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Christian Seiler
,
Ronald W. A. Hutjes
, and
Pavel Kabat

Abstract

Climate-related disasters in Bolivia are frequent, severe, and manifold and affect large parts of the population, economy, and ecosystems. Potentially amplified through climate change, natural hazards are of growing concern. To better understand these events, homogenized daily observations of temperature (29 stations) and precipitation (68 stations) from 1960 to 2009 were analyzed in this study. The impact of the positive (+) and negative (−) phases of the three climate modes (i) Pacific decadal oscillation (PDO), (ii) El Niño–Southern Oscillation (ENSO) with El Niño (EN) and La Niña (LN) events, and (iii) Antarctic Oscillation (AAO) were assessed. Temperatures were found to be higher during PDO(+), EN, and AAO(+) in the Andes. Total amounts of rainfall, as well as the number of extreme events, were higher during PDO(+), EN, and LN in the lowlands. During austral summer [December–February (DJF)], EN led to drier conditions in the Andes with more variable precipitation. Temperatures increased at a rate of 0.1°C per decade, with stronger increases in the Andes and in the dry season. Rainfall totals increased from 1965 to 1984 [12% in DJF and 18% in June–August (JJA)] and decreased afterward (−4% in DJF and −10% in JJA), following roughly the pattern of PDO. Trends of climate extremes generally corresponded to trends of climate means. Findings suggest that Bolivia’s climate will be warmer and drier than average in the near-term future. Having entered PDO(−) in 2007, droughts and LN-related floods can be expected in the lowlands, while increasing temperatures suggest higher risks of drought in the Andes.

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Christian Seiler
,
Ronald W. A. Hutjes
, and
Pavel Kabat

Abstract

Bolivia is facing numerous climate-related threats, ranging from water scarcity due to rapidly retreating glaciers in the Andes to a partial loss of the Amazon forest in the lowlands. To assess what changes in climate may be expected in the future, 35 global circulation models (GCMs) from the third and fifth phases of the Coupled Model Intercomparison Project (CMIP3/5) were analyzed for the Bolivian case. GCMs were validated against observed surface air temperature, precipitation, and incoming shortwave (SW) radiation for the period 1961–90. Weighted ensembles were developed, and climate change projections for five emission scenarios were assessed for 2070–99. GCMs revealed an overall cold, wet, and positive-SW-radiation bias and showed no substantial improvement from the CMIP3 to the CMIP5 ensemble for the Bolivian case. Models projected an increase in temperature (2.5°–5.9°C) and SW radiation (1%–5%), with seasonal and regional differences. In the lowlands, changes in annual rainfall remained uncertain for CMIP3 whereas CMIP5 GCMs were more inclined to project decreases (−9%). This pattern also applied to most of the Amazon basin, suggesting a higher risk of partial biomass loss for the CMIP5 ensemble. Both ensembles agreed on less rainfall (−19%) during drier months (June–August and September–November), with significant changes in interannual rainfall variability, but disagreed on changes during wetter months (January–March). In the Andes, CMIP3 GCMs tended toward less rainfall (−9%) whereas CMIP5 tended toward more (+20%) rainfall during parts of the wet season. The findings presented here may provide inputs for studies of climate change impact that assess how resilient human and natural systems are under different climate change scenarios.

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Richard Harding
,
Martin Best
,
Eleanor Blyth
,
Stefan Hagemann
,
Pavel Kabat
,
Lena M. Tallaksen
,
Tanya Warnaars
,
David Wiberg
,
Graham P. Weedon
,
Henny van Lanen
,
Fulco Ludwig
, and
Ingjerd Haddeland

Abstract

Water-related impacts are among the most important consequences of increasing greenhouse gas concentrations. Changes in the global water cycle will also impact the carbon and nutrient cycles and vegetation patterns. There is already some evidence of increasing severity of floods and droughts and increasing water scarcity linked to increasing greenhouse gases. So far, however, the most important impacts on water resources are the direct interventions by humans, such as dams, water extractions, and river channel modifications. The Water and Global Change (WATCH) project is a major international initiative to bring together climate and water scientists to better understand the current and future water cycle. This paper summarizes the underlying motivation for the WATCH project and the major results from a series of papers published or soon to be published in the Journal of Hydrometeorology WATCH special collection. At its core is the Water Model Intercomparison Project (WaterMIP), which brings together a wide range of global hydrological and land surface models run with consistent driving data. It is clear that we still have considerable uncertainties in the future climate drivers and in how the river systems will respond to these changes. There is a grand challenge to the hydrological and climate communities to both reduce these uncertainties and communicate them to a wider society.

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Reinder A. Feddes
,
Holger Hoff
,
Michael Bruen
,
Todd Dawson
,
Patricia de Rosnay
,
Paul Dirmeyer
,
Robert B. Jackson
,
Pavel Kabat
,
Axel Kleidon
,
Allan Lilly
, and
Andrew J. Pitman

From 30 September to 2 October 1999 a workshop was held in Gif-sur-Yvette, France, with the central objective to develop a research strategy for the next 3–5 years, aiming at a systematic description of root functioning, rooting depth, and root distribution for modeling root water uptake from local and regional to global scales. The goal was to link more closely the weather prediction and climate and hydrological models with ecological and plant physiological information in order to improve the understanding of the impact that root functioning has on the hydrological cycle at various scales. The major outcome of the workshop was a number of recommendations, detailed at the end of this paper, on root water uptake parameterization and modeling and on collection of root and soil hydraulic data.

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Ingjerd Haddeland
,
Douglas B. Clark
,
Wietse Franssen
,
Fulco Ludwig
,
Frank Voß
,
Nigel W. Arnell
,
Nathalie Bertrand
,
Martin Best
,
Sonja Folwell
,
Dieter Gerten
,
Sandra Gomes
,
Simon N. Gosling
,
Stefan Hagemann
,
Naota Hanasaki
,
Richard Harding
,
Jens Heinke
,
Pavel Kabat
,
Sujan Koirala
,
Taikan Oki
,
Jan Polcher
,
Tobias Stacke
,
Pedro Viterbo
,
Graham P. Weedon
, and
Pat Yeh

Abstract

Six land surface models and five global hydrological models participate in a model intercomparison project [Water Model Intercomparison Project (WaterMIP)], which for the first time compares simulation results of these different classes of models in a consistent way. In this paper, the simulation setup is described and aspects of the multimodel global terrestrial water balance are presented. All models were run at 0.5° spatial resolution for the global land areas for a 15-yr period (1985–99) using a newly developed global meteorological dataset. Simulated global terrestrial evapotranspiration, excluding Greenland and Antarctica, ranges from 415 to 586 mm yr−1 (from 60 000 to 85 000 km3 yr−1), and simulated runoff ranges from 290 to 457 mm yr−1 (from 42 000 to 66 000 km3 yr−1). Both the mean and median runoff fractions for the land surface models are lower than those of the global hydrological models, although the range is wider. Significant simulation differences between land surface and global hydrological models are found to be caused by the snow scheme employed. The physically based energy balance approach used by land surface models generally results in lower snow water equivalent values than the conceptual degree-day approach used by global hydrological models. Some differences in simulated runoff and evapotranspiration are explained by model parameterizations, although the processes included and parameterizations used are not distinct to either land surface models or global hydrological models. The results show that differences between models are a major source of uncertainty. Climate change impact studies thus need to use not only multiple climate models but also some other measure of uncertainty (e.g., multiple impact models).

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