Hydro-climatic change in irrigated world regions

Hydro-climatic changes in irrigated world regions Shilpa Muliyil Asokan

Department of Physical Geography and Quaternary Geology Stockholm University Stockholm 2013

1

Shilpa Muliyil Asokan

© 2013 Shilpa Muliyil Asokan ISSN: 1653-7211 ISBN: 978-91-7447-641-5 Paper I © 2008 John Wiley & Sons, Ltd. Paper II © 2010 American Geophysical Union Paper III © 2010 American Geophysical Union Paper IV © 2013 Springer Science + Business Media Dordrecht Paper V © 2012 Shilpa Muliyil Asokan Cover: Top panel: Irrigation water diversion in Mahanadi River Basin (Photo: Orissa Economy); Bottom panel: The effect of irrigation water diversion in Aral Sea Drainage Basin - the desiccated Aral Sea (Photo: Olov Johansson) Layout: Shilpa Muliyil Asokan, Susanne Ingvander (except Paper I, II, III and V)

2

Hydro-climatic change in irrigated world regions

Abstract Understanding of hydro-climatic changes in the world’s river basins is required, for instance towards ensuring future food security. Different regional basins experience different levels of hydro-climatic change and different water resource impacts of climate change depending on the endorheic (discharging into terminal inland water) or exorheic (discharging into the ocean) nature of a hydrological basin, along with the climatic conditions and human land-use and water-use practices (for instance for agriculture and its irrigation) within the basin. This thesis has analyzed long-term hydro-climatic changes in two main irrigated regions of the world: the Mahanadi River Basin (MRB) in India and the Aral region in Central Asia, including the terminal Aral Sea and the hydrological basin draining into it. The thesis applies a basin-wise, data-driven water balance-constrained approach to understanding and quantifying the hydro-climatic changes, and to distinguish their main drivers in the past century and for future scenario projections. Results point at human water-use and water re-distribution for irrigation within a basin as a major driver of water balance and water resource changes, which also affect surface temperature in the region. Cross-regional comparison for the two different study basins clarifies the importance of different perspectives on hydro-climatic change. One perspective may for instance focus on the climatically important changes of water, vapor and latent heat fluxes at the land surface, while another may focus on changes to water resource availability in the landscape, with different change drivers then being dominant from these different perspectives on continental water change. Thesis results show that irrigationdriven changes in evapotranspiration and latent heat fluxes and associated temperature changes at the land surface may be greater in regions with small relative irrigation impacts on water availability in the landscape (here represented by MRB) than in regions with severe such impacts (here represented by the Aral region). This implies that one cannot from the knowledge about only one aspect of hydro-climatic change (such as temperature and precipitation changes at the land surface as given from climate modeling) simply extrapolate the impact importance of those changes for other types of water changes (such as on availability of water resources) in a region. Climate model projections from General Circulation Models (GCMs) used in the fourth assessment report (AR4) of the Intergovernmental Panel on Climate Change (IPCC) were also studied in the thesis regarding their performance against hydrologically important, basin-scale observational temperature and precipitation datasets. Results show lack of consistency in individual GCM performance with regard to temperature and to precipitation, implying difficulties to identify well-performing GCMs with regard to both of these variables in a region. For the example case of the Aral region, the thesis shows that consideration of the ensemble mean of different GCM outputs may instead provide robust projection of future hydro-climate changes in a regional hydrological basin. Keywords: Climate change, hydro-climatic change, evapotranspiration, irrigation, water demand, water balance, land-use, water-use, hydrological catchment, Aral Sea, India, Mahanadi River Basin

3

Shilpa Muliyil Asokan

4

Hydro-climatic change in irrigated world regions

Svensk sammanfattning Förståelse av förändringar i hydroklimatet inom olika avrinningsområden i världen krävs, till exempel för att säkra livsmedelsförsörjningen i framtiden. Beroende på avrinningsområdets karaktär, som om det är endoreiskt (dränerar till inlandsvatten) eller exoreiskt (dränerar till havet), samt på klimatförhållandena och den mänskliga användningen av mark och vatten (t.ex. för jordbruk och bevattning) inom området, påverkas hydroklimatet och vattenresurserna olika av motsvarande klimatförändring i olika regionala områden. Denna avhandling har analyserat långsiktiga förändringar i hydroklimatet inom två olika bevattnade regioner i världen: Mahanadi-flodens avrinningsområde (MRB) i Indien och Aral-regionen i Centralasien, som omfattar Aralsjön och hela det endoreiska avrinningsområde som dräneras in till den. Avhandlingen tillämpar en datadriven vattenbalansbaserad metod på avrinningsområdesskalan för att förstå och kvantifiera förändringarna i hydroklimat, samt identifiera och särskilja de viktigaste drivkrafterna för dessa förändringar under det senaste århundradet och för framtida modellprojektioner. Resultaten pekar på den mänskliga användningen och omfördelningen av vatten för bevattningsändamål som en viktig drivkraft för förändringar i vattenbalans och vattenresurstillgång, vilka i sin tur också påverkar temperaturen i regionen. Jämförelse av förändringarna i hydroklimat mellan de två studerade avrinningsområdena visar betydelsen av olika perspektiv på dessa förändringar. Ett perspektiv kan exempelvis fokusera på förändringar i flödena av vatten, vattenånga och latent värme vid markytan, som är viktiga för området klimat, medan ett annat perspektiv fokuserar på förändringar i vattentillgång i landskapet. Olika drivkrafter till förändring blir då viktiga ur dessa olika perspektiv på det kontinentala vattnet. Resultaten i avhandlingen visar att bevattningsdrivna förändringar i evapotranspiration och latent värmeflöde, samt relaterade temperaturförändringar vid markytan kan vara större i regioner där bevattningens effekt på vattentillgången i landskapet är relativt liten (som i MRB) än i regioner med stor bevattningspåverkan på vattentillgången (som i Aral-regionen). Detta innebär att man inte kan från kunskap om endast en vattenförändringsaspekt (till exempel förändringar i nederbörd och temperatur vid markytan, givna från atmosfärisk klimatmodellering) enkelt dra slutsatser om effekter på andra vattenaspekter (till exempel på vattentillgången i landskapet) inom en region. Avhandlingen jämför också projektioner av globala klimatmodeller (GCM:er) i den internationella klimatpanelens (IPCC) fjärde utvärderingsrapport (AR4) med hydrologiskt viktiga observationsdata för temperatur och nederbörd i de studerade avrinningsområdena. Resultaten visar inkonsekventa jämförelseresultat för temperatur och nederbörd från enskilda GCM:er, som innebär att det är svårt att hitta en enskild GCM som ger bra resultat i relation till data för båda dessa variabler i en region. För Aralregionen visar avhandlingen att medelvärdesbildade resultat från många olika GCM:er kan i stället ge mer rättvisande projektioner av förändringar av hydroklimat inom regionala avrinningsområden.

5

Shilpa Muliyil Asokan

6

Hydro-climatic change in irrigated world regions

Hydro-climatic change in irrigated world regions Shilpa Muliyil Asokan Department of Physical Geography and Quaternary Geology, Stockholm University, Sweden

LIST OF APPENDED PAPERS This doctoral thesis consists of a summary and six papers. The papers are referred to as Paper I-VI in the summary text. Paper I Asokan, S. M. and Dutta, D, 2008, Analysis of water resources in the Mahanadi River Basin, India under projected climate conditions, Hydrological Processes, 22: 3589-3603. doi: 10.1002/hyp.6962 Paper II Asokan, S. M., Jarsjö, J. and Destouni, G, 2010, Vapor flux by evapotranspiration: effects of changes in climate, land-use and water-use, Journal of Geophysical Research – Atmospheres, 115, D24102. doi: 10.1029/2010JD014417 Paper III Destouni, G., Asokan, S. M. and Jarsjö, J, 2010, Inland hydro-climatic interaction: Effects of human water use on regional climate, Geophysical Research Letters, 37, L18402. doi: 10.1029/2010GL044153 Paper IV Asokan, S. M. and Destouni, G, Irrigation effects on hydro-climatic change: Basin-wise water balanceconstrained quantification and cross-regional comparison, manuscript accepted for publication in the Surveys in Geophysics. doi: 10.1007/s10712-013-9223-5 Paper V Jarsjö, J., Asokan, S. M., Prieto, C., Bring, A. and Destouni, G, 2012, Hydrological responses to climate change conditioned by historic alterations of land-use and water-use, Hydrology and Earth System Sciences, 16, 1335 – 1347. doi: 10.5194/hess-16-1335-2012 Paper VI: Appendix to Paper V Asokan, S. M. and Destouni, G, Climate model performance versus basin-scale hydro-climatic data.

CO-AUTHORSHIP The co-authorship of the papers reflects the collaborative nature of the underlying research. For Papers I, II, IV and VI, I was the main responsible for writing the papers and for most of the computations and analysis, with all co-authors collaborating on and contributing to study design, analysis and some computations. For Papers III and V, I did many of the included computations, and participated in the design and analysis of the papers.

LIST OF RELATED PAPERS NOT APPENDED IN THE THESIS Järsjo, J., Asokan, S. M., Shibuo, Y. and Destouni, G, 2008, Water Scarcity in the Aral Sea Drainage Basin: Contributions of Agricultural Irrigation and a Changing Climate, NATO Science for Peace and Security Series C: Environmental Security. Dordrecht: Springer, Pages: 99-108. 7

Shilpa Muliyil Asokan

ABBREVIATIONS AND SYMBOLS AR4 AS ASDB CAWATERinfo CRU TS DEM DHM DS E Ep ET F FAO GCM GDP IISDHM IPCC km2 km3 yr-1 MCM mm season-1 mm yr-1 MODIS MRB msl P R RCM RGI SRTM T TAR UNEP WHO W m-2 °C ΔET ΔP ΔR ΔT ΔTcl ΔTDS ΔTDS-irr ΔTirr ΔTirr-gs ΔTirr-ngs ΔTshr ΔTWS ΔTWS-irr

8

Fourth Assessment Report Aral Sea Aral Sea Drainage Basin Central Asia Water Information database Climate Research Unit time-series Digital Elevation Model Distributed Hydrologic Model Water storage change Evaporation Potential evapotranspiration Evapotranspiration Latent heat flux Food and Agriculture Organization General Circulation Model Gross Domestic Product Institute of Industrial Science Distributed Hydrological Model Intergovernmental Panel on Climate Change Square kilometer Cubic km per year Million cubic meter Millimeter per season Millimeter per year Moderate Resolution Imaging Spectrometer Mahanadi River Basin Mean Sea Level Precipitation Runoff Regional Climate Model Registrar General Method of India Shuttle Radar Topography Mission Temperature Third Assessment Report United Nations Environment Programme World Health Organization Watts per squared meter Degrees Celsius Changes in evapotranspiration Changes in precipitation Changes in runoff Changes in temperature Temperature change representing the regional manifestation of global climate change Temperature change in the dry season Temperature change component due to irrigation in the dry season Annual average change in temperature due to irrigation Average change in temperature due to irrigation in the growing season Average change in temperature due to irrigation in the non-growing season Temperature change component of Aral Sea shrinkage Temperature change in the wet season Temperature change component due to irrigation in the wet season

Hydro-climatic change in irrigated world regions

1 INTRODUCTION

Regional land use changes affect the Earth’s hydrological cycle, as the land surface is an interface to the atmosphere that interacts with climate in regulating the partitioning of precipitation on land into evapotranspiration (ET) and runoff (R) (Gordon et al., 2005; Douglas et al., 2006; Donohue et al., 2007; Shibuo et al., 2007; Destouni et al., 2013). Human activities that have affected vegetation and changed land- and water-uses, for instance by irrigation, have therefore considerably impacted the water cycle (Foley et al., 2005; Shibuo et al., 2007; Piao et al., 2007; Weiskel et al., 2007; Wisser et al., 2010; Destouni et al., 2013). Hydro-climatic impacts of land-use and water-use changes have been quantified as increases or decrease of ET. For instance, deforestation may decrease ET and increase R, while opposite impacts may result from new forest establishment on previously sparsely vegetated land (Vanlill et al., 1980; Gordon et al., 2005; Loarie et al., 2011). Furthermore, conversion of natural unplowed land to cultivated land may often increase ET (Loarie et al., 2011; Destouni et al., 2013), but such conversions may under some conditions also decrease it (Schilling et al., 2008). Change from agriculture to forest may further initially decrease ET (Qiu et al., 2011) and later increase it (Donohue et al., 2007). Regarding irrigation of agricultural areas, human re-distribution of water for irrigation, in addition to the actual land irrigation itself, (Shibuo et al., 2007; Lobell et al., 2009; Lee et al., 2011; Törnqvist and Jarsjö, 2012) has been estimated to increase net water fluxes from the land surface to the atmosphere by about 2000 km3 per year (Foley et al., 2005; Gordon et al., 2005), constituting a considerable part of the total human freshwater withdrawals and losses through total ET from land (Destouni et al., 2013). Human modifications through non-irrigated agriculture and hydropower (Destouni et al., 2013), and deforestation (Gordon et al., 2005) have also been shown to increase ET, possibly each of them by as much as the ET increase by irrigation. The role of irrigated areas in modifying regional climate through enhanced ET has been investigated by several researchers. The first investigations along these lines considered relative ET flux as a constant across the global irrigated areas (for instance, the flux was considered as 40% of the total irrigation water use

by Boucher et al., 2004). Later on, researchers modeled the ET flux using land surface models based on the area equipped for irrigation (Sacks et al., 2009), however the modeled water quantity used in the irrigated fields was then mostly considered unlimited. Also, irrigation water was applied to entire grid cells even if only a part of the cell was irrigated. These simplifications led to overestimation of vapor and associated latent heat flux to the atmosphere. A more recent study by Lobell et al. (2009) on the irrigation-induced climate change over eight major irrigated regions of the world described a more developed climate modeling approach by taking into account soil saturation thresholds. Nevertheless, the modeled water-use quantity can also in this approach be higher than actual regional water use, for instance in the case of Aral Sea region of Central Asia, for which actual water use has been quantified in a hydrological modeling study by Shibuo et al. (2007). The interactive processes between the land surface and the climate regime in the atmosphere vary between regions (e.g., Koster et al., 2004). Major factors that govern the geographic variability of irrigation-induced regional climate change have been assumed to depend on the extent of irrigated land area by Lobell et al. (2009), who reported temperature biases for climate simulations that neglected irrigation. For the Aral Sea region in Central Asia, lack of agreement between observed temperature records and climate model simulation results, which did also account for irrigation, was attributed to inaccuracy with regard to the quantity of water being applied for irrigation in that region. This indicates an effect of human water-use, and not only land-use, in modifying the regional water cycle. Furthermore, regarding the linkage between the climate regime and the land surface processes, Schär et al. (2004) and Seneviratne et al. (2006) have identified a soil moisture precipitation feedback to regional climate, even in regions with assumed weak soil moisture-atmosphere coupling (Koster et al., 2004). However, the fact that soil moisture change is not only a climate change feedback, but also depends on human water use, such as the applied quantity of irrigation water on land, again indicates an important direct role of humans in altering regional water cycles, which needs to be further 9

Shilpa Muliyil Asokan

investigated. Regionally, the impacts on water resources from changes in global atmospheric circulation and climate overlap with the impacts from land-use and water-use changes (Lobell and Field, 2007). The overlap makes it hard to distinguish between different hydrological cause-effect relations and impacts (Milly et al., 2002; Piao et al., 2007; Destouni et al., 2008). Although recent technological possibilities, such as Moderate Resolution Imaging Spectrometer [MODIS] (King et al., 1992) and ET algorithm techniques based on satellite remote sensing (Zhang et al., 2010), provide more tools, for instance for ET change distinction and quantification, they have the limitation of only reflecting changes in relatively recent time periods that overlap with the accessibility to these technologies (Douglas et al., 2006; Cheng et al., 2011; Loarie et al., 2011). For more general and relevant understanding of the hydrological effects of both global climate change and regional land and water use changes there is a need for distinguishing and quantifying irrigation and other change drivers across different times and relevant water management scales; the latter are commonly those of regional drainage basins. The topographical water divides that define the regional drainage basins constrain the flows of water and waterborne substances through the landscape, and so does the associated environmental impacts of manmade changes to these flows (Jarsjö and Destouni 2004; Darracq et al., 2005; Shibuo et al., 2007; Destouni and Darracq 2009; Törnqvist et al., 2011; Visser et al., 2012; Destouni et al., 2013). Yet, also on those scales, several scientific questions are open and in need of further investigation in the context of hydro-climatic change history and projection, where the latter perspective also includes the large spatial scale discrepancy between typical hydrological

10

drainage basins and the coarse resolution of general circulation models (GCMs) (Milly et al., 2005; Groves et al., 2008). This thesis aims to understand, distinguish and quantify hydro-climatic changes caused by irrigation, with particular focus on two geographically and hydro-climatically different main irrigated regions of the world: India, with the Mahanadi River Basin (MRB) as case study, and Central Asia, with the Aral Sea Drainage Basin (ASDB) as case study. The thesis compiles data and quantifies historic to present hydrological conditions for the MRB case (Papers I-II), and utilizes and extends previous such compilations and quantifications for the ASDB case (Jarsjö and Destouni, 2004; Shibuo et al., 2007). With this hydrological quantification basis, the thesis develops and uses a data driven, water-balance constrained approach to understanding and quantifying long-term hydrological changes and their drivers historically (Papers II-IV) and in future change scenarios (Paper V). This approach honors the fundamental water balance constrains within each hydrological basin, taking also into account human use, re-distribution and export/import of water, in addition to the hydrology of each basin. Furthermore, the thesis quantifies and compares regional climate implications, in terms of the temperature change contributions of historic changes to ET induced by irrigation and other change drivers in the two regional basins (Papers III-IV). As a benchmarking assessment for future hydro-climatic change projections, and projection comparison with the soon forthcoming 5th IPCC report, the Appendix to Paper V contain a compilation and comparison for both the regional case studies the available basinscale hydro-climatic data with corresponding results from different General Circulation Models (GCMs), as reported in Assessment Report Four (AR4) of the Intergovernmental Panel on Climate Change (IPCC) (IPCC, 2007).

Hydro-climatic change in irrigated world regions

2 METHODOLOGY

2.1 Case Study Areas Figure 1 shows the two case study basins considered in this study: the Mahanadi River Basin (MRB) in India (Figure 1a) and the Aral Sea Drainage Basin (ASDB) in Central Asia (Figure 1b). MRB is located in East Central India within geographical co-ordinates of 80º30’ to 86º50’ E and 19º20’ to 23º35’ N. ASDB is located in Central Asian region within geographical

co-ordinates of 54º30’ to 78º30’ E and 34º30’ to 52º30’ N. Major land use and associated water use changes that have taken place in both these basins in the 20th century are related to intense irrigation of agricultural areas, with Figure 1 showing the irrigation distribution within the basins.

Figure 1. Location map and irrigated area percentage within the investigated basins. (a) Mahanadi River Basin (MRB) in India and (b) Aral Sea Drainage Basin (ASDB) in Central Asia. The red line shows the boundary (water divide) of the basins and the green scale shows the percentage of irrigated area within the basins (according to Siebert et al., 2007). The blue lines seen within the basins are the major rivers. The light and dark shades of blue in (b) illustrate the outline of Aral Sea before (pre-1950) and after (in 2005) its major shrinkage.

MRB is an exorheic basin that drains into Bay of Bengal, which eventually joins the Indian Ocean. The basin covers an area of about 135 084 km2 within the Orissa and Chhattisgarh provinces. The Mahanadi River with a total length of 851 km originates at an elevation of about 442 m above Mean Sea Level (msl). The Hirakud dam with its reservoir, built in 1956 with a gross water storage capacity of 8136 × 106 m3, is an engineered water storage structure that facilitates different human water uses, including water use for irrigation in the basin. Agricultural areas in MRB are cultivated throughout the year. The cropping seasons are broadly classified into Kharif (rainfed cultivation) and Rabi (irrigated cultivation) seasons. The Kharif season extends from June till November and the Rabi

season spans from December till May. Out of the total annual irrigation water demand of 11km3 in MRB, the Kharif season utilizes 7km3 and Rabi season demands 4km3. ASDB is an endorheic basin with its two major rivers Amu Darya and Syr Darya draining into the terminal Aral Sea. The total basin area is about 1 854 534 km2 (towards the end of the 20th century) which spreads almost the entire region of Central Asia, occupying 1.3% of the Earth’s land surface. Major land and water use changes in the Aral Sea region were initiated during the 1960s with the commencement of extensive water diversion and irrigation schemes leading to dramatic shrinkage of the Aral Sea (Shibuo et al., 2007). Several irrigation canals were constructed, 11

Shilpa Muliyil Asokan

the largest of which is the Karakum canal, reported to export about 8–12 km3 of water from the ASDB to other areas annually (Glantz, 2005). Along with the water diversion from the two major rivers, the irrigation of land in the ASDB (furrow irrigation has been, and is still the predominant irrigation method in the basin) has increased regional evapotranspiration and dramatically decreased the river runoff into the Aral Sea (Figure 2), leading to the equally dramatic shrinkage of the latter (Shibuo et al., 2007).

Figure 2. River discharge from the ASDB into the Aral Sea during the 20th century. The grey line indicates the discharge into the Aral Sea through the principal rivers Amu Darya and Syr Darya (Mamatov, 2003, Shibuo et al., 2007). Running average (30 years) is shown in thin black line.

2.2 Basin-scale hydro-climatic data The time series plots of basin-scale annual average (a) temperature and (b) precipitation in MRB and Aral region for the 20th century is illustrated in Figure 3. The observation data is obtained from the CRU T S 2.1 database by Mitchell and Jones (2005). This figure clearly illustrates the significant difference in the climatic settings of the two basins. The average rainfall in MRB during the 20th century is about five times higher than that of the average ASDB rainfall. The average temperature in Aral region is about onethird of the MRB average temperature during the last

century. In order to understand the change effects of the main Hirakud dam and water reservoir, and associated irrigation water-use changes in MRB, we compared in Papers II and IV the hydro-climatic conditions during the time period 1956–2000 with those in the pre-reservoir time period of 1901–1955. Similarly, in the case of Aral region, we compared in Paper III the hydro-climatic conditions during the time period 1983–2002 with the relatively undisturbed hydrological conditions during the time period 1901–1950, before the commencement of the major irrigation schemes in that basin. The available hydro-climatic data show then directly an increase in temperature by 0.26°C, and a decrease in precipitation by 60 mm/year from (1901–1955) to (1956–2000) in MRB. For Aral region, the hydro-climatic data show an increase in temperature by 1.1°C, and an increase in precipitation by 11 mm/year from (1901–1950) to (1983–2002). In addition to temperature and precipitation data, runoff data is also needed to close, assess and interpret basin-scale water balances and changes to them over longer, historic time periods like the whole 20th century. For Aral region, river discharge data was obtained for most part of the 20th century (even though on different time aggregation scales for different time periods in the century; Figure 2) as reported by Shibuo et al. (2007). For MRB, however, river discharge data was obtained only for the limited time period from 1990 to 2000 from the Central Water Commission, New Delhi. This data was compiled and reported in detail by Asokan (2005), and Paper I summarized and utilized it for some first scoping calculations and assessments of present seasonal hydrological conditions and their possible future changes due to climate change in the region. The limited time period of runoff data for MRB implied that longer-term changes to annual average hydro-climatic and water balance conditions in the basin could not be directly assessed based only on data. A different type of hydrological modeling, with focus on longer-term changes to annual average hydro-climatic conditions and basin-scale water

Figure 3. Annual average temperature (a) and precipitation (b) within the Mahanadi River Basin (color red) and the Aral Region (color blue); based on data from Mitchell and Jones [2005]. Running averages (10 years) are shown in black thin lines.

12

Hydro-climatic change in irrigated world regions

balance, rather than the short-term, intra-annual fluctuation modeling used in Paper I, was needed to bridge the historic data gaps in the MRB, and address its long-term change similarly as for the Aral region. Even though more runoff data was available for the latter, such a modeling approach had been developed and used also for the Aral region by Shibuo et al. (2007). A similar approach to historic data gap bridging and interpretation was then also developed for the MRB in Paper II, and used there and further in Paper IV for assessment and interpretation of historic hydro-climatic changes, drivers and impacts in that basin, and for consistent cross-regional comparison with corresponding long-term Aral region results in Paper IV.

2.3 Data driven water-balance constrained quantification of hydro-climatic change A drainage basin is a basic hydrological spatial unit that naturally (topographically) confines the different water flow components of the hydrological system, and hence the perturbations of hydro-climatic change can be best captured and understood at this scale. The understanding of and distinction between different hydrological change components, and their drivers and effects can be greatly aided and improved by honoring and accounting for the water flux constraints implied by the fundamental water balance quantification ET=P−R−DS (where P is precipitation, R is runoff and DS is water storage change) which applies to all hydrological drainage basins, with the effects of water storage change, DS, often being small on larger than annual temporal scales (Destouni et al., 2013). The basin-scale water balance constraints imply that the commonly difficult to measure and quantify ET term can be derived from directly measured P data across the basin surface and R data at the basin outlet; the latter should then ideally be measured data over the same time period as the P data, but if that is not available over the whole period, model interpolation/ extrapolation of R has to be used to bridge the R data gaps, as done here for the MRB case. The hydrological modeling approach can then be lumped (consider average water flows and their balance over a whole drainage basin) or distributed (resolve water fluxes also in grid cells within the basin) depending on the aim of the investigation. In this thesis, both approaches have been used for different study aims across the Papers II−V, and have also been compared in Paper IV for the case of MRB. In the distributed hydrological modeling approach, the basin area is divided into grid cells of equal (in the present work) or variable size, with the climate and land use data as input across the grid cells to generate output precipitation surplus at each individual cell and routed along different hydrological pathways (the runoff and water balance in the catchments of which must then be separately honored and checked against different local data) and ultimately in total towards the outlet of the basin. This methodology provides a thorough understanding of the spatial variability

of hydrological processes, in addition to the integral hydrological change that is reflected by the total runoff at the outlet of a whole basin. For instance, the intensity of ET at different parts of a drainage basin (say, at irrigated versus non-irrigated parts) can be well represented through such a distributed hydrological modeling approach. Furthermore, also larger-scale comparisons, for instance with climate model results, can be facilitated by this approach considering only or mostly the total quantity of water flows, including total ET from the land surface to the atmosphere, through a whole hydrological basin.

2.4 Using temperature change seasonality to distinguish irrigation and other drivers of hydro-climatic change Hydro-climatic changes, not least those in the two regional cases of this thesis, are driven by both global climate change and regional land-water-use changes, including irrigation (Shibuo et al., 2007; Paper II). It is then important to distinguish, understand and quantify the drivers and impacts of different contributions to total observed hydro-climatic change. Papers III and IV develop and apply a novel methodology to distinguishing irrigation from other drivers of hydro-climatic change, based on the seasonality of observed temperature changes, along with hydrological and water-use data (Paper III for the Aral region) combined with scenarios (to analyze implications of data gaps; Paper IV for the MRB) for the different irrigation conditions in different seasons. Calculations of temperature change contributions from irrigation in regional hydrological basins can use straightforward physical relations (described in detail in Papers III-IV) between observed seasonal temperature changes, and hydrologically determined seasonal changes in ET and associated changes in latent heat flux over each regional basin. Furthermore, the latent heat flux and temperature change contributions from the regional manifestation of global climate change can then also be calculated as the difference between the observed annual average temperature change, and the calculated temperature change contribution from irrigation in each basin.

2.5 Linking climate model outputs with hydro-climatic change quantification After development of a relevant, water-balance constrained hydrological model based on data for historic, multi-decadal hydro-climatic dynamics and changes in a regional basin (Paper II), one can also further use that model development for projections of possible future hydrological changes in the basin. This was done for the Aral region in Paper V, using GCM output for future climate change scenarios, along with future scenario assumptions also for the regional land-water-use conditions, as drivers of the hydrological modeling. The aim of Paper V was to understand how, and to what extent, future climate 13

Shilpa Muliyil Asokan

change and human re-distributions of water can affect future hydrology and water resource conditions in the basin. Such interactions of climate change and localregional water resource conditions are not well resolved in current GCMs, or in regional climate models (RCMs). To quantify these interactions, Paper V used the climate (temperature and precipitation) outputs from 14 GCMs in the IPCC AR4 (IPCC, 2007), which are tabulated in Table 1. For the large Aral region, with typically many GCM grid cells falling within the basin area, GCM outputs could be used directly, without any downscaling, as input data to the hydrological modeling in Paper V. Furthermore, the same GCM grid cell magnitude and reasoning for not requiring a particular downscaling procedure for the Aral region apply also for the follow-

up, benchmarking comparison in the Appendix to Paper V between basin-scale results of individual GCMs and corresponding basin-scale observations (CRU TS 2.1 climate data) of temperature and precipitation. Also for the much smaller MRB case, where only some models had relatively many grid cells within the basin area (as listed in the Appendix to Paper V), the corresponding comparison was carried out without any downscaling procedure. This was done in order to directly compare results on model performance versus observations between the two different basin scales, and thereby distinguish and understand effects of possible GCM bias versus effects of insufficient GCM grid resolution on resulting GCM result and observation discrepancies for the different basins.

Table 1. List of the AR4 GCMs, with their respective agency, used in this thesis.

GCM CSIRO: Mk3.0 ECHAM5/MPI-OM GFDL:CM2.0; GFDL:CM2.1 HadCM3 MIROC3.2

CNRM-CM3 ECHO-G GISS-ER HadGEM1 INMCM3.0 IPSL-CM4 MRI-CGCM2.3.2 CCSM3 PCM

14

Center and Location CSIRO (Australia) Max Planck institute of Meteorology (Germany) Geophysical Fluid Dynamics Laboratory, NOAA Hadley Centre for Climate Prediction and Research, Met Office (United Kingdom) Center for Climate System Research, The University of Tokyo; National Institute for Environmental Studies, and Frontier Research Center for Global Change (Japan) Centre National de Recherches Meteorologiques, Meteo France (France) Meteorological Institute of the University of Bonn (Germany), Institute of KMA (Korea), and Model and Data Group Goddard Institute for Space Studies, NASA (USA) Hadley Centre for Climate Prediction and Research, Met Office (United Kingdom) Institute of Numerical mathematics, Russian academy of Science, Russia Institut Pierre Simon Laplace (France) Meteorological Research Institute (Japan) National Center for Atmospheric Research (United States) National Center for Atmospheric Research (United States)

Hydro-climatic change in irrigated world regions

3 RESULTS

3.1 MRB data compilation and scoping assessments − Paper I Paper I summarizes the first data compilation and scoping assessment, particularly of the large hydrological seasonality in MRB, clarifying and quantifying the signature, monsoon-dependent hydro-climatic identity of this regional basin. The climate in MRB is tropical hot and humid monsoonal, and its temporal rainfall pattern is dominated by the monsoon cycles, with about 70% of the precipitation occurring during the south-west monsoon period (June to October; Paper I). There are thus only a few months of heavy rainfall during the Wet Season in the MRB that can provide the total irrigation water amount used for irrigation during both the Wet and the Dry Seasons. Figure 4 shows the time series plot of Wet Season and Dry Season precipitation in MRB during the 20th century. This seasonality, along with that in observed temperature and that in availability and use of water for irrigation were further considered in Paper IV, and used there to distinguish irrigation and other drivers of hydro-climatic change, with results discussed in section 3.3 below.

Figure 4. Seasonal plot of observed precipitation in MRB during the 20th century. Wet Season (color blue) represents the months from June to November, and Dry Season (color brown) represents the months from December to May. Running seasonal averages (10 years) are shown in black thin lines.

Spatially, the first data compilation in Paper I clarified that the more upstream part of MRB is comparatively dry, receiving an average annual rainfall of about 1000 mm, which then increases towards the central part of the basin with average annual rainfall of about 1300 mm, and even more further downstream in the relatively wet coastal belt with average annual rainfall of about 1700 mm. This spatial distribution in precipitation reflects also the spatial distribution of water availability and use for irrigation. This distribution pattern was further considered in Paper II, and used there to distinguish hydro-climatic change effects of land-use versus water-use, with results discussed in section 3.2 below.

3.2 Quantification of long-term hydro-climatic change in MRB − Paper II The spatially distributed modeling of long-term hydrological change in MRB clarified in particular the role of human water-use in the long-term change pattern of ET in the basin. Specifically, the basin’s map of water-use (for irrigation) is significantly different from that of land-use (irrigated area), and the map of greatest modeled ET changes in the basin corresponds well to the actual water-use map, rather than to the land-use map (Figure 5). This finding questions a common assumption in climate modeling that it is mainly land-use that determines the climate effects of irrigation. This assumption implies that, under dry conditions, irrigation is applied in the modeling to maintain relatively high soil moisture contents that can in turn sustain some assumed land-use production of biomass. However, when/where ever the actual water availability in space and/or time is insufficient to maintain such high soil water contents, the modeled water-use for irrigation and the corresponding ET flux contribution of the irrigation will be higher than the real quantities. Furthermore, these model quantities will also tend to be higher under dry than under humid spatiotemporal conditions, failing to reflect the quite opposite reality of the water availability/scarcity relation to the water use for irrigation at ground level (see discussion for MRB in section 3.1 above, and results for both regions in section 3.3.1 below). 15

Shilpa Muliyil Asokan

Figure 5. Distributed maps of (a) water-use for irrigation and (b) evapotranspiration changes from the period 1901−1955 to the period 1990−2000 in MRB.

3.3 Distinguishing irrigation and other drivers of hydro-climatic change – Papers III−IV Paper III developed the approach to distinguishing and quantifying irrigation and other drivers of hydroclimatic change, through the seasonality of changes in temperature (ΔT) and applied it to the whole ASDB and Aral Sea region. Paper IV further applied the same methodology to the MRB, distinguishing and utilizing also there ΔT differences between the Wet Season and Dry Season, and also carrying out a crossregional comparison between Aral region and MRB results in this context.

internal flow in the total land region, including both the Aral Sea and its drainage basin. The net total water flow balance in this land region is therefore quantified as P−ET, which is here still negative and thus imbalanced at the end of the 20th century, so that the Aral Sea continued to shrink also after that time. In contrast, R from the exorheic MRB flows into the ocean, which is not particularly dependent on just that basin flow into it, and R is here an external flow component for the whole MRB land system. The net total water flow balance in this land region is then quantified as P−ET−R, which was essentially zero and thus balanced at the end as in the beginning of the 20th century.

3.3.1 Annual hydro-climatic changes and their cross-regional comparison Table 2 summarizes data and results from distributed hydrological modeling for hydro-climatic conditions and flows, and their changes, in both MRB and Aral region. Model results include also those from a hypothetical simulation scenario of only climate (and no irrigation) change from the beginning to the end of the 20th century, which quantifies hydrological flow changes only due to the observed climate (temperature and precipitation) change. These simulated climate-induced changes can then be compared to corresponding observed data and modeled results for the realistic conditions of both climate and irrigation change over the same period. Figure 6 illustrates the cross-regional comparison between changes in different annual average water flows in MRB and Aral region. In absolute terms, all water flow changes are much smaller in Aral region than in MRB. Yet, the small runoff (R) and associated other flow changes in Aral region have led to one of the world’s worst environmental disasters in modern time: the Aral Sea desiccation and associated ecosystem collapse (Gaybullaev et al., 2012). With regard to annual average regional water balance, the terminal Aral Sea is entirely dependent on R into it from the endorheic ASDB, and R constitutes here an 16

Figure 6. Water flux changes (for precipitation, ∆P, evapotranspiration, ∆ET, and runoff, ∆R) in mm per year from the period 1901−1955 to 1956−2000 for the MRB, and from 1901−1950 to 1983−2002 for the Aral region. The magenta error bars for ∆R and ∆ET for the distributed hydrological modeling of MRB show the possible error range associated with these results by considering the available runoff observation data for the 1956-2000 instead of the modeled runoff result for that period (Table 2) To maintain basin-scale water balance, the ∆R error range must also be mirrored as a corresponding ∆ET error range. Black error bars show 80% confidence intervals (Appendix A, Table A1 of Paper IV) for the other ∆ET and ∆R results, as well as for ∆P, for both regions.

Hydro-climatic change in irrigated world regions

The irrigation-driven contributions to the annual average water flow changes can be quantified from the result difference between the realistic ClimateIrrigation scenario and the hypothetical Climate scenario in Table 2. In both MRB and Aral region, the irrigation-driven changes are then greater than the climate-driven ones. In absolute terms, less irrigation water is used in Aral region than in MRB, even though the smaller Aral region water use has had much more dramatic and severe regional impacts (the R decrease and associated Aral Sea shrinkage) than the larger water use in MRB. The regional impact differences are due to the much smaller original (and current) water availability (in terms of both precipitation and runoff, Table 2) in Aral region than in MRB. Furthermore, the differences in regional water use emphasize the point made in section 3.2 above, regarding the real water use for irrigation being greater for greater water availability, rather than the opposite assumption often made in climate modeling.

3.3.2 Seasonal hydro-climatic change implications and cross-regional comparison Table 3 summarizes seasonal temperature and precipitation data and their changes from the beginning to the end of the 20th century for both MRB and Aral region. In MRB, there is very large difference in precipitation and small difference in temperature between the Wet Season and the Dry Season. The basis for season differentiation and the temporal extent of the different seasons is therefore here determined by precipitation rather than by temperature. The opposite applies in Aral region, with much more evenly distributed precipitation and much larger difference in temperature between the growing and the non-growing season. The basis and temporal extents of the different seasons here are therefore primarily given by temperature rather than by precipitation differences.

Table 2. Summary of basin scale data and model results for MRB and Aral region. Pre-reservoir

Climate-

Climate: hypothetical

Irrigation

scenario of only climate change

Mahanadi River Basin

1901-1955

1956-2000

1956-2000

Data-given average temperature (°C)

25.19

25.45

25.45

Data-given annual average precipitation (mm yr-1)

1334

1274

1274

Total modeled ET (mm yr-1)

668

706

656

-

81

-

Modeled runoff at basin outlet (mm yr-1)

666

568

618

Observed runoff at basin outlet (mm yr-1)

-

515*

-

1901-1950

1983-2002

1983-2002

7.5

8.6

8.6

249

260

260

250

265

260

-

23

-

35

7

36

38

6

-

Data-given irrigation water-use within the basin (mm yr-1)

Aral Region Data-given average temperature

(°C)

Data-given annual average precipitation (mm

yr-1)

Total modeled ET from whole region – Aral Sea and its drainage basin (mm

yr-1)

Modeled irrigation water-use within the basin (mm yr-1) Modeled runoff from drainage basin into Aral Sea (mm

yr-1)

Observed runoff from drainage basin into the Aral Sea (mm yr-1)

*Average runoff from available observations for the period 1990-2000 [Asokan, 2005] Table 3. Average seasonal temperature and precipitation at the beginning and the end of 20th century for the Mahanadi River Basin (MRB) and Aral region. Temperature (oC) Mahanadi River Basin

Precipitation (mm yr-1)

1901-1955

1956-2000

1901-1955

1956-2000

Dry Season

24.44

24.74

198

190

Wet Season

25.93

26.16

2470

2358

Aral Region

1901-1950

1983-2002

1901-1950

1983-2002

Growing Season

14.7

15.5

192

193

Non-growing Season

-0.1

1.54

354

381

17

Shilpa Muliyil Asokan

Furthermore, Figure 7 illustrates the cross-regional comparison of seasonal ΔT between MRB and Aral region. In MRB, the basin-average ΔT during the Wet Season is an increase of 0.23°C, which is smaller than the ΔT change (also increase) of 0.30°C during the Dry Season. In Aral region, the basin-average ΔT during the growing season is an increase of 0.81°C, while that during the non‐growing season is an almost double increase of 1.67°C. The smaller basin average temperature change during the Wet Season for MRB (in comparison with the Dry Season), and during the growing season in Aral region (in comparison with the non-growing season) is consistent with more irrigation water

being used during Wet Season in MRB, and the only irrigation applied in Aral region during the growing season. Figure 8 summarizes the results obtained in Papers III-IV by utilizing the different seasonal temperature changes in Figure 7 to distinguish and quantify the change contributions to total annual average ΔT due to irrigation (ΔTirr), and due to the regional manifestation of global climate change (ΔTcl) for both MRB and Aral region. Note that the ΔTcl result then quantifies the total climate‐driven surface temperature change and not only the latent heat‐related change contribution, whereas ΔTirr is entirely due to the latent heat flux change driven by regional irrigation practices.

Figure 7. Changes in average seasonal surface temperature (ΔT) in degrees Celsius for the Mahanadi River Basin (MRB) and the Aral region including the Aral Sea Drainage Basin and the Aral Sea. (3a) The change from 1901−1955 to 1956−2000 for the Wet Season in MRB. (3b) The change from 1901−1955 to 1956−2000 for the Dry Season in MRB. (3c) The change from 1901−1950 to 1983−2002 for the growing season in the Aral region. (3d) The change from 1901−1950 to 1983−2002 for nongrowing season in the Aral region. All these seasonal temperature changes (ΔT) are significant at least at the confidence level p=0.999 (see Appendix A, Table A1 of Paper IV). Source: Mitchell and Jones [2005].

18

Hydro-climatic change in irrigated world regions

3.4 Assessing climate model applicability to basin scale hydro-climatic change – Paper V and its Appendix

Figure 8. Changes in surface temperature (∆T) for the Mahanadi River Basin (from 1901−1955 to 1956−2000) and for the Aral Sea region (from 1901−1950 to 1983−2002). Error bars show 80% confidence intervals for the data-given total average annual T changes in both regions (Appendix A, Table A1 of Paper IV).

In summary, the cross-regional comparison in Paper IV shows that differences in seasonal temperature changes are exhibited and can be used beyond the original approach development and application in the Aral region (Paper III) to distinguish and quantify irrigation effects on hydro-climatic change across different land regions. An important insight gained from the present comparison is that irrigation-driven absolute changes in the atmospherically important ET and latent heat (F) fluxes, and through these also in surface temperature (T), may be greater in land regions with small relative water resource changes, like MRB, than in land regions with severe water resource changes, like the Aral region. Therefore, the regional climate importance of natural or anthropogenic climate change drivers, versus that of regional land and water use drivers cannot simply be judged from the water resource severity of the latter change drivers. Conversely, one cannot from just climate modeling of T, ET and F changes in a region judge the severity of their water resource effects in the landscape. From both change assessment perspectives, actual water balance constraints need to be accounted for in order to realistically link and accurately assess different inter-connected hydro-climatic change drivers and effects in a land region.

Paper V considered future climate change scenarios for Aral region by using the spatially distributed outputs of the 14 GCMs from the IPCC AR4 (IPCC, 2007) and comparing them with corresponding earlier GCM results from the Third Assessment Report of IPCC (IPCC, 2001) and CRU TS 2.1 observation records. The Appendix to Paper V compiles corresponding AR4 GCM outputs and comparisons with observation records also for MRB. Figure 9 summarizes and illustrates individual and ensemble mean results from AR4 GCMs for temperature (T) and precipitation (P), and their comparison with CRU TS 2.1 observation records, for both MRB and Aral region. The T projections from all the GCMs show an increasing trend, irrespective of whether they overor under-estimate T compared with observation data. The ensemble mean results of all the GCMs yields T and its change trend at comparable range to CRU observations in both case study regions. However, with regard to P, some GCMs show an increasing trend, while others show a decreasing trend. In the case of MRB, the ensemble mean projection of all the GCMs underestimates P relative to CRU observations, while in Aral region, the ensemble mean projection overestimates P. Acknowledging such climate model biases and understanding how to best handle them in hydrological modeling and assessments is important for relevant projection of basin-scale hydrological changes in response to future climate change scenarios. Paper V develops and applies such a handling approach to projection of future hydrological change in Aral region based on the AR4 GCM results shown in Figure 9. Regardless of the uncertainty implied by individual GCM biases, hydrological model results in Paper V driven by either bias-corrected or not bias- corrected ensemble mean GCM results and projections for Aral region converge in indicating continued decrease in the regional river discharges, if the present irrigation practices are maintained also in the future (Figure 10). Note then that both the historic and the projected future decreases in river discharge in this region have occurred and are expected to continue in spite of historic and ensemble-mean projected increases in P. These divergent R and P changes re-confirm the concluding assessment in section 3.3 above, that regional climate changes in T and P alone cannot be extrapolated to runoff and water condition changes at the land surface. Nevertheless, T and P are still important hydrological drivers, and therefore we have further assessed the performance of individual GCMs in simulating these climate drivers T and P on hydrological basin scales under different geographic and hydro-climatic settings. The Appendix to Paper V quantifies and Figure 11 summarizes and illustrates the relative model bias of GCM results for T (Figure 11a) and P (Figure 11b) relative to corresponding 19

Shilpa Muliyil Asokan

Figure 9. Observed (grey line; with running average in black) and projected (14 AR4 GCMs) temperature in (a) MRB and (b) Aral region, and precipitation in (c) MRB and (d) Aral region. Thick red line and the corresponding data label show the uncalibrated ensemble mean values of the GCM projections.

Figure 10. Observed and projected total river discharge to the Aral Sea. The observed runoff changes so far are primarily due to irrigation expansion, whereas the future runoff results assume maintained irrigation practices following the 1984– 1989 period, and quantify the effect of climate change from the reference period 1961–1990 to 2010–2039. The error bar indicates the range of runoff projection obtained by running the hydrological model with the 14 IPCC AR4 GCM temperature and precipitation projections.

observations for both MRB and Aral region. Relative model bias is calculated as the difference between the GCM simulations of basin-average T and P and the corresponding basin-average T and P based on observation data from the CRU TS 2.1 database, normalized with the latter basin-average observation 20

values. The T results in Figure 11 show less GCM scatter than the P results for both regional basins. The GCM results for P show greater deviation from observations than the T results, with general P underprediction bias in MRB, and over-prediction bias in Aral region, as also indicated by the ensemble mean results in Figure 9. GCMs with good performance with respect to the T observations in both basins (GFDL:CM2.0_2.1, HadCM3, IPSL-CM4, MRICGCM2.3.2 etc.) are not necessarily the best ones with regard to P observations, making it difficult to choose one/few best GCMs for hydro-climatic change projection in a region. The comparison with observation data in Figure 11 does not indicate greater GCM bias for the small MRB than for the large Aral region, implying that the deviations of climate model results from observations may in both cases depend more on actual model bias than on scale effects. The Appendix to Paper V includes also results for relative model performance, indicating how well a particular GCM performs with respect to the typical GCM result. It is then only with this perspective of comparing model results with each other that there is an apparent difference in GCM performance between the large Aral region (with smaller scatter around typical GCM result) and the small MRB (with greater scatter around typical GCM result).

Hydro-climatic change in irrigated world regions

Figure 11. Relative model bias of individual GCMs with respect to corresponding CRU TS 2.1 observation data for (a) temperature and (b) precipitation in MRB and the Aral region.

21

Shilpa Muliyil Asokan

22

Hydro-climatic change in irrigated world regions

4 DISCUSSION

Results in this thesis show that the effect of human water use on the water balance of a hydrological basin depends on whether the nature of that basin is endorheic or exorheic. For an endorheic basin (in this case the ASDB), the runoff R is an internal flow component, and therefore the human water diversion for irrigation which is reflected in the runoff R, has an impact on the terminal surface water body (here the Aral Sea) to which it drains. However, for an exorheic basin (in this case the MRB), where the R is an external flow component from the hydrological basin system, the effect of the same human water use is smaller because it impacts a relatively small fraction of the total discharge into the ocean. Understanding the role of the nature of the hydrological basin is hence important for understanding hydro-climatic change impacts. There has been a lack of accounting for the ground level reality of actual human water use, and water availability and distribution in the landscape in climate modeling. For instance, climate models may assume application of more (unlimited) irrigation water than is actually available under dry conditions, which leads to overestimation of ET flux from the land surface and therefore fails to reflect the actual regional water status. Furthermore, such an assumption can also lead to higher modeled ET flux in relatively dry regions/seasons than in relatively wet regions/seasons. A basin-wise, data-driven and water balance-constrained approach to ET quantification can reflect the actual water resource status of the basins. Paper II has quantified ET estimation error with the water balance closure to be around 10-15%, which is significantly lesser than ET estimation errors without such water balance constraints, reported to be around 30-50% (Kite and Droogers 2000). Remotesensing possibilities to estimate ET, such as based on MODIS (King et al., 1992) and other ET algorithm techniques (Zhang et al., 2010) represent additional tools for estimation of large-scale ET, but are limited to relatively recent time periods (Dougluas et al., 2006; Cheng et al., 2011; Loarie et al., 2011). Accurate assessment of vapor flux to the atmosphere by ET from irrigated land areas is important for quantification of associated temperature change at the land surface, which is an essential component of local-regional climate change. Thesis results have distinguished and quantified the irrigation and other drivers of hydro-climatic change through a novel data-driven approach based on temperature change

seasonality. We found that the total and the climatedriven land surface T changes are larger in the Aral region, whereas the net total irrigation effect is much smaller in this region than in MRB (both in absolute terms and relative to climate-driven T change). Had ASDB been an exorheic basin, there would not have been such irrigation-driven surface water shrinkage as that of the terminal Aral Sea, and the irrigationdriven temperature change (cooling effect) would have been much larger (cooling of −0.6°C instead of −0.1°C) in the Aral region, which would then also be larger than the cooling effect in the MRB. Furthermore, thesis results show relatively greater changes in irrigation-driven ET, latent heat flux and surface temperature in MRB, which is a region with relative small water availability changes, in comparison to the Aral region, which has had severe such changes. This implies that the severity of changes to water resource availability in a land region cannot be directly assessed from the magnitude of climaterelated changes at the land surface, and vice versa. Actual water balance constraints and hydrological basin conditions need to be taken into account for accurate understanding of the interconnection between different hydro-climatic changes, in interactions with the atmosphere at the land surface, and water processes and conditions through the whole landscape. The thesis also analyzed basin-scale results of global climate models in relation to each other and to available observation data of T and P for the two study regions. Deviations between observations and climate model outputs appear to depend more on GCM biases than on the spatial scale difference between the two different regional basins. Lack of consistency between individual GCM performance for T and P results imply difficulty in identifying one (or more) generally well-performing GCM(s) for a region, and hence the ensemble mean of the available GCMs may be more useful for hydro-climatic change assessment and projection. Projection results for future changes in the Aral region further show that climate model outputs cannot alone judge future changes in water resource availability and change vulnerability in the region. Scenarios of future development of human water use for irrigation and other major water uses must also be accounted for to accurately understand and project possible future hydrological and water resource changes.

23

Shilpa Muliyil Asokan

24

Hydro-climatic change in irrigated world regions

5 CONCLUSIONS This thesis has developed and successfully applied a consistent basin-wise, data-driven water balanceconstrained approach to quantification of long-term hydro-climatic change in two, hydro-climatically different irrigated regions in the world. The approach has distinguished and quantified the role and impacts of climate and land-water-use (in particular related to irrigation) changes as drivers of hydro-climatic change, both historically and in future scenario projections. Important results and implications from the two basin applications and their cross-regional comparison can be summarized as: • It is important to account for human water use, and its change and spatial patterns, in addition to atmospheric climate change, in order to understand and accurately interpret past and future hydro-climatic changes in land regions. • Human redistribution of water within hydrological basins can alter both the water balance and the surface temperature of land regions. • Climate model results for precipitation in a region cannot be simply extrapolated to regional changes in water flow, water balance and water availability in the landscape.

•

•

•

•

Good GCM performance with regard to regional T data is not often accompanied by similarly good performance with regard to P data, and vice versa, implying difficulties to identify a generally wellperforming GCM for regional hydro-climatic change assessment. GCM performance relative to basin-scale T and P observations is not increasing for the larger land areas when compared to the smaller land areas, implying that model-observation differences may depend more on actual model bias than on scale effects. The ensemble mean of available GCM outputs provide robust projection of future hydroclimatic changes, rather than a particular GCM as the driver of hydrological modeling. A basin-scale, water balance-constrained approach to quantification of long-term hydroclimatic change dynamics can aid in correcting model biases and bridging resolution gaps in land surface representations of climate models, by more accurately accounting for and quantifying land-water-use effects, such as those of irrigation, on water and latent heat fluxes, as well as on temperature change at the land surface.

25

Shilpa Muliyil Asokan

26

Hydro-climatic change in irrigated world regions

ACKNOWLEDGEMENTS I would like to express my sincere gratitude to my principal supervisor Georgia Destouni for providing me the opportunity to pursue my doctoral research at the department. The scientific-discussions with her have always been filled with valuable insights and in-depth expertise which have incrementally improved my perspective on the subject matter in particular, and research in general. The supervision that I have received from her throughout the research period has been fundamental for the completion of this thesis. I am grateful to my co-supervisor, Jerker Jarsjö, for his generous advices and discussions. The thesis has greatly benefited from the valuable scientific discussions and contributions by him. Thanks are also due to Dr. Dushmanta Dutta for guiding me during the initial stages of my research career. I take this opportunity to thank my colleagues at the department, collaboration with whom has been invaluable. I extend my thanks to Yoshihiro Shibuo for introducing me to the world of complex hydrologic modelling and yet making it appear so simple. Thanks are also due to Carmen Prieto, Amelie Darracq, Claudia Teutschbein, Arvid Bring, Rebecka Törnqvist and many other friends for the long scientific and not-so scientific discussions along the hallway. Many thanks to Peter Jansson and Helle Skånes for their guidance on all the scientific and administrative follow-up procedures leading towards the disputation. Thanks to Ingrid Stjernquist for the comments and suggestions during the internal review of my thesis. A special thanks to Susanne Ingvander for helping me with the formatting of my thesis. Thanks are also due to Susanna Blåndman and Carina Henriksson for providing complete administrative assistance all throughout. Also, I would like to thank the reviewers of my manuscripts for providing constructive feedback and insightful comments. I am thankful for the constant support from my family and friends back home. Special thanks to my dear parents Muliyil Asokan and Sajini Asokan for always believing in me and supporting my idea of pursuing graduate study in an international university at the first place. I would like to thank my elder sister Greeshma for being patient with me and guiding me through all life situations. Thanks to my younger sister Theertha, I really enjoyed the competition we had on who will be the first to have the Dr. degree, well you won, and I am happy for you. Finally, I would like to thank my husband Abhi for the past decade of togetherness – from my friend to my life partner to the doting papa of our two year old daughter Trisha. Thank you for all these happening years of friendship, family, travelling and parenting, I thoroughly enjoyed it and am looking forward for many more to come. Our daughter Trisha joined us two years back, and since then she holds the key to my mood swings, when she smiles I smile, and when she becomes sad, so do I. Thank you my dear, for you are the most wonderful gift that I have ever received.

FINANCIAL SUPPORT Work in this thesis has been financially supported by the Swedish International Development Cooperation Agency (SIDA) and the Swedish Research Council (VR; project number 2006-4366). Work has also been carried out within the frameworks of the Bert Bolin Centre for Climate Research (supported by a Linnaeus grant from the Swedish research councils VR and Formas) and the strategic environmental research project EkoKlim at Stockholm University.

27

Shilpa Muliyil Asokan

28

Hydro-climatic change in irrigated world regions

REFERENCES

Asokan, S. M, 2005, Water resources analysis under projected climate conditions in the Mahanadi River Basin, India, Master thesis No. WM-04-13, Asian Institute of Technology, Thailand

applying uncertain global climate change projections for regional water management planning, Water Resources Research, 44, W12413, doi:10.1029/2008WR006964

Boucher, O., Myhre, G. and Myhre, A, 2004, Direct human influence of irrigation on atmospheric water vapor and climate. Climate Dynamics, 22, 597−603. doi:10.1007/s00382004-0402-4

IPCC, 2001, Climate Change 2001: Synthesis report, A contribution of working groups I, II and III to the Third Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom, and New York, 398 pp. IPCC, 2007, Climate change 2007: Synthesis report, Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom, and New York, 996 pp. Jarsjö, J. and Destouni, G, 2004, Groundwater discharge into the Aral Sea after 1960, Journal of Marine Systems, 47, 109–120. doi:10.1016/j.jmarsys.2003.12.013

Cheng, L., Xu, Z., Wang, D. and Cai, X, 2011, Assessing interannual variability of evapotranspiration at the catchment scale using satellite-based evapotranspiration data sets, Water Resources Research, 47, W09509. doi:10.1029/2011WR010636 Darracq, A., Greffe, F., Hannerz, F., Destouni, G. and Cvetkovic, V, 2005, Nutrient transport scenarios in a changing Stockholm and Malaren valley region, Sweden. Water Science and Technology, 51(3–4), 31–38 Destouni, G., Hannerz, F., Prieto, C., Jarsjö, J. and Shibuo, Y, 2008, Small unmonitored near coastal catchment areas yielding large mass loading to the sea, Global Biogeochemical Cycles, 22, GB4003. doi:10.1029/2008GB003287 Destouni, G. and Darracq, A, 2009, Nutrient cycling and N2O emissions in a changing climate: the subsurface water system role. Environmental Research Letters, 4, 035008, 7pp. doi:10.1088/1748-9326/4/3/035008 Destouni, G., Jaramillo, F. and Prieto, C, 2013, Hydroclimatic shifts driven by human water use for food and energy production, Nature Climate Change. doi:10.1038/NCLIMATE1719 Donohue, R. J., Roderick, M. L. and McVicar, T. R, 2007, On the importance of including vegetation dynamics in Budyko’s hydrological model, Hydrology and Earth System Sciences, 11, 983–995. doi:10.5194/hess-11-983-2007 Douglas, E. M., Niyogi, D., Frolking, S., Yeluripati, J. B., Pielke, R. A., Niyogi, N., Vörösmarty, C. J. and Mohanty, U. C, 2006, Changes in moisture and energy fluxes due to agricultural land use and irrigation in the Indian Monsoon Belt, Geophysical Research Letters, 33, L14403, doi:10.1029/2006GL026550 Foley, J. A., DeFries, R., Asner, G. P., Barford, C., Bonan, G., Carpenter, S. R., Chapin, F. S., Coe, M. T., Daily, G. C., Gibbs, H. K., Helkowski, J. H., Holloway, T., Howard, E. A., Kucharik, C. J., Monfreda, C., Patz, J. A., Prentice, I. C., Ramankutty, N. and Snyder, P. K, 2005, Global consequences of land use, Science, 309, 570–574. doi:10.1126/science.1111772 Gaybullaev, B., Chen, S. C. and Kuo, Y. M, 2012, Large-scale desiccation of the Aral Sea due to over-exploitation after 1960, Journal of Mountain Science, 9, 538−546. doi:10.1007/s11629012-2273-1 Glantz, M. H, 2005, Water, climate, and development issues in the Amu Darya Basin, Mitigation Adaptation Strategies for Global Change, 10, 23– 50. doi:10.1007/s11027-005-7829-8. Gordon, L., Steffen, W., Jonsson, B., Folke, C., Falkenmark, M. and Johannessen, A, 2005, Human modification of global water vapor flows from the land surface, Proceedings of the National Academy of Sciences of the United States of America, 102, 7612–7617. doi: 10.1073/pnas.0500208102 Groves, D. G., Yates, D. and Tebaldi, C, 2008, Developing and

King, M., Kaufman, Y., Menzel, W. and Tanre, D, 1992, Remotesensing of cloud, aerosol, and water-vapor properties from the moderate resolution imaging spectrometer (MODIS), IEEE Transactions on Geoscience and Remote Sensing, 30, 2–27. Kite, G. W. and Droogers, P, 2000, Comparing evapotranspiration estimates from satellites, hydrological models and field data, Journal of Hydrology, 229, 3–18. Koster. et al., 2004, Regions of strong coupling between soil moisture and precipitation, Science, 305, 1138−1140. doi:10.1126/science.1100217 Lee, E., Sacks, W. J., Chase, T. N. and Foley, J. A, 2011, Simulated impacts of irrigation on the atmospheric circulation over Asia, Journal of Geophysical Research, 116, D08114, 30. doi:10.1029/2010JD014740 Loarie, S. R., Lobell, D. B., Asner, G. P., Mu, Q. and Field, C. B, 2011, Direct impacts on local climate of sugar-cane expansion in Brazil, Nature Climate Change, 1, 105–109. doi:10.1038/ nclimate1067 Lobell, D. andField, C. B, 2007, Global scale climate-crop yield relationships and the impacts of recent warming, Environmental Research Letters, 2, 014002. doi:10.1088/17489326/2/1/014002 Lobell, D., Bala, G., Mirin, A., Phillips, T., Maxwell, R. and Rotman, D, 2009, Regional differences in the influence of irrigation on climate, Journal of Climate, 22, 2248−2255. doi: 10.1175/2008JCL12703.1 Mamatov, S, 2003, Study of the groundwater contribution to the Aral Sea region water supply and water quality: Strategies for reversibility and pollution control – INTAS project 1014, EUINTAS Aral Sea Basin Call 2000, Group CR5 Status Report, European Commission, Bruxelles, Belgium. Milly, P. C. D, Wetherald, R. T., Dunne, K. A, and Delworth, T. L, 2002, Increasing risk of great floods in a changing climate, Nature, 415, 514–517, 2002. Milly, P. C. D., Dunne, K. A. and Vecchia, A. V, 2005, Global pattern of trends in streamflow and water availability in a changing climate, Nature, 438, 347–350. doi:10.1038/ nature04312 Mitchell, T. D. and Jones, P. D, 2005, An improved method of

29

Shilpa Muliyil Asokan constructing a database of monthly climate observations and associated high-resolution grids, International Journal of Climatology, 25, 693-712. doi: 10.1002/joc.1181 Piao, S., Friedlingstein, P., Ciais, P., De Noblet-Ducoudre´N, Labat, D. and Zaehle, S, 2007, Changes in climate and land use have a larger direct impact than rising CO2 on global river runoff trends, Proceedings of the National Academy of Sciences of the United States of America, 104, 15242–15247. doi:10.1073/pnas.0707213104 Qiu, G. Y., Yin, J., Tian, F. and Geng, S, 2011, Effects of the “Conversion of Cropland to Forest and Grassland Program” on the water budget of the Jinghe River Catchment in China, Journal of Environmental Quality, 40, 1745–1755. doi: 10.2134/jeq2010.0263 Sacks, W. J., Cook, B. I., Buenning, N., Levis, s. and Helkowski, J. H, 2009, Effects of global irrigation on the near‐surface climate, Climate Dynamics, 33, 159–175, doi:10.1007/s00382008-0445-z Seneviratne, S. I., Luthi, D., Litschi, M. and Schär, C, 2006, Landatmosphere coupling and climate change in Europe, Nature, 443, 205−209. doi: 10.1038/nature05095

Global map of irrigation areas version 4.0.1, map, Johann Wolfgang Goethe Univ., Frankfurt am Main, Germany. Törnqvist, R., Jarsjö, J. and Karimov, B, 2011, Health risks from large-scale water pollution: Trends in Central Asia, Environment International 37: 435−442. doi:10.1016/j. envint.2010.11.006 Törnqvist, R. and Jarsjö, J, 2012, Water Savings Through Improved Irrigation Techniques: Basin-Scale Quantification in SemiArid Environments, Water Resources Management, 26, 949962. doi: 10.1007/s11269-011-9819-9 Vanlill, W. S, Kruger, F. J. and Vanwyk, D. B, 1980, The Effect Of Afforestation With Eucalyptus grandis Hill Ex Maiden and Pinus patula Schlecht. et. Cham. on streamflow from experimental catchments at Mokobulaan, Transvaal, Journal of Hydrology, 48, 107–118. Visser, A., Kroes, J., Van Vliet, M. T. H., Blenkinsop, S., Fowler, H. J. and Broers, H. P, 2012, Climate change impacts on the leaching of a heavy metal contamination in a small lowland catchment, Journal of Contaminant Hydrology, 127, 47−64. doi: 10.1016/j.jconhyd.2011.04.007

Schär, C., Vidale, P. L., Luthi, D., Frei, C., Haberli, C., Liniger, M. A. and Appenzeller, C, 2004, The role of increasing variability in European summer heat waves, Nature, 427, 332−336. doi: 10.1038/nature02300

Weiskel, P. K., Vogel, R. M., Steeves, P. A., Zarriello, P. J., DeSimone, L.A. and Ries III, K. G, 2007, Water use regimes: Characterizing direct human interaction with hydrologic systems, Water Resources Research, 43, W04402. doi:10.1029/2006WR005062

Shibuo, Y., Jarsjö, J. and Destouni, G, 2007, Hydrological responses to climate change and irrigation in the Aral Sea drainage basin, Geophysical Research Letters, 34, L21406. doi:10.1029/2007GL031465

Wisser, D., Fekete, B. M., Vörösmarty, C. J. and Schumann, A. H, 2010, Reconstructing 20th century 20 global hydrography: a contribution to the Global Terrestrial Network-Hydrology (GTN-H), Hydrology and Earth System Sciences, 14, 1–24. doi:10.5194/hess-14-1-2010

Schilling, K. E., Jha, M. K., Zhang, Y. K., Gassman, P. W. and Wolter, C. F, 2008, Impact of land use and land cover change on the water balance of a large agricultural watershed: Historical effects and future directions, Water Resources Research, 44, W00A09, doi:10.1029/2007WR006644 Siebert, S., Döll, P., Feick, S., Hoogeveen, J. and Frenken, K, 2007,

30

Zhang, K., Kimball, J. S., Nemani, R. R. and Running, S. W, 2010, A continuous satellite-derived global record of land surface evapotranspiration from 1983 to 2006, Water Resources Research, 46 W09522, doi:10.1029/2009WR008800