Assessment of human impact on water resources

The human impact is an essential determinant of hydrological behaviours in most of the societally relevant catchments all over the world. Human impact is playing a significant role on fresh water resources and in some regions is actually conditioning water resources availability. Experience suggests that human impact assessment is an essential prerequisite to ensure the sustainable design of water resources systems.

There is the widespread perception that the whole globe is significantly suffering from human impact, therefore determining a globally critical situation for climate and water resources management. Actually, most of the globe surface is not significantly human impacted, if one excludes the potential effect of human induced climate change, whose hydrological implications are the subject of a vivid and to some extent controversial debate. However, the most important catchments for society are typically those where humans live and exploit resources and these are of course human impacted, to a different extent.

Human impact affects environment in several ways (see Figure 1 and Figure 2). There is the general perception that the human impact always induces negative consequences. However, the opposite actually happens: most of the consequences of the human impact are positive, as humans make an effort to shape the environment to meet their needs. However, there undoubtedly are negative consequences from the human impact. Unexpected consequences are concerning, especially those originated by non-linear effects that may give rise to the so-called tipping points.

Figure 1 and Figure 2: example of human impact over a catchment. Source of Figure 1: http://pacificwater.org/

The increase in world's population makes environmental impact more pressing (see Figure 3), although the major impacts are concentrated in the high income regions which are more densely populated but are relatively limited in extension and therefore do not contribute much to population increase.

Figure 3: recent population growth with projections. For more details see here. By Bdm25 - Own work, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=51036438.

Human impact should be adequately considered when planning the exploitation of water resources and mitigation of natural hazards. Therefore, it should be adequately considered in hydrology. Actually, human impact is a key element that is considered by integrated water resources management, but scientists are today dedicating much attention to improving our understanding and interpretation through models of human impact.

Assessing human impact is assuming a critical role in the design of water resources systems. In fact, the development of society is exacerbating water conflicts at several levels therefore stimulating the debate to identify solutions. After the introduction of integrated water resources management and the second Dublin statement water development and management is increasingly based on a participatory approach by involving stakeholders at all levels. Involvement is usually developed through meetings were assessment of the state of water resources and solutions are critically discussed.

The above assessment is often based on the analysis of the current status of water resources. This approach is increasingly criticized in view of the human impact that is expected to significantly change the status of water resources in the future. It follows that the assessment of the human impact is becoming a necessary step of the consultation process and, consequently, the sustainable design of water resources systems. The problem is that the are not yet technical guidelines on how the human impact on water resources should be estimated.

Here below we propose a technical framework for estimating human impact on water resources. The framework is evolving basing on the assessment of the most recent scientific literature.

We propose that the assessment of human impact on water resources is carried out by following the steps below:

• Perceptional assessment of the effects of human impact and their priority;
• Identification of objective and robust methods for assessing each effect;
• Development of future scenarios;
• Uncertainty assessment.

The impact of humans on water resources is the subject of a vivid debate within the scientific community. Water is essential for life and humans need more and more water, while at the same their action is compromising the quantity and quality of freshwater, but is not fully clear what are the most concerning impacts and therefore their priority.

We define the "effect of human impact" as the consequence of human action, either to be designed or already in place, which turns into an impact on human society. For instance, an effect of human impact that is already in place is climate change. The effect of a dam, where the dam can be a possible alternative solution to resolve the previously induced human impact by climate change, may be increased flood risk, changed groundwater levels downstream, degraded stream ecology and so forth. Assessing the effect of human impact may be a straightforward task. For example, the human impact may be given by a new water withdrawal from a river. In this case it is very simple to identify its effects in terms of reduced streamflow.

In other cases assessing the human impact is more challenging. For instance, in many cases climate change is claimed to be a relevant effect; actually, it is not easy to estimate if climate change is affecting the status of water resources.

Assessing human impact first of all requires an awareness of the most frequent effects. The effect of human impact on freshwater resources may take place by conditioning their quantitative availability and/or their quality. Human impact may be direct or indirect, and may take place at several different spatial and temporal scales. Examples of human interventions that may affect freshwater resources quantity and quality are:

• Human impact like increased emissions of greenhous gases that induce climate change at the local and global level;
• Regulation of river flow and water storage in lakes;
• River diversion;
• River damming;
• River training, including river diversions and river tunnelling;
• Fresh water withdrawal;
• Land-use change, including agriculture, drainage, afforestation and deforestation.
• Irrigation and intensive agriculture.
• Changes of social and environmental conditions, including agricultural changes;
• Groundwater withdrawals;

Assigning priorities to impact through discussion with stakeholders is a complex task, which is usually carried out through an interaction of decision makers with stakeholders, in order to elaborate a strategy for mitigation and recovery. Such interaction is prescribed in many countries by laws in force, issued either at the national or regional level. The negotiation may require an in-depth analysis of technical issues and therefore the support of external consultants is often required. In such a situation, the external consultant may be required to be independent and unbiased in presenting a truthful picture of the system and the implication of each alternative scenario or decision.

The support of a decision support system (DSS) is helpful, is it provides a demonstration of rigor and unbiasedness. However, in most practical cases the use of a decision support system is not possible for limited resources or time. It is essential for the external consultant to be up-to-date on the theory and practice of decision making, in order to identify efficient DDSs that can be used to get to target. If a DDS is used, then transparency becomes an essential requirement which may lead to prefer a DDS that is based on object-oriented programming.

In most of the cases the target of the decision is to assign a weight to each effect of human impact. Determination of weights is essential to assign priorities to each effect. Effects with lower weight will considered less important with respect to others. The weight is a number between 0 and 1 that is assigned to each effect so that all the weights sum up to 1. There are several decision support systems that can be used to assign weights to the above effects. They are discussed here. It is interesting to mention here that the literature recently supported the "bottom-up" approach which focuses on current evidence and current vulnerability to each effect to assess their weight.

Whatever DSS is chosen, a solution that turns out to be helpful in many situation is to split a complicated decision into a cascade of subsequent easier decisions. To this end, we propose that we first make a preliminary qualitative ranking of the impacts from the most important to the less important. Such preliminary qualitative ranking will help to converge towards a quantitative assessment.

In order to get the first preliminary ranking of impact we suggest to first categorize the impact in two categories only by discussing with the stakeholders: hard impacts and soft impacts which may be identified basing on hard facts and soft facts, basing on current evidence and knowledge.

A decision support system (DSS) may be used to support data systematisation, data analysis and impact assessment. An interesting example is the Sparrow modeling program by the United States Geological Survey. DSS are very helpful in setting up a visual perspective of the problem at hand. Looking at a picture - instead of a table with numbers - helps the discussion considerably. Decision support systems will be discussed here below.

Once a preliminary qualitative ranking is accepted, an objective way of assigning weight to each effect should be agreed by the stakeholders. A decision criteria should be agreed. The analytic hierarchy process (AHP) may be used, which is a structured technique for organizing and analyzing complex decisions, based on mathematics and psychology. Within this process, weights to each effect may be assigned through pairwise comparison; it is a method based on comparing effects in pairs to judge which of them is believed more important. The method of pairwise comparison is used in the scientific study of preferences.

We are focusing on the details of pairwise comparison in a subsequent lecture. Here we note that it is an interesting option to explore and weigh competing alternatives, which has been applied to a number of water resources management problems.

Hitherto we mentioned that models may be used to quantitatively assess the human impact. Models may range from simple empirical approaches to physically based methods to estimate climate change and other effects. Climate change is very often a matter of concern when talking about water resources so it will used as an example here below. Climate change may be estimated through:

• Analysis of historical data, which is often based on trend analysis;
• Modeling.

Application of the above approaches is discussed in what follows.

Historical data analysis is a frequently used approach to assess human impact. It has been recently criticized because the representativity of past data to assess changes in environmental variables has been widely questioned, for the presence of errors and gaps in observations and the reduced spatial density of monitoring stations. In several cases questions arise on the homogeneity of data, for example, about whether the series is equally reliable throughout its length. For this reason, in some fields like climatic research the use of models is a frequently used approach. In hydrology and water resources management past data analysis is usually preferred for the applied character of the related assessments. When design variables are to be determined in practice, data analysis is frequently preferred as a more reliable approach with respect to model application, in particular when uncertainty of models cannot be convincingly assessed.

Several approaches can be applied to assess change, and therefore human impact, by analysing historical environmental data. The most used approach is trend analysis of a historical time series. If the trend can be assumed to be linear, trend analysis can be undertaken through regression analysis, as described in Trend estimation. If the trends have other shapes than linear, trend testing can be done by non-parametric methods.

Linear trend estimation is essentially based on fitting a linear straight line interpolating the data. A linear function f(x) must satisfy two conditions:

f(a + x) = f(a) + f(x); f(ax) = a f(x).

Therefore, a given time series y(t) is assumed to be decomposed in a straight line plus a noise:

y(t) = a + bt + ε(t)

where a and b are the intercept and slope of the trend line, respectively, and ε(t) is a randomly distributed residual whose statistical properties are assumed not to change in time. If the slope b is significantly positive or negative, then a trend in the data exists which provides an indication of the related change. This approach is indicated for the estimation of changes that are gradually occurring in time, while it is not representative of sudden changes like river diversions, river damming or the perturbation given by other infrastructures.

To estimate a and b and the statistical behaviors of ε(t), which can be considered as parameter of the above linear model, the least squares approach is usually applied.This method minimizes the sum of the squared values of ε(t), as computed by the difference between the y(t) values and the available and corresponding observations:

min ∑(y(t) - a - bt)²

where y(t) is the observation. By using proper inference methods a confidence band can be computed for the value of the slope. In statistics, confidence bands or confidence intervals potentially include the unobservable true parameter of interest. How frequently the estimated confidence band contains the true parameter if the experiment is repeated on different time series is called the confidence level.

More generally, given the availability of a hypothesis testing procedure that can test the null hypothesis b = 0 against the alternative that b ≠ 0, then a confidence interval with confidence level γ = 1 − α can be defined as the interval around 0 containing any b value for which the corresponding null hypothesis b = 0 is not rejected with probability 1 − α. According to the above formulation, α is called significance level.

Computation of the confidence interval is affected by the assumptions on ε(t) and in particular the assumptions related to its probability distribution. Usually the residuals are assumed to follow a normal distribution and to be uncorrelated. These assumptions heavily impact the estimation of the confidence interval. In particular, the presence of correlation widens the width of the confidence interval significantly. Therefore, the question often arises on the possible presence of correlation in environmental variables, and in particular the present of short correlation extended over long time ranges, which corresponds to the presence of long term cycles. If the considered time series is affected by such long term periodicities, then distinguishing between a linear trend and a long term cycle may be difficult. For instance, characterising the current trend that is observed in the global temperature is challenging if one accepts the idea that the climate system may be affected by such long term cycles.

An idea of the variability of environmental data and related tendencies and cycles may be given by Figure 4 and Figure 5, which display the progress of a trend line estimated in a moving window starting from 1920 and encompassing 50 observations of annual maxima (Figure 4) and annual minima (Figure 5) of the Po River daily flows at Pontelagoscuro. The considered time series is extended from 1920 to 2009 and therefore provides the opportunity of testing how the slope of the regression line changed along the observation period. Even if 50 years is a long period, which is usually considered extended enough to allow a reliable trend estimation, one can see that the slope of the regression line is continuously changing, therefore proving that natural fluctuations lead to considerable changes in the dynamics of the river flow which cannot be easily interpreted through a linear trend.

Figure 4. Regression line estimated along a moving window encompassing 50 years of annual maxima of the Po River at Pontelagoscuro. Increasing and decreasing slopes are depicted in red and blue, respectively.

Figure 5. Regression line estimated along a moving window encompassing 50 years of annual minima of the Po River at Pontelagoscuro. Increasing and decreasing slopes are depicted in blue and red, respectively.

Models for assessing the hydrological impact of climate change - and in general human impact on water resources - may be classified between:

• Methods representing the human impact as an external forcing.
• Methods embedding humans as an inherent component of the hydrological cycle.

Methods representing the human impact as an external forcing are based on the study of pristine hydrological systems to obtain an estimate of the desired design variable in absence of human impact. Then, humans are modelled through a separate approach, therefore determining a correction of the design variable to take human impact into account. A very simple example is the estimation of the flow duration curve (FDC) for a river affected by upstream water withdrawals. One may first estimate the flow duration curve in undisturbed conditions, and then estimate the amount of water withdrawal which is subsequently subtracted from the unimpacted FDC. This approach may ignore downstream effects of the water withdrawal, like for instance increased infiltration in the river bed given by the groundwater deficit originated by the withdrawal itself. Therefore, the actual human impact on the undisturbed flow duration curve may go beyond the water withdrawal alone. These effects, that may be difficult to estimate, are called "feedbacks" of the human activity into hydrological processes, which are affecting the dynamics of water systems. Therefore, there is an underlying assumption that the human and the water system evolve independently each other.

Representing the human impact as an external forcing is a simple solution that in many cases captures the relevant behaviors of the integrated human-water system. It has been used in most of the practical applications, to design water supply systems, urban drainage systems, flood mitigation actions and so forth. Its advantages are robustness and therefore reduced uncertainty with respect to more detailed approaches. The method can be applied to either deterministic or stochastic models. However, in the latter case a probabilistic interpretation of the human behaviours is needed if uncertainty in the human impact is to be taken into account.

Methods embedding humans an an inherent component of the hydrological cycle are based on the integration of the human dynamics into the models leading to the estimation of the design variables. Such embedding generally leads to an increased number of model parameters and therefore to an increased estimation variance, but allows for a possible integration of any interaction between water and humans, therefore providing the means to account for the relevant feedbacks. A simple example is given by a model that may estimate effective evapotranspiration as a function of the water withdrawals, for irrigation, therefore modifying the representation of the hydrological cycle depending on human impact.

A relevant question is how to represent the human system, which is very complex by its nature. As for the case of hydrological models, one may decide to adopt a deterministic versus a stochastic approach. The deterministic approach may be adopted by expliciting the dependence of some of the model variables on selected social dynamics, by ensuring feedbacks (otherwise one may fall back to the case of external forcing). A relevant question related to the deterministic approach is its representativity for the human dynamics. While hydrology is by its nature governed by physical deterministic equations, and uncertainty is determined by imprecise knowledge or imprecise representation of the system geometry or measured input and output variables, human dynamics are not governed by deterministic relationships. Therefore, the use of deterministic dynamics is even less justified for human systems with respect to hydrological systems. Therefore, the use of deterministic social-hydrological models turns out not to be useful for engineering design. Their application may serve for improving our understanding of the outcome that may result from assigned conditions, namely, to assess "what if" scenarios.

Scenarios are projections of the future and the possible ways a system might develop. Scenarios help focus thinking on the most important factors driving change in any particular field. Scenarios are not predictions. They are conceived to help one to explore different ways the future might unfold. They are typically finalized to policy making.

Future scenarios may be derived through extrapolation in the future of the same method that has been used for human impact assessment. If historical analysis was used and a trend line was estimated, extrapolating the same line into the future may allow one to derive future scenarios. If modeling was used, the same model forced by future effects can be applied to elaborate projections. This is the method that is typically applied to derive future climate scenarios.

Uncertainty assessment for human impact is a challenging task. Human impact is uncertain by its inherent nature and assessing such uncertainty requires, in turn, a refined understanding of human impact, which is difficult to capture by definition. Uncertainty assessment should lead to defining confidence for future scenarios. The proper method to be used depends on the approach that has been adopted for assessing impact and developing the projections. Proper assumptions are needed to make sure that uncertainty is well represented, and testing should be carried out to ensure that models are reliable. If historical analysis is used one may apply statistical methods and statistical testing for uncertainty assessment. We do not provide more details here.

If models are used to elaborate projections, several techniques may be used to estimate uncertainty. In few words, we can say that one only needs the identification of any relevant uncertainty source and running the simulation models for several different values of any uncertain external forcing and/or parameter, extracted from suitable probable configurations. In this case as well we do not provide further details. A blueprint for uncertainty assessment is described by Montanari and Koutsoyiannis (2012). The theoretical part of the paper is essentially technical but the applications therein presented are conceptually simple.

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Last modified on April 15, 2021