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Artificial drainage of agricultural land, for example with ditches or drainage tubes, is used to avoid water logging and to manage high groundwater tables. Among other impacts it influences the nutrient balances by increasing leaching losses and by decreasing denitrification. To simulate terrestrial transport of nitrogen on the global scale, a digital global map of artificially drained agricultural areas was developed. The map depicts the percentage of each 5’ by 5’ grid cell that is equipped for artificial drainage. Information on artificial drainage in countries or sub-national units was mainly derived from international inventories. Distribution to grid cells was based, for most countries, on the "Global Croplands Dataset" of Ramankutty et al. (1998) and the "Digital Global Map of Irrigation Areas" of Siebert et al. (2005). For some European countries the CORINE land cover dataset was used instead of the both datasets mentioned above. Maps with outlines of artificially drained areas were available for 6 countries. The global drainage area on the map is 167 Mio hectares. For only 11 out of the 116 countries with information on artificial drainage areas, sub-national information could be taken into account. Due to this coarse spatial resolution of the data sources, we recommended to use the map of artificially drained areas only for continental to global scale assessments. This documentation describes the dataset, the data sources and the map generation, and it discusses the data uncertainty.
A data set of monthly growing areas of 26 irrigated crops (MGAG-I) and related crop calendars (CC-I) was compiled for 402 spatial entities. The selection of the crops consisted of all major food crops including regionally important ones (wheat, rice, maize, barley, rye, millet, sorghum, soybeans, sunflower, potatoes, cassava, sugar cane, sugar beets, oil palm, rapeseed/canola, groundnuts/peanuts, pulses, citrus, date palm, grapes/vine, cocoa, coffee), major water-consuming crops (cotton), and unspecified other crops (other perennial crops, other annual crops, managed grassland). The data set refers to the time period 1998-2002 and has a spatial resolution of 5 arc minutes by 5 arc minutes which is 8 km by 8 km at the equator. This is the first time that a data set of cell-specific irrigated growing areas of irrigated crops with this spatial resolution was created. The data set is consistent to the irrigated area and water use statistics of the AQUASTAT programme of the Food and Agriculture Organization of the United Nations (FAO) (http://www.fao.org/ag/agl/aglw/aquastat/main/index.stm) and the Global Map of Irrigation Areas (GMIA) (http://www.fao.org/ag/agl/aglw/aquastat/irrigationmap/index.stm). At the cell-level it was tried to maximise consistency to the cropland extent and cropland harvested area from the Department of Geography and Earth System Science Program of the McGill University at Montreal, Quebec, Canada and the Center for Sustainability and the Global Environment (SAGE) of the University of Wisconsin at Madison, USA (http://www.geog.mcgill.ca/~nramankutty/ Datasets/Datasets.html and http://geomatics.geog.mcgill.ca/~navin/pub/Data/175crops2000/). The consistency between the grid product and the input data was quantified. MGAG-I and CC-I are fully consistent to each other on entity level. For input data other than CC-I, the consistency of MGAG-I on cell level was calculated. The consistency of MGAG-I with respect to the area equipped for irrigation (AEI) of GMIA and to the cropland extent of SAGE was characterised by the sum of the cell-specific maximum difference between the MGAG-I monthly total irrigated area and the reference area when the latter was exceeded in the grid cell. The consistency of the harvested area contained in MGAG-I with respect to SAGE harvested area was characterised by the crop-specific sum of the cell-specific difference between MGAG-I harvested area and the SAGE harvested area when the latter was exceeded in the grid cell. In all three cases, the sums are the excess areas that should not have been distributed under the assumption that the input data were correct. Globally, this cell-level excess of MGAG-I as compared to AEI is 331,304 ha or only about 0.12 % of the global AEI of 278.9 Mha found in the original grid. The respective cell-level excess of MGAG-I as compared to the SAGE cropland extent is 32.2 Mha, corresponding to about 2.2 % of the total cropland area. The respective cell-level excess of MGAG-I as compared to the SAGE harvested area is 27 % of the irrigated harvested area, or 11.5 % of the AEI. In a further step that will be published later also rainfed areas were compiled in order to form the Global data set of monthly irrigated and rainfed crop areas around the year 2000 (MIRCA2000). The data set can be used for global and continental-scale studies on food security and water use. In the future, it will be improved, e.g. with a better spatial resolution of crop calendars and an improved crop distribution algorithm. The MIRCA2000 data set, its full documentation together with future updates will be freely available through the following long-term internet site: http://www.geo.uni-frankfurt.de/ipg/ag/dl/forschung/MIRCA/index.html. The research presented here was funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) within the framework of the research project entitled "Consistent assessment of global green, blue and virtual water fluxes in the context of food production: regional stresses and worldwide teleconnections". The authors thank Navin Ramankutty and Chad Monfreda for making available the current SAGE datasets on cropland extent (Ramankutty et al., 2008) and harvested area (Monfreda et al., 2008) prior to their publication.
The Global Irrigation Model (GIM) is used within the framework of the global hydrological model WaterGAP to calculate monthly irrigation crop water use. Results on a 0.5 degrees grid include, consumption (ICU) and, via division by irrigation efficiencies, water withdrawal (IWU). The model distinguishes up to two cropping periods of rice and non-rice crops, each grown for 150 days, using a grid of area equipped for irrigation (AEI). Historical development of AEI and fraction of area actually irrigated (AAI) was previously considered via scaling of cell-specific results with country-specific factors for each year. In this study, GIM was adapted to use the new Historical Irrigation Data set (HID) with cell-specific AEI for 14 time slices between 1900 and 2005. AEI grids were temporally interpolated, and using the optional grid of AAI/AEI, results for years 1901-2014 were generated (runs "HID-ACT"). Thus, new installation or abandonment of irrigation infrastructure in new grid cells can be represented in a spatially explicit manner. For evaluated years 1910, 1960, 1995, and 2005, ICU from HID-ACT was superior to country-specific scaled results (run "HID-ACTHIST") in representing historical development of the spatial pattern. Compared to US state-level reference data, spatial patterns were better, while country totals were not always better. For calculating the cropping periods, 30-years climate means are needed, the choice of which is relevant. Four chosen periods before 1981-2010 all resulted in considerable, pertaining changes of ICU spatial pattern, and various percent changes in country totals. This might be because of already present climate change.
Within the framework of the Transboundary Waters Assessment Programme (TWAP), initiated by the Global Environment Facility (GEF), we contributed to a comprehensive baseline assessment of transboundary aquifers (TBAs) by quantifying different groundwater indicators using the global water resources and water use model WaterGAP 2.2. All indicators were computed under current (2010) and projected conditions in 2030 and 2050 for 91 selected TBAs larger than 20,000 km2 and for each nation’s share of the TBAs (TBA-CU: country unit). TBA outlines were provided by the International Groundwater Resources Assessment Centre (IGRAC). The set of indicators comprises groundwater recharge, groundwater depletion, per-capita groundwater recharge, dependency on groundwater, population density, and groundwater development stress (groundwater withdrawals to groundwater recharge). Only the latter four indicators were projected to 2030 and 2050. Current-state indicators were quantified using the Watch Forcing Data climate dataset, while projections were based on five climate scenarios that were computed by five global climate models for the high-emissions scenario RCP 8.5. Water use projections were based on the Shared Socio-economic Pathway SSP2 developed within ISI-MIP. Furthermore, two scenarios of future irrigated areas were explored. For individual water use sectors, the fraction of groundwater abstraction was assumed to remain at the current level.
According to our assessment, aquifers with the highest current groundwater depletion rates worldwide are not transboundary. Exceptions are the Neogene Aquifer System (Syria) with 53 mm/yr between 2000 and 2009 and the Indus River Plain aquifer (India) with 28 mm/yr. For current conditions, we identified 20 out of 258 TBA-CUs suffering from medium to very high groundwater development stress, which are located in the Middle East and North Africa region, in South Asia, China, and the USA. Considering projections, ensemble means of per-cent changes or percent point changes to current conditions were determined. Per-capita groundwater recharge is projected to decrease in 80-90% of all TBA-CUs until 2030/2050. Due to the strongly varying projections of the global climate models, we applied a worst-case scenario approach to define future hotspots of groundwater development stress, taking into account the strongest computed increase until either 2030 or 2050 among all scenarios and individual GCMs. Based on this approach, the number of TBA-CUs under at least medium groundwater development stress increases from 20 to 58, comprising all hotspots under current conditions. New hotspots are projected to develop mainly in Sub-Saharan Africa, China, and Mexico.
The Land and Water Development Division of the Food and Agriculture Organization of the United Nations and the Johann Wolfgang Goethe University, Frankfurt am Main, Germany, are cooperating in the development of a global irrigation-mapping facility. This report describes an update of the Digital Global Map of Irrigation Areas for the continents of Africa and Europe as well as for the countries Argentina, Brazil, Mexico, Peru and Uruguay in Latin America. For this update, an new inventory of subnational irrigation statistics was compiled. The reference year for the statistics is 2000. Adding up the irrigated areas per country as documented in the report gives a total of 48.8 million ha while the total area equipped for irrigation at the global scale is 278.8 million ha. The total number of subnational units in the inventory used for this update is 16 822 while the number of subnational units in the global inventory increased to 26 909. In order to distribute the irrigation statistics per subnational unit, digital spatial data layers and printed maps were used. Irrigation maps were derived from project reports, irrigation subsector studies, and books related to irrigation and drainage. These maps were digitized and compared with satellite images of many regions. In areas without spatial information on irrigated areas, additional information was used to locate areas where irrigation is likely, such as land-cover and land-use maps that indicate agricultural areas or areas with crops that are usually grown under irrigation.
The assumption that mankind is able to have an in uence on global or regional climate, respectively, due to the emission of greenhouse gases, is often discussed. This assumption is both very important and very obscure. In consequence, it is necessary to clarify definitively which meteorological elements (climate parameters) are in uencend by the anthropogenic climate impact, and to which extent in which regions of the world. In addition, to be able to interprete such an information properly, it is also necessary to know the magnitude of the different climate signals due to natural variability (for example due to volcanic or solar activity) and the magnitide of stochastic climate noise. The usual tool of climatologists, general circulation models (GCM) suffer from the problem that they are at least quantitatively uncertain with regard to the regional patterns of the behaviour of climate elements and from the lack of accurate information about long-term (decadal and centennial) forcing. In contrast to that, statistical methods as used in this study have the advantage to test hypotheses directly based on observational data. So, we focus to the very reality of climate variability as it has occurred in the past. We apply two strategies of time series analyis with regard to the observed climate variables under consideration. First, each time series is splitted into its variation components. This procedure is called 'structure-oriented time series separation'. The second strategy called 'cause-oriented time series separation' matches various time series representing various forcing mechanisms with those representing the climate behaviour (climate elements). In this way it can be assessed which part of observed climate variability can be explained by this (combined) forcing and which part remains unexplained.
During the 1980s and early 1990s, the importance of small firm growth and industrial districts in Italy became the focus of a large number of regional development studies. According to this literature, successful industrial districts are characterized by intensive cooperation and market producer-user interaction between small and medium-sized, flexibly specialized firms (Piore and Sabel, 1984; Scott, 1988). In addition, specialized local labor markets develop which are complemented by a variety of supportive institutions and a tradition of collaboration based on trust relations (Amin and Robins, 1990; Amin and Thrift, 1995). It has also been emphasized that industrial districts are deeply embedded into the socio-institutional structures within their particular regions (Grabher, 1993). Many case studies have attempted to find evidence that the regional patterns identified in Italy are a reflection of a general trend in industrial development rather than just being historical exceptions. Silicon Valley, which is focused on high technology production, has been identified as being one such production complex similar to those in Italy (see, for instance, Hayter, 1997). However, some remarkable differences do exist in the institutional context of this region, as well as its particular social division of labor (Markusen, 1996). Even though critics, such as Amin and Robins (1990), emphasized quite early that the Italian experience could not easily be applied to other socio-cultural settings, many studies have classified other high technology regions in the U.S. as being industrial districts, such as Boston s Route 128 area. Too much attention has been paid to the performance of small and medium-sized firms and the regional level of industrial production in the ill-fated debate regarding industrial districts (Martinelli and Schoenberger, 1991). Harrison (1997) has provided substantial evidence that large firms continue to dominate the global economy. This does not, however, imply that a de-territorialization of economic growth is necessarily taking place as globalization tendencies continue (Storper, 1997; Maskell and Malmberg, 1998). In the case of Boston, it has been misleading to define its regional economy as being an industrial district. Neither have small and medium-sized firms been decisive in the development of the Route 128 area nor has the region developed a tradition of close communication between vertically-disintegrated firms (Dorfman, 1983; Bathelt, 1991a). Saxenian (1994) found that Boston s economy contrasted sharply with that of an industrial district. Specifically, the region has been dominated by large, vertically-integrated high technology firms which are reliant on proprietary technologies and autarkic firm structures. Several studies have tried to compare the development of the Route 128 region to Silicon Valley. These studies have shown that both regions developed into major 2 agglomerations of high technology industries in the post-World War II period. Due to their different traditions, structures and practices, Silicon Valley and Route 128 have followed divergent development paths which have resulted in a different regional specialization (Dorfman, 1983; Saxenian, 1985; Kenney and von Burg, 1999). In the mid 1970s, both regions were almost equally important in terms of the size of their high technology sectors. Since then, however, Silicon Valley has become more important and has now the largest agglomeration of leading-edge technologies in the U.S. (Saxenian, 1994). Saxenian (1994) argues that the superior performance of high technology industries in Silicon Valley over those in Boston is based on different organizational patterns and manufacturing cultures which are embedded in those socio-institutional traditions which are particular to each region. Despite the fact that Saxenian (1994) has been criticized for basing her conclusions on weak empirical research (i.e. Harrison, 1997; Markusen, 1998), she offers a convincing explanation as to why the development paths of both regions have differed.1 Saxenian s (1994) study does not, however, identify which structures and processes have enabled both regions to overcome economic crises. In the case of the Boston economy, high technology industries have proven that they are capable of readjusting and rejuvenating their product and process structures in such a way that further innovation and growth is stimulated. This is also exemplified by the region s recent economic development. In the late 1980s, Boston experienced an economic decline when the minicomputer industry lost its competitive basis and defense expenditures were drastically reduced. The number of high technology manufacturing jobs decreased by more than 45,000 between 1987 and 1995. By the mid 1990s, however, the regional economy began to recover. The rapidly growing software sector compensated for some of the losses experienced in manufacturing. In this paper, I aim to identify the forces behind this economic recovery. I will investigate whether high technology firms have uncovered new ways to overcome the crisis and the extent to which they have given up their focus on self-reliance and autarkic structures. The empirical findings will also be discussed in the context of the recent debate about the importance of regional competence and collective learning (Storper, 1997; Maskell and Malmberg, 1998). There is a growing body of literature which suggests that some regional economies During the 1980s and early 1990s, the importance of small firm growth and industrial districts in Italy became the focus of a large number of regional development studies. According to this literature, successful industrial districts are characterized by intensive cooperation and market producer-user interaction between small and medium-sized, flexibly specialized firms (Piore and Sabel, 1984; Scott, 1988). In addition, specialized local labor markets develop which are complemented by a variety of supportive institutions and a tradition of collaboration based on trust relations (Amin and Robins, 1990; Amin and Thrift, 1995). It has also been emphasized that industrial districts are deeply embedded into the socio-institutional structures within their particular regions (Grabher, 1993). Many case studies have attempted to find evidence that the regional patterns identified in Italy are a reflection of a general trend in industrial development rather than just being historical exceptions. Silicon Valley, which is focused on high technology production, has been identified as being one such production complex similar to those in Italy (see, for instance, Hayter, 1997). However, some remarkable differences do exist in the institutional context of this region, as well as its particular social division of labor (Markusen, 1996). Even though critics, such as Amin and Robins (1990), emphasized quite early that the Italian experience could not easily be applied to other socio-cultural settings, many studies have classified other high technology regions in the U.S. as being industrial districts, such as Boston s Route 128 area. Too much attention has been paid to the performance of small and medium-sized firms and the regional level of industrial production in the ill-fated debate regarding industrial districts (Martinelli and Schoenberger, 1991). Harrison (1997) has provided substantial evidence that large firms continue to dominate the global economy. This does not, however, imply that a de-territorialization of economic growth is necessarily taking place as globalization tendencies continue (Storper, 1997; Maskell and Malmberg, 1998). In the case of Boston, it has been misleading to define its regional economy as being an industrial district. Neither have small and medium-sized firms been decisive in the development of the Route 128 area nor has the region developed a tradition of close communication between vertically-disintegrated firms (Dorfman, 1983; Bathelt, 1991a). Saxenian (1994) found that Boston s economy contrasted sharply with that of an industrial district. Specifically, the region has been dominated by large, vertically-integrated high technology firms which are reliant on proprietary technologies and autarkic firm structures. Several studies have tried to compare the development of the Route 128 region to Silicon Valley. These studies have shown that both regions developed into major 2 agglomerations of high technology industries in the post-World War II period. Due to their different traditions, structures and practices, Silicon Valley and Route 128 have followed divergent development paths which have resulted in a different regional specialization (Dorfman, 1983; Saxenian, 1985; Kenney and von Burg, 1999). In the mid 1970s, both regions were almost equally important in terms of the size of their high technology sectors. Since then, however, Silicon Valley has become more important and has now the largest agglomeration of leading-edge technologies in the U.S. (Saxenian, 1994). Saxenian (1994) argues that the superior performance of high technology industries in Silicon Valley over those in Boston is based on different organizational patterns and manufacturing cultures which are embedded in those socio-institutional traditions which are particular to each region. Despite the fact that Saxenian (1994) has been criticized for basing her conclusions on weak empirical research (i.e. Harrison, 1997; Markusen, 1998), she offers a convincing explanation as to why the development paths of both regions have differed.1 Saxenian s (1994) study does not, however, identify which structures and processes have enabled both regions to overcome economic crises. In the case of the Boston economy, high technology industries have proven that they are capable of readjusting and rejuvenating their product and process structures in such a way that further innovation and growth is stimulated. This is also exemplified by the region s recent economic development. In the late 1980s, Boston experienced an economic decline when the minicomputer industry lost its competitive basis and defense expenditures were drastically reduced. The number of high technology manufacturing jobs decreased by more than 45,000 between 1987 and 1995. By the mid 1990s, however, the regional economy began to recover. The rapidly growing software sector compensated for some of the losses experienced in manufacturing. In this paper, I aim to identify the forces behind this economic recovery. I will investigate whether high technology firms have uncovered new ways to overcome the crisis and the extent to which they have given up their focus on self-reliance and autarkic structures. The empirical findings will also be discussed in the context of the recent debate about the importance of regional competence and collective learning (Storper, 1997; Maskell and Malmberg, 1998). There is a growing body of literature which suggests that some regional economies an develop into learning economies which are based on intra-regional production linkages, interactive technological learning processes, flexibility and proximity (Storper, 1992; Lundvall and Johnson, 1994; Gregersen and Johnson, 1997). In the next section of this paper, I will discuss some of the theoretical issues regarding localized learning processes, learning economies and learning regions (see, also, Bathelt, 1999). I will then describe the methodology used. What follows is a brief overview of how Boston s economy has specialized in high technology production. The main part of the paper will then focus on recent trends in Boston s high technology industries. It will be shown that the high technology economy consists of different subsectors which are not tied to a single technological development path. The various subsectors are, at least partially, dependent on different forces and unrelated processes. There is, however, tentative evidence which suggests that cooperative behavior and collective learning in supplierproducer- user relations have become important factors in securing reproductivity in the regional structure. The importance of these trends will be discussed in the conclusions.
A new global crop water model was developed to compute blue (irrigation) water requirements and crop evapotranspiration from green (precipitation) water at a spatial resolution of 5 arc minutes by 5 arc minutes for 26 different crop classes. The model is based on soil water balances performed for each crop and each grid cell. For the first time a new global data set was applied consisting of monthly growing areas of irrigated crops and related cropping calendars. Crop water use was computed for irrigated land and the period 1998 – 2002. In this documentation report the data sets used as model input and methods used in the model calculations are described, followed by a presentation of the first results for blue and green water use at the global scale, for countries and specific crops. Additionally the simulated seasonal distribution of water use on irrigated land is presented. The computed model results are compared to census based statistical information on irrigation water use and to results of another crop water model developed at FAO.
A data set of annual values of area equipped for irrigation for all 236 countries in the world during the time period 1900 - 2003 was generated. The basis for this data product was information available through various online data bases and from other published materials. The complete time series were then constructed around the reported data applying six statistical methods. The methods are discussed in terms of reliability and data uncertainties. The total area equipped for irrigation in the world in 1900 was 53.2 million hectares. Irrigation was mainly practiced in all the arid regions of the globe and in paddy rice areas of South and East Asia. In some temperate countries in Western Europe irrigation was practiced widely on pastures and meadows. The time series suggest a modest rate of increase of irrigated areas in the first half of the 20th century followed by a more dynamic development in the second half. The turn of the century is characterized by an overall consolidating trend resulting at a total of 285.8 million hectares in 2003. The major contributing countries have changed little throughout the century. This data product is regarded as a preliminary result toward an ongoing effort to develop a detailed data set and map of areas equipped for irrigation in the world over the 20th century using sub-national statistics and historical irrigation maps.