20 Jan 2021
20 Jan 2021
Untangling irrigation effects on maize water and heat stress alleviation using satellite data
- School of Global Policy and Strategy, University of California, San Diego, CA USA
- School of Global Policy and Strategy, University of California, San Diego, CA USA
Abstract. Irrigation has important implications for sustaining global food production, enabling crop water demand to be met even under dry conditions. Added water also cools crop plants through transpiration; irrigation might thus play an important role in a warmer climate by simultaneously moderating water and high temperature stresses. Here we use satellite-derived evapotranspiration estimates, land surface temperature (LST) measurements, and crop phenological stage information from Nebraska maize to quantify how irrigation relieves both water and temperature stresses. Our study shows that, unlike air temperature metrics, satellite-derived LST detects significant irrigation-induced cooling effect, especially during the grain filling period (GFP) of crop growth. This cooling is likely to extend the maize growing season, especially for GFP, likely due to the stronger temperature sensitivity of phenological development during this stage. The analysis also suggests that irrigation not only reduces water and temperature stress but also weakens the response of yield to these stresses. Specifically, temperature stress is significantly weakened for reproductive processes in irrigated crops. The attribution analysis further suggests that water and high temperature stress alleviation contributes to 65 % and 35 % of yield benefit, respectively. Our study underlines the relative importance of high temperature stress alleviation in yield improvement and the necessity of simulating crop surface temperature to better quantify heat stress effects in crop yield models. Finally, untangling irrigation effects on both heat and water stress mitigation has important implications for designing agricultural adaptation strategies under climate change.
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Peng Zhu and Jennifer Burney
Status: open (until 17 Mar 2021)
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RC1: 'Comment on hess-2020-627', Anonymous Referee #1, 26 Feb 2021
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This is an interesting article describing effects of high temperature and drought on maize yield and yield components in Nebraska. The authors used remote sensing to detect high temperature stress and drought stress and also tested whether four different crop models can reproduce the effects detected by remote sensing. The article is well written, good to understand and figures are of high quality. However, I cannot recommend to publish the present version of the article in HESS. My major criticisms are:
1) The major source to describe high temperature and drought stress in maize are land surface temperature and ET detected by remote sensing. I think that the temperature based indicators LST and EDD are highly determined by the ratio ET/PET which was used to describe drought impacts. Which factor different from drought can explain canopy temperature differences between well watered and rainfed maize fields? Or in other words: can differences in LST and EDD at the same location happen independently of drought stress? I don't think so. If so, for example because of different LAI, then this is likely an affect of drought in previous growth stages.
It is well understood that transpiration cooling is directly controlled by the stomata conductance and vapor pressure deficit, which are again controlled by drought. This is also the reason why canopy temperature differences are often used as indicator for drought stress or even for irrigation scheduling. Consequently I think that EDD differences or LST differences between irrigated and rainfed maize in the same region are just another manifestation of differences in drought stress between irrigated and rainfed fields. From that perspective I cannot understand why the collinearity tests performed for the variables included in equation 7-9 did not show critical values.
2) The authors showed that there are considerable differences in the growing season length of irrigated and rainfed maize and suggest that the differences are mainly an effect of cooler canopy temperature under well watered conditions (lines 322-337). Another potential reason could be the so called drought escape effect. It is known that many crops speed up their phenological development under drought to make sure that grains reach physiological maturity before the stress becomes so strong that the crop has to die. Again, in that case it would be a drought effect and not an effect of higher temperatures. I agree that it is not so easy to find out which effect really matters. I suggest to test the GDD computed in equation 3 for years with similar canopy temperature but different drought stress (ET/PET ratio). For example, a year that is warm and wet should result in similar canopy temperatures compared to a year that is a bit cooler but dry. Important is that the test has to be made for the same location (county) to avoid that cultivar differences between warmer and cooler regions disturb the relationship. If for years with similar canopy temperature but different ET/PET ratio the GDD is similar, then the shorting of the growing period is independently of drought and the drought escape mechanism can be excluded. If GDD is, for similar canopy temperatures, positively correlated with the ET/PET ratio, then this would point to the drought escape mechanism.
Specific comments:
Line 175 (equation 3): Why was it decided to set the high temperature threshold to 30 dC? In the literature heat stress thresholds for maize are typically higher, about 34 dC (Sanchez et al., 2014).
Line 262 (equation 7): How was LST and ET/PET computed? As mean for the whole growing period? In the variable explanation (line 265) you call LST "local crop temperature stress" but shouldn't you then better use EDD here?
Lines 280-292: Any reason why delta EDD and delta ET/PET are NOT highly correlated?
Lines 363-365: "As shown in Figure 7, we found that temperature sensitivity of yield was significantly weakened from − 6.9%/â to −1%/â in irrigated vs. rainfed areas ..."
=> shouldn't this be vice versa (lower sensitivity in irrigated maize)?
Lines 438-442: The assimilation of satellite derived LST might in fact reduce crop model uncertainty but this helps only when LST data are available. Crop models are also often used for climate change impact analysis but for simulation of potential futures LST is not available. Another disadvantage could be that LST is sensor and satellite specific, for example due to the different overpass times. Therefore another recommendation could be to improve crop models so that they can reproduce the effects that were found in the present study and use remotely sensed LST for validation.
Figure 8: It seems that there is also considerable drought stress in irrigated maize because the ET/PET ratio is often much lower than 1. Any explanation why yield under irrigated conditions is often much higher for similar ET/PET ratios? Because irrigated maize is more often grown in cooler regions?
References:
Sanchez, B., Rasmussen, A. and Porter, J.R. (2014). Temperatures and the growth and development of maize and rice: a review. Global Change Biology 20, 408–417.
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CC1: 'Comment on hess-2020-627', Yan Li, 03 Mar 2021
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I came across this paper which is a very nice study. The authors used remote sensing data and statistical methods to quantify irrigation effects on maize yield in Nebraska. It is found that water and high temperature stress alleviation contributes to 65% and 35% of yield benefit. 1) This paper reminds a recent paper by Li et al in Global Change Biology. Interestingly, these two studies are very similar in many aspects (topic, data, and Nebraska) but show quantitively different results. Li2020 reported that 16% of irrigation yield increase is due to irrigation cooling, while the rest (84%) is due to water supply and other factors. This work (Line 373-383) reported a 79% (water) and 21% (temperature) contribution for water and temperature respectively using eq 8 while the numbers became 65% and 35% with Eq 9. However, the later numbers appeared in the abstract. This shows that there might be large uncertainty in the reported contribution numbers. The important questions are how to understand these different results and which one is more reliable? I feel that numbers with range is more appropriate than just a single number. A comparison or discussion with Li2020 and with the authors' own results would be very needed for the readers to understand the robustness and possible causes for different results. 2) In my opinion, the usage of 1km Daymet air temperature is not suitable for studying the irrigation cooling. The gridded air temperature data were typically produced by interpolation of weather stations with many assumptions and tricks. The irrigation cooling signals would be lost in the interpolation and processing. This is probably the primary reason why air temperature showed no cooing compared to LST. Ref Li, Y., Guan, K., Peng, B., Franz, T. E., Wardlow, B., & Pan, M. (2020). Quantifying irrigation cooling benefits to maize yield in the US Midwest. Global Change Biology, 26(5), 3065–3078. https://doi.org/10.1111/gcb.15002
Peng Zhu and Jennifer Burney
Peng Zhu and Jennifer Burney
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