Environmental Adaptation Measures

The Syr Darya and Rhine Basins are used for demonstrating the AMR framework for evaluating adaptation strategies on the basis of environmental indicators (see Chapter 1). The main environmental issues and policy goals of the basins are briefly described and expressed in terms of indicators. This information is based on the reports of the basin studies conducted during the ADAPT project.

For the Syr Darya, the largest environmental problems correlate with the main activity in the basin, agriculture, and are partly caused by the way the water resources are managed (Savoskul et al., 2003). Agriculture pollutes the water with pesticides, herbicides, insecticides and mineral fertilizers. Furthermore, the agricultural use of water is so high that the inflow to the Aral Sea has decreased drastically and the sea level is dropping, which has led to the collapse of ecosystems in the Aral Sea region since the 1960s. Besides the current pressures on the system, climate change will also impose additional pressures on the system in the future. These pressures are called exogenous influences in the framework, because they cannot be influenced by actions in the basin. Other exogenous influences are population growth and economic growth. For the Syr Darya it is expected that under climate change the runoff peak in spring will

Box 4.1. Ecosystem-related environmental indicators. The indicators fit into the AMR methodology described in Chapter 1.

WATER QUALITY

State indicators

[ ]BOD Biochemical oxygen demand concentration. This measures the amount of oxygen required or consumed for the microbiological decomposition of organic material. It indicates how much organic matter is discharged by human activities. The BOD is lowered when sewage treatment plants are constructed. High BOD levels generally indicate low water quality.

[ ]NaCI The concentration of NaCI in the river near the mouth will increase when the sea level rises and when the fresh water flow of the river decreases. Also the overuse of the freshwater resources by agriculture can lead to salinization. It may disturb the freshwater habitats, reduce food production and endanger surface drinking-water facilities. It is best to choose a representative station downstream in an area where salinization occurs. It is possible to value on the ordinal scale when no conductivity data are available.

[ ]PCB This indicator measures the concentration of pollutants like pesticides, herbicides, insecticides and industrial by-products. PCB is an example of these pollutants and is a chronic pollutant. Concentrations of such contaminants in fish give an indication of the concentration of these pollutants in water and aquatic soil. These pollutants are obviously bad for human health. The most widespread pollutants in the ADAPT basins are PCB, DDT, mercury, lead, dioxin, other heavy metals and pesticides. These chemicals bind on fat and accumulate in the food chain. Other fish species can be used as indicators. For instance, for the Rhine the 'Eel' species is used.

[ ]Fertilizer The concentration of P or N in water is an indication of eutrofication by fertilizers. High values indicate poor water quality.

WATER QUANTITY

Outflow to sea The outflow to the sea is equal to the water available for the ecosystems near the mouth of the river such as mangrove and wetlands. It is also an indicator for the minimum flow during the year and can be used as an industrial indicator for the navigability of the river.

High flow The occurrence of a runoff that passes a certain threshold for the river. Riverine ecosystems need to be flooded once a year. When this does not happen for some consecutive years, these ecosystems will disappear. The environmental high flow is part of the environmental water resource (EWR).

Low flow Is the same as outflow to the sea, but more upstream, and ecosystems have a minimum requirement for the amount of water that is available in the EWR.

Decision indicators

% Freedom

No. fish ha Ecosystem ha Badland/ desert

Longitudinal and lateral freedom are indicators of the pristinity of the river system. The indicator for lateral freedom provides the percentage of the river area that is not protected by man-made dykes. The higher this percentage,fewer dykes or protective measures have been applied. More dykes generally protect the river from flooding but prevent exchange of nutrients and fish to the flood-plains. This influences biodiversity negatively. Decrease in lateral freedom also influences the flood dynamics of the river basin, and hence the ecosystem equilibrium. Longitudinal freedom relates to the number of dams and barrages in the river. These man-made obstructions hinder fish migration, and hence lower biodiversity and ecological quality. Decrease in longitudinal freedom also influences the flood dynamics of the river basin (high flow, low flow), and hence the ecosystem equilibrium. The number of indicator fish species is a bio-indicator for water quality. The fish need a certain minimal quality of water and quality of habitat (geomorphologic) to be able to survive and reproduce in a river system. If the fish are able to survive, the overall quality of the system and water quality are good. In some rivers, the species can be salmon or trout, but any other fish can be taken. Instead of one fish species, the diversity of fish species can be used.

Forest, wetland, upstream forest, etc. The area of forest species or wetlands in the basin is an indicator for the size of the ecosystem habitat. Large areas have a larger natural value as their capacity to host species (biodiversity) is greater compared with smaller areas. Larger areas also better regulate changes in erosion rates and sediment loads. The area of wetlands is an indicator for the size of this habitat and it measures indirectly the biodiversity of the area. The area of badland or desert is an indicator for the size of this non-inhabitable land. An increase in area in the basin means that the lands are deteriorating in quality.

Table 4.1. Environmental adaptation measures in Syr Darya and Rhine Basins.

Measure

Syr Darya

Rhine

Decrease agricultural land

X

X

Use less fertilizer

X

Use less herbicide

X

X

Change management of dams (minimum flow requirements)

X

Decrease amount of water for irrigation

X

X

Build fish traps

X

X

Develop sewage treatment plants

X

Develop dykes

X

Increase floodplain storage and retention

X

Temperature control devices

X

start 3-4 weeks earlier and will decrease in volume compared to the current situation. This is expected to negatively influence the environment through a decrease in water availability in summer, probably below the EWR of the basin (see Chapter 6 for more information). The set of indicators that best allows monitoring the state of the water resources in the Syr Darya Basin are: 'outflow to the Aral Sea' and concentrations of 'PCB', 'fertilizer' and 'NaCl'. The DIs used are: 'area of badlands/desert' and 'longitudinal freedom'.

In the recent past, the environmental issues in the Rhine Basin mainly relate to pollution of the river, especially during the period between 1960 and 1975. After this period the inflow of pollutants from industry and cities was constrained by the construction of sewage treatment plants and international treaties (Klein et al., 2003). Currently, the most pressing issues related to the environment are: extreme high flows, low flows (which cause an increase in water temperature and an increase in concentration of pollutants), input of pollutants and habitat suitability for the return of fish species like salmon and the return of bird species such as the black stork (de Bruin et al., 1997). For the Rhine the SIs used are: 'concentration of BOD', 'low flow' and 'fertilizer'. The DIs are: 'fish diversity', 'upstream forest', 'floodplain forest' and 'wetlands'.

The change in the state of the water resources in the two basins is simulated for two time slices, 2010-2039 and 2070-2099, and two scenarios, A2 and B2 (see Chapters 2, 5 and 6). First, with the help of models and experts, the values of the SIs and DIs are estimated for the future without considering adaptation. These values are compared with the current situation. A change in DI and SI values indicates an impact. This provides a baseline to which the different adaptation strategies will be compared. Next, adaptation strategies are formulated from various measures, ranging from technical measures to policy measures.

For a water manager in the Syr Darya Basin, the environmental focus would lie on the reduction of pollutants, the supply of more water to the wetlands in the basin and an increase in outflow to the Aral Sea. The resulting set of measures (from here called a strategy) is listed in Table 4.1. For the Rhine Basin, the environmental measures would focus on restoring the habitats in the river and on the floodplains to make them suitable again for supporting certain (fish) species.

In Table 4.1, adaptation measures for the Syr Darya and the Rhine Basins are listed. The measures mainly address the improvement of environmental quality, and are almost all structural engineering measures, as opposed to non-structural measures such as water pricing and efficient water demand management. This could be a result of the time frames and the scope of the ADAPT project, with a greater emphasis on structural than non-structural measures. Structural measures are considered good measures for the long term as opposed to non-structural measures, which are more suitable for the shorter term (Dvorak et al., 1997).

Next, models were used to evaluate the adaptation strategies by calculating new values for the SIs and DIs. The measures and strategies are evaluated by comparing the difference in the DIs for the different adaptation strategies and by comparing them to the pre-set goals. By varying the measures and running the models for these slightly different strategies, the effects will change and the best strategy can be found in an iterative way. For both storylines, A2 and B2, and for the two time slices, 2010-2039 and 2070-2099, the simulation model is run.

For each basin, originally four adaptation strategies were developed, which focused on enhancing environment, food security, industrial production and a strategy that is a mix of these three. In the basin chapters, all these strategies are evaluated through the values of indicators. In Table 4.1, only the values of the indicators for 'no adaptation' (baseline) and the environmental strategy are shown for the two time slices. The effectiveness of the strategies can be evaluated through this table.

From Table 4.2 it can be shown that not all the indicators apply to both basins. For example, the state indicator 'concentration PCB' applies to the Syr Darya, but not to the Rhine. This can be explained by the use of products that contain PCBs. These products have been prohibited for many years in the Rhine Basin, but are still allowed in the Syr Darya Basin. It is also possible for other indicators to be unsuitable because that species or substance does not occur in that region.

So far, only planned adaptation measures have been described. Apart from planned adaptation, spontaneous adaptation will occur. These are the responses of the ecosystems as described earlier. For the environment, the most important spontaneous adaptation is the expected increase in water efficiency by plants, as a result of higher CO2 concentrations in the atmosphere. In Chapters 5-12, the different strategies and effectiveness of the measures are explained more thoroughly. The migration of species is not taken into account in the development of adaptation strategies and in the evaluation of the strategies, although it is expected to occur in all the basins. With the available data on tree species, for instance, it was not possible to make estimations like the ones made for agricultural crops (see Chapter 3).

Effects of measures as explored above are meant to enhance the quality of the environment, but some of them are not beneficial for the other users in the basin. For instance, increasing the winter flow in the river by releasing water from reservoirs decreases food yield through less irrigation water and reducing the the possibility to produce hydropower in summer. The table clearly shows that a measure that benefits one goal is probably negative for the realization of another, so in the development of adaptation strategies trade-offs have to be made between the different users and their goals.

Table 4.2. Results for the Syr Darya and Rhine Basins for the A2 scenario.

Rhine

Syr Darya

No adaptation

Environmental strategy

Environmental No adaptation strategy

No adaptation

Indicators

2039

2099

2039

2099

2039

2099

2039

2099

State indicators

[BOD]

0

0

+/-

+/-

+

+

Fertilizer

0

0

0

+

--

--

+/-

+

[PCB]

n/a

n/a

n/a

n/a

--

--

+/-

+/-

[NaCl]

n/a

n/a

n/a

n/a

-

--

-

--

Outflow to sea

n/a

n/a

n/a

n/a

n/a

2.1

n/a

3.8

Radioactive pollution

n/a

n/a

n/a

n/a

-

-

+

+ +

Decision indicators

No. fish (diversity)

0

0

+

+

ha upstream forest

0

0

+

+ +

n/a

n/a

n/a

n/a

ha floodplain forest

0/+

+

+

+ +

n/a

n/a

n/a

n/a

and wetlands

% longitudinal freedom

0

0/+

+

+

25

34

25

30

% lateral freedom

0

0/+

+

+

n/a

n/a

n/a

n/a

ha of badland/desert

n/a

n/a

n/a

n/a

+ +

---

0

---

+ , Positive effect of the strategy on this indicator (quantitative effect can be an increase or a decrease!).

-, Negative effect of the strategy on the indicator. 0, No effect of the strategy on this indicator.

0 0

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