Distributed modelling of climate change impacts on snow sublimation in Northern Mongolia

Sublimation of snow is an important factor of the hydrological cycle in Mongolia and is likely to increase according to future climate projections. In this study the hydrological model TRAIN was used to assess spatially distributed current and future sublimation rates based on interpolated daily data of precipitation, air temperature, air humidity, wind speed and solar radiation. An automated procedure for the interpolation of the input data is provided. Depending on the meteorological parameter and the data availability for the individual days, the most appropriate interpolation method is chosen automatically from inverse distance weighting, Ordinary Least Squares interpolation, Ordinary or Universal Kriging. Depending on elevation simulated annual sublimation in the period 1986–2006 was 23 to 35 mm, i.e. approximately 80% of total snowfall. Moreover, future climate projections for 2071–2100 of ECHAM5 and HadCM3, based on the A1B emission scenario of the Intergovernmental Panel on Climate Change, were analysed with TRAIN. In the case of ECHAM5 simulated sublimation increases by up to 17% (26...41 mm) while it remains at the same level for HadCM3 (24...34 mm). The differences are mainly due to a distinct increase in winter precipitation for ECHAM5. Simulated changes of the all-season hydrological conditions, e.g. the sublimation-to-precipitation ratio, were ambiguous due to diverse precipitation patterns derived by the global circulation models

Participatory agro-climate information services: A key component in climate resilient agriculture

The brief promotes participatory agro-climate information services as a key component in achieving climate-smart agriculture. The brief emphasizes that actionable agro-climate information starts with—and responds to—gender-based needs of farmers, integrated at all stages of the value chain. Timely forecasts and accurate agroclimate advisories have been proven to provide farmers with production, adaptation, and mitigation benefits

Modelling historical and current irrigation water demand on the continental scale

Abstract. Water abstractions for irrigation purposes are higher than for any other pan-European water use sector and have a large influence on river runoff regimes. This modelling experiment assesses historic and current irrigation water demands for different crops in five arc minute spatial resolution for pan-Europe. Two different modelling frameworks have been applied in this study. First, soft-coupling the dynamic vegetation model LPJmL with the land use model LandSHIFT leads to overestimations of national irrigation water demands, which are rather high in the southern Mediterranean countries. This can be explained by unlimited water supply in the model structure and illegal or not gauged water abstractions in the reported data sets. The second modelling framework is WaterGAP3, which has an integrated conceptual crop specific irrigation module. Irrigation water requirements as modelled with WaterGAP3 feature a more realistic representation of pan-European water withdrawals. However, in colder humid regions, irrigation water demands are often underestimated. Additionally, a national database on crop-specific irrigated area and water withdrawal for all 42 countries within pan-Europe has been set up and integrated in both model frameworks

Quantification of uncertainties in global grazing systems assessments

Livestock systems play a key role in global sustainability challenges like food security and climate change, yet, many unknowns and large uncertainties prevail. We present a systematic, spatially explicit assessment of uncertainties related to grazing intensity (GI), a key metric for assessing ecological impacts of grazing, by combining existing datasets on a) grazing feed intake, b) the spatial distribution of livestock, c) the extent of grazing land, and d) its net primary productivity (NPP). An analysis of the resulting 96 maps implies that on average 15% of the grazing land NPP is consumed by livestock. GI is low in most of worlds grazing lands but hotspots of very high GI prevail in 1% of the total grazing area. The agreement between GI maps is good on one fifth of the world's grazing area, while on the remainder it is low to very low. Largest uncertainties are found in global drylands and where grazing land bears trees (e.g., the Amazon basin or the Taiga belt). In some regions like India or Western Europe massive uncertainties even result in GI > 100% estimates. Our sensitivity analysis indicates that the input-data for NPP, animal distribution and grazing area contribute about equally to the total variability in GI maps, while grazing feed intake is a less critical variable. We argue that a general improvement in quality of the available global level datasets is a precondition for improving the understanding of the role of livestock systems in the context of global environmental change or food security

Climate change impacts on Central Asian water resources

Central Asia is in large parts dominated by low precipitation and, consequentially, by low water availability. Therefore, changes of natural water resources induced by climate change are of high interest. The aim of this study is to analyse the potential impact of climate change on Central Asian water resources until the end of the 21st century and to point out the main affected regions. Thus, simulations with the large-scale hydrology model WaterGAP3 for the baseline and scenario periods were performed with outputs from three General Circulation Models (GCMs: ECHAM5, IPSL-CM4, and CNRM-CM3) and two IPCC-SRES emission scenarios (A2 and B1). The results show that mean modelled annual water availability increases for all scenarios and GCMs while CNRM-CM3 induces the wettest water situation for the 2085s and ECHAM5 the lowest water availability. Furthermore, robust trends to wetter or dryer conditions could be found for many basins. A seasonal shift of mean modelled water availability could be derived for ECHAM5 which does not show a second peak during summer. The application of daily input data showed no improvement of modelled monthly river discharges for most Central Asian basins compared to monthly input data

Modelling historical and current irrigation water demand on the continental scale: Europe

Water abstractions for irrigation purposes are higher than for any other pan-European water use sector and have a large influence on river runoff regimes. This modelling experiment assesses historic and current irrigation water demands for different crops in five arc minute spatial resolution for pan-Europe. Two different modelling frameworks have been applied in this study. First, soft-coupling the dynamic vegetation model LPJmL with the land use model LandSHIFT leads to overestimations of national irrigation water demands, which are rather high in the southern Mediterranean countries. This can be explained by unlimited water supply in the model structure and illegal or not gauged water abstractions in the reported data sets. The second modelling framework is WaterGAP3, which has an integrated conceptual crop specific irrigation module. Irrigation water requirements as modelled with WaterGAP3 feature a more realistic representation of pan-European water withdrawals. However, in colder humid regions, irrigation water demands are often underestimated. Additionally, a national database on crop-specific irrigated area and water withdrawal for all 42 countries within pan-Europe has been set up and integrated in both model frameworks