Use of numerical methods in assessing the impact of projects on water levels

You can act as long as you don’t harm, that is, the requirement for an environmental impact assessment

Undertakings that may have a significant impact on the environment, can be implemented only under the condition of obtaining a decision on environmental conditions. In such cases, a detailed analysis and assessment of the impact of the investment on all environmental components is carried out as part of the preparation of a complete report.

The protection of the water environment from the negative impact of investment activities applies to everyone, both service providers, i.e. relevant governmental and local governmental units, and service recipients – individual and collective users, including investors planning to implement projects.

According to the binding jurisprudence of the Court of Justice of the EU,[1] in order for us to speak of harm, the impairment of natural conditions and negative changes must be measurable. Thus, the requirement to assess the risk of deterioration and failure to achieve the required classification of a surface water body (water body) for planned projects indicates the need to quantify the results of the impact assessment in order to provide evidence in the decision-making process. In addition, according to the operative part of the judgment, deterioration of the status of a water body occurs when at least one of the quality elements is downgraded by one class, even if this deterioration is not expressed in an overall change in the classification of the entire water body. In the case of a water body in poor condition, any deterioration in the value of the indicator due to the impact of the planned project is clearly interpreted as deterioration, despite the lack of a change in class[2]. In view of the above interpretation, the transition of an indicator from a given class to a lower class, as a result of the impact of a given project, must be expressed numerically.

Despite this necessity, the use of tools that allow quantitative assessment of changes occurring in the inland water environment is still not a common and required practice in such key aspects of water resources protection as the degree of impact of projects on the state of surface waters.

Numerical methods are not widely used in evaluation practice so far, expert methods are more popular. Such an approach makes it impossible to meet the basic criterion for assessing the project’s impact on water quality. Numerically expressed changes allow to answer questions:

1. Will the planned investment involve a reclassification of impacted waters to a lower quality class?
2. Will the planned investment be so insignificant that it will not cause a downward change in the class of waters, and therefore will not cause deterioration in their condition or hinder the achievement of environmental goals?

The development and implementation of environmental impact assessments of projects based on numerical tools should lead to streamlined procedures, both on the part of applicants and authorities issuing environmental decisions. This should also lead to less uncertainty in the results of these assessments.

Describe, but also calculate, or the use of mathematical models in procedures

In recent decades, there have been studies and publications in the nature of guidelines or recommendations, identifying mathematical models as tools to support water resources management and conservation (BDMF, 2000[3]; UNECE, 2007[4]; Black et al, 2011[5]; GWP, 2013[6]; EC, 2013[7]; EC, 2015[8]). The effectiveness of using numerical tools for quantitative assessment of environmental changes, as giving much more precise results than qualitative analysis, has been proven in practice.

The first described use of a mathematical model to identify the causes of changes in water quality (impact assessment) took place in the 1920s. In the 1970s. This model, as well as subsequent ones, was tailored to meet the specific needs of water resources management[9] and was applied in assessing the impact of projects. However, applications of such solutions are rare, globally dispersed and not driven by a systems approach (CEGIS, 2015[10]; AECL, 2017[11]; CEGIS, 1018[12]; MRC, 2018[13]). What’s more, one can also find criticisms of reports made with the support of mathematical models, such as. SWAT. They do not deny the legitimacy of using such models, but stress the need to prepare them properly[14].

However, despite a significant increase in interest in the use of mathematical equations to describe natural phenomena and the legal requirement for quantitative assessments, numerical methods for assessing the impact of investments on water levels are not widely used. The reason for this is certainly not the lack of availability of tools. So there are other factors that make it difficult to implement such a system. One is the level of complexity of the statistical tools, knowledge of which is the basis for the evaluation. Lack of awareness among those/entities deciding on impact assessment procedures about the applicability of such tools and their effectiveness (under certain conditions) may also be a problem, as well as the high cost and time-consuming nature of the implementations, the additional expert knowledge required, and the relatively large number of inputs[15] .

The dissonance between the use of, often complex, mathematical tools and evaluation practice may be due to timeframes, available financial and human resources. The lack of an adequate exchange of information between the scientific community, developing the methodological basis and tools, and the community of practitioners, participating in the process of assessing the impact of projects on the aquatic environment, both on the side of the investor (developer) and the authorities (receiver) was highlighted already several decades ago[16]. Despite the passage of time, the problem is still present, and the situation has even worsened.

Can biological processes be translated into models?

Moving away from assessing water quality as an economic resource and replacing it, as a consequence of the entry into force of the WFD, with an assessment based on the control of the condition of aquatic ecosystems, was a revolutionary step and most appropriate. At the same time, the notorious problems of trying to find cause-and-effect relationships between aquatic flora and fauna and anthropopressure through mathematical models have long been known[17].

The history of models describing the behavior of aquatic biology and its response to environmental pollution dates back to the middle of the last century, and the first extensive descriptions involved calculations based on equations that approximate environmental processes[18]. Such models are constantly being developed to reflect the behavior of particular groups of organisms. However, they still require very detailed parameterization of each group of organisms, and their preparation is very time-consuming. As a result, the cost and time to perform the assessment would be much greater, but the results more precise. However, there are cases in which the use of such models is the best option. This applies to assessments of the impact of investments on lakes, reservoirs or other water bodies, which, due to increased nutrient loading, changes in oxygen conditions or water temperature, may contribute to the transformation of habitat conditions.

A key prerequisite for conducting modeling is the availability of appropriate input data. In the context of assessments, consider the accuracy of results from the state environmental monitoring (PMŚ) network, which often serve as the basis for conducting environmental assessments. Classification of physicochemical indicators is based on averaged values from samples taken several times during the year, while assessment of biological elements (except phytoplankton) is usually based on a single sampling, and often at a different time, or even location, than sampling for physicochemical analyses. This type of monitoring, its frequency and spatial inconsistency can be used to assess water status, but is not suitable for assessing the impact of projects, especially those planned far from measurement and control points. A solution could be to mandate pre-investment monitoring in a specific area. Executing project developers would be legally obliged to conduct an inventory of the quality of the aquatic environment in order to document the status prior to the commencement of project activities, and such results would then feed into PME data. Performing determinations of physicochemical and biological parameters at the same intervals and intake sites would likely allow better identification of the relationship between biological and physicochemical elements. Currently, PMS results in this area do not always allow to identify patterns of relationships, and sometimes even make it difficult to find those described in the literature.

Uncertainty of the result, or whether models can be trusted

One of the key issues in evaluating methodologies based on mathematical modeling is the uncertainty of the results. This topic should be looked at from two perspectives. One is the uncertainty of the assessment itself, fraught with factors such as the use of complementary input data from outside the PME and assumptions/simplifications of numerical methods in which environmental processes are described by formulas and fixed parameters. These additional factors mean that the cumulative uncertainty of the impact assessment can reach or even exceed 100%.

On the other hand, in order to assess the usefulness of the impact assessment results, it would be possible to compare the uncertainty of the state monitoring assessment with the uncertainty of the assessment made by numerical tools. In this way, we admit the imperfection of the system, but since we assume that the impact assessment (or rather, the method of its preparation) has similar or less uncertainty, such results should be acceptable. As Moges notes[19] et al, describing in detail the issue of uncertainty in hydrological modeling, the assessment of uncertainty, despite awareness of its existence, is rare in project planning and water management practice. This is largely a consequence of the lack of a quantitative approach to water impact assessments, which prevents any assessment of uncertainty or allows it only by reference to uncertainty associated with water assessments.

Performing uncertainty assessments is an element that constitutes good practice in modeling natural processes. This is a common activity in climate change research, as well as in the practical use of this research to implement international climate and atmospheric protection policies[20]. In the application of mathematical modeling to the analysis of flow and water quality in rivers, uncertainty analysis is indicated as a key element preceding the interpretation of results[21]. In the current situation, where most environmental impact reports do not consider the effects of planned activities on the risk of not achieving environmental goals at all, or describe them in a qualitative way, the implementation of quantitative assessment methods should make it easier to interpret the results. On the other hand, going further in the direction of implementing good practices, we could make interpretation difficult anew by presenting results like “The projected suspended solids concentration in the receiver will be, for example, 14.28 mg/L during the construction phase of the project, with a model uncertainty of 5%.” Is the risk of exceeding the good status limit (14.7 mg/L) real? Should this uncertainty be covered by measures to minimize the impact of the project, which entails additional costs? The problem of uncertainty in quantitative assessments is still ongoing, and attempts to solve it can be found in publications from different regions of the world[22].

Ecosystems, as systems of many variables, are non-linear systems, consisting of a network of interconnected and continuously interacting components including biotic and abiotic elements. Due to the fact of structural complexity, the operation of ecosystems is inherently difficult to discern. The non-linearity of interactions, random distribution and adaptability to external conditions cause the components of the environment, and thus the entire ecosystem, to change continuously. In proportion to the increase in awareness of the complexity of ecosystems, the amount of information available about their condition has grown. With the increase in data from physical measurements of the environment, there has been a surge in the development of numerical tools to analyze them. Regardless of the course of action that would be taken on the impact assessment, based on the arguments cited above, the numerical method to support expert evaluation should be an integral part of the procedure for assessing the impact of the investment on water status.

In the article, I used, among other things. From the works:

[1] Judgment in Case C-461/13 Bund für Umwelt und Naturschutz Deutschland e.V. v. Bundesrepublik Deutschland; CJEU, 2015.

[2] OJ. C 352, 30.11.2013.

[3] BDMF., 2000. protocols for water and environmental modeling. Bay-Delta Modeling Forum. Ad hoc Modeling Protocols Committee, BDMF 2000-01.

[4] UNECE, 2007. Recommendations on Payments for Ecosystem Services in Integrated Water Resources Management. United Nations Economic Commission for Europe.

[5] Black D.C, Wallbrink P.J., Jordon P.W., Waters D., Carroll C., Blackmore J.M. 2011. Guidelines for
Water management modelling: towards best practice model application. eWater Cooperative research Centre, Canberra, Australia.

[6] GWP. 2013. The role of decision support systems and models in integrated river basin management. Technical Focus Paper, Global Water Partnership.

[7] EC, 2013. System of Environmental-Economic Accounting 2012 – Experimental Ecosystem Accounting. White cover publication, pre-edited text subject to official editing. European Commission, Organization for Economic Co-operation and Development, United Nations, World Bank Group.

[8] EC. 2015. Guidance document on the application of water balances for supporting the implementation of the WFD. Directorate-General for Environment (European Commission). https://doi.org/10.2779/352735.

[9] Thomann R. V., 1963. Mathematical model for dissolved oxygen. Proceedings of American Society ofCil’il Engineers, Journal ofSanitary Engineering Dil’ision 89(SA5): 1-30.

[10] CEGIS, 2015. EIA Study for Repowering of Unit-4 of Ghorashal Power Station. Center for Environmental and Geographic Information Services.

[11] AECL, 2017. Environmental & Social Impact Assessment and Management Plan for 750 MW Gas based Power Station at Meghnaghat, NaraynGanj, Bangladesh. Adroit Environment Consultants Ltd.

[12] CEGIS, 2018. Inception Report on Environmental Impact Assessment (EIA) of Ghorasal Polash Urea Fertilizer Project, Polash, Narsingdi. Center for Environmental and Geographic Information Services.

[13] MRC, 2018. Guidelines for Transboundary Environmental Impact Assessment in the Lower Mekong Basin (Working Document). Mekong River Commission for Sustainable Development.

[14] Huszar P., Petermann P., Leite A., Resende E., Schnack E., Schneider E., Francesco F., Rast G., Schnack J., Wasson J., Garcia Lozano L., Dantas M., Obrdlik P., Pedrono R., 1999. Fact of fiction: A revive of the Hydrovia Paraguay-Parana Official studies. Toronto, Canada, World Wildlife Fund / World Wide Fund for Nature (WWF). 217 pp.

[15] Loucks D. P. van Beek E., 2017. Water Resource Systems Planning and Management: An Introduction to Methods, Models, and Applications. Springer, Cham. ISBN: 978-3-319-44234-1.

[16] Orlob G. T., 1979. Mathematical modelling and simulation of water quality: a survey of the state-of-the-art, Hydrological Sciences Journal, 24:2, 151-156.

[17] Quetglas A., Ordines F. and Guijarro B. The Use of Artificial Neural Networks (ANNs) in Aquatic Ecology Instituto Español de Oceanografía, Centre Oceanogràfic de les Balears Spain, DOI: 10.5772/16092, Source: InTech, 2014.

[18] Jørgensen S. E., 1976. A eutrophication model for a lake. Ecol. Modelling 2: 147-163.

[19] Moges E., Demissie Y., Larsen L., Yassin F., 2021. Review: Sources of Hydrological Model Uncertainties and Advances in Their Analysis. Water 2021, 13, 28.

[20] Frey C., Hanle L., 2006. 2006 IPCC Guidelines for National Greenhouse Gas Inventories: Volume 1: General Guidance and Reporting: Chapter 3 – Uncertainties.

[21] Abbaspour K.C., Rouholahnejad E. Vaghefi S., Srinivasan R., Yanga H., Kløved B., 2015. A continental-scale hydrology and water quality model for Europe: Calibration and uncertainty of a high-resolution large-scale SWAT model. Journal of Hydrology, Volume 524, May 2015, Pages 733-752; Abbaspour K.C., Vaghefi S.A., Srinivasan R.A., 2018. Guideline for Successful Calibration and Uncertainty Analysis for Soil and Water Assessment: A Review of Papers from the 2016 International SWAT Conference. Water 2018, 10, 6.

[22] Lees J., Jaeger J.A.G., Gunn J.A.E., Noble B.F., 2016 Analysis of uncertainty consideration in environmental assessment: an empirical study of Canadian EA practice, Journal of Environmental Planning and Management, 59:11, 2024-2044.