Worldwide, more than 40% of all natural hazards and about half of all deaths are the result of flood disasters. In northern Namibia flood disasters have increased dramatically over the past half-century, along with associated economic losses and fatalities. There is a growing concern to identify these extreme precipitation events that result in many hydro-meteorological disasters. This study presents an up to date and broad analysis of the trends of hydro-meteorological events using extreme daily precipitation indices, daily precipitation data from the Grootfontein rainfall station (1917–present), regionally averaged climatologies from the gauged gridded Climate Research Unit (CRU) product, archived disasters by global disaster databases, published disaster events in literature as well as events listed by Mendelsohn, Jarvis and Robertson (2013) for the data-sparse Cuvelai river basin (CRB). The listed events that have many missing data gaps were used to reference and validate results obtained from other sources in this study. A suite of ten climate change extreme precipitation indices derived from daily precipitation data (Grootfontein rainfall station), were calculated and analysed. The results in this study highlighted years that had major hydro-meteorological events during periods where no data are available. Furthermore, the results underlined decrease in both the annual precipitation as well as the annual total wet days of precipitation, whilst it found increases in the longest annual dry spell indicating more extreme dry seasons. These findings can help to improve flood risk management policies by providing timely information on historic hydro-meteorological hazard events that are essential for early warning and forecasting.
More than 40% of all natural hazards and about half of all deaths worldwide are the result of flood disasters (Emergency Disasters Database [EM-DAT]
Statistics of fatalities by flood disaster events from 1950 to 2015 in Africa.
In northern Namibia (
(a) Location of the Cuvelai River Basin in Africa. (b) The location of the CRB in southern Africa and (c) the CRB with the location of the study area (box) between southern Angola and northern Namibia. The figure also shows all the national rainfall stations as well as the new automatic rainfall stations from the WeatherNet.
Inventory data for Namibia from 1900–2014 showing the number of people affected and the casualties caused by flooding.
There is a low to medium confidence in the historical extreme rainfall trends observed over most of Africa, because of partial lack of data, lack of literature, and inconsistency of reported patterns in the literature (Conway
More research has been undertaken on the observed monthly climate over the southern Africa region resulting from the scarcity and paucity of daily precipitation data. Unganai and Mason (
Future projections for southern Africa are:
medium confidence – that the projected droughts will intensify in the 21st century in some seasons, resulting from reduced precipitation or increased evapotranspiration
low confidence – that the projected heavy precipitation will increase. The projected changes for the climatological dry southwest parts of southern Africa have signalled drying in the annual mean as well as during the austral summer months (James & Washington
Furthermore, a projected delay in the onset of seasonal rains in southern Africa is caused by rainfall decreases during austral spring months, and this is also projected for the region (Seth
In countries qualified as ‘developing’, such as Namibia, observed climate change time series data and detailed quantitative flood events archives are usually unavailable (Filali-Meknassi, Ouarda & Wilcox
Flood levels from year-to-year in the CRB, northern Namibia.
exceptionally high flows (major flood events) for 11 years: 1949, 1953, 1956, 1970, 1976, 1977, 1995, 2004, 2008, 2009 and 2011
18 years of medium flows (medium floods)
whilst no or only negligible flow (small floods) for 12 years were reported (
This archive has also been used to better understand the flooding occurrences in the CRB (Hipondoka
Extreme precipitation events play an important role in monitoring and predicting the occurrence of flood disaster events (Hofer & Messerli
Hence, the aim of this article is to develop an up to date archive of flood events by comparing referenced flood events listed above to events (years) derived from:
extreme precipitation indices calculated from data obtained from the Grootfontein rainfall station (longest continuous time series data for northern Namibia)
regional rainfall stations (New
global disasters databases
rainfall climatologies of the Grootfontein station
averaged data (area and time – annual) from the CRU TS 3.21 product (Harris
This article contributes towards supplementing geospatial data scarcity that delays the implementation of an efficient and effective flood risk management system in Namibia and also the advancement of climate change adaptation strategies. An updated flood disaster event archive is also essential as flood disaster events are short lived and their spectacular impact is soon forgotten, thus, awareness of the flood disaster event quickly subsides (Barnolas & Llasat
The CRB is an area of 167 600 km2 and is located between 14°E and 15° E longitude, and 15°S and 20° S latitude. It extends over 450 km from north to south and is located between Angola and Namibia. It is surrounded by the Kunene and Okavango rivers to the north-west and north-east respectively (
Mean annual rainfall for the CRB derived from CRU 3.21 data. The original data covered a period from 1901–2012 at a gridded spatial resolution of 0.5° and were converted into contours.
The dominant vegetation types of the area are the
A sound and reliable archive on floods and flooding is important to insurance companies, research institutes, and government and financial organisations that can benefit from accurate data. It is also useful for more appropriate and suitable flood management and protection measures, both structural and non-structural, for analysing locational strategies for disaster risk mitigation. Furthermore, it will help institutions optimise their investments to alleviate poverty and to stimulate (economic) prosperity and, finally, many stakeholders are also interested in collecting correct information to improve their knowledge on floods and flooding.
The specific objectives of this article are to:
list flood events according to their magnitudes (
document flood events published in literature based on hydro-meteorological data
calculate extreme precipitation indices for the Grootfontein station, and compare them to the regional trends (New
calculate climatologies for the Grootfontein rainfall station
calculate area averaged climatologies and also anomalies for the CRB using the CRU product data
validate the listed flood events (
Reported flood events in the Cuvelai basin for the last 73 years from 1941 to 2013.
Magnitude of flood disaster | Years |
---|---|
High flows occurred in 11 years (major flood disaster events) | 1949, 1953, 1956, 1970, 1976, 1977, 1995, 2004, 2008, 2009, and 2011 |
Medium flows occurred in 18 years (medium floods) | 1943, 1946, 1955, 1975, 1979, 1983, 1985, 1987, 1991, 1994, 1997, 1999, 2000, 2001, 2005, 2006, 2010, and 2012 |
Negligible flow occurred in 12 years (small floods) | 1941, 1942, 1944, 1947, 1950, 1958, 1982, 1984, 1990, 1993, 1996, and 1998 |
No flow occurred in 18 years | 1945, 1948, 1951, 1952, 1954, 1957, 1959, 1969, 1978, 1980, 1981, 1986, 1988, 1989, 1992, 2002, 2003 and 2013 |
No data available in 13 years | 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1971, 1972, 1973, and 1974 |
This article has used, as reference, an existing list of flood events (
The sources used in this article were:
flood events from the archive (
daily rainfall data from the Grootfontein station that will be used to calculate these indices as utilised by New
daily rainfall data to calculate climatologies (Grootfontein)
information from the global disaster databases such as the EM-DAT
rain-gauged gridded precipitation (CRU) data
published literature on flood events in northern Namibia.
exceptional high flows (major floods events) – 11 times
medium flows (medium floods) – 18 times
negligible flow (small floods) – 12 times
no flow – 18 times
years with no data – 13 times.
These flood events (years) were used as references to validate results obtained from other sources that enabled the creation of an up to date archive of flood events for the study area. The archive of hydro-meteorological events (droughts and floods) will help to identify weather and climate extreme events that have caused large flood disasters in the study area. This is especially relevant as a changing and variable climate can lead to changes in the frequency, intensity, spatial extent, duration, and timing of these events (Niang
Rainfall daily data from several stations in the Cuvelai basin were evaluated using two criteria. Firstly, the selection of rainfall gauge stations must have at least 30 years of data over a long-term period, including the climate (normal) standard period from 1961 to 1990. Secondly, the stations must have limited missing data (less than 10%) in the overall long-term record (New
The Emergency Disasters Database (EM-DAT
Annual precipitation climatologies and composites from the CRU product (Harris
Flood events were identified from climate change extreme precipitation indices. Extreme precipitation indices are valuable for climate monitoring but are not recommended for use for activities such as weather forecasting (Klein-Tank
precipitation frequency (R20 mm and R10 mm)
precipitation intensity (RX1day, RX5day, simple daily intensity index [SDII], R99p and R95p)
precipitation duration (consecutive dry days [CDD] and consecutive wet days [CWD])
precipitation amount (PRCPTOT).
Alternatively, the indices can be classified by their:
percentile (R95p and R99p)
absolute (RX1day, RX5day, R10 mm and R20 mm)
duration (CDD and CWD) values even though the total cumulative precipitation (PRCPTOT) and SDII do not fit this classification.
Percentile-based indices (very wet days – R95p and extremely wet days – R99p) represent the amount of rainfall falling above the 95th (R95p) and 99th (R99p) percentiles. They also include, but are not limited to, the most extreme precipitation events in a year. Absolute indices represent maximum or minimum values within a year and they include the maximum 1–day precipitation amount (RX1day) and maximum 5–day precipitation amount (RX5day). Threshold indices, in contrast, are defined as the number of days on which a precipitation value falls above or below a fixed threshold and include the annual occurrence of the number of heavy precipitation days greater than (>) 10 mm (R10 mm) and the number of very heavy precipitation days > 20 mm (R20 mm). Duration indices are defined as periods of excessive wetness or dryness and they include the indices of the CDD and the CWD. The CDD index is defined as the length of the longest dry spell in a year whilst the CWD index is the longest wet spell in a year. Other indices include annual precipitation total (PRCPTOT) and the SDII. They do not fall into any of the above categories but changes in them could have significant societal impacts (Klein-Tank
Values for the ten indices were calculated from the Grootfontein daily rainfall station data. Also, linear trends for these indices for both the Grootfontein station as well as the regional rainfall stations (New
The ten climate change extreme daily indices used for this study.
Index | Description | Definition | Units | Grootfontein Station Trend | Regional Trend |
---|---|---|---|---|---|
PRCPTOT | wet day precipitation | annual total precipitation from wet days | mm | Positive | Negative |
SDII | simple daily intensity index | average precipitation on wet days | mm/d | Negative | Positive |
CDD | consecutive dry days | maximum number of consecutive dry days | days | Positive | Positive |
CWD | consecutive wet days | maximum number of consecutive wet days | days | Positive | Negative |
R10 mm | heavy precipitation days | annual count of days when RR >= 10 | days | Negative | Negative |
R20 mm | very heavy precipitation days | annual count of days when RR >= 20 | days | Positive | Negative |
R95p | very wet day precipitation | annual total precipitation when RR > 95th percentile of 1961–1990 daily rainfall | mm | Negative | Positive |
R99p | extremely wet day precipitation | annual total precipitation when RR > 99th percentile of 1961–1990 daily rainfall | mm | Positive | Positive |
RX1day | maximum 1–day precipitation | annual maximum 1–day precipitation | mm | Positive | Positive |
RX5day | maximum 5–day precipitation | annual maximum consecutive 5–day precipitation | mm | Negative | Positive |
The regional trends for the abovementioned indices were obtained from New
EM-DAT (
United Nations Office for the Coordination of Humanitarian Affairs (OCHA) – Reliefweb (
International Flood Network (IFNET
Dartmouth Flood Observatory (De Groeve
The DFO and EM-DAT databases have the most extensive records of flood events and their impacts, but underreporting, especially of relatively small and frequent floods, is a great obstacle to reliable validating risk estimates, in these databases and others (Adhikari
Fourthly, the daily precipitation data of Grootfontein were used to derive the annual cumulative rainfall sum for each year and a least square trend line was calculated.
The regional averaged (spatial) climatologies for the region (
Published literature on reported flood events (precipitation based) were obtained and used to verify the reference flood events listed in
These referenced flood events were compared to events derived from the extreme precipitation indices, literature, global disaster databases, the Grootfontein rainfall station's climatologies and regional averaged CRU data.
The flood disaster events mentioned in the media and elsewhere prompted questions about whether such events were becoming more frequent or not. An up to date archive is needed to inform and help in the reporting of unquantified disaster events, especially in an area such as the CRB. The CRB has a chronic shortage of long-term hydro-meteorological data that are also needed for rigorous frequency analyses. This can help with the reporting of whether an event can be classified as one of the worst flood disaster events or heaviest-ever rainfall events in the region (Grobler
The annual precipitation of Grootfontein range from 1917 to 2014 (
The annual rainfall of Grootfontein from 1917 to 2014 with a least square linear regression and 10–year moving average trend lines.
The second decade (1951–1960) started with dry conditions (1952 recorded only 267 mm), although it was one of the wettest periods in the entire time series. Other studies from southern Africa also reported drought conditions for the year 1952 (Rouault & Richard
Dry conditions continued into the fourth decade (1971–1980) whilst wetter conditions occurred at the end of the decade. The highest annual accumulative rainfall of 950 mm (1978) was recorded whilst 886 mm (1974) was measured earlier. Rouault and Richard (
The fifth decade (1981–1990) started with wet conditions and ended with dry conditions. The highest rainfall was 662 mm (1982) whilst the lowest within this decade was 319 mm (1984). Floods (1981, 1988 and 1989) and droughts (1982, 1983, 1984 and 1987) were reported by Rouault and Richard (
The sixth decade (1991–2000) was relatively dry compared to the second and the fourth decades whilst the last decade (2001–2013) was the driest for the Grootfontein rainfall station. In the sixth decade, 729 mm (1994) was recorded whereas the lowest recorded annual accumulative rainfall figures of 68 mm (2004) and 75 mm (2007) were recorded in the last decade which was one of the driest decades (DWA
To summarise, using an annual accumulative total of 900 mm for the Grootfontein rainfall station, the wettest spells (highest seven totals), in decreasing order, can also be identified as: 1978 (950 mm), 1923 (915 mm), 1934 (898 mm), 1954 (897 mm), 1974 (885 mm), 1944 (881 mm) and 1950 (873 mm).
Descriptive statistics for the Grootfontein rainfall station derived from a 96–year time series: 1917–2014.
Statistical parameters | Values |
---|---|
Mean | 498.1375 |
Standard Error | 19.22688 |
Median | 478.5 |
Mode | 425 |
Standard Deviation | 188.3842 |
Sample Variance | 35488.61 |
Kurtosis | −0.00836 |
Skewness | 0.325199 |
Range | 882 |
Minimum | 68.2 |
Maximum | 950.2 |
Sum | 47821.2 |
Count | 96 |
Extreme precipitation indices were used to identity and validate flood events in this study. Identifying extreme precipitation events are important as these events are often associated with flood hazards and, ultimately, higher risk of the vector and epidemic diseases such as malaria and cholera (Anyamba
Regionally, studies have shown that the most extreme daily precipitation indices, over southern Africa, showed approximately identical proportions of increasing as well as decreasing trends for the subregion, even though a very small number of station trends are statistically significant for any index (New
The southern African region has many diverse climatic zones which can therefore produce extensive trends in inter-annual and decadal-scale variability of rainfall, hence, secular trends would not be easily detected. New
An increase of the CDD index over southern Africa was reported by New
Large parts of the region experience one long rainy season from October to April (Yuan
Ten climate change extreme precipitation indices for the Grootfontein rainfall station: (a) annual count of days when RR > 10 mm; (b) annual count of days when RR >= 20 mm; (c) annual total precipitation when RR > 95th percentile of 1961–1990 daily rainfall; (d) annual total precipitation when RR > 99th percentile of 1961–1990 daily rainfall; (e) annual maximum 1–day precipitation; (f) annual maximum consecutive 5–day precipitation; (g) maximum number of consecutive wet days; (h) maximum number of consecutive dry days; (i) annual total precipitation from wet days and (j) average precipitation on wet days.
Ten climate change extreme precipitation indices for the Grootfontein rainfall station: (a) annual count of days when RR > 10 mm; (b) annual count of days when RR >= 20 mm; (c) annual total precipitation when RR > 95th percentile of 1961–1990 daily rainfall; (d) annual total precipitation when RR > 99th percentile of 1961–1990 daily rainfall; (e) annual maximum 1–day precipitation; (f) annual maximum consecutive 5–day precipitation; (g) maximum number of consecutive wet days; (h) maximum number of consecutive dry days; (i) annual total precipitation from wet days and (j) average precipitation on wet days.
Ten climate change extreme precipitation indices for the Grootfontein rainfall station: (a) annual count of days when RR > 10 mm; (b) annual count of days when RR >= 20 mm; (c) annual total precipitation when RR > 95th percentile of 1961–1990 daily rainfall; (d) annual total precipitation when RR > 99th percentile of 1961–1990 daily rainfall; (e) annual maximum 1–day precipitation; (f) annual maximum consecutive 5–day precipitation; (g) maximum number of consecutive wet days; (h) maximum number of consecutive dry days; (i) annual total precipitation from wet days and (j) average precipitation on wet days.
The increases for the extreme indices calculated for the Grootfontein station compared favourably to those reported by New
The inter-annual and decadal-scale variability of precipitation over the study area (spatially-averaged box around the Cuvelai basin) for CRU data are depicted in
Standardised anomalies of the spatial (regional) averaged CRU data with a 10–year moving average trend line.
Flood disaster events archive compiled from hydro-meteorological data, global disaster data bases, extreme precipitation indices and disaster events published in literature.
Decades | Table 1 | Hydrologic modelled or annual observed flood peaks | Published events (Precipitation based) | Global disaster archives | Climatology | Extreme precipitation indices |
---|---|---|---|---|---|---|
1940 – 1950 | 8 flood events: 1949H, 1943M, 1946M, 1941L, 1942L, 1944L, 1947L, |
BAR Namibia (2012): |
Engert ( |
- | Grootfontein: 1944, 1947 |
R20 mm: |
R95p: |
||||||
R99p: |
||||||
Van Langenhove ( |
DWA ( |
CRU: 1942, 1944, 1945, 1947 |
||||
PRCPTOT: |
||||||
DWA ( |
Rouault and Richard ( |
RX1day: |
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RX5day: |
||||||
SDII: |
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CWD: |
||||||
CDD: |
||||||
1951 – 1960 | 4 flood events: 1953H, |
BAR Namibia (2012): 1951L |
Engert ( |
- | Grootfontein: 1951, 1953, 1954, |
R20 mm: 1951, 1953, |
R95p: 1954 |
||||||
Van Langenhove ( |
Jury ( |
R99p: 1954, |
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PRCPTOT: 1951, 1953, 1954, |
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RX1day: 1954, |
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DWA ( |
Rouault and Richard ( |
CRU: 1951, 1954, |
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RX5day: 1953, 1954, |
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SDII: 1950, 1951, 1953, 1954, |
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CWD: 1951, 1953, |
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CDD: 1952, 1955 |
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1961 – 1970 | No records: 1960 –1968 | BAR Namibia (2012): |
Engert ( |
- | Grootfontein: 1961, |
R20 mm: 1961, 1966 |
1 flood event: 1970H | R95p: |
|||||
R99p: |
||||||
Van Langenhove ( |
Jury ( |
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PRCPTOT: 1961, |
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RX1day: 1961, 1962, |
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RX5day: 1961, |
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CRU: |
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DWA ( |
Rouault and Richard ( |
SDII: 1961, 1962, |
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CWD: |
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CDD: 1963 |
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1971 – 1980 | 4 flood events: |
BAR Namibia (2012): 1971M, 1974M, |
Engert ( |
- | Grootfontein: 1972, 1974, |
SDII: |
CDD: 1975 | ||||||
Jury ( |
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Van Langenhove ( |
||||||
Jury |
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CRU: 1971, 1974, |
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DWA ( |
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Rouault and Richard ( |
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1981 – 1990 | 6 flood events: 1983M, 1985M, 1987M, 1982L, |
Van Langenhove ( |
Engert ( |
- | Grootfontein: 1982, 1987 |
R20mm: 1990 |
R95p: 1990 | ||||||
R99p: 1990, 1996, 1997 |
||||||
Jury ( |
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PRCPTOT: 1990 | ||||||
RX1day: 1990 | ||||||
RX5day: 1989, 1990 | ||||||
SDII: 1989 | ||||||
DWA ( |
Jury |
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CWD: 1990 | ||||||
CDD: 1989 |
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Rouault and Richard ( |
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1991–2000 | 9 flood events: 1995H, 1991M, 1994M, 1997M, 1999M, |
BAR Namibia (2012): |
Jury ( |
- | Grootfontein: 1993, 1994, 1997 |
R20 mm: 1993, 1994, 1997, |
R95p: 1994, 1997, |
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R99p: |
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PRCPTOT: 1993, 1994, 1997 |
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Van Langenhove ( |
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RX1day: 1994, 1996, 1997, 1998 |
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Rouault and Richard ( |
RX5day: 1994, 1996, 1997 |
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SDII: 1994 |
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CRU: 1991, 1997 |
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Van Langenhove ( |
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CWD: 1991, 1993, 1994, 1997 |
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CDD: 1991, 1992, 1994, 1996, 1998, 1999 |
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2001 – 2013 | 9 flood events: 2004H |
BAR Namibia (2012): |
Jury ( |
EM-DAT ( |
Grootfontein: |
R20 mm: |
R95p: |
||||||
R99p: |
||||||
Van Langenhove ( |
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NMS (2015a): Ondangwa – |
PRCPTOT: |
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Mufeti and Katjizeu ( |
RX1day: |
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CRU: |
RX5day: |
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SDII: |
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Van Langenhove ( |
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CWD: - | ||||||
CDD: |
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DWA ( |
Note: Common years amongst the different sources are in bold (for example 1944, 1947 and 1950).
H, Major flood disaster (high flows); M, Medium floods (medium flows); L, negligible flow (Small floods); BAR: H = 160–300m3/s; M = 301–500m3/s; L = 501–700m3/s
For the first decade (1940–1950) eight flood disaster events were reported and three of these events were reported by BAR Namibia (
The second decade (1951–1960) had only four referenced events and only one flood disaster event (1956), which was commonly occurring amongst the different sources used to identify them.
The third decade (1961–1970) lacks sufficient observational data for the period 1960–1968 (
The fourth decade (1971–1980) had the fewest recorded flood events from all sources, only one index (SDII) reported an extreme event for 1975. Generally, 1975 was reported by most of the sources as a year during which flood events had occurred. This decade also began with dry spells and ends with wet spells that included extreme precipitation for 1974 and 1978 for the Grootfontein station. Regionally, it was a wet year with only 1973 appearing to be a dry year. The extreme precipitation indices failed to record this, as these years were not plotted, resulting from the rigorous rainfall selection criteria. Other studies such as Washington and Preston (
The fifth decade (1981–1990) started as a wet year (
The sixth decade covers the period 1991–2000 and includes the driest periods in southern Africa. The CDD index reported 7 years as very dry periods whilst the year 2000 is widely reported (
For the last decade (2001–2013) the highest number of reported flood events was nine (
One of the main objectives of this study is to identify drought and flood events during periods where no observational hydro-meteorological data are available (
Lastly, as a result of increasing changes in the climate, land-use and extreme precipitation, disaster risk reduction and flood risk management strategies are essential in adapting to the envisaged increasing flood risk. This necessitates an accurate inventory of hydro-meteorological events, which can also be updated to provide a quantified spatial and temporal distribution of floods, including magnitude, frequency, and duration. The major limitation of this article is that it does not include methods that can help with the identification of historical records of catastrophic floods, such as records of physical signs of water levels on old buildings, memories of old citizens, historical documents, news reports and archive reports from meteorological and hydrological national services as well as detail discharge and rainfall frequency analysis resulting from data scarcity.
The present contribution produced an up to date archive of flood events (years) by validating and supplementing referenced flood events, and also comparing them against indices of extreme precipitation (years) derived from daily climate data as well as data derived from other sources for northern Namibia.
Generally, for the extreme precipitation indices, no significant regional trends were identified, which does not come as a surprise for a continent where different factors affect regional rainfall, and where there are few consistent and statistically significant trends in extreme precipitation indices. Also, for the statistical regional trends, similar to other studies: an increase in average dry spell length was found for other indices such as the average rainfall intensity and annual 1–day maximum rainfall indices, whilst decreasing trends were found for the Grootfontein station. Furthermore, for the Grootfontein station, there is an indication of decreasing trends, for both total annual precipitation as well as for the total number of wet days annual precipitation.
The study appended the hydro-meteorological records, for the developed archive, for the ‘no records’ period of 1960–1968 by adding the years 1963 and 1967 and possibly 1968 as flood prone years. It also identified years when floods would have not been possible and, hence, these years require in-depth hydrologic and hydraulic modelling to confirm the occurrences of flood hazard events.
These findings highlighted the lack of and difficulty with obtaining observed hydro-meteorological data and performing analyses on them in Africa, where a large rural population entirely relies on precipitation for its water supply. This also, ultimately, determines strategies of food production and also the mobility of migrant groups.
The results from this article will provide an improved understanding of past extreme precipitation events that are required for scientists, practitioners, policymakers and civil society to better compare and refer to past and present flood and drought hazard events.
This article updated the inventory of past flood events by using different sources such as: hydro-meteorological data, global disaster databases, climate change extreme precipitation indices and disaster events published in literature.
The authors declare that they have no financial or personal relationships which may have inappropriately influenced them in writing this article.
F.C.P. (University of Canterbury) did the analysis, wrote and compiled the article as part of a chapter for his PhD thesis in the Geography Department at the University of Canterbury, Christchurch, New Zealand. C.G. (University of Canterbury) and P.Z-R. (University of Canterbury) provided guidance and also edited earlier versions of the article. They also helped with the conceptual framework of the article.