Zahra Kalantari

The superior effect of nature based solutions in land management for enhancing ecosystem services

The rehabilitation and restoration of land is a key strategy to recover services -goods and resources- ecosystems offer to the humankind. This paper reviews key examples to understand the superior effect of nature based solutions to enhance the sustainability of catchment systems by promoting desirable soil and landscape functions. The use of concepts such as connectivity and the theory of system thinking framework allowed to review coastal and river management as a guide to evaluate other strategies to achieve sustainability. In land management NBSs are not mainstream management. Through a set of case studies: organic farming in Spain; rewilding in Slovenia; land restoration in Iceland, sediment trapping in Ethiopia and wetland construction in Sweden, we show the potential of Nature based solutions (NBSs) as a cost-effective long term solution for hydrological risks and land degradation. NBSs can be divided into two main groups of strategies: soil solutions and landscape solutions. Soil solutions aim to enhance the soil health and soil functions through which local eco-system services will be maintained or restored. Landscape solutions mainly focus on the concept of connectivity. Making the landscape less connected, facilitating less rainfall to be transformed into runoff and therefore reducing flood risk, increasing soil moisture and reducing droughts and soil erosion we can achieve the sustainability. The enhanced eco-system services directly feed into the realization of the Sustainable Development Goals of the United Nations.

Distinction, quantification and mapping of potential and realized supply-demand of flow-dependent ecosystem services

This study addresses and conceptualizes the possible dependence of ecosystem services on prevailing air and/or water flow processes and conditions, and particularly on the trajectories and associated spatial reach of these flows in carrying services from supply to demand areas in the landscape. The present conceptualization considers and accounts for such flow-dependence in terms of potential and actually realized service supply and demand, which may generally differ and must therefore be distinguished due to and accounting for the prevailing conditions of service carrier flows. We here concretize and quantify such flow-dependence for a specific landscape case (the Stockholm region, Sweden) and for two examples of regulating ecosystem services: local climate regulation and storm water regulation. For these service and landscape examples, we identify, quantify and map key areas of potential and realized service supply and demand, based for the former (potential) on prevailing relatively static types of landscape conditions (such as land-cover/use, soil type and demographics), and for the latter (realized) on relevant carrier air and water flows. These first-order quantification examples constitute first steps towards further development of generally needed such flow-dependence assessments for various types of ecosystem services in different landscapes over the world.

Flood probability quantification for road infrastructure: Data-driven spatial-statistical approach and case study applications

Climate-driven increase in the frequency of extreme hydrological events is expected to impose greater strain on the built environment and major transport infrastructure, such as roads and railways. This study develops a data-driven spatial-statistical approach to quantifying and mapping the probability of flooding at critical road-stream intersection locations, where water flow and sediment transport may accumulate and cause serious road damage. The approach is based on novel integration of key watershed and road characteristics, including also measures of sediment connectivity. The approach is concretely applied to and quantified for two specific study case examples in southwest Sweden, with documented road flooding effects of recorded extreme rainfall. The novel contributions of this study in combining a sediment connectivity account with that of soil type, land use, spatial precipitation-runoff variability and road drainage in catchments, and in extending the connectivity measure use for different types of catchments, improve the accuracy of model results for road flood probability.

Predicting and communicating flood risk of transport infrastructure based on watershed characteristics

This research aims to identify and communicate water-related vulnerabilities in transport infrastructure, specifically flood risk of road/rail-stream intersections, based on watershed characteristics. This was done using flooding in Värmland and Västra Götaland, Sweden in August 2014 as case studies on which risk models are built. Three different statistical modelling approaches were considered: a partial least square regression, a binomial logistic regression, and artificial neural networks. Using the results of the different modelling approaches together in an ensemble makes it possible to cross-validate their results. To help visualize this and provide a tool for communication with stakeholders (e.g., the Swedish Transport Administration - Trafikverket), a flood thermometer indicating the level of flooding risk at a given point was developed. This tool improved stakeholder interaction and helped highlight the need for better data collection in order to increase the accuracy and generalizability of modelling approaches.

Natural hazard susceptibility assessment for road planning using spatial multi-criteria analysis

Inadequate infrastructural networks can be detrimental to society if transport between locations becomes hindered or delayed, especially due to natural hazards which are difficult to control. Thus determining natural hazard susceptible areas and incorporating them in the initial planning process, may reduce infrastructural damages in the long run. The objective of this study was to evaluate the usefulness of expert judgments for assessing natural hazard susceptibility through a spatial multi-criteria analysis approach using hydrological, geological, and land use factors. To utilize spatial multi-criteria analysis for decision support, an analytic hierarchy process was adopted where expert judgments were evaluated individually and in an aggregated manner. The estimates of susceptible areas were then compared with the methods weighted linear combination using equal weights and factor interaction method. Results showed that inundation received the highest susceptibility. Using expert judgment showed to perform almost the same as equal weighting where the difference in susceptibility between the two for inundation was around 4%. The results also showed that downscaling could negatively affect the susceptibility assessment and be highly misleading. Susceptibility assessment through spatial multi-criteria analysis is useful for decision support in early road planning despite its limitation to the selection and use of decision rules and criteria. A natural hazard spatial multi-criteria analysis could be used to indicate areas where more investigations need to be undertaken from a natural hazard point of view, and to identify areas thought to have higher susceptibility along existing roads where mitigation measures could be targeted after in-situ investigations.

Soil moisture remote-sensing applications for identification of flood-prone areas along transport infrastructure

The expected increase in precipitation and temperature in Scandinavia, and especially short-time heavy precipitation, will increase the frequency of flooding. Urban areas are the most vulnerable, and specifically, the road infrastructure. The accumulation of large volumes of water and sediments on road-stream intersections gets severe consequences for the road drainage structures. This study integrates the spatial and temporal soil moisture properties into the research about flood prediction methods by a case study of two areas in Sweden, Västra Götaland and Värmland, which was affected by severe flooding in August 2014. Soil moisture data are derived from remote-sensing techniques, with a focus on the soil moisture-specific satellites ASCAT and SMOS. Furthermore, several physical catchments descriptors (PCDs) are analyzed and the result shows that larger slopes and drainage density, in general, mean a higher risk of flooding. The precipitation is the same; however, it can be concluded that more precipitation in most cases gives higher soil moisture values. The lack, or the dimensioning, of road drainage structures seems to have a large impact on the flood risk as more sediment and water can be accumulated at the road-stream intersection. The results show that the method implementing soil moisture satellite data is promising for improving the reliability of flooding.