Ecosystem services from combined natural and engineered water and wastewater treatment systems: Going beyond water quality enhancement

Combined natural and engineered water and waste water systems (cNES) are nature-based solutions that utilise naturally occurring processes to remove impurities from water and therefore contribute to the ecosystem service of water quality enhancement. We hypothesise that these systems may also have a potential to deliver ecosystem services other than their primary purpose of water purification and we use spatially-explicit modelling tools to determine these benefits. We focused on three different types of cNES: bank filtration (BF), managed aquifer recharge/soil aquifer treatment (MAR/SAT), and constructed wetlands (CW), and combined the ecosystem services cascade, DESSIN and CICES conceptual frameworks with multiple InVEST 3.4.4 models to investigate the spatial distribution of intermediate ecosystem services within the sites as well as in the surrounding landscape. We also determined the role of habitats present within the sites in wider landscape’s connectivity to the nearest Natura 2000 areas using the Circuitscape 4.0 model, assessed the public perception of the aesthetic value of two of the cNES technologies, i.e. CW and MAR/SAT, via an online survey, and linked the determined ecosystem services to their likely beneficiaries. Our results indicated that the sites characterised with semi-natural ecosystems had a good potential for ecosystem services provision and that the selected cNES technologies were favourably received by the public as compared to their engineered equivalents. We concluded that determination of ecosystem services potential from nature-based solutions, such as cNES technologies, should be done in consideration of various contextual factors including the type of habitats/ecosystems present within the proposed solutions, the location within the landscape as well as properties and ecosystem services potential of the areas surrounding the sites, all of which can be facilitated by deployment of spatially-explicit ecosystem service models at early stages of the planning process

Using variograms to detect and attribute hydrological change

There have been many published studies aiming to identify temporal changes in river flow time series, most of which use monotonic trend tests such as the Mann–Kendall test. Although robust to both the distribution of the data and incomplete records, these tests have important limitations and provide no information as to whether a change in variability mirrors a change in magnitude. This study develops a new method for detecting periods of change in a river flow time series, using temporally shifting variograms (TSVs) based on applying variograms to moving windows in a time series and comparing these to the long-term average variogram, which characterises the temporal dependence structure in the river flow time series. Variogram properties in each moving window can also be related to potential meteorological drivers. The method is applied to 91 UK catchments which were chosen to have minimal anthropogenic influences and good quality data between 1980 and 2012 inclusive. Each of the four variogram parameters (range, sill and two measures of semi-variance) characterise different aspects of the river flow regime, and have a different relationship with the precipitation characteristics. Three variogram parameters (the sill and the two measures of semi-variance) are related to variability (either day-to-day or over the time series) and have the largest correlations with indicators describing the magnitude and variability of precipitation. The fourth (the range) is dependent on the relationship between the river flow on successive days and is most correlated with the length of wet and dry periods. Two prominent periods of change were identified: 1995–2001 and 2004–2012. The first period of change is attributed to an increase in the magnitude of rainfall whilst the second period is attributed to an increase in variability of the rainfall. The study demonstrates that variograms have considerable potential for application in the detection and attribution of temporal variability and change in hydrological systems

A conceptual model for climatic teleconnection signal control on groundwater variability in the UK and Europe

The ability to predict future variability of groundwater resources in time and space is of critical importance to drought management. Periodic control on groundwater levels from oscillatory climatic systems (such as the North Atlantic Oscillation) offers a potentially valuable source of longer term forecasting capability. While some studies have found evidence of the influence of such climatic oscillations within groundwater records, there is little information on how periodic signals propagate between a climatic system and a groundwater resource. This paper develops a conceptual model of this relationship for groundwater resources in the UK and Europe, based on a review of current research. The studies reviewed here reveal key spatial and temporal signal modulations between climatic oscillations, precipitation, groundwater recharge and groundwater discharge. Generally positive correlations are found between the NAO (as a dominant influence) and precipitation in northern Europe indicating a strong control on water available for groundwater recharge. These periodic signals in precipitation are transformed by the unsaturated and saturated zones, such that signals are damped and lagged. This modulation has been identified to varying degrees, and is dependent on the shape, storage and transmissivity of an aquifer system. This goes part way towards explaining the differences in periodic signal strength found across many groundwater systems in current research. So that an understanding of these relationships can be used by water managers in building resilience to drought, several research gaps have been identified. Among these are improved quantification of spatial groundwater sensitivity to periodic control, and better identification of the hydrogeological controls on signal lagging and damping. Principally, research needs to move towards developing improved predictive capability for the use of periodic climate oscillations as indicators of longer term groundwater variability

Understanding the potential of climate teleconnections to project future groundwater drought

Predicting the next major drought is of paramount interest to water managers globally. Estimating the onset of groundwater drought is of particular importance, as groundwater resources are often assumed to be more resilient when surface water resources begin to fail. A potential source of long-term forecasting is offered by possible periodic controls on groundwater level via teleconnections with oscillatory ocean–atmosphere systems. However, relationships between large-scale climate systems and regional to local-scale rainfall, evapotranspiration (ET) and groundwater are often complex and non-linear so that the influence of long-term climate cycles on groundwater drought remains poorly understood. Furthermore, it is currently unknown whether the absolute contribution of multi-annual climate variability to total groundwater storage is significant. This study assesses the extent to which multi-annual variability in groundwater can be used to indicate the timing of groundwater droughts in the UK. Continuous wavelet transforms show how repeating teleconnection-driven 7-year and 16–32-year cycles in the majority of groundwater sites from all the UK's major aquifers can systematically control the recurrence of groundwater drought; and we provide evidence that these periodic modes are driven by teleconnections. Wavelet reconstructions demonstrate that multi-annual periodicities of the North Atlantic Oscillation, known to drive North Atlantic meteorology, comprise up to 40 % of the total groundwater storage variability. Furthermore, the majority of UK recorded droughts in recent history coincide with a minimum phase in the 7-year NAO-driven cycles in groundwater level, providing insight into drought occurrences on a multi-annual timescale. Long-range groundwater drought forecasts via climate teleconnections present transformational opportunities to drought prediction and its management across the North Atlantic region

Impact of rapid urban expansion on green space structure

Rapid urban expansion has had a significant impact on green space structure. A wide variety of modelling approaches have been tested to simulate urban expansion; however, the effectiveness of simulations of the spatial structure of urban expansion remains unexplored. This study aims to model and predict urban expansion in three cities (Kuala Lumpur, Metro Manila and Jakarta), all experiencing rapid urban expansion, and to identify which are the main drivers, including spatial planning, in the resulting spatial patterns. Land Change Modeller (LCM)-Markov Chain models were used, parameterised on changes observed between 1988/1989 and 1999 and verified with the urban form observed for 2014. These models were then used to simulate urban expansion for the year 2030. The spatial structure of the simulated 2030 land use was then compared with the 2030 master plan for each city using spatial metrics. LCM-Markov Chain models proved to be a suitable method for simulating the development of future land use. There were also important differences in the projected spatial structure for 2030 when compared to the planned development in each city; substantive differences in the size, density, distance, shape and spatial pattern. Evidence suggests that these spatial patterns are influenced by the forms of rapid urban expansion experienced in these cities and respective master planning policies of the municipalities of the cities. The use of integrated simulation modelling and landscape ecology analytics supplies significant insights into the evolution of the spatial structure of urban expansion and identifies constraints and informs intervention for spatial planning and policies in cities

Are existing soils data meeting the needs of stakeholders in Europe? An analysis of practical use from policy to field

Soils form a major component of the natural system and their functions underpin many key ecosystem goods and services. The fundamental importance of soils in the environment means that many different organisations and stakeholders make extensive use of soils data and information in their everyday working practices. For many reasons, stakeholders are not always aware that they are reliant upon soil data and information to support their activities. Various reviews of stakeholder needs and how soil information could be improved have been carried out in recent years. However, to date, there has been little consideration of user needs from a non-expert perspective. The aim of this study was to explore the use of explicit and hidden soil information in different organisations across Europe and gain a better understanding of improvements needed in soil data and information to assist in practical use by non-expert stakeholders. An on-line questionnaire was used to investigate different uses of soils data and information with 310 responses obtained from 77 organisations across Europe. Results illustrate the widespread use of soil data and information across diverse organisations within Europe, particularly spatial products and soil functional assessments and tools. A wide range of improvements were expressed with a prevalence for finer scale resolution, trends over time, future scenarios, improved accuracy, non-technical supporting information and better capacity to use GIS. An underlying message is that existing legacy soils data need to be supplemented by new up-to-date data to meet stakeholder needs and information gaps

Physical soil quality indicators for monitoring British soils

The condition or quality of soils determines its ability to deliver a range of functions that support ecosystem services, human health and wellbeing. The increasing policy imperative to implement successful soil monitoring programmes has resulted in the demand for reliable soil quality indicators (SQIs) for physical, biological and chemical soil properties. The selection of these indicators needs to ensure that they are sensitive and responsive to pressure and change e.g. they change across space and time in relation to natural perturbations and land management practices. Using a logical sieve approach based on key policy-related soil functions, this research assessed whether physical soil properties can be used to indicate the quality of British soils in terms of its capacity to deliver ecosystem goods and services. The resultant prioritised list of physical SQIs were tested for robustness, spatial and temporal variability and expected rate of change using statistical analysis and modelling. Six SQIs were prioritised; packing density, soil water retention characteristics, aggregate stability, rate of erosion, depth of soil and soil sealing. These all have direct relevance to current and likely future soil and environmental policy and are appropriate for implementation in soil monitoring programs

A simple method for determination of fine resolution urban form patterns with distinct thermal properties using class-level landscape metrics

Context Relationships between land surface temperature (LST) and spatial configuration of urban form described by landscape metrics so far have been investigated with coarse resolution LST imagery within artificially superimposed land divisions. Citywide micro-scale observations are needed to better inform urban design and help mitigate urban heat island effects in warming climates. Objectives The primary objective was to sub-divide an existing high-resolution land cover (LC) map into groups of patches with distinct spatial and thermal properties suitable for urban LST studies relevant to micro-scales. The secondary objective was to provide insights into the optimal analytical unit size to calculate class-level landscape metrics strongly correlated with LST at 2 m spatial resolution. Methods A two-tiered unsupervised k-means clustering analysis was deployed to derive spatially distinct groups of patches of each major LC class followed by further subdivisions into hottest, coldest and intermediary sub-classes, making use of high resolution class-level landscape metrics strongly correlated with LST. Results Aggregation class-level landscape metrics were consistently correlated with LST for green and grey LC classes and the optimal search window size for their calculations was 100 m for LST at 2 m resolution. ANOVA indicated that all Tier 1 and most of Tier 2 subdivisions were thermally and spatially different. Conclusions The two-tiered k-means clustering approach was successful at depicting subdivisions of major LC classes with distinct spatial configuration and thermal properties, especially at a broader Tier 1 level. Further research into spatial configuration of LC patches with similar spatial but different thermal properties is required

Digital soil assessment for quantifying soil constraints to crop production: a case study for rice in Punjab, India

Assessments of land capability for particular functions such as food production need to allow for uncertainties both in the criteria used to specify the function and in information on relevant soil properties. In this paper, we evaluate the use of digital soil assessment (DSA) for dynamic assessment of soil capability allowing for both uncertainties and spatial variability in soil properties and flexibility in the values of assessment criteria. We do this for soil constraints to rice production in the state of Punjab, India, where soil salinity and alkalinity are potentially important constraints to cropping. In DSA, spatial predictions of soil properties and associated uncertainties made with digital soil mapping (DSM) are used to assess soil functions. We use a combination of DSM and Monte Carlo simulation methods to estimate the spatial variation in soil electrical conductivity (ECe) and pH to 20 cm depth in soils across Punjab. We then use the estimates and associated uncertainties to assess the likelihood that soil salinity or alkalinity or both could constrain rice production. Results show that allowing for prediction uncertainties of soil attributes results in far smaller areas affected by salinity (1.2 vs. 2.0 Mha) and alkalinity (3.0 vs. 3.2 Mha). Results also show the importance of correctly setting threshold values for constraint criteria and the flexibility of the DSA approach for setting thresholds

Harvest monitoring of Kenyan tea plantations with X-band SAR

Tea is an important cash crop in Kenya, grown in a climatically restricted geographic area where climatic variability is starting to affect yield productivity levels. This paper assesses the feasibility of monitoring tea growth between, but also within fields, using X-band COSMO-SkyMed SAR images (five images at VV polarization and five images at HH polarization). We detect the harvested and nonharvested areas for each field, based on the loss of interferometric coherence between two images, with an accuracy of 52% at VV polarization and 74% at HH polarization. We then implement a normalization method to isolate the scattering component related to shoot growth and eliminate the effects of moisture and local incidence angle. After normalization, we analyze the difference in backscatter between harvested and nonharvested areas. At HH polarization, our backscatter normalization reveals a small decrease (∼0.1 dB) in HH backscatter after harvest. However, this decrease is too small for monitoring shoot growth. The decrease is not clear at VV polarization. This is attributed to the predominantly horizontal orientation of the harvested leaves