Dynamic assessment of the impact of drought on agricultural yield and scale-dependent return periods over large geographic regions

Yu, Chunyan; Li, Changsheng; Xin, Qinchuan; Chen, Han; Zhang, Jie; Zhang, Feng; Li, Xuecao; Clinton, Nick; Huang, Xiao; Yue, Yali; Gong, Peng

doi:10.1016/j.envsoft.2014.08.004

Cited by 51 publications

(34 citation statements)

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“…However, the accuracy of this application would depend strongly on the accuracy and resolution of the weather data inputs (e.g., precipitation and temperature) for future periods. Several previous studies have implemented crop yield predictions based on different scenarios that used historical weather data from a dry, a wet, and a normal year [42], [43]. However, due to high variability of weather data in the future, this method would inevitably contain relatively large errors.…”

Section: Discussionmentioning

confidence: 98%

Jointly Assimilating MODIS LAI and ET Products Into the SWAP Model for Winter Wheat Yield Estimation

Huang

et al. 2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

138

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Leaf area index (LAI) and evapotranspiration (ET) are two crucial biophysical variables related to crop growth and grain yield. This study presents a crop model-data assimilation framework to assimilate the 1-km moderate resolution imaging spectroradiometer (MODIS) LAI and ET products (MCD15A3 and MOD16A2, respectively) into the soil water atmosphere plant (SWAP) model to assess the potential for estimating winter wheat yield at field and regional scales. Since the 1-km MODIS products generally underestimate LAI or ET values in fragmented agricultural landscapes due to scale effects and intrapixel heterogeneity, we constructed a new cost function by comparing the generalized vector angle between the observed and modeled LAI and ET time series during the growing season. We selected three parameters (irrigation date, irrigation depth, and emergence date) as the reinitialized parameters to be optimized by minimizing the cost function using the shuffled complex evolution method-University of Arizona (SCE-UA) optimization algorithm, and then used the optimized parameters as inputs into the SWAP model for winter wheat yield estimation. We used four data-assimilation schemes to estimate winter wheat yield at field and regional scales. We found that jointly assimilating MODIS LAI and ET data improved accuracy (R 2 = 0.43, RMSE = 619 kg · ha −1 ) than assimilating MODIS LAI data (R 2 = 0.28, RMSE = 889 kg · ha −1 ) or ET data (R 2 = 0.36, RMSE = 1561 kg · ha −1 ) at the county level, which indicates that the proposed estimation method is reliable and applicable at a county scale.Index Terms-Data assimilation, evapotranspiration (ET), leaf area index (LAI), remote sensing, soil water atmosphere plant (SWAP) model.

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Section: Discussionmentioning

confidence: 98%

Jointly Assimilating MODIS LAI and ET Products Into the SWAP Model for Winter Wheat Yield Estimation

Huang

et al. 2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

138

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“…Realistic crop growth models (Di Paola et al, ; Kroes et al, ; Mearns et al, ; Morgan et al, ; Steduto et al, ; van Diepen et al, ) are arguably one of the best tools to quantify the influences of short‐ and long‐term variations in weather conditions and human management on soil moisture availability, crop growth, and harvest yields (Jin et al, ; Niyogi et al, ; Singh et al, ) at regional (Yu et al, ) and global (Asseng et al, ) scales. In China, for example, the Environmental Policy Integrated Climate model was applied to evaluate the impacts of drought on yields of cereal crops in China (Jia et al, ; Li et al, ).…”

Section: Introductionmentioning

confidence: 99%

Assessing the Impacts of Extreme Agricultural Droughts in China Under Climate and Socioeconomic Changes

Huang

Han

et al. 2018

Earth's Future

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Agricultural food production in China is deeply vulnerable to extreme droughts. Although there are many studies to evaluate this issue from different aspects, comprehensive assessments with full consideration of climate change, crop rotations, irrigation effects, and socioeconomic factors in broad scales have not been well addressed. Considering both the probability of drought occurrence and the consequential yield losses, here we propose an integrated approach for assessing past and future agricultural drought risks that relies on multimodel ensemble simulations calibrated for rice, maize, and wheat (RMW) in China. Our results show that irrigation has reduced drought‐related yield losses by 31 ± 2%; the largest reductions in food production were primarily attributable to socioeconomic factors rather than droughts during 1955–2014. Unsustainable water management, especially groundwater management, could potentially cause disastrous consequences in both food production and water supply in extreme events. Our simulations project a rise of 2.5~3.3% in average rice, maize, and wheat productivity before 2050 but decrease thereafter if climate warming continues. The frequency of extreme agricultural droughts in China is projected to increase under all examined Representative Concentration Pathway (RCP). A current 100‐year drought is projected to occur once every 30 years under RCP 2.6, once every 13 years under RCP 4.5, and once every 5 years under RCP 8.5. This increased occurrence of severe droughts would double the rate of drought‐induced yield losses in the largest warming scenario. Policies for future food security should prioritize sustainable intensification and conservation of groundwater, as well as geographically balanced water resource and food production.

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“…A similar and very important issue involves the evaluation of drought impacts on agriculture. Crop yields and drought occurrence statistics are closely related [Yu et al, 2014a[Yu et al, , 2014bMkhabela et al, 2010], but consistency analysis of drought trends derived from drought indices and agricultural drought survey is sparse. There is little work relating drought-covered area data derived from agriculture surveys with drought Journal of Geophysical Research: Atmospheres 10.1002/2015JD024285 indices in China [Zhang and Zhou, 2015].…”

Section: Introductionmentioning

confidence: 99%

Assessing spatiotemporal variation of drought in China and its impact on agriculture during 1982–2011 by using PDSI indices and agriculture drought survey data

Yan

Wang

et al. 2016

JGR Atmospheres

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Inspired by concerns of the effects of a warming climate, drought variation and its impacts have gained much attention in China. Arguments about China's drought persist and little work has utilized agricultural drought survey area to evaluate the impact of natural drought on agriculture. Based on a newly revised self‐calibrating Palmer Drought Severity Index (PDSI) model driven with air‐relative‐humidity‐based two‐source (ARTS) E0 (PDSIARTS; Yan et al., 2014), spatial and temporal variations of drought were analyzed for 1982–2011 in China, which indicates that there was nonsignificant change of drought over this interval but with an extreme drought event happened in 2000–2001. However, using air temperature (Ta)‐based Thornthwaite potential evaporation (EP_Th) and Penman‐Monteith potential evaporation (EP_PM) to drive the PDSI model, their corresponding PDSITh and PDSIPM all gave a significant drying trend for 1982–2011. This suggests that PDSI model was sensitive to EP parameterization in China. Annual drought‐covered area from agriculture survey was initially adopted to evaluate impact of PDSI drought on agriculture in China during 1982–2011. The results indicate that PDSIARTS drought area (defined as PDSIARTS < −0.5) correlated well with the agriculture drought‐covered area and PDSIARTS successfully detected the extreme agriculture drought in 2000–2001 during 1982–2011, i.e., climate factors dominated the interannual changes of agriculture drought area, while PDSITh and PDSIPM drought areas had no relationship with the agriculture drought‐covered area and overestimated the uptrend of agriculture drought This study highlights the importance of coupling PDSI with drought survey data in evaluating the impact of natural drought on agriculture.

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Dynamic assessment of the impact of drought on agricultural yield and scale-dependent return periods over large geographic regions

Cited by 51 publications

References 50 publications

Jointly Assimilating MODIS LAI and ET Products Into the SWAP Model for Winter Wheat Yield Estimation

Jointly Assimilating MODIS LAI and ET Products Into the SWAP Model for Winter Wheat Yield Estimation

Assessing the Impacts of Extreme Agricultural Droughts in China Under Climate and Socioeconomic Changes

Assessing spatiotemporal variation of drought in China and its impact on agriculture during 1982–2011 by using PDSI indices and agriculture drought survey data

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