Review and classification of indicators of green water availability and scarcity

Abstract

Research on water scarcity has mainly focussed on blue water (ground- and surface water), but green water (soil moisture returning to the atmosphere through evaporation) is also scarce, because its availability is limited and there are competing demands for green water. Crop production, grazing lands, forestry and terrestrial ecosystems are all sustained by green water. The implicit distribution or explicit allocation of limited green water resources over competitive demands determines which economic and environmental goods and services will be produced and may affect food security and nature conservation. We need to better understand green water scarcity to be able to measure, model, predict and handle it. This paper reviews and classifies around 80 indicators of green water availability and scarcity, and discusses the way forward to develop operational green water scarcity indicators that can broaden the scope of water scarcity assessments.

Hydrol. Earth Syst. Sci., 19, 45814608, 2015 www.hydrol-earth-syst-sci.net/19/4581/2015/ doi:10.5194/hess-19-4581-2015 Author(s) 2015. CC Attribution 3.0 License.

Review and classication of indicators of green water availability and scarcity

J. F. Schyns, A. Y. Hoekstra, and M. J. Booij

Twente Water Centre, University of Twente, Enschede, the Netherlands

Correspondence to: J. F. Schyns ([email protected])

Received: 20 May 2015 Published in Hydrol. Earth Syst. Sci. Discuss.: 11 June 2015 Revised: 4 November 2015 Accepted: 5 November 2015 Published: 18 November 2015

Abstract. Research on water scarcity has mainly focussed on blue water (ground- and surface water), but green water (soil moisture returning to the atmosphere through evaporation) is also scarce, because its availability is limited and there are competing demands for green water. Crop production, grazing lands, forestry and terrestrial ecosystems are all sustained by green water. The implicit distribution or explicit allocation of limited green water resources over competitive demands determines which economic and environmental goods and services will be produced and may affect food security and nature conservation. We need to better understand green water scarcity to be able to measure, model, predict and handle it. This paper reviews and classies around 80 indicators of green water availability and scarcity, and discusses the way forward to develop operational green water scarcity indicators that can broaden the scope of water scarcity assessments.

1 Introduction

Freshwater is a renewable resource that is naturally replenished over time when moving through the hydrological cycle (Oki and Kanae, 2006; Hoekstra, 2013). Precipitation forms the input of freshwater on land. Subsequently, it takes the blue or the green pathway back to the ocean and atmosphere before eventually returning as precipitation again (Falken-mark, 2003; Falkenmark and Rockstrm, 2006, 2010). The water that runs off to the ocean via rivers and groundwater is called the blue water ow. The green water ow is formed by the water that is temporarily stored in the soil and on top of vegetation and returns to the atmosphere as evaporation instead of running off (Hoekstra et al., 2011). As suggested by Savenije (2004), in this paper we use the term evaporation

(instead of the often used term evapotranspiration) to refer to the vapour ux from land to atmosphere, which includes soil evaporation, evaporation of intercepted water, transpiration and in some cases (e.g. rice or swamp vegetation) open-water evaporation. About three-fth of the precipitation over land takes the green path and two-fth the blue path (Oki and Kanae, 2006).

Both blue and green water ows are made productive for human purposes. Blue water is used for industrial and domestic purposes and irrigation in agriculture. Green water sustains crop production, grazing lands, forestry and terrestrial ecosystems (Rockstrm, 1999; Rockstrm et al., 1999;Savenije, 2000; Gerten et al., 2005). These systems provide food, bres, biofuels, timber and livestock products and other ecosystem services humans benet from (Millennium Ecosystem Assessment, 2005; Gordon et al., 2010).

Although freshwater is renewable, this does not mean that its availability is unlimited. In fact, freshwater is also a nite resource (Hoekstra, 2013). Over a certain period, there falls a certain amount of precipitation. This limits both blue and green water availability in time. Human society cannot appropriate more water than is available. The niteness of freshwater, in combination with the various competing demands for water, makes water a scarce resource.

Water scarcity is becoming increasingly important for multiple reasons. The growing world population leads to rising demands for food, energy and other water-consuming goods and services (Hejazi et al., 2014; WWAP, 2015). Moreover, peoples diets are changing toward more livestock-based products, due to rising incomes and continuing urbanization (Molden, 2007). Such diets are more water and land intensive (Erb et al., 2009; Kastner et al., 2012;Odegard and van der Voet, 2014). Policies towards more en-

Published by Copernicus Publications on behalf of the European Geosciences Union.

4582 J. F. Schyns et al.: Review and classication of indicators of green water availability and scarcity

ergy production from biomass create additional pressure on water and land (Hejazi et al., 2014). Additionally, a changing climate with increased variability and more extremes (IPCC, 2013) amplies water scarcity (WWAP, 2014).

Given that green and blue water resources are limited and there are competing demands for both, green water as well as blue water are scarce. Therefore, it is surprising that research and debate on water scarcity have been, and still are, mainly focussed on blue water (Vrsmarty et al., 2000, 2010; Rijsberman, 2006; Wada et al., 2011; Hoekstra et al., 2012; WWAP, 2014, 2015). Although the importance of green water has increasingly gained acceptance since Falken-mark (1995) drew attention to it in the mid-1990s (Savenije, 2000; Rockstrm, 2001; Rijsberman, 2006; Liu et al., 2009;Hanasaki et al., 2010; Hoekstra and Mekonnen, 2012), the notion of green water scarcity is addressed in the literature to a limited extent (Falkenmark et al., 2007; Falkenmark, 2013a, b). While the need to incorporate green water in water scarcity indicators and assessments has already been expressed since the beginning of this millennium (Savenije, 2000; Rockstrm, 2001; Rijsberman, 2006; Falkenmark and Rockstrm, 2006), only a few attempts have been made so far in the form of combined greenblue water scarcity assessments (Rockstrm et al., 2009; Gerten et al., 2011; Kummu et al., 2014) (discussed in detail in Sect. 3.2).

Green water scarcity refers to the competition over limited green water resources and allocation over competing demands. This allocation occurs mostly implicitly and indirectly, since generally it is land that is been allocated to a certain use. This indirectness of allocation, together with the absence of a price, makes green water scarcity invisible in our economy. This does not mean, though, that green water resources are not scarce, since using green water for one purpose makes it unavailable for another purpose. We need to measure how scarce green water is in order to answer questions like following: Can we produce enough food, feed, bres, bioenergy and forestry products with limited availability of water resources and suitable land? How can we do so without compromising natural ecosystems and other sectors that put a claim on water and land resources? For studying these crucial questions, a sole assessment of blue water scarcity is insufcient.

Therefore, it is due time that more attention is given to green water scarcity and how we can measure it. In this review, we make an inventory of existing indicators of green water availability and scarcity, and classify them based on their scope and purpose of measurement. The classication allows us to discuss similarities and differences between indicators and give advice on how the various indicator classes could be used to measure different kinds of green water availability or scarcity. This is useful in order to properly include limitations in green water availability in water scarcity assessments.

A review of green water scarcity indicators is new in its kind. Past reviews of water scarcity indicators (Savenije,

2000; Rijsberman, 2006) date back a while and hence do not include recent developments in the eld, especially those related to the inclusion of green water. There exist multiple reviews of indicators of aridity (Walln, 1967; Walton, 1969; Stadler, 2005) and drought (World Meteorological Organization, 1975; Wilhite and Glantz, 1985; Maracchi, 2000;Tate and Gustard, 2000; Keyantash and Dracup, 2002; Heim, 2002; Hayes, 2007; Kallis, 2008; Mishra and Singh, 2010;Sivakumar et al., 2011). We classify and discuss these indicators in an overarching way. First, we discuss the multiple dimensions of water availability and scarcity, and sharpen the scope of this review (Sect. 2). Next, we classify and review green water availability and scarcity indicators (Sect. 3). Finally, we draw conclusions and discuss future research directions (Sect. 4).

2 Multiple aspects of water availability and scarcity

The concepts of water availability and scarcity are examined in Sect. 2.1 to 2.4. We will reect on these concepts in broad terms, not yet focussing on green water. In Sect. 2.5, we detail the scope of the indicators discussed in this paper.

2.1 Water availability and scarcity

A straightforward denition of water scarcity is an excess of water demand over available supply (FAO, 2012). Various other denitions of water scarcity exist that aim to be more inclusive.

An imbalance between supply and demand of freshwater in a specied domain (country, region, catchment, river basin, etc.) as a result of a high rate of demand compared with available supply, under prevailing institutional arrangements (including price) and infrastructural conditions. (FAO, 2015)

When an individual does not have access to safe and affordable water to satisfy her or his needs for drinking, washing or their livelihoods we call that person water insecure.When a large number of people in an area are water insecure for a signicant period of time, then we can call that area water scarce. (Rijsberman, 2006)

Considering these denitions, we can conclude that water scarcity is not something that is experienced by a single person at a particular moment (day or week). Rather, it is experienced by a larger community within a certain geographic area (e.g. catchment or country) and relates to larger timescales (months or years).

The concept of scarcity describes a relation between humans and nature (Baumgrtner et al., 2006). Nevertheless, we can distinguish water scarcity mainly caused by natural conditions of low water availability from scarcity mainly induced by a large human demand relative to natural availability. The latter can also occur in naturally water-abundant areas (Pereira et al., 2002).

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Until now we have spoken about physical water scarcity, referring to the situation where there is insufcient water to meet human demand. If human, institutional and nancial capital limit access to water, the term economic water scarcity applies (Seckler et al., 1999; Molden, 2007).In a broader sense, Ohlsson (2000) denes social resource scarcity as the situation in which social resources required to successfully adapt to physical water scarcity fall short.

2.2 Relative and absolute water scarcity

According to economic theory, water is a scarce good because it carries opportunity costs, which are the benets foregone from possible alternative uses of the water (FAO, 2004).This is a form of relative scarcity based on the assumption of substitutability of goods (Baumgrtner et al., 2006). Water can be scarce in the relative sense also in water-abundant areas, because allocating water to purpose A implies it cannot be allocated to purpose B. In other words, water for purpose A is scarce in relation to water for other purposes. In common language we are inclined to say that sometimes water is scarce and at other times it is not. In economic sense, water is always scarce; the degree of water scarcity can vary though, and it can even be zero if alternative uses and thus competition is absent.

We speak of absolute scarcity when according to Baumgrtner et al. (2006) scarcity concerns a non-substitutable means for satisfaction of an elementary need and cannot be levied by additional production. This means that in an area with a limited amount of water resources (that cannot be increased), at a certain level of consumption, water for elementary purposes (e.g. drinking and food production) will no longer be substitutable with water used for less essential purposes. In this case, there is absolute scarcity of water. Whether water is scarce in the absolute or relative sense thus depends on the degree of water scarcity: relative water scarcity turns into absolute scarcity when the boundaries of water exploitation are approached.

2.3 Blue and green water

Freshwater essentially stems from precipitation, which partitions into green and blue water (Falkenmark and Rockstrm, 2006, 2010). As discussed in the introduction of this paper, water availability and scarcity can pertain to both blue or green water resources, separately or in combination (Falken-mark, 2013a).

In contrast to the clear denition of blue water, various denitions of green water exist, dening it as an inow (precipitation), a stock (rainwater in the soil) or an outow (evaporation of rainwater). Often, the term green water is used to refer to rainwater stored in the soil or more specically plant-available soil moisture in the unsaturated zone (Falken-mark et al., 2007; Falkenmark, 2013a); in this context the term green water is interpreted as a stock. Commonly, the

distinction is made between green water stock and green water ow (Falkenmark and Rockstrm, 2006, 2010). The latter is an outow, usually dened as actual evaporation over land (referring to the entire landatmosphere vapour ux; see comment in the introduction), but it has also been dened as transpiration only (Savenije, 2000). Furthermore, some authors include precipitation (i.e. an inow) in the denition of green water (Weiskel et al., 2014). The latter is in contrast with the denition of Falkenmark and Rockstrm (2006) (adhered to in this paper) that precipitation is the undifferentiated freshwater resource. Scholars who have tried to quantify green water availability in water scarcity assessments dened it as the actual evaporation ux over land to the atmosphere (Rockstrm et al., 2009; Gerten et al., 2011; Kummu et al., 2014) (Sect. 3).

While not always made explicit in denitions, an accurate description of the green water storage and ow excludes the part of the storage and vapour ow that originates from blue water resources, which have been redirected to the soil moisture stock by means of irrigation, capillary rise or natural ooding (Hoekstra et al., 2011). In such cases, the green and blue contributions to the soil moisture can be tracked with a model-based water balance approach (see Chukalla et al., 2015).

2.4 Water quantity and quality

Water scarcity is not only a function of the quantity of the water resource in relation to the demand, but also the quality of the resource in relation to the required quality for its end purpose (Pereira et al., 2002). If there is sufcient water available for a certain purpose, but it is polluted to such an extent that it is not usable for that purpose, then water can be considered scarce as long as the means are not available for cleaning the water to a desirable level. Pollution of water resources can thus aggravate water scarcity (FAO, 2012).

Water quality in the case of green water differs from that of blue water. The quality of green water depends on soil properties such as nutrient availability, nutrient retention capacity and the presence of salts and toxic substances. However, close ties with blue water quality do exist. For example, blue water used for irrigation can increase soil salinity when the water is brackish or saline and it can also ush out excess nutrients and other substances.

2.5 Scope of the review and classication

This paper focuses on green water, water quantity and physical water scarcity and treats of both green water availability and scarcity. In the next section, we consider indicators within this scope, including indicators of aridity, agricultural, meteorological and vegetation drought, soil moisture availability and overall greenblue water scarcity. The focus of this paper implies that several concepts and indicators fall outside the scope of the classication. The concepts and in-

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dicators focussing on blue water that are outside the scope of this paper are the following:

Hydrological drought: concerns the effects of dry periods on surface and subsurface ows and stocks and is therefore related to blue water. Examples of associated indicators are surface water supply index (Shafer and Dezman, 1982), Palmer Hydrological Drought Index (Karl, 1986) and several indicators reviewed by Smakhtin (2001).

Blue water scarcity: measures demand for blue water resources versus blue water availability and is thus purely related to blue water. Examples of associated indicators are the water crowding indicator (Falkenmark et al., 1989), the withdrawal-to-discharge ratio (Vrsmarty et al., 2000), water poverty index (Sullivan et al., 2003), water stress indicator (Smakhtin et al., 2004), water stress index (Pster et al., 2009), dynamic water stress index (Wada et al., 2011) and blue water scarcity (Hoekstra et al., 2012). Note that some of these indicators also incorporate more than only physical elements of water scarcity (e.g. water poverty index).

The concepts related to broader forms of water scarcity than physical water scarcity that are outside the scope of this paper are the following:

Socio-economic drought: concerns imbalances in supply and demand of economic goods due to the physical characteristics of drought (Wilhite and Glantz, 1985;American Meteorological Society, 2013) with effects on the economy and society. The American Meteorological Society (2013) mentions the following effects: loss of income from lower crop yields, reduced spending in rural communities, health issues and mass migration.

Social resource scarcity: see Sect. 2.1.

Furthermore, the review and classication in this paper excludes indicators that measure drought by combining multiple drought indicators (classied individually) and sometimes other information such as land use maps. Examples of such indicators are the US Drought Monitor (Svoboda et al., 2002) and the Vegetation Drought Response Index (Brown et al., 2008).

3 Green water availability and scarcity indicators

We have identied around 80 indicators of green water availability and scarcity, which we classify into the following categories:

1. Green water availability indicators show whether green water availability is low or high and are insensitive to actual water demand. In other words, when the water

demand increases, indicator values will not reect this.

Within this category we distinguish absolute and relative green water availability indicators:

(a) Absolute green water availability indicators measure actual conditions of green water availability (in an absolute sense).

(b) Relative green water availability indicators measure actual conditions of green water availability compared to conditions that are perceived as normal, which is often dened as the climate-average or median value of the variable of interest.

Note that this distinction between absolute and relative indicators is unrelated to and different from the concepts of relative and absolute scarcity earlier discussed in Sect. 2.2.

1. Green water scarcity indicators incorporate elements of both water availability and demand and therefore respond in contrast to green water availability indicators to changes in water demand as well. We distinguish three different options to measure green water scarcity conceptually (explanation in Sect. 3.2):

(a) green water crowding;

(b) green water requirements for self-sufciency versus green water availability;

(c) actual green water consumption versus green water availability.

The usage of terms like water availability and water demand can be confusing because in different contexts they have different meanings. The term green water availability is basically used in two different ways. When we speak of green water availability indicators (Sect. 3.1), we refer to indicators that measure the availability of green water in one way or another, without considering availability in relation to an actual demand for green water. This is in contrast with green water scarcity indicators that always compare demand to availability. In the case of green water scarcity indicators, the term green water availability specically refers to the part of the green water ow available for biomass production for human purposes (Sect. 3.2). Also the term demand occurs in two different contexts. When we speak of demand in the context of green water scarcity, we refer to the demand for green water, associated with the production of biomass for human purposes. In the discussion of agricultural drought indicators in Sect. 3.1, the term crop moisture/evaporation/water demand is used to refer to the water needs of the crop for non-water limited growth.

The indicator categories will be discussed in the following sections. Table 1 provides an overview of the categories and summarizes what they measure, which human factors directly inuence them and what they are used for. Furthermore, the conceptual diagram in Fig. 1 displays the indicator categories and the factors that inuence them.

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Table 1. Overview of indicator categories.

Indicator category (parent category)

Measures Human factors of direct inuence

Purposes

Aridity (absolute green water availability)

Long-term annual climatic balance between precipitation and evaporation.

Classication of climates; characterization of (semi)-arid zones.

Agricultural drought (absolute green water availability)

Actual soil moisture availability versus crop water demand for non-water limited growth.

Soil management affecting inltration and groundwater recharge (percolation); crop management.

Assessing the extent to which crop growth is adversely affected by limiting soil moisture conditions; linking drought conditions to yield losses.

Absolute soil moisture (absolute green water availability)

Actual soil moisture availability.

Soil management affecting inltration and groundwater recharge (percolation).

Monitoring spatial and temporal variation in soil moisture availability; analysing the correlation between soil moisture availability and crop evaporation and yields; warning for onset of agricultural drought.

Agricultural suitability under rain-fed conditions (absolute green water availability)

Land suitability for rain-fed crop production based on climate-average temperature and precipitation conditions, crop and soil characteristics and terrain slope.

Level of agricultural inputs and management.

Agro-ecological zoning; determining a locations potential for rain-fed agriculture (yield gap analysis).

Drought monitoring as a basis for early warning systems and decision-support tools; assessing drought severity based on intensity, duration and spatial extent; comparison of historic drought events.

Meteorological drought (relative green water availability)

Whether there is relatively little precipitation or whether the normal balance between precipitation and potential evaporation is distorted.

Vegetation drought (relative green water availability)

Greenness of vegetation relative to historical observations of greenness.

Pruning or clearing; prevention of plant disease.

Assessment of drought impact on vegetation; early drought detection; studying the correlation between vegetation health and soil moisture availability, thermal conditions and crop yields.

Relative soil moisture (relative green water availability)

Whether the soil is dryer or wetter than normal.

Soil management affecting inltration and groundwater recharge (percolation).

Monitoring spatial and temporal variation in relative soil moisture availability; analysing the correlation between soil moisture availability and crop yields.

Studying green water availability in relation to hypothetical green water requirements for self-sufciency; identifying geographic areas that have too limited green water availability for self-sufciency and are dependent on blue water resources and virtual water import (assessing food security).

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Green water crowding (green water scarcity)

The potential of a geographic area to reach self-sufciency based on its available green water resources.

Consumption pattern (diet composition); population growth; land use changes.

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Table 1. Continued.

Green water requirements for self-sufciency versus green water availability (green water scarcity)

Idem to green water crowding indicators.

Consumption pattern (diet composition); population growth; crop and soil management affecting water productivities; land use changes.

Idem to green water crowding indicators.

Actual green water consumption versus green water availability (green water scarcity)

The degree to which the available green water resources in a geographic area have been appropriated, i.e. the extent to which the green water footprint has reached its maximum sustainable level.

Consumption pattern (diet composition); population growth; production pattern; crop and soil management affecting water productivities; land use changes.

Studying the competition over limited green water resources and allocation over competing demands.

Figure 1. Conceptual diagram of indicator categories and the factors that inuence them.

3.1 Green water availability indicators

Indicators of green water availability fall apart in indicators that measure availability in an absolute sense or in terms of relative to normal conditions. These two categories are treated in the next two subsections. Descriptions of various specic green water availability indicators that fall in the two categories are included in Appendices A and B. The indicator abbreviations used in this section are dened in these appendices.

3.1.1 Absolute green water availability indicators

Indicators in this category measure green water availability in a certain area (or location) and period (or moment) in an absolute sense. We nd here indicators of aridity, agricultural drought, soil moisture and agricultural suitability, which are subsequently discussed in the following. Aridity indicators

are purely climatic, while the others are also inuenced by the characteristics and management of the soil and vegetation.

Aridity indicators

Aridity is seen as a permanent feature of a climate, consisting of low average annual precipitation and/or high evaporation rates, often resulting in low soil moisture availability (Pereira et al., 2002; Heim, 2002; Kallis, 2008). As such, one can say that an aridity map shows the preconditions for vegetation (Falkenmark and Rockstrm, 2004). Aridity indicators are usually based on long-term average annual comparisons of precipitation versus potential evaporation, temperature or atmospheric saturation decit, whereby the latter two were often used in the twentieth century as proxies for potential evaporation due to lack of data. They have been used for the classication of climates, specically the characterization of (semi-)arid zones. Some more recently developed aridity indicators compare the actual rather than potential evaporation rate with precipitation (ER, HU-ER). These indicators reect the actual availability of water at a given location (also from lateral uxes) for meeting the evaporative demand of the atmosphere.

The SCMD by Wilhelmi et al. (2002) is somewhat different than the classical aridity indicators. It shows the probability of seasonal crop moisture deciency based on a combination of long-term precipitation records and area-weighted evaporation of the mixture of crops grown in the study area. Wilhelmi and Wilhite (2002) apply the SCMD to assess agricultural drought vulnerability in Nebraska. We classify the SCMD here under the aridity indicators, because like most aridity indicators, it measures precipitation versus evaporation and is calculated for a historical time period, thus representing a long-term average.

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Agricultural drought indicators

According to the World Meteorological Organization (1975), agricultural drought indicators indirectly express the degree to which growing plants have been adversely affected by an abnormal moisture deciency, which may be the result of an unusually small moisture supply or an unusually large moisture demand (World Meteorological Organization, 1975). Formulated differently by Sivakumar (2011): Agricultural drought depends on the crop evapotranspiration demand and the soil moisture availability to meet this demand.

Therefore, the bulk of agricultural drought indicators measures crop-available water compared to crop water needs for non-water limited growth (i.e. potential evaporation) and are usually applied on a daily, weekly, monthly or seasonal basis (Woli et al., 2012). Some indicators measure the plant water decit more specically by looking at the difference between actual and potential transpiration (e.g. DTx and WDI). Agricultural drought indicators can be inuenced by soil management that affects the rates of inltration and percolation and thus the water available to the crop.

Drought is typically a relative-to-normal phenomenon as will be discussed in Sect. 3.1.2. Agricultural drought indicators, which measure actual relative to potential evaporation, are relative indicators in another way, though. They do not compare actual with normal conditions. Instead, they compare moisture supply with a crop water demand in the ideal case of non-water limited growth. Therefore these indicators actually measure absolute green water availability (actual evaporation), set against this crop water demand. In fact, these indicators say more about the demand for blue water (irrigation) to ensure non-water limited crop growth than they do about green water availability. Some indicators do somehow compare the actual to potential evaporation ratio with a multi-year average (or median) of this ratio and are thus in essence relative indicators according to our classication.Examples are the CMI, DSI and GrWSI and anomalies of the ESI and WS. Nevertheless, they are classied as agricultural drought indicators because they, like most of the others, measure actual to potential evaporation.

A note is required on the GWSI by Nunez et al. (2013) of which the name suggests that it is a green water scarcity indicator. Nevertheless, we classify it as an agricultural drought indicator, because it measures actual moisture supply versus crop-specic reference evaporation, albeit on a larger timescale (3-year crop rotation) than most other agricultural drought indicators.

Absolute soil moisture indicators

Multiple indicators provide a measure of the absolute amount of soil moisture available at a given location and moment (or summed over a period), be it on the basis of eld measurements (e.g. SMIX, SMI) and/or modelling of the soil water balance (e.g. Avg-GWS and SD-GWS) or remote sens-

ing data (e.g. TVDI, MPDI). They can be used for monitoring spatial and/or temporal variations in soil moisture availability. Temporal analysis of soil moisture availability can warn for the onset of agricultural drought, or in contrast, the proneness to ash oods (Hunt et al., 2009). Several of these indicators have been introduced and applied as indicators of agricultural drought (e.g. ADD, SMDI, SMIX, SMI), analysing the correlation between soil moisture availability and crop yields. Therefore, they are typically calculated on intra-annual timescales.

It should be noted that the soil moisture can partially be blue also under rain-fed conditions due to capillary rise or natural ooding (Sect. 2.3). This note also applies to the other indicators that are not purely based on climatic factors (Fig. 1).

Agricultural suitability under rain-fed conditions

Maps that classify land according to agricultural suitability under rain-fed conditions (green water only) are indirect measures of green water availability in the absolute sense.Up to date, two global studies have made such land suitability classications for rain-fed crop production for climate-average temperature and precipitation conditions and taking into account crop characteristics, various soil parameters and terrain slope: GAEZ (IIASA/FAO, 2012) and GLUES (Zabel et al., 2014). The GAEZ study additionally considers various levels of agricultural input/management. Both studies classify lands as not suitable, marginally suitable, moderately suitable or highly suitable. This classication shows where the climate, soil and topographic conditions are more or less suitable for agricultural production with green water only.In other words, where aridity maps show the preconditions for vegetation in general (Falkenmark and Rockstrm, 2004), these maps show the preconditions for rain-fed crop production, therein considering crop, soil and terrain parameters in addition to climate.

.2 Relative green water availability indicators

Indicators in this category measure green water availability relative to a normal condition and are usually calculated on intra-annual scales. As opposed to aridity, drought is often dened as a condition relative to what is perceived as a normal amount of precipitation or balance between precipitation and evaporation (World Meteorological Organization, 1975;Wilhite and Glantz, 1985). Droughts are often termed temporary, uncertain and difcult to predict features characterized by lower-than-average precipitation (Pereira et al., 2002;Heim, 2002; Kallis, 2008; Mishra and Singh, 2010; FAO, 2015). Therefore, indicators of meteorological drought and vegetation drought are classied into the category of relative green water availability indicators. Indicators that measure soil moisture in a relative sense are included in this category as well. Just like aridity indicators, meteorological drought

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ity, which is experienced by a community (numerous people) within a certain geographic area (e.g. catchment or country) over a signicant period of time (months or years). We can then dene green water scarcity as the degree of competition over limited green water resources, whereby the demand for green water resources to sustain the production of a desirable level of biomass-based products within a certain geographic area is somehow compared to the available green water resources in space and time.

Since production of biomass-based products (food, bres, biofuels, timber) generally takes place in cycles of 1 year (or more in case of perennials and forestry), this denition of green water scarcity incorporates the signicant-period-of-time element in the imbalance between green water demand and availability. Furthermore, limited production of biomass-based products affects numerous people, both producers and consumers.

As opposed to the indicators discussed in Sect. 3.1, indicators of green water scarcity thus need to include a measure of green water demand, associated with the production of biomass for human purposes, compared to green water availability. In other words, they should measure the green water demand related to crop production, grazing lands and forestry in relation to green water availability. Note that the term green water availability here refers to the part of the green water ow available for biomass production for human purposes (in space and time); it thus excludes green water ows that are effectively unavailable, for instance green water ows in unsuitable areas (e.g. because of steep slopes) or green water ows in cold parts of the year unsuitable for growth.

We distinguish three different options to measure green water scarcity conceptually:

1. green water crowding: per capita available green water resources in an area compared to a global average threshold representing the amount of green water required to sustain a persons standard consumption pattern of biomass-based products;

2. green water requirements for self-sufciency versus green water availability: green water requirements for producing the consumed biomass-based products within a certain geographic area, assuming self-sufciency within the geographic area, compared to the green water resources in the geographic area;

3. actual green water consumption versus green water availability: actual green water consumption in a certain geographic area (associated with the actual production of biomass for human purposes) compared to green water availability in the area. This type of indicator thus acknowledges the possibility of virtual water trade as opposed to assuming self-sufciency as in the previous two types of indicators.

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4588 J. F. Schyns et al.: Review and classication of indicators of green water availability and scarcity

indicators are solely based on climatic variables. The other two subcategories are also affected by the soil and vegetation and how they are managed. The three subcategories are sequentially discussed in the following.

Meteorological drought indicators

Meteorological drought indicators fall apart in indicators that are solely based on precipitation (e.g. SPI) and those that consider both precipitation and potential evaporation (e.g.PDSI, RDI, SPEI). These indicators show whether there is relatively little precipitation or whether the normal balance between precipitation and evaporation is distorted. Unlike aridity indicators, which are generally based on long-term annual averages reecting climate, these indicators capture variations in the weather. They are applied for monitoring the intensity, duration and spatial extent of droughts and determining drought severity based on these characteristics. This is useful for recognizing droughts and comparing them with past drought, which serves as a basis for early warning systems and decision-support tools.

Vegetation drought indicators

Vegetation drought indicators show the drought impact on vegetation by measuring the weather-related variations in greenness of vegetation. They reect whether vegetation greenness is deviating from regular conditions. They can be used for studying the correlation between vegetation health and soil moisture availability, thermal conditions and crop yields (Kogan, 2001). Since the vegetation drought indicators we have identied are all based on remote sensing observations, the indicators do not directly show whether deviations are caused by relatively dry weather (i.e. meteorological drought) or by other factors inuencing vegetation growth (e.g. plant diseases or human interference such as pruning and clearing). Satellite-based vegetation drought indicators respond to subtle changes in vegetation canopy, which makes them suitable for early drought detection (Kogan, 2001).

Relative soil moisture indicators

In contrast to the absolute soil moisture indicators discussed in Sect. 3.1.1, these indicators measure the moisture conditions at a given location relative to a normal condition. Identied examples are the PZI, SMAI and SD. These indicators have similar uses as absolute soil moisture indicators.They are also used to correlate soil moisture conditions to crop yields and are considered suitable for measuring agricultural droughts (Keyantash and Dracup, 2002; Narasimhan and Srinivasan, 2005).

3.2 Green water scarcity indicators

As put forward in Sect. 2, water scarcity pertains to a situation with a high water demand compared to water availabil-

J. F. Schyns et al.: Review and classication of indicators of green water availability and scarcity 4589

In Sect. 3.2.1 and 3.2.2, we discuss existing indicators that measure overall greenblue water scarcity and reect on how these indicators could be adapted to measure green water scarcity specically, according to above-mentioned options(1) and (2). In Sect. 3.2.3, we elaborate upon a third way of measuring green water scarcity that has yet to be brought into practice. The challenges for operationalization of these green water scarcity indicators are discussed in Sect. 3.2.4.Finally, in Sect. 3.2.5 we reect on green water scarcity indicators versus indicators that measure overall greenblue water scarcity.

3.2.1 Green water crowding

Rockstrm et al. (2009) introduced a combined greenblue water shortage index, which compares the sum of green and blue water availability with a global average threshold of 1300 m3 cap1 yr1. This threshold represents the green and blue water requirements for sustaining a global average standard diet. When greenblue water availability drops below the threshold, this indicates a shortage of greenblue water resources in the study area and reects the areas dependency on external water resources. The greenblue water shortage index is an indicator of water crowding, similar to Falkenmarks blue-water-focussed water crowding indicator (Falkenmark et al., 1989).

Similar to the indicator by Rockstrm et al. (2009), an indicator of green water crowding could be dened as the per capita available green water resources in an area compared to a global average threshold representing the amount of green water required to sustain a persons standard consumption pattern. We intentionally speak here of a consumption pattern, because green water is required not only to produce food, but also to produce other biomass-based products humans consume, such as bres, biofuels and forestry products.As such, the measure of green water requirements we propose here is broader than the denition of a standard diet according to Rockstrm et al. (2009) (and Gerten et al., 2011;Kummu et al. 2014), which only pertains to water requirements for food production.

Rockstrm et al. (2009) dene green water availability as the soil moisture available for productive vapour ows from agricultural land. Technically, they calculate green water availability as actual evaporation from existing cropland and permanent pasture, reduced by a factor 0.85 that accounts for minimum evaporation losses that are unavoidable in agricultural systems (Rockstrm et al., 2009). This denition is dependent on the extent of agricultural land and excludes available green water on lands that are currently uncultivated, but have potential to be used productively in a sustainable manner.

3.2.2 Green water requirements for self-sufciency versus green water availability

Gerten et al. (2011) and Kummu et al. (2014) elaborated on the work by Rockstrm et al. (2009) by further developing and applying the overall greenblue water scarcity indicator. Instead of using a global average, Gerten et al. (2011) calculate the greenblue water requirements for sustaining a standard diet on the national level based on local crop water productivities and compare this with the sum of green and blue availability in each country of the world. The resulting greenblue water scarcity indicator, computed for each country, is dened as the ratio between greenblue water availability and greenblue water requirements for producing the standard diet. They dene green water availability similar to Rockstrm et al. (2009), but a bit more conservative: they do not assume year-round evaporation from areas covered with their category of other crops that they parameterized as perennial grass, since this category includes non-food crops and crops that grow only during a part of the year (Gerten et al., 2011).

Whereas the studies by Rockstrm et al. (2009) and Gerten et al. (2011) are based on climate-averages, Kummu et al. (2014) apply the greenblue water scarcity indicator by Gerten et al. (2011) on a year-by-year basis to account for inter-annual climate variability on the scale of food producing units, the scale at which demand for water and food is assumed to be managed according to the authors. Kummu et al. (2014) measure the frequency of years in which green blue water availability falls short of greenblue water requirements, on which they base their classication of green blue scarcity: no scarcity, occasional scarcity (subdivided in four levels) or chronic scarcity.

The greenblue water scarcity indicator shows the potential of a geographic area (e.g. country or food producing unit) to reach food self-sufciency and reects its dependency on trade in agricultural commodities and associated virtual water (Kummu et al., 2014). A similar indicator for green water could show an areas green water demand (for self-sufciency in biomass-based products, for sustaining the standard consumption pattern) compared to green water availability in the area. It would also reect an areas dependency on internal blue water resources and virtual water trade.

For the potential green water scarcity indicators discussed in Sect. 3.2.1 and 3.2.2, a more comprehensive denition of green water availability is advised than the one applied by Rockstrm et al. (2009), Gerten et al. (2011) and Kummu et al. (2014). An example of a more comprehensive denition is discussed in the following section.

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4590 J. F. Schyns et al.: Review and classication of indicators of green water availability and scarcity

3.2.3 Actual green water consumption versus green water availability

The green water scarcity indicator by Hoekstra et al. (2011) compares the actual green water consumption in an area associated with the actual biomass production pattern (hence considering virtual water trade as opposed to assuming self-sufciency) with green water availability in the area. Green water scarcity is dened as the ratio of the total green water footprint in a catchment in a period (e.g. a year) over green water availability.

The sum of green water footprints equals all actual evaporation (Eact) related to biomass production for human purposes (i.e. agriculture and forestry) excluding the part of the vapour ow that originates from blue water resources (irrigation). Note that for cases where land use is partly natural and partly for human production (e.g. a semi-natural production forest), the green water demand related to human production would need to be expressed as a fraction of the total green water ow. Methods to do so for a production forest are discussed by van Oel and Hoekstra (2012). Green water availability is dened as total Eact over the catchment minus Eact from land reserved for natural vegetation (so-called environmental green water requirement) and minus Eact from land that cannot be made productive, e.g. in areas or periods of the year that are unsuitable for crop growth (Hoekstra et al., 2011). In fact, green water availability dened like this, represents the maximum sustainable green water footprint in the catchment and period under consideration. Hence, the green water scarcity ratio shows the extent to which the green water footprint has reached its maximum sustainable level. Of course, this denition can also be applied to other geographical units than a catchment.

The denition of green water availability by Hoekstra et al. (2011) is more comprehensive than the one used by Rock-strm et al. (2009), Gerten et al. (2011) and Kummu et al. (2014). However, this is also the reason why the indicator has not been made operational yet. Difculties remain in estimating the amount of land that needs to be reserved for nature and when and where the green water ow cannot be made productive (Hoekstra et al., 2011). These challenges are discussed in the following section.

Furthermore, the indicator does not deal with green water scarcity at a particular site as looked upon by Falken-mark et al. (2007) and Falkenmark (2013a). They describe green water scarcity as an issue of lower-than-potential plant-accessible water in the root zone and the occurrence of un-productive evaporation losses from the eld, which results in lower yields than potentially achievable. First, blue water losses in the form of surface run-off and percolation decrease the plant-accessible water in the root zone (smaller green water ow) (Rockstrm and Falkenmark, 2000). Such losses are the result of a soils low inltration capacity (e.g. soil crusting) and poor soil water holding capacity, but can be caused or aggravated by human action through soil mismanagement

(Falkenmark, 2013a). Second, low root/crop water uptake capacity leads to unproductive evaporation losses (green water ow not entirely productive) (Rockstrm and Falkenmark, 2000). Transpiration is a productive form of green water use, contributing to biomass production, while other components of the evaporative ow are regarded as unproductive (Rock-strm and Falkenmark, 2000; Rockstrm, 2001; Rockstrom et al., 2007; Savenije, 2004). Rockstrom et al. (2007) express the productivity of green water use as the ratio of transpiration to evaporation. Rockstrm et al. (2009) call this the transpiration efciency. This transpiration efciency is complementary to the green water scarcity indicator by Hoekstra et al. (2011). A green water scarcity assessment based on both will give insight into the severity of green water scarcity: areas that are considered highly greenwater scarce, but have a low transpiration efciency, may have options to improve the latter and thereby yields, which may lower the green water scarcity.

3.2.4 Challenges for operationalization of green water scarcity indicators

Operationalization of green water scarcity indicators faces three major challenges, particularly regarding the quantication of green water availability.

First, the determination of which areas and periods of the year the green water ow can be used productively is not straightforward. Absolute green water availability indicators, in particular land classications of agricultural suitability, can provide insight in the availability of green water in the spatial dimension. Relative green water availability indicators can enrich the picture by showing which areas are prone to large inter- and intra-annual variations in green water availability, making these areas less suitable for (certain types of) biomass production. To estimate which part of the green water ow can be used productively in time, advanced crop growth models (like APSIM, McCown et al., 1995 and Holzworth et al., 2014; AquaCrop, Steduto et al., 2009; CropSyst, Stckle et al., 2003; EPIC, Jones et al., 1991 or SWAP/WOFOST, van Dam et al., 2008) can be used to simulate water-limited yields and actual evaporation for various cropping periods and different types of soil, crop and agricultural water management (e.g. adding blue water in the form of decit irrigation during a dry spell, might make it possible for the crop to survive and use the green water ow later in the year productively).

Second, estimating green water consumption of forestry is difcult, because it entails separation of production forest evaporation into green and blue parts. This is problematic, because trees generally root so deep that, by means of capillary rise, they directly take up water from groundwater (blue) in addition to the soil moisture (green) (Hoekstra, 2013).

Third, research is required to determine the environmental green water requirements, i.e. the green water ow that should be preserved for nature, similar to the environmen-

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J. F. Schyns et al.: Review and classication of indicators of green water availability and scarcity 4591

tal ow requirements for blue water. Key here is the identication of areas that need to be reserved for nature and biodiversity conservation. It is known that the current network of protected areas is insufcient to conserve biodiversity (Rodrigues et al., 2004a, b; Venter et al., 2014; Butchart et al., 2015) and that attention should be paid to conservation of biodiversity in production landscapes that are shared with humans (Baudron and Giller, 2014). The 11th Aichi Biodiversity Target is to expand the protected area network, which currently has a terrestrial coverage of about 14.6 % (Butchart et al., 2015), to at least 17 % terrestrial coverage by 2020 (Convention on Biological Diversity, 2010). However, to properly assess the limitations to green water availability, spatially explicit information on the additional areas to be preserved is required. The best-available data regarding this are recently published work by Montesino Pouzols et al. (2014). These authors have mapped global and national priority areas for expansion of the protected area network on0.2 degree spatial resolution and assessed associated conservation gains (Montesino Pouzols et al., 2014; Brooks, 2014).

3.2.5 Measuring green water scarcity versus overall greenblue water scarcity

In Sect. 3.2.1 and 3.2.2, we mentioned a few indicators that measure overall greenblue water scarcity (Rockstrm et al., 2009; Gerten et al., 2011; Kummu et al., 2014). Whereas useful for getting an overall picture of water scarcity, a disadvantage of these indicators is that a high degree of green water scarcity can be masked by a low degree of blue water scarcity and vice versa. Imagine for example a river basin where nearly all land is in use and natural forest is under pressure by conversion to cropland (high degree of green water scarcity), while there is enough blue water available to irrigate crop-lands if necessary (low degree of blue water scarcity). Measuring increasing green water scarcity could be relevant, for instance, for the Amazon basin in South America, where increasingly natural forest and associated green water ows are turned into use and competition is essentially about land and associated green water resources, while blue water resources are abundant and blue water scarcity is low. Therefore, for studying green water scarcity, an indicator specically comparing green water demand and green water availability can be more appropriate.

4 Conclusions and future research

In this paper we have reviewed and classied around 80 indicators of green water availability and scarcity. This list of indicators is extensive, but not exhaustive. Nevertheless, we are condent to have identied the most widely used and cited indicators.

The number of green water availability indicators by far outnumbers the existing green water scarcity indicators. This reects that the concept of green water scarcity is still largely unexplored. Indicators of overall greenblue water crowding and scarcity have been developed by Rockstrm et al. (2009), Gerten et al. (2011) and Kummu et al. (2014). These have potential to be tailored to measure green water crowding and green water requirements for self-sufciency versus green water availability. The green water scarcity indicator by Hoekstra et al. (2011) measures actual green water consumption versus green water availability, but has not yet been operationalized due to several challenges discussed in Sect. 3.2.4. The biggest challenge is to determine which part of the green water ow can be made productive in space and time. Application of both absolute and relative green water availability indicators will provide insight into where the green water ow can be made productive for human purposes. Simulations with crop growth models for different management strategies can be used to assess during which parts of the year the green water ow can be made productive.

Future research should be aimed at overcoming these challenges to make the green water scarcity indicators discussed in this paper operational. We also encourage the development of additional denitions of green water scarcity indicators to the ones discussed here. The conceptual denition of green water scarcity we introduced in Sect. 3.2 can be a starting point for this.

Despite scientic obstacles on the way, it is time that the scope of water scarcity assessments is broadened to include green water. We hope that this paper is a stepping stone towards this goal by bringing structure in the large pool of green water availability indicators and discussing the way forward to develop operational green water scarcity indicators. Practitioners and scholars might also nd the classication of indicators provided in this paper insightful and helpful for choosing the indicator that suits their purpose.

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4592 J. F. Schyns et al.: Review and classication of indicators of green water availability and scarcity

Appendix A: Absolute green water availability indicators

Absolute green water availability indicators are included in

Tables A1 to A4. The following are some often used symbols in this appendix: Eact is actual evaporation, Epot potential evaporation, Epot,c crop-specic potential evaporation,

Epot,ref potential evaporation of FAO reference crop, P precipitation, S soil moisture, T air temperature, Tract actual transpiration and Trpot potential transpiration.

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Table A1. Aridity indicators.

Name Abbreviation Formula/description Reference

Rainfallevaporation ratio RER P

EowEow is open-water evaporation.

Transeau (1905)

Rain factor RF PT Lang (1920) Koloskov index KI P

PTSum over vegetative period

Koloskov (1925) as cited by World Meteorological Organization (1975)

de Martonnes aridity index dM-AI P

T +10

de Martonne (1926) as cited by Thornthwaite (1931), Budyko (1958) and de Martonne (1942)

Precipitationsaturation decit ratio

PDR P

DD is mean annual atmospheric saturation decit.

Meyer (1926) as cited by Thornthwaite (1931) and Budyko (1958)

Reichels aridity index R-AI N[notdef]P

T +10

N is number of rainy days.

Reichel (1928) as cited by Perez-Mendoza et al. (2013)

Marcovitchs index MI 0.5L2 [notdef] [parenleftBig]

100 P

2L is the total number of 2 or more consecutive days above 90 F for the months of June, July, August and

September; Total P for those months.

Marcovitch (1930)

Shostakovich index SI PT

P during vegetative period and mean T over this period.

Shostakovich (1932) as cited by Jenny (1994)

Embergers aridity index E-AI 100P

(M+m)(Mm)

M is mean temperature of the warmest month and m = mean temperature of the coldest month.

Emberger (1932) as cited by Walln (1967)

12

Precipitationeffectiveness index

PE

Pn=110 PnEpotn Thornthwaite (1931)

Hydrothermal coefcient HC P

PT [notdef]T >10 C

Selianinov (1930, 1937) as cited by Budyko (1958) and World Meteorological Organization (1975)

Kppen classication KC Threshold for classifying area as semi-arid:

P = 2(T + 14) (summer rainfall)

P = 2T (winter rainfall)

Threshold for classifying area as arid: P = T + 14 (summer rainfall)

P = T (winter rainfall)

P is annual precipitation amount in cm yr1 T is mean annual temperature in C

Kppen (1931)

PmaxPmin Pavg

flat is latitude factor, Tmax temperature of the long-term mean warmest month, Tmin temperature of the long-term mean coldest month, Pmax largest annual precipitation amount on record, Pmin smallest annual precipitation amount on record and Pavg average annual precipitation amount on record.

Gorczynski (1940)

Aridity coefcient AC flat [notdef] (Tmax Tmin) [notdef]

Modied de Martonne aridity index

MdM-AI 1

[parenleftBig]

PT +10 +

12Pd Td+10

2 Pd is precipitation in the driest month and Td temperature in the driest month.

de Martonne (1942)

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Table A1. Continued.

Name Abbreviation Formula/description Reference

Popovs aridity index P-AI Peff

2.4(tt[prime])r

Peff is annual amount of precipitation available to plants; r = factor depending on day length and t t[prime] an

nual mean wet bulb depression in C.

Popov (1948) as cited by World Meteorological Organization (1975)

Moisture index, aridity index, humidity index

Im; Ia; Ih Ia =

100d

Epot

Ih =

100s

Epot

Im = Ih 0.6Ia

where d is a water deciency when P <Epot and s is a water surplus when P >Epot.

Im is an overall measure of the moisture conditions of a region, giving more weight to Ih, since s in one season can partially compensate for d in another season.

Thornthwaite (1948)

100PEpot +12Pw Epot,w

2 Pw is precipitation of the wettest month of the year (in cm month1) and Epot,w potential evaporation of the wettest month of the year (in cm month1).

Capot-Rey (1951)

Capot-Reys aridity index CR-AI 1

Budyko (1958)

Gaussen classication GC P 2T UNESCO (1963)

Slys climatic moisture index SCMI P

P +S+I

I is irrigation requirement for non-water limited growth,P and I during growing season and S at start of growing season. The index is made purely climatic by xed assumptions on the non-climatic factors.

Sly (1970)

Radiational index of dryness RID R

L[notdef]P

R is mean annual net radiation and L latent heat of vaporization of water

Hargreaves (1972)

Evaporation ratio ER EactP Peixoto and Oort (1992) UNEPs aridity index AI P

Epot Middleton and Thomas

(1992, 1997)

Seasonal crop moisture deciency

SCMD Probability of seasonal crop moisture deciency based on a combination of long-term precipitation records and area-weighted Eact of the mixture of crops grown in the study area.

Although most crops studied by Wilhelmi et al. (2002) are considered well-watered (Eact = Epot,c), for wheat

and grasses Eact is estimated as the Eact associated with a certain threshold yield, representing so-called critical crop water requirements (Wilhelmi et al., 2002).

Moisture availability index MAI-H Pdep

Epot

Pdep is dependable precipitation, which is the precipitation amount with a specied probability of occurrence.

Wilhelmi et al. (2002); Wilhelmi and Wilhite (2002)

Climatic moisture index CliMI P

Epot 1 when P < Epot

1

Epot

P when P Epot

Vrsmarty et al. (2005)

Hydrologic unit evaporation ratio

HU-ER EactP

Theoretically equivalent to ER (above), but applied to the level of a hydrologic unit.

Weiskel et al. (2014)

Greenblue index GBI Indicates whether vertical precipitation and evaporation uxes dominate in a hydrologic unit (compared to lateral blue water ows) during a period of interest. Distinction between semi-arid and arid areas can be made when combined with a precipitation map.

Weiskel et al. (2014)

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Table A2. Agricultural drought indicators.

Name Abbreviation Formula/description Reference

Bovas drought index BDI 10(S+P )

PTS (in mm) of the top 100 cm of soil at the beginning of the growing season, P during growing season and sum of T from the rst day T is above 0 C.

Bova (1941) as cited by World Meteorological Organization (1975)

Epot McGuire and Palmer

(1957)

Water requirement satisfaction index WRSI Eact

Epot[notdef]Kc

Kc is crop coefcient that accounts for the difference in evaporation between the considered crop and a reference grass surface.

WRSI is usually evaluated as sum over the growing season.

FAO (1986); Verdin and Klaver (2002)

Moisture adequacy index MAI P +S

EactEpot Jackson et al. (1981);

Moran et al. (1994)

Evaporative stress index ESI Idem to CWSI. Anderson et al. (2007a,b); Yao et al. (2010)

Water stress ratio WS EpotEact

Epot

In fact, idem to CWSI

Crop water stress index CWSI 1

Narasimhan and Srinivasan (2005)

Crop moisture index CMI Abnormal evaporation decit, dened as the difference between Eact and climatologically expected weekly evaporation. Whereby the latter is the normal value adjusted up or down according to the departure of the weeks temperature from normal (Wilhite and Glantz, 1985).

Palmer (1968)

Stress day index SDI Product of a stress day factor (SD) that measures the degree and duration of plant water decit and a crop susceptibility factor (CS), which is specic for the crop species and growth stage, indicating a crops susceptibility to water decit. Various denitions of SD are proposed based on Tract and Trpot and/or leaf and soil water potential.

Hiler and Clark (1971)

Crop-specic drought index CSDI

n

Qi=1

[parenleftBig] [summationtext]E

act PEpot,c

i i

Index i depicts the crop growth stage. Exponent i expresses the relative sensitivity of the crop to moisture stress during stage i.

Meyer et al. (1993) initially developed the CSDI for corn. Later on, the index was also applied for soybean, wheat and sorghum (Wu et al., 2004).

Meyer et al. (1993)

~ Trpot Tract

Transpiration decit that has been built up during a period of x days before.

Marletto et al. (2005)

Integrated transpiration decit DTx

Actual to potential canopy conductance LTA gact

gpot

Ratio of actual to potential canopy conductance. It describes the extent to which transpiration and photosyn-thesis are co-limited by soil water decits (Gerten et al., 2007).

Gerten et al. (2005)

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Table A2. Continued.

Name Abbreviation Formula/description Reference

Water decit index WDI 1

Tract

Epot,ref Woli et al. (2012)

Mu et al. (2013)

Nunez et al. (2013)

Green water stress index GrWSI Eact/Epot

Eact/Epot Wada (2013)

Tract

Trpot Woli et al. (2012)

Agricultural reference index for drought

ARID 1

MODIS Global Terrestrial Drought Severity Index

DSI Standardized sum of the standardized ratio of Eact to

Epot and the standardized normalized difference vegetation index (NDVI). The latter only during the snow-free growing season.

Green water scarcity index GWSI min(Peff,Ept,c)

Peff

Ratio of the green water consumption of a 3-year crop rotation (in m3 m2 rotation1) over the effective precipitation during the same period (Peff in m3 m2 rotation1). Peff represents inltrated precipitation as a proxy for crop-available green water. Green water consumption is dened as the minimum of Peff and Epot,c. Therefore, the index is 1 if Peff Epot,c

and ranges from 0 to 1 if Peff >Epot,c. It measures to which extent available green water during the 3-year period was sufcient to meet the evaporative demand of the crop rotation during that period.

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Table A3. Absolute soil moisture indicators.

Name Abbreviation Formula/description Reference

Antecedent precipitation index API k [notdef] APIi1 + Pi

API on day i is calculated by multiplying API of the previous day with a factor k (e.g. 0.9) and adding the P during day i. By combining the amount and timing of precipitation, the index is a proxy for available soil moisture.

McQuigg (1954)

Agricultural drought day ADD

Pi=1 Lday[vextendsingle][vextendsingle][vextendsingle] wp

L = length of the period considered

Rickard (1960)

Kulik (1958) as citedby World Meteorological Organization (1975)

Kuliks drought indicator KU

P[notdef]day[notdef]S<SthresS in tilled layer of soil (top 20 cm).

KeetchByram drought index KBDI The amount of net precipitation (precipitation minus evaporation) that is required to ll up the soil moisture to eld capacity.

Keetch and Byram (1968)

Hollinger et al. (1993) as cited by Byunand Wilhite (1999)

Soil moisture drought index SMDI

365

Pi=1

l2

Soil moisture index SMIX

t2

Rt1

Rl1

Sdldt

t1 and t2 are usually start and end of growing seasons (authors also take t2 somewhat before end of the cropping period); l1 and l2 are the soil depths over which integration takes place; l1 is the soil surface; and l2 represents the rooting depth, which depends on the crop type and stage of growth.

Isard et al. (1995)

Water stress coefcient Ks StotSdepl

(1p)[notdef]Stot

Stot is total available soil water in the root zone (mm), Sdepl root zone depletion (mm) and p part of total available soil water in the root zone that a crop can extract from the root zone without suffering from water stress.

Allen et al. (1998)

Sandholt et al. (2002)

Temperaturevegetation dryness index

TVDI Surface soil moisture availability based on an empirical parameterization of the relationship between NDVI and land surface temperature (LST) derived from satellite observations.

Ghulam et al. (2007a, b)

Modied perpendicular drought index

MPDI Soil moisture and vegetation status on the basis of near-infrared and red spectral reectance space.

Schuol et al. (2008)

Average green water storage availability

Avg-GWS Long-term average number of months in which

S > 1 mm m1.

Schuol et al. (2008)

Standard deviation of green water storage availability

SD-GWS Standard deviation of the number of months in which

S > 1 mm m1.

Soil moisture index SMI 5 + 10

WP

FC WP is volumetric soil moisture content (cm m1), WP volumetric soil moisture content at wilting point (cm m1) and FC = volumetric soil moisture content at eld capacity (cm m1).

Hunt et al. (2009)

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Table A4. Agricultural suitability under rain-fed conditions.

Name Abbreviation Formula/description Reference

GAEZ crop-specic suitability under rain-fed conditions

Zabel et al. (2014)

GAEZ Crop-specic suitability under rain-fed conditions is based on estimates of agro-ecologically attainable yields. First, agro-climatically attainable yields are determined based on a water balance approach that calculates Eact and additionally considers crop water requirements and a crops sensitivity to water stress during the various stages of growth to calculate a yield reduction factor due to water limitations. Second, agro-climatically attainable yields are further reduced by agro-edaphic constraints.

IIASA/FAO (2012)

GLUES crop-specic suitability under rain-fed conditions

GLUES Crop-specic suitability under rain-fed conditions is based on a fuzzy logic approach with crop-specic membership functions for climatic, soil and topo-graphic conditions. Yield estimates are not provided by the GLUES methodology.

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Appendix B: Relative green water availability indicators

Relative green water availability indicators are included in

Tables B1 to B4. The following are some often used symbols in this appendix: Epot is potential evaporation, Epot,ref potential evaporation of FAO reference crop, P precipitation and NDVI normalized difference vegetation index.

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4600 J. F. Schyns et al.: Review and classication of indicators of green water availability and scarcity

Table B1. Meteorological drought indicators based on precipitation only.

Name Abbreviation Formula/description Reference

Days of rain DoR

Pday

[vextendsingle][vextendsingle]

P <Pthres Munger (1916); Kincer (1919);Blumenstock (1942)

Percent of average precipitation

PoAP P

P Bates (1935); Hoyt (1936) as

cited by World Meteorological Organization (1975)

Foley (1957) as cited by World Meteorological Organization (1975) and Keyantash and Dracup (2002)

Foley drought index

FDI Cumulative deciency (excess) of P in certain month (period) compared to the long-term average P for that month (period), expressed in thousands of annual P .

Rainfall anomaly index

RAI [notdef]3

P P

PextPPext is average of the 10 most extreme precipitation amounts on record (largest for positive and smallest for negative anomalies). Can be calculated on weekly, monthly or annual timescale (Wanders et al., 2010).

Van Rooy (1965) as cited by Keyantash and Dracup (2002)

Deciles In which decile of a long-term record of precipitation events a certain precipitation event falls.

Gibbs and Maher (1967) as cited by Wilhite and Glantz (1985)

Bhalme and Mooley (1980)

Bhalme and Mooley drought index

BMDI The percentage departure of monthly rainfall from the long-term mean weighted by the reciprocal of the coefcient of variation.

McKee et al. (1993)

Standardized precipitation index

SPI Precipitation deviation for a normally distributed probability density with a mean of zero and standard deviation of 1.

Gommes and Petrassi (1994)

National rainfall index

NRI National average of annual precipitation weighed according to the long-term average precipitation of all individual stations in a country.

Byun and Wilhite (1999)

Effective drought index

EDI Ratio of the difference between effective precipitation (EP, calculated from equations based on precipitation) and its 5-day-running mean over the standard deviation of this difference.

Du et al. (2013)

Precipitation condition index

PCI P Pmin

PmaxPmin

P inputs refer to monthly amounts.

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J. F. Schyns et al.: Review and classication of indicators of green water availability and scarcity 4601

Table B2. Meteorological drought indicators based on precipitation and a measure of potential evaporation.

Name Abbreviation Formula/description Reference

Palmer drought severity index PDSI Accumulated weighted differences between actual precipitation and precipitation requirement of evaporation (Wilhite and Glantz, 1985).

Palmer (1965); Alley (1984)

Reconnaissance drought index RDI Standardized ratio of P to Epot based on a log-normal distribution.

Tsakiris and Vangelis (2005); Tsakiris et al. (2007)

Vicente-Serrano et al. (2009)

Standardized precipitation evapotranspiration index

SPEI Standardized difference between P and Epot based on a log-logistic distribution.

Water surplus variability index WSVI Standardized difference between P and Epot,ref based on a logistic distribution.

Gocic and Trajkovic (2014)

Table B3. Vegetation drought indicators.

Name Abbreviation Formula/description Reference

Normalized difference vegetation index anomaly

NDVIA NDVI NDVI Tucker (1979);

Myneni et al. (1998)

Vegetation condition index VCI NDVINDVImin

NDVImaxNDVImin

NDVImin is multi-year minimum of smoothed weekly NDVI and NDVImax multi-year maximum of smoothed weekly

NDVI

Kogan (1990, 1995)

Vegetation health index VHI a [notdef] VCI + b [notdef] TCI

a is coefcient quantifying share of VCI contribution in the combined condition, b coefcient quantifying share of TCI contribution in the combined condition, TCI Temperature Condition Index and VCI Vegetation Condition Index

Kogan (2001)

Standardized vegetation index SVI NDVI deviation for a normally distributed probability density with a mean of zero and standard deviation of 1.

Peters et al. (2002)

Gu et al. (2007)

Normalized difference water index anomaly

NDWIA Adaptation of NDVI (Gao, 1996) compared to its multi-year mean.

Saleska et al. (2007)

Enhanced vegetation index anomaly

EVIA EVI anomaly. EVI is an improvement over

NDVI, which keeps sensitivity over densely vegetated areas (Huete et al., 1994).

Brown et al. (2008)

Percent of average seasonal greenness

PASG SG

SG [notdef] 100 %

SG is seasonal greenness, dened as accumulated NDVI above background NDVI during a specied period.

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4602 J. F. Schyns et al.: Review and classication of indicators of green water availability and scarcity

Table B4. Relative soil moisture availability indicators.

Name Abbreviation Formula/description Reference

Soil water decit SD (& SMDI) Difference between mean weekly and long-term median S, divided by the difference between long-term minimum (maximum) and median S.

Narasimhan and Srinivasan (2005)

Palmer (1965); Alley (1984)

Palmer Z-index (a.k.a. Palmer moisture anomaly index)

PZI Moisture anomaly for the current period from the climate-average moisture conditions for that period.

Soil moisture anomaly index SMAI

[notdef] 100 %

is volumetric soil moisture content

Bergman et al. (1989)

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J. F. Schyns et al.: Review and classication of indicators of green water availability and scarcity 4603

Author contributions. Conceived and designed the study:A. Y. Hoekstra, J. F. Schyns and M. J. Booij. Executed the study:J. F. Schyns. Wrote the paper: J. F. Schyns, A. Y. Hoekstra andM. J. Booij.

Acknowledgements. The present work was (partially) developed within the framework of the Panta Rhei Research Initiative of the International Association of Hydrological Sciences (IAHS) and has been made possible by grants from the Water Footprint Network and Deltares.

Edited by: N. Ursino

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