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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.
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.
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.
Für eine möglichst vollständige analytische Beschreibung werden in der statistischen Klimatologie beobachtete Klimazeitreihen als Realisation eines stochastischen Prozesses, das heißt als eine Folge von Zufallsvariablen verstanden. Die Zeitreihe soll im wesentlichen durch eine analytische Funktion der Zeit beschrieben werden können und die Beobachtung nur durch Zufallseinflüsse von dieser Funktion abweichen. Diese analytische Funktion setzt sich aus der Summe zeitlich strukturierter Komponenten zusammen, welche aus klimatologischem Blickwinkel interpretierbar erscheinen. Es werden Funktionen zugelassen, die den Jahresgang, Trends, episodische Komponenten und deren Änderung beschreiben. Die Extremereignisse sind als eine besondere weitere Komponente in die Zeitreihenanalyse aufgenommen und als von Änderungen in den Parametern der Verteilung unabhängige, extreme Werte definiert. Die Zufallseinflüsse sollen zunächst als Realisierungen unabhängiger normalverteilter Zufallsvariablen mit dem Erwartungswert Null und im Zeitablauf konstanter Varianz interpretiert werden können. In diesem Fall beschreibt die analytische Funktion der Zeit, die Summe detektierter strukturierter Komponenten, den zeitlichen Verlauf des Mittels. Ein zu einem bestimmten Zeitpunkt tatsächlich beobachteter Wert kann dann als eine mögliche Realisation einer Zufallsvariablen interpretiert werden, die der Gaußverteilung mit dem Mittelwert µ(t) zur Zeit t und konstanter Varianz genügt. Da die zugrundeliegenden Annahmen, unter Verwendung klimatologisch interpretierbarer Basisfunktionen, in der Analyse von Klimazeitreihen, die nicht die Temperatur betreffen, zumeist nicht erfüllt sind, wird in eine Verallgemeinerung des Konzepts der Zeitreihenzerlegung in einen deterministischen und einen statistischen Anteil eingeführt. Zeitlich strukturierte Änderungen werden nun in verschiedenen Verteilungsparametern frei wählbarer Wahrscheinlichkeitsdichtefunktionen gesucht. Die gängige Beschränkung auf die Schätzung einer zeitlich veränderlichen Lokation wird aufgehoben. Skalenschätzer sowie Schätzer fär den Formparameter spielen ebenso relevante Rollen fär die Beschreibung beobachteter Klimavariabilität. Die Klimazeitreihen werden wieder als Realisation eines Zufallprozesses verstanden, jedoch genügen die Zufallsvariablen nun einer frei wählbaren Wahrscheinlichkeitsdichtefunktion. Die zeitlich strukturierten Änderungen in den Verteilungsparametern werden auf Basis der gesamten Zeitreihe für jeden Zeitpunkt geschätzt. Die aus der Analyse resultierende analytische Beschreibung in Form einer zeitabhängigen Wahrscheinlichkeitsdichtefunktion ermöglicht weiterhin die Schätzung von Über- und Unterschreitungswahrscheinlichkeiten beliebig wählbarer Schwellenwerte für jeden Zeitpunkt des Beobachtungszeitraums. Diese Methode erlaubt insbesondere eine statistische Modellierung monatlicher Niederschlagsreihen durch die Zerlegung in einen deterministischen und einen statistischen Anteil. In dem speziellen Fall von 132 Reihen monatlicher Niederschlagssummen deutscher Stationen 1901-2000 gelingt eine vollständige analytische Beschreibung der Reihen durch ihre Interpretation als Realisation einer Gumbel-verteilten Zufallsvariablen mit variablem Lage- und Streuparameter. Auf Basis der gewonnenen analytischen Beschreibung der Reihen kann beispielsweise im Westen Deutschlands auf Verschiebungen der jährlichen Überschreitungsmaxima des 95%-Perzentils von den Sommer- in die Wintermonate geschlossen werden. Sie werden durch relativ starke Anstiege in der Überschreitungswahrscheinlichkeit (bis 10%) in den Wintermonaten und nur geringe Zunahmen oder aber Abnahmen in den Sommermonaten hervorgerufen. Dies geht mit einer Zunahme der Unterschreitungswahrscheinlichkeit in den Winter- und einer Abnahme in den Sommermonaten einher. Monte-Carlo-Simulationen zeigen, daß jahreszeitlich differenzierte Schätzungen von Änderungen im Erwartungswert, also gebräuchliche Trends, auf Basis der Kleinst-Quadrate-Methode systematischen Bias und hohe Varianz aufweisen. Eine Schätzung der Trends im Mittel auf Basis der statistischen Modellierung ist somit ebenso den Kleinst-Quadrate-Schätzern vorzuziehen. Hinsichtlich der Niederschlagsanalysen stellen jedoch aride Gebiete, mit sehr seltenen Niederschlägen zu bestimmten Jahreszeiten, die Grenze der Methode dar, denn zu diesen Zeitpunkten ist eine vertrauenswürdige Schätzung einer Wahrscheinlichkeitsdichtefunktion nicht möglich. In solchen Fällen ist eine grundsätzlich andere Herangehensweise zur Modellierung der Reihen erforderlich.
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.
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.
Der "Regionalatlas Rhein-Main" wird zum 75-jährigen Jubiläum der "Rhein-Mainischen Forschung" veröffentlicht. Er verfolgt das Ziel,
- einne Überblick über die regionale Struktur des Rhein-Main-Gebietes zu verschaffen,
- Politik, Wirtschaft und Verwaltung Grundlagendaten in regionalisierter Form für ihre Entscheidungen an die Hand zu geben, und
- den im Rhein-Main-Gebiet lebenden Menschen die regionalen Strukturen ihres Lebensraumes näher zu bringen.
Vorwort: Klima ist vor allem deswegen nicht nur von wissenschaftlichem, sondern auch von öffentlichem Interesse, weil es veränderlich ist und weil solche Änderungen gravierende ökologische sowie sozioökonomische Folgen haben können. Im Detail weisen Klimaänderungen allerdings komplizierte zeitliche und räumliche Strukturen auf, deren Erfassung und Interpretation alles andere als einfach ist. Bei den zeitlichen Strukturen stehen mit Recht vor allem relativ langfristige Trends sowie Extremereignisse im Blickpunkt, erstere, weil sie den systematischen Klimawandel zum Ausdruck bringen und letztere wegen ihrer besonders brisanten Auswirkungen. Mit beiden Aspekten hat sich unsere Arbeitsgruppe immer wieder eingehend befasst. Hinsichtlich der Extremereignisse bzw. Extremwertstatistik sei beispielsweise auf die Institutsberichte Nr. 1, 2 und 5 sowie die dort angegebene Literatur hingewiesen. Hier geht es wieder einmal um Klimatrends und dabei ganz besonders um die räumlichen Trendstrukturen. Der relativ langfristige und somit systematische Klimawandel läuft nämlich regional sehr unterschiedlich ab, was am besten in Trendkarten zum Ausdruck kommt. Solche regionalen, zum Teil sehr kleinräumigen Besonderheiten sind insbesondere beim Niederschlag sehr ausgeprägt. Zudem sind die räumlichen Trendstrukturen auch jahreszeitlich/monatlich sehr unterschiedlich. In unserer Arbeitsgruppe hat sich Herr Dr. Jörg Rapp im Rahmen seiner Diplom- und insbesondere Doktorarbeit intensiv mit diesem Problem beschäftigt, was zur Publikation des „Atlas der Niederschlags- und Temperaturtrends in Deutschland 1891-1990“ (Rapp und Schönwiese, 2. Aufl. 1996) sowie des „Climate Trend Atlas of Europe – Based on Observations 1891-1990“ (Schönwiese und Rapp, 1997) geführt hat. Die große Beachtung dieser Arbeiten ließ es schon lange als notwendig erscheinen, eine Aktualisierung vorzunehmen. Dies ist zunächst für den Klima-Trendatlas Deutschland geschehen, der nun für das Zeitintervall 1901-2000 vorliegt (Institutsbericht Nr. 4, 2005). Hier wird nun auch eine entsprechende Aktualisierung für Europa vorgelegt, und zwar auf der Grundlage der Berechnungen, die Reinhard Janoschitz in seiner Diplomarbeit durchgeführt hat. Dabei besteht eine enge Querverbindung zum Projekt VASClimO (Variability Analysis of Surface Climate Observations), das dankenswerterweise vom Bundesministerium für Bildung und Forschung (BMBF) im Rahmen von DEKLIM (Deutsches Klimaforschungsprogramm) gefördert worden ist (siehe Institutsbericht Nr. 6, in den vorab schon einige wenige Europa-Klima-Trendkarten einbezogen worden sind). Mit der Publikation des hier vorliegenden „Klima-Trendatlas Europa 1901-2000“ werden in insgesamt 261 Karten (davon 17 Karten in Farbdarstellung in den Text integriert) wieder umfangreiche Informationen zum Klimawandel in Europa vorgelegt. Sie beruhen vorwiegend auf linearen Trendanalysen hinsichtlich der bodennahen Lufttemperatur und des Niederschlags für die Zeit 1901-2000 sowie für die Subintervalle 1951-2000, 1961-1990 und 1971-2000, jeweils aufgrund der jährlichen, jahreszeitlichen und monatlichen Beobachtungsdaten. Die Signifikanz der Trends ist im (schwarz/weiß wiedergegebenen) Kartenteil durch Rasterung markiert. Da sich die Analyse eng an die oben zitierte Arbeit von Schönwiese und Rapp (1997) anlehnt, wo ausführliche textliche Erläuterungen zu finden sind (ebenso in Rapp, 2000) wurde hier der Textteil sehr knapp gehalten.
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.
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.