Abstract: Climate change and human activities are two major factors affecting water resource change. It is important to understand the roles of the major factors in affecting runoff change in different basins for watershed management. Here, we investigated the trends in climate and runoff in seven typical catchments in seven basins across China from 1961 to 2014. Then we attributed the runoff change to climate change and human activities in each catchment and in three time periods (1980s, 1990s and 2000s), using the VIC model and long-term runoff observation data. During 1961-2014, temperature increased significantly, while the trends in precipitation were insignificant in most of the catchments and inconsistent among the catchments. The runoff in most of the catchments showed a decreasing trend except the Yingluoxia catchment in the northwestern China. The contributions of climate change and human activities to runoff change varied in different catchments and time periods. In the 1980s, climate change contributed more to runoff change than human activities, which was 84%, 59%,-66%,-50%, 59%, 94%, and -59% in the Nianzishan, Yingluoxia, Xiahui, Yangjiaping, Sanjiangkou, Xixian, and Changle catchment, respectively. After that, human activities had played a more essential role in runoff change. In the 1990s and 2000s, human activities contributed more to runoff change than in the 1980s. The contribution by human activities accounted for 84%,- 68%, and 67% in the Yingluoxia, Xiahui, and Sanjiangkou catchment, respectively, in the 1990s; and -96%,-67%,-94%, and -142% in the Nianzishan, Yangjiaping, Xixian, and Changle catchment, respectively, in the 2000s. It is also noted that after 2000 human activities caused decrease in runoff in all catchments except the Yingluoxia. Our findings highlight that the effects of human activities, such as increase in water withdrawal, land use/cover change, operation of dams and reservoirs, should be well managed. (C) 2017 Elsevier B.V. All rights reserved.

Abstract: Ruminant production systems are important producers of food, support rural communities and culture, and help to maintain a range of ecosystem services including the sequestering of carbon in grassland soils. However, these systems also contribute significantly to climate change through greenhouse gas (GHG) emissions, while intensi- fication of production has driven biodiversity and nutrient loss, and soil degradation. Modeling can offer insights into the complexity underlying the relationships between climate change, management and policy choices, food production, and the maintenance of ecosystem services. This paper 1) provides an overview of how ruminant systems modeling supports the efforts of stakeholders and policymakers to predict, mitigate and adapt to climate change and 2) provides ideas for enhancing modeling to fulfil this role. Many grassland models can predict plant growth, yield and GHG emissions from mono-specific swards, but modeling multi-species swards, grassland quality and the impact of management changes requires further development. Current livestock models provide a good basis for predicting animal production; linking these with models of animal health and disease is a prior- ity. Farm-scale modeling provides tools for policymakers to predict the emissions of GHG and other pollutants from livestock farms, and to support the management decisions of farmers from environmental and economic standpoints. Other models focus on how policy and associated management changes affect a range of economic and environmental variables at regional, national and European scales. Models at larger scales generally utilise more empirical approaches than those applied at animal, field and farm-scales and include assumptions which may not be valid under climate change conditions. It is therefore important to continue to develop more realistic representations of processes in regional and global models, using the understanding gained from finer-scale modeling. An iterative process of model development, in which lessons learnt from mechanistic models are ap- plied to develop ‘smart’ empirical modeling, may overcome the trade-off between complexity and usability. De- veloping the modeling capacity to tackle the complex challenges related to climate change, is reliant on closer links between modelers and experimental researchers, and also requires knowledge-sharing and increasing technical compatibility across modeling disciplines. Stakeholder engagement throughout the process of model development and application is vital for the creation of relevant models, and important in reducing problems re- lated to the interpretation of modeling outcomes. Enabling modeling to meet the demands of policymakers and other stakeholders under climate change will require collaboration within adequately-resourced, long-term inter-disciplinary research networks

Abstract: Croplands play an important role in the carbon budget of many regions. However, the estimation of their carbon balance remains difficult due to diversity and complexity of the processes involved. We report the coupling of a one-dimensional soil water, heat, and CO2 flux model (SOILCO2), a pool concept of soil carbon turnover (RothC), and a crop growth module (SUCROS) to predict the net ecosystem exchange (NEE) of carbon. The coupled model, further referred to as AgroC, was extended with routines for managed grassland as well as for root exudation and root decay. In a first step, the coupled model was applied to two winter wheat sites and one upland grassland site in Germany. The model was calibrated based on soil water content, soil temperature, biometric, and soil respiration measurements for each site, and validated in terms of hourly NEE measured with the eddy covariance technique. The overall model performance of AgroC was sufficient with a model efficiency above 0.78 and a correlation coefficient above 0.91 for NEE. In a second step, AgroC was optimized with eddy covariance NEE measurements to examine the effect of different objective functions, constraints, and data-transformations on estimated NEE. It was found that NEE showed a distinct sensitivity to the choice of objective function and the inclusion of soil respiration data in the optimization process. In particular, both positive and negative day- and nighttime fluxes were found to be sensitive to the selected optimization strategy. Additional consideration of soil respiration measurements improved the simulation of small positive fluxes remarkably. Even though the model performance of the selected optimization strategies did not diverge substantially, the resulting cumulative NEE over simulation time period differed substantially. Therefore, it is concluded that data transformations, definitions of objective functions, and data sources have to be considered cautiously when a terrestrial ecosystem model is used to determine NEE by means of eddy covariance measurements. (C) 2017 Elsevier B.V. All rights reserved.