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Author |
Luo, K.; Tao, F.; Deng, X.; Moiwo, J.P. |
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Title |
Changes in potential evapotranspiration and surface runoff in 1981-2010 and the driving factors in Upper Heihe River Basin in Northwest China |
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Journal Article |
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Year |
2017 |
Publication |
Hydrological Processes |
Abbreviated Journal |
Hydrol. Process. |
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Volume |
31 |
Issue |
1 |
Pages |
90-103 |
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Keywords |
driving factor; potential evaporation; surface runoff; SWAT model; Upper Heihe River Basin; SWAT Hydrologic Model; Pan Evaporation; Vegetation Model; Climate-Change; Water; Trends; Precipitation; Uncertainty; Variability; Generation |
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Abstract |
Changes in potential evapotranspiration and surface runoff can have profound implications for hydrological processes in arid and semiarid regions. In this study, we investigated the response of hydrological processes to climate change in Upper Heihe River Basin in Northwest China for the period from 1981 to 2010. We used agronomic, climatic and hydrological data to drive the Soil and Water Assessment Tool model for changes in potential evapotranspiration (ET0) and surface runoff and the driving factors in the study area. The results showed that increasing autumn temperature increased snow melt, resulting in increased surface runoff, especially in September and October. The spatial distribution of annual runoff was different from that of seasonal runoff, with the highest runoff in Yeniugou River, followed by Babaohe River and then the tributaries in the northern of the basin. There was no evaporation paradox at annual and seasonal time scales, and annual ET0 was driven mainly by wind speed. ET0 was driven by relative humidity in spring, sunshine hour duration in autumn and both sunshine hour duration and relative humility in summer. Surface runoff was controlled by temperature in spring and winter and by precipitation in summer (flood season). Although surface runoff increased in autumn with increasing temperature, it depended on rainfall in September and on temperature in October and November. Copyright (C) 2016 John Wiley & Sons, Ltd. |
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2018-08-23 |
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0885-6087 |
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CropM, ft_macsur |
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MA @ admin @ |
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5207 |
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Doro, L.; Jones, C.; Williams, J.R.; Norfleet, M.L.; Izaurralde, R.C.; Wang, X.; Jeong, J. |
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Title |
The Variable Saturation Hydraulic Conductivity Method for Improving Soil Water Content Simulation in EPIC and APEX Models |
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Journal Article |
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Year |
2017 |
Publication |
Vadose Zone Journal |
Abbreviated Journal |
Vadose Zone Journal |
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Volume |
16 |
Issue |
13 |
Pages |
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Keywords |
Conservation Effects Assessment; Runoff Simulation; Unsaturated Soils; United-States; Porous-Media; Moisture; Flow; Productivity; Transport; Denitrification |
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Soil water percolation is a key process in the life cycle of water in fields, watersheds, and river basins. The Environmental Policy Integrated Climate (EPIC) and the Agricultural Policy/Environmental eXtender (APEX) are continuous models developed for evaluating the environmental effects of agricultural management. Traditionally, these models have simulated soil water percolation processes using a tipping-bucket approach, with the rate of flow limited by the saturated hydraulic conductivity. This simple approach often leads to inaccuracy in simulating elevated soil water conditions where soil water content (SWC) levels may remain above field capacity under prolonged wet weather periods or limited drainage. To overcome this deficiency, a new sub-model, the variable saturation hydraulic conductivity (VSHC) method, was developed for simulating soil water percolation processes using a nonlinear equation to estimate the effective hydraulic conductivity as a function of the SWC and soil properties. The VSHC method was evaluated at three sites in the United States and two sites in Europe. In addition, a numerical solution of the Richards equation was used as a benchmark for SWC comparison. Results show that the VSHC method substantially improves the accuracy of the SWC simulation in long-term simulations, particularly during wet periods. At the watershed scale, results on the Riesel Y2 watershed indicate that the VSHC method enhances model performance in the high-flow regime of channel peak flows because of the improved estimation of SWC, which implies that the improved SWC simulation at the field scale is beneficial to hydrologic modeling at the watershed scale. |
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2018-09-07 |
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1539-1663 |
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CropM, ft_macsur |
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MA @ admin @ |
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5208 |
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Kipling, R.P.; Bannink, A.; Bellocchi, G.; Dalgaard, T.; Fox, N.J.; Hutchings, N.J.; Kjeldsen, C.; Lacetera, N.; Sinabell, F.; Topp, C.F.E.; van Oijen, M.; Virkajärvi, P.; Scollan, N.D. |
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Title |
Modelling European ruminant production systems: Facing the challenges of climate change |
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Report |
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Year |
2017 |
Publication |
FACCE MACSUR Reports |
Abbreviated Journal |
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Volume |
10 |
Issue |
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Pages |
L1.1-D1 |
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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 |
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LiveM |
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MA @ admin @ |
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4947 |
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Kersebaum, K.-C.; Wallor, E.; Ventrella, D.; Cammarano, D.; Choucheney, E.; Ewert, F.; Ferrise, R.; Gaiser, T.; Garofalo, P.; Giglio, L.; Giola, P.; Hoffmann, M.; Laan, M.; Lewan, E.; Maharjan, G.R.; Moriondo, M.; Mula, L.; Nendel, C.; Pohankova, E.; Roggero, P.P.; Trnka, M.; Trombi, G. |
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Title |
Comparison of site sensitivity of crop models using spatially variable field data from Precision Agriculture |
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Report |
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Year |
2017 |
Publication |
FACCE MACSUR Reports |
Abbreviated Journal |
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Volume |
10 |
Issue |
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Pages |
C1.1-D2 |
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Keywords |
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Abstract |
Site conditions and soil properties have a strong influence on impacts of climate change on crop production. Vulnerability of crop production to changing climate conditions is highly determined by the ability of the site to buffer periods of adverse climatic situations like water scarcity or excessive rainfall. Therefore, the capability of models to reflect crop responses and water and nutrient dynamics under different site conditions is essential to assess climate impact even on a regional scale. To test and improve sensitivity of models to various site properties such as soil variability and hydrological boundary conditions, spatial variable data sets from precision farming of two fields in Germany and Italy were provided to modellers. For the German 20 ha field soil and management data for 60 grid points for 3 years (2 years wheat, 1 year triticale) were provided. For the Italian field (12 ha) information for 100 grid points were available for three growing seasons of durum wheat. Modellers were asked to run their models using a) the model specific procedure to estimate soil hydraulic properties from texture using their standard procedure and use in step b) fixed values for field capacity and wilting point derived from soil taxonomy. Only the phenology and crop yield of one grid point provided for a basic calibration. In step c) information for all grid points of the first year (yield, soil water and mineral N content for Germany, yield, biomass and LAI for Italy) were provided. First results of five out of twelve participating models are compared against measured state variables analysing their site specific response and consistency across crop and soil variables. (Main text to be published in a peer-reviewed journal) |
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MA @ admin @ |
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4951 |
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Webber, H.; Martre, P.; Asseng, S.; Kimball, B.; White, J.; Ottman, M.; Wall, G.W.; De Sanctis, G.; Doltra, J.; Grant, R.; Kassie, B.; Maiorano, A.; Olesen, J.E.; Ripoche, D.; Rezaei, E.E.; Semenov, M.A.; Stratonovitch, P.; Ewert, F. |
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Title |
Canopy temperature for simulation of heat stress in irrigated wheat in a semi-arid environment: A multi-model comparison |
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Journal Article |
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Year |
2017 |
Publication |
Field Crops Research |
Abbreviated Journal |
Field Crops Research |
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Volume |
202 |
Issue |
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Pages |
21-35 |
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Keywords |
Crop model comparison; Canopy temperature; Heat stress; Wheat |
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Even brief periods of high temperatures occurring around flowering and during grain filling can severely reduce grain yield in cereals. Recently, ecophysiological and crop models have begun to represent such phenomena. Most models use air temperature (Tair) in their heat stress responses despite evidence that crop canopy temperature (Tc) better explains grain yield losses. Tc can deviate significantly from Tair based on climatic factors and the crop water status. The broad objective of this study was to evaluate whether simulation of Tc improves the ability of crop models to simulate heat stress impacts on wheat under irrigated conditions. Nine process-based models, each using one of three broad approaches (empirical, EMP; energy balance assuming neutral atmospheric stability, EBN; and energy balance correcting for the atmospheric stability conditions, EBSC) to simulate Tc, simulated grain yield under a range of temperature conditions. The models varied widely in their ability to reproduce the measured Tc with the commonly used EBN models performing much worse than either EMP or EBSC. Use of Tc to account for heat stress effects did improve simulations compared to using only Tair to a relatively minor extent, but the models that additionally use Tc on various other processes as well did not have better yield simulations. Models that simulated yield well under heat stress had varying skill in simulating Tc. For example, the EBN models had very poor simulations of Tc but performed very well in simulating grain yield. These results highlight the need to more systematically understand and model heat stress events in wheat. |
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2016-10-31 |
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ISSN |
0378-4290 |
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Call Number |
MA @ admin @ |
Serial |
4824 |
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