2.1 Data

The data used for the study are summarised in Table 2. Runoff and water withdrawals (WWs) were calculated using the PCR-GLOBWB 30 arcmin model (Wada et al., 2011a, 2013; Wanders et al., 2018). PCR-GLOBWB is a conceptual, process-based water balance model. In brief, it simulates for each grid cell and for each time step (daily) the water balance in two vertically stacked soil layers and an underlying ground water layer, as well as the water exchange between the layers and between the top layer and the atmosphere (rainfall, evaporation, and snowmelt) (Wada et al., 2013). Discharge estimates from the model are extensively validated against observations from the Global Runoff Data Centre (GRDC) in existing publications by Wada et al. (2013, 2014). The return flows from industrial and domestic sectors have been taken into account in PCR-GLOBWB and the recycling ratios for industrial and domestic sectors have been estimated (roughly 40–80 %) at a country level and validated based on Wada et al. (2011a, 2014).

Total WW was calculated for each SBA as the sum of three water use sectors: irrigation, domestic, and industrial. The water use data for these sectors were obtained from the same model as the discharge simulations (Wada et al., 2011a, 2013; Wanders et al., 2018). Water use estimates have also been previously validated against reported country data, notably FAO AQUASTAT, by Wada et al. (2011a). In this analysis, water withdrawals refer to the total amount of water withdrawn, but not necessarily consumed, by each sector, much of which is returned to the water environment where it may be available to be withdrawn again. However, estimation of return flows is uncertain and they may not necessarily be available to downstream users, for example because of pollution, timing of the flows, or infiltration to groundwater (Wada et al., 2011a, b). Thus, the return flows were not subtracted from withdrawals in this analysis.

To provide an indication of need for water (rather than withdrawals), population density information was obtained from the HYDE 3.2 dataset for each year from 1981 to 2010 (Klein Goldewijk et al., 2010). The data were first aggregated from 5 to 30 arcmin resolution and then for each SBA for every year over the 30-year period.

The 30 arcmin raster dataset DDM30 (Döll, 2002) described drainage direction for both surface flow routing in PCR-GLOBWB and definition of upstream–downstream links.

Figure 2Upstream–downstream relationship between sub-basin areas (SBAs) in the Oder basin and average simulated annual water availability for 1981–2010. Drainage network and sub-basin division are based on DDM30 (Döll, 2002) and country borders (Natural Earth, 2017) with additional manual assignment of border cells.

Download

Country boundaries were first rasterised from Natural Earth admin 0 boundaries (Natural Earth, 2017). Border cells were then manually assigned to countries to provide meaningful hydrological relationships. In general, single cell SBAs were avoided. Cells where country borders follow a river were treated as separate “shared” zones. What we refer to as a “country” raster therefore includes both countries and shared zones.

2.2 Methods

2.2.2 Interpretation of upstream dependency in terms of water scarcity

Looking at the average availability of water (1981–2010) for the SBAs of the Oder basin provides an illustration of the concept of upstream dependency (Fig. 2). The headwater SBA (OdSBA_CZ) obviously has no upstream dependency; the three types of water availability are the same. But in the case of SBAs OdSBA_PO-A, OdSBA_PO-B, and OdSBA_GE, upstream water availability and withdrawals influence water availability. These are the SBAs we are most interested in.

Dependency on upstream water can be assessed by comparing an SBA's scarcity category across the different water availability types (i.e. local runoff, natural discharge, actual discharge – see definitions in Table 1). We calculated scarcity using water stress and water shortage indices. Water stress refers to impacts from high use of water, while water shortage refers to impacts from insufficient water availability per person (Falkenmark et al., 2007; Kummu et al., 2016).

The stress indicator was calculated as WW.local/avail and the shortage indicator is calculated as avail/population.local. The stress indicator includes environmental flow requirements (EFRs), assuming 30 % of the water is needed to satisfy the EFRs (Falkenmark et al., 2007). To determine whether water stress or shortage occurs, we respectively used the thresholds of 0.2 and 1000 m³ cap⁻¹ yr⁻¹, as defined by Falkenmark et al. (2007) and used by other research too (Liu et al., 2017). Crossing these thresholds leads to impacts from insufficient water availability per person, potentially limiting economic development and human health and well-being (Falkenmark et al., 2007). Using annual values may mask water scarcity during the dry season. Falkenmark's per capita water availability as a measure of water scarcity has limitations as an indicator. Nevertheless, both stress and shortage are useful indicators of the more general concept of scarcity. Shortage, measured by per capita water availability, captures an important intuition that sufficiency of water availability depends on population. Even though the thresholds are arbitrary, using both indicators provides a useful balance to understand the development of water scarcity (Kummu et al., 2016), as well as illustrating the generality of the analysis framework. The use of these thresholds is in line with existing studies and while interpretation of the results is limited by the simplicity of the indicators, they provide a first step in understanding upstream dependency.

Annual stress and shortage were calculated using WWs and population for 2010 with (1) local runoff, (2) natural discharge, and (3) actual discharge. Equations for water stress are

$$\begin{array}{}\text{(1)}& {\displaystyle }& {\displaystyle \frac{WW.local}{avail.local}};\text{(2)}& {\displaystyle }& {\displaystyle \frac{WW.local}{avail.natural}};\text{(3)}& {\displaystyle }& {\displaystyle \frac{WW.local}{avail.natural-WW.upstream}}.\end{array}$$

Equations for water shortage are

$$\begin{array}{}\text{(4)}& {\displaystyle }& {\displaystyle \frac{avail.local}{population.local}};\text{(5)}& {\displaystyle }& {\displaystyle \frac{avail.natural}{population.local}};\text{(6)}& {\displaystyle }& {\displaystyle \frac{avail.natural-WW.upstream}{population.local}}.\end{array}$$

The water scarcity status was categorised as “No scarcity” (N) and “Scarcity” (S) using average annual water availability from 1981 to 2010. The 30-year period was used to capture the current hydro-climatic characteristics. Figure 3a represents scarcity for the three water availability types for the Oder basin under average conditions, shown within the Falkenmark matrix (Falkenmark et al., 2007; Kummu et al., 2016) which shows stress and shortage together. Archetypes in the Falkenmark matrix describe the water scarcity status (corresponding to position on the plot) and where both shortage and stress occur, according to which occurs first (Kummu et al., 2016). Figure 4 defines the four possible different dependency categories in terms of the scarcity an SBA can face, as illustrated by the discussion below.

Figure 3Scarcity and dependency category for the Oder sub-basin areas (SBAs) under annual average conditions. The Falkenmark matrix (a) and plot of water availability required to avoid stress (b) show changes in stress and shortage under different types of water availability (see definitions in Table 1). The inset map represents the Oder SBAs corresponding dependency categories. Scarcity and dependency categories for each SBA for the year 2010 were calculated using a water stress threshold value of 0.2 and water shortage threshold value of 1000 m³ cap⁻¹ yr⁻¹.

Download

Figure 4Definition of potential upstream water dependency categories. Dependency categories are obtained by summarising three letter codes representing the scarcity category using local runoff, natural discharge, and actual discharge respectively (see definitions in Table 1).

Download

None of the SBAs have any shortage as the per capita water availability has never dropped below 1000 m³ cap⁻¹ yr⁻¹. OdSBA_PO-A is stressed (S) under all three water availability types, and OdSBA_CZ is not stressed (N). OdSBA_GE and OdSBA_PO-B would both be stressed (S) only if they were restricted to their local runoff (Fig. 3a). After accounting for inflows from upstream (natural discharge), the stress level decreased from 0.25 to 0.01 (N) for OdSBA_GE and from 0.35 to 0.01 (N) for OdSBA_PO-B (Fig. 3a). This change in stress category means that both of these SBAs are dependent on upstream water to avoid stress. We further see that upstream WW increases the stress level relative to natural conditions (to 0.02 for both; Fig. 3a and c), but the threshold for stress was not crossed. The stress level changed without changing the stress category, such that the category of the dependency was not affected; we have a “hidden” rather than “open” dependency (definitions in Table 1). In the case of OdSBA_PO-A, local runoff is not sufficient to meet needs and that upstream water availability and WWs do not influence the scarcity category of this SBA. This SBA is under the same scarcity conditions regardless of upstream influence (Fig. 3a and b), and it is thus categorised as “No dependency”, though the intensity of scarcity is still affected by upstream WW. The dependency category of an SBA can then be summarised using three letter codes representing the scarcity category using local runoff, natural discharge, and actual discharge respectively: OdSBA_GE and OdSBA_PO-B are SNN, OdSBA_PO-A is SSS, and OdSBA_CZ is NNN (Fig. 3a).

2.2.3 Determinants of dependency category and possible transitions in them

In order to evaluate possible responses to dependency, we need to understand what determines a dependency category and what can be done to achieve or to avoid change. Annual water availability can be thought of as a constraint on the environment in which a society operates. Society is able to influence that constraint, for example by building reservoirs (Veldkamp et al., 2017) – captured to some extent by the model. However, for a given hydro-climate and state of development, it is useful to think of the current water availability regime as an integral, defining characteristic of a system regime. As population and WW increase in a region, the occurrence of shortage, stress, and upstream dependency is determined by the volumes of the three types of water availability. A region will face scarcity or dependency as a result of

• insufficient local runoff (avail.local);

• insufficient discharge, from local runoff and possible upstream inflows (avail.natural);

• insufficient discharge after upstream WW (i.e. water withdrawals) (avail.actual).

From a resilience perspective, these volumes of water can be thought of as thresholds, where an SBA would be under the “No scarcity” category when its average local runoff (avail.local) is sufficient to meet the water demand in a given year and “Scarcity” category when its average natural discharge (avail.natural) is insufficient in relation to its water demand. In this study, “demand” is used as a high-level umbrella term covering both actual withdrawals (for the stress indicator) and need for water (population, for the shortage indicator).

Figure 5Typology of possible transitions in dependency category, as local water demand or upstream water withdrawals (WW) increase or decrease (a). Upstream WWs decrease the downstream water availability, while local water demand increases the pressure on available resources. The current (2010) status of OdSBA_GE and OdSBA_PO-B (for both stress and shortage) is shown in the transition map (b). See definitions of terminology in Table 1.

Download

Figure 6Dependency categories for each sub-basin area (SBA) for the year 2010 using (a) a water stress threshold value of 0.2 and (b) a water shortage threshold value of 1000 m³ yr⁻¹ cap⁻¹. See definitions of dependency categories in Fig. 4 and definitions of key terminology in Table 1.

Download

Figure 5 shows the ordering of possible thresholds for an SBA based on water availability, and how the shortage and stress categories vary as demand changes. To allow comparison, water availability, population, and withdrawal are all expressed as percentages respectively of avail.natural, carrying capacity (avail.natural∕1000), and sustainable yield (avail.natural × 0.2). The current status of OdSBA_GE and OdSBA_PO-B is shown in the figure. Currently OdSBA_GE and OdSBA_PO-B are in the “SNN” category for stress and “NNN” category for shortage (Fig. 3). They have hidden dependencies (avoiding stress) as the average year inflows after upstream WW are sufficient to meet water demand. If the water demand in these SBAs were to increase to a level where the average year inflows after upstream WW (avail.actual) would not be enough to meet demand, the SBA would next transition from SNN to the SNS category. Thus, with the increase in demand, the dependency category (e.g. “SNS”) would change based on the thresholds it crosses and ultimately the basin would become SSS, indicating that an SBA would be under scarcity under each type of water availability considered. The same thing would happen with shortage as the population increases (Fig. 5). Over time, this change in dependency category could go forward and backward as water demand of the SBA increases or decreases. This order of thresholds determines the transition in dependency category for OdSBA_GE and OdSBA_PO-B as local demand increases or decreases.

Table 3Number of SBAs under different dependency categories in the year 2010.

Download Print Version | Download XLSX

So far, we have conceptualised change in dependency category in the context of a fixed set of water availability thresholds, obtained directly from estimated water availability volumes. The order of thresholds determines the transition in dependency category as local demand increases or decreases. In fact, even if upstream WW changes the values of the thresholds, their order will remain the same. These scarcity thresholds are naturally ordered because local water necessarily becomes insufficient before upstream water availability types: local ≤ actual ≤ natural. We do, however, distinguish between headwaters vs. middle stream and downstream SBAs.

Headwaters are the simplest case. Given they are the most upstream SBAs, they rely solely on local runoff, Increases in an SBA's demand cause a transition from “No scarcity” to the “Scarcity” category. Decrease in demand would have the opposite effect (Fig. 5).

In the case of middle stream and downstream SBAs, transition occurs between four scarcity categories, which are connected by a simple map of transitions: NNN-SNN-SNS-SSS. Transition in the scarcity category depends on both local demand and upstream WW. The experience of dependency in the Oder basin is therefore generally applicable to all middle and downstream SBAs. As the local demand increases, the SBA moves from NNN to SNN, exposing it to a “hidden dependency” as local runoff become insufficient, but the SBA still receives sufficient upstream inflows to meet the local demand. The next transition between SNN to SNS is dependent on both local demand and upstream WW until local demand increases to the level where all available water become insufficient – the SBA becomes SSS. The decrease in local demand and upstream WW will have the opposite effect.

Thus SBA crosses thresholds which not only change the scarcity category but also change the dependency category, considered in this study as transitions between different “system regimes”. Note that we focus on the effect of increasing or decreasing local demand and upstream WW, leaving changes in water availability to future work.

4.1 What are the implications for mitigation and prevention of scarcity?

The literature on resilience and complex adaptive systems emphasises that it is difficult to predict what will happen in the future, but we can identify what are the transitions that might occur to prepare ourselves such that the system either avoids or manages those transitions. According to our framework, the starting point is a system regime with low water demand, easily satisfied by local runoff (NNN). There is no need to use upstream inflows, such that upstream withdrawals have no effect on local water scarcity. The need to engage with upstream water users begins with an increase in local water demand, transitioning to a system regime where scarcity depends on upstream withdrawals (SNN). It is in the interest of both downstream and upstream users to avoid transitioning to an open dependency (SNS), which could happen because of either increases in local demand or upstream WW (as well as changes in climate). Despite the invisible consequences of a transition to SNN, it would be worthwhile to expose the hidden dependency. Negotiation to reduce WWs may fix an open dependency, but there is also another possible outcome. If local water demand continues to increase, the dependence on upstream disappears again. Very high water demand cannot be met even with upstream inflows (SSS), such that upstream withdrawals can no longer solely cause scarcity, even if they contribute to its severity. In this system regime, negotiation with upstream regions is not sufficient to avoid scarcity, so it may be more worthwhile to look for other solutions, such as those within the political economy (Allan, 2002).

Understanding these transitions provides a basic level of guidance for a region. In a no dependency system regime (e.g. most SBAs analysed), efforts can be made to keep water demand at low enough levels to be self-sufficient. If water demand is expected to increase, monitoring is useful to avoid being surprised by the breaking of a hidden dependency. While our analysis shows relatively few open or hidden dependencies in 2010, population growth and associated water demand means that the need for water scarcity-related negotiation in transboundary basins could become a much greater issue in the future. It is specifically the emergence of dependencies that introduces the need for negotiation. Treaties have an indirect effect on physical upstream water dependency by limiting or coordinating development of water resources locally and upstream. Treaty design can be innovated to include functions that improve the stability of the dependency and hence prevent scarcity from occurring. If decision makers cannot avoid a transition to scarcity (i.e. an open dependency), perhaps due to factors outside their control, then coordination can at least facilitate adaptation to cope with physical water scarcity. There are regions where physical water scarcity is to some extent expected – development is limited by water availability, such that fully utilising other resources (e.g. land) requires more water than is available. In addition, it should be pointed out that negotiation for rights to upstream inflows is only one strategy among many to try to meet water demand. In such cases, treaties can focus on mitigating the severity of impacts of scarcity.

Downstream areas with increasing water demand should be mindful that, in a way, they are “choosing” to have to deal with dependencies and potential scarcity. If upstream withdrawals are stable, it can be argued that any conflict is effectively of their own making. Scarcity and dependency only emerge as problems when local demand crosses a threshold. This gives the impression that it is the local user that is responsible for the new problem, even though it may simply be that they are late to the game. On the other hand, if upstream withdrawals later increase, downstream regions might argue that they would not need to deal with scarcity, were it not for upstream actions. These interpretations of responsibility rely on the idea of precedence. The precedence paradigm is visible in prior appropriations regimes in the USA, while negotiated allocations are arguably implemented by water markets in Australia and elsewhere (Grafton et al., 2011). Even in a negotiated approach, however, existing water needs and WWs are often taken into account, including at an international level – hybrid approaches are common. The UN Watercourses Convention of 1997 also refers to the no harm principle (article 7), which works in tandem with consideration as to whether a given water use is reasonable and equitable (UN Watercourses Convention, 2018). These examples illustrate the close connection between water allocation and different views about responsibility for transitions.

4.2 Relation to existing work

Our work distinguishes between dependency and scarcity and recognises that dependency is primarily about potential for future scarcity, which transboundary cooperation aims to mitigate. To judge the importance of transboundary cooperation, it is more important to look at areas under no scarcity which are dependent on upstream inflows. The “open dependency” category (SNS) and SSS only include cases where institutional arrangements have failed to prevent scarcity from occurring. Our work, however, highlights that negotiation to avoid needing to cope with scarcity is only part of the issue. As demand increases, negotiation among riparian countries will eventually turn to discussion of intensity and frequency of scarcity, and the level of demand at which it occurs. Other existing work also distinguishes different types of rivers and basins to help understand why some riparian countries on international rivers have been able to successfully negotiate treaties and others have not – taking into account, for example, size of population, GDP, upstream–downstream relationship, and asymmetries in economic and political power among riparian states (Delbourg and Strobl, 2012; Song and Whittington, 2004; Wolf et al., 2003). Increasing water scarcity has been identified as a risk factor, but has not previously been systematically explored in terms of upstream dependency. Our dependency category typology complements this existing work, and relations to other typologies could be explored in the future.

One of the main advantages of our analytical framework, compared to existing knowledge, is that it highlights the possible “hidden” dependency of upstream water, which has not been assessed in these terms before. Previous studies on transboundary river basins identified clear evidence of the impacts of upstream water use to downstream water availability and water scarcity level (Al-Faraj and Scholz, 2015; Munia et al., 2016; Nepal et al., 2014; Veldkamp et al., 2017). It has already been found that about 0.95–1.44 billion transboundary people are under stress because of local water use, while upstream WWs increased the stress level by at least 1 percentage-point for 30–65 SBAs, affecting 0.29–1.13 billion people (Munia et al., 2016). Our analysis provides a different view of the issue by revealing that 386 million people (14 % of the total transboundary population) are dependent on upstream water to avoid possible stress because of their own water demand and 306 million people (11 % of the total transboundary population) are dependent on upstream water to avoid possible shortage (Table 3). Along with previous work, including a broader discussion of hydro-political dependency (Brochmann et al., 2012; Giordano and Wolf, 2003; Gleick, 2014; Jägerskog and Zeitoun, 2009; Mirumachi, 2013, 2015; Wolf, 1998, 1999, 2007), our analysis highlights the importance of local demand in causing scarcity and dependency. If local demand stays low enough and local water resources are sufficient to meet the demand, neither scarcity nor dependency occurs, and transboundary cooperation is not needed. This point has been made in existing literature (e.g. related to social construction of scarcity) but is not yet widely recognised.

4.3 Limitations and future work

In our analysis, we used WWs, which refer to the total amount of water withdrawn, but not necessarily consumed, by each sector; much of which is returned to the water environment where it may be available to be withdrawn again. The return flows from industrial and domestic sectors have been taken into account in PCR-GLOBWB and the recycling ratios for industrial and domestic sectors have been estimated and validated at a country level based on Wada et al. (2011a, 2014). However, in this paper, estimation of return flows is uncertain and they may not necessarily be available to downstream users, for example because of pollution, timing of the flows, or infiltration to groundwater (Wada et al., 2011a). We therefore did not include return flows when calculating water stress, but those could be taken into account in the future.

EFRs (i.e. environmental flow requirements) are important in transboundary water management. The stress indicator used in the analysis includes EFRs, assuming 30 % of the water is needed to satisfy the EFRs (Falkenmark et al., 2007). We do not account for EFR in a spatially disaggregated way as the analysis is conducted at the SBA scale, where spatially variable EFRs influence the dependency category, adding additional complexity to the transition map. EFRs are in any case a rather complex issue and not easy to quantify (Pastor et al., 2014). Global scale EFR methods could be criticised for not adequately capturing on-the-ground conditions – our treatment of environmental flows is fit for purpose given that our focus is on the resilience-based analytical framework.

Nuances of water availability were not taken into account in this analysis. Industrial or domestic pollution may occur in upstream parts of a basin, which might make water unusable for irrigation or domestic purposes (Thebo et al., 2017). Availability of green water has not been considered either. Green water increases the amount of locally available water by including soil water in addition to runoff. This affects scarcity, as the need for blue water should vary in response to changing green water availability, e.g. when there is less green water available, more blue water is needed. Decreases in availability of blue water (e.g. due to upstream withdrawals) may also push a region to use more green water. While green water is an important part of the local water availability, it does not affect inflows from upstream, by definition. Water is called “green water” when evapotranspiration occurs directly from rain or soil water, without runoff occurring. There is no additional effect on avail.natural, other than that on avail.local. Incorporating green water into the analysis will not affect avail.actual data either, as upstream withdrawals are in principle already accounted for in the water use model (including the effects of green water availability). The thresholds for both water shortage and stress are highly uncertain, so the effect of green water on the results is difficult to anticipate.

The main emphasis of the paper was the development of the analytical framework to understand the concept of upstream dependency from a resilience perspective. In this study, we provide the first attempt to link the dependency order to management strategies that could be taken to ease the possible scarcity situation. In future studies, in order to evaluate which transitions are actually plausible in the future, the analytical framework could also be applied to water availability and demand scenarios based on future climate change scenarios (representative concentration pathways, RCPs; Van Vuuren et al., 2011) as well as shared social pathway scenarios (SSP; O'Neill et al., 2014). In doing so, the scarcity criteria could also be revisited, given the simplicity of the indicators and thresholds used here, as acknowledged in Sect. 2.2.2. The analysis can be integrated with the concept of “adaptation tipping points (ATP)” to understand what strategies are needed (Kwadijk et al., 2010) to cope with the scarcity status. Additional insights may be gained using other thresholds and/or other water scarcity indicators, such as food self-sufficiency (Gerten et al., 2011; Kummu et al., 2014) or sustainability of water withdrawals (Wada and Bierkens, 2014). Future work could also quantify “distance” from a threshold, which would further address the distinction between how close these basins are to scarcity.

Our method was applied here at the basin scale, considering only international transboundary basins. It can, however, also be applied to understand the dependency at different scales to interpret, for example, more localised water dependencies, e.g. between states within countries (Garrick, 2015). Moreover, instead of using average water availability, analysis can be performed using water availability for each year to capture variability. Thus, the evolution of scarcity and dependency of an SBA for a given climate can be categorised into different transition pathways along which an SBA progresses as its water demand or water availability changes. An early attempt at this was made in the “discussion paper” version of this article (Munia et al., 2017). In connecting to management, the relevance of frequency of scarcity could be further examined in order to provide a more meaningful distinction between scarcity that occurs every year and scarcity that occurs in some year: at what frequency of scarcity do management options need to be implemented permanently rather than only adaptively e.g. trading of temporary vs. permanent water allocations (Bjornlund, 2003).