Agricultural drought risk

In several countries, losses from agricultural drought not only pose risks to the national econ-omy but can also lead to devastating effects on the rural population. With climate change, pat-terns of agricultural drought can be expected to change.

Agricultural drought is probably the most social-ly constructed of all disaster risks (UNISDR, 2011a).

Besides insufficient rainfall, agricultural drought is associated with other factors such as tempera-ture and wind, which influence evaporation, tran-spiration and the soil’s capacity to hold moisture.

However, while agricultural drought hazard occurs when there is insufficient moisture in the soil to meet the needs of a particular crop at a given time and location, it is also associated with factors such as land degradation, inappropriate land-use and cropping patterns, over-extraction of ground water, and overgrazing. Low-income rural house-holds and communities may have no alternative but to farm or graze marginal, drought-prone and degraded land. And with little capacity to mobilize assets, they are vulnerable to even small shortfalls in production and have low levels of resilience.

The direct impacts of agricultural drought are reduced crop, rangeland and forest productivity, reduced water levels, increased fire hazard, dam-age to wildlife and fish habitats, and increased livestock and wildlife mortality. The indirect impacts include reduced income from agricul-ture and increased food and timber prices, which in turn lead to wider impacts such as malnutrition (especially among children), increased unem-ployment, migration, reduced tax revenues and the risk of foreclosures on bank loans to farmers.

Although agricultural droughts can persist for several years, even a short, intense drought can cause significant damage to the local economy (FAO, 2013a).

In sub-Saharan Africa, only 1 per cent of the farmed area is irrigated (Ward et al., 2014), while 52 per cent of land is degraded to some degree (Erian et al., 2014). Despite increasing productivi-ty, the total productivity gap between the region and developing countries as a whole is still wid-ening (Figure 3.33).

Figure 3.33 Total factor productivity (TFP) index

(Source: USDA Economic Research Service.)

In many low-income countries in this region, agri-culture remains a critical economic sector. In many of those countries where economic activity and employment are concentrated in agriculture, such as Eritrea and Ethiopia, a significant propor-tion of the populapropor-tion is undernourished, and a significant proportion of the area covered by veg-etation is affected by high levels of land degrada-tion and agricultural drought hazard (Table 3.2).

In these countries, agricultural drought not only poses risks to the national economy but also leads to devastating effects on the rural population.

In Malawi, for example, agriculture is responsible for around 30 per cent of GDP. Estimated annual losses due to drought represent about 1 per cent

Table 3.2 Agriculture, land degradation and drought in sub-Saharan Africa

(Source: UNISDR with data from FAO, 2014 and Erian et al., 2014.)

Note: Where data is not included in the table, it is because no data was readily available.

of GDP, and the probable maximum loss (PML) from a 1-in-25-year drought is equal to 10 per cent of GDP. In addition, a 1-in-25-year drought would exacerbate income poverty by 17 per cent, which would mean an additional 2.1 million peo-ple falling below the poverty line (World Bank et al., no date).

The picture is equally critical in West Africa. Mali, for example, faces a 10 per cent probability of suffering production losses amounting to US$48

million or larger in 50 years for millet alone (Fig-ure 3.34). In Senegal, millet production losses for the same return period are US$15 million or more.

According to the IPCC, “climate change is very likely to have an overall negative effect on yields of major cereal crops across Africa, with strong regional variability in the degree of yield reduc-tion” (IPCC, 2014). However, this regional variabil-ity would be considerable and may even involve

Figure 3.34 Probability of production loss in West Africa

(Source: Jayanthi, 2014.)

increases in maize production in eastern Africa (IPCC 2014).

In Kenya, Malawi and Niger, income from agri-culture respectively contributes 30 per cent, 30 per cent, and 38 per cent to each country’s GDP.

Estimated average annual losses (AAL) vary with and without near-term climate change in all three

Figure 3.35 Drought AAL and PML100, with and without climate change

(Source: Jayanthi, 2014.)

countries (Figure 3.35). While maize production in Malawi is expected to face higher AAL with cli-mate change, Kenya and Niger show reduced AAL figures for the same climate change scenario, both in terms of absolute values and as a percent-age of their GDP for maize and millet production, respectively.

Figure 3.36 Malawi maize production loss in tons with respect to 2007 countrywide production of maize

(Source: Jayanthi, 2014.)

Table 3.3 Estimated maize production losses in Malawi with and without climate change

(Source: Jayanthi, 2014.)

For example, losses in maize production from a 1-in-25-year drought in Malawi are estimated to be 23 per cent higher in 2016-2035 than in 1981-2010 based on near-future climate change sce-narios (Figure 3.36).

Climate change could result in significant addi-tional losses in maize production (Table 3.3) and potentially push countries like Malawi over a resil-ience threshold in terms of the national economy as well as poverty.

In contrast, climate change could have a positive impact on maize and millet yields in Kenya and Niger, respectively (Jayanthi, 2014). The results also show that the impact of climate change could be different depending on the intensity of drought.

The agricultural drought risk to maize in the Kenya Rift Valley, for example, is forecast to decline in the near future (2016-2035) due to the impact of climate change. In the climate change scenario, PML100 (probable maximum loss cor-responding to a 1-in-100-year drought) would fall

Table 3.4 Estimated maize production losses in the Rift Valley, Kenya with and without climate change

(Source: Jayanthi, 2014.)

Figure 3.37 Probable losses in maize production in the Rift Valley, Kenya with and without climate change

(Source: Jayanthi, 2014.)

from 866,440 tons (baseline) to 351,225 tons. The average annual loss (AAL) is thus projected to be 48,463 tons (1.78 per cent of the total maize pro-duction in the Rift Valley Province in 2012), a full 38 per cent lower than the baseline AAL of 78,190 tons (2.86 per cent of the total maize production in Rift Valley Province in 2012; see Table 3.4).

While losses due to frequent droughts (return periods shorter than 5 years) would be similar

to the observed losses for the 1981-2010 period, losses from more severe and infrequent droughts would be significantly lower (Figure 3.37). For example, a crop loss of 390,000 tons with a cur-rent probability of 1 in 20 years would have an occurrence probability of 1 in 100 years under the near-term climate change scenario.

Notes

1 The global AAL for earthquake and tropical cyclone wind has changed compared to the figures published in GAR13 due to changes in the methodologies for seismic and tropical cyclone hazard assessments. Details on the improvements to the meth-odology can be found in Annex 1 and in CIMNE-INGENIAR, 2014.

2 Persons aged 15 to 64 based on data from the United Nations;

see http://esa.un.org/unpd/wpp/index.htm.

3 World Bank definition of poverty line: those living on less than US$1.25 per day.

4 Calculations based on data from the EIA: http://www.eia.gov.

5 Based on United States Government census data: https://www.

census.gov/hhes/families/data/cps2012.html.

6 Based on data from the World Bank: http://data.worldbank.

org/.

7 This is defined as armed criminal violence in situations that are not identified as conflict or armed conflict.

8 For example, models from the Global Earthquake model (http://www.globalquakemodel.org) or Deltares (http://www.

deltares.nl/en).

9 The Global Risk Assessment was conducted in a partnership of 20 institutions. The probabilistic risk model for all hazards was developed and run by CIMNE and INGENIAR LTDA on the CAPRA modelling platform. The exposure model at the global scale was developed by UNEP-GRID and CIMNE in collaboration with WAP-MERR, EU-JRC, Kokusai Kogyo and Beijing Normal University. The hazard models were developed by CIMNE and INGENIAR LTDA (cy-clones and earthquakes, with inputs from GEM for earthquakes), CIMA and UNEP-GRID (floods), NGI and Geoscience Australia (tsu-namis and volcanoes), and GVM and Geoscience Australia (volca-noes). Vulnerability was modelled by CIMNE and INGENIAR LTDA for Latin America and the Caribbean, and by Geoscience Australia for the Asia-Pacific region. In other regions, HAZUS vulnerabil-ity functions developed by USGS were used. Agricultural drought risk assessments were undertaken by ACSAD and FEWSNET. Peer reviews were conducted by WMO (hydro-meteorological hazard models), UNESCO (geohazard models), and an ad-hoc group of seismic hazard and exposure experts. For more details on part-ners and their contributions, see Annex 1.

10 http://www.ecapra.org/.

11 Throughout this chapter, capital investment refers to gross fixed capital formation (GFCF) based on data from 2013.

12 Capital stock refers to a country’s building stock, compris-ing residential and commercial buildcompris-ings, schools and hospitals, based on the exposure model (see Annex 1 for more details).

13 All regions are according to World Bank country and regional classification; see http://data.worldbank.org/about/country-and-lending-groups.

14 See Annex 1 for full risk results by geographical region.

15 http://data.worldbank.org/.

16 Please see Annex 1 for more details on hazard-specific risk re-sults and graphs depicting key economic and social development metrics.

17 The GAR15 risk model considers only tropical cyclones (i.e.

hurricanes on the Saffir Simpson Scale), including strong winds and storm surges. Other tropical circulations, such as tropical de-pressions or tropical storms, are not considered. These kinds of events usually involve lower wind speeds, and therefore effects such as strong winds and storm surge are usually not present in those cases. Thus, although rare but potentially intense storms

near the equator can exist—as witnessed during the Category 5 Typhoon Bopha in Mindanao in 2012—tropical cyclones do not typically occur at those latitudes. This is because of the Coriolis Effect and the fact that storms rotate clockwise in the southern hemisphere and anticlockwise in the northern hemisphere with-out crossing over.

18 http://www.earthobservatory.nasa.gov/IOTD/view.php?id

=7079.

19 Hurricanes: The Greatest Storms on Ear th. http://

earthobservatory.nasa.gov/Features/Hurricanes/ (accessed 10

23 The provisional results presented here give an overview of the risks associated with river flooding. Factors other than the depth of the water also have a considerable influence on loss, which means that there is greater uncertainty compared with other haz-ards.

24 Most SIDS are located in the region of Latin America and the Caribbean or East Asia and the Pacific.

25 The five historical eruptions responsible for the majority of fatalities are: Tambora, Indonesia in 1815 (60,000 fatalities);

Krakatau, Indonesia in 1883 (36,417 fatalities); Pelée, Martinique in 1902 (28,800 fatalities); Nevado del Ruiz, Colombia in 1985 (23,187 fatalities); Unzen, Japan in 1792 (14,524 fatalities).

26 Developed by Aspinall et al. (2011), the Population Exposure Index (PEI) is one of the prominent indices used in assessing vol-cano risk. it is based on the population within 10, 30, and 100 km of a volcano, which is then weighted according to evidence on historical distributions of fatalities within a given distance from volcanoes. The PEI is divided into seven levels, from sparsely to very densely populated areas. The results of the index show that just 4 per cent of volcanoes account for 60 per cent of the total population exposed.

27 A Volcano Hazard Index (VHI) has also been developed to characterize the hazard level of volcanoes based on their record-ed eruption frequency, modal and maximum recordrecord-ed volcanic explosivity levels, and the occurrence of pyroclastic density cur-rents, lahars and lava flows. Only half of the historically active vol-canoes have sufficiently detailed eruptive histories to calculate VHI.

28 For this loss estimate, a simplified methodology emulating volcanic ash fall for multi-scale analysis was used for probabilis-tic hazard modelling of volcanic ash fall in the Asia-Pacific region (different from the model used in the production of maps in Figure 3.35).

29 It should be noted that these values only represent the losses from structural damage, which are only a fraction of potential economic losses that can be caused by ash fall. This also does not include the losses to the aviation industry from airborne ash.