A gridded dataset of consumptive water footprints, evaporation, transpiration, and associated benchmarks related to crop production in China during 2000–2018
Abstract. Evapotranspiration over crop growth period, also referred to as the consumptive water footprint of crop production (WFCP), is an essential component of the hydrological cycle. However, the existing high-resolution consumptive WFCP datasets do not distinguish between soil evaporation and crop transpiration and disregard the impacts of different irrigation practices. This restricts the practical implementation of existing WFCP datasets for precise crop water productivity assessments, agricultural water-saving evaluations, the development of sustainable irrigation techniques, cropping structure optimisation, and crop-related interregional virtual water trade analysis. This study establishes a 5-arcmin gridded dataset of monthly green and blue WFCP, evaporation, transpiration, and associated unit WFCP benchmarks for 21 crops grown in China during 2000–2018. The data simulation was based on calibrated AquaCrop modelling under furrow-, sprinkler-, and micro-irrigated as well as rainfed conditions. Data quality was validated by comparing the current results with multiple public datasets and remote-sensing products. The improved gridded WFCP dataset effectively compensated for the gaps in the existing datasets through: (i) revealing the intensity, structure, and spatiotemporal evolution of both productive and non-productive blue and green water consumption on a monthly scale, and (ii) including crop-by-crop unit WFCP benchmarks according to climatic zones.
Wei Wang et al.
Status: open (until 01 Jul 2023)
- RC1: 'Comment on essd-2023-102', Anonymous Referee #1, 01 Jun 2023 reply
Wei Wang et al.
Wei Wang et al.
Viewed (geographical distribution)
This study constructed a gridded dataset for crop water consumption in China. The advantages of this dataset are the separate estimation of blue and green evaporation (E), transpiration (T), the use of the local information on irrigation type per crop, and the long time series. Even though comparisons with other studies on ET look good, I have many concerns about methods that are not clear in the paper. Without this information, it is hard for me to conclude on the dataset quality and reliability. I suggest a major revision and reconsideration for publication unless the authors can address the following concerns/questions.
1. The aquaCrop model setting is absent.
1-A) There are different versions of AquaCrop and available on different platforms, e.g. Windows interface for FAO AquaCrop v7, v6 or before, and Python and MATLAB open source versions. Which AquaCrop version was used for this study?
1-B) As I understand, publicly available AquaCrop cannot simulate perennial crops. How did the authors deal with perennial crops using AquaCrop, e.g. tea, and apple?
1-C) Using AquaCrop needs crop characteristics as input, e.g. the maximum canopy cover, canopy cover decline coefficient, canopy growth coefficient, and many others. Where did these inputs come from and what are the inputs?
1-D) What do the authors mean by calibrated AquaCrop? How did the authors calibrate AquaCrop? Section 2.3.1 does not explain the calibration on AquaCrop.
2. Reliability in separating E and T.
2-A) Distinguishing T from ET is not traditionally in water footprint studies. One challenge is that the ratio of T to ET depends not only on crop growth, and irrigation type but also on field management, e.g. weeds, soil fertility, mulching. As for large-scale assessment, there is always lacking data for field management, thus, it is difficult to estimate T and E separately. However, total ET is more robust to field management, so lacking such data will not be a big issue for total ET. Here the authors try to explicitly distinguish E and T from ET but still have very limited ability to describe the field management in the model. In this sense, how do the authors evaluate the reliability of separating E and T?
2-B) Did the authors further distinguish the color of E and T? and how?
3. Irrigation and soil moisture assumption. In Table 5, comparing the previous studies, this study has a higher water footprint. According to my experience using these datasets, they generally overestimate water consumption compared to hydrological models and recent crop models for some crops because of two reasons (maybe more): first, they assume irrigation once there is a water deficit, even though the water deficit would be tiny; second, when setting the initial soil moisture, they assumed the field capacity of the soil moisture at the beginning of each year. Both are unrealistic and will lead to overestimation of water consumption. How do the authors set irrigation rules and soil moisture in the model and did the authors have a spin-up for the model?
4. Usability and quality of the benchmark
4-A) Figure 6 shows the benchmark at the grid level. However, if we have a close look at the graph, we find usually, the resolution is the provincial level, e.g. the whole province is efficient or not. This is because the calibration and scaling were on the provincial level. If it is the case, scaling factors seem to play a critical role here other than irrigation, climate, and so on. What do the authors think of the reliability of the benchmark in this case?
4-B) The whole of China was divided into two climate zones for the benchmark without considering soil type. This seems oversimplified to me. Furthermore, this dataset provides the benchmark for each year, but the idea for a benchmark is to provide a kind of efficient “reference”. So, why not derive using the whole time series? Imagine one wants to use the benchmark for future analysis, which year should be selected? How do the authors suggest using their benchmark?
1. In the title, “consumptive water footprints”. By definition, water footprint is consumptive water use. Is consumptive redundant here? Or “consumptive water use”?
2. Line 95-102. The authors basically only rely on the global datasets for land use from the year 2000 and then scale to each year. Are there other better datasets that have better quality/time coverage in China?
3. What is CC in Equation 11? Canopy cover?
4. I didn’t see the causal relationship between the number in lines 222-228 and why it is important to distinguish irrigation type. Lines 263-269 do explain some.
5. In the paper, e.g. line 240, the authors discussed the monthly blue and green water consumption but is not available in the dataset. Some items are available monthly and some annually. What are the criteria to decide which data is open to the public or not? And the dataset lacks projected coordinate system information.
6. Line 245-247, why sprinkler irrigation has the highest value? Because of crop type?
7. Line 298-300, can the authors explain why the benchmarks of some crops are higher in arid zones and others are higher in humid zones?
8. in GAEZ+2015 and MapSPAM2010, they also have other crops, did the authors include them in the comparison? And GAEZ+2015 is developed for the year 2015 and MapSPAM2010 is developed for the year 2010, why not use the corresponding years other than the average between 2001 and 2008?
1. Table 3 doesn’t seem consistent with the text. I only checked for rice. In Table 3, the total water consumption of rice is 81847+58979+4629+5540=150995 M m3 and in the text, it is 143 G m3.