FutureWater

Wageningen, Netherlands

FutureWater

Wageningen, Netherlands

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Van Der Hoek W.,National Institute for Public Health and the Environment | Hunink J.,FutureWater | Vellema P.,Animal Health Service | Droogers P.,FutureWater
International Journal of Environmental Health Research | Year: 2011

The Netherlands is facing a Q fever epidemic in which dairy goats are implicated. People living close to an affected farm have an increased risk. However, no human cases were reported around a number of farms with serious Q fever problems. To assess the role of local environmental conditions which may add to the transmission or risk of Q fever, we gathered datasets on vegetation, land use, soil characteristics, and weather conditions in 5 km areas around infected farms. Areas without transmission had a higher vegetation density and relatively shallow groundwater conditions. Vegetation and soil moisture are relevant factors in the transmission of Coxiella burnetii from infected farms to humans, by reducing the amount of dust available for dispersion of the bacteria. The findings suggest that intensive goat and sheep husbandry should be avoided in areas that are characterized by a combination of arable land with deep groundwater and little vegetation. © 2011 Taylor & Francis.


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).


Lutz A.F.,FutureWater | Lutz A.F.,University Utrecht | Immerzeel W.W.,FutureWater | Immerzeel W.W.,University Utrecht | And 3 more authors.
Nature Climate Change | Year: 2014

Rivers originating in the high mountains of Asia are among the most meltwater-dependent river systems on Earth, yet large human populations depend on their resources downstream1. Across High Asias river basins, there is large variation in the contribution of glacier and snow melt to total runoff 2, which is poorly quantified.The lack of understanding of the hydrological regimes of High Asias rivers is one of the main sources of uncertainty in assessing the regional hydrological impacts of climate change3. Here we use a large-scale, high-resolution cryospheric-hydrological model to quantify the upstream hydrological regimes of the Indus, Ganges, Brahmaputra, Salween and Mekong rivers. Subsequently, we analyse the impacts of climate change on future water availability in these basins using the latest climate model ensemble. Despite large differences in runoff composition and regimes between basins and between tributaries within basins, we project an increase in runoff at least until 2050 caused primarily by an increase in precipitation in the upper Ganges, Brahmaputra, Salween and Mekong basins and from accelerated melt in the upper Indus Basin. These findings have immediate consequences for climate change policies where a transition towards coping with intra-annual shifts in water availability is desirable. © 2014 Macmillan Publishers Limited. All rights reserved.


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 | 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.


Hunink J.E.,FutureWater | Immerzeel W.W.,FutureWater | Immerzeel W.W.,University Utrecht | Droogers P.,FutureWater
Remote Sensing of Environment | Year: 2014

Understanding the spatial and temporal variability of precipitation in tropical high mountain areas remains a key challenge. Point measurements are often not sufficient to capture the strong spatial variability particularly in mountain regions. Satellite remote sensing allows capturing the spatial heterogeneity of precipitation, yet it is generally characterized by significant bias. Rainfall satellite products such as those coming from the Tropical Rainfall Measuring Mission (TRMM) are being continuously improved and an increasing amount of high- and medium-resolution remote sensing data on biophysical surface properties is becoming available. Here we present a methodology that blends two TRMM products with remote sensing data on vegetation and topography to quantify the spatial distribution of precipitation in areas where direct observations are lacking. The approach assumes that vegetation cover, the topography and satellite-derived estimates of rainfall are reasonable indirect measures of ground-based precipitation. The methodology is evaluated for an area in the Andes of Ecuador. The results show that around 40% of the variance in weekly precipitation is explained by these proxies. During the drier periods of the year, vegetation is the strongest proxy. In the very wet areas and during the wet periods vegetation is usually in a climax development phase with no development trends to correlate with rain, and the other proxies dominate precipitation estimation. A cross-validation procedure in which each one of the weather stations is sequentially excluded from the analysis, was applied to test the performance of the methodology. The performance was satisfactory, and as expected it is related to the density of the weather station network and temporal rainfall variability. Overall we conclude that the methodology is useful for areas with very high variable conditions, where sufficient ground-data is available to establish the relationships with the different remote sensing datasets. © 2013 Elsevier Inc.


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.


Droogers P.,FutureWater | Immerzeel W.W.,FutureWater | Lorite I.J.,Centro Alameda del Obispo
Agricultural Water Management | Year: 2010

Water managers and policy makers need accurate estimates of real (actual) irrigation applications for effective monitoring of irrigation and efficient irrigation management. However, this information is not readily available at field level for larger irrigation areas. An innovative inverse modeling approach was tested for a field in an irrigation scheme in southern Spain where observed actual evapotranspiration by satellites was used to assess irrigation application amounts. The actual evapotranspiration was used as the basis for an optimization procedure using the physical based SWAP model and the parameter optimization tool PEST. To evaluate the proposed techniques two steps were taken. First, actual observed evapotranspiration from remote sensing was used to optimize two parameters of the SWAP model to determine irrigation applications. Second, a forward-backward approach was applied to test the minimum overpass return time of satellites and the required accuracy of remotely sensed actual evapotranspiration for accurate assessment of irrigation applications. Results indicate that irrigation application amounts can be estimated reasonably accurately, providing data are available at an interval of 15 days or shorter and the accuracy of the signal is 90% or higher. © 2010 Elsevier B.V. All rights reserved.


Terink W.,FutureWater | Immerzeel W.W.,FutureWater | Droogers P.,FutureWater
International Journal of Climatology | Year: 2013

The Middle East and North Africa (MENA) region can be considered as the most water-scarce region of the world. The Intergovernmental Panel on Climate Change projects strong changes in climate across MENA, further exacerbating pressure on available water resources. The objective of this study is to undertake a climate change assessment for 22 MENA countries in order to quantify the problems these countries may encounter up to 2050. To evaluate climate change in MENA, nine global circulation models representing two future periods (2020-2030 and 2040-2050) were statistically downscaled and compared with a current climate, defined as the period 2000-2009. Besides precipitation only this study also focuses on change in water demand by vegetation reference evapotranspiration (ETref). It was found that for both future periods the annual precipitation sum will decrease for the majority of countries, with decreases of 15-20% for the latter period. For some countries, e.g. Djibouti and Yemen, an increase in annual precipitation of 15-20% was found. The annual ETref shows an increase for all countries for both future periods, with the strongest increases for the latter period. For the extreme situation, it was found that the minimum monthly and annual precipitation sum does not become smaller in the future climate. It in fact increases. In contrast, the maximum monthly and annual ETref increases for all countries. This indicates that projected changes in demand are likely to have a more adverse effect than changes in supply. Spatial analysis showed that the largest precipitation decreases are to be found in southern Egypt, Morocco, central and coastal Algeria, Tunisia, central Libya, Syria, and central and eastern Iran. A case study for Morocco revealed that the potential water deficit, which is already apparent for the current climate, becomes even larger for the future climate. © 2013 Royal Meteorological Society.

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