Blind Spots in Water Management, and How Natural Sciences Could Be Much More Relevant

Abstract

Estimates of crop evapotranspiration (ET) to measure the freshwater use indicator water footprint (WF) have undoubtedly been popular and implemented (Chapagain and Hoekstra, 2004), as well as the more recent extension to subnational regions and watersheds (Mekonnen and Hoekstra, 2010a; Hoekstra and Mekonnen, 2012; Mekonnen and Hoekstra, 2012). As reviewed by (Chenoweth et al., 2014; Lovarelli et al., 2016), these studies have gone from estimating products’ water trade on a global scale, to rigorous quantification for specific crops and geographical areas. Many studies have extended the coverage and precision of estimates. However, when it comes to the implementation of these improvements in local and river-basin water management, we find management problems that are ultimately unaddressed. It is here that, in our opinion, the Plant Water Sciences (PWS) have to shed light on these “blind spots.” We also illustrate these general ideas with two examples. Firstly, defining boundaries on what to account for human appropriation is a multidisciplinary and, to this point, open debate regarding WF calculations. Launiainen et al. (2014) questioned WF’s appropriateness for evaluating the water use in forestry and forest-based production. They pleaded for the exclusion of rain-fed forestry and forest-based production in WF, arguing that managed forest ET is indistinguishable from those of unmanaged forests. At a global level, there were case studies on some forest products such as paper WF (van Oel and Hoekstra, 2012), but these were not as systematic as those for crops and livestock WF. Nevertheless, we do need a clear split between both human and natural water usage in order to manage existing water resources. Secondly, to further improve estimates, we find areas where WF can benefit from more precise studies of plant water ET and dynamics. Recent studies (Schyns et al., 2017; Schyns and Vanham, 2019) have estimated the WF (of production) of wood for lumber, pulp, paper, fuel, and firewood, but more can be done to calculate the WF on the consumer side, computing the responsibility of the demand (typically households/individuals) in the WF. Regarding livestock WF, the challenges involve discerning dry matter composition (concentrates/roughages). Furthermore, WF would probably benefit from updated estimates on roughages ET/WF, especially pasture/grass (estimated and briefly explained in Mekonnen and Hoekstra, 2010a). The relation and distinction of evaporation (E) to transpiration (T), absent in many studies until very recently, is very important from an economic perspective since T is productive and E is not (E can occur from soil but also from intercepted water on leaves). Recently, Nouri et al. (2019) found that mulching reduced irrigation needs by 3.6%, and when combined with drip irrigation, by 4.7%. There is thus an important need for studies on “partitioning” of ET and T (see review in Kool et al., 2014). Thirdly, while there have been great advances in developing spatial and temporally explicit information, there is a divergence between the geographic and temporal units used by natural scientists and those used by social scientists for resource management (typically, river basins and long periods of time). If plant sciences do not provide information for the geographic areas in which decisions are taken, their work will be overlooked by social scientists. Regarding the temporal dimension, there are a lack of studies looking beyond a point in time (even when the evaluations of evapotranspiration are averaged over periods of time), and looking at the effects derived from land use and cover changes. In other words, studies looking at the dynamics of the resource rather than a static picture are needed. In summary, very detailed and methodical studies from the natural sciences coexist with rough approximations and simulations of data, such as those used by hydro-economic models (see for example Harou et al., 2009; Kahil et al., 2018 for a review of this form of work). Fourthly, there are also blind spots regarding the estimation of historical WF time series. Historic economic analyses on water consumption usually rely on multiple sources of information: censuses, statistics on climate, precipitation, irrigation systems, agricultural production, yield, inputs used, water uses, etc. Information on crop water consumption (in m³ per unit of production) was also used in the past, relating it to scarcity and sustainability. How do we estimate the evolution of these coefficients over time? The answer likely lies in developing a methodology that allows us to obtain them from data on changes in irrigation systems, yields, harvest indexes, soils, etc. Dalin et al. (2012) and Duarte et al. (2014) initiated attempts to generalize coefficient changes over time based on yield changes. There is room for improvement in these estimates, e.g., incorporating not only the effect of changes in yield (as crop output per unit area), but also the changes in the harvest index (the ratio of grain yield to biomass when the crop matures), notably being increased (greater part of the biomass allocated to the grain) in many countries with the Green Revolution. As happened previously with the concept of Integrated Water Resources Management (IWRM), a vast literature and discussion of a topic does not directly entail practical utility. One step further is needed. We cannot expect one indicator to be able to resolve everything, but we can provide additional data to complement it (e.g., Vanham et al., 2016, investigated whether the WF indicator addresses the food–energy–water ecosystem nexus, finding potential components to be included). Lund (2015) correctly highlighted that water management has always required not only physical sciences, but also social ones. Indeed, natural sciences have often not counted so much as it should on water management practices....