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• Published: 26 March 2021

Evaluating the economic impact of water scarcity in a changing world

• Flannery Dolan   ORCID: orcid.org/0000-0001-8916-3903 1 ,
• Jonathan Lamontagne   ORCID: orcid.org/0000-0003-3976-1678 1 ,
• Robert Link 2 ,
• Mohamad Hejazi 3 , 4 ,
• Patrick Reed   ORCID: orcid.org/0000-0002-7963-6102 5 &
• Jae Edmonds   ORCID: orcid.org/0000-0002-3210-9209 3  

Nature Communications volume  12 , Article number:  1915 ( 2021 ) Cite this article

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• Climate and Earth system modelling
• Environmental economics

Water scarcity is dynamic and complex, emerging from the combined influences of climate change, basin-level water resources, and managed systems’ adaptive capacities. Beyond geophysical stressors and responses, it is critical to also consider how multi-sector, multi-scale economic teleconnections mitigate or exacerbate water shortages. Here, we contribute a global-to-basin-scale exploratory analysis of potential water scarcity impacts by linking a global human-Earth system model, a global hydrologic model, and a metric for the loss of economic surplus due to resource shortages. We find that, dependent on scenario assumptions, major hydrologic basins can experience strongly positive or strongly negative economic impacts due to global trade dynamics and market adaptations to regional scarcity. In many cases, market adaptation profoundly magnifies economic uncertainty relative to hydrologic uncertainty. Our analysis finds that impactful scenarios are often combinations of standard scenarios, showcasing that planners cannot presume drivers of uncertainty in complex adaptive systems.

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Introduction

Global water scarcity is a leading challenge for continued human development and achievement of the Sustainable Development Goals 1 , 2 . While water scarcity is often understood as a local river basin problem, its drivers are often global in nature 3 . For instance, agricultural commodities (the primary source of global water consumption 4 ), are often traded and consumed outside the regions they are produced 5 . These economic trade connections mean that global changes in consumption result in impacts on local water systems 6 . Likewise, local water system shocks can also propagate globally 7 , 8 . Water is a critical input to other sectors, such as energy, transportation, and manufacturing 9 , 10 , so that changes in the regional water supply or sectoral demand can propagate across sectors and scales. Continued population growth, climate change, and globalization ensure that these multi-region, multi-sector dynamics will become increasingly important to our understanding of water scarcity drivers and impacts 11 .

Quantifying water scarcity and its impacts are active and growing research areas 12 . Early and influential work in the area largely focused on supply-oriented metrics of scarcity: per-capita water availability 13 , the fraction of available water being used 14 , and more sophisticated measures that account for a region’s ability to leverage available water given its infrastructure and institutional constraints 15 . Recent work proposes indicators such as water quality 16 , green water availability 17 , and environmental flow requirements 18 that focus on specific facets of water scarcity. Qin et al. incorporate the flexibility of current modes of consumption to identify regions where adaptation to scarcity may be relatively difficult 19 . Other recent work focuses on the water footprint of economic activity 20 , 21 making it possible to identify the economic drivers of scarcity (through virtual water trade) 6 , 8 . Yet knowledge gaps remain concerning how the economic costs of future water scarcity will propagate between sectors and regions as society adapts to scarcity, and how the cost of this adaptation depends on uncertainties in the projections of future conditions.

From the economic perspective, water scarcity impacts arise when the difficulty of obtaining water forces a change in consumption. For instance, abundant snowmelt may be of little use to would-be farmers if barriers (cost, institutional, etc.) prevent them from utilizing it. They will be forced to go elsewhere for water or engage in other activities, and this bears an economic cost that is not reflected in conventional water scarcity metrics. When water becomes a binding constraint, societies adapt through trade and shifting patterns of production, and the cost of that adaptation is tied to the difficulty of adopting needed changes. Changing annual cropping patterns to conserve water is easier and will impact an economy less than shuttering thermal power generation during prolonged drought 19 . In a globalized economy, the impact of such adaptation cannot be assessed in a single basin or sector in isolation, as hydrologic changes in one region reverberate across sectors around the world 3 , 22 . Indeed, reductions in water supply in one region may increase demands for water in another, simultaneously inducing both physical scarcity and economic benefit in ways that are difficult to anticipate ex ante 23 . Our primary research question is how these dynamics will impact society in the future, and how both the magnitude and direction of those impacts depend on future deeply uncertain conditions 24 .

To address this question, we deploy a coupled global hydrologic-economic model with basin-level hydrologic and economic resolution 25 to compute the loss (or gain) of economic surplus due to that scarcity in each basin across a range of deeply uncertain futures. Here “economic surplus” refers to the difference between the value that consumers place on a good and the producers’ cost of providing that good 26 . The surplus is a measure of the value-added, or societal welfare gained, due to some economic activity. The change in economic surplus is an appealing metric because it captures how the impact of resource scarcity propagates across sectors and regions that depend on that resource. Change in surplus has been used in past studies to assess the impacts of water policies and to understand how to efficiently allocate water in arid regions 27 , 28 , though it has not typically been used to analyze the impact of water scarcity itself. One exception is a study by Berritella et al., who used the loss in equivalent variation, another welfare metric, to measure the effects of restricting the use of groundwater 29 . On a broader scale, our analysis tracks the impacts of scarcity in hundreds of basins across thousands of scenarios, revealing important global drivers of local impacts that are often missed when the spatial and sectoral scope is defined too narrowly.

Global water scarcity studies depend on long-term projections of climate, population growth, technology change, and other factors that are deeply uncertain, meaning that neither the appropriate distribution nor the correct systems model is agreed upon 24 , 30 . Complicating matters, the coupled human-earth system is complex, exhibiting nonlinearities and emergent properties that make it difficult to anticipate important drivers in the scenario selection process. In such a case, focusing on a few scenarios, as is common in water scarcity studies, risks missing key drivers and their interactions 31 . In contrast, recent studies advocate exploratory modeling 32 to identify important global change scenarios 33 , 34 . In that approach, the uncertainty space is searched broadly and coupled-systems models are used to test the implications of different assumptions on salient measures of impact across a scenario ensemble 35 . Exploratory modeling is especially important in long-term water scarcity studies, where we show that meaningful scenarios vary widely from basin-to-basin, highlighting the inadequacy of relying on a few global narrative scenarios.

By analyzing a large ensemble of global hydro-economic futures, we arrive at three key insights. First, basin-level water scarcity may be economically beneficial or detrimental depending on a basin’s future adaptive capacity and comparative advantage, but that advantage is highly path-dependent on which deeply uncertain factors emerge as the basin-specific drivers of consequential outcomes. For instance, in the Lower Colorado Basin, the worst economic outcomes arise from limited groundwater availability and high population growth, but that high population growth can also prove beneficial under some climatic scenarios. In contrast, the future economic outcomes in the Indus Basin depend largely on global land-use policies intended to disincentive land-use change in the developing world. Our second insight is that those land-use policies often incentivize unsustainable water consumption. In the case of the Indus Basin, limiting agricultural extensification results in intensification through increased irrigation that leads to unsustainable overdraft of groundwater, with similar dynamics playing out elsewhere. Third, our results show that the nonlinear nature of water demand can substantially amplify underlying climate uncertainty, so that small changes in runoff result in large swings in economic impact. This is pronounced in water-scarce basins (like the Colorado) under high-demand scenarios. Collectively, these insights suggest that understanding and accounting for the adaptive nature of global water demand is crucial for determining basin-level water scarcity’s path-dependent and deeply uncertain impacts.

Global-to-basin impacts

We calculate both physical water scarcity (Fig.  1B ) and its economic impact (Fig.  1C ) over the 21st century for 235 river basins for each of the 3000 global change scenarios, simulated using the Global Change Analysis Model (GCAM) integrated assessment model 36 . With the effects of inter-basin trade, hydrologic basins may experience highly positive or highly negative economic impact due to water scarcity (Fig.  1A ). Here, economic impact is defined as the difference in total surplus in water markets (Supplementary Fig.  1 ) between a control scenario with unlimited water and an experimental scenario with limited water supply (Supplementary Fig.  2 ). Water scarcity usually induces negative economic impact (loss of surplus), although positive economic impact from global water scarcity can arise if a basin holds a comparative advantage over others. With this comparative advantage, a basin can become a virtual water exporter through inter-basin trade 37 , meaning it will export water-embedded goods to other regions. Though some basins experience positive impact more often than others (across the scenario ensemble), all basins experience both negative and positive impacts in some scenarios (Supplementary Table  1 ): no basin has a universally positive or negative outlook. As may be expected, the basins with the highest number of positive impact scenarios are those that are relatively water-rich by conventional measures (Fig.  1B ), for example, the Orinoco River in northern South America (Fig.  1A ).

The scatter plot in panel A shows the two metrics in panels B and C plotted against each other in four basins. Each point represents the maximum absolute value of that metric over time in each scenario. The map in panel B shows WTA in each water basin while the map in panel C shows the log-modulus of economic impact. Both maps plot the maximum absolute value of the metric over time and the median across all scenarios. The correspondence between the two metrics is not perfect. Some water-scarce basins have more capacity to handle water scarcity and thus are not as impacted economically as others.

We measure physical water scarcity using the Withdrawal-To-Availability ratio (WTA) which is computed by dividing water withdrawals by renewable supply. The correspondence between the WTA and the economic impact metric is not perfect (Fig.  1B and C ). In some scenarios (for instance, those with restricted reservoir storage), basins with high physical scarcity have a small negative or even positive economic impact, and in others, basins with low physical scarcity have a negative economic impact (Fig.  1A ). This highlights the importance of capturing the interdependencies between physical and economic factors that affect the welfare of a basin.

Several basins show high variance in economic impacts, including the Indus River Basin, the Arabian Peninsula, and the Lower Colorado River Basin (Supplementary Fig.  4 ). In addition to variance in economic impacts, those basins exhibit a wide range of physical water scarcity, are geographically diverse and are of geopolitical importance. The Orinoco Basin is also highlighted as an example of a basin that is not physically water-scarce and experiences slightly positive economic impact in most scenarios (Fig.  1A and B ). Such water-rich basins are particularly well-positioned to produce more water-intensive products to offset lost production in water-scarce basins (Supplementary Fig.  3 ), though the stylized water markets as represented in GCAM (and indeed in all other global hydro-economic models) may overstate these benefits for some basins compared to real-world conditions. The market representation assumes that all agents have an equal opportunity to acquire water and that water is allocated in the most economically efficient manner (except for agricultural subsidies 38 ). In reality, water rights frameworks and barriers to trade may block potential users from putting the water to more economically beneficial use.

The distributions of the plotted scenarios in the four selected basins (Fig.  1A ) give some indication of the relationship between water scarcity and economic impact in each basin. The bi-modal spread of the scenario points (Fig.  1A ) shows that higher physical water scarcity can be associated with both highly positive and severely negative economic impacts. When the distributions are wide and shallow (e.g., the Indus Basin in Fig.  1A ), smaller changes in physical scarcity lead to much higher changes in economic impact compared with other basins (Table  1 ). This occurs if the basin cannot easily supplement renewable supply with other water sources and the price of water rises precipitously. Shifts in demand subject to these high prices lead to large swings in economic impact.

The direction of shifts in demand depends on a basin’s comparative advantage (or disadvantage) due to the scenario assumptions and how these assumptions affect other basins around the world. As evidenced by the positive scenarios in water-scarce basins in Fig.  1 , this comparative advantage can arise from mechanisms other than abundant water supply (e.g., higher agricultural productivity, different dietary or technological preferences, or a lower population). The equilibrium demand over the renewable supply (the WTA) could be the same in two scenarios with very different economic impacts depending on if the scenario assumptions enable a basin’s comparative advantage in one but are detrimental in another (Fig.  1A ). Influential factors that determine economic impact are basin-specific (examples given in the next section). The changes in demand and resulting impacts due to these factors underscore the importance of projecting basin-level scarcity in a global context that allows for market adaptation.

Climate system uncertainty amplification

The market response to water scarcity within a hydrologic basin usually amplifies the uncertainty in hydro-climatic projections (Fig.  2 , Supplementary Fig.  5 ), leading to higher changes in economic impact. Analysis of the scenario ensemble revealed that differences in Earth System Model (ESM) forcing often determines the sign of impact (SA Figs.  6 – 9 ). The ESMs contribute precipitation and temperature projections to the hydrologic model used by GCAM, generating water runoff estimates (see “Methods” section). Surface water supply fluctuations heavily affect changes in economic surplus within these hydrologic basins. Other important factors include reservoir expansion (in Arabia and the Orinoco), land-use scenario (in the Indus and Orinoco), and agricultural productivity (in Arabia, the Indus, and the Orinoco) (Supplementary Figs.  6 – 9 ).

Uncertainty over time plots of the four chosen basins. Values are taken relative to the 2015 baseline. Uncertainty prior to 2015 is illustrative only. The scenario group shown in A – D has the lowest mean climate-induced economic impact uncertainty over time out of the 600 groups. The scenario group shown in E – H has the highest mean climate-induced economic impact uncertainty over time. In most scenarios, runoff uncertainty is amplified by the human system, leading to higher uncertainty in economic impact.

Climate uncertainty is one dimension over which decision-makers have very little control (as opposed to socioeconomic trajectories, agricultural advancements, reservoir storage, etc.). To isolate the uncertainty in the economic impact due to this fundamental climate uncertainty, 600 groups of five scenarios were created by holding all factors constant, except the ESM (of which 5 were considered). The difference between the maximum impact in this group of five and the minimum is one measure of climate-induced impact uncertainty. This uncertainty is plotted in blue in Fig.  2 compared to the runoff uncertainty in red. We find that the economic impact uncertainty is usually higher than the runoff uncertainty (Supplementary Fig.  5 ). Here, runoff uncertainty is the difference between the maximum runoff and the minimum runoff in the set of five scenarios. Peaks and troughs in Fig.  2 correspond to slight deviations in climate forcing in the ESMs. This in turn leads to differences in the runoff, which changes the unit costs of water, causing market adaptations and thus amplifying the economic surplus change (Supplementary Fig.  10 ).

High economic impact uncertainty relative to runoff uncertainty indicates that the market is very sensitive to changes in water supply. In high-demand scenarios (e.g., those with a high population and high food demand), the price of water steeply rises when shifting toward nontraditional water sources such as non-renewable reserves and desalination (Supplementary Fig.  11A ). When this occurs, deviations in supply lead to highly nonlinear impacts (Fig.  2E–H ). Vulnerable basins in these high-demand scenarios see steep and rapid declines in economic impact (Fig.  2E, H ). Scenarios where the economic impact continues dropping through the end of the century are of particular concern and suggest that a basin no longer has the economic capacity to stabilize these negative impacts. We will henceforth call this loss in capacity an ‘economic tipping point’.

Importantly, the conditions that lead to tipping points can vary substantially across basins. For instance, in the Arabian Peninsula, tipping point conditions include low groundwater availability and pricing carbon emissions from all sectors (see “Methods” section). Even with ample groundwater supply, tipping points can occur with high population and low GDP (SSP 3 socioeconomic assumptions) in addition to pricing carbon emissions from all sectors. In some scenarios, we can see that the Arabian Peninsula experiences a positive impact mid-century by relying on relatively inexpensive water resources. After these resources run out subject to the constraints, the economic impact becomes more negative until the end of the century (Fig.  2A ) and the basin utilizes an increasing amount of desalinated water (Supplementary Fig.  11B ). The lack of perfect foresight within GCAM helps explain this short-term thinking, though historically the area has withdrawn groundwater at unsustainable rates 39 .

Meanwhile, the Lower Colorado River Basin experiences an economic tipping point when there is low groundwater availability, low agricultural productivity (SSP 3 agriculture and land use assumptions), and high wealth socioeconomic trajectories (SSP 5 socioeconomics). The uncertainty in economic impact in the Lower Colorado Basin is the highest out of all of the highlighted basins (Fig.  2C ) and is one of the basins with the highest uncertainty in economic impact in the world.

Importantly, the factors that cause economic tipping points in these basins are not the same, nor do they always follow a well-defined global narrative such as the canonical SSPs. Table  2 shows the basins with the most highly negative impact values out of all the time periods in every scenario. Most of these scenarios contain a mixture of SSP elements (e.g., SSP 5 socioeconomics and SSP 4 agriculture in the Sabarmati). There are noticeable trends in the factors, for instance, high wealth socioeconomic trajectories (SSP 5) and the Universal Carbon Tax often lead to tipping points. However, the factors are not all the same in each basin (e.g., in the Ganges-Brahmaputra).

Mitigation-scarcity trade-offs

Pricing carbon emissions from the land-use sector often contributes to an economic tipping point because basins respond by intensifying agricultural land and increasing irrigation, thus exacerbating scarcity. When food demand increases, GCAM responds either by expanding agricultural land or intensifying existing agricultural land. With no price put on land-use change emissions (under the Fossil Fuel and Industrial Carbon Tax, or FFICT) it is more cost-effective to expand. Indeed, we find that scenarios with the FFICT use more agricultural land than the Universal Carbon Tax (UCT) scenarios (Fig.  3A ). Conversely, the carbon prices under the UCT disincentivize expansion and therefore prompt intensification. Carbon prices are derived from the continued ambition scenario of the Nationally Determined Contributions in a future with medium challenges to adaptation and mitigation 40 (see “Methods” section).

Density plots depicting the difference in tax regimes. The plot in A depicts the sum of global cropland over time under the two carbon tax regimes. The density plot in B shows water withdrawals in the Arabian Peninsula in FFICT (orange) and UCT (cyan) scenarios. The density plot in C depicts the shadow price of water in the Indus River basin in the two tax cases. Values in B and C are averaged over time. Total agricultural land increases under the FFICT while water price and water withdrawals increase under the UCT.

When intensification occurs, yields are increased by irrigating crops more instead of relying on rainwater. The intensity of agricultural land management also increases. These changes prompt greater water withdrawals (Fig.  3B ). The shift from rainwater toward irrigated water also increases the price of water in the UCT scenarios (Fig.  3C ). These results are especially significant in basins sensitive to land-use change. A previous study found that the FFICT prompts greater water withdrawals 41 . However, the study used a previous version of GCAM that did not have intensification options and assumed unlimited water. In that version, water use was proportional to land use. Therefore, when the UCT disincentivized expansion, water use was also limited. When extensification-intensification dynamics are considered, we find a substitution between water use and agricultural expansion. This finding emphasizes the importance of considering all trade-offs in mitigation policy options.

In this study, we use an economic surplus metric in order to quantify the economic impacts of water scarcity and the uncertainty of this impact due to different factors (i.e., population, agricultural productivity, etc.). Theoretically, basins would withdraw less when exposed to a limited supply of water and thus experience a negative economic impact, yet we find some basins capitalize on their water resources and become virtual water exporters in the face of global water scarcity. This dynamic would not be captured by looking at physical water scarcity metrics alone, nor by assessing economic impact at the basin-scale.

These variable responses to water scarcity are sometimes due to highly uncertain and largely uncontrollable factors such as the climate system. When normalized by a 2015 baseline, we find that uncertainty of economic impact due to Earth System Model forcing alone is often several thousand times higher than the uncertainty in the forcing itself (Fig.  2 ). Across the sampled states of the world, we find that slight deviations in precipitation drivers are almost always amplified as they propagate through markets. Since we have little control over uncertainty in the climate system, basin economies that are sensitive to fluctuations in hydro-climactic forcings will need especially robust water resource management frameworks in the future. Further, basins with the highest amount of impact variability due to climate uncertainty are often in politically unstable regions such as the Middle East. Thus, there is an even greater need to manage water resources in the most efficient way possible in the face of extreme uncertainty of economic impacts due to climate in these basins. Planners must also be aware of factors (e.g., population growth or carbon pricing regimes) that lead to economic tipping points in unstable basins.

Under the assumption that food production will always meet demand, implementing a Universal Carbon Tax prompts the intensification of agricultural land due to the increased cost of converting land for agricultural use. The intensification is enabled by increased irrigation and greater water withdrawals (Fig.  3 ). Thus, the effects of pricing carbon in a land-use policy on land intensification-extensification dynamics need to be taken into account in basins exhibiting high levels of water stress.

We find that most scenarios of interest (i.e., those that resulted in extremely high or low economic impact) are composed of a mix of SSP dimensions. This demonstrates the importance of using a scenario discovery framework in the context of a highly uncertain problem such as modeling water resources and the drawbacks of focusing on a limited set of narratives. In addition, the dimensions of high importance in certain basins are of less importance in others. Indeed, every dimension varied in this study was the most influential factor in determining the economic impact of water scarcity in at least one basin (Supplementary Fig.  12 ). Scenario discovery addresses this by identifying the most critical scenario components to the specific analysis context. There is no reason to expect universal shared scenarios will capture key challenges in each basin (or indeed in any), and it is very difficult to anticipate what combinations of factors present challenges in every basin before doing extensive exploration. Scenario discovery is a promising approach to identify relevant scenarios to inform water scarcity analyses. In addition, while this work assessed the economic impact in water markets alone, future work could make use of a Computable General Equilibrium model where the interactions between all markets would be accounted for (see “Methods” section). Indeed, we hope this work provides the basis for similar analyses across a range of hydro-economic models to ascertain the sensitivity of our results to model structure. Confidence in our metric depends on the fidelity of the selected hydro-economic model, so future work would benefit from expanded data collection of socio-technological drivers of regional and sectoral water consumption to improve those underlying models. This study’s use of a coupled partial equilibrium-hydrologic model to perform an extensive uncertainty analysis is novel to the integrated assessment modeling literature and enables the discovery of important multi-scale dynamics such as a basin’s wide range of adaptive responses to water scarcity.

Human-earth system model

Multiple factors affect water demand including population, wealth abundance and distribution, agricultural technology and practices, technological improvements, and carbon and land-use policy. These factors all interact with each other and with the climate system. It is therefore necessary to use a model that includes detailed representations of these systems and the interdependent endogenous choices that shape them. To this end, we have used a partial equilibrium model in order to represent the affected systems with as much detail as possible.

This study makes use of the Global Change Analysis Model (GCAM), a human-Earth system model that has been used by numerous agencies to make informed policy decisions 36 . GCAM is a complex model that decomposes the world into 32 geopolitical regions, 384 land-use regions, and 235 water basins 36 . GCAM includes coupled representations of the Earth’s climate, economic, hydrologic, land-use, and energy systems. These systems are expressed in varying degrees of detail. Population and GDP growth are represented as simple exogenous model inputs. Energy and land-use systems are represented in more detail, with shares of supplies and technologies competing using a logit model 36 . Renewable technologies within the model become more efficient over time and therefore some processes such as solar energy production become more competitive. Nonrenewable resources such as oil and fossil groundwater are modeled with graded supply curves and become more expensive as the levels are used up over time. Shares of energy production technologies may change based on different policy choices. For example, a carbon tax may increase the feasibility of using renewable energy sources. These policies may also impact the shares of land uses (e.g., the carbon tax may prompt afforestation).

Water demand and supply

GCAM allows users to specify water constraints and to link water supply to Xanthos, an extensible hydrologic model 42 . Previous versions of GCAM have introduced the water system but have limited its capabilities to computing water demands. The current system calculates both supply and demand and balances the two quantities by solving for an equilibrium regional shadow price for water 38 , 43 , 44 . Water demand in GCAM is modeled through six sectors: irrigation, livestock, municipal, manufacturing, primary energy, and electricity generation 25 . Irrigation demand is based on biophysical water demand estimates for twelve crop classes 25 . Water demand for irrigation is determined by deducting green water (i.e., water available for use by plants) on irrigated areas and green water on rain-fed areas from total water consumption. Livestock water demand is computed using the consumptive rates for six livestock types (cattle, buffalo, sheep, goats, pigs, and poultry) and estimates of livestock density in 2000 25 . Water withdrawals for electricity generation are related to the amount of electricity generated in each region. Once-through cooling systems compete with evaporative cooling systems with the latter becoming more prevalent over time 25 . Water use in the primary energy sector (i.e., the water used to extract natural resources) is calculated using estimates of energy production in each region along with water use coefficients. Municipal water demand is modeled using population, GDP, and assumptions about technological efficiencies 36 , 41 . Finally, manufacturing water demand is the total industrial water withdrawals less the energy-sector water withdrawals 25 . Consumption is calculated using exogenous consumption to withdrawal ratios for industrial manufacturing 25 .

Water supply in GCAM is modeled using three sources: surface water and renewable groundwater, nonrenewable groundwater, and desalinated seawater. Similar to technology use within GCAM, these sources of water compete using a logit structure based on price. Surface water is typically used first in larger quantities than its competing sources as it is the cheapest source of water. The upper limit of surface water in a basin is taken to be the mean average flow modeled using Xanthos, which calculates water supply at a monthly time step using evapotranspiration, water balance, and routing modules 42 . Accessible water 38 is assumed to be the volume of runoff available even in dry years in addition to reservoir storage capacity (after removing environmental flow requirements). The estimates of accessible water and basin runoff are used as inputs in GCAM. After the renewable water supply is fully consumed, GCAM will either use desalinated water or nonrenewable groundwater depending on the relative shares computed in the price-based logit structure 38 . Nonrenewable groundwater increases in price as more of the resource is consumed. The groundwater supply curves account for geophysical characteristics such as aquifer thickness and porosity, as well as economic factors such as the cost of installing and operating the well. As the price of extraction rises, desalination becomes more competitive, resulting in wider use of desalinated water 44 .

Basin-specific water policies are not represented within GCAM or indeed any global model. The level of detail needed to represent existing water markets and policies exceeds the capabilities of a global model. GCAM does, however, enforce a subsidy on water for agricultural sectors 36 . Imposing this subsidy in GCAM’s water markets allows water to be allocated first to agricultural producers. This behavior mimics the effect of traditional water rights in that senior rights are usually given to agricultural producers. The water markets within GCAM operate by generating a “shadow” price of water, which reflects the economic value of the last unit of water in terms of the water’s contribution to production. When water supply becomes a binding constraint in a particular water basin, the shadow price of water rises because users cannot use more water than there is in the basin. This forces a reduction in the production of the goods and services that rely on water as an input. Clearly, this approach is a simplification, but it marks an improvement over what is most often done where the implications of water scarcity are ignored (i.e., direct and indirect feedbacks associated with unsatisfied water demands are not captured, and analyses are limited to how water scarcity may increase or decrease in the future without a mechanism for dynamic adaptation measures).

We compute the difference in total economic surplus in these simplified water markets (i.e., the sum of producer surplus and consumer surplus) between a control scenario with no water constraints and its paired limited water scenario (see next section).

Capturing economic impact in the entire economy would require a general equilibrium model. However, general equilibrium models necessarily lose some detail in sectoral resolution so that they can capture market interactions. Water is a non-substitutable input to most markets in the human system and so most market interactions will be represented by the changes in water markets when conditions are perturbed. The surplus change in the water markets includes both direct effects (e.g., restricted supply) and indirect effects (e.g., demand shifts in adjacent markets). There may be economic effects not captured by looking at water markets alone, which could be investigated in future work that employs a computable general equilibrium model. Numerous previous studies have assessed economic impact in water markets using both types of models 45 .

Scenario design

We utilize a scenario discovery approach 35 to study the uncertainty in physical water scarcity and its economic impacts. Using this approach, scenarios are generated using all possible combinations of discrete levels of uncertain factors. All scenarios are weighted equally during scenario exploration so as not to presume the likelihood of outcomes a priori. Doing so may leave the system vulnerable to unanticipated events. In addition, in complex adaptive systems such as the human-Earth system, the main drivers of an outcome of interest may be non-intuitive and context-specific 34 . The traditional “predict-then-act” approaches 46 to planning implies a more complete understanding of the system and of future circumstances than is often the case, which can, in turn, lead to myopic decisions 35 . Alternatively, scenario discovery gives equal weight to all possible future system trajectories (i.e., population, wealth, energy prices) and finds the most influential factors driving outcomes of interest-based on the results of all scenarios. Planners can then make robust management decisions based on the influential factors and their uncertainties as opposed to designing based on a few future projections.

In this study, we use scenario discovery to determine the relative influence of seven dimensions in driving highly consequential economic outcomes due to water scarcity (Supplementary Fig.  2 ). These factors include socioeconomic conditions ( S ), agricultural yield assumptions ( G ), groundwater supply ( W ) and reservoir storage ( R ) levels, climate trajectories ( E ), and land-use scenarios ( T ). All factors are represented in Eq. ( 1 ) and are discussed in more detail below. Every scenario n is composed of a distinct combination of the levels of each factor.

Settings for the first three dimensions are taken from GCAM’s implementation of the Shared Socioeconomic Pathways (SSPs) 47 , 48 , 49 . The SSPs are based on plausible but distinct narratives that envision how the century will unfold 47 . The five SSPs correspond to the four combinations of high and low challenges to adaptation and mitigation of climate change with a fifth narrative that lies in the middle of the adaptation-mitigation challenge plane. The implementations of the SSPs within GCAM are made up of factors including population and GDP, agricultural yields, carbon sequestration implementation, renewable energy use, fossil fuel extraction cost, and energy demand 48 . This study included the population/GDP component and the agricultural component of the SSPs. The remaining components of the SSP framework were linked to either the population/GDP or agricultural component. For instance, in one scenario, SSP 3 fossil fuel extraction costs and renewable energy assumptions would be present with SSP 3 socioeconomics and SSP 5 agriculture assumptions; the converse scenario of this dimension would include SSP 5 fossil fuel extraction costs, renewable energy assumptions, and agriculture yields and SSP 3 socioeconomics. This switch ( L ) represents the third dimension of the design. Previous work found the socioeconomic and agricultural and land use elements of the SSPs had the most profound impact on water use 34 , thus we linked the other elements to ensure the scenario design emphasized potentially impactful factors.

The next three dimensions relate directly to the water supply. Groundwater availability is constrained at different levels (5%, 25%, and 40% of the physical water availability) that reflect the economic feasibility of extracting groundwater using the methodology within Turner et al. (2019a) 50 . We also vary reservoir storage estimates using two extremes following the methodology in Turner et al. (2019b) 44 . A restricted scenario indicates that reservoir storage remains constant from the present to the end of the century while an expanded scenario expresses a linear increase from current levels to maximum storage capacity (meaning all accessible water is stored) at the end of the century 44 . The Earth System Model forcing trajectories used as input to Xanthos were also varied between GFDL, MIROC, IPSL, HadGEM2, and NorESM 51 , 52 , 53 , 54 , 55 .

The final dimension corresponds to land-use scenarios formed by mitigation policies. The first, a Universal Carbon Tax (UCT) scenario, imposes a carbon tax on all sectors of the economy including emissions from land-use change. This scenario has many different land-use implications than the alternative scenario that employs the Fossil Fuel and Industrial Carbon Tax (FFICT) which does not price changes in land use (e.g., preserving grasslands and forests rather than expanding agriculture). To construct these scenarios, we use a carbon price trajectory that approximates the continued ambition scenario of the Nationally Determined Contributions (NDCs) as implemented in Fawcett (2015) and revised in Cui et al. (2018) 40 , 43 . This scenario assumes that countries continue decarbonization at the same rate as was necessary to meet the NDCs by 2030. The price of carbon at the reference scenario (SSP 2) for the continued ambition trajectory was used globally for all scenarios. The price begins at $21/ton of CO2 and increases to $233/ton of CO2 by the end of the century. These carbon prices are applied to all sectors (under the UCT) or to every sector but land-use change (under the FFICT).

In total, all unique combinations of the levels of these dimensions (i.e., the size of the set in Eq.  1 ) yield 3000 scenarios. Of the 3000, the total surplus could be calculated for 2876 scenarios without integration errors. Importantly, using a single carbon price trajectory while varying other socioeconomic and climatological factors yields a spread of emission trajectories. This will produce inconsistencies in a given scenario to the extent inputs depend on exogenous forcing trajectories. In this study, this is most important to the generation of hydrologic realizations (to compute available renewable water), where the Xanthos model was forced using several downscaled ESM simulations of RCP 4.5 even though the actual forcing trajectories varied across scenarios. Since climate change will impact the water cycle 56 , the amount of renewable water would also be different in each scenario had Xanthos been run endogenously. However, the magnitude of this difference is highly uncertain, as climate models have been found to cause as much or more uncertainty in hydrologic realizations as the RCPs themselves 57 . Thus, it is not clear that imposing an emissions cap to ensure consistency in forcing would better characterize hydrologic uncertainty. Future studies, for instance, those focused on the cost of meeting mitigation targets, may instead choose to vary prices rather than emissions, but this is beyond the scope of this work.

In addition to the dimensional components of the design, we added further inputs to reflect the recent advances of GCAM. Agricultural yield inputs based on Earth System Model, Representative Concentration Pathway (RCP) 58 and SSP were included, as well as hydropower inputs based on SSP, RCP, and ESM, and technological water demand estimates based on SSP 49 , 59 .

Water scarcity metrics

Many different metrics for measuring water scarcity have been proposed 56 , 60 , 61 . The most commonly used metrics typically compute physical water scarcity and exclude the socioeconomic information necessary to understand adaptive capacity. For example, the Water-To-Availability ratio (WTA) is computed as water withdrawals over renewable water supply 14 , 25 . Several holistic metrics exist that include socioeconomic information such as the Human Development Index 62 , though these metrics face the challenge of subjectively determining how to weight socioeconomic indicators relative to one another 60 .

This study examines water scarcity vulnerability using a metric that accounts for the economic impact of water scarcity within a hydrologic basin. We use the change in economic surplus in water markets between a basin with unlimited water and one with physical constraints on the water supply to calculate this economic impact. This difference consists of the direct impacts of changes in the water supply, as well as the indirect impacts from markets that rely on water. From this point on, we will refer to the surplus change in water markets as simply the surplus change, or economic impact.

Change in economic surplus has been used in many disciplines since its inception 63 . It has been used to assess the impact of climate change on agriculture 64 , 65 , as well as potential infrastructure projects 66 and adaptation policies 67 , 68 . Its continued use is due in part to its ease of implementation, its theoretical simplicity, and its ability to capture changes across sectors. These qualities are highly beneficial in a water scarcity metric. Computing the loss of surplus due to some factor requires a counterfactual scenario in which that factor is absent. This presents a problem when applying this type of metric to any historical data, including water scarcity: water scarcity has always been present. Even a synthetic history with unlimited water would be inadequate as all other historical values depend on historical water scarcity levels. Still, our metric has significant advantages over conventional physical water metrics that lack information about the ability of the basin to respond to water stress.

Here economic impact is defined as:

where T represents the total economic surplus (Supplementary Fig.  1 ). In this study, the economic impact is reported using its log-modulus and has units billions of 2020 US dollars:

Thus, an impact value of −2 would correspond to a loss of 100 billion 1975 US dollars or 2.3% of US GDP in 2018 after adjusting for inflation 69 .

Sign changes in economic impact correspond to shifts in water demand in a basin between unlimited and limited water scenarios. If the total surplus gained from increased withdrawals exceeds the consumer surplus lost by low-demand consumers when water limitations are imposed, basins experience a positive impact. This counter-intuitive case could result when basins become virtual water exporters when global physical constraints are imposed. With water constraints in place, such regions now have a comparative advantage in producing water-intensive goods (notably agricultural products, see Supplementary Fig.  3 ); therefore, they capture greater market share in the water-constrained scenarios. The increased production of these goods translates into a positive shift in demand in water markets. The additional economic activity also increases the value of water as consumers’ willingness to pay for goods increases. This additional economic activity manifests as a larger economic surplus, which translates to a more positive impact. The magnitude of the metric gives an indication of the difficulty of overcoming water scarcity within a basin since the economic impact depends on the value put on water. Higher values of water correspond to higher magnitudes of economic impact.

To uncover the most influential factors that lead a basin to experience positive versus negative impact, we used the Classification and Regression Trees (CART) algorithm 70 . The CART algorithm has been found useful in determining important factors and scenarios of interest in previous studies 34 , 35 . CART operates by performing binary splits of the data to create the purest possible subgroups. In this study, we use CART to identify the factors that lead to the worst-case scenarios with respect to the economic impact metric. Examining this continuous metric necessitates the use of the regression approach of CART. The regression approach uses an Analysis of Variance (ANOVA) method to discover the purest subgroups. Splits work to maximize the variance between groups and minimize variance within groups.

Data availability

Requests for raw data should be made to [email protected]. Processed data is available at https://doi.org/10.5281/zenodo.4470017 71 .

Code availability

Code to generate the main text figures and calculate economic impact can be found at https://doi.org/10.5281/zenodo.4470017 71 .

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Acknowledgements

This research was supported by the U.S. Department of Energy, Office of Science, as part of research in MultiSector Dynamics, Earth and Environmental System Modeling Program. The authors would also like to acknowledge Sean Turner, Chris Vernon, and Abigail Snyder for their help at the beginning of the project.

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F.D. ran the model, processed the output, and analyzed the data. J.L. provided guidance throughout the process and proposed the initial experimental design. R.L. provided the necessary computational capabilities to output the economic impact metric. M.H. and P.R. helped propose the initial narratives of the paper. J.E. proposed the economic impact metric. All authors wrote the manuscript.

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Dolan, F., Lamontagne, J., Link, R. et al. Evaluating the economic impact of water scarcity in a changing world. Nat Commun 12 , 1915 (2021). https://doi.org/10.1038/s41467-021-22194-0

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Article Contents

Introduction, linking xylem structural components and their functions, functionality of the xylem network: bottleneck for efficiency or smart design for safety, what is the appropriate approach to investigate the regulation of sap flow dynamics, toward real-time imaging of flow dynamics in the xylem network, future directions, acknowledgements.

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Investigating water transport through the xylem network in vascular plants

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Hae Koo Kim, Joonghyuk Park, Ildoo Hwang, Investigating water transport through the xylem network in vascular plants, Journal of Experimental Botany , Volume 65, Issue 7, April 2014, Pages 1895–1904, https://doi.org/10.1093/jxb/eru075

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Our understanding of physical and physiological mechanisms depends on the development of advanced technologies and tools to prove or re-evaluate established theories, and test new hypotheses. Water flow in land plants is a fascinating phenomenon, a vital component of the water cycle, and essential for life on Earth. The cohesion-tension theory (CTT), formulated more than a century ago and based on the physical properties of water, laid the foundation for our understanding of water transport in vascular plants. Numerous experimental tools have since been developed to evaluate various aspects of the CTT, such as the existence of negative hydrostatic pressure. This review focuses on the evolution of the experimental methods used to study water transport in plants, and summarizes the different ways to investigate the diversity of the xylem network structure and sap flow dynamics in various species. As water transport is documented at different scales, from the level of single conduits to entire plants, it is critical that new results be subjected to systematic cross-validation and that findings based on different organs be integrated at the whole-plant level. We also discuss the functional trade-offs between optimizing hydraulic efficiency and maintaining the safety of the entire transport system. Furthermore, we evaluate future directions in sap flow research and highlight the importance of integrating the combined effects of various levels of hydraulic regulation.

In land plants, water and minerals are taken up from the soil by the roots and transported through the xylem network to the leaves. Some trees can lift water over distances of more than 100 metres from the roots to the uppermost leaves ( Ryan et al. , 2006 ). This ability has fascinated scientists through the centuries and the study of plant hydraulics remains an active topic of research open to new methods of investigation ( Tyree, 2003 ). Independent of plant size, water movement is at the crossroads of all plant growth and development processes, from transpiration and photosynthesis to the distribution of organic and inorganic molecules throughout the plant.

When Einstein formulated the equation for the interconversion of matter and energy in 1905, it inspired decades of research and the revision of the law of conservation of energy. In plant physiology, the cohesion-tension theory (CTT) represents a similar conceptual breakthrough; however, some aspects of the CTT still require experimental validation. The CTT originated at the end of the 19th century, when Boehm (1893) proposed an initial framework, based on the cohesion and adhesion properties of water, to explain water transport in plants. The CTT attributes the main driving force for water transport to the tension (i.e. negative hydrostatic pressure) generated at the leaf surface by evaporation. The fundamental principles of the CTT summarized by Dixon and Joly (1894) have withstood persistent challenges ( Zimmermann et al. , 1994 ; Canny, 1995 ; Milburn, 1996 ; Meinzer et al. , 2001 ; Zimmermann et al. , 2004 ) and remain the most comprehensive explanation of water transport in plants ( Angeles et al. , 2004 ). The CTT is based on the physical properties of water: cohesion between dipolar water molecules gives water its high tensile strength, which maintains hydraulic continuity throughout the plant vasculature. The negative pressure that causes water to move up through the xylem develops at the surface of cell walls, which act as a very fine capillary wick. Water molecules adhere to the cellulose microfibrils and other hydrophilic components of the wall ( Somerville et al. , 2004 ; Oda and Hasezawa, 2006 ). As water evaporates from a thin film permeating through an extensive system of intercellular air spaces in the substomatal chambers of leaves, cohesive forces result in the formation of curved air/water interfaces. The surface tension at the interface induces a negative pressure and that generates the motive force that drives sap ascent in the xylem ( Zimmermann, 1983 ). Ultimately, the surface tension generated at the air/water interfaces of cell walls is assumed to be transmitted through a continuous water column to the roots. However, this system is highly prone to cavitation owing to the metastable state of water ( Tyree and Sperry, 1989 ; Hacke and Sperry, 2001 ; Meinzer et al. , 2001 ; Zimmermann et al. , 2004 ; Herbert et al. , 2006 ). Cavitation in the xylem can lead to a major reduction in hydraulic conductivity. Such a disruption in water flow poses a serious threat to photosynthetic efficiency and plant survival.

The first extensive integration of water transport and xylem structure was proposed by Zimmermann ( Zimmermann et al. , 1971 ; Zimmermann, 1983 ) and was updated by Tyree and Zimmerman (2002) . The overview presented by Holbrook and Zwieniecki (2005) is probably the most comprehensive formulation of vascular transport in plants. However, new conceptual models and experimental methods that emerged in the past decade have brought new insights. Many research groups now examine the plant-water relationship at various scales, from the level of the cellular water exchange to that of the whole-plant canopy. In that respect, although the discovery of aquaporins ( Murata et al. , 2000 ) represent a significant advance in our understanding of intercellular water flow, we will restrict our review to water flow in the xylem network. Numerous tools have been developed to probe the mechanism underlying the passive transport of water in plants. During the past two decades, the concept of passive water transport has been heatedly debated in the scientific community ( Zimmermann et al. , 1994 ; Canny, 1995 ; Milburn, 1996 ; Meinzer et al. , 2001 ; Zimmermann et al. , 2004 ). In this review, we highlight the major experimental tools that have provided insight into sap flow through the xylem network. From the broader perspective of the Blue Revolution ( Pennisi, 2008 ), understanding how water is transported from the soil through the intricate plant xylem network to the atmosphere can lead to innovative ways to optimize each drop of water in applied scientific fields such as molecular biology and agronomy, and in breeding programmes that seek to improve drought-resistance in crop plants. Some industrial applications based on our understanding of microfluidics and nanofluidics have already started to emerge in the form of plant-inspired devices, such as synthetic trees ( Wheeler and Stroock, 2008 ). In recent reviews, Pittermann (2010) presented an integrated approach of the evolution of the plant vascular system, and Lucas et al. (2013) summarized our current understanding of plant vascular biology and emphasized the major impact of the tracheophyte-based vascular system on all terrestrial organisms. Two recent international meetings (the 9th International Workshop on Sap Flow, 2013, and the Third International Conference on Plant Vascular Biology, 2013) demonstrated that sap flow is an area of prolific and inspiring research. However, there is no agreement as to which methods are best for examining sap flow or how the new results contribute to unravel sap flow dynamics in vascular plants.

In this review, we briefly retrace the scientific investigation of water transport in vascular plants, and evaluate basic concepts and theories in light of new experimental methods. We will assess our current understanding of the structure/function relationships of the xylem hydraulic architecture and provide an overview of experimental tools and methods used to unravel sap flow dynamics through the xylem network. Real-time imaging emerges as the most promising approach for integrating the xylem network structure and its multiple layers of regulation.

Our understanding of xylem hydraulic properties has evolved with the development of theoretical modelling and novel experimental tools to visualize the cross-sectional and three-dimensional structure of xylem. Tracheary elements (TEs) are the elementary units of xylem. After a complex process of differentiation, TEs lose their nuclei and cell contents, leaving behind a central lumen surrounded by secondary cell walls, which together form tracheids or vessels ( Fukuda, 1997 ). The structural characteristics of tracheids in conifers and vessel elements in angiosperms have been well characterized using optical and electron microscopy. The diameter of TEs varies from a few micrometres to a few hundred micrometres. Their association in series to form long-distance pathways can attain a few millimetres up to several metres. Torus-margo or pit membranes integrated in the secondary cell wall provide various levels of subcellular resistance to water flow ( Schulte and Castle, 1993 ; Hacke et al. , 2006 ; Sperry et al. , 2007 ). Although the structural characteristics of TEs are well established, our understanding of water flow dynamics is limited to the tissue or organ level. From a bottom-up perspective, water and minerals from the soil are absorbed through apoplastic and symplastic pathways into protoxylem vessels of the roots ( Passioura, 1988 ). Then, long-distance transport in the stem is generally attributed to large metaxylem vessels. The vascular bundles in leaves become highly branched reducing the distance of most leaf cells to less than a few hundred micrometres from a vessel ( Fig. 1 ). The mesophyll at the interface with air represents the highest resistance to water flow ( Cochard et al. , 2004 ; Sack et al. , 2008 ).

Main characteristics of the xylem network. Organization and characteristics of the xylem network: water flow throughout the plants depends on characteristics of the xylem in different organs. Water absorbed by the roots moves radially from small protoxylem vessels, which have high hydraulic resistance, to larger metaxylem vessels, with reduced hydraulic resistance. In the stem, the number and organization of vessels vary along the height of the plant height. Packing and tapering functions can be used to characterize each level of organization. In the leaves, water travels through small xylem vessels. During transpiration, negative hydrostatic pressure is generated at the interface between mesophyll cells and air.

In analogy with Ohm’s law, water uptake and transport are associated with a hydraulic flow process that is controlled by resistance and hydraulic gradients ( van den Honert, 1948 ). The overall resistance is determined by soil water potential, conducting vessels, transpiration rate, plant height, and gravity. In this physical conceptualization of the soil-plant-atmosphere continuum, the tension of the driving force of sap ascent continuously decreases in the direction of flow, and the pressure gradient is proportional to the evaporative flux density from the leaves ( Tyree, 1997 ). The xylem provides a low-resistance pathway for long-distance water movement by minimizing the pressure gradients required to transport water from the soil to the leaves ( Jeje and Zimmermann, 1979 ). In its most simplified representation, the xylem is often modelled as an assemblage of ‘unit pipes’ ( Shinozaki et al. , 1964 ) and water flow is generally approximated with the Hagen-Poiseuille equation ( Dimond, 1966 ; Schulte et al. , 1989 ; Lewis and Boose, 1995 ). The pipe model has contributed to the estimation of canopy-level parameters by incorporating variations in vessel size and number at the tissue and organ levels, and was also used to understand tree growth, resource allocation, and plant biomechanics ( Niklas et al. , 2006 ). However, the functionalities of the xylem network integrate different structural organization at the tissue and organ levels that cannot be supported by this simplified model. Hydraulic resistance is highly variable depending on the species and organ. Unravelling how water is collected from all the vessels in the roots, passes through the stem, and is distributed in the leaves requires an integrated functional approach at the whole-plant level ( Sperry, 2003 ; Loepfe et al. , 2007 ; Page et al. , 2011 ).

At the tissue level, the hydraulic conductivity per unit of cross-sectional area generally defines efficiency. The constraints on the maximum diameter, length, and number of xylem vessels for a given cross-sectional area limit efficiency: this is related to a species-dependent limit on conduit frequency. For instance, vessel lumens in angiosperms occupy less than 10% of the cross-sectional wood area at the mid-point of their diameter range, whereas tracheid lumens in conifers can occupy over 40% ( Sperry et al. , 2008 ). Such variation is due to the lower investment in mechanical strength in angiosperms, which rely on wood fibres, whereas conifer tracheids provide both transport and support functions. Conduit diameter and frequency are not the only factors determining efficiency of water flow, because the conductivity of conduits of a given diameter can also vary. In angiosperms, simple or schalariform perforation plates and conduit end walls create differences in actual conductivity compared with the theoretical maximum set by the Hagen-Poiseuille equation. Lumen and end-wall resistance is relatively constant and flow resistance through pits does not increase with cavitation safety. Pit membrane porosity does not seem to be related to cavitation pressure ( Hacke et al. , 2006 ). Despite difference in size, the end wall resistance at a given diameter seems to be relatively similar between conifers and angiosperms. The presence of specialized structures, such as the torus-margo in conifers, greatly reduces the resistance of inter-tracheid water flow ( Pittermann et al. , 2005 ).

In summary, the most important structural features of the xylem at the cellular level are conduit diameter, length, wall features (i.e. annular, spiral, or reticulate thickening or pits), and the presence or absence of end walls (simple or scalariform). At the tissue level, inter-conduit pitting (determined by the density and size of torus-margo or pit membrane) and the number of conduits define the connectivity of the xylem network.

The two main functions of the xylem hydraulic network in vascular plants are (i) to supply water and minerals to all tissues and (ii) to provide mechanical support. In living organisms, similar functions are generally carried out by similar structures. A large diversity in xylem hydraulic architecture exists between organs and among species, and the initial structures are even modified during growth and development. Among seed plants, coniferous, diffuse-porous, and ring-porous trees have radically different xylem anatomy ( McCulloh et al. , 2010 ). Within angiosperms, the vascular bundles of dicot and monocot plants have distinct organizations that vary in different organs. These differences in organization are ultimately due to differences in conduit tapering and packing of similar elementary structures. However, the functional consequences of these distinct organizations are not well understood at either the conduit or the whole-organism level. The integration of organ-level variation in xylem architecture at the whole-plant level is essential for unravelling the mechanisms that maintain the integrity of water transport from roots to leaves. The elementary elements of the system (i.e. tracheids or vessel elements) are organized to withstand strong physical constraints and simultaneously achieve efficient water transport with minimal resistance, while protecting against cavitation ( Tyree and Sperry, 1989 ). To integrate structural characteristics into functional roles, it requires determining how the dynamic hydraulic properties at the cellular level are incorporated into tissue and organ levels. For example, the hydraulic efficiency per conduit diameter and length is higher for conifer tracheids than for angiosperm vessel elements; however, the wider diameter and greater length of angiosperm vessels provide greater conductivity per xylem area.

In terms of the biophysical mechanisms underlying these processes, the major challenge is to understand how the trade-off between efficiency and safety is achieved at different levels of organization. The hydraulic regulation attributed to the xylem is generally considered to depend on the specific organization in each organ; however, the respective contributions are difficult to integrate into the entire network ( Fig. 1 ). Unravelling the relationship between the structural complexity of hydraulic architecture and efficiency/safety functions remains one of the main issues in understanding plant-water relations. In leaves, direct pressure-drop measurements confirmed that mesophyll cells are the major component of hydraulic resistance, even though the vascular system accounts for the longest distance ( Cochard et al. , 2004 ). From the soil to the atmosphere, the relationship between hydraulic resistance and stomatal conductance is a key component ( Cruiziat et al. , 2002 ) as environmental factors can influence the efficiency of water absorption and uptake. When transpiration is high, maintaining the continuity of flow in individual vessels is seriously challenged owing to cavitation risks ( Cochard, 2006 ). However, cavitation can be reduced by the hydraulic capacitance of the xylem and the water storage capacity of each organ, and the network organization can also provide alternative pathways to avoid disruption of water flow ( Tyree and Ewers, 1991 ; Sperry et al. , 2008 ; Höltta et al. , 2009 ). Ultimately, water transport and gas exchange in the leaves have a major physiological effect on the photosynthetic capacity of the plant ( Tyree and Ewers, 1991 ).

The structural model of the hydraulic transport system proposed by West, Brown, and Enquist (WBE model; 1999) has been widely used to explain the maintenance of a constant flow rate along the entire flow path. In this model, it is assumed that plants minimize the effect of hydraulic resistance imposed by increasing height and total path-length conductance by tapering the xylem conduits. Plant size is related to the geometry of the branching architecture and metabolism. Based on the fact that all living organisms contain a transport system for aqueous materials, the plant vascular system should minimize the hydrodynamic resistance of nutrient transport, while maximizing the exchange surface with the environment ( Petit and Anfodillo, 2009 ). The ideas that (i) all plants adopt a universal architecture of the xylem transport system, and (ii) hydraulic efficiency is independent of plant height are very attractive. Although a wide range of plants seemed to comply with these assumptions ( West et al. , 1999 ), numerous studies challenged the validity of a universal rule given the diversity of vascular plants ( Dodds et al. , 2001 ; Coomes, 2006 ; Apol et al. , 2008 ), and hydraulic constraints seem to increase with plant height ( Koch et al. , 2004 ). Despite controversies, the WBE model highlights the value of architectural modelling in simplifying plant diversity and stimulated prolific empirical research. Now, complementary models of the vascular system not only include a more realistic view of the hydraulic architecture ( Savage et al. , 2010 ), but also incorporate physiological considerations ( von Allmen et al. , 2012 ).

Although the plant xylem is non-living tissue, there is an extraordinary degree of coordination between the hydraulic capacity and photosynthetic assimilation because both of these pathways intersect at stomata during the exchange of water and CO 2 at the leaf surface ( Brodribb, 2009 ). The rate of transpiration and gas exchange via stomata are limited by the xylem hydraulic system. Packing and taper functions are the backbone of a robust framework for modelling network transport ( Sperry et al. , 2008 ; McCulloh et al. , 2010 ). Strength and storage requirements set a packing limit and influence the conducting capacity ( Zanne et al. , 2010 ). Theoretically, a small number of wide conduits are more efficient than a large number of narrow ones. This is reflected by the more efficient networks of ring-porous trees compared with conifers ( McCulloh et al. , 2010 ). Without tapering of the xylem conduits, branches would have the highest conductivity in a tree. In other words, tapering counterbalances the decline in conductance due to increasing path length, but maintaining similar conductivity requires an increase in the number of xylem vessels per unit cross-sectional area as conduits become narrower. The organization of the xylem network thus defines the functional trade-off between efficiency and safety in each organ.

Building on these concepts, Höltta et al. (2011) proposed a carbon-cost gain model, which calculates the xylem structure that maximizes carbon-use efficiency while simultaneously accounting for intervessel pit structures that increase flow resistance. As the water potential is lower at the plant apex, fewer pores in the pits near the apex would also restrict the spreading of embolisms. An optimal hydraulic structure would have conduits that decrease in size from the base to the apex (defining tapering function). In parallel, the vulnerability to cavitation can be reduced by increasing conduit number (defining the packing function). Indeed, whole-plant carbon-use efficiency demands that conduit size decreases and conduit number increases simultaneously ( Lancashire and Ennos, 2002 ; Choat et al. , 2003 ; Höltta et al. , 2009 ).

The theoretical and conceptual bases of water transport and xylem hydraulic architecture have been examined by various experimental methods ( Fig. 2 ). Technical reliability of new methodology is of prime importance in investigating the processes of water transport. Moreover, subsequent results are rarely cross-validated with those obtained using other methods. A difficulty in making proper comparisons is that the measurement techniques do not address the same level of the xylem network. For instance, the technical limitations of new methods in measuring internal pressure or vulnerability to cavitation have sometimes resulted in a misunderstanding of the elementary processes and have given erroneous interpretations. The invasive methods using excised tissues do not change the internal xylem structure, but water flow generated artificially in isolated leaves, stems or roots does not accurately reflect water flow in intact plants.

Methods and instruments used to analyse sap flow in plants. A. Schematic representation of different methods used to measure sap flow velocity. In heat-based methods, heat sensors (heat pulse velocity, heat field deformation, or thermal dissipation) are installed radially into a segment. In radioisotope or dye methods, tracers are injected into the xylem or uptaken from a cut segment. B. Methods used to measure negative pressure in the xylem. The observation scale and measurement target (i.e. cell, tissue, or organ) differ between indirect (i.e. pressure bomb, centrifugation) or direct (i.e. cell pressure probe) methods. C. Simultaneous visualization of xylem structure and sap flow using magnetic resonance, neutron or synchrotron X-ray imaging methods. The temporal and spatial resolutions vary for each imaging method.

Three categories of methods are currently available for investigating xylem sap flow: (i) continuous measurement of sap flow velocity (to confirm the relationship between transpiration and water uptake); (ii) internal pressure measurement (to confirm that negative hydrostatic pressure is the main driving force of sap flow); and (iii) visualization of sap flow through the xylem. Experimental data obtained using these different methods were frequently not in agreement, because the scale of the xylem architecture examined (from the whole-plant network to individual vessels) generally differed. Futhermore, sap flow dynamics were not always measured with the same hydraulic parameters. Therefore, it is crucial to understand the advantages and limitations of different techniques to compare the characteristics of sap flow across different species.

Continuous sap-flow monitoring has been most commonly used to measure water flux through the stems and branches of trees, but the resolution is not sufficient for determining leaf-level responses to environmental changes. Flow monitoring techniques using tracers and histological sections enabled the identification of the water-conducting vessels of the xylem network and provided a snapshot of how they function under different environmental conditions. The injection of different dyes (e.g. fuchsin or safranin) is the most common method used to visualize water-conducting pathways at the tissue level in conifers ( Harris, 1961 ; Kozlowski and Winget, 1963 ; de Faÿ et al. , 2000 ), dicotyledonous trees ( Kramer and Kozlowski, 1960 ; Ellmore and Ewers, 1986 ), and herbaceous plants ( Hargrave et al. , 1994 ; Tang and Boyer, 2002 ). Recently, a number of concerns have been raised in interpreting the results of dye injection ( Umebayashi et al. , 2007 ). First, the type of dye and the method used for sample preparation greatly affect the distribution and diffusion of the dye through the xylem. Second, the diversity in plant size, and vessel size and organization do not generally allow extrapolation of the results obtained for a stem, root, or leaf sections to other organs. Third, it is difficult to compare the results of studies conducted at the whole-plant level under various environmental conditions with those obtained from the isolated tissues. Using improved preparation methods, stabilized dye can enable the identification of water-conducting vessels in trees at the cellular level ( Sano et al. , 2005 ); however, it remains technically challenging to visualize sap flow at the subcellular level ( Geitmann, 2006 ). Dye injection is a relatively easy technique, but it gives misleading interpretations about the functional water-conducting pathways if the procedures are not well defined and standardized ( Umebayashi et al. , 2007 ).

More accurate modelling of leaf and plant-level responses to abiotic stresses is essential to predict the canopy response to future climate change. In forest ecosystems, water fluxes in trees can be monitored at the stem or leaf level ( Fig. 2A ). Heat-balance and heat-pulse methods estimate whole-plant water flow using heat-based sensors ( Smith and Allen, 1996 ). In both cases, probes inserted into the stem of a tree generate heat that is used as a tracer. The heat-balance method calculates the mass flow of sap in the stem from the amount of heat taken up by the moving sap stream. In the heat-pulse method short pulses of heat are applied, and the mass flow of sap is determined from the velocity of the heat pulses moving along the stem ( Cohen et al. , 1981 ; Burgess et al. , 2001 ). The thermal dissipation method, which is based on the propagation of heat pulses and was initially developed by Huber (1932) and refined by Vieweg and Ziegler (1960) , is also widely used to estimate sap flow rates. The direction of volume flow is derived from the asymmetry of thermal dissipation; however, reliable estimates of the sap-conducting surface area and size are essential to compare the deduced sap flow rates with the actual sap flow rates ( Green et al. , 2003 ). One of the major limitations of theses techniques is that the inserted probes disrupt the sap stream, which alters the thermal homogeneity of the sapwood. Recently, mathematical corrections of sap velocity include effects due to heat-convection ( Vandegehuchte and Steppe, 2012 b ) or natural temperature gradients ( Lubczynski et al. , 2012 ). In ecophysiological studies, technically improved probes are now available for continuous sap flow measurements in trees ( Burgess et al. , 2001 ). A sophisticated four-needle, heat-pulse sap flow probe even permits measurement of non-empirical sap flux density and water content ( Vandegehuchte and Steppe, 2012 a ).

Measurements of sap flow alone do not provide sufficient spatial resolution to evaluate the variations in xylem water transport properties. Spatial variations in xylem structure and hydraulic properties have to be compared with the actual patterns of in vivo water flow dynamics. Measuring the sap flow (i.e. the velocity and amount of water transported through the xylem) and pressure (i.e. the driving force responsible for the transport) are technically and conceptually challenging. A reliable interpretation of instrumentation outputs requires an integrated understanding of both the structural complexity and technical limits of each measurement method. In particular, the velocity or pressure measurements should be evaluated with respect to the hydraulic architecture of the xylem network. Tension measured using pressure bombs and xylem pressure probes were only in accordance for non-transpiring leaves and differed considerably for transpiring leaves ( Melcher et al. , 1998 ). The deviations were later attributed to technical limitations, as the range of sensitivity of the initially developed pressure probes was below 0.8MPa, and insertion of the glass tip of the probe frequently disrupted a vessel under tension ( Wei et al. , 1999 a , b ; Wei et al. , 2001 ) ( Fig. 2B ). Pressure probes can now be used to measure negative pressures; however, theoretical values of up to –10MPa cannot be verified. The existence of negative hydrostatic pressure is no longer a question. Meanwhile, how this pressure is transmitted through the xylem network requires a better understanding of the relationship between changes in pressure and network architecture.

A lack of consistency between results obtained using tracer dyes and probes called into question which velocity component each method measures. Flow velocities obtained from heat-pulse or particle-type tracers, such as radioisotopes, probably differ owing to the way in which axial and radial flow components are measured. Vessels involved in the flow and the total lumen area are generally not known and it is technically difficult to insert the glass tip of a pressure probe into a vessel without causing cavitation ( Heine and Farr, 1973 ; Dye et al. , 1992 ). In a tropical forest canopy, axial long-distance flow and transport of radial water were affected by the internal water-exchange capacity and the transpiration stream ( James et al. , 2003 ). An inverse relationship between the internal water-exchange capacity and the specific hydraulic conductivity confirmed a trade-off between transport efficiency and water storage. By combining the thermal-dissipation technique with infrared gas analysis, sap flow and transpiration could be measured simultaneously ( Ziegler et al. , 2009 ).

Since the formulation of the CTT, multiple instruments and techniques have been developed to measure the negative pressure in xylem vessels. Inspired by Renner’s (1911) technique using a potometer attached to an excised leafy twig, Scholander et al. (1965) developed the pressure bomb technique. It rapidly became a reference tool to measure negative hydrostatic pressures in excised leaves. Despite initial disagreement between the results obtained from the pressure bomb, in situ psychrometry ( Turner et al. , 1984 ), and the root pressurization method ( Passioura and Munns, 1984 ; Passioura, 1988 ), the high negative value given by the pressure bomb was considered to be the decisive proof supporting the CTT. Later on, cell pressure probes developed by Balling and Zimmermann (1990) gave access to in vivo measurements of pressure in individual xylem vessels ( Pockman et al. , 1995 ). Measurements of xylem pressures, leaf balancing pressures, transpiration rates, and leaf hydraulic properties are now possible; however, the reasons behind the large variations in pressure obtained using different techniques need further investigation. Better integration of the hydraulic regulation at each level of organization of the xylem network should thus be the next step ( Fig. 2B ). How is water from individual vessels in the roots transmitted to a network of vessels in the stem? How is long-distance water transport redistributed to vessels in the leaves? How is each level of hydraulic regulation coordinated at the whole-plant level?

Visualization of in vivo water flow dynamics using magnetic resonance imaging (MRI) and synchrotron X-ray imaging provided the first tools for examining flow regulation and a specific level of structural organization. In particular, it is now possible to visualize the functionality of individual xylem vessels under different environmental conditions. Nuclear magnetic resonance (NMR) or magnetic resonance imaging (MRI) is the least invasive method to investigate sap flow, and provide spatially and temporally resolved information on sap flow at the level of membrane, cell-to-cell, and long-distance transport ( Witsuba et al. , 2000 ; Scheenen et al. , 2007 ). Relative differences in flow volume in different vascular bundles suggested that each vascular bundle is under different tension. Also, root pressure can be estimated non-destructively by taking continuous measurement of sap flow and variations in root segments of different stem diameter and integrating this information with a mechanistic flow and storage model ( De Swaef et al. , 2013 ) ( Fig. 2C ).

Numerous studies examine sap flow as a combination of flow velocity and pressure measurements under different environmental conditions ( Witsuba et al. , 2000 ). A wide range of devices are available to measure pressure and flow at different scales: mobile MRI systems for outdoor tree measurements ( Kimura et al. , 2011 ), patch-clamp pressure-probes to monitor leaf water status non-invasively and record variations in turgor pressure gradients in leaves ( Zimmermann et al. , 2008 ), and even simultaneous dendrometer and leaf patch-clamp pressure-probe measurements for the effects of microclimate and soil moisture on diurnal variations in leaf turgor pressure and water in stems ( Ehrenberger et al. , 2012 ). Now, measurements of sap flow velocity, xylem pressure at the level of individual vessels and in-vivo real-time visualization are required to completely unravel the dynamics of sap flow regulation in the xylem network.

Real-time imaging methods, such as synchrotron X-ray imaging, have recently revealed that radial flow of water can occur during refilling of dehydrated xylem vessels in monocot leaves ( Kim and Lee, 2010 ) and in the roots of Arabidopsis plants ( Lee et al. , 2013 b ). A major challenge for plants under high evaporative demand or low soil water availability is to resist cavitation and/or recover from the reduction in water transport ( Hacke and Sperry, 2001 ; Lens et al. , 2013 ). Embolism of xylem vessels reduces hydraulic conductivity, and the percentage loss of conductivity (PLC) is used to estimate cavitation and embolism repair ( Zwieniecki and Holbrook, 1998 ). For a long time, the experimental research was focused on trying to identify how frequently embolism occurred and how it could be repaired, especially in trees. The refilling of embolized vessels is not explained by thermodynamic laws ( Holbrook and Zwieniecki, 1999 ). However, the latest comparison of different methods used to measure the PLC showed that embolism repair is largely due to technical artefacts ( Wheeler et al. , 2013 ). The ability to limit embolism occurrence is a major component of hydraulic safety and the frequent cavitation reported in earlier studies was due to erroneous interpretations. In particular, inappropriate dehydration methods to generate vulnerability curves led to an overestimation of the vulnerability to cavitation ( Cochard et al. , 2013 ). Nonetheless, the ability of plants to refill embolized vessels during transpiration cannot be neglected and the biophysical mechanisms that enable plants to do so remain to be elucidated ( Zwieniecki and Holbrook, 2009 ). Synchrotron X-ray computed tomography is an extremely promising method to visualize and quantify refilling dynamics ( Brodersen et al. , 2010 ; Brodersen et al. , 2013 ). In grapevines, water influx in the embolized vessels has been attributed to adjacent vessels or the surrounding living tissue. These advances in imaging techniques provide sufficient spatial and temporal resolution to visualize axial, radial, and reverse flow ( Lee et al. , 2013 a ; Lens et al. , 2013 ). Although such methods cannot be used on trees due to limitations in sample size and field of view, the experimental results obtained from model plants can be integrated into a broader framework to understand the hydraulic regulation of active water flow. If refilling under tension is indeed a physical process, we need to re-evaluate the reality of this phenomenon and identify the source of the driving force that draws water into embolized vessels, localize the origin of this water, and determine how embolized and functional vessels are hydraulically compartmentalized ( Holbrook and Zwieniecki, 1999 ). Real-time, high-resolution imaging methods are ideal for visualizing dynamic processes such as embolism repair ( Brodersen et al. , 2010 ; Brodersen et al. , 2013 ). Although these methods can only be used in some small model plants, the visualization of flow dynamics in the xylem network opens new insights in understanding the hydraulic efficiency/safety trade-offs ( Kim and Lee, 2010 ; Brodersen and McElrone, 2013 ). Ultimately, the different structural and functional components, such as sugar metabolism, capacitive effect ( Höltta et al. , 2009 ), the presence of bordered pit membranes ( Zwieniecki and Holbrook, 2009 ), venation architecture, and leaf size ( Scoffoni et al. , 2011 ) must be incorporated in a functional model to fully comprehend the hydraulic regulation at the entire plant level.

A multitude of tools and methods are now available to study water transport from the level of individual xylem vessels to the whole plant. It is crucial to consolidate our current knowledge in order to guide future research on plant water transport in the most relevant directions. Whereas plant physiologists are the ones who better understand the complexity of this transport system, they need support from physicists to validate the results obtained with new methods. Molecular biologists should also play a key role in incorporating the role of aquaporins in regulating plant water transport, especially in the roots and leaves. Ecologists, agronomists, and breeders can benefit tremendously by including the basic processes of water transport in their modelling and selection approaches. Currently, it is difficult to attribute structural characteristics of the xylem network to specific functions related to efficiency or safety. Developing new tools and methods that connect flow and structure at different scales is probably the most promising approach for gaining new insight into hydraulic regulation along the transpiration stream. Using a combination of structural and functional methods, it is now possible to distinguish between water-conducting and non-functional vessels. However, given the diversity of plant hydraulic architecture and dimensions, the same methods cannot be applied to all plants.

Advanced high-resolution imaging methods such as MRI, synchrotron X-ray imaging, and neutron-based imaging, now allows the analysis of flow dynamics at the organ level, as reported for rice leaves, grapevine stems, or Arabidopsis roots ( Kim and Lee, 2010 ; Brodersen, 2013 ; Lee et al. , 2013 a ; Warren et al. , 2013 ). The next major step will be to reconstitute a realistic 3D map of the hydraulic network of the whole organism starting with small model plants, such as Arabidopsis . At the subcellular level, the combination of scanning electron microscopy (nano-scale) and macroscopic techniques will enable investigations of the relationship between cell wall characteristics and the xylem network ( McCully et al. , 2009 ; Zehbe et al. , 2010 ; Page et al. , 2011 ). Atomic force microscopy will provide information about the surface chemistry of xylem cell walls. Confocal microscopy of leaves can provide insight into the relationship between leaf water dynamics and transpiration ( Botha et al. , 2008 ; Fitzgibbon et al. , 2010 ; Wuyts et al. , 2010 ). On the other hand, portable devices such as portable MRI are being developed to measure sap flow under real-field conditions. Infrared imaging techniques can provide a detailed map of surface temperatures and promote insight into water distribution, evaporation, ice formation, and sap flow. The development of enhanced computing power will also give rise to more realistic models and simulations of sap flow.

Transport of water and minerals is at the centre of all metabolic processes in plants, yet many variables and parameters related to this transport are unknown. In a broader perspective, a functional framework of the xylem network that integrates water flow dynamics at various levels of organizations can lead the development of bio-inspired technologies based on sap flow in plants. For decades, research on water transport in plants has hinged on a reference theory. To move forward, the research should now focus on unravelling how water transport through the xylem network is regulated using ingenious combinations of advanced techniques that probe the structure-function relationships of this fascinating transport system.

This work was supported by the Advanced Biomass R&D Center of the Global Frontier Project funded by the MEST (ABC-2011-0028378) to Ildoo Hwang.

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Water transport, perception, and response in plants

Affiliations.

• 1 Department of Plant Biology, Carnegie Institution for Science, 260 Panama Street, Stanford, CA, 94305, USA.
• 2 Department of Biology, Stanford University, 371 Serra Mall, Stanford, CA, 94305, USA.
• 3 Department of Plant Biology, Carnegie Institution for Science, 260 Panama Street, Stanford, CA, 94305, USA. [email protected].
• 4 Department of Biology, Stanford University, 371 Serra Mall, Stanford, CA, 94305, USA. [email protected].
• PMID: 30747327
• DOI: 10.1007/s10265-019-01089-8

Sufficient water availability in the environment is critical for plant survival. Perception of water by plants is necessary to balance water uptake and water loss and to control plant growth. Plant physiology and soil science research have contributed greatly to our understanding of how water moves through soil, is taken up by roots, and moves to leaves where it is lost to the atmosphere by transpiration. Water uptake from the soil is affected by soil texture itself and soil water content. Hydraulic resistances for water flow through soil can be a major limitation for plant water uptake. Changes in water supply and water loss affect water potential gradients inside plants. Likewise, growth creates water potential gradients. It is known that plants respond to changes in these gradients. Water flow and loss are controlled through stomata and regulation of hydraulic conductance via aquaporins. When water availability declines, water loss is limited through stomatal closure and by adjusting hydraulic conductance to maintain cell turgor. Plants also adapt to changes in water supply by growing their roots towards water and through refinements to their root system architecture. Mechanosensitive ion channels, aquaporins, proteins that sense the cell wall and cell membrane environment, and proteins that change conformation in response to osmotic or turgor changes could serve as putative sensors. Future research is required to better understand processes in the rhizosphere during soil drying and how plants respond to spatial differences in water availability. It remains to be investigated how changes in water availability and water loss affect different tissues and cells in plants and how these biophysical signals are translated into chemical signals that feed into signaling pathways like abscisic acid response or organ development.

Keywords: Aquaporins; Drought stress; Hydropatterning; Plant water relations; Stomatal regulation; Water perception.

Publication types

• Dehydration
• Plant Physiological Phenomena*
• Plant Transpiration
• Plants / metabolism
• Rhizosphere
• Water / metabolism*
• Water / physiology

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• DE-AR 1565-1555/Advanced Research Projects Agency-Energy (ARPA-E), U.S. Department of Energy

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Average passenger capacity of ocean-going cruise vessels worldwide 2018-2026

Average passenger capacity carried by ocean-going vessels in the cruise industry worldwide from 2018 to 2023, with a forecast until 2026

Maritime choke points

• Premium Statistic Panama Canal: number of transits 2014-2022
• Premium Statistic Number of transits in the Suez Canal 1976-2022
• Premium Statistic Number of transits through the Turkish Straits 2018-2021
• Premium Statistic Number of transits through the Malacca Straits 2000-2017
• Basic Statistic Oil flows - Strait of Hormuz 2014-2020

Panama Canal: number of transits 2014-2022

Number of transits in the Panama Canal from 2014 to 2022

Number of transits in the Suez Canal 1976-2022

Number of ships passing through the Suez Canal from 1976 to 2022

Number of transits through the Turkish Straits 2018-2021

Number of ships passing through the Bosphorus and the Dardanelles (Canakkale) from 2018 to 2021

Number of transits through the Malacca Straits 2000-2017

Number of ships passing through the Malacca Straits from 2000 to 2017

Oil flows - Strait of Hormuz 2014-2020

Oil flows through the Strait of Hormuz between 2014 and 2020 (in million barrels per day)

Maritime safety

• Basic Statistic Number of pirate attacks worldwide 2010-2022
• Basic Statistic Actual and attempted piracy attacks worldwide by country 2022
• Basic Statistic Worldwide ship losses by vessel type 2013-2022
• Premium Statistic Number of crew members attacked by maritime pirates 2015-2022

Number of pirate attacks worldwide 2010-2022

Number of pirate attacks against ships worldwide from 2010 to 2022

Actual and attempted piracy attacks worldwide by country 2022

Number of actual and attempted piracy attacks in selected territories worldwide in 2022, by country or location

Worldwide ship losses by vessel type 2013-2022

Number of ship losses worldwide between 2013 and 2022, by vessel type

Number of crew members attacked by maritime pirates 2015-2022

Number of crew members killed or injured by maritime pirates from 2015 to 2022

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New discovery for deep water transport in the Atlantic Meridional Overturning Circulation

Ocean waves crash into the shore. Credit: Pixabay

A new scientific study supported by the Climate Program Office’s Climate Variability & Predictability (CVP) Program explores how ocean currents in the North Atlantic Ocean move deep waters and influence global climate patterns, with a specific focus on the Atlantic Meridional Overturning Circulation (AMOC). CVP-supported scientist Zhengyu Liu of The Ohio State University led a team of international and U.S. researchers to discover a new pathway for deep water ocean transport. Zhengyu Liu is funded through a CVP grant to improve our understanding of multidecadal variability in the Atlantic Ocean through defining the roles of climate feedbacks and teleconnections.

Traditionally, researchers thought that the Deep Western Boundary Current was the main pathway for deep water movement in the North Atlantic, but this study, published in Nature Geoscience suggests there’s another significant route called the Eastern Pathway (EP) east of the Mid-Atlantic Ridge.

Click to read the full article

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