Grid-size effects on estimation of evapotranspiration and gross primary production over a large Loess Plateau basin, China

Mo, Xingguo; Liu, Suxia; Chen, Dan; Lin, Zhifeng; Guo, R.T.; Wang, Kun

doi:10.1623/hysj.54.1.160

Cited by 18 publications

(11 citation statements)

References 39 publications

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“…For a specific model, the DAE on different response variables ranked differently. This is in line with Mo et al (2009), who found that the DAE on gross primary production (GPP) is more significant than that on ET. The differences could result from the different algorithms used to simulate different response variables.…”

Section: Variation Of Dae Across Production Conditions Crops Responsupporting

confidence: 90%

Effect of weather data aggregation on regional crop simulation for different crops, production conditions, and response variables

Zhao¹,

Hoffmann²,

Bussel³

et al. 2015

Clim. Res.

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Co-author addresses in the Supplement (www. int-res. com/ articles/ suppl/ c065 p141 _ supp. pdf) ABSTRACT: We assessed the weather data aggregation effect (DAE) on the simulation of cropping systems for different crops, response variables, and production conditions. Using 13 processbased crop models and the ensemble mean, we simulated 30 yr continuous cropping systems for 2 crops (winter wheat and silage maize) under 3 production conditions for the state of North Rhine-Westphalia, Germany. The DAE was evaluated for 5 weather data resolutions (i.e. 1, 10, 25, 50, and 100 km) for 3 response variables including yield, growing season evapotranspiration, and water use efficiency. Five metrics, viz. the spatial bias (Δ), average absolute deviation (AAD), relative AAD, root mean squared error (RMSE), and relative RMSE, were used to evaluate the DAE on both the input weather data and simulated results. For weather data, we found that data aggregation narrowed the spatial variability but widened the Δ, especially across mountainous areas. The DAE on loss of spatial heterogeneity and hotspots was stronger than on the average changes over the region. The DAE increased when coarsening the spatial resolution of the input weather data. The DAE varied considerably across different models, but changed only slightly for different production conditions and crops. We conclude that if spatially detailed information is essential for local management decision, higher resolution is desirable to adequately capture the spatial variability for heterogeneous regions. The required resolution depends on the choice of the model as well as the environmental condition of the study area.

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Section: Variation Of Dae Across Production Conditions Crops Responsupporting

confidence: 90%

Effect of weather data aggregation on regional crop simulation for different crops, production conditions, and response variables

Zhao¹,

Hoffmann²,

Bussel³

et al. 2015

Clim. Res.

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“…, and "3 • C warming level" referred to an increase of 2.39 • C (= 3 − 0.61 • C) compared with the baseline, respectively. As large differences existed in temperature simulations among CMIP5 models and RCP scenarios, we applied a widely used time sampling method (James et al, 2017;Mohammed et al, 2017;Marx et al, 2018) to each GCM under each RCP scenario (referred to as GCM-RCP combination hereafter). A 20-year moving window, which has the same length of the baseline period, was used to determine the first period reaching a specific warming level for each combination, with the period median year referred to as the "crossing year".…”

Section: Determination Of Years Reaching Specific Warming Levelsmentioning

confidence: 99%

More severe hydrological drought events emerge at different warming levels over the Wudinghe watershed in northern China

Jiao

Yuan

2019

Hydrol. Earth Syst. Sci.

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Abstract. Assessment of changes in hydrological droughts at specific warming levels is important for an adaptive water resources management with consideration of the 2015 Paris Agreement. However, most studies focused on the response of drought frequency to the warming and neglected other drought characteristics, including severity. By using a semiarid watershed in northern China (i.e., Wudinghe) as an example, here we show less frequent but more severe hydrological drought events emerge at 1.5, 2 and 3 ∘C warming levels. We used meteorological forcings from eight Coupled Model Intercomparison Project Phase 5 climate models under four representative concentration pathways, to drive a newly developed land surface hydrological model to simulate streamflow, and analyzed historical and future hydrological drought characteristics based on the standardized streamflow index. The Wudinghe watershed will reach the 1.5, 2 and 3 ∘C warming levels around 2015–2034, 2032–2051 and 2060–2079, with an increase in precipitation of 8 %, 9 % and 18 % and runoff of 27 %, 19 % and 44 %, and a drop in hydrological drought frequency of 11 %, 26 % and 23 % as compared to the baseline period (1986–2005). However, the drought severity will rise dramatically by 184 %, 116 % and 184 %, which is mainly caused by the increased variability in precipitation and evapotranspiration. The climate models and the land surface hydrological model contribute to more than 80 % of total uncertainties in the future projection of precipitation and hydrological droughts. This study suggests that different aspects of hydrological droughts should be carefully investigated when assessing the impact of 1.5, 2 and 3 ∘C global warming.

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“…Therefore, quantitative study of the effects of elevated CO 2 concentration and increased temperature on crop physiological processes and yield has become key to addressing climate change. Several studies have related changes in agricultural phenomena such as crop yield, growth period, and water use to temperature increases (Gbetibouo and Hassan 2005;Aggarwal et al 2006a,b;Dhungana et al 2006;Tao et al 2003Tao et al , 2008bChallinor and Wheeler 2008;Mo et al 2009;Challinor et al 2010;Laux et al 2010;Liu and Yuan 2010;Rotter et al 2011;Tao and Zhang 2011).…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Change of Wheat Productivity and Water Use in China for Global Mean Temperature Changes of 1°, 2°, and 3°C

Liu¹,

Tao²

2013

Journal of Applied Meteorology and Climatology

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Impacts of climate change on agriculture are a major concern worldwide, but uncertainties of climate models and emission scenarios may hamper efforts to adapt to climate change. In this paper, a probabilistic approach is used to estimate the uncertainties and simulate impacts of global warming on wheat production and water use in the main wheat cultivation regions of China, with a global mean temperature (GMT) increase scale relative to 1961-90 values. From output of 20 climate scenarios of the Intergovernmental Panel on Climate Change Data Distribution Centre, median values of projected changes in monthly mean climate variables for representative stations are adapted. These are used to drive the Crop Environment Resource Synthesis (CERES)-Wheat model to simulate wheat production and water use under baseline and global warming scenarios, with and without consideration of carbon dioxide (CO 2 ) fertilization effects. Results show that, because of temperature increase, projected wheat-growing periods for GMT changes of 18, 28, and 38C would shorten, with averaged median values of 3.94%, 6.90%, and 9.67%, respectively. There is a high probability of decreasing (increasing) changes in yield and water-use efficiency under higher temperature scenarios without (with) consideration of CO 2 fertilization effects. Elevated CO 2 concentration generally compensates for the negative effects of warming temperatures on production. Moreover, positive effects of elevated CO 2 concentration on grain yield increase with warming temperatures. The findings could be critical for climate-change-driven agricultural production that ensures global food security.

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Grid-size effects on estimation of evapotranspiration and gross primary production over a large Loess Plateau basin, China

Cited by 18 publications

References 39 publications

Effect of weather data aggregation on regional crop simulation for different crops, production conditions, and response variables

Effect of weather data aggregation on regional crop simulation for different crops, production conditions, and response variables

More severe hydrological drought events emerge at different warming levels over the Wudinghe watershed in northern China

Probabilistic Change of Wheat Productivity and Water Use in China for Global Mean Temperature Changes of 1°, 2°, and 3°C

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