-Natal, South Africa

JC Smithers I, II, * ; J Streatfield II ; RP Gray II ; EGM Oakes II

I Bioresources Engineering, School of Engineering, University of KwaZulu-Natal, Private Bag X01, Scottsville, 3209, South Africa II Jeffares & Green (Pty) Ltd, 6 Pin Oak Avenue, Hilton, Pietermaritzburg, 3201, South Africa

Estimates of design floods are required for the design of hydraulic structures and to quantify the risk of failure of the structures. Many international studies have shown that design floods estimated using a regionalised method result in more reliable estimates of design floods than values computed from a single site or from other methods. A number of regional flood frequency analysis (RFFA) methods have been developed, which cover all or parts of South Africa. These include methods developed by Van Bladeren (1993), Mkhandi et al. (2000), Görgens (2007) and Haile (2011). The performance of these methods has been assessed at selected flow-gauging sites in the province of KwaZulu-Natal (KZN), South Africa. It is recommended that the limitations of available flow records to estimate extreme flow events need to be urgently addressed. From the results for KZN the JPV method, with a regionalised GEV distribution with the veld zone regionalisation, generally gave the best performance when compared to design floods estimated from the annual maximum series extracted from the observed data. It is recommended that the performance of the various RFFA methods needs to be assessed at a national scale and that a more detailed regionalisation be used in the development of an updated RFFA method for South Africa.

Keywords: regional flood frequency analysis, KwaZulu-Natal

INTRODUCTION

The design of hydraulic structures (e.g. dams, flood attenuation structures, culverts) requires the estimation of a design flood which is the magnitude of the flood associated with a given probability of exceedance or return period in years. Practitioners in South Africa generally estimate design floods by performing a frequency analysis of gauged flow data at a given location, if flow data are available at the site of interest, or by using an event-based rainfall-runoff model, for example, using the rational method, unit hydrograph method, or Soil Conservation Services method adapted for conditions in South Africa (SCS-SA). However, limitations of event-based methods include the assumption that the exceedance probability of the flood event is the same as the exceedance probability of the rainfall event, i.e., the 100-year return period flood event is assumed to result from a 100-year return period rainfall event, and the antecedent soil moisture condition in the catchment prior to extreme rainfall events is not taken into account.

When observed flow data are available, design floods can be estimated by performing a frequency analysis of the data, which generally involves fitting probability distributions to the annual maximum series (AMS) extracted from the data. The selected probability distribution is assumed to represent the population of all extreme events from the site. Hence, the longer the period of record, the better the assumption that the selected distribution represents the distribution of the population of all extreme events at the site.

A limitation of using a single-site approach to flood frequency analysis is that relatively few gauging stations in South Africa have long record lengths (e.g. > 50 years) and this limits the confidence in design floods estimated using data from a single site, particularly when using shorter record lengths and when estimating design values for longer return periods (e.g. 100 years). In addition, design floods generally need to be estimated at sites where observed flood data are not available and thus rainfall-based methods or regionalised methods need to be used to estimate design floods at ungauged sites.

Given the relatively short flow-record lengths generally available it is necessary to use data from similar and nearby locations to improve the reliability of design flood estimates (Stedinger et al. 1993). This approach is known as regional flood frequency analysis (RFFA) and utilises data from several sites to estimate the frequency distribution of floods at each site. As summarised by Smithers (2012), many studies have shown that RFFA will result in more accurate and consistent estimates than at-site analyses (e.g. Cordery and Pilgrim, 2000; Hosking and Wallis, 1997; Smithers and Schulze, 2000a; Smithers and Schulze, 2000b).

RFFA usually assumes that relatively homogenous flood regions can be identified where the frequency distributions of floods at different sites are similar after site-specific scaling. Generally, growth curves (ratio of design flood/index flood vs. return period), or regionalised scaled distribution parameters, are developed for each region. Regionalised relationships are then developed to estimate the index flood/scaling value (e.g. mean annual flood) at ungauged sites in a region. A critical aspect of RFFA is the identification of relatively homogenous flood response regions.

A number of RFFA studies have included parts of or the entire area of South Africa in their analyses. These include Van Bladeren (1993), Mkhandi et al. (2000), Görgens (2007) and Haile (2011). None of these RFFA methods are currently widely used in practice to estimate design floods. The objective of this paper is to give a brief background to the methods and to compare the performance of these RFFA methods in the province of KwaZulu-Natal (KZN) in South Africa.

REGIONAL FLOOD FREQUENCY ANALYSIS METHODS FOR KWAZULU-NATAL

The following sections provide a brief background to the RFFA methods used in this study.

Mkhandi Method

Mkhandi et al. (2000) performed a regional frequency analysis of annual maximum flood data using data from 407 screened stations in southern Africa. As shown in Fig. 1 , 13 flood regions were identified in South Africa. The Pearson Type 3 (P3) distribution fitted by probability weighted moments (PWM) was found to be the best distribution in all regions in South Africa, with the exception of SAF13 where the Log-Pearson Type 3 (LP3) distribution fitted by the Method of Moments (MM) was used.

The Mean Annual Flood ( MAF ) was used as an index flood to scale the data. Equation 1 was used to estimate the MAF at ungauged sites (Mkhandi et al., 2000).

MAF = CONSTANT × AREA EXPONENT (1)

where: MAF = mean annual flood (m 3 ∙ s -1 ), CONSTANT = regionalised parameter, AREA = catchment area (km 2 ), and EXPONENT = regionalised parameter.

Van Bladeren Method

Van Bladeren (1993) derived growth curves using both continuously recorded data and discharges derived from historical flood information from the Natal and Transkei regions. Design floods were estimated using the General Extreme Value (GEV) distribution fitted by PWM. The MAF was used as the index flood in the derivation of the growth curves and regionalised regressions were derived to estimate the MAF as a function of catchment area. Regionalisation was initially based on the Regional Maximum Flood (RMF) regions identified by Kovács (1988) and was further refined within the RMF regions based on the skewness of the data. The method developed by Van Bladeren (1993) is applicable for RMF Regions 5.0 to 5.6 in Natal and Transkei.

Haile Method

A RFFA in southern Africa was undertaken by Haile (2011) who used data from 459 gauging stations in 5 countries (Namibia, Malawi, Zambia, Zimbabwe and South Africa). After screening of the data, only 122 stations were included in further analyses ( Fig. 2 ) with 92 stations from South Africa, of which 8 stations were used for independent testing of the method. Nine homogenous regions were identified, with 5 of these regions in South Africa, as shown in Fig. 3 . The generalised Pareto (GPA), Pearson Type 3 (P3), three-parameter log-normal (LN3) and the GEV distributions were found to be suitable to model the distributions of the AMS of floods in southern African catchments. The median of the AMS ( MEF ) was used as the index to scale the values. From independent assessment of design floods estimated in the 9 regions using the regionalised flood frequency relationships, it was concluded that the regional approach was satisfactory (Haile, 2011). Both linear and exponential relationships were developed to estimate the MEF as a function of catchment area. In some regions where the negative constant in the linear relationship resulted in a negative MEF , exponential relationships were developed in this study to estimate the MEF from catchment area using information from Haile (2011).

As part of the development of the Joint Peak-Volume (JPV) methodology, Görgens (2007) developed a regionalised index flood approach to design flood estimation for South Africa. Data from 74 flow gauging stations and inflows to dams were used in the regionalisation, with the distribution of stations as shown in Fig. 4 . The means of the peak discharges and volumes were used as index values to standardise the values.

Regionalisation

Görgens (2007) used both a fixed pool group, referred to as 'wide' pooling, and adjustable pooling groups, referred to as 'narrow' pooling. For the fixed pool grouping, Görgens (2007) developed growth curves for both the well-established K-regions (Kovacs 1988) and the veld type zones (HRU 1972) in his regionalisation. As shown in Fig. 5 , the K-regions were grouped into 3 regions: (i) high-K ( K > 5), (ii) mid-K ( K =5), and (iii) low-K ( K < 5).

As shown in Fig. 6 , the veld type zones were grouped into 3 groups (Görgens 2007) as: (i) Group A (Veld Type Zone 2), (ii) Group B (Veld Type Zones 4, 5, 6, 7), and (iii) Group C (Veld Type Zones 1, 3, 8, 9).

Pooling of statistical parameters

Pooled values for the coefficient of skewness ( g ) and coefficient of variation ( CV ) were weighted according to the record length and the inverse of a similarity distance ( Dist i,j ), computed using Eq. 2 (Görgens, 2007).

Dist i,j = similarity distance measure between Stations i and j

Area i = area for Catchment i (km 2 )

S i = slope for Catchment i

MAR90 = mean annual runoff (mm), determined from the WR90 study (Midgley et al., 1994) σ = standard deviation of descriptors of catchments in the pooling group

Estimation of the index value at ungauged sites

Linear regression relationships were developed by Görgens (2007) to estimate the index flood, as shown in Eq. 3. The catchment descriptors ( Des i ) included catchment area, equal area catchment slope, MAR90, and an index of the veld type zone or K-region.

ln( Index Flood Peak ) = B o + B 1 ln( Des 1 ) + B 2 ln( Des 2 ) + B 3 ln( Des 3 ) + … .(3)

DATA USED IN THE STUDY

Stream flow-gauges located in KZN were selected for inclusion in the analysis based on the attributes of the gauges. The criteria used were length of record, with gauges included for record lengths > 20 years, start and end date of flow record, the percentage of values in the AMS where the recorded stage exceeded the limits of the rating curve for the flow-gauging station, the number of values in the AMS which were flagged as having missing data during the year, and the period of the year when the missing data occurred.

After the initial selection of gauges, additional gauges with 15-20 years of record were investigated for inclusion in the analysis in areas which did not have any gauges included in the initial selection. The location of all of the gauges in KZN is displayed in Fig. 7 . The distribution of the length of record of the selected gauges used in the analysis is shown in Fig. 8 . The record lengths ranged from 13 to 83 years with a median value of 40 years.

The reliability of design values estimated from the observed data is dependent on the quality of the observed data and length of available record. As shown in Fig. 9 , 67.4% of the gauges in KZN had a single unique maximum value in the AMS, whereas 15.7% of the gauges had the same maximum value in more than 20% of the years, which is an indication that the rating table used to convert the observed river stage into discharge had been exceeded. For the selected gauges used in this study, a single unique maximum value in the AMS was found at 87.8% of the gauges, and 12.2% of the sites had up to 15% of the years with the same maximum value. In these cases, the exceeded values were treated as missing data.

This section contains the results from the application of the JPV, Haille, Van Bladeren and Mkhandi RFFA methods at the 41 selected flow-gauging sites and a comparison of the estimated design floods to the design floods computed from the observed flow data at the sites. Alexander (1990, 2001) recommended the use of the LP3 probability distribution for design flood estimation in South Africa, while Görgens (2007) used both the LP3 and GEV distributions and, according to Van der Spuy and Rademeyer (2010), both distributions are applicable in South Africa. Hence both the LP3 and GEV distributions, fitted by L-moments (Hosking, 1990; Hosking and Wallis, 1990), were used to estimate the design floods based on the statistics of the AMS at each selected gauge.

For each RFFA method, site and distribution, a mean absolute relative error (MARE) was computed as shown in Eq. 4:

MARE M,D = mean absolute relative error (%) for RFFA Method = M and probability distribution = D (LP3 or GEV) for all stations (41) used E M,T = design flood estimated using RFFA Method = M and for return period = T (2, 5, 10, 20, 50 or 100 years) O D,T = design flood estimated using observed AMS and probability distribution = D (LP3 or GEV) for return period = T (2, 5, 10, 20, 50 or 100 years)

The results from an analysis of the performance of the RFFA methods are shown in Table 1 , which includes both the MARE M,D values for each method and the average slope between the 2 to 100 year return period floods computed at each site using the selected method (Estimated) and estimated from the observed data at the site (Observed). While the Haile method resulted in the smallest MARE M,D value, the average slope of the estimated vs. observed floods is considerably less than 1, indicating that the Haile method generally underestimates the floods computed from the observed data. This general underestimation by the Haile method is confirmed by the performance of the RFFA methods in estimating the 50-year return period floods shown in Fig. 10 . The results in Fig. 10 also confirm the poor performance of the JPV method when the regionalised LP3 distribution is used. Based on the results in Table 1 and the typical results shown in Fig. 10 , the JPV method using the regionalised GEV distribution and veld-zone regionalisation performed the best of the RFFA methods considered in this study.

DISCUSSION, CONCLUSIONS AND RECOMMENDATIONS

The availability of gauged flows for large events with extreme discharges remains a challenge in South Africa. For many flow-gauging stations investigated in this study, exceedance of the rating table by observed river stage was evident by values in the AMS which are constant and equal to the maximum rated discharge for the flow-gauging structure. Stations which have more than 20% of the values in the AMS which exceed the maximum rated discharge were excluded from the study and, for the retained stations, the years with values which exceed the maximum rated discharge were assumed to be missing. This analysis did not account for different rating tables covering different periods of record, which would result in the rating tables being exceeded more frequently than indicated in this analysis.

The frequency with which recorded flow stages exceed the maximum rated level needs to be quantified and the impact of not including these extreme events in the estimation of design floods in South Africa must be quantified. Methods to extend the rating tables, and thus provide an estimate of discharge for all observed stage levels, need to be urgently developed. It is expected that the estimation of discharge for all of the observed stage levels will impact both on the volume of runoff measured and the design floods estimated from the observed peak discharge data.

The results presented for the 50-year return period illustrate the differences in the design floods estimated from the observed data when using the GEV and LP3 distributions, particularly for longer return periods. Despite the quality screening of the stations included in the study, the design floods estimated at a few stations seem to not be consistent with other stations in the region. Hence, the need for consistent screening and checking of the flow data is required in order to identify reliable data records that can be used for design flood estimation.

A number of RFFA studies have been developed which include all of South Africa (Haile, Mkhandi and JPV methods), and regions of South Africa (Van Bladeren). Despite the advantages of a regional approach to design flood estimation, RFFA methods are not widely used in South Africa. Of the RFFA methods assessed in KZN, the Haile method gave the best performance in terms of the MARE, but consistently underestimated the design floods computed from the observed data when using either the GEV or LP3 distribution. The poor performance of the JPV method with the regionalised LP3 distribution needs to be investigated. However, the JPV method with the regionalised GEV distribution generally performed well, with the veld zone regionalisation giving better results than the RMF K-region regionalisation. The results from this study are applicable only to KZN and the performance of the various RFFA methods at a national scale needs to be investigated.

Only the studies reported by Mkhandi et al. (2000), Haile (2011) and Görgens (2007) encompass the whole of South Africa. Ideally, regionalisation in a RFFA should be performed using site characteristics as this enables independent testing of the regions for homogeneity using the at-site data, and the independent allocation of a site to a region based on the site characteristics. Discontinuities at regional boundaries need to be investigated and the alternative approach of transferring hydrological information from gauged to ungauged sites within a region should be evaluated.

Both Mkhandi et al. (2000) and Haile (2011) utilised the statistics of the at-site data with various homogeneity tests to identify homogenous flood regions in their study areas. Görgens (2007) did not update flood regions in South Africa and used both the RMF K-regions (Kovács, 1988) and the veld type zones (HRU, 1972) in his regionalisation, and it is recommended that a more detailed regionalisation should be used in the development of an updated RFFA method for South Africa.

ACKNOWLEDGEMENTS

The results in this paper have been generated as part of a flood risk study being undertaken by Jeffares & Green for the Department of Human Settlements in KwaZulu-Natal and the funding for the project, and permission to publish the results, are gratefully acknowledged.

ALEXANDER WJR (1990) Flood Hydrology for Southern Africa. SANCOLD, Pretoria.         [  Links  ]

ALEXANDER WJR (2001) Flood Risk Reduction Measures . University of Pretoria, Pretoria.         [  Links  ]

CORDERY I and PILGRIM DH (2000) The state of the art of flood prediction. In: Parker DJ (ed.) Floods. Volume II. Routledge, London, UK.         [  Links  ]

GÖRGENS AHM (2007) Joint peak-volume (JPV) design flood hydrographs for South Africa. WRC Report No. 1420/3/07. Water Research Commission, Pretoria. 241 pp.         [  Links  ]

HAILE AT (2011) Regional flood frequency analaysis in Southern Africa. MSc Thesis, University of Oslo, Norway. 129 pp.         [  Links  ]

HOSKING JRM (1990) L-moments: analysis and estimation of distribution using linear combinations of order statistics. J. R. Stat. Soc. 52 (1) 105-124.         [  Links  ]

HOSKING JRM and WALLIS JR (1990) Regional flood frequency analysis using L-moments. Research Report RC 15658. IBM Research Division, Yorktown Heights, New York, USA. 12 pp.         [  Links  ]

HOSKING JRM and WALLIS JR (1997) Regional Frequency Analysis: An Approach Based on L-Moments . Cambridge University Press, Cambridge, UK.         [  Links  ]

HRU (HYDROLOGICAL RESEARCH UNIT) (1972) Design flood determination in South Africa. Report No. 1/72. Hydrological Research Unit, Department of Civil Engineering, University of the Witwatersrand, Johannesburg.         [  Links  ]

KOVACS ZP (1988) Regional maximum flood peaks in South Africa. Technical Report TR137. Department of Water Affairs, Pretoria, RSA.         [  Links  ]

MIDGLEY DC, PITMAN WV and MIDDLETON BJ (1994) Surface water resources of South Africa 1990. WRC Report No. 298/1/94. Water Research Commission, Pretoria.         [  Links  ]

MKHANDI SH, KACHROO RK and GUNASEKARA TAG (2000) Flood frequency analysis of southern Africa: II. Identification of regional distributions. Hydrol. Sci. J. 45 (3) 449-464.         [  Links  ]

SMITHERS JC (2012) Review of methods for design flood estimation in South Africa. Water SA 38 (4) 633-646.         [  Links  ]

SMITHERS JC and SCHULZE RE (2000a) Development and evaluation of techniques for estimating short duration design rainfall in South Africa. WRC Report No. 681/1/00. Water Research Commission, Pretoria. 356 pp.         [  Links  ]

SMITHERS JC and SCHULZE RE (2000b) Long duration design rainfall estimates for South Africa. WRC Report No. 811/1/00. Water Research Commission, Pretoria. 69 pp.         [  Links  ]

STEDINGER JR, VOGEL RM and FOUFOULA-GEORGIOU E (1993) Frequency analysis of extreme events. In: Maidment DR (ed.) Handbook of Hydrology. McGraw-Hill, New York.         [  Links  ]

VAN BLADEREN D (1993) Application of historical flood data in flood frequency analysis, for the Natal and Transkei region. In: Proceedings of Sixth South African National Hydrological Symposium. Department of Agricultural Engineering, University of Natal. 359-366.         [  Links  ]

VAN DER SPUY D and RADEMEYER PF (2010) Flood Frequency Estimation Methods as Applied in the Department of Water Affairs. Department of Water Affairs, Pretoria. 98 pp.         [  Links  ]

Received 16 September 2014 Accepted in revised form 18 March 2015

* To whom all correspondence should be addressed. e-mail: [email protected]

A Critical Analysis of the Impacts of and Responses to the April-May 2022 Floods in KwaZulu-Natal

04 Apr 2023

Working Paper

This paper is an analysis of the April-May 2022 floods that struck the KwaZulu-Natal (KZN) province, centred in the City of eThekwini, which led to the loss of lives and livelihoods, displacement of people, extensive damage to infrastructure and disruption of services.

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• Climate change made extreme rains in 2022 South Africa floods ‘twice as likely’

Ayesha Tandon

The extreme rainfall that triggered one of South Africa’s deadliest disasters of this century was made more intense and more likely because of climate change, a new “rapid-attribution” study finds.

Over 11-12 April 2022, intense rains hit the eastern coast of South Africa – causing floods and landslides across the provinces of KwaZulu-Natal and the Eastern Cape. More than 400 people died as a result of the floods, which also destroyed more than 12,000 houses and forced an estimated 40,000 people from their homes.

The World Weather Attribution service finds that climate change doubled the likelihood of the event – from an event expected once every 40 years to once every 20. It adds that rainfall over the two-day period was 4-8% more intense than it would have been without climate change.

The study also explores the role of structural inequality in vulnerability to flooding, noting that forced relocation moved marginalised groups of people onto land that was more prone to flooding. 

“In South Africa, the legacy of apartheid is really key,” an author on the study told a press briefing, adding that “even though apartheid was formally dismantled more than 30 years ago, these structural inequalities persist”.

‘Catastrophic’ flooding

Over 11-12 April 2022, close to a year’s worth of rain fell on the eastern coast of South Africa, causing one of the deadliest natural disasters to hit the country in the 21st century.

In the provinces of KwaZulu-Natal and the Eastern Cape, the deluge triggered “ catastrophic ” floods and sudden landslides that “ devastated ” the region. More than 12,000 houses were destroyed, forcing an estimated 40,000 people from their homes.

Meanwhile, 630 schools were affected in the KwaZulu-Natal province, impacting around 270,000 students. Overall, the rain drove $1.57bn in damages to infrastructure.

At least 51 people have died after flooding and mudslides in South Africa’s eastern KwaZulu-Natal province, according to local media. President @CyrilRamaphosa is heading to the affected areas pic.twitter.com/ecpm7NrF07 — Bloomberg Quicktake (@Quicktake) April 24, 2019

The South African military deployed 10,000 troops to help search the wre c kage for survivors, the death toll quickly rose into the hundreds.

Authorities say that the city of Durban was the most severely affected, with an estimated 450 people killed in the city. In the Port of Durban – one of the largest shipping terminals in Africa  – “dozens of heavy shipping containers were dislodged from storage and strewn across the Indian Ocean port during the deluge”.

South Africa’s president, Cyril Ramaphosa, called the floods a “catastrophe of enormous proportions,” and “the biggest tragedy we have ever seen”. He declared a national state of disaster on 18 April. BBC News reported at the time:

“On a visit to affected areas in KwaZulu-Natal, the president said climate change was to blame, but some communities disagree. They say poor drainage and building standards have increased the scale of the disaster.”

The timing of the flood made it particularly damaging because South Africa was still recovering from a string of storms and cyclones. “The new disaster comes after three tropical cyclones and two tropical storms hit south-east Africa in just six weeks in the first months of this year,” the Guardian reported.

Extreme rainfall

The intense rainfall was caused by a “ cut-off low ” – a mid-latitude depression, where air of polar origin is cut off from the main subpolar belt of low pressure and cold air. This type of weather system is common in South Africa in April, according to the study – and results in heavy rainfall around one-fifth of the time.

On 7 April, the South African Weather Service issued a warning for disruptive rainfall. And as the storm drew closer, the severity of the warning level was raised . However, the study says “the warnings had limited reach and that the people who did receive them may not have known what to do based on them”.

The rainfall began on 9 April , and reached its peak intensity a few days later. In this study, the authors analyse the impact of climate change on maximum two-day rainfall over 11-12 April.

The map below shows the total rainfall on the eastern coast of South Africa over 11-12 April, where darker blues indicate more intense rainfall. The red outline indicates the area analysed in the study.

The South African Weather Service gave the World Weather Attribution team daily rainfall observations for 194 stations in the affected region. According to the study, 70 of the stations had a continuous string of data over 1950-2022 and five were able to provide the required data required in time for the study.

These stations and recorded rainfall trends are shown in the maps below. Green arrows indicate a trend of increasing rainfall over 1950-2022, while red arrows indicate a decrease. The map on the left shows the 70 stations with suitable data. The map on the right shows the 5 stations selected for the study.

Taking an average over the entire area studied, the rainfall over 11-12 April was a 1-in-20 event overall. However, the rainfall varied highly between different weather stations.

“The rainfall was very high in very small locations,” Dr Frederieke Otto – senior lecturer in climate science at the Grantham Institute for Climate Change and the Environment at Imperial College London and co-author of the study – told the press briefing, adding that “there were a few locations where at raised more than 350mm over two days”.

For example, due to high rainfall at the Mount Edgecombe and Mapumulo Prison weather stations, the authors calculated return periods of 1-in-200 years and 1-in-30 years for these stations, respectively.

Otto added that “there is a lag between the data being measured and being made available for people to analyse – that is why we can only use these five [stations].”

Vanetia Phakula – a meteorologist at the South African Weather Service – added that compared to other regions in Africa, South Africa has reasonably good data. However, she noted that many stations in South Africa have been forced to close due to lack of funding.

Attribution is a fast-growing field of climate science that aims to identify the “fingerprint” of climate change on extreme-weather events, such as heatwaves and floods. In this study, the authors investigate the impact of climate change on rainfall in South Africa over the 11-12 April period.

To conduct attribution studies , scientists use models to compare the world as it is to a “counterfactual” world without human-caused climate change. This study aims to distinguish the “signal” of climate change in South Africa’s rainfall from natural variability.  The plot below shows a time series of annual maxima of two-day average rainfall over the east coast of South Africa, based on the ERA5 reanalysis dataset , which combines observed data with model simulations. The green line shows a 10-year running average.

The authors conclude that the extreme rainfall was made twice as likely due to climate change – increasing from a 1-in-40 to a 1-in-20 year event. They also find that the event was made 4-8% more intense due to climate change.

(The findings are yet to be published in a peer-reviewed journal. However, the methods used in the analysis have been published in previous attribution studies .)

“We have quite high confidence in the results that we have for this study,” Otto told Carbon Brief at the press briefing. 

The authors conclude that the event was “not unprecedented”, and that additional factors played a role in “making this meteorological event so impactful and worth studying”.

‘Legacy of apartheid’

Kwazulu-Natal is the second-most populous province in South Africa with about 11.5 million people. However, the study notes that not all people were impacted equally by the flood. Poorer and more marginalised communities, such as migrants, were disproportionately vulnerable.

For example, in its coverage of the flooding at the time, the Guardian notes that people living in makeshift settlements were particularly vulnerable to the flooding:

“Poor people living in makeshift settlements built on unstable, steep-sided gorges around Durban were worst affected by the floods. Most have inadequate or no drainage systems and homes are sometimes flimsy shacks that offer little protection against the elements.”

Dr Christopher Jack – deputy director of the Climate System Analysis Group at the University of Cape Town , and science advisor Red Cross Climate Centre – told the press conference that flooding and landslides are a “chronic problem” in South Africa.

When considering vulnerability in South Africa, “the legacy of apartheid is really key”, he explained, adding that the “forced relocation of people” led to deep structural inequalities.

For example, the study highlights the Group Areas Act of 1958 – in which the Durban City Council assigned racial groups to different residential and business sections – resulting in “the displacement of many non-white communities into less desirable and, in some cases, more flood exposed areas”.

“Even though apartheid was formally dismantled more than 30 years ago, these structural inequalities persist, and we still see them represented in our city structures. And we still see them manifest and magnified when events such as this occur.”

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22 April 2022

Understanding the deadly landslides in the Durban area of South Africa

Posted by Dave Petley

In the last week I have posted extensively about the deadly landslides on 11 April 2022 in the Philippines .  At around the same time an event of a similar scale occurred in a completely different location.  It is reasonable to ask why I have not written about that one.

The event in question occurred in the Durban area of South Africa.  Once again it was triggered by heavy rainfall.  Reliefweb posted an update on this event yesterday – the statistics are appalling:

According to national authorities, 443 people died in KwaZulu-Natal and over 40,000 are missing. More than 40,000 people have been displaced, while nearly 4,000 houses were destroyed and more than 8,000 others were damaged, mostly across Durban City and its surrounding areas. A National State of Disaster has been declared in response to the floods and landslides, and rescue teams have been deployed to the affected areas to provide humanitarian assistance to those most affected. 

Hopefully the number missing is primarily the result of challenges of documenting displaced people, but even if the death toll remains at its current level, the picture is truly grim.

The challenge that I have had with this event has been obtaining reliable information about what has actually happened.  It is not at all clear to me as to why this is the case.  However, The Conversation has now published an article by Charles MacRo bert of Stellenbosch University , who provides a readily understandable explanation for the underlying causes of the landslides in this area.  It is worth a read – the underlying problems are weak geology.

In the coastal region, the problems are associated with a large, vegetated dune formation that is prone to rapid erosion:

Ground adjacent to the sea from Durban to Mtunzini (a coastal town 140km north of Durban) is almost exclusively made up of ancient red sand dunes termed the Berea formation. South of the Durban harbour these sands form a ridge called the Bluff and north of the harbour they form the Berea Ridge. In some places these sand dunes are extremely steep…The investigation showed the slopes’ stability was not significantly affected by rainfall. That makes sense as these slopes have been battered by storms over geological time. But concentrated flows from poorly controlled flood water or broken water pipes were found to be catastrophic.

This is dramatically illustrated at the town of Umdloti:-

Highly destructive gully formation at Umdloti, near to Durban in South Africa. Image from the North Coast Courier.

Meanwhile, inland the geology also causes problems.  Part of the area is underlain by the shales of the Pietermaritzburg formation, which consist of thin layers of clay and silt.  These shales weather easily and trap high pore water pressures, making them very susceptible to failure.  Other parts of the region are underlain by sandstones of the Natal group, which also contain layers of clay that trap high pore water pressures.

Understanding the distribution of these landslides, and the associated floods, is going to be a challenge but is urgently needed.  Meanwhile, the best pictoral record I can find has been posted to Facebook by Kierran Allen Photography .  This is an example of one of their images:-

A landslide in the Durban area of South Africa, triggered by the April 2022 rainfall event. Image by Kierran Allen Photography.

The devastating impact of such a landslide, even though it is small, is clear to see.

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May 16, 2022

Climate Change Doubled the Likelihood of Devastating South African Floods

Hundreds of people were killed and thousands of homes destroyed in Durban after torrential rains unleashed flooding

By Chelsea Harvey & E&E News

Part of Caversham road in Pinetown washed away on April 12, 2022 in Durban, South Africa. The fatal floods have been attributed to climate change.

Darren Stewart/Gallo Images/Getty Images

CLIMATEWIRE | Parts of South Africa are still reeling nearly a month after heavy rains and catastrophic floods wracked the coastal city of Durban and surrounding areas, killing hundreds of people and destroying thousands of homes. Now, scientists say the extreme rainfall was worsened by the influence of climate change.

According to a new analysis by the research consortium World Weather Attribution, the likelihood of an event this severe happening at all has more than doubled because of global warming. The amount of rainfall in this case was also 4 percent to 8 percent more intense than it would have been without the influence of climate change.

The findings are “consistent with scientific understanding of how climate change influences heavy rainfall in many parts of the world,” said lead study author Izidine Pinto, a climate scientist at the University of Cape Town and an adviser at the Red Cross Red Crescent Climate Centre.

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A warmer atmosphere can hold more water, allowing storms to dump more rain. That doesn’t necessarily mean storms will happen more frequently — but in many places, they’ll be stronger when they do happen.

This region of southern Africa, he added, is one of those places. The latest report from the Intergovernmental Panel on Climate Change concludes that extreme rainfall is likely to intensify there as the planet continues to warm.

South Africa is no stranger to heavy rainfall as it is. Durban, in particular, has seen a number of similar disasters in recent years, including a devastating series of floods and landslides as recently as spring 2019.

The latest event was triggered by days of torrential rainfall over South Africa’s east coast, especially the provinces of Eastern Cape and KwaZulu-Natal. Some locations recorded around 14 inches of rain over just two days.

It’s the latest event investigated by World Weather Attribution, which specializes in studying the links between climate change and individual extreme weather events, a field of research known as attribution science. Founded in 2014, the group has analyzed dozens of climate-related disasters around the world, including heat waves, floods, droughts and storms.

Recent studies from WWA have found that climate change worsened the extreme rainfall produced by tropical cyclones in Madagascar, Mozambique and Malawi earlier this year. It made the heavy rainfall and severe floods that devastated Western Europe last year much more likely. And the astonishing heat wave that scorched northwestern North America last summer would have been virtually impossible without the influence of global warming.

Attribution science, itself, is a relatively young field. But it’s advanced rapidly since its start about two decades ago. Scientists are now able to investigate the effects of climate change on the frequency and intensity of a wide variety of different weather events.

They’re getting faster at it, too. While some studies previously may have required weeks or months to complete, scientists now can analyze many kinds of events in near real time.

The study on South Africa uses the same general method applied in many attribution studies. It uses climate models to compare simulations of the real world with simulations of a hypothetical world in which climate change doesn’t exist. The difference between these simulations can demonstrate the influence of global warming on extreme events.

In this case, some locations were affected worse than others. Some of the heaviest-hit weather stations recorded rainfall qualifying as a 1-in-200-year event — an extremely rare disaster. Averaged across the whole region, though, the heavy rainfall constituted about a 1-in-20-year event. That means in any given year, there would be about a 1-in-20, or 5 percent, chance of such an event occurring.

The WWA team opted to look at the region as a whole, where it would have the most data to work with. They found that the influence of climate change has approximately doubled the risk of such severe rainfall. In a world without global warming, in other words, this event only would have had about a 2.5 percent chance of occurring in any given year.

Still, it’s not just the severity of the rainfall that led to its devastating outcome. Structural inequalities in the affected areas also worsened the impact. Many of the people most vulnerable to floods and landslides in and around Durban live in informal settlements and in homes that are easily washed away.

In South Africa, “the legacy of apartheid is really key,” said study co-author Christopher Jack, a climate scientist at the University of Cape Town and adviser to the Red Cross Red Crescent Climate Centre.

“The forced relocation into specific areas across the country — in particular, into cities — have set up these deeply rooted structural inequalities where people have been forced to live in unsuitable areas,” he said. “Even though apartheid was formally dismantled more than 30 years ago, these structural inequalities persist.”

Events like the recent floods underscore the deep connections between climate change and social inequality. Numerous studies have pointed out the disproportionate impacts that global warming and climate-related disasters have on certain populations. As extreme weather events worsen, so will their impacts on the world’s most vulnerable people.

At the same time, even adaptation plans designed to protect vulnerable populations are strained by the speed at which climate change is progressing around the world, Jack noted.

“We can’t seem to do it rapidly enough to avoid event after event with devastating impacts,” he said. “We need to scale up our response to climate change if we want to avoid seeing these kinds of impacts in the future.”

Reprinted from E&E News with permission from POLITICO, LLC. Copyright 2022. E&E News provides essential news for energy and environment professionals.

South Africa: KwaZulu-Natal Floods

Following severe flooding in Mozambique and Madagascar earlier in the year, further storms affected Durban and surrounding areas of South Africa in April 2022, causing over 400 fatalities.

Between 8 and 21 April 2022, the slow-moving storm Issa brought long periods of heavy rain to KwaZulu-Natal which caused flooding and mudslides in Durban and surrounding areas, affecting over 40,000 people and leaving a trail of destruction. The region has been declared a state of disaster by President Cyril Ramaphosa and many people were still without power or drinking water several days after the waters began to recede (AfricaNews, 2022).

More than 440 people are known to have died, with the search continuing for a further 63 people reported missing at the time of writing. Mudslides as well as swollen rivers were responsible for many of these deaths (BBC, 2022a). Search and rescue operations being run from Virginia Airport in Durban were still sending out teams on Sunday 24 April to search for missing people thought to have been swept away by the floods (Durban Mercury, 2022).

One factor exacerbating the effects of the storms was the fact that many homes in lower-income neighbourhoods of Durban – the worst affected city – are built on open, unsafe ground in low-lying areas, leaving low-income families particularly vulnerable to flooding and landslides (ABC News, 2022).

At least 13,000 homes have been damaged, and community spokespeople have said that poor drainage and building standards have increased the scale of the disaster (BBC, 2022b).

Other issues affecting the province following the flooding include damage to the water supply, with 80% of the drinking water network out of order according to authorities; over 600 schools facing disruption or temporary closures; roads becoming impassable, and bridges being swept away (CNN, 2022).

So far, R1 billion (USD $67 million) has been allocated by the South African government to help KwaZulu-Natal (KZN) respond to the floods, but local officials believe they will need at least twice that amount to complete their work in the region (Thesouthafrican.com, 2022a). The Premier of KZN, Sihle Zikalala has stated that more than R1.9 billion (USD $120 million) will be needed (Durban Mercury, 2022).

The story of the kzn floods

A strong low pressure weather system off the east coast of southern Africa – in itself not an unusual occurrence, with such systems often causing localised flooding and large wave events in the autumn – brought rain to the region from 8 April 2022 (Thesouthafrican.com, 2022b).

Figure 1: Satellite observed rainfall animation over South Africa from 6 to 21 April 2022. Data source: NASA GPM, 2022 (video produced by JBA Risk Management, 2022).

The low pressure system was enhanced by an influx of low-level moist air feeding in from the southern Indian ocean. The airflow originated from a warmer sub-tropical climate, which increased the system’s capacity to hold moisture. The combined effects of additional heat and moisture enabled the system to produce more rainfall, exceeding the expectations of the southern African meteorological community (South African Weather Service, 2022).

The South African Weather Service issued a Level 5 warning for coastal areas and interior areas of KwaZulu-Natal, which was subsequently upgraded to Level 8 and then to Level 9, reflecting disruptive rainfall, widespread flooding, sinkholes, mudslides, and major traffic disruption (South African Weather Service, 2022).

Durban recorded 91mm of rain in 24 hours from 10-11 April, with more flooding occurring on 12 April in the wider area after more rain fell over the region. Margate, Sezela, Mount Edgecombe and parts of Durban all received more than 300mm of rain in the 24 hours to 12 April (Floodlist, 2022a). This is more than four times the average amount for the entire month of April (CNN, 2022).

Durban, a thriving port city, has experienced flooding events every year since 2016, but previous storms have typically dropped half this amount (100-150mm) in any 24-hour period (Thesouthafrican.com, 2022b).

Several rivers, including the Amanzimtoti, Umbilo and Umgeni, overflowed, causing widespread damage to informal settlements and other riverside communities (Floodlist, 2022b).

Financial losses

Flood-related claims of R245 million had been received by 14 April. About R45 million of the claims were from residential, motor and commercial policies. The remaining R200 million came from industrial policies covering plants, factories, equipment, and shipping and maritime policies (Daily Maverick, 2022). This is the biggest natural disaster affecting the insurance industry in South Africa since the 2017 Knysna fires which led to R7 billion in claims (Daily Maverick, 2022).

Previous large floods in this area

Durban easter floods 2019: 18-22 april 2019.

There were over 85 fatalities in Eastern Cape and KwaZulu-Natal, and economic losses cost the KwaZulu-Natal Government USD $75 million and cost the Disaster Management Centre USD $45 million (WillisRe, 2019; France24, 2022; News24, 2019). The 2019 Durban Easter Floods were also caused by heavy rains due to a low pressure system (France24, 2022; News24, 2019).

South Africa floods 1987: 25-29 September 1987

Around 400 people were killed, and 50,000 left homeless in KwaZulu-Natal (Government of South Africa, 2022b). Informal settlements were wholly destroyed (Integrated Emergency Response, 2020). The total cost of damages to agriculture, communications, infrastructure and property was R400 million (Government of South Africa, 2022b). Floods were also caused by a low pressure system (Platford, 1988), resulting in high rainfall – some areas received up to 900mm of rain in four days (South Coast Sun, 2016). This is considered the most devastating flood in South Africa in history (Integrated Emergency Response, 2020).

How does climate change impact these events?

Following comments from South Africa’s President, the issue of climate change has been widely discussed in relation to this event. With the flooding coming hard on the heels of a season in which three tropical cyclones and two tropical storms hit south-east Africa in just six weeks , experts are highlighting similarities in the underlying causes of these extreme events (Guardian, 2022).

Scientists from several nations collaborating on research commissioned by the World Weather Attribution organisation have analysed how human-induced climate change affected the 3-day average annual maximum rainfall in the regions worst hit by Tropical Storms Ana and Batsirai in January-February 2022. Their conclusion that the region is expected to see increased likelihood and intensity of rainfall caused by tropical storms has led many commentators to link the KZN flooding to the season’s other storms (World Weather Attribution, 2022).

Experts at the South African Weather Service (SAWS) say severe and extreme weather events are becoming more frequent and intense as a result of the changing climate (BBC, 2022c).

However, poor infrastructure, urban sprawl and a lack of resources have also been blamed for the severe effects of the floods (BBC, 2022c), with many of the homes affected being flimsily built and lacking adequate drainage systems which mean that they offer little protection from the elements (Guardian, 2022).

JBA’s southern Africa focus

With increasing interest in our flood maps and probabilistic models from re/insurers and Disaster Risk Reduction organisations active in this region, JBA is working with new partners in the area.

We have recently partnered with Reinsurance Solutions Intermediary Services (RSIS) , an African based reinsurance broker who will use JBA’s global flood modelling capability to provide flood risk analytics and catastrophe modelling services for its insurance clients across Africa.

Flood risk is a significant peril in the continent, especially in eastern and southern regions, with the 2020 East Africa floods affecting at least 700,000 people and the 2019 Easter floods in South Africa causing R650 million (USD $40 million) worth of storm damage.

"Flood risk is a peril that is often overlooked in Africa but can be extremely costly when it is not taken into account. We are proud to partner with JBA Risk Management, global leader in flood risk. Their outstanding analytics and insights complement our comprehensive service offering, ensuring our clients are fully informed and have a more complete understanding of the cover that they require. We believe in partnering with the right people, and this is one such partnership." -  Thato Raboroko, RSIS Head of Analytics. 

Get in Touch

It is vital that organisations act now to understand both current and future flood risk. As well as offering probabilistic models and high-resolution river and surface water hazard maps for all countries globally, JBA enables organisations to understand their risk to flood through the delivery of bespoke flood assessment reports and portfolio analysis. For more information, get in touch with the team at JBA.

This report is covered by JBA’s website terms – please read them  here .

We take your privacy seriously. We will securely store the data that you share. We will not share your data with any third party. If you would like to unsubscribe at any time please contact us at  [email protected]   with the subject line Opt-out or call JBA Risk Management Marketing on   01756 799919 . All updates will also give you the option to unsubscribe. Read our complete privacy policy   here .

ABC News, 2022. South Africa’s Durban still recovering from deadly floods. [online] Available at: https://abcnews.go.com/International/wireStory/south-africas-durban-recovering-deadly-floods-84260585 [Accessed 26 April 2022]

AfricaNews, 2022. As South Africa flood toll nears 400, rescuers continue search for missing. [online] Available at: https://www.africanews.com/2022/04/16/as-south-africa-flood-toll-nears-400-rescuers-continue-search-for-missing/ [Accessed 26 April 2022]

BBC, 2022a. KwaZulu-Natal floods: South Africa army sends 10,000 troops. [online] Available at: https://www.bbc.co.uk/news/world-africa-61113807 [Accessed 26 April 2022]

BBC, 2022b. Durban flood survivors: South Africans homeless, hurt and heartbroken. [online] Available at: https://www.bbc.co.uk/news/world-africa-61105463 [Accessed 26 April 2022]

BBC, 2022c. Durban floods: Is it a consequence of climate change? [online] Available at: https://www.bbc.co.uk/news/61107685 [Accessed 26 April 2022]

CNN, 2022. South Africa flooding: over 300 killed after flooding washed away roads, destroyed homes in South Africa. [online] Available at: https://edition.cnn.com/2022/04/13/africa/south-africa-rain-floods-climate-intl/index.html [Accessed 26 April 2022]

Daily Maverick, 2022. SA insurance industry drowning in claims after KZN flash floods, Covid-19 and July riots. [online] Available at: https://www.dailymaverick.co.za/article/2022-04-24-sa-insurance-industry-drowning-in-claims-after-kzn-flash-floods-covid-19-and-july-riots/ [Accessed 26 April 2022]

Durban Mercury (IOL), 2022. Cost of KZN flood disaster escalates. [online] Available at: https://www.iol.co.za/dailynews/news/kwazulu-natal/cost-of-kzn-flood-disaster-escalates-bcffc014-7be6-4e4f-a976-6b35f84ae6f1 [Accessed 26 April 2022]

Floodlist, 2022a. South Africa – Heavy Rain Causes Floods and Mudslides in KwaZulu-Natal. [online] Available at: https://floodlist.com/africa/south-africa-floods-mudslides-kwazulu-natal-april-2022 [Accessed 26 April 2022]

Floodlist, 2022b. Death Toll in KwaZulu-Natal Floods Over 300. [online] Available at: https://floodlist.com/africa/south-africa-kwazulu-natal-floods-april-2022 [Accessed 26 April 2022]

France24, 2022. What's behind South Africa's flood disaster? [online] Available at: https://www.france24.com/en/live-news/20220418-what-s-behind-south-africa-s-flood-disaster [Accessed 27 April 2022]

Government of South Africa, 2022b. Remarks by Premier of Kwazulu-Natal Sihle Zikalala on prayer held in memory of the victims of recent floods. [online] Available at: https://www.gov.za/speeches/remarks-premier-kwazulu-natal-sihle-zikalala-prayer-held-memory-victims-recent-floods-21 [Accessed 27 April 2022]

Guardian, 2022. After the relentless rain, South Africa sounds the alarm on the climate crisis. [online] Available at: https://www.theguardian.com/world/2022/apr/24/south-africa-floods-rain-climate-crisis-extreme-weather [Accessed 26 April 2022]

Integrated Emergency Response, 2020. The worst South African floods. [online] Available at: https://www.ier.co.za/the-worst-south-african-floods/ [Accessed 27 April 2022]

News24, 2019. KZN floods: Death toll up to 85. [online] Available at: https://www.news24.com/News24/kzn-flooding-death-toll-up-to-85-20190425 [Accessed 27 April 2022]

Platford, G. G. (1988). Protection against flood damage. Proceedings of South Africa Sugar Technologists’ Association.

The South African, 2022a. KZN floods: Nearly R2 billion NEEDED ‘to complete our work’ - Zikalala. [online] Available at: https://www.thesouthafrican.com/news/kzn-floods-nearly-r2-billion-needed-to-complete-our-work-zikalala/ [Accessed 26 April 2022]

The South African, 2022b. Warnings for floods in SA: Planning for future climate change. [online] Available at: https://www.thesouthafrican.com/lifestyle/environment/flooding-weather-forecast-systems-kzn-floods/ [Accessed 26 April 2022]

South African Weather Service, 2022. Extreme rainfall and widespread flooding overnight: KwaZulu-Natal and parts of Eastern Cape. [online] Available at: http://www.weathersa.co.za/Documents/Corporate/Medrel12April2022_12042022142120.pdf [Accessed 27 April 2022]

South Coast Sun, 2016. A river ran through it. [online] Available at: https://southcoastsun.co.za/81662/a-river-ran-through-it/ [Accessed 27 April 2022]

WillisRe, 2019. Summary of Natural Catastrophe Events 2019. [online] Available at: https://www.willistowerswatson.com/-/media/WTW/Insights/2020/01/Willis-Re-Summary-of-Natural-Catastrophe-Events-2019.pdf?modified=20200128113240 [Accessed 27 April 2022]

World Weather Attribution, 2022. Climate change increased rainfall associated with tropical cyclones hitting highly vulnerable communities in Madagascar, Mozambique and Malawi. [online] Available at: https://www.worldweatherattribution.org/climate-change-increased-rainfall-associated-with-tropical-cyclones-hitting-highly-vulnerable-communities-in-madagascar-mozambique-malawi/ [Accessed 26 April 2022]

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South Africa

A rapid geospatial analysis of the flood impacts on crops in KwaZulu-Natal province of South Africa in 2022

Attachments.

An analysis to assess the impacts of floods on cropland in KwaZulu-Natal province was performed using existing data, GIS and remote sensing. The crop mask was derived from the South African National Land Cover map (SANLC, 2018). The water mask was derived from the Joint Research Centre (JRC) water body data (2020). Sentinel 1 SAR was used to assess flood extent.

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Giews country brief: south africa (26-august-2022), a rapid geospatial analysis of the flood impacts on crops in kwazulu-natal and eastern cape provinces of south africa in 2022, a rapid geospatial analysis of the flood impacts on crops in eastern cape province of south africa in 2022.

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