Periodic Reporting for period 2 - SIZE (The role of size in the sustainability of irrigation systems)
Reporting period: 2021-09-01 to 2022-08-31
The main target of SIZE has been to study how size (i.e. the physical extension) conditions specific attributes of irrigation systems, including their capacity to cope with unexpected shocks. Following the line of evidence gathered for organisms, financial corporations and cities, size (e.g. the area, the dimensions) seemed to be a master variable conditioning the behavior of irrigation systems as well. However, our knowledge was comparatively fragmentary and lacked in formalization due to overreliance in case studies, which set our capacity to ensure their sustainability in fragile grounds. SIZE aimed at breaking through this limitation by combining allometry, the study of global irrigation datasets, dynamic modelling and uncertainty and sensitivity analysis. Overall, SIZE was designed to increase our knowledge on size as a universal contstraint and to endow us with a better understanding of the role that the size of irrigated areas played in conditioning their sustainability.
The project has retrieved data on irrigation attributes (e.g. volume of water withdrawn for irrigation, irrigation efficiency, production) from several large-scale datasets, as well as data on the global extension of irrigation from the irrigated area maps available up to 2022. It has also mined the data to identify scaling relationships, model the relation between irrigated area and these attributes and appraise uncertainties and sensitivities. The project has led to 14 papers, including one Nature, two Nature Communications and one Science Advances. The main results of the project are summarized below.
SIZE has shown that the extension of irrigation is the main variable conditioning the volume of water withdrawn for irrigation agriculture, e.g. the latter can be simply predicted as a function of the former, which tends to scale linearly. This relation seems to hold at different geographical scales (region, country, global level) and may help modelers build simpler, lighter global irrigation models, thus opening the door to less computationally-demanding, more transparent irrigation algorithms.
The project has also observed that the size of irrigated areas expands as a function of population. Aiming at estimating how large would irrigated areas be in 2050, we noted that current models severely underestimate the potential extension of irrigation because they fail to acknowledge uncertainties in population growth rates. Current models may significantly minimize the potential future impact of irrigated agriculture in freshwater resources or its role in fostering land degradation processes.
We noted that size does not have any perceptible effect on the irrigation efficiency of a given irrigation system. This result questions the reliability of several global irrigation models grounded on the assumption that larger irrigated areas are intrinsically less efficient than smaller ones. Such models may benefit from a more nuanced conceptualization of irrigation efficiency as to better appraise uncertainties and guide policy-making in the real world, where technologies do not have essential properties but depend on the social-environmental substratum to deliver as expected.
Due to its reliance on uncertainty and sensitivity analysis, SIZE has revealed other limitations of current global irrigation models and the state-of-the-art. We observed that models used to predict global irrigation water withdrawals miss uncertainties that may span up to two orders of magnitude at the grid cell level (the minimum geographical unit in which these models conduct simulations). This means that their irrigation water withdrawal estimates are spuriously precise, a flaw that promotes tunnel vision and risks misleading water policies. And these uncertainties are unlikely to disappear with more complex models: we provided numerical proof that the addition of model detail aiming at yielding more accurate estimates may in fact have the opposite effect –to promote fuzzier estimates. This suggests that the current quest towards ever-detailed hydrological models as a means to get sharper estimates or insights should be reassessesed.
SIZE has shown that the extension of irrigation is the main variable conditioning the volume of water withdrawn for irrigation agriculture, e.g. the latter can be simply predicted as a function of the former, which tends to scale linearly. This relation seems to hold at different geographical scales (region, country, global level) and may help modelers build simpler, lighter global irrigation models, thus opening the door to less computationally-demanding, more transparent irrigation algorithms.
The project has also observed that the size of irrigated areas expands as a function of population. Aiming at estimating how large would irrigated areas be in 2050, we noted that current models severely underestimate the potential extension of irrigation because they fail to acknowledge uncertainties in population growth rates. Current models may significantly minimize the potential future impact of irrigated agriculture in freshwater resources or its role in fostering land degradation processes.
We noted that size does not have any perceptible effect on the irrigation efficiency of a given irrigation system. This result questions the reliability of several global irrigation models grounded on the assumption that larger irrigated areas are intrinsically less efficient than smaller ones. Such models may benefit from a more nuanced conceptualization of irrigation efficiency as to better appraise uncertainties and guide policy-making in the real world, where technologies do not have essential properties but depend on the social-environmental substratum to deliver as expected.
Due to its reliance on uncertainty and sensitivity analysis, SIZE has revealed other limitations of current global irrigation models and the state-of-the-art. We observed that models used to predict global irrigation water withdrawals miss uncertainties that may span up to two orders of magnitude at the grid cell level (the minimum geographical unit in which these models conduct simulations). This means that their irrigation water withdrawal estimates are spuriously precise, a flaw that promotes tunnel vision and risks misleading water policies. And these uncertainties are unlikely to disappear with more complex models: we provided numerical proof that the addition of model detail aiming at yielding more accurate estimates may in fact have the opposite effect –to promote fuzzier estimates. This suggests that the current quest towards ever-detailed hydrological models as a means to get sharper estimates or insights should be reassessesed.
The project has made several contributions that have gone beyond the state-of-the-art. Firstly, it has shown that irrigated areas in 2050 may be much more larger than previously thought. This is because current models do not take into account uncertainties in population growth rates, a key variable conditioning the extension of irrigation. Secondly, it suggests that irrigated areas are a key variable determining irrigation water withdrawals at large scales (e.g. global, national levels). We therefore may not need complex algorithms to simulate irrigation water withdrawals: we can produce almost identical global or country-level irrigation water withdrawal estimates with a simple linear regression against the extension of irrigation, thus saving financial and computational resources. Thirdly, SIZE has observed that the extension of irrigation does not clearly condition the efficiency of a given irrigation system, meaning that both small/large systems can have either high/low efficiencies. These results suggest that models should abandon the assumption that large systems are intrisincially less efficient than small ones if they wish to produce results that match the ambiguity of real-life irrigation. Fourthly, it has discovered that irrigation water withdrawal estimates produced by Global Models are unreliable as they disregard several key uncertainties. These models might therefore be misguiding our policies on irrigation by conveying unwarranted accuracy.