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Wageningen, Netherlands

Immerzeel W.W.,FutureWater | Immerzeel W.W.,University Utrecht | Bierkens M.F.P.,University Utrecht | Bierkens M.F.P.,Deltares
International Journal of Climatology | Year: 2010

Empirical and numerical studies aiming at predicting inter-annual monsoon variability have thus far shown limited predictive capability. In this study, we develop a spatially explicit seasonal prediction methodology for south-west Asian monsoon (SWM) rainfall in the river basins of the Indus, Brahmaputra and Ganges, using multiple regression linear models in combination with satellite-derived snow cover. We show that the use of recent time series of remotely sensed snow cover, in combination with indices of global ocean and atmospheric modes (ENSO, NAO), can predict average monsoon precipitation with reasonable accuracy and with greater accuracy in specific regions. Maps of the relative contribution of predictor variables to the regression model show that the spring snow cover on the Tibetan plateau is the most important predictor of monsoon precipitation, especially in inland regions. © 2009 Royal Meteorological Society.

Immerzeel W.W.,FutureWater | Immerzeel W.W.,University Utrecht | van Beek L.P.H.,University Utrecht | Konz M.,ETH Zurich | And 3 more authors.
Climatic Change | Year: 2012

The analysis of climate change impact on the hydrology of high altitude glacierized catchments in the Himalayas is complex due to the high variability in climate, lack of data, large uncertainties in climate change projection and uncertainty about the response of glaciers. Therefore a high resolution combined cryospheric hydrological model was developed and calibrated that explicitly simulates glacier evolution and all major hydrological processes. The model was used to assess the future development of the glaciers and the runoff using an ensemble of downscaled climate model data in the Langtang catchment in Nepal. The analysis shows that both temperature and precipitation are projected to increase which results in a steady decline of the glacier area. The river flow is projected to increase significantly due to the increased precipitation and ice melt and the transition towards a rain river. Rain runoff and base flow will increase at the expense of glacier runoff. However, as the melt water peak coincides with the monsoon peak, no shifts in the hydrograph are expected. © 2011 The Author(s).

Immerzeel W.W.,FutureWater | Immerzeel W.W.,University Utrecht | Van Beek L.P.H.,University Utrecht | Bierkens M.F.P.,University Utrecht | Bierkens M.F.P.,Deltares
Science | Year: 2010

More than 1.4 billion people depend on water from the Indus, Ganges, Brahmaputra, Yangtze, and Yellow rivers. Upstream snow and ice reserves of these basins, important in sustaining seasonal water availability, are likely to be affected substantially by climate change, but to what extent is yet unclear. Here, we show that meltwater is extremely important in the Indus basin and important for the Brahmaputra basin, but plays only a modest role for the Ganges, Yangtze, and Yellow rivers. A huge difference also exists between basins in the extent to which climate change is predicted to affect water availability and food security. The Brahmaputra and Indus basins are most susceptible to reductions of flow, threatening the food security of an estimated 60 million people.

Droogers P.,FutureWater | Bouma J.,Wageningen University
International Journal of Water Resources Development | Year: 2014

Accelerating future water shortages require development of operational water governance models, as illustrated by three case studies: (1) upstream-downstream interactions in the Aral Sea basin, where science acts as problem recognizer, emphasizing scoping policies; (2) impact and adaptation of climate change on water and food supply in the Middle East and North Africa, where science acts as a mediator between perspectives, emphasizing scoping and a start of implementation policies; and (3) green water credits in Kenya, where science acts as advocate, emphasizing scoping and implementation policies in close interaction with stakeholders, including impulses from applied to basic research. © 2014 Taylor & Francis.

Quiroz R.,International Potato Center | Yarleque C.,International Potato Center | Posadas A.,International Potato Center | Mares V.,International Potato Center | Immerzeel W.W.,FutureWater
Environmental Modelling and Software | Year: 2011

Quantifying rainfall at spatial and temporal scales in regions where meteorological stations are scarce is important for agriculture, natural resource management and land-atmosphere interactions science. We describe a new approach to reconstruct daily rainfall from rain gauge data and the normalized difference vegetation index (NDVI) based on the fact that both signals are periodic and proportional. The procedure combines the Fourier Transform (FT) and the Wavelet Transform (WT). FT was used to estimate the lag time between rainfall and the vegetation response. Subsequently, third level decompositions of both signals with WT were used for the reconstruction process, determined by the entropy difference between levels and R2. The low-frequency NDVI data signal, to which the high frequency signal (noise) extracted from the rainfall data was added, was the base for the reconstruction. The reconstructed and the measured rainfall showed similar entropy levels and better determination coefficients (>0.81) than the estimates with conventional statistical relations reported in the literature where this level of precision is only found for comparisons at the seasonal levels. Cross-validation resulted in ≤10% entropy differences, compared to more than 45% obtained for the standard method when the NDVI was used to estimate the rainfall in the same pixel where the weather station was located. This methodology based on high resolution NDVI fields and data from a limited number of meteorological stations improves spatial reconstruction of rainfall. © 2010 Elsevier Ltd.

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