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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 |
Type |
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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Martre, P.; Wallach, D.; Asseng, S.; Ewert, F.; Jones, J.W.; Rötter, R.P.; Boote, K.J.; Ruane, A.C.; Thorburn, P.J.; Cammarano, D.; Hatfield, J.L.; Rosenzweig, C.; Aggarwal, P.K.; Angulo, C.; Basso, B.; Bertuzzi, P.; Biernath, C.; Brisson, N.; Challinor, A.J.; Doltra, J.; Gayler, S.; Goldberg, R.; Grant, R.F.; Heng, L.; Hooker, J.; Hunt, L.A.; Ingwersen, J.; Izaurralde, R.C.; Kersebaum, K.C.; Müller, C.; Kumar, S.N.; Nendel, C.; O’Leary, G.; Olesen, J.E.; Osborne, T.M.; Palosuo, T.; Priesack, E.; Ripoche, D.; Semenov, M.A.; Shcherbak, I.; Steduto, P.; Stöckle, C.O.; Stratonovitch, P.; Streck, T.; Supit, I.; Tao, F.; Travasso, M.; Waha, K.; White, J.W.; Wolf, J. |
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Title |
Multimodel ensembles of wheat growth: many models are better than one |
Type |
Journal Article |
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Year |
2015 |
Publication |
Global Change Biology |
Abbreviated Journal |
Glob. Chang. Biol. |
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Volume |
21 |
Issue |
2 |
Pages |
911-925 |
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Keywords |
Climate; Climate Change; Environment; *Models, Biological; Seasons; Triticum/*growth & development; ecophysiological model; ensemble modeling; model intercomparison; process-based model; uncertainty; wheat (Triticum aestivum L.) |
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Abstract |
Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models. |
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1354-1013 |
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CropM, ftnotmacsur |
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MA @ admin @ |
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4665 |
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Müller, C.; Waha, K.; Bondeau, A.; Heinke, J. |
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Title |
Hotspots of climate change impacts in sub-Saharan Africa and implications for adaptation and development |
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Journal Article |
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Year |
2014 |
Publication |
Global Change Biology |
Abbreviated Journal |
Glob. Chang. Biol. |
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Volume |
20 |
Issue |
8 |
Pages |
2505-2517 |
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Keywords |
Africa South of the Sahara; *Climate Change; Crops, Agricultural; Environment; Hydrology; *Models, Theoretical; Uncertainty; adaptation; climate change; development; impacts; modeling; sub-Saharan Africa |
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Abstract |
Development efforts for poverty reduction and food security in sub-Saharan Africa will have to consider future climate change impacts. Large uncertainties in climate change impact assessments do not necessarily complicate, but can inform development strategies. The design of development strategies will need to consider the likelihood, strength, and interaction of climate change impacts across biosphere properties. We here explore the spread of climate change impact projections and develop a composite impact measure to identify hotspots of climate change impacts, addressing likelihood and strength of impacts. Overlapping impacts in different biosphere properties (e.g. flooding, yields) will not only claim additional capacity to respond, but will also narrow the options to respond and develop. Regions with severest projected climate change impacts often coincide with regions of high population density and poverty rates. Science and policy need to propose ways of preparing these areas for development under climate change impacts. |
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1354-1013 |
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CropM, ft_macsur |
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MA @ admin @ |
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4534 |
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Bassu, S.; Brisson, N.; Durand, J.-L.; Boote, K.; Lizaso, J.; Jones, J.W.; Rosenzweig, C.; Ruane, A.C.; Adam, M.; Baron, C.; Basso, B.; Biernath, C.; Boogaard, H.; Conijn, S.; Corbeels, M.; Deryng, D.; De Sanctis, G.; Gayler, S.; Grassini, P.; Hatfield, J.; Hoek, S.; Izaurralde, C.; Jongschaap, R.; Kemanian, A.R.; Kersebaum, K.C.; Kim, S.-H.; Kumar, N.S.; Makowski, D.; Müller, C.; Nendel, C.; Priesack, E.; Pravia, M.V.; Sau, F.; Shcherbak, I.; Tao, F.; Teixeira, E.; Timlin, D.; Waha, K. |
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Title |
How do various maize crop models vary in their responses to climate change factors |
Type |
Journal Article |
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Year |
2014 |
Publication |
Global Change Biology |
Abbreviated Journal |
Glob. Chang. Biol. |
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Volume |
20 |
Issue |
7 |
Pages |
2301-2320 |
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Keywords |
Carbon Dioxide/metabolism; *Climate Change; Crops, Agricultural/growth & development/metabolism; Geography; Models, Biological; Temperature; Water/*metabolism; Zea mays/*growth & development/*metabolism; AgMIP; [Co2]; climate; maize; model intercomparison; simulation; uncertainty |
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Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2 ], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly -0.5 Mg ha(-1) per °C. Doubling [CO2 ] from 360 to 720 μmol mol(-1) increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2 ] among models. Model responses to temperature and [CO2 ] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information. |
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1354-1013 |
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CropM |
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MA @ admin @ |
Serial |
4510 |
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Author |
De Swaef, T.; Bellocchi, G.; Aper, J.; Lootens, P.; Roldan-Ruiz, I. |
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Title |
Use of identifiability analysis in designing phenotyping experiments for modelling forage production and quality |
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Journal Article |
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Year |
2019 |
Publication |
Journal of Experimental Botany |
Abbreviated Journal |
J. Experim. Bot. |
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Volume |
70 |
Issue |
9 |
Pages |
2587-2604 |
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Keywords |
Breeding; grassland modelling; identifiability analysis; perennial; ryegrass; phenotyping; sensitivity analysis; pasture simulation-model; practical identifiability; crop; water; parameters; systems; carbon; uncertainty; sensitivity; emissions |
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Abstract |
Agricultural systems models are complex and tend to be over-parameterized with respect to observational datasets. Practical identifiability analysis based on local sensitivity analysis has proved effective in investigating identifiable parameter sets in environmental models, but has not been applied to agricultural systems models. Here, we demonstrate that identifiability analysis improves experimental design to ensure independent parameter estimation for yield and quality outputs of a complex grassland model. The Pasture Simulation model (PaSim) was used to demonstrate the effectiveness of practical identifiability analysis in designing experiments and measurement protocols within phe-notyping experiments with perennial ryegrass. Virtual experiments were designed combining three factors: frequency of measurements, duration of the experiment. and location of trials. Our results demonstrate that (i) PaSim provides sufficient detail in terms of simulating biomass yield and quality of perennial ryegrass for use in breeding, (ii) typical breeding trials are insufficient to parameterize all influential parameters, (iii) the frequency of measurements is more important than the number of growing seasons to improve the identifiability of PaSim parameters, and (iv) identifiability analysis provides a sound approach for optimizing the design of multi-location trials. Practical identifiability analysis can play an important role in ensuring proper exploitation of phenotypic data and cost-effective multi-location experimental designs. Considering the growing importance of simulation models, this study supports the design of experiments and measurement protocols in the phenotyping networks that have recently been organized. |
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2020-02-14 |
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0022-0957 |
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LiveM, ft_macsur |
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MA @ admin @ |
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5231 |
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