International Activities Related to Dryland Degradation Assessment and Drought Early Warning

Land degradation usually occurs on drylands (arid, semiarid, and dry subhumid areas). According to the United Nations Convention to Combat Desertification held in Paris in 1994 (UNCCD, 1999), drylands are defined as those lands (other than polar and subpolar regions) where the ratio of annual precipitation to potential evapotranspiration falls within the range of 0.05–0.65. Land degradation causes reduction in the biological or economic productivity of those lands that may support cropland, rangelands, forest, and woodlands. Land degradation threatens culturally unique agropastoral and silvopastoral farming systems and nomadic and transhumance systems. The consequences of land degradation are widespread poverty, hunger, migration, and creation of a potential cycle of debt for the affected populations. Historical awareness of the land degradation was cited, mainly at the local and regional scales, by Plato in the 4th century B.C in the Mediterranean region, and in Mesopotamia and China (WRI, 2001). The occurrence of the “dust bowl” in the United States during the 1930s affected farms and agricultural productivity, and several famines and mass migrations, especially in Africa during the 1970s, were important landmarks of land degradation in the 20th century. It is estimated that more than 33% of the earth’s land surface and 2.6 billion people are affected by land degradation and desertification in more than 100 countries. About 73% of rangelands in dryland areas and 47% of marginal rain-fed croplands, together with a significant percentage of irrigated croplands, are currently degraded (WRI, 2001). In sub-Saharan Africa, land degradation is widespread (20–50% of the land) and affects some 200 million people. This region experiences poverty and frequent droughts on a scale not known anywhere else in the world. Land degradation is also severe and widespread in Asia, Latin America, as well as other regions of the globe. Continuous land degradation is accelerating the loss of agricultural productivity and food production in the world. Over the next 50 years, food production needs to triple in order to provide a nutritionally adequate diet for the world’s growing population. This will be difficult to achieve even under favorable circumstances.

World Meteorological Organization and Agricultural Droughts

Adaptation strategies for coping with agricultural droughts must be based on a better understanding of the climatic conditions of the location or region under consideration. As the United Nations’ specialized agency with responsibility for meteorology and operational hydrology, the World Meteorological Organization (WMO), since its inception, has been addressing the issue of agricultural droughts. This chapter presents a short overview of the various initiatives undertaken by WMO in this respect. The fight against drought receives a high priority in the long-term plan of WMO, particularly under the Agricultural Meteorology Programme, the Hydrology and Water Resources Programme, and the Technical Cooperation Programme. WMO actively involves the National Meteorological and Hydrological Services (NMHSs), regional and subregional meteorological centers, and other bodies in the improvement of hydrological and meteorological networks for systematic observation, exchange and analysis of data for better monitoring of droughts, and use of medium- and long-range weather forecasts, and assists in the transfer of knowledge and technology. Following is a brief description of various activities undertaken by WMO in the combat against drought. WMO has been in the forefront of research on interactions of climate, drought, and desertification from its beginnings in the mid-1970s, when it was suggested that human activities in drylands could alter surface features that would lead to an intensification of desertification processes and trends. WMO has been in the forefront of research on interactions of climate, drought, and desertification from its beginnings in the mid-1970s, when it was suggested that human activities in drylands could alter surface features that would lead to an intensification of desertification processes and trends. Human-induced changes in dryland surface conditions and atmospheric composition can certainly have an impact on local and regional climate conditions because they directly affect the energy budget of the surface and the overlying atmospheric column. These changes to the energy balance have been simulated in many numerical modeling studies covering almost all dryland areas of the world.

A Drought Warning System for Thailand

Thailand is located between 5°30' and 20°30'N latitudes and between 97°30' and 105°30'E longitudes. Geographically, the country can be divided into northern, northeastern, central, and southern regions. Most of the country experiences distinct wet and dry climates, except some parts of the southern region, which experience a wet and humid climate. Of the country’s total area (514,000 km2), 41% is under agricultural use (Office of Agricultural Economics, 1999) with 92% of it being rainfed. Drought normally occurs during the hot season (March–April) and sometimes during dry season (November–April) due to inadequate rains. In recent times, the occurrence of drought has increased in Thailand, threatening sustainability of agricultural production. According to Department of Local Administration (1998), droughts of varying intensity occur in 67 out of 76 provinces of Thailand almost every year. During the period from 1987 to 1997, drought impacted a total of 5.44 million ha of agricultural land, causing $1.4 billion in losses. Droughts of varying intensity or severity occur in different regions of Thailand. A drought is categorized as severe, moderate, slight, or none drought using a radiative index (RI) determined during the rainy season (May– October). The RI for a region is determined using the number of rainy days, percentage of irrigated area, groundwater availability, topography, land use, soil, drainage density, and watershed size. If RI ranges from 1.0 to 1.2 for 15 consecutive days for a region or area, the region is said to be affected by slight drought. If RI exceeds 1.2 for 30 consecutive days, the region is considered to be affected by moderate drought, and if RI exceeds 1.0 for more than 30 consecutive days, severe drought is said to have occurred in the region. Using these criteria, the percentage of area affected by different drought categories has been determined in Thailand. It can be observed from table 25.1 that the northeastern region is the most droughtprone in Thailand. A drought index, D, is also used to monitor drought conditions in Thailand.

Harnessing Radio and Internet Systems to Monitor and Mitigate Agricultural Droughts in Rural African Communities

Humankind has not yet discovered a way to prevent drought entirely. Hence, the provision of timely and accurate climate and weather information can help rural and semiurban producers to better prepare for and mitigate the effects of insufficient precipitation (IRI, 2001). Communicating drought information to remote rural populations, however, has been a major challenge in Africa (Stern and Easterling, 1999). Seasonal rainfall forecasts, precipitation, and stream flow monitoring products, key environmental information, and even lifesaving early warnings are commonly trapped in the information bottleneck of Africa’s capital cities, due to the relative lack of infrastructure in rural areas (Glantz, 2001). Without access to reliable communication networks, the majority of Africa’s farmers and herders are cut off from the scientific and technological advances that support agricultural decision-making in other parts of the world. Before the proliferation of radios, cell phones, and televisions, Africans used local methods—interpreting wind speed and direction, cloud formations, vegetation, and insect and bird migrations, for example—to predict weather patterns and the advent or cessation of precipitation. This chapter describes a Radio and Internet (RANET; http://www.ranetproject.net) system for communicating drought information to the rural communities in Niger and Uganda. This system was developed under a disaster mitigation program funded by the U.S. Agency for International Development (USAID). The need for a drought communications system tailored to the realities of rural Africa was initially communicated to the director of the African Centre of Meteorological Applications for Development (ACMAD; http:// www.acmad.ne) by a nomad in the desert of southeastern Algeria when he declined the gift of a radio offered by the young meteorologist researching desert locusts near Djanet. The nomad did agree that information was vital to his survival. “Just tell me where it has rained. I will know where to take my flocks” (personal communication with Boulahya, Hirir, Algeria, February 1988). He explained that he was familiar with every rise and fall of the terrain and would lead his animals every rainy season to meet the water as it flowed in streams to form pools at low spots in the landscape.

Monitoring Agricultural Drought in the Near East

The countries of North Africa and West Asia, hereafter referred to as the “Near East,” cover a large part of the world (more than 7,200,000 km2). This region is characterized by diverse but generally dry climates, in which evaporation exceeds precipitation. The level of aridity is indicated by the aridity index, the ratio of annual precipitation to annual potential evapotranspiration, calculated by the Penman method (UNESCO, 1979). The degree of aridity is shown spatially in figure 16.1 and summarized per country in table 16.1. These data show that the region is characterized by humid, subhumid, semiarid, and arid to hyperarid moisture regimes. In addition, temperature regimes vary considerably, particularly due to the differences in altitudes and, to a lesser extent, due to the oceanic/continental influences. For most of the region, the precipitation generally occurs during the October–April period and thus is concentrated over the winter season. Table 16.1 shows that, with more than 90% of the land area in hyperarid, arid, or semiarid moisture regimes, aridity is very significant in the Near East. Turkey is better endowed with surface and groundwater resources due to the orographic capture of Atlantic cyclonal precipitation, but much of the interior is semiarid. If one excludes the hyperarid zones, which cover the driest deserts and have no potential for agricultural use, nearly 34% of the region, or about 2,460,000 km2, is dryland (i.e., the area with arid or semiarid moisture regime). These are the areas with some potential for either dryland farming (in semiarid zones) or for extensive rangeland (in arid zones). In the Near East countries, agriculture contributes about 10–20% to the gross domestic product and is therefore a major pillar of their economies. However, the indirect importance of agriculture is larger because it provides the primary goods that constitute the majority of merchandise exports and because of the relatively high number of people employed in agriculture. Because of the high degree of aridity in large parts of the region, agriculture in the Near East is particularly vulnerable to drought. Most of the agricultural systems depend on rainfall.

Agricultural Drought in North-Central Mexico

Drought is the most significant natural phenomenon that affects the agriculture of northern Mexico. The more drought-prone areas in Mexico fall in the northern half of the country, in the states of Chihuahua, Coahuila, Durango, Zacatecas, and Aguascalientes (figure 10.1). The north-central states form part of the Altiplanicie Mexicana and account for 30.7% of the national territory of 1,959,248 km2. This area is characterized by dry and semidry climates (Garcia, 1981) and recurrent drought periods. The climate of Mexico varies from very dry to subhumid. Very dry climate covers 21%, dry climate covers 28%, and temperate subhumid and hot subhumid climates prevail in 21% and 23% of the national territory, respectively. About 20 years ago, almost 75% of Mexico’s agricultural land was rainfed, and only 25% irrigated (Toledo et al., 1985), making the ratio of rainfed to irrigated area equal to 3. However, for the northern states this ratio was 3.5 during the 1990–98 period (table 10.1). Because of higher percentage of rain-fed agriculture, drought is a common phenomenon in this region, which has turned thousands of hectares of land into desert. Though the government has built dams, reservoirs, and other irrigation systems to alleviate drought effects, rain-fed agriculture (or dryland farming) remains the major form of cultivation in Mexico. In Mexico, there is no standard definition for agricultural drought. However, the Comisión Nacional del Agua (CNA; i.e., National Water Commission), which is a federal agency responsible for making water policies, has coined its own definition for drought. This agency determines whether a particular region has been affected by drought, by studying rainfall records collected from the national climatic network. The national climatic network is spread throughout the country and is managed by the Servicio Meteorológico Nacional (SMN; i.e., National Meteorological Services). The CNA determines, for a municipal region, if the rainfall is equal to or less than one standard deviation from the long-term mean over a time period of two or more consecutive months. If it is, then the secretary of state declares drought for the region.

Active Microwave Systems for Monitoring Drought Stress

Remote sensing can provide timely and economical monitoring of large areas. It provides the ability to generate information on a variety of spatial and temporal scales. Generally, remote sensing is divided into passive and active depending on the sensor system. The majority of remote-sensing studies concerned with drought monitoring have involved visible–infrared sensor systems, which are passive and depend on the sun’s illumination. Radar (radio detection and ranging) is an active sensor system that transmits energy in the microwave region of the electromagnetic spectrum and measures the energy reflected back from the landscape target. The energy reflected back is called backscatter. The attraction of radar over visible– infrared remote sensing (chapters 5 and 6) is its independence from the sun, enabling day/night operations, as well as its ability to penetrate cloud and obtain data under most weather conditions. Thus, unlike visible–infrared sensors, radar offers the opportunity to acquire uninterrupted information relevant to drought such as soil moisture and vegetation stress. Drought conditions manifest in multiple and complex ways. Accordingly, a large number of drought indices have been defined to signal abnormally dry conditions and their effects on crop growth, river flow, groundwater, and so on (Tate and Gustard, 2000). In the field of radar remote sensing, much work has been devoted to developing algorithms to retrieve geophysical parameters such as soil moisture, crop biomass, and vegetation water content. In principle, these parameters would be highly relevant for monitoring agricultural drought. However, despite the existence of a number of radar satellite systems, progress in the use of radar in environmental monitoring, particularly in respect to agriculture, has been slower than anticipated. This may be attributed to the complex nature of radar interactions with agricultural targets and the suboptimal configuration of the satellite sensors available in the 1990s (Ulaby, 1998; Bouman et al., 1999). Because most attention is still devoted to the problem of deriving high-quality soil moisture and vegetation products, there have been few investigations on how to combine such radar products with other data and models to obtain value-added agricultural drought products.

Techniques to Predict Agricultural Droughts

In general, the techniques to predict drought include statistical regression, time series, stochastic (or probabilistic), and, lately, pattern recognition techniques. All of these techniques require that a quantitative variable be identified to define drought, with which to begin the process of prediction. In the case of agricultural drought, such a variable can be the yield (production per unit area) of the major crop in a region (Kumar, 1998; Boken, 2000). The crop yield in a year can be compared with its long-term average, and drought intensity can be classified as nil, mild, moderate, severe, or disastrous, based on the difference between the current yield and the average yield. Regression techniques estimate crop yields using yield-affecting variables. A comprehensive list of possible variables that affect yield is provided in chapter 1. Usually, the weather variables routinely available for a historical period that significantly affect the yield are included in a regression analysis. Regression techniques using weather data during a growing season produce short-term estimates (e.g., Sakamoto, 1978; Idso et al., 1979; Slabbers and Dunin, 1981; Diaz et al., 1983; Cordery and Graham, 1989; Walker, 1989; Toure et al., 1995; Kumar, 1998). Various researchers in different parts of the world (see other chapters) have developed drought indices that can also be included along with the weather variables to estimate crop yield. For example, Boken and Shaykewich (2002) modifed the Western Canada Wheat Yield Model (Walker, 1989) drought index using daily temperature and precipitation data and advanced very high resolution radiometer (AVHRR) satellite data. The modified model improved the predictive power of the wheat yield model significantly. Some satellite data-based variables that can be used to predict crop yield are described in chapters 5, 6, 9, 13, 19, and 28. The short-term estimates are available just before or around harvest time. But many times long-term estimates are required to predict drought for next year, so that long-term planning for dealing with the effects of drought can be initiated in time.

Monitoring Agricultural Drought Using El Niño and Southern Oscillation Data

Almost all the studies performed during the past century have shown that drought is not the result of a single cause. Instead, it is the result of many factors varying in nature and scales. For this reason, researchers have been focusing their studies on the components of the climate system to explain a link between patterns (regional and global) of climatic variability and drought. Some drought patterns tend to recur frequently, particularly in the tropics. One such pattern is the El Niño and Southern Oscillation (ENSO). This chapter explains the main characteristics of the ENSO and its data forms, and how this phenomenon is related to the occurrence of drought in the world regions. Originally, the name El Niño was coined in the late 1800s by fishermen along the coast of Peru to refer to a seasonal invasion of south-flowing warm currents of the ocean that displaced the north-flowing cold currents in which they normally fished. The invasion of warm water disrupts both the marine food chain and the economies of coastal communities that are based on fishing and related industries. Because the phenomenon peaks around the Christmas season, the fishermen who first observed it named it “El Niño” (“the Christ Child”). In recent decades, scientists have recognized that El Niño is linked with other shifts in global weather patterns (Bjerknes, 1969; Wyrtki, 1975; Alexander, 1992; Trenberth, 1995; Nicholson and Kim, 1997). The recurring period of El Niño varies from two to seven years. The intensity and duration of the event vary too and are hard to predict. Typically, the duration of El Niño ranges from 14 to 22 months, but it can also be much longer or shorter. El Niño often begins early in the year and peaks in the following boreal winter. Although most El Niño events have many features in common, no two events are exactly the same. The presence of El Niño events during historical periods can be detected using climatic data interpreted from the tree ring analysis, sediment or ice cores, coral reef samples, and even historical accounts from early settlers.

NOAA/AVHRR Satellite Data-Based Indices for Monitoring Agricultural Droughts

Operational polar-orbiting environmental satellites launched in the early 1960s were designed for daily weather monitoring around the world. In the early years, they were mostly applied for cloud monitoring and for advancing skills in satellite data applications. The new era was opened with the series of TIROS-N launched in 1978, which has continued until present. These satellites have such instruments as the advanced very high resolution radiometer (AVHRR) and the TIROS operational vertical sounder (TOVS), which included a microwave sounding unit (MSU), a stratospheric sounding unit (SSU), and high-resolution infrared radiation sounder/2 (HIRS/2). These instruments helped weather forecasters improve their skills. AVHRR instruments were also useful for observing and monitoring earth surface. Specific advances were achieved in understanding vegetation distribution. Since the late 1980s, experience gained in interpreting vegetation conditions from satellite images has helped develop new applications for detecting phenomenon such as drought and its impacts on agriculture. The objective of this chapter is to introduce AVHRR indices that have been useful for detecting most unusual droughts in the world during 1990–2000, a decade identified by the United Nations as the International Decade for Natural Disasters Reduction. Radiances measured by the AVHRR instrument onboard National Oceanic Atmospheric Administration (NOAA) polar-orbiting satellites can be used to monitor drought conditions because of their sensitivity to changes in leaf chlorophyll, moisture content, and thermal conditions (Gates, 1970; Myers, 1970). Over the last 20 years, these radiances were converted into indices that were used as proxies for estimating various vegetation conditions (Kogan, 1997, 2001, 2002). The indices became indispensable sources of information in the absence of in situ data, whose measurements and delivery are affected by telecommunication problems, difficult access to environmentally marginal areas, economic disturbances, and political or military conflicts. In addition, indices have advantage over in situ data in terms of better spatial and temporal coverage and faster data availability. The AVHRR-based indices used for monitoring vegetation can be divided into two groups: two-channel indices, and three-channel indices.