el niño-southern oscillation (enso) cycle events and their
TRANSCRIPT
El Niño-Southern Oscillation (ENSO) cycle events and their impacts
in Zimbabwe
Zimbabwe Resilience Building Fund
El Niño-Southern Oscillation (ENSO) cycle events and their impacts in Zimbabwe
December 2016
© UNDP 2017
This publication may be reproduced in whole or in part and in any format for educational, research and/or non-profit purposes without special permission from the copyright holder, provided acknowledgement of the source is made. Analysis and write-up are credited to staff from the following organisations, ZRBF, AGRITEX, Meteorological Services Department, Livestock Production Division and the Zimbabwe National Water Authority (ZINWA).
These are: Vhusomuzi Sithole, Rufael Fassil, Natalia Perez, Takudzwa Mutize, Hillary Mujiyo, Robson Chihumba,Vimbai Mamombe, Blessing Shumba, Nyashadzashe Viriri and Debra Musiwa.
UNDP would appreciate receiving a copy of any publication or article that uses this report as a source. No use of this publication or any of its contents may be made for resale or for any other commercial purpose whatsoever without prior permission in writing from UNDP.
For bibliographic and reference purposes, this publication should be referred to as:
“UNDP (2017), El Niño-Southern Oscillation (ENSO) cycle events and their impacts in Zimbabwe”.
To obtain copies of this publication, please contact:
UNDP Zimbabwe, Zimbabwe Resilience Building Fund (ZRBF) Management Unit Arundel Office Park, Block 7, Norfolk Rd, Mt. Pleasant, Harare, Zimbabwe +263 4 338836-44
Contents 1. EXECUTIVE SUMMARY 5
2. INTRODUCTION 6
3. METHODOLOGY 9
3.1 Data Analysis 9
3.2 Scenario building to inform climate change and resilience building projects in
Zimbabwe 12
4.3 Impacts on maize production 14
4.4 Impacts on cattle production 17
4.5 Impacts on water resources 19
4.6 Scenario planning 21
Annex 2: Dam levels by catchment area 28
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List of Figures Figure 1: Zimbabwe Standardized Precipitation Index (SPI) and Sea Surface
Temperature (SST) anomalies 11
Figure 2: National maize production statistics since 1980 12
Figure 3: Maize yield map for the 2015/16 agricultural season. 12
Figure 4: Maize yield map for the 2014/15 agricultural season. 13
Figure 5: Provincial maize yield for the different ENSO classes 13
Figure 6: Friedman’s test results for different EBSO classes 14
Figure 7: Cattle Populations for the period 2000-2016 14
Figure 8: Friedman’s test results on cattle population across different ENSO classes 15
Figure 9: Percentage Inflow for all major dams (1972-2016) 15
Figure 10: Dam level figures per catchment across different ENSO classes 16
Figure 11: Friedman’s test results for dam levels in different catchments 16
List of Tables Table 1: Proposed data sources 8
Table 2: Classification of ENSO events for the period 1951-2016 10
Table 3: Categories of rainfall received under different ENSO events 11
Table 4: Summary of impacts under different La Niña scenarios 17
Table 5: Summary of implications to climate change and resilience programmes in
Zimbabwe 17
1. EXECUTIVE SUMMARY The 2015/16 rainfall season resulted in Zimbabwe experiencing low and erratic rains mainly due to El Niño conditions. The Seasonal Outlook for the 2016/17 season predicted normal to above normal rainfall due to La Niña/Neutral conditions in the Pacific Ocean. This technical note evaluate impacts of the different phases of the El Niño-Southern Oscillation (ENSO) cycle on dam levels, maize and cattle production. The impacts were evaluated through trends analysis and overlaying ENSO events and each of the three variables (dam levels, maize and cattle production). The objectives of the technical note are to document the impacts of the 3 phases of ENSO (El Niño, Neutral and La Niña) in Zimbabwe and develop planning scenarios. These scenarios examine the possible dam levels, maize and cattle production in a season when La Niña/Neutral conditions are predicted. Potential consequences on resilience programme activities were also documented in these scenarios.
Results show that the 62% of El Niño events have led to below normal rainfall in Zimbabwe since 1970. In turn, the low rainfall has negatively affected dam levels, maize and cattle production in the country. On the other hand 67% of La Niña events over the same time period have resulted in above normal rains. The improved rain resulted in higher dam levels, maize and livestock production when compared to El Niño years. It is important to note that some of the La Niña events have resulted in flooding in some parts of the country thereby negatively affecting maize and cattle production. The last phase of ENSO, saw the country receiving above normal rainfall 54% of these Neutral years. Dam levels were average compared to El Niño and La Niña. However, neutral years have resulted in the highest yields due to optimum rains. Statistical analysis proved that dam levels, maize as well as cattle populations significantly differ when compared across different ENSO classes. When the rainfall, dam level, maize and cattle production analysis was related to resilience programming activities and the national seasonal climate forecast for 2016, there is evidence that most of the activities will be disrupted if the country experiences medium or strong La Niña. However, if a weak La Niña or Neutral phenomenon occur, information from previous years show less chances of disruption with maize and cattle population expected to be higher compared to strong La Niña or El Niño years.
In a nutshell, this technical note shows that that ENSO events significantly impacted the production systems in the country. It is therefore recommended for resilience building initiatives to plan according to predicted phases of ENSO.
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6 PAGE 2. INTRODUCTION
The 2015-2016 drought experienced across Zimbabwe has been attributed to the El Niño phenomenon. The most affected regions include the western, southern and south- eastern districts of Zimbabwe. Zimbabwe experienced similar drought conditions in 1982, 1992 and 2012. El Niño and La Niña are opposite phases of the El Niño-Southern Oscillation (ENSO)1 cycle. The “El Niño” phenomenon refers to the large-scale ocean- atmosphere climate interaction due to warming of sea surface temperatures across the central and east-central Equatorial Pacific Ocean, which results in drought conditions for Southern Africa2. Meteorological drought results from prolonged periods of below average rainfall for a season or more3. In Zimbabwe, the El Niño phenomenon often leads to drought conditions with negative effects on livelihoods. On the other hand, the La Niña phenomenon is characterised by anomalously cool sea surface temperatures in the Pacific Ocean thereby affecting global climate in an opposite way to El Niño. In the case of Zimbabwe, often leading to increased rainfall conditions.
In Zimbabwe, legal and institutional frameworks for disaster risk management are well established at both local and national levels. Disaster management is multi-sectoral and involves various relevant ministries and institutions, coordinated by the Department of Civil Protection (DCP) within the context of the National Civil Protection Committee (NCPC). The institutions include government ministries, parastatals, private sector, United Nations agencies, and non-governmental organisations. These various institutions provide different capacities for DRM in the country.
A number of policies and legislation guide the operations of DRM at various scales in the country. The Hyogo Framework for Disaster Risk Reduction and Management was the widely used policy framework in developing the various national statutes. The National statutes include the Civil Protection Act No. 5 of 1989, National Climate Change Strategy for Zimbabwe, National Water Policy, Meteorological Services Act and Zimbabwe Food Security Strategy. The DCP is guided by the Civil Protection Act which provides for the establishment of the Department of Civil Protection and setting up of the
1. NOAA (2016), What is La Niña? http://www.pmel.noaa.gov/elnino/what-is-la-nina. 2. NOAA (2016), What are El Niño and La Niña? http://oceanservice.noaa.gov/facts/ ninonina.html 3. Chitongo (2013), Towards Comprehensive Disaster Risk Management in Zimbabwe: Evaluating Masvingo Rural District’s Community Drought Management Program (MRDCCDMP), International Journal of Economy, Management and Social Sciences, 2(8).
National Disaster Management Committee. In addition, international organisations such as UNDP and UNOCHA are also guided by the provisions of the HYOGO framework for Disaster Risk Reduction and Management (2005-2015), the Sendai Framework for Disaster Reduction (2015-2030), the United Nations Framework Convention on Climate Change (UNFCC) and the Paris Agreement on Climate Change which entered into force on 4 November 2016. This technical note also supports the national efforts in meeting the SDGs especially Goals number 2 (Zero Hunger), 3 (Good Health and well-being), 10 (Reduced Inequality) and 13 (Climate Action). The technical note will further more contribute to Outcome 1 (Food and Nutrition Security) of the Zimbabwe United Nations Development Assistance Framework (ZUNDAF) 2016-2020.
The Government of Zimbabwe is partnering UNDP in areas related to climate change, resilience and poverty reduction. Most of these initiatives, particularly the climate change and resilience programmes, seek to assist the government in fostering preparedness, early warning, early action and resilience building activities in the face of disasters. Since the government’s declaration of the 2015-2016 drought as a State of Disaster, UN Agencies and civil society organisations are rendering humanitarian assistance in a number of affected districts. This is a costly exercise, which diverts funds from productive investment and which has proven to have a negative impact on the beneficiary population in the form of dependency syndrome. In order to minimise the impact of similar situations in the future it is essential for all mandated government departments to work together in developing a long term strategy for mitigating impacts of both El Niño and La Niña. There are a number of questions concerning the El Niño and La Niña impacts which this technical note attempts to answer based on analysis of data collected by four national departments in collaboration with the Zimbabwe Resilience Building Fund (ZRBF). These include Agricultural Extension Services Department (AGRITEX), Division of Livestock Production and Development, Meteorological Services Department (MSD), Zimbabwe National Water Authority (ZINWA), and the Department of Civil Protection (DCP).
2.1 Justification and Objectives
In 2016, Zimbabwe, like the rest of the Southern African region, was experiencing the consequence of the worst drought in 35 years. The 2015-2016 drought resulted in loss of both domestic4 and wild animals, crop failure as well as lack of adequate water availability for both domestic use and industrial use. This has culminated in low yields and food insecurity in many parts of the country. The subsequent 2016 ZimVAC estimated that about 4.1 million people will need food assistance during the peak of the season (January
4. El Niño and drought take a toll on Zimbabwe’s cattle, Thomson Reuters Foundation News, Tuesday 12 January 2016, http://news.trust.org/item/20160112054900-bk2q7/?- source=spotlight.
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to March 2017)5. Figures from the Livestock Production and Development Division show that there were more than 25 000 cattle deaths which are attributed to the El Niño induced drought countrywide. The government declared the drought conditions a State of Disaster on the 4th of February 2016.
The frequency of unreliable weather patterns (e.g. false and late starts of agricultural season combined with long of mid-season dry spells) as well as anticipation of El Niño and La Niña conditions create the need for accurate and reliable information. Information generation and dissemination will foster learning from past experiences as well as creating early warning systems resulting in early action on forecasted events. This technical note seeks to contribute some of the information on the consequences of the three phases of ENSO, namely El Niño, La Niña and Neutral.
Specific Objectives
The specific objectives of this technical note are to:
• Document the causes, historical trends and effects of the El Niño phenomenon in Zimbabwe,
• Document the causes, historical trends and effects of the La Niña phenomenon in Zimbabwe, and
• Develop scenarios on for the La Niña phenomenon and Neutral phases, as predicted in the latest weather outlook.
5. Zimbabwe Vulnerability Assessment Committee (ZimVAC) 2016 Rural Livelihoods Assessment.
3. METHODOLOGY The analysis of ENSO impacts was carried out at a national level, based on existing quantitative data obtained from the mandated government departments. Government reports, bulletins and official communication were used as complementary qualitative data sources. Quantitative data on drought and El Niño frequency were obtained from the meteorological datasets. The following table summarises the various data sources.
Table 1: Proposed data sources
Variable Data source Period of time
El Niño /La Niña frequency and impacts MSD 1951-present
El Niño and La Niña’s impacts on rainfall MSD 1971-present
El Niño /La Niña effects on crops AGRITEX 1980-present
El Niño /La Niña effects on livestock LPD 1980-present
El Niño /La Niña effects on dam levels ZINWA 1972-present
Scenario building to inform climate change and resilience building projects in Zimbabwe
UNDP
3.1 Data Analysis The methods used in data analysis were determined by the type of data obtained namely meteorological, crops production, livestock production and water resources. Each method is described in detail in the below.
ENSO Trends Analysis Sea surface temperatures anomalies in the Pacific Ring were analysed and subsequently seasons were classified into Weak, Moderate, Strong, Very Strong, El Niño events. Sea surface temperature is the temperature of the top millimetre of the ocean’s surface. An anomaly is a departure from average conditions. Sea surface temperatures anomaly maps compare temperatures in a given month to the long- term average temperature of that month. At irregular intervals (roughly every 3-6 years), the sea surface temperatures in the Pacific Ocean along the equator become warmer or cooler than normal. These anomalies are the hallmark of El Niño and La Niña climate cycles, which can influence weather patterns across the globe. On the other hand, strong, localized sea surface temperature anomalies may reveal that an ocean current has veered off its usual path for a time or is stronger or weaker than usual. Sea surface temperature anomalies that persist over many years can be signals of regional or global climate change, such as global warming.
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Meteorological Data Analysis RRainfall data for the years 1981 to 2016 were merged with information on occurrence of El Niño, Neutral and La Niña events, to determine the impacts on Zimbabwe’s rainfall. In consultation with other specialists, total rainfall for the months which are critical for crop growth was plotted against time. The meteorological data were used to classify rainfall seasons from 1981 to present into different ENSO classes.
The Standardized Precipitation Index (SPI) is a tool which was developed primarily for defining and monitoring drought. It allows an analyst to determine the rarity of a drought at a given time scale (temporal resolution) of interest for any rainfall station with historic data (McKee et al 1993; McKee et al 1995; Guttman, 1998; Guttman, 1999; Trenberth et al., 2014). It can also be used to determine periods of anomalously wet events. The SPI is not a drought prediction tool. Mathematically, the SPI is based on the cumulative probability of a given rainfall event occurring at a station. The historic rainfall data of the station is fitted to a gamma distribution, as the gamma distribution has been found to fit the precipitation distribution quite well. This is done through a process of maximum likelihood estimation of the gamma distribution parameters, α and β. In simple terms, the process described above allows the rainfall distribution at the station to be effectively represented by a mathematical cumulative probability function. Therefore, based on the historic rainfall data, an analyst can then tell what is the probability of the rainfall being less than or equal to a certain amount. Thus, the probability of rainfall being less than or equal to the average rainfall for that area will be about 0.5, while the probability of rainfall being less than or equal to an amount much smaller than the average will be also be lower (0.2, 0.1, 0.01 etc., depending on the amount). Therefore if a particular rainfall event gives a low probability on the cumulative probability function, then this is indicative of a likely drought event. Alternatively, a rainfall event which gives a high probability on the cumulative probability function is an anomalously wet event. Negative SPI values represent rainfall deficit, whereas positive SPI values indicate rainfall surplus. Intensity of drought event can be classified according to the magnitude of negative SPI values such that the larger the negative SPI values are, the more serious the event would be. For example, negative SPI values greater than 2 are often classified as extremely dry conditions. An advantage in using SPI is that only rainfall data are needed for its computation. SPI can also be compared across regions of different climatic zones. The SPI index for Zimbabwe for the period 1960 to date, was calculated from rainfall data collected from 44 stations across the country.
Crop and Livestock Production Data Analysis Using data from the crop and livestock surveys, national, provincial and district level maps and trends were plotted. Using maize production data from different ENSO classified years, analysis was done to check if there was any significant difference in
production levels between the years affected by El Niño events vs “normal” years. The non-parametric, Friedman’s test was used to detect differences in production levels across multiple years. The Friedman’s test was selected since the data was found not to be normally distributed.
The null hypothesis for the Friedman’s test was:
Ho: μ1 = μ2 = μ3 Means of production data (crop/livestock) are not significantly different across the different years
H1: μ1 ≠ μ2 ≠ μ3 Means of production data (crop/livestock) are significantly different across the different years
Water Resources Data Analysis Dam levels data were analysed for trends in water levels, as well as occurrence of flooding events over the period 1972 to 2016. Similarly, an analysis of the significant difference among ENSO years using the Friedman’s test was conducted to test if there were any significant difference in inflows across the years.
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3.2 Scenario building to inform climate change and resilience building projects in Zimbabwe Based on these analyses, the next stage involved developing scenarios of how the La Niña and Neutral phenomenon will affect agricultural production (crops and livestock) as well as water resources in the coming seasons. Different classes of La Niña were used to build the scenarios. Conclusions were made on the El Niño/ La Niña impacts on water resources, crop and livestock production in Zimbabwe. These fed into the evaluation on the implications of the different scenarios to climate and resilience building programmes in Zimbabwe. The information generated in this exercise is complementary to the 2016/2017 seasonal outlook since it evaluates past experiences as a way of providing information on anticipated impacts.
4. RESULTS Results on ENSO Trends as well as impacts on rainfall, dam levels, maize and cattle production in Zimbabwe are presented as follows:
4.1 ENSO Trends
An analysis of the sea surface temperatures anomalies in the pacific ring produced the following classification of seasons.
Table 2: Classification of ENSO events for the period 1951-2016
El Niño La Niña Neutral Weak Mod Strong Very
Strong Weak Mod Strong Neutral
1951-52 1963-64 1957-58 1982-83 1950-51 1955-56 1973-74 1960-61
1952-53 1986-87 1965-66 1997-98 1954-55 1970-71 1975-76 1962-63
1953-54 1987-88 1972-73 2015-16 1964-65 1998-99 1988-89 1966-67
1958-59 1991-92 1967-68 1999-00 1978-79
1968-69 2002-03 1971-72 2007-08 1980-81
1969-70 2009-10 1974-75 2010-11 1985-86
1976-77 1983-84 1989-90
1977-78 1984-85 1993-93
1979-80 1995-96 2008-09
1994-95 2000-01 2001-02
2004-05 2011-12 2003-04
The most notable information coming out the classification is that:
• The 2015/16 season is ranked among the very strong El Niño conditions
• 1991/92 and 2002/2003 seasons were both in the Moderate El Niño class
• 2014/15 and 2003/04 are among the years classified as Neutral years
4.2 Impacts on rainfall
Figure 1: Zimbabwe Standardized Precipitation Index (SPI) and Sea Surface Temperature (SST) anomalies
Results showed that rainfall in Zimbabwe is affected by ENSO conditions. High sea surface temperature (SST) anomalies, which are indicative of the El Niño phenomena, often result in negative standardised precipitation index (SPI). El Niño years are coinciding with negative SPI values in Zimbabwe. On the other hand, La Niña years coincide with wet conditions in Zimbabwe.
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Table 3: Categories of rainfall received under different ENSO events
Above Normal Below Normal Total
El Niño 8 38% 13 62% 21
La Niña 10 67% 5 33% 15
Neutral 13 54% 11 46% 24
60
An analysis of the past 60 years show that the majority of El Niño years (62%) resulted in below normal rainfall in Zimbabwe. Majority of La Niña years (67%) result in above normal rainfall in Zimbabwe while the remaining displayed the opposite (33%).
4.3 Impacts on maize production
An analysis of the maize production trends since 1980 revealed that production and yield (tonnes per hectares) have been declining gradually. The period between 2000 and 2016 was characterised by an increase in area under cultivation even though maize production was declining. Since maize is the staple food in Zimbabwe, reduced yields compromise food security in most parts of the country.
Figure 2: National maize production statistics since 1980
.
Figure 3: Maize yield map for the 2015/16 agricultural season.
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Figure 4: Maize yield map for the 2014/15 agricultural season.
When the yield values are analysed together with the ENSO classification, results showed that yield for the different categories of El Niño events were significantly different. These statistics assist in understanding the impact of ENSO conditions to maize yields in Zimbabwe. For the period 1980-2016, El Niño years have been characterised by lower yields compared to Neutral and la Niña years at national level. Based on the illustration below, Masvingo is worst affected by El Niño events compared to other provinces. All provinces except Mashonaland Central have the best maize yields during Neutral phase of ENSO.
Figure 5: Provincial maize yield for the different ENSO classes
Results from the Friedman’s test gave a p-value which is less than 0.05 hence the decision to reject the null hypothesis that there was no significant difference in maize yield across different ENSO events in all the provinces. Through a statistical test, there is evidence that different ENSO events result in wide margins in maize yields.
Figure 6: Friedman’s test results for different EBSO classes
4.4 Impacts on cattle production Cattle population showed an increasing or decreasing trend when the country experienced La Niña and El Niño conditions respectively. Since Very Strong El Niño conditions often lead to drought, the negative effect on cattle population is well pronounced with many drought-related cattle deaths (e.g. 2015/2016 season).
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Figure 7: Cattle Populations for the period 2000-2016
TThe Friedman’s test showed that changes in cattle population due to ENSO conditions were statistically significant (Figure 8). The p value was less the 0.05, hence the null hypothesis that there was no significant difference in cattle populations across different ENSO events was rejected. Cattle populations across ENSO events are so different that they can be proven through a statistical test.
Figure 8: Friedman’s test results on cattle population across different ENSO classes
4.5 Impacts on water resources Figure 9 below depicts the annual dam inflow trends (as a %) between 1972 and 2016 plotted against La Niña, Neutral and El Niño events. Results showed that Neutral and La Niña were characterised by positive inflows. The strong La Niña phenomenon experienced in 1974 saw the country experiencing the highest inflows since 1972. On the other hand, El Niño years coincided with negative inflows, leading to reduced water in dams for use by households and communities. It is also worth noting that the contribution of groundwater has an effect on dam levels figures in years of water deficit. Figure 10 below show that in most instances surplus come after years of deficit.
Figure 9: Percentage Inflow for all major dams (1972-2016)
An analysis of the trends on selected ENSO classified years show that dam levels also respond to ENSO events (Figure 10). This analysis was done per catchment (Figure 1). Runde catchment was the worst affected during the El Niño events. For example, during the 2015/16 season, the catchment has the lowest percentage dam levels in the country. The dam levels trends for individual catchments are shown in Annex 2.
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Figure 10: Dam level figures per catchment across different ENSO classes
The Friedman’s test further showed that there was a significant difference in dam levels in catchments across different ENSO events. It was thus concluded that different changes in ENSO events also results in changes in dam levels across the country.
Figure 11: Friedman’s test results for dam levels in different catchments
4.6 Scenario planning The SST anomalies in the Pacific zone reflected the occurrence of La Niña conditions. The Southern African Development Community’s (SADC) Climate Services Centre and Zimbabwe’s Meteorological Services Department have already issued out the regional and national seasonal outlook reports for the 2016/17 rainfall season. These outlook reports are also indicating La Niña conditions. As a result, normal to above normal rainfall is expected for most parts of the country. Using historical figures in Zimbabwe, the following table illustrates the three different La Niña phenomenon as well as Neutral classes and their probable impacts to dam levels as well as maize and cattle production.
Table 4: Summary of impacts under different La Niña scenarios
La Niña Class Impact on maize production
Impact on cattle population
Impact on dam levels
Neutral Average maize yields
Low water levels for most inland dams.
Weak La Niña Average maize yields
Few deaths due to lack of pastures
Low dam levels
More pastures leading to improved cattle conditions
Average dam levels
Strong La Niña Above average yields due to increased water from direct rainfall as well as dams
A lot of water for pastures often resulting in waterlogging of low lands and diseases outbreak due to waterborne pathogens
High dam levels
Given the above summary on the likely impacts under the three La Niña classes, implications to climate change and resilience programme are shown in Table 5 below.
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Table 5: Summary of implications to climate change and resilience programmes in Zimbabwe
ENSO Class
Potential Impact Recommended Interventions
Neutral Fair chances of normal to above rainfall leading to fairly high maize yields, dam inflows and no negative effects on cattle populations in the country. Overall verdict: Little or no impact on programming
1. Improved crop, livestock and agro- forestry production (climate-smart)
2. Livelihood diversification 3. Small productive infrastructure 4. Integrated DRM and Resilience
planning at district and ward level 5. Access, control and sustainable use of
community and household assets 6. Sustainable and equitable utilisation of
natural resources 7. Integrated water resources management
Weak La Niña
There will be fair chances of normal to above rainfall leading to fairly high maize yields, dam inflows and no negative effects on cattle populations in the country. Overall verdict: Little or no impact on programming
1. Improved crop, livestock and agro- forestry production (climate-smart)
2. Livelihood diversification 3. Small productive infrastructure 4. Integrated DRM and Resilience
planning at district and ward level 5. Access, control and sustainable use of
community and household assets 6. Sustainable and equitable utilisation of
natural resources 7. Integrated water resources management
Moderate La Niña
More impact on programming with rains supporting crop and livestock production in most parts of the country
1. Improved crop, livestock and agro- forestry production (climate-smart)
2. Livelihood diversification 3. Small productive infrastructure 4. Integrated DRM and Resilience
planning at district and ward level 5. Access, control and sustainable use of
community and household assets 6. Sustainable and equitable utilisation of
natural resources 7. Integrated water resources management
Strong La Niña
A lot of rains to support crop, livestock and agro- forestry production but high chances of flooding in low lands
1. Improved crop, livestock and agro- forestry production (climate-smart)
2. Livelihood diversification 3. Small productive infrastructure 4. Integrated DRM and Resilience
planning at district and ward level 5. Access, control and sustainable use of
community and household assets 6. Sustainable and equitable utilisation of
natural resources 7. Integrated water resources management
5. CONCLUSION Following the above analysis, the following overall conclusions can be drawn: The presence of ENSO conditions significantly influence weather patterns in Zimbabwe. In Zimbabwe, 62% of El Niño events resulted in drier conditions accompanied by below normal rainfall, usually experienced from October to March. 62% of La Niña was characterized by above rainfall during the rainy season stretching from October to March.
• Since 1971, 62% of the years which experienced the El Niño phenomenon, Zimbabwe received below normal rainfall thereby impacting negatively on dam levels, maize production and cattle populations. On the other hand, 67% of La Niña conditions have resulted in above normal rainfall with higher dam levels, maize and cattle populations compared to El Niño events.
• 54% of the Neutral years have resulted in above rainfall in the country. These years also saw most provinces receiving higher maize yields compared to other years due to less chances of either moisture stress or flooding.
• These trends prove the importance of Sea Surface Temperate (SST) anomaly figures (which are used to determine ENSO phases) as possible early warning indicators when planning resilience programme activities.
• With the 2016/17 climate forecast predicting conditions ranging between La Niña and Neutral, the scenarios developed anticipate little to no disruption to resilience programme activities. However, the developed scenarios also include moderate and strong La Niña, in case conditions in the Pacific Ocean change. Besides planning for resilience activities, these scenarios are also useful for early recovery activities as the country is still recovering from the Strong El Niño phenomenon of the 2015/2016 agricultural season.
• All the work presented in this note highlight the strong need for resilience programme activities to use early warning information, in the form of ENSO information, for planning purposes. ENSO phenomenon proved to have a strong bearing on the country’s rainfall, dam levels, maize and cattle production.
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Zimbabwe Resilience Building Fund
El Niño-Southern Oscillation (ENSO) cycle events and their impacts in Zimbabwe
December 2016
© UNDP 2017
This publication may be reproduced in whole or in part and in any format for educational, research and/or non-profit purposes without special permission from the copyright holder, provided acknowledgement of the source is made. Analysis and write-up are credited to staff from the following organisations, ZRBF, AGRITEX, Meteorological Services Department, Livestock Production Division and the Zimbabwe National Water Authority (ZINWA).
These are: Vhusomuzi Sithole, Rufael Fassil, Natalia Perez, Takudzwa Mutize, Hillary Mujiyo, Robson Chihumba,Vimbai Mamombe, Blessing Shumba, Nyashadzashe Viriri and Debra Musiwa.
UNDP would appreciate receiving a copy of any publication or article that uses this report as a source. No use of this publication or any of its contents may be made for resale or for any other commercial purpose whatsoever without prior permission in writing from UNDP.
For bibliographic and reference purposes, this publication should be referred to as:
“UNDP (2017), El Niño-Southern Oscillation (ENSO) cycle events and their impacts in Zimbabwe”.
To obtain copies of this publication, please contact:
UNDP Zimbabwe, Zimbabwe Resilience Building Fund (ZRBF) Management Unit Arundel Office Park, Block 7, Norfolk Rd, Mt. Pleasant, Harare, Zimbabwe +263 4 338836-44
Contents 1. EXECUTIVE SUMMARY 5
2. INTRODUCTION 6
3. METHODOLOGY 9
3.1 Data Analysis 9
3.2 Scenario building to inform climate change and resilience building projects in
Zimbabwe 12
4.3 Impacts on maize production 14
4.4 Impacts on cattle production 17
4.5 Impacts on water resources 19
4.6 Scenario planning 21
Annex 2: Dam levels by catchment area 28
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List of Figures Figure 1: Zimbabwe Standardized Precipitation Index (SPI) and Sea Surface
Temperature (SST) anomalies 11
Figure 2: National maize production statistics since 1980 12
Figure 3: Maize yield map for the 2015/16 agricultural season. 12
Figure 4: Maize yield map for the 2014/15 agricultural season. 13
Figure 5: Provincial maize yield for the different ENSO classes 13
Figure 6: Friedman’s test results for different EBSO classes 14
Figure 7: Cattle Populations for the period 2000-2016 14
Figure 8: Friedman’s test results on cattle population across different ENSO classes 15
Figure 9: Percentage Inflow for all major dams (1972-2016) 15
Figure 10: Dam level figures per catchment across different ENSO classes 16
Figure 11: Friedman’s test results for dam levels in different catchments 16
List of Tables Table 1: Proposed data sources 8
Table 2: Classification of ENSO events for the period 1951-2016 10
Table 3: Categories of rainfall received under different ENSO events 11
Table 4: Summary of impacts under different La Niña scenarios 17
Table 5: Summary of implications to climate change and resilience programmes in
Zimbabwe 17
1. EXECUTIVE SUMMARY The 2015/16 rainfall season resulted in Zimbabwe experiencing low and erratic rains mainly due to El Niño conditions. The Seasonal Outlook for the 2016/17 season predicted normal to above normal rainfall due to La Niña/Neutral conditions in the Pacific Ocean. This technical note evaluate impacts of the different phases of the El Niño-Southern Oscillation (ENSO) cycle on dam levels, maize and cattle production. The impacts were evaluated through trends analysis and overlaying ENSO events and each of the three variables (dam levels, maize and cattle production). The objectives of the technical note are to document the impacts of the 3 phases of ENSO (El Niño, Neutral and La Niña) in Zimbabwe and develop planning scenarios. These scenarios examine the possible dam levels, maize and cattle production in a season when La Niña/Neutral conditions are predicted. Potential consequences on resilience programme activities were also documented in these scenarios.
Results show that the 62% of El Niño events have led to below normal rainfall in Zimbabwe since 1970. In turn, the low rainfall has negatively affected dam levels, maize and cattle production in the country. On the other hand 67% of La Niña events over the same time period have resulted in above normal rains. The improved rain resulted in higher dam levels, maize and livestock production when compared to El Niño years. It is important to note that some of the La Niña events have resulted in flooding in some parts of the country thereby negatively affecting maize and cattle production. The last phase of ENSO, saw the country receiving above normal rainfall 54% of these Neutral years. Dam levels were average compared to El Niño and La Niña. However, neutral years have resulted in the highest yields due to optimum rains. Statistical analysis proved that dam levels, maize as well as cattle populations significantly differ when compared across different ENSO classes. When the rainfall, dam level, maize and cattle production analysis was related to resilience programming activities and the national seasonal climate forecast for 2016, there is evidence that most of the activities will be disrupted if the country experiences medium or strong La Niña. However, if a weak La Niña or Neutral phenomenon occur, information from previous years show less chances of disruption with maize and cattle population expected to be higher compared to strong La Niña or El Niño years.
In a nutshell, this technical note shows that that ENSO events significantly impacted the production systems in the country. It is therefore recommended for resilience building initiatives to plan according to predicted phases of ENSO.
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The 2015-2016 drought experienced across Zimbabwe has been attributed to the El Niño phenomenon. The most affected regions include the western, southern and south- eastern districts of Zimbabwe. Zimbabwe experienced similar drought conditions in 1982, 1992 and 2012. El Niño and La Niña are opposite phases of the El Niño-Southern Oscillation (ENSO)1 cycle. The “El Niño” phenomenon refers to the large-scale ocean- atmosphere climate interaction due to warming of sea surface temperatures across the central and east-central Equatorial Pacific Ocean, which results in drought conditions for Southern Africa2. Meteorological drought results from prolonged periods of below average rainfall for a season or more3. In Zimbabwe, the El Niño phenomenon often leads to drought conditions with negative effects on livelihoods. On the other hand, the La Niña phenomenon is characterised by anomalously cool sea surface temperatures in the Pacific Ocean thereby affecting global climate in an opposite way to El Niño. In the case of Zimbabwe, often leading to increased rainfall conditions.
In Zimbabwe, legal and institutional frameworks for disaster risk management are well established at both local and national levels. Disaster management is multi-sectoral and involves various relevant ministries and institutions, coordinated by the Department of Civil Protection (DCP) within the context of the National Civil Protection Committee (NCPC). The institutions include government ministries, parastatals, private sector, United Nations agencies, and non-governmental organisations. These various institutions provide different capacities for DRM in the country.
A number of policies and legislation guide the operations of DRM at various scales in the country. The Hyogo Framework for Disaster Risk Reduction and Management was the widely used policy framework in developing the various national statutes. The National statutes include the Civil Protection Act No. 5 of 1989, National Climate Change Strategy for Zimbabwe, National Water Policy, Meteorological Services Act and Zimbabwe Food Security Strategy. The DCP is guided by the Civil Protection Act which provides for the establishment of the Department of Civil Protection and setting up of the
1. NOAA (2016), What is La Niña? http://www.pmel.noaa.gov/elnino/what-is-la-nina. 2. NOAA (2016), What are El Niño and La Niña? http://oceanservice.noaa.gov/facts/ ninonina.html 3. Chitongo (2013), Towards Comprehensive Disaster Risk Management in Zimbabwe: Evaluating Masvingo Rural District’s Community Drought Management Program (MRDCCDMP), International Journal of Economy, Management and Social Sciences, 2(8).
National Disaster Management Committee. In addition, international organisations such as UNDP and UNOCHA are also guided by the provisions of the HYOGO framework for Disaster Risk Reduction and Management (2005-2015), the Sendai Framework for Disaster Reduction (2015-2030), the United Nations Framework Convention on Climate Change (UNFCC) and the Paris Agreement on Climate Change which entered into force on 4 November 2016. This technical note also supports the national efforts in meeting the SDGs especially Goals number 2 (Zero Hunger), 3 (Good Health and well-being), 10 (Reduced Inequality) and 13 (Climate Action). The technical note will further more contribute to Outcome 1 (Food and Nutrition Security) of the Zimbabwe United Nations Development Assistance Framework (ZUNDAF) 2016-2020.
The Government of Zimbabwe is partnering UNDP in areas related to climate change, resilience and poverty reduction. Most of these initiatives, particularly the climate change and resilience programmes, seek to assist the government in fostering preparedness, early warning, early action and resilience building activities in the face of disasters. Since the government’s declaration of the 2015-2016 drought as a State of Disaster, UN Agencies and civil society organisations are rendering humanitarian assistance in a number of affected districts. This is a costly exercise, which diverts funds from productive investment and which has proven to have a negative impact on the beneficiary population in the form of dependency syndrome. In order to minimise the impact of similar situations in the future it is essential for all mandated government departments to work together in developing a long term strategy for mitigating impacts of both El Niño and La Niña. There are a number of questions concerning the El Niño and La Niña impacts which this technical note attempts to answer based on analysis of data collected by four national departments in collaboration with the Zimbabwe Resilience Building Fund (ZRBF). These include Agricultural Extension Services Department (AGRITEX), Division of Livestock Production and Development, Meteorological Services Department (MSD), Zimbabwe National Water Authority (ZINWA), and the Department of Civil Protection (DCP).
2.1 Justification and Objectives
In 2016, Zimbabwe, like the rest of the Southern African region, was experiencing the consequence of the worst drought in 35 years. The 2015-2016 drought resulted in loss of both domestic4 and wild animals, crop failure as well as lack of adequate water availability for both domestic use and industrial use. This has culminated in low yields and food insecurity in many parts of the country. The subsequent 2016 ZimVAC estimated that about 4.1 million people will need food assistance during the peak of the season (January
4. El Niño and drought take a toll on Zimbabwe’s cattle, Thomson Reuters Foundation News, Tuesday 12 January 2016, http://news.trust.org/item/20160112054900-bk2q7/?- source=spotlight.
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to March 2017)5. Figures from the Livestock Production and Development Division show that there were more than 25 000 cattle deaths which are attributed to the El Niño induced drought countrywide. The government declared the drought conditions a State of Disaster on the 4th of February 2016.
The frequency of unreliable weather patterns (e.g. false and late starts of agricultural season combined with long of mid-season dry spells) as well as anticipation of El Niño and La Niña conditions create the need for accurate and reliable information. Information generation and dissemination will foster learning from past experiences as well as creating early warning systems resulting in early action on forecasted events. This technical note seeks to contribute some of the information on the consequences of the three phases of ENSO, namely El Niño, La Niña and Neutral.
Specific Objectives
The specific objectives of this technical note are to:
• Document the causes, historical trends and effects of the El Niño phenomenon in Zimbabwe,
• Document the causes, historical trends and effects of the La Niña phenomenon in Zimbabwe, and
• Develop scenarios on for the La Niña phenomenon and Neutral phases, as predicted in the latest weather outlook.
5. Zimbabwe Vulnerability Assessment Committee (ZimVAC) 2016 Rural Livelihoods Assessment.
3. METHODOLOGY The analysis of ENSO impacts was carried out at a national level, based on existing quantitative data obtained from the mandated government departments. Government reports, bulletins and official communication were used as complementary qualitative data sources. Quantitative data on drought and El Niño frequency were obtained from the meteorological datasets. The following table summarises the various data sources.
Table 1: Proposed data sources
Variable Data source Period of time
El Niño /La Niña frequency and impacts MSD 1951-present
El Niño and La Niña’s impacts on rainfall MSD 1971-present
El Niño /La Niña effects on crops AGRITEX 1980-present
El Niño /La Niña effects on livestock LPD 1980-present
El Niño /La Niña effects on dam levels ZINWA 1972-present
Scenario building to inform climate change and resilience building projects in Zimbabwe
UNDP
3.1 Data Analysis The methods used in data analysis were determined by the type of data obtained namely meteorological, crops production, livestock production and water resources. Each method is described in detail in the below.
ENSO Trends Analysis Sea surface temperatures anomalies in the Pacific Ring were analysed and subsequently seasons were classified into Weak, Moderate, Strong, Very Strong, El Niño events. Sea surface temperature is the temperature of the top millimetre of the ocean’s surface. An anomaly is a departure from average conditions. Sea surface temperatures anomaly maps compare temperatures in a given month to the long- term average temperature of that month. At irregular intervals (roughly every 3-6 years), the sea surface temperatures in the Pacific Ocean along the equator become warmer or cooler than normal. These anomalies are the hallmark of El Niño and La Niña climate cycles, which can influence weather patterns across the globe. On the other hand, strong, localized sea surface temperature anomalies may reveal that an ocean current has veered off its usual path for a time or is stronger or weaker than usual. Sea surface temperature anomalies that persist over many years can be signals of regional or global climate change, such as global warming.
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Meteorological Data Analysis RRainfall data for the years 1981 to 2016 were merged with information on occurrence of El Niño, Neutral and La Niña events, to determine the impacts on Zimbabwe’s rainfall. In consultation with other specialists, total rainfall for the months which are critical for crop growth was plotted against time. The meteorological data were used to classify rainfall seasons from 1981 to present into different ENSO classes.
The Standardized Precipitation Index (SPI) is a tool which was developed primarily for defining and monitoring drought. It allows an analyst to determine the rarity of a drought at a given time scale (temporal resolution) of interest for any rainfall station with historic data (McKee et al 1993; McKee et al 1995; Guttman, 1998; Guttman, 1999; Trenberth et al., 2014). It can also be used to determine periods of anomalously wet events. The SPI is not a drought prediction tool. Mathematically, the SPI is based on the cumulative probability of a given rainfall event occurring at a station. The historic rainfall data of the station is fitted to a gamma distribution, as the gamma distribution has been found to fit the precipitation distribution quite well. This is done through a process of maximum likelihood estimation of the gamma distribution parameters, α and β. In simple terms, the process described above allows the rainfall distribution at the station to be effectively represented by a mathematical cumulative probability function. Therefore, based on the historic rainfall data, an analyst can then tell what is the probability of the rainfall being less than or equal to a certain amount. Thus, the probability of rainfall being less than or equal to the average rainfall for that area will be about 0.5, while the probability of rainfall being less than or equal to an amount much smaller than the average will be also be lower (0.2, 0.1, 0.01 etc., depending on the amount). Therefore if a particular rainfall event gives a low probability on the cumulative probability function, then this is indicative of a likely drought event. Alternatively, a rainfall event which gives a high probability on the cumulative probability function is an anomalously wet event. Negative SPI values represent rainfall deficit, whereas positive SPI values indicate rainfall surplus. Intensity of drought event can be classified according to the magnitude of negative SPI values such that the larger the negative SPI values are, the more serious the event would be. For example, negative SPI values greater than 2 are often classified as extremely dry conditions. An advantage in using SPI is that only rainfall data are needed for its computation. SPI can also be compared across regions of different climatic zones. The SPI index for Zimbabwe for the period 1960 to date, was calculated from rainfall data collected from 44 stations across the country.
Crop and Livestock Production Data Analysis Using data from the crop and livestock surveys, national, provincial and district level maps and trends were plotted. Using maize production data from different ENSO classified years, analysis was done to check if there was any significant difference in
production levels between the years affected by El Niño events vs “normal” years. The non-parametric, Friedman’s test was used to detect differences in production levels across multiple years. The Friedman’s test was selected since the data was found not to be normally distributed.
The null hypothesis for the Friedman’s test was:
Ho: μ1 = μ2 = μ3 Means of production data (crop/livestock) are not significantly different across the different years
H1: μ1 ≠ μ2 ≠ μ3 Means of production data (crop/livestock) are significantly different across the different years
Water Resources Data Analysis Dam levels data were analysed for trends in water levels, as well as occurrence of flooding events over the period 1972 to 2016. Similarly, an analysis of the significant difference among ENSO years using the Friedman’s test was conducted to test if there were any significant difference in inflows across the years.
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3.2 Scenario building to inform climate change and resilience building projects in Zimbabwe Based on these analyses, the next stage involved developing scenarios of how the La Niña and Neutral phenomenon will affect agricultural production (crops and livestock) as well as water resources in the coming seasons. Different classes of La Niña were used to build the scenarios. Conclusions were made on the El Niño/ La Niña impacts on water resources, crop and livestock production in Zimbabwe. These fed into the evaluation on the implications of the different scenarios to climate and resilience building programmes in Zimbabwe. The information generated in this exercise is complementary to the 2016/2017 seasonal outlook since it evaluates past experiences as a way of providing information on anticipated impacts.
4. RESULTS Results on ENSO Trends as well as impacts on rainfall, dam levels, maize and cattle production in Zimbabwe are presented as follows:
4.1 ENSO Trends
An analysis of the sea surface temperatures anomalies in the pacific ring produced the following classification of seasons.
Table 2: Classification of ENSO events for the period 1951-2016
El Niño La Niña Neutral Weak Mod Strong Very
Strong Weak Mod Strong Neutral
1951-52 1963-64 1957-58 1982-83 1950-51 1955-56 1973-74 1960-61
1952-53 1986-87 1965-66 1997-98 1954-55 1970-71 1975-76 1962-63
1953-54 1987-88 1972-73 2015-16 1964-65 1998-99 1988-89 1966-67
1958-59 1991-92 1967-68 1999-00 1978-79
1968-69 2002-03 1971-72 2007-08 1980-81
1969-70 2009-10 1974-75 2010-11 1985-86
1976-77 1983-84 1989-90
1977-78 1984-85 1993-93
1979-80 1995-96 2008-09
1994-95 2000-01 2001-02
2004-05 2011-12 2003-04
The most notable information coming out the classification is that:
• The 2015/16 season is ranked among the very strong El Niño conditions
• 1991/92 and 2002/2003 seasons were both in the Moderate El Niño class
• 2014/15 and 2003/04 are among the years classified as Neutral years
4.2 Impacts on rainfall
Figure 1: Zimbabwe Standardized Precipitation Index (SPI) and Sea Surface Temperature (SST) anomalies
Results showed that rainfall in Zimbabwe is affected by ENSO conditions. High sea surface temperature (SST) anomalies, which are indicative of the El Niño phenomena, often result in negative standardised precipitation index (SPI). El Niño years are coinciding with negative SPI values in Zimbabwe. On the other hand, La Niña years coincide with wet conditions in Zimbabwe.
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Table 3: Categories of rainfall received under different ENSO events
Above Normal Below Normal Total
El Niño 8 38% 13 62% 21
La Niña 10 67% 5 33% 15
Neutral 13 54% 11 46% 24
60
An analysis of the past 60 years show that the majority of El Niño years (62%) resulted in below normal rainfall in Zimbabwe. Majority of La Niña years (67%) result in above normal rainfall in Zimbabwe while the remaining displayed the opposite (33%).
4.3 Impacts on maize production
An analysis of the maize production trends since 1980 revealed that production and yield (tonnes per hectares) have been declining gradually. The period between 2000 and 2016 was characterised by an increase in area under cultivation even though maize production was declining. Since maize is the staple food in Zimbabwe, reduced yields compromise food security in most parts of the country.
Figure 2: National maize production statistics since 1980
.
Figure 3: Maize yield map for the 2015/16 agricultural season.
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Figure 4: Maize yield map for the 2014/15 agricultural season.
When the yield values are analysed together with the ENSO classification, results showed that yield for the different categories of El Niño events were significantly different. These statistics assist in understanding the impact of ENSO conditions to maize yields in Zimbabwe. For the period 1980-2016, El Niño years have been characterised by lower yields compared to Neutral and la Niña years at national level. Based on the illustration below, Masvingo is worst affected by El Niño events compared to other provinces. All provinces except Mashonaland Central have the best maize yields during Neutral phase of ENSO.
Figure 5: Provincial maize yield for the different ENSO classes
Results from the Friedman’s test gave a p-value which is less than 0.05 hence the decision to reject the null hypothesis that there was no significant difference in maize yield across different ENSO events in all the provinces. Through a statistical test, there is evidence that different ENSO events result in wide margins in maize yields.
Figure 6: Friedman’s test results for different EBSO classes
4.4 Impacts on cattle production Cattle population showed an increasing or decreasing trend when the country experienced La Niña and El Niño conditions respectively. Since Very Strong El Niño conditions often lead to drought, the negative effect on cattle population is well pronounced with many drought-related cattle deaths (e.g. 2015/2016 season).
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Figure 7: Cattle Populations for the period 2000-2016
TThe Friedman’s test showed that changes in cattle population due to ENSO conditions were statistically significant (Figure 8). The p value was less the 0.05, hence the null hypothesis that there was no significant difference in cattle populations across different ENSO events was rejected. Cattle populations across ENSO events are so different that they can be proven through a statistical test.
Figure 8: Friedman’s test results on cattle population across different ENSO classes
4.5 Impacts on water resources Figure 9 below depicts the annual dam inflow trends (as a %) between 1972 and 2016 plotted against La Niña, Neutral and El Niño events. Results showed that Neutral and La Niña were characterised by positive inflows. The strong La Niña phenomenon experienced in 1974 saw the country experiencing the highest inflows since 1972. On the other hand, El Niño years coincided with negative inflows, leading to reduced water in dams for use by households and communities. It is also worth noting that the contribution of groundwater has an effect on dam levels figures in years of water deficit. Figure 10 below show that in most instances surplus come after years of deficit.
Figure 9: Percentage Inflow for all major dams (1972-2016)
An analysis of the trends on selected ENSO classified years show that dam levels also respond to ENSO events (Figure 10). This analysis was done per catchment (Figure 1). Runde catchment was the worst affected during the El Niño events. For example, during the 2015/16 season, the catchment has the lowest percentage dam levels in the country. The dam levels trends for individual catchments are shown in Annex 2.
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Figure 10: Dam level figures per catchment across different ENSO classes
The Friedman’s test further showed that there was a significant difference in dam levels in catchments across different ENSO events. It was thus concluded that different changes in ENSO events also results in changes in dam levels across the country.
Figure 11: Friedman’s test results for dam levels in different catchments
4.6 Scenario planning The SST anomalies in the Pacific zone reflected the occurrence of La Niña conditions. The Southern African Development Community’s (SADC) Climate Services Centre and Zimbabwe’s Meteorological Services Department have already issued out the regional and national seasonal outlook reports for the 2016/17 rainfall season. These outlook reports are also indicating La Niña conditions. As a result, normal to above normal rainfall is expected for most parts of the country. Using historical figures in Zimbabwe, the following table illustrates the three different La Niña phenomenon as well as Neutral classes and their probable impacts to dam levels as well as maize and cattle production.
Table 4: Summary of impacts under different La Niña scenarios
La Niña Class Impact on maize production
Impact on cattle population
Impact on dam levels
Neutral Average maize yields
Low water levels for most inland dams.
Weak La Niña Average maize yields
Few deaths due to lack of pastures
Low dam levels
More pastures leading to improved cattle conditions
Average dam levels
Strong La Niña Above average yields due to increased water from direct rainfall as well as dams
A lot of water for pastures often resulting in waterlogging of low lands and diseases outbreak due to waterborne pathogens
High dam levels
Given the above summary on the likely impacts under the three La Niña classes, implications to climate change and resilience programme are shown in Table 5 below.
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Table 5: Summary of implications to climate change and resilience programmes in Zimbabwe
ENSO Class
Potential Impact Recommended Interventions
Neutral Fair chances of normal to above rainfall leading to fairly high maize yields, dam inflows and no negative effects on cattle populations in the country. Overall verdict: Little or no impact on programming
1. Improved crop, livestock and agro- forestry production (climate-smart)
2. Livelihood diversification 3. Small productive infrastructure 4. Integrated DRM and Resilience
planning at district and ward level 5. Access, control and sustainable use of
community and household assets 6. Sustainable and equitable utilisation of
natural resources 7. Integrated water resources management
Weak La Niña
There will be fair chances of normal to above rainfall leading to fairly high maize yields, dam inflows and no negative effects on cattle populations in the country. Overall verdict: Little or no impact on programming
1. Improved crop, livestock and agro- forestry production (climate-smart)
2. Livelihood diversification 3. Small productive infrastructure 4. Integrated DRM and Resilience
planning at district and ward level 5. Access, control and sustainable use of
community and household assets 6. Sustainable and equitable utilisation of
natural resources 7. Integrated water resources management
Moderate La Niña
More impact on programming with rains supporting crop and livestock production in most parts of the country
1. Improved crop, livestock and agro- forestry production (climate-smart)
2. Livelihood diversification 3. Small productive infrastructure 4. Integrated DRM and Resilience
planning at district and ward level 5. Access, control and sustainable use of
community and household assets 6. Sustainable and equitable utilisation of
natural resources 7. Integrated water resources management
Strong La Niña
A lot of rains to support crop, livestock and agro- forestry production but high chances of flooding in low lands
1. Improved crop, livestock and agro- forestry production (climate-smart)
2. Livelihood diversification 3. Small productive infrastructure 4. Integrated DRM and Resilience
planning at district and ward level 5. Access, control and sustainable use of
community and household assets 6. Sustainable and equitable utilisation of
natural resources 7. Integrated water resources management
5. CONCLUSION Following the above analysis, the following overall conclusions can be drawn: The presence of ENSO conditions significantly influence weather patterns in Zimbabwe. In Zimbabwe, 62% of El Niño events resulted in drier conditions accompanied by below normal rainfall, usually experienced from October to March. 62% of La Niña was characterized by above rainfall during the rainy season stretching from October to March.
• Since 1971, 62% of the years which experienced the El Niño phenomenon, Zimbabwe received below normal rainfall thereby impacting negatively on dam levels, maize production and cattle populations. On the other hand, 67% of La Niña conditions have resulted in above normal rainfall with higher dam levels, maize and cattle populations compared to El Niño events.
• 54% of the Neutral years have resulted in above rainfall in the country. These years also saw most provinces receiving higher maize yields compared to other years due to less chances of either moisture stress or flooding.
• These trends prove the importance of Sea Surface Temperate (SST) anomaly figures (which are used to determine ENSO phases) as possible early warning indicators when planning resilience programme activities.
• With the 2016/17 climate forecast predicting conditions ranging between La Niña and Neutral, the scenarios developed anticipate little to no disruption to resilience programme activities. However, the developed scenarios also include moderate and strong La Niña, in case conditions in the Pacific Ocean change. Besides planning for resilience activities, these scenarios are also useful for early recovery activities as the country is still recovering from the Strong El Niño phenomenon of the 2015/2016 agricultural season.
• All the work presented in this note highlight the strong need for resilience programme activities to use early warning information, in the form of ENSO information, for planning purposes. ENSO phenomenon proved to have a strong bearing on the country’s rainfall, dam levels, maize and cattle production.
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