Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ect land-atmospheric feedbacks and may ff 8240 8239 This discussion paper is/has beenSciences under (HESS). review Please for refer the to journal the Hydrology corresponding and final Earth paper System in HESS if available. assumptions. We discuss thesources pros of and cons uncertainty of andthat available highlight current algorithms, limitations address capability in various demands of is current rather large-scale applications. limited, models particularly Weulations. with in conclude respect We to terms argue future that projections oftheir and current online representing impact limitations sim- on in human the simulating water demand Earth various algorithms System human are and demands mainly and large-scalealgorithms due models. and to To data the fill for uncertainties representing thesetested, in various intercompared gaps, data water and the improved demands support, and available should human models, be water systematically demands should be considered ter resource management into twoas interdependent well elements, as related water toon supply water how demand and various allocation. water InLand demands this Surface Schemes have paper, and been we Globalclassified survey included Hydrological based the in Models. on current The large-scale the available literature models, algorithms type including are of demand, mode of simulation and underlying modeling interactions in time andmanagement space. in There Earth are Systemis various models. increasing First, reasons rapidly the to at extent include theance of water global between human scale water resource water and demands requirements and it and across is supply various crucial temporal under and to various spatialhuman–water analyze scenarios scales. interactions, Second, the of recent manifested possible climate observations through imbal- show change water that stantially resource alter management, can the sub- terrestrialfurther water interact cycle, with climate a and contribute to sea-level change. Here, we divide the wa- Human activities have causedinterconnections various between changes humans in and thereflected in the Earth models Earth System, that and simulate Systemactivity the hence, should is Earth the water be System resource processes. recognized management One that and key determines anthropogenic the dynamics of human–water Abstract Hydrol. Earth Syst. Sci. Discuss.,www.hydrol-earth-syst-sci-discuss.net/11/8239/2014/ 11, 8239–8298, 2014 doi:10.5194/hessd-11-8239-2014 © Author(s) 2014. CC Attribution 3.0 License. On inclusion of water resource management in Earth System modelsPart – 1: Problem definition and representation of water demand A. Nazemi and H. S. Wheater Global Institute for Water Security,Saskatoon, University SK, of S7N Saskatchewan, 11 3H5, Innovation Canada Boulevard, Received: 12 June 2014 – Accepted: 23Correspondence June to: 2014 A. – Nazemi Published: ([email protected]) 21 July 2014 Published by Copernicus Publications on behalf of the European Geosciences Union. 5 25 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | generation and calculations (see ff ff . Nonetheless, despite a large ff 8242 8241 ine LSSs are computationally much less demanding; they ects of land responses on the atmospheric system. ffl ff ine simulations, typically at global, regional or large catch- ffl ine hydrological modeling (e.g. Liang et al., 1994; Pietroniro et al., 2007; ffl , 1978; Dickinson, 1983, 1984; Sellers et al., 1986, 1994, 1996a; Nicholson, ff The importance of representing the water cycle in LSSs is well-established (see proved simulations of soil moisture dynamics and runo flow modeling capability ofparameterizations of WATFLOOD CLASS (Kouwen (Verseghy, 2000). et Similarlythe al., Oleson representation et 1993) of al. with (2008) hydrology improved in theOleson et the land-surface al., 3rd 2004), generation by Community(Beven including Land a and Model simple Kirkby, hydrological (CLM3; 1979), modelshowed inspired and that by TOPMODEL a these developments simpleglobal made groundwater water significant model. cycle. improvements Lawrence The inland et simulation model representing al. by results the (2011) including further anrevised improved developed snow numerical the and solution Oleson for evaporation et unsaturated parameterizations. soil al. These as (2008) modifications well resulted as in im- water and energy balancepart) calculations. to These unrealistic deficienciessponse assumptions in have and LSSs been (Soulis incomplete et attributedattempts, parameterizations al., therefore, (in 2000; of have Music focused catchment and on Caya,routing re- including 2007; processes Sulis catchment in et scale LSSs. al., For runo 2011). instance, Further Pietroniro et al. (2007) combined the stream- Adam et al., 2007; Livnehmodels, et so al., 2011) called and GlobalIn often Hydrologic early compared Models to LSSs, large-scale hydrology (GHMs) hydrological wasabe, – conceptualized 1969), see as but this a Haddeland representation simple etcomplexity has lumped progressively al. and bucket been explicit (2011). model improved physics (Man- by into includingDeardor more canopy, soil moisture and1988; runo Pitman et al.,tainties 1990). remained Despite these in improvements, the major hydrological limitations simulations and of uncer- LSSs, causing systematic bias in Pitman, 2003 and references therein)LSSs and in there representing has been variousture, progressive components development vegetation, of of snowmelt the and hydrologichydrological response cycle, evaporation. at such As the as catchment these andquently soil processes larger in mois- scales, also o LSSs have determine been the applied fre- require atmospheric driving variablesbut and do simulate not land-surface represent responses the to e climate general applications for LSSs.weather-forecasting models, First, as LSSs they provide aretions the essential to dynamics the components of atmospheric surface ofcations boundary models climate condi- are (Verseghy, 1991; and generally Verseghy termed et(e.g. in al., Entekhabi 1993). the and Such LSS Eagleson, appli- communityapplication 1989; as relates online Noilhan to or andment o coupled Planton, scales, simulations 1989). for assessment A ofland-surface second impacts processes. area of O climate of or other environmental changes on portion of the Earthphysical System. processes related LSSs to contain soil,count interconnected vegetation and for modules water, their that over a characterize influenceseither gridded on explicitly mesh, mass or and ac- and implicitlyious include energy temporal the exchanges. and dynamics A spatial of LSS, scales these therefore, (see physical should Trenberth, processes 1992; at Sellers, 1992). var- There are two The Earth System isEarth’s an surface. integrated These system processestions include that between a unifies atmosphere, wide the land spectrum and physicaland oceans of processes carbon and feedbacks at that cover and the global support interac- From cycles the planetary of advent climate, of life water digital (e.g. computers,tify Earth Schellnhuber, past System 1999; models changes Kump have andinclude been et the to computational al., key to predict 2010). components iden- themosphere that and future represent oceans of various (Claussen PlanetSchemes functions et (LSSs) Earth. al., of are These sub-models 2001; the within models Schlosser land, Earth normally et System at- al., models 2007). that represent Land-Surface the land and unknown future climate. 1 Background and scope in conjunction with water supply and allocation, particularly in the face of water scarcity 5 5 25 20 10 15 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | cient pa- ffi ine LSSs or ffl ects on lakes and wetlands, ff that is geographically and temporally ff en et al., 2007, 2011). This has initiated ff en, 2003; Crutzen, 2006). Recent climate ff 8244 8243 ect the natural cycles within the Earth System orts, therefore, should be made to (1) revisit the ff ff ects on the terrestrial water cycle can be ignored. ects such as the emission of greenhouse gases and ff ff ects include pertubed flow regimes and reduced inputs to wetlands, ff ects livelihoods as well as local, regional and global economies (e.g. Nilsson ff ort has been made to represent human–water interactions (e.g. Trenberth and ff As human life and water availability are tightly interconnected (see Sivapalan et al., Although some anthropogenic e While external forcing, mainly the energy flux from the Sun, is the main driver economic development. There are major concerns about how future demand should 2012), current and futurehuman changes society, in and the these issues waterGHMs. can availability Although be are human explored of water to use major a stilland importance large accounts below extent to for the with a surface o small (see proportionterrestrial Oki of evaporation and and total Kanae, water 54 2006), % on it ofavailable currently (Postel surface et includes runo al., around 1996). 26 % Therehighly of are populated already regions major of water thehuman security globe concerns demands (e.g. across are Falkenmark, 2013; growing Schiermeier, rapidly, 2014) due and to increasing population as well as socio- groundwater depletion in somemid-west, areas and of Iran the (Giordano, globe, 2009;sive such Rodell groundwater et as pumping al., Indian is 2009;for peninsula, also Gleeson example the by associated et US salt with al., water 2012). potential intrusionquality Exten- (Sophocleous, long impacts, 2002; however, term remain Antonellini beyond contamination, et the al., scope 2008). of Water this paper. Downstream e lakes, seas and oceans. Vörösmarty andto Sahagian (2000) internal argued sinks that river havesumption discharges decreased of remarkably water, resulting dueColorado in to River seasonal the (e.g. decline Cayan storage,such et in diversion as al., flows and the 2010) of death con- and of majorgroundwater extreme the rivers abstractions Aral e such are Sea as (e.g. associatedbaseflow Precoda, the with contributions 1991; declining and Small et groundwater loss al., levels, of 2001). reduced wetlands. In parallel, Current assessments reveal significant This assumption is highly questionablesurface and processes can result (see in Gleick thedecrease et neglect downstream of al., flows, important often 2013). land- human substantially, For and demands instance, dam considerably surface changes operation water the toral timing, withdrawals streamflow supply volume, (e.g. various peak Meybeck, and 2003; the Vörösmarty et age al., of 1997, natu- 2007; Tang et al., 2010). still widely assumed that human e Asrar, 2012; Lawrence et al., 2012; Oki et al., 2013). In current LSS applications, it is et al., 2005). Duringthan the past 6-fold, century, with humanmunicipal water around consumption consumption, 5, has respectively increased 18ing (see more and such Shiklomanov, intensive 10 1993, demands times has 1997,respect required to increase 2000). large both changes in Supply- land in use agricultural, the and natural industrial water landscape, resource with and management. land-use change have been incorporatedZhao in et al., Earth 2001; System Karl models andless Trenberth, (e.g. e 2003; Lenton, Brovkin et 2000; al., 2006; Solomon et al., 2009), tem (perhaps) more than any othera new driver geological (Ste epoch, informally termedthat the the “Anthropocene”, natural in processes which within it the ishumans land recognized (see surface McNeil, are highly 2000). controlled The andgreatly water regulated a cycle by is one set of these processes, which also of the Earth System,human internal activities disturbances can such(Vitousek substantially as et a volcanic al., eruptions, 1997;post-industrial wildfires Trenberth human and activities and from Dai, theturbed 2007; mid-20th the century Bowman onwards, Earth et have System severely al.,warming per- (Crutzen 2009). and and In other Ste particular, globalnow changes become have one raised the of consciousness the that great humans forces have of nature, introducing change to the Earth Sys- and incomplete as current simulations still(see cannot Lawrence match past et hydrological al., observations 2012).assumptions Major concerning e the dominantscale hydrological land-surface responses; processes and thatrameterizations (2) to determine improve represent larger the missing description processes of water with cycle e in LSSs. body of research, representations of hydrological processes in LSSs remain imperfect 5 5 25 20 10 15 25 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 8246 8245 erent aspects of water resource management ff ect water resource management at the larger scale, such ff . For instance Gao et al. (2012) noted that the “. . . results from ff ects (e.g. Sacks et al., 2009; Destouni et al., 2010; Gerten et al., 2011; ff ine mode. ffl ine mode, in particular within the context of GHMs. Nonetheless, there are still ffl First, multiple factors a Due to the importance of anthropogenic activities in determining the future of the Apart from hydrologic and water security relevance discussed above, anthropogenic which requires allocation decisions. At this stage of model development, however, it trial storage and runo global reservoir simulations arereservoir questionable” storage”. as Third, “there there arewater is no resource a direct models major observations gap andthe of between large-scale scale the applications scope at and of research whichsub-grid local needs. resolution local operational Essentially, of water current resource large-scaletion models, management in which takes large-scale requires models narrowing place the formore is resolu- explicit sub-grid representation often heterogeneity (see within into Wood the dition, et grid al., there calculations 2011) is for or (and implicit adding will parameterization. increasingly In be) ad- competition between various water demands there is considerable lackoperation of of regional water and global resourcesproperly data tuned systems, concerning or and validated. the therefore,instance, This actual large-scale to major use models limitation use and has cannot estimatedabout led be human demand the operation as research can community, also a for introduce surrogate large uncertainty for into the simulations actual of terres- use. Lack of data in o fundamental obstacles in includingeven water in resource o systems within large-scale models as climate, hydrology, land-cover and socio-economymanagements. as Moreover, real-world well management as decisions landues often and and environment include political cultural concerns. val- Theseand various the influences interactions are among so them far are considered widely in unseen isolation (e.g. Beddington, 2013). Second, tion and redistribution of availablethat water sources this for is various subject humanresentation demands to and operational of note and water policy resourceavailable, constraints. important management Although progress in a is fully being Earthgradually coupled made, System shaping and rep- around more models describing generally is a di not body of currently literature is resource management as a set of anthropogenic activities related to storage, abstrac- those activities manifested through water resource management. We consider water global water cycleGlobal and Energy and human Water livelihoods, Exchangesgaps project the in (WRCP-GEWEX) describing World has human–water recently ClimateSystem interactions identified modeling as Research (GEWEX, one 2012). Programs’ of Thescientific aim the of grand and this challenges data review in is challenges,research. to Earth the consider We the state associated note of thatsurface current interventions, human–water practice, including interactions land-use and change includethis directions paper and a for and water a future wide resource companion management. spectrum paper In (hereafter of Nazemi and land- Wheater, 2014), we focus on Pokhrel et al., 2012; Hossaindevelopment, et the available al., quantitative 2012; understandings Dadson aboutare these et limited. online al., implications To 2013). explore At thesepled this issues stage Earth it of is System model necessary models,interactions to and within include LSS this these computational requires processes schemes. in explicit cou- representation of human–water required for impact assessments. activities might have broader implicationsfully for explored, the and remain water in cycle; some althoughthat cases these controversial. human For are instance, interventions to it be has throughgional been evaporation shown irrigation and near can surface change temperature.ural” These atmospheric soil changes boundary can moisture conditions disturb andfeedback and the “nat- e may hence interact re- with regional climate through demand and scarcity (e.g.Döll, 2009; Postel Taylor et et al., al.,et 2013; al., 1996; 2013; Hanasaki Arnell, Millano et 1999,security et al., necessitate 2004; al., 2013a, a 2013; Tao b; detailed Mehta et Wada understandingand et et al., of space; al., al., water 2003; 2013). availability and 2013; Such and Schewe therefore, demand important large-scale in threats time models, to water including both GHMs and LSSs, are be supplied, particularly considering that climate change will likely amplify global water 5 5 25 20 10 15 25 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | en et al., 2011). ff erence between irrigated ff ine impacts. We further discuss current ffl 8248 8247 ine and online implications. For the purpose of ffl ect land surface-atmosphere feedbacks (see Eltahir, 1998). Pokhrel et al. (2012) ff set the increasing temperature in the region. Agricultural management in irrigated Apart from driving hydrological changes, irrigation-induced changes in soil-moisture Conceptually, water resource management at larger scales can be seen as an in- ff o evapotranspiration, and therefore transformsspiration the due surface to energy irrigation balance.2006; Evapotran- leads Betts et to al., cooling 2007; Saeedcloud of et cover al., the and 2009; land Destouni chance et surface ofDouglas al., (e.g. convective et 2010), precipitation as Haddeland al., well (e.g. 2009; et as Moore Hardingalso enhanced al., and alter and Rojstaczer, regional Snyder, 2001; 2012a, circulation b; patternsareas Qian due and to et neighboring temperature regions al., di (e.g. 2013).et Kueppers Irrigation et al., may al., 2013). 2007; DeAngelis Oversignals. et Gerten highly al., et 2010; irrigated al. Wei regions, (2011), for this instance, can showed that mask the important irrigation in climate South change Asia has the main supplier of globaltive irrigation irrigative use needs, at accounting the for global 57 % scale of (Siebert the etcan al., total a 2010). consump- showed that increased soil water content through irrigation substantially enhances of the world’s food (Abdullah,irrigated 2006). (Portmann Currently, et around al., 25 %at 2010). of the This harvested global accounts scale crop for (Döll areatotal some et is water al., 90 withdrawals % 2009; from of Siebert surfaceGerten water et and and consumption al., groundwater Rost, 2010), resources which 2010). (Wisserdisturb is Clearly et the around al., “natural supplying 70 % condition” 2008; such ofGaybullaev by a the decreasing et large streamflow al., water volume 2012; (e.g.2009; demand Gleeson Meybeck, Lai can et 2003; et severely al., al., 2012; Wada 2014) et al., and 2010, groundwater 2012, levels 2014). Currently, (e.g. surface Rodell water is et al., Human water demands canrigation be divided is into the irrigative dominant1950s, and due human non-irrigative to water categories. population use Ir- growthThis and and has technological major has development importance significantly (Ste for intensified global since food security, the as it produces approximately 40 % 2 Types of human demand and their impacts on the water cycle at larger scales,tions respectively. and In highlight current Sect. limitations 5,water and uncertainties demand we in and briefly estimating associated currentgaps explore and online in future state-of-the-art and Sect. 6 o applica- andsummaries provide first some part suggestions for of future oursenting developments. survey human Finally, and Sect. water outlines 7 demand. our main findings with respect to repre- focus in this paperWheater (2014) on on the water supply representationmand and into allocation. of In irrigative water Sect. and 2 demand, non-irrigativeon we sub-demands the and further and terrestrial divide in briefly human water the de- highlightvide cycle their an Nazemi and impacts overview and land-atmospheric of feedbacks. available Sections representations 3 of and irrigative 4 and pro- non-irrigative demands the high level of calculationsto within computational optimization barriers algorithms as cannot well be as maintained, data due availability issues. tegration of two interactiveply elements, and related allocation: to water waterfrom demand demand water as drives sources well waterof and as allocation, the water determines which land-surface. sup- results This theour in has extent survey, extraction and both of reflecting o change the state in of hydrological algorithm development elements and data availability, we the local scale, detailedclimate information and water on supply physicalsee conditions and e.g. are Nazemi operational available et (or systems al.,as can as 2013) an be and well optimization generated the as problem. as competition Asscales scenarios; between the demands to simulation is regional scale often and moves reflected from global local scales, and the small data basin availability degrades considerably and is still unclear how operational policies should best be reflected at larger scales. At 5 5 25 20 10 15 25 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ine ex- ffl ine irrigation demand ffl ine applications can vary from ffl ine vs. online). Tables 1 and 2 sum- ffl ects of agricultural management, how- ine representations. In contrast, a wide ff ffl 8250 8249 ine simulations at both regional (Table 1) and ffl ects on the water cycle and climate (e.g. Lobell et al., ff erences in population, income, life style and technological devel- ff ects on the terrestrial water cycle and future water security. Also, for some ff Essentially, irrigation algorithms require identifying the extent of irrigated regions Non-irrigative water demands include municipal and industrial uses, energy-related are met (e.g. Döllseason and can Siebert, be identified 2002). based In on more biophysical detailed conditions models of the crop optimal growth growing and/or soil and growing seasons. Thecrop location and types area can of2008; be irrigation Siebert extracted districts et and from al., the(e.g. regional 2005, associated Adegoke 2007; and et Portmann global al., etidentifying al., data 2003; growing 2010) seasons. Qian sets The and/or et choice (e.g. remotely al., ofin sensed USDA, these the 2013). data options host 2002, There depends model. on In arecrops the simpler two level models, can of general where grow detail no approaches when energy-balance for and calculation is where available, simple temperature- and precipitation-based criteria embedded), as well as forcingamples and (see land-use Tables data, 1 has1 and been h 2). used (e.g. in Leng Model current et resolutionsmeters o al., in (e.g. 2013) Sibert o to and 1kilometers day Döll, (e.g. (e.g. 2010; Haddeland Gueneau Nakayama et and etcalculations al., Shankman, have al., 2007) been 2013) in 2012) already to time performed in few and globally hundred space. few under kilo- Moreover, future climate o conditions. similar to forested land,ing bare our soil presentation, and we snow(regional classify cover vs. the (Polcher global) current et and/or representations mode al.,marize with of 2011). simulation respect representative For (o to examples simplify- the ofglobal scale o (Table 2) scales. Tableline 3 applications presents have some mainly online beenlutions performed examples. with at In shorter rather brief, simulation fine current, periodsspectrum temporal on- than and of o spatial host reso- models (i.e. large-scale models in which the irrigation algorithm is 3.1 Framework and general procedure Irrigated lands normally introduceand heterogeneity into GHMs. the Such computational sub-grid grids heterogeneity of LSSs can be represented as an additional “tile” 3 Available representations of irrigative demand in large-scale models large-scale mining activities, inthe which the associated extent changes ofto water in land-atmospheric withdrawals soil is feedbacks. To considerable, moisture thenon-irrigative and best withdrawals has of land-cover not our can yet knowledge, been be online explored consideration in potentially the of relevant literature. ity and temperature (e.g.water Maybeck, demands 2003; are Förste currently andtrial on Lilliestam, development. a This 2010). can rapid Non-irrigative increase incline2013a–d). water due Recognizing stress to in non-irrigative both growing water time uses populationsible and is and e space therefore indus- (Hejazi relevant et in al., terms of pos- tion, however, has significant spatial2013) as variability regional (Vassolo di andopments Döll, can 2005; alter Flörke the et extent2003; al., of Flörke non-irrigative and demand Alcamo, significantly 2004;dominantly (e.g. Hejazi a Alcamo et et consumptive al., al., 2013a). waterdrawal However, is use, while consumptive irrigation only (e.g. is a Hanasaki pre- fore, et small partially al., portion or 2013a). of Non-irrigative totallydegrees withdrawals, the return of there- non-irrigative time to with- lag. surface Still, water this or can groundwater considerably systems perturb with the varying streamflow regime, qual- 2006; Kucharik and Twine, 2007). Associatedever, e remain beyond the scope of this paper. withdrawals, and othercontribute agricultural a uses, lesser proportion such to total as human water livestock. use at Non-irrigative the demands global scale. This propor- areas can also have other e 5 5 25 20 10 15 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | in irrigated ff erent algorithms together is the ff ), which provides annual data on ine applications, the irrigation rate ffl 8252 8251 cients. Various methods are used to characterize the reference evapotranspira- ffi In the most simplistic bottom-up representations, the irrigation demand at every pacity (e.g. Nakayama andthe Shankman, water 2013); required therefore, to the bring irrigation the water soil need moisture is to field capacity. The description of the able bottom-up algorithms fromand the highlight most their simplistic strengths and to weaknesses. most comprehensive algorithm time step isration the (e.g. water Lobell required etextreme to al., demand bring 2006; condition the Hardingquirement and soil and (Sacks clearly moisture et Snyder, overestimates at 2012a, al.,soil the the 2009). moisture b), actual requirement In root which during irrigation a zone describes the water more to an growing realistic re- season satu- is but considered still to naïve be representation, the the field ca- coe tion, such as FAOand Penman-Monteith modified (Allen Hargreaves et (Farmer al.,Rosenberg, 1993 et 1998), for Priestley al., more and examples). 2011)rather The arbitrary Taylor to choice (1972) and of depends name these largely asupported on formulations the has in few data the remained (see availability host as model. McKenney well Here as and the we level try of to detail sort and briefly explain the currently avail- rithms try to mimic theinclude optimal a crop range growth at of irrigatedlines modeling sub-grid for assumptions tiles. calculating and These the are algorithms irrigationcomponent heavily water in influenced requirements the by (see bottom-up FAO’scalculation Allen guide- approaches et of that al., ties potential 1998).ideal di The evapotranspiration, key conditions which with determines nois the mainly water based crop deficit. on water The calculatinging the calculation use it evapotranspiration of for as in a potential a reference function evapotranspiration crop and of correct- crop type and crop development stage using a set of empirical calculation of irrigation demand is mainly pursued through bottom-up3.3 schemes. Bottom-up algorithms for calculating irrigationDespite demand major limitations due to the heterogeneity in soil and crops, bottom-up algo- irrigation practices are highly variable within a country and a typical year. As a result, 3.2 Top-down algorithms for calculating irrigationIn demand top-down approaches,timated the based irrigation onlated demand downscaling (e.g. Voisin is either et notTop-down al., historical approaches 2013) directly (e.g. are information, obtained highly calculated, Sackster at influenced et but national use, by or al., es- such the geopoliticalhttp://www.fao.org/nr/water/aquastat/main/index.stm availability as 2009) scales. of FAO’s or global Information simu- data System on on wa- Water and Agriculture (AQUASTAT; each grid can be thennon-irrigated calculated portions as of the the sum grid ofcan (e.g. the Haddeland flux be et contributions al., further from 2006; introduced irrigated Pokhrel(e.g. et and to Sorooshian al., et climate 2012), al., and models 2011; as Harding coupled and Snyder, surface 2012a, boundary b). conditions ideal crop growth, inprocedures addition to are available available water. for Aand calculating variety are of the reviewed top-down irrigation furtherthe and demand actual bottom-up below. evapotranspiration in If would be large-scale thestandard equal models irrigation to conditions crop-specific demand (see evapotranspiration under Allen iscan et completely perturb al., soil fulfilled, moisture 1998). then content,tiles In evaporation, (e.g. deep o Hanasaki percolation et and al., runotions, 2008a, the b; vertical vapor Wada and et heat al., fluxes 2011, need 2012, to 2014). be In also considered. online The applica- total fluxes for necessary to obtain mature andet optimal al., plant 2012). biomass This (e.g.models latter Rost approach and et is al., to applied 2008; somegation mainly Pokhrel demands in extent the (and in context under LSSs. ofsimulation some After time global assumptions, step vegetation actual the can irrigation growing be withdrawals) calculated. season at The is each irrigation identified, demand is the the irri- water required for water, canopy and energy balance conditions to estimate the cropping period that is national (and inmainly some using land-use, cases technological and/or alsoalgorithms socio-economic proxies. for sub-national) Current calculating top-down irrigation scales. demand are Downscaling however rather is simplistic performed since annual 5 5 25 20 10 15 20 10 15 25 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | er- ff ects. ff e 2 orts have ff ects of other ff ects. Moreover, ff e 2 ective rainfall (Haddeland et al., 2006, 2007; ff 8254 8253 emission, crop growth and irrigation water requirement. 2 on photosynthesis are wholly ignored. 2 ects of both carbon and water in vegetation can provide a basis for ff ective rainfall is used as a surrogate for available crop water. In more ff orts try to overcome these limitations by defining irrigation demand based ff ine example, Hanasaki et al. (2008a) assumed that paddy and non-paddy crops ffl Some e More realistic definition of irrigation water demand would be based on the di with current climate changecamo scenarios et (see al., e.g. 2007). Arnell, For 2004; irrigation, intermediate Fischer scenarios et describe al., changes 2007; in Al- irrigated 3.4 Projection of irrigative demand From water and foodscenarios, security it perspectives, is particularly crucialsibilities to under investigate for various future irrigation global irrigation deficit. change demandios Climate and (IPCC, model assess 2000) projections various under have pos- rithms IPCC been (e.g. emission widely Arnell, scenar- used 1999;been to Wada also force et bottom-up made al., irrigation to 2013; demand include Rosenzweig algo- intermediate et al., socio-economic 2013). scenarios E that can match explicit linkage between CO This would be important forsome future recent predictions simulations under increasing showed CO thatgrowth model the is irrigation used; requirement and2013). changes this if can a improve the dynamic partitioning of latent heat flux (e.g. Lu, on potential transpiration2011, instead 2012) and/or of using more potentialet comprehensive vegetation evapotranspiration al. schemes. For (e.g. (2008) example Rost managed Wada coupled Land a et scheme transpiration al., (LPJmL;tation deficit Bondeau growth algorithm et module with al., basedGerten the 2007), on et Lund–Potsdam–Jena which carbon al., and hasspheric water a 2004). water availability detailed The deficit, (see vege- crop soil SitchConsidering moisture, water et the plant e limitation al., hydraulic 2003; was states calculated as well based as on the the CO atmo- this quantification may result in overestimatingerly the represent irrigation the demand dynamics and of maythat not vegetation prop- (Polcher crop et growth al., is 2011).drivers Second, a such it function as is CO of assumed water availability only; therefore, the e considers both transpiration from crop and evaporation from soil. It has been noted that simulation of irrigation demands. First, FAO’s definition of irrigation water requirement ence between the crop-dependent potentialter. evapotranspiration This and definition available crop has wa- Table been 2). In widely earlier used examples (e.g. indevelopment Döll is global and described Siebert, irrigation by 2002; demand constant detion projections Rosnay monthly and et (see multipliers al., the for 2003), potential e crop advanced evapotranspira- algorithms, the correctionmate, stage factors of are vegetation considered andmoisture root content as can growth. functions be Moreover, used actual of insteadGueneau evapotranspiration of daily or et e soil al., cli- 2012). There are two key limitations associated with this approach to Qian et al. (2013) implemented rootthe growth overestimation in of their demand irrigation due demandculating to algorithm the to a root avoid constant growth root isremain zone. beyond It also the should subject scope be to of noted uncertainty; this that however, paper. cal- associated limitations constant depth at the globaltion scale. for Yoshikawa non-paddy et soil al. moistureto (2013) requirement the later and requirement updated for used the wheat. 60stant assump- % This of is percentage again field of rather capacity, referring unrealisticrequirement the as is (1) field by ignored; capacity assuming aresult and for con- in (2) misestimating all the a crop irrigationitations. constant demand. types, For There root instance, the are Sorooshian zone attempts et diversity to al.content depth address in (2011) can these at assumed crop change lim- that the in the water global required each soil grid scale moisture based can on the dominant crop. Leng et al. (2013) and requirements, as the evaporation oftenfield reaches capacity. The potential threshold level at beforecrop-dependent, which the but the soil often evaporation reaches reaches consideredan potential as o evaporation is a constantrequire value soil in moisture large-scale content models. of As 100 or 75 % of the field capacity at the root zone with irrigation demand based on the field capacity can also overestimate the actual water 5 5 25 20 10 15 25 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 8256 8255 spatial resolution using livestock data of Steinfeld ◦ 0.5 × ◦ ciency as well as crop using empirical approaches. For example, ffi ects of various drivers on irrigation, they remain uncertain as their simu- ff withdrawals Municipal, industrial and energy-related water demands are the most dominant forms Unlike irrigation demand, top-down approachestional have or been geopolitical data widely to used basin to or transfer grid na- scales. Various downscaling procedures have However, the later consumescooling less technology. Climate than can be half anothertive of important and the factor non-consumptive controlling water, both withdrawals consumed2013a; consump- (e.g. Voisin by et Wada recirculating al., etirrigative 2013), al., water but 2011, demand. it 2013a; has Hejazi been et often ignored al., 4.2 as an explicit Top-down driver algorithms for of estimation non- of grid-based non-irrigative both consumptive and non-consumptivetype uses of can technology. significantly Macknickwater change withdrawals et based and al. consumption on for (2011), the the most for US. electricity Comparing generation instance, technologies to providedcooling within recirculating requires estimates 10 cooling of to technology, 100 total they times noted more that water withdrawal once-through per unit of electric generation. 2008a; Wada et al.,factor 2014). (e.g. National Gleick, 1996; Gross Cole, Domesticthat, 2004; in Product Wada general, et (GDP) water al., uses is 2011). perdue also capita Hughes are to et a more al. low-tech strong in (2010) developing waterhigher showed than developed delivery GDP countries and may trigger industrialization.Strzepek more It et municipal al. must water (2010) besource argued use noted, industry that per however, and industrial capita that decreases waterdustrial (Alcamo when use technology et a increases is al., country with another 2007). moves the important toward level factor the of for service re- non-irrigative sector. In- use as the extent of et al. (2006). Daily demand was considered as aof function of non-irrigative daily uses, and temperature. canand be technological considered factors, with as high complex variabilitysignificant functions in of factor time socio-economic and driving space. these Population is withdrawals the (e.g. most Alcamo et al., 2003; Hanasaki et al., livestock requirements at 0.5 demand per livestocket head al., (e.g. 2013d). Wada et Wada et al., al. 2011; (2014) Strzepek made et a al., further 2012b; Hejazi improvement by estimating daily 4 Available representations of non-irrigative demand 4.1 Forms and drivers of non-irrigativeNon-irrigative demand water demands relate to arelated wide range uses, of municipal, as industrial andboth well energy- consumptive as and non-consumptive other withdrawals. Amongmand agricultural these, livestock is water water de- assumed needs fully (e.g. consumptive, livestock), and and can be include estimated by livestock number and the dynamic e lations are rather coarse andtion they often of consider growths in irrigation economy development only andwidely as energy-use; ignored a therefore, (Hejazi water func- et availability constraints al., are 2013d). gation expansion to socio-economicrelationships factors can are contain not large fully uncertainties.expansion known More and and dynamic linkage current socio-economic between empirical driversenergy-carbon irrigation can models. be One providedAssessment emerging by Model model coupled (GCAM; of socio-economy- has Wise such been and recently a implemented Calvin, kind forand 2009; simulating demands is the Wise (Hejazi future the et et expansions al., Globalrequirements in al., 2013b–d) (e.g. irrigation Change as 2009a, Chaturvedi areas well et b). as al., policy GCAM 2013a, implications b). for Although, irrigation these water models can represent Hanasaki et al.developed (2013a) Shared recently Socio-economic proposed PathwaysMoss intermediate (SSPs; et al., Kriegler scenarios 2010), et based which(RCPs; al., are Meinshausen on consistent et 2012; newly with al., see Representativenarios 2011; also Concentration Taylor using et Pathways empirical al., procedures, 2012). however, Constructing is intermediate uncertain sce- as mechanisms that link irri- areas, irrigation e 5 5 25 20 10 15 25 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ects ff . It should ◦ ine applications ffl erent sources. As an example, ff erence in the implemented irrigation ff orts, which can be classified through explicit ff 8258 8257 erent global information sources has provided ff ine applications, online simulations are still under ffl erent proxies (see Table 4). These top-down schemes ff demand calculations. Sacks etalgorithms al. by (2009) downscaling tried the to AQUASTAT irrigative overcome the water use limitations in data demand to the grid scale. Recent studies have showncan that generally including irrigation improve in climateinstance, coupled simulations. Saeed land-surface et With schemes al. respect (2009)western to showed India regional that and representing temperature, Pakistan irrigationbe can for activities reduce noted, over climate however, north- model thatof simulation there bias irrigation are by on still 5 Lobell large regional et disagreements and al., in global quantifying 2006), temperature the mainly (see e attributed e.g. to Boucher the di et al., 2004 vs. in contrast include both irrigativeand and future non-irrigative demands, conditions, performed and undersummarize provide current the relatively recent more applications consistent and highlight results. the Here, limitations we in5.1 briefly current simulations. Online representation are consequently projected. 5 State of large-scale modeling applications The algorithms reviewed infline Sects. applications. 3 Comparing and todevelopment; 4 o they have only had include aunder irrigation, current wide conditions, mainly range and of implemented present online at rather and contradictory regional results. of- scale O and et al., 2013a; Hejazi et al.,global 2013a). and The parameters regional can data. beestimated assigned In using based implicit the on available procedures, integrated economy firstios. and the By population production considering models (or the ortion) population) prescribed amount and is scenar- of accounting water for withdrawal technological per and/or socio-economic unit shifts, of water production withdrawals (or popula- simple parametric structures (e.g. Strzepek et al., 2012b; Flörke et al., 2013; Hanasaki and implicit algorithms. In explicit algorithms,described changes as in functions water of withdrawals changes are in directly socio-economy, technology and water price using understand the mechanisms controlling water usetions and have water coarse allocation. temporal Current and projec- spatialas resolution functions and describe of non-irrigative2013; socio-economic demands Blanc and et technological al.,can developments 2013; be (e.g. Hejazi characterized Davies by et intermediate al., etbriefly socio-economic explained 2013b, al., above and d; for technological irrigation Voisin scenarios, expansioncan et (see as be Sect. al., further 3.4). 2013). The downscaled projected using Theseble demands various changes 6 proxy summarizes variables, as some explained representative in e Sect. 4.2. Ta- Recently Wada et al. (2011, 2014)to and disaggregate Voisin et annual al. data (2013) to developed monthly simple algorithms and daily estimates4.3 (see Table 5). Projection of non-irrigative demand Characterizing the past and future evolution of non-irrigative demands is required to Hanasaki et al.tions (2008a) and merged national the boundarythe FAO-AQUASTAT data consumptive information with ratios from of population Columbiamunicipal Shiklomanov water distribu- (2000) University withdrawals to and (CIAT, 2005) uses come atvarious up and the industrial with global scale. gridded uses More industrial resulted detailedponents. and information in For on instance, breaking Vassolo the anduses Döll industrial (2005) related withdrawals distinguished to into between thermoelectric industrial theirporal power disaggregation water com- of generation annual and withdrawals, however, manufacturing has received production. much Tem- less attention. are heavily influenced by thescaling availability of algorithms national and within globalwhich datasets the is and a Water the global down- – water2007). budget Currently, Global and the use Assessment model availabilitythe (WaterGAP; and of opportunity Alcamo di Prognosis et to al., scheme, generate 1997, gridded 2003, products from di been suggested, based on di 5 5 20 25 15 10 25 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ects on ff ects are associated ff ects of irrigation do not expand ff 8260 8259 erent demand algorithms have been used to simulate ff erences in the host climate and LSS models, irrigation de- ff erences in estimates of water demand and use. This can be referred to ff ine representation ffl erences in data support, demand calculation schemes and host models – see ff ine representation of water demands is more common and a wide variety of GHMs In summary despite di Online simulations under future climate change are limited and have been performed Irrigation-induced precipitation has been studied for quite some time and has been ffl the di the discussion of Sect. 6 below. O and LSSs in conjunction withthe di dynamics of water demandglobal under both simulations current under and current futureet conditions. conditions al. The are available (2014) compared andet and Chaturvedi al. summarized et (2003) in al.simulations and Wada (2013a, are Hejazi mainly b) et compared forexhibit al. large at irrigative (2013b) di countrywide, demands for continental and total and in water global Alcamo consumption. scales, In and brief, current with irrigation. Large uncertainties,surface–climate however, modeling, exist in which currentarea. emphasize coupled on irrigation–land– the need for more5.2 research in O this simulation with andnorthern without India irrigation and bothwas increase showed enhanced in a with precipitation irrigation. decreasediscovered, over They in if noted the the precipitation that global southern the over scalelights peninsular; increase simulations the are in the not importance precipitation latter dynamically cannotdynamic of downscaled. downscaling be This including of high- future irrigation climate change schemes scenarios. in regionalmand climate algorithms models and for simulation settings, significant feedback e mainly at regional scales.to Gerten dynamically et downscale al. (2011) theSouthern used future Asia a simulations and nested ofThey considered regional concluded a climate two that model global modes including climateincrease, of irrigation predicted model simulations, without can over representing result with irrigation. the in or With roughly respect without to half irrigation. future of precipitation, the temperature creases. Harding and Snyderprecipitation (2012a, also b), depend however, on noted the thatmoisture antecedent conditions, the soil further extent moisture. irrigation of They can result arguedWith e in respect that suppression to in of the low regional scale soil precipitation. ofover disturbance, California’s Central Sorooshian Valley et significantly al. decreases (2011) locallocal showed temperature that precipitation; and irrigation increases however, theyfar argued from that the place theargued where e that irrigation irrigation takes place.southwestern in In US California’s and contrast, Central can Lo increase Valley and the intensifies flow Famiglietti in (2013) the the water Colorado River. cycle in the irrigation-induced precipitation; and (2) estimatingchange the in amount precipitation. and DeAngelis spatial etitation extension al. of increased (2010) in noted the thatintensive Great the irrigation. growing Plains Using season vapor of precip- tracking theirrigated analysis, lands US they adds indicated during to that the evaporation downwind 20th from precipitation, century which increases as as a the result evaporation of in- shown to have(e.g. a Barnston and significantdespite Schickedanz, footprint regional 1984; decline, on Moore Tuinenbergin et local and climate al. Rojstaczer, and stations (2011) 2001). located found regionalet in For a al. the precipitation instance positive (2011) irrigated tested precipitation regions patterns fouris trend of climate the the main models Southern reason and for Asia. arguedare precipitation that Lucas-Picher bias still lack over large Indian of disagreements Monsoon representing in area. irrigation (1) Nonetheless, identifying there the dominant mechanisms that drive the at global scale theby temperature temperature cooling warming in in some some otherchanges. regions areas There due due are, to however, to climate, some irrigation land-coverdid limitations and is in not circulation cancelled their vary study, as between80 % the years of irrigation and the demand annual they LAI. applied These assumptions irrigation can only result in when large the uncertainty. LAI is around They concluded that irrigation has significant importance for regional temperature, but 5 5 25 20 10 15 25 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | - ffi ect ff ciency, ffi erences in ff ected by mit- ciency is can- ff ffi changes that a 2 ciency in future simulations. ffi ects on crop transpiration e erent climate model simulation, irri- ff ff erent projections of non-irrigative demands ff mitigation policies can result in high deploy- 8262 8261 2 ect of water scarcity by regulating more water e ff er an ideal platform as they have the explicit modules required for ff increases might have beneficial e 2 ects of mitigation, however, have large regional variation. For irrigative demands, As the current global potential for expanding water demand is rather limited (Rost Similar conclusions were obtained with respect to non-irrigative demands. Alcamo Normally, future projections of water demands include more uncertainty than simu- ff a slight decrease in municipal withdrawals in the year 2100 under a high-tech scenario, e Fischer et al.igation (2007) showed actions, that which somecrop depend regions water on may specific requirement beet combinations and al. negatively of projected (2013) a CO precipitation showedment that and of applying other temperature high-tech CO changes.low solutions Kyle for water electrical requirements. generation Hejazi (e.g.important et solar factor al. power) in (2013c) that mitigating further have thecient showed e options that for irrigation. taxation Hejazi can et be al. an (2013a) further showed the possibility of even to moderate human waterbe demands. introduced In into such large-scale caseshas models prescribed been for “policy” shown impact that scenarios mitigation assessment. can For can Using significantly example, this decrease Hanasaki future approach, et global it ation water al. demand. in (2013a) showed industrial approximately and 7-fold municipal and demands, 2.5-fold vari- depending on the SSP considered. The (Gleick, 2003), in which thelighted divergence between as modeling results the becomes projectionmand more high- horizon and increases associated (see waterin Davis current use). et data These availability al., uncertainties forsocio-economic 2013, supporting and can for technological robust be scenarios, and electrical as referred reliablein de- well projections, to demand as di calculation some limitations algorithms, underlying which assumptions can limit their e et al., 2009; Gerten and Rost, 2010), adaptation and mitigation strategies are required of Sect. 6. et al. (2007) andtrial water Hejazi uses, et if al.however, not large (2013d) controlled, discrepancies can showed between be that di a increasing major domestic threat and for water indus- security. There are, considering dynamic interactions of carbon, vegetation and water – see the discussion which LSSs can o ios (Taylor et al., 2012),in these future studies demand generally isdemand concluded likely, with that in possibly a mid-latitude one-month significant orsociated regions increase more with (Wada shift the et in predictionsnoted al., the that (see peak 2013), CO irrigation Rosenzweig but etif large al., other uncertainties 2013). factors are are Moreover,Nonetheless, not as- both it limiting studies still (see remains alsocelled unclear Gerten out whether by et increased increased al., transpiration transpiration 2011; duestudies, e to therefore, Konzmann are increasing et required biomass al., in and this plant 2013). direction growth. More (see Gerten, 2013). This is a context for irrigation demand projections withgation algorithms respect and to host di these large-scale uncertainties models. would One be possible usinget approach multi-model approach to al. as account (2011) recommended for by and Gosling et Haddeland al. et (2013) al. and (2011) Rosenzweig et and al. implemented (2013). to Based some on extent the by latest Wada IPCC climate scenar- a function of both projectedshowed irrigated land that and the climate impact changecould from of 1990 be climate to nearly 2080. change They asThere on are, large increasing however, two irrigation as sets water the oftion requirement uncertainty demand. changes associated First, initiated with gridded future by climateing projections products socio-economic current of have and irriga- developments. significant future deficiencies climate, in particularlyKunstmann, represent- with 2012; respect Grey to et precipitationgation (e.g. al., Lorenz demand 2013). and at This the can sub-grid further scale. Second, propagate there to are estimation large of disagreements irri- between lation of current conditionsand/or as socio-economic they and are technological alsojections, with conditioned scenarios. or on Considering without uncertain considering futurehave irrigation climate mainly climate expansion, futures projected irrigation pro- increase demandAs in algorithms an irrigation earlier demand example, under Fischer climate et change al. scenarios. (2007) estimated irrigation water requirement as 5 5 25 20 10 15 25 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ine global simulations ffl erent simulation products under erent climate models can be di- ff ff ine human water demand simula- ffl er from major sources of uncertainty, ff erent combinations of temporal/spatial ff 8264 8263 ine impacts on the Earth System and human livelihood. ffl ine projections agree on large impacts of future change in cli- ffl ; however, the temporal resolution does change the mean evap- ff ects of fine modeling resolution seem to be in general less sig- ff balance. The issues around modeling resolution are explored more in ine runs. Compton and Best (2011) conducted o ff ffl erence in terms of water demand per capita between the simulated products ff data required for executingnoted demand that calculation even algorithms. the Siebert locationsand et sub-grid of variability al. irrigation of (2005) crops districts within are irrigated uncertain are not in generally many available. Wisser regions Uncertainty in current data support: major uncertainties are associated with the Large uncertainties are also associated with o In summary, current o 1. nificant di of WaterGAP and reportedto AQUASTAT data. (i) These available data uncertaintiesThese support, are sources (ii) mainly are demand related widely calculationindependently. connected Here algorithms and we and cannot briefly (iii) befuture discuss host developments. easily these models. addressed sources and and quantified propose few directions for evaporation and runo oration/runo Nazemi and Wheater (2014). tions under current and future conditions. Lissner et al. (2012), for instance, noticed sig- tation of irrigation.resolutions, For Sorooshian instance, et using al. (2011) sixcoupled concluded irrigation–land–climate di that models spatial can significantly andprecipitation change temporal both simulations resolution temperature in over and irrigatedrepresenting grids the physical and processes aatmosphere. controlling fine The the level e feedbacks of betweennificant irrigation detail in and is o requiredand for showed that fine spatial resolution has little importance on long-term modeling of and feedbacks between landIdeally, and the optimal atmosphere modeling should resolution be shouldnonetheless, represented be the identified and choice based described. of oncomputational physical resolution realism; resources in and coupled simulations databeen is availability. shown If mainly that constrained these finer by are temporal not and spatial limiting resolutions factors, can it improve has online represen- appropriate temporal and spatial resolutions, in which the relevant physical processes vergent (Koster et al.,jor 2004; challenge Pitman for et coupled al., irrigation–land–surface–climate 2009; simulations Dadson is the et choice al., of 2013). Another ma- Major gaps remain instanding their the online current and capabilityThese o in gaps modeling are water partiallycesses, demands due which and to is under- inherentcomputational more barriers, complexity significant one in main in challenge modelingsociated in coupled with Earth online simulation coupling simulations System is land modes. pro- boundary the and Apart uncertainty condition, atmospheric as- from models, the as various simulations given obtained a by unique di land-surface which is revealed bycurrent large and discrepancies future between conditions. di identify We the now research turn needs to and discuss priorities. these gaps in more details and 6 Discussions a detailed track ofdent the on water cycle how (see reasonableability Chenoweth the and et human water al., demands allocation 2013) and areis and currently described production is not in as available highly time and well depen- and therefore as space. the water Such assessments avail- remain a widely levelmate, uncertain. of socio-economy accuracy and technology ontation water and demands and mitigation the strategiesprojections, importance for however, of are adap- managing rather future limited water and security su threats. Available electricity water withdrawals ifalso high-tech showed solutions that are promoting employed.controlling international Large-scale regional demand, trade models in can which water-limited beproducts regions can a from import strong other water-expensive adaptation areaset (e.g. option al., Siebert for 2013). and Assessment Döll, of 2010; trade Hanasaki et scenarios al., and 2010; water Konar footprinting, however, needs despite significant population growth. Davies et al. (2013) showed similar results for 5 5 25 20 10 15 25 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | erent sources ff ects on demand ff erences in underlying assump- ff erences between simulations of irri- ff erences in the global simulations ob- ff ects of increased carbon concentration ff 8266 8265 ects between irrigation and atmosphere can con- ff erent methods for calculating reference evapotranspi- ff ine irrigation demand simulations based on GHMs might be biased ffl climate and type offurther crops. This research. can Another be avenuethe considered demand for as simulations future an using data development important assimilationopportunities can need and will model for be be calibration. improving These discussed further inNon-irrigative Nazemi demand: and the Wheater current (2014). and modeling available capability downscaling is and temporallyfor projection seasonal coarse algorithms variations in mainly water demand. do Theretural are not uncertainties also account parametric in and struc- functionaleconomic mappings and technological that proxies. linkhow At water these this demand uncertainties stage, propagate to ittant into socio- is avenue future not for projections. fully future Thisjection understood exploration. is algorithms Developing an robust for impor- downscaling non-irrigativefuture development. and demands Future pro- developments is should consider another limitationsable important in data avail- need and for futurevariability in scenarios non-irrigative as demands. well as the diversity and spatiotemporal stage of research, di ration and corresponding demandlyzed simulations and compared have to not identify appropriate yet algorithms been with fully respect to ana- region, Irrigative demand: limitationscertainty in in current describing algorithmsCurrent the mainly bottom-up crop include moisture algorithms thewater requirements do un- requirements in not at time appropriatelyversity. the and This consider sub-grid space. can plant-specific scalewidely-used result due irrigation to in demand missing estimates misestimatingquire based soil the several on and input irrigation FAO guidelines crop variables demand. (see often2013b di- for re- e.g. Moreover, simplifications), Farmer and given et theables al., need for 2011 for downscaling future and of Hejazi simulations, climate(e.g. vari- et these Vörösmarty, al., can 1998; be Oudin outperformed et by al., simpler 2005; models Wisser et al., 2010). At current b. a. as they inherently ignore climatesent feedbacks. Moreover, important GHMs often processes cannot such repre- on as irrigation the demand. e This limitation may result in major deficiencies in simulating Uncertainty in host models:mand host simulations, models particularly can forof add irrigation. irrigation substantial As demand uncertainty noted involves totime in solving step de- Sect. the and 3, this soil theactual is water evapotranspiration determined calculation balance and by soil at how moisture everyHaddeland the are simulation relevant et parameterized natural al. in processes, the (2011) suchtained host showed as model. from major six di LSSstions, and process five representations, GHMs andthat due related considering to parameterizations. feedback di Itsiderably e change is potential also evaporation shown therefore (e.g. o Blyth and Jacobs, 2011; Lu, 2013); gorithms. At this stagecompared of with respect research, to the theirsimulations. uncertainty various This and datasets is the a are associated major e not need for systematically futureUncertainty exploration. in demandnon-irrigative calculation demands. algorithms: this includes both irrigative and et al. (2008) argued thatand major crop uncertainties maps are and associated this withgation can forcing, water irrigation result requirement. in The issues large around di demands data support as applies well. to non-irrigative ForMacknick the et case al. of (2011) water notedhave that use numerous “federal for gaps data electricity and sets methodological generationpropagate on inconsistencies”. into in water Data structural the use and uncertainty in US, parametric can and power identification plants can during further model development extendof to global future and projections. regional Thevarying data availability degrees has of of resulted di quality, in which emergence can of potentially various support datasets, demand with calculation al- 3. 2. 5 5 25 20 10 15 25 20 10 15 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ects; ff e 2 orts are also re- ff erent geographic regions ff orts are however needed to trans- ff ected in time and space by human ac- ects of water demand on both terrestrial ff erent datasets, demand algorithms and ff ff 8268 8267 increases can significantly change vegetation 2 concentration and future water stress, as they often include many 2 First author would like to thank Amir Aghakouchak for his valuable inputs regimes (Gerten et al., 2004). From this perspective, it can be con- ff tainties in irrigation algorithmsdisjointed and and large-scale distinguished. host Thisgorithms requires models with “mix have multiple and not host match” been modelssensitivity multiple analysis. to fully This demand conduct can al- a be systematic considered intercomparison as and an important research direction. climate change scenarios asdynamics CO (e.g. Prudhomme et al.,and 2013), runo which can furthercluded that alter online the evaporation LSSsincreasing are CO superior to GHMsof with the respect required to computational simulationsclimate, components under carbon, for vegetation and investigating water interactions cycles. between fer E recent demand calculationLSSs. algorithms In developed addition, in although the itels context has been of are argued GHMs more that into the significant uncertainties than in in host mod- climate forcing (e.g. Wada et al., 2013), uncer- As a final remark, it must be noted that the e with water supply andin allocation, water cycle. which This determinewater scarcity is the is particularly a extent major important limiting ofcompetition factor for for human over water available future intervention demand water and predictions, canhow sources. as substantially In water increase the Nazemi supply increasing andintegrated and Wheater with allocation various (2014), water have we demands beensub-grid review and scales. natural represented land-surface at processes at larger grid scales and and been demand and consumption (Calvin etdevelopment al., due 2013). to This seemssocio-economic the to and limitations be energy more models. in of current a long-term demand algorithms,water LSSs cycle and as water security well cannot be as fully studied unless considered in conjunction considering data limitations as welldemand; as diversity and and finally spatiotemporal variability (3) into transferring human LSSs the for algorithms (a) developedand in improved (b) the irrigation further context demand coupled of studies calculationtions GHMs with under with climate respect increasing models to CO to interactionschange address between various conditions. carbon, scientific Apart irrigation ques- and fromquired climate to these under link immediate climate with researchthe socio-economic needs, dynamic and e energy interactions models between to natural have and a anthropogenic full drivers understanding of of human water models, three main directions(1) are systematic suggested intercomparisons for between futurehost models di developments. and These associated include uncertaintiesas well with as respect various to socio-economic di gorithms and for climate calculating conditions; (2) both developing irrigative improved and al- non-irrigative demands in time and space Considering current gaps in representing the anthropogenic demands in large-scale on type of demand,cations modeling were procedure overviewed; and and underlying limitations assumptions. in Current knowledge appli- are identified and discussed. named the “Anthropocene”. Anthropogenic activities,resented therefore, are in required to models bemodeling that rep- and land–atmosphere are feedback representations. used Currentactions human–water for are inter- mainly impact manifested assessments, throughther water large-scale broken resource down management, hydrological into which two canwater interacting be supply components, and fur- related allocation. In to this watermand paper demand in we as considered large-scale the well models. representation as Water ofirrigative demand water was de- categories. further divided We into summarized irrigative and current non- demand calculation algorithms based 7 Summary and concluding remarks The terrestrial water cycletivities has been during greatly the a recent past, to the extent that the current geological era has been Acknowledgements. and discussions during early stages ofby the this Canada survey. 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P.: Global modeling of withdrawal, allocation and 5 5 10 20 30 25 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ine ine 0.08 0.5 0.5 0.5 0.5 0.5 0.5 ffl ffl ◦ ◦ × ◦ ◦ ◦ ◦ 1 1 × × × × × × 0.125 0.125 ◦ ◦ ◦ ◦ ◦ ◦ 10 km × × × × 0.5 0.5 2.5 ◦ ◦ × ◦ ◦ ◦ 1 × × × ◦ ◦ ◦ × ◦ resolution resolution 6 h 10 km resolution resolution ) ) http:// http:// ) NCEP (Kalnay et al., 1996) Dirmeyer, 2003) gov/merra/ gmao.gsfc.nasa. http://cascade. www.ecmwf.int/ products/data/ usgs.gov 1998) (China) and Asia 8294 8293 ective rainfall and WaterGAP CRU TS 1.0 (New 24 h 0.5 ff cients obtained from Allen Döll, 2008) Jones, 2005) ffi ective rainfall Indian ORCHIDEE ISLSCP-I 24 h 1 ff erence between current and ideal plant available (van Beek MERRA ( ff erence between actual and potential transpiration PCR- CRU TS 1.0 (New 24 h 0.5 erence between Smith (1992) e erence between available plant-moisture and an LPJmLerence between current and 75 % of field capacity. H07 CRU TS 2.1erence between actual and crop-dependent reference GCWM 24 h NCEP-DOE 0.5 CRU TS 2.1 24 h 24 h 1 0.08 erence between current and ideal Contermi- CLM4erence between current soil NLDAS Changjing, NICE 1 h ECMWF ( 0.125 erence between e erence between current soil Colorado VIC (Liangerence between actual Adam and and 3 h USA 0.5 CLM3.5 (Oleson NCC (Ngo-Duc 6 h 2.5 ciency. ff ff ff ff ff ff ff ff ff ff ffi et al., 1998). et al., 2011) moisture at field capacity with dynamic root zone. et al., 2011) evaporation based on potential canopycarbon conductance and of water (Sitch et al., 2003). et al., 2007)crop growth representation based onet SWIM al., (Krysanova 1998). Jones, 2005) et al., 2008a, b) 2002); GSWP-2 et al., 1998). (Zhao and 2003) et al., 2005) et al. (1998). Döll (2008) and Hanasaki (2013a, b). et al., 2014) evapotranspiration (Allen et al.,1998) without considering irrigation e et al., 1993) et al., 1996b) FAO Penman–Monteith crop-specificevapotranspiration and soil moisturecontent Mekong at field capacity. (east Asia) (2003); Maurer et al. (2002) et al., 2010) Taylor (1972) crop-specific and transpiration (Allen (van Beek GIAM(Thenkabail et al., 2009) et al., 1998)et al., 2010) Jones, 2005); the di et al., 2010) Taylor (1972). Crop coe 2008);NASS (USDA,2002) CLM4 (Levis and Sacks, 2011; Levis on CLM4CNcrop crop growth model of et al., 2012). Chaturvedi et al., 2011) Wise et al., 2009a) using et methods al., of 2003) 2011; Li et al., et al. (2013a, b). Hejazi et al. (2013a), Siebert and 2013; Tesfa FRIS (USDA,2008) Farmer et al. (2011). Crop growth and irrigation losses included. 2008) Representative examples for including global irrigation in large-scale models (o Representative examples for including regional irrigation in large-scale models (o (2013) basins et al. (2013) and Gutman, soil moisture content basedShankman et al., 2010)Voisin nous USAet al. (2013) at (Lawrence the field capacity. projections in Crop area (Cosgrove estimations (Wise and Calvin, 2009; Downscaling GCAM model west River (Lawrence et al., ( US et mid- al., 2011) SCLM-MOSART CASCaDE 1 h 0.125 Leng MODIS (Ozdogan Di Nakayama Liu (1996, in Di Haddeland Döll andHaddeland Di SiebertGueneau GAEZ (IIASA/ Haddeland et al. (2006) Di North VIC (Liang Maurer 24 h 0.5 Reference Irrigation datade Rosnay Döll Irrigation and demand Di Region Host model Forcing Temporal Spatial et al. (2003) Siebert (2002) and FAO potential Peninsula (Ducoudré (Sellers and Chinese; see Liu moisture content and soil moisture Yellow (Nakayama et al. (2006) Siebert (2002) moisture content and minimum ofet al. (2007) et al. (2005) (USA) andet et al. al., (2012) 1994) FAO, 2012); Lettenmaier potential evapotranspiration based on et al., 2004, et al., 2005) America et al., 1994) et al. (2002) (2011, 2012) (Portmann according to van Beek et al. (2011), using Priestley and GLOBWB et al., 1999, 2000) Siebert(2002)et al. (2006) (2000) (2000)et al. (2008) Priestley and Taylor (1972) crop specific (2005, potential 2007); evaporation is based on FAO Penman (1998) Monteith. procedure. evapotranspiration(2008, and 2009) Allen et al. (1998) multipliers. (Alcamo (2007) et and al., Sud, 2003) et al. (2008a, b) et al., 1999, 2000) (2000) updated Priestley and Taylor (1972) et potential al., 1996b) Irrigation applied 30 days prior to planting. DetailedWada (Bondeau et al. (Vörösmarty (Hanasaki MIRCA2000 (Mitchell and (Mitchell and Pokhrel (Kanamitsu etet Di al., al. (2012)Wada et (2007) al. Siebert et(2013a) al. MIRCA2000 Procedure of Hanasaki et al. (2008a, b). Crop is (Portmann calendar based on Constant Potential 50 evapotranspiration mm (Allen surface water depth for MASTIRO paddy Irrigation until 20 days PCR- before harvesting. For non-paddy areas, Kim et al. (2009); (Takata et al., GLOBWB 6 h GPCC ERA-Interim (Rudolf (Dee 24 h et al., 2011); 1 0.5 Hanasaki Döll and Siebert Di Siebert and MIRCA2000 Di Döll andHanasaki Döll and Siebert Di DöllWisser and Siebert Similar to Döll and Siebert (2002). Reference Siebert et al.Rost et al. Similar to Haddeland et al. TRIP (2006) (Oki using Allen et Siebert al. et al. ISLSCP-I Di (Sellers WBM 24 h 0.5 CRU TS 2.1 24 h 0.5 Reference Irrigation data Irrigation demand Host model Forcing Temporal Spatial Döll (2010) (Portmann evapotranspiration computed according to Priestley and (Siebert and (Mitchell and Table 2. mode). Table 1. mode). Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ◦ ◦ ◦ ◦ 12 km 36 km 12 km 40 km 10 km 10 km 4 km 0.5 0.5 0.5 × × × × × × 2.8 ◦ × 1 × × × × ◦ ◦ ◦ ◦ × ◦ resolution ) ). model resolution resolution 2006) http://www.platts.com http://www.ngdc.noaa.gov/dmsp to withdrawal (Shiklomanov, 2000). 8295 8296 erence High LEAF-2 RAMS 30 serence 10 km Great Noah (Ek WRF 30 s and 10 km ff ff ) data based on WWDR-II (Global) Roots grow based on the greenness index. (USA) between actual and saturated Soil moisture). Plains irrigatedAQUASTAT water uses applied at (Walko Global (Pielke CLM3.5when the soil moisture drops below CAMwhich nested the plant in stressedfield (a capacity, depending percentage on of thecontinues crop) to nested and field in capacity). (USA) 20 Central min et al., 2.8 2003) MM5 (Chen 1 h 2001a, b) 12 km constant rate when LAI exceeds 80 % of the (Oleson (Collins http://wiki.umd.edu/gcam ) maximum annual value. et al., 2008) et al., 2004, national boundary information, further convertedconsumption to estimates. water national boundaries (CIAT, 2005); ratio of consumption Allocating constant flow to eachof unit production according to typecooling of system. production volumes along with waterintensity for each unit ofin production each sector. Downscaling totaldemand to the grid-scale basedcity on nighttime light. volumes (UN, 1997; CIA, 2001); Sectoral intensity (Shiklomanov, 2000; WRI, 2000); Night city light pollution (US Air Force, downscaled as a function ofpopulation. Population densityassumed static in time. et al. and (2011, methodology 2013a) of Wada ratio of rural to urban(constant population for each country) and percentage of population with access to drinking water. withdrawals based on proportion ofurban population. drinking water (WRI, 1998) et al., 1995) http:// ) a maximum depletion threshold beyond Valley and Dudhia, 36 km ) (USA) et al., 2000) et al., 1992) in 1 min 40 km Manufacturing Estimating country-wide sectoral Industrial production demand Industrial Downscaling county-wide industrial Population (van Woerden http://landsat.gsfc. http://www.cimis.water. Representative examples for calculating grid-based non-irrigative demands using et al., 2010) threshold is fixed at 50 % of filed capacity. Plains et al., 2005) nasa.gov/ www.fao.org/nr/water/ aquastat/main/index.stm ca.gov/cimis 2002) Representative examples for including irrigation in coupled land-surface models (online et al. (2008a) industrial scale by weighting population and withdrawals, Population and (Global) Hejazi Municipal and Demand estimates of GCAM model Global population density 0.5 Hanaskai Domestic and Countrywide data downscaled to gridet al. (2013b) industrial ( countrywide AQUASTAT 1 et al. (2003) withdrawals based on population,and Döll(2005) cooling power production based on et al., 1995); Access to downscaling global estimates. (Global) Plants Data Set ( (Global) Reference Estimated Downscaling procedureVassolo Thermoelectric Calculating the gridded data for Data support Targeted World Electric Power 0.5 Alcamo Domestic Distributing country-level Population (van Woerden 0.5 (2009) et al. (2011) ( (2013) Gutman, 2008; Ozdogan on Ozdogan et al. (2010), moisture Great et al., 2003) (Skamarock Reference Irrigation dataAdegoke LandSatSacks et al. ( FAO-AQUASTAT Irrigation demandSorooshian CIMIS-MODIS Target soil moisture deficit (di Harding Target soil moisture deficit (Irrigation starts Region MODIS (Friedl et al., California 2002; Host LSS Noah Target (EkQian soil et moisture al. deficit (di Climate NCAR- MODIS (Ozdogan and Temporal 30 min Spatial Similar to Sorooshian et al. (2011). Based 4 km Southern Noah (Ek WRF 3 h 12 km et al. (2003) ( and Snyder(2012a, b) Ozdogan and Gutman, 2008); NASS (USDA, between actual and saturated soil moisture at depth of 2 Plains m). et al., 2003) (Skamarock 25 s (USA) et al., 2005) Table 4. downscaling coarse scale estimates. Table 3. mode). Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ) resolution resolution http://cascade. wr.usgs.gov ) (global) 8298 8297 ) datasets. Technological change influenced ciency, which further determines extent of water use. (global) ffi http://themasites.pbl.nl/tridion/en/themasites/hyde/ http://www.unep.org/ industry and transportation uses andmonthly capturing fluctuations the in building usecooling based degree on days. heating/ electrical demand. Manufacturing water usestructural computed intensity as and a rates function of of manufacturing baseline gross value and technological change. a function of population and change in municipal intensity, varying based on GDP. a function of operational e demand for secondary energy.technological proxies. Net municipal waterurban demand to calculated total as population a and function recycling of ratio. fraction of (global) consumptions estimated based on level of technological change.and simulated explicitly. Public supplyGDP considered per as capita. a Self-supply function considered ofconsidered as population as function and a of function sectoral of GDP. mining’s Mining GDP. supply (global) capita and GDP considering growth rate and climatic and water availability factors.and UNEP ( (global) demand industrial andmining consumption to meet electrical demand estimated using Strzepek et al. (2012a). Other demands categorized into three groups: public supply, self-supply and mining supply (US) Representative examples for disaggregating annual non-irrigative demand into Representative examples for projection of non-irrigative water demands using socio- Voisin et al.(2013) Electrical Dividing electrical use into industry, transportation and CASCaDE building sectors. Assuming uniform distribution for ( Reference EstimatedWada et al.(2011, 2013) Disaggregation procedure Municipal and livestock as a function of Downscaling temperature. annual demand to monthly fluctuations CRU (New et al., Data support 1999, 2000) Hejazi et al.(2013a) MunicipalHejazi et al.(2013b,d) IndustrialWada et al. Withdrawal per capita(2013a) explicitly determined as a function of GDP Industrial per and capita, water Manufacturing water demand municipal is explicitly simulated based on population price and and Industrial GDP. technological and Annual development. municipal Technological withdrawal development taken considered from as WWDR-II dataset (Shiklomanov, Geopolitical Water demand for primary 1997; Annual Vörösmarty energy et scaled al., by 2005) amount and of backcasted fuel explicitly production using and economic Geopolitical water and Annual Countrywide regions regions (global) Hanasaki et al.(2013a) Industrial andBlanc et al.(2013) municipal Explicit simulation of industrial withdrawal as Electrical, a function of electricity production and domestic, water intensity which decreases linearly Five in year time. Electrical Municipal demand water use projected calculated implicitly Countrywide as using ReEDS (Short et al., 2009) integration and with USREP model (Rausch and Mowers, 2013). Water interval withdrawal and Annual Assessment sub-regions Davies et al.(2013) Electrical Implicit simulation – changes in regional cooling system shares estimated based on Annual shift from wet to dry cooling Geopolitical technologies. Reductions in water withdrawal and regions ReferenceAlcamo Simulated et demands al.(2003a) Simulation procedure Domestic andStrzepek et al.(2012b) Municipal and industrial Explicit simulationFlörke of et change al. in industrial(2013) and industrial domestic withdrawal Explicit as simulation functions of of change in Domestic municipal and water use as a usage function intensity of and population technological and change. Usage intensities industrial are functions of Annual GDP. Explicit per simulation capita of income. domestic Industrial demand water using use Alcamo Annual considered et as Countrywide al. a (2003) function with of parameterization water use per Assessment Annual based on HYDE ( Countrywide sub-regions Temporal Spatial Table 6. economic variables. Table 5. monthly estimates.