Understanding the potential of groundwater teleconnections to forecast hydrological extremes

Groundwater teleconnections is a growing area of research seeking to detect and understand relationships between wide-scale ocean-atmosphere oscillations and groundwater response. Such relationships can yield important predictive information on groundwater variability and extremes for future years or decades. However, due to the complex non-linear relationships between large-scale climate systems and regional to local-scale rainfall, ET and groundwater; detecting wide-scale evidence of such groundwater teleconnections, and their influence on drought and groundwater flooding, has been difficult. Here, we present the biggest groundwater teleconnection study to date, using an improved wavelet-based methodology to (1) quantify the strength of annual to multi-annual cyclical behaviour in monthly groundwater levels in 60 UK reference boreholes; (2) Analyse rainfall and ET to assess the contribution of teleconnections for these periodicities, and (3) evaluate how indicative these cycles are of groundwater extremes in the UK. Our results are the first to quantify the relative strength of seasonal and extra-seasonal variance in monthly groundwater levels, indicating that �7-year cycles in Chalk (limestone) and sandstone groundwater levels are often comparable to seasonality in defining total groundwater level variability.We demonstrate that the �7 year periodicity in groundwater results from a rainfall-based teleconnection with the North Atlantic Oscillation; documenting a clear alignment with every major recorded instance of groundwater drought (and recent instances of groundwater flooding) in the UK. An understanding that the severity of groundwater drought, and to some extent flooding, is enhanced on a 7-year cycle, produced through a teleconnection, provides significant opportunity for forecasting of future groundwater extremes. This understanding will becoming increasingly critical given the expected increased pressure on groundwater resources as a result of climate change, particularly in the UK and Europe

The influence of soil properties in estimating soil moisture from satellite C-band synthetic aperture radar

Regularly updated soil moisture maps are needed that combine wide scale for regional and national studies, whilstcapturing the field-scale variability that is needed by many applications in agriculture, hydrology and meteorology.C-band SAR satellites, such as Sentinel-1, offer the high spatial and temporal resolution required, but the estimationof soil moisture from SAR requires correction for many contributing factors including vegetation, soil roughness,soil texture and temperature. This paper reviews and predicts the significance of soil texture and organic mattercontent to the errors that may be present in any estimation that is made using default assumptions. We showthat each factor may contribute to a 10% error if an incorrect assumption is made. Soil moisture retrieval overagricultural fields in northern latitudes requires any algorithm to account for rapid and large changes in SARbackscatter due to crop growth and harvesting, tillage operations and freezing of the soil surface. This has particularsignificance for the extending the use of change detection approaches into arable farming areas. We discuss theprospect for developing a model to guide the setting of soil roughness parameters based on land use, soil textureand tillage, and for automatic correction for frozen soil. Successful implementation will improve the accuracy andvalidity of estimating soil moisture from C-band SAR satellite data, at the field scale

Robust spatial estimates of biomass carbon on farms

The drive for farm businesses to move towards net zero greenhouse gas emissions means that there is a need to develop robust methods to quantify the amount of biomass carbon (C) on farms. Direct measurements can be destructive and time-consuming and some prediction methods provide no assessment of uncertainty. This study describes the development, validation, and use of an integrated spatial approach, including the use of lidar data, and Bayesian Belief Networks (BBNs) to quantify total biomass carbon stocks (Ctotal) of i) land cover and ii) landscape features such as hedges and lone trees for five case study sites in lowland England. The results demonstrated that it was possible to develop and use a remote integrated approach to estimate biomass carbon at a farm scale. The highest achievable prediction accuracy was attained from models using the variables AGBC, BGBC, DOMC, age, height, species and land cover, derived from measured information and from literature review. The two BBN models successfully predicted the test values of the total biomass carbon with propagated error rates of 6.7 % and 4.3 % for the land cover and landscape features respectively. These error rates were lower than in other studies indicating that the seven predictors are strong determinants of biomass carbon. The lidar data also enabled the spatial presentation and calculation of the variable C stocks along the length of hedges and within woodlands.Natural Environment Research Council (NERC): NE/L002493/

Improved soil moisture estimation with Sentinel-1 for arable land at the field scale

Abstract and Presentatio

Bundling ecosystem services at a high resolution in the UK: trade-offs and synergies in urban landscapes

Context Ecosystem service bundles can be defined as the spatial co-occurrence of ecosystem services in a landscape. The understanding of the delivery of multiple ecosystem services as bundles in urban areas is limited. This study modelled ecosystem services in an urban area comprising the towns of Milton Keynes, Bedford and Luton. Objectives The objectives of this study were to assess (1) how ecosystem service bundles scale at a 2 m spatial resolution and (2) identify and analyse the composition of ecosystem service bundles. Methods Six ecosystem services were modelled with the InVEST framework at a 2 m resolution. The correlations between ecosystem services were calculated using the Spearman rank correlation coefficient method. Principal Component Analysis and K-means cluster analysis were used to analyse the distributions, spatial trade-offs and synergies of multiple ecosystem services. Results The results showed that regulating services had the tendency to form trade-offs and synergies. There was a significant tendency for trade-offs between supporting service Habitat quality and Pollinator abundance. Four bundle types were identified which showed specialised areas with prevalent soil erosion with high levels in water supply, areas with high values in nutrient retention, areas with high levels in carbon storage and urban areas with pollinator abundance. Conclusions This study demonstrates the existence of synergies and trade-offs between ecosystem services and the formation of ecosystem service bundles in urban areas. This study provides a better understanding of the interactions between services and improve the management choices in ecosystem service provision in urban and landscape planning

Using Bayesian Belief Networks to assess the influence of landscape connectivity on ecosystem service trade-offs and synergies in urban landscapes in the UK

Context Landscape connectivity is assumed to influence ecosystem service (ES) trade-offs and synergies. However, empirical studies of the effect of landscape connectivity on ES trade-offs and synergies are limited, especially in urban areas where the interactions between patterns and processes are complex. Objectives The objectives of this study were to use a Bayesian Belief Network approach to (1) assess whether functional connectivity drives ES trade-offs and synergies in urban areas and (2) assess the influence of connectivity on the supply of ESs. Methods We used circuit theory to model urban bird flow of P. major and C. caeruleus at a 2 m spatial resolution in Bedford, Luton and Milton Keynes, UK, and Bayesian Belief Networks (BBNs) to assess the sensitivity of ES trade-offs and synergies model outputs to landscape and patch structural characteristics (patch area, connectivity and bird species abundance). Results We found that functional connectivity was the most influential variable in determining two of three ES trade-offs and synergies. Patch area and connectivity exerted a strong influence on ES trade-offs and synergies. Low patch area and low to moderately low connectivity were associated with high levels of ES trade-offs and synergies. Conclusions This study demonstrates that landscape connectivity is an influential determinant of ES trade-offs and synergies and supports the conviction that larger and better-connected habitat patches increase ES provision. A BBN approach is proposed as a feasible method of ES trade-off and synergy prediction in complex landscapes. Our findings can prove to be informative for urban ES management

Understanding the importance of landscape configuration on ecosystem service bundles at a high resolution in urban landscapes in the UK

Context Landscape structure is thought to affect the provision of ecosystem service bundles. However, studies of the influence of landscape configuration on ecosystem service trade-offs and synergies in urban areas are limited. This study used Bayesian Belief Networks to predict ecosystem service trade-offs and synergies in the urban area comprising the towns of Milton Keynes, Bedford and Luton, UK. Objectives The objectives of this study were to test (1) a Bayesian Belief Network approach for predicting ecosystem service trade-offs and synergies in urban areas and (2) assess whether landscape configuration characteristics affect ecosystem service trade-offs and synergies. Methods Bayesian Belief Network models were used to test the influence of landscape configuration on ecosystem service interactions. The outputs of a Principal Component Analysis (PCA) on six ecosystem services and landscape configuration metrics were used as response and explanatory variables, respectively. We employed Spearman’s rank correlation and principal component analysis to identify redundancies between landscape metrics. Results We found that landscape configuration affects ecosystem service trade-offs and synergies. A sensitivity analysis conducted on the principal components showed that landscape configuration metrics core area (CORE) and effective mesh size (MESH) are strong influential determinants of ecosystem service trade-offs and synergies. Conclusions This study demonstrates that landscape configuration characteristics affect ecosystem service trade-offs and synergies and that a core set of metrics could be used to assess ecosystem service (ES) trade-offs and synergies. The findings may be relevant to planning and urban design and improved ecosystem management

Spatial modelling approach and accounting method affects soil carbon estimates and derived farm-scale carbon payments

Improved farm management of soil organic carbon (SOC) is critical if national governments and agricultural businesses are to achieve net-zero targets. There are opportunities for farmers to secure financial benefits from carbon trading, but field measurements to establish SOC baselines for each part of a farm can be prohibitively expensive. Hence there is a potential role for spatial modelling approaches that have the resolution, accuracy, and estimates to uncertainty to estimate the carbon levels currently stored in the soil. This study uses three spatial modelling approaches to estimate SOC stocks, which are compared with measured data to a 10 cm depth and then used to determine carbon payments. The three approaches used either fine- (100 m × 100 m) or field-scale input soil data to produce either fine- or field-scale outputs across nine geographically dispersed farms. Each spatial model accurately predicted SOC stocks (range: 26.7–44.8 t ha−1) for the five case study farms where the measured SOC was lowest (range: 31.6–48.3 t ha−1). However, across the four case study farms with the highest measured SOC (range: 56.5–67.5 t ha−1), both models underestimated the SOC with the coarse input model predicting lower values (range: 39.8–48.2 t ha−1) than those using fine inputs (range: 43.5–59.2 t ha−1). Hence the use of the spatial models to establish a baseline, from which to derive payments for additional carbon sequestration, favoured farms with already high SOC levels, with that benefit greatest with the use of the coarse input data. Developing a national approach for SOC sequestration payments to farmers is possible but the economic impacts on individual businesses will depend on the approach and the accounting method.Natural Environment Research Council (NERC): NE/L002493/

The relationship between spatial configuration of urban parks and neighbourhood cooling in a humid subtropical city

Context Urban parks are essential for maintaining aesthetics within cities and keeping their its energy balance by helping mitigate the Urban Heat Island (UHI) effect through controlling ambient and land surface temperature (LST). Objectives To investigate the impact of cooling in terms of distance by variously configured urban parks of a humid subtropical city, using landscape metrics and open-source data. Methods Land use (LU) was obtained through maximum likelihood classification of 3 m resolution aerial RGB-NIR imagery supported by ground control points and park boundaries collected during field survey. LST at matching resolution was obtained through downscaling of Landsat-8 LST at 30/100m resolution, calculated with the Radiative Transfer Equation (RTE). Landscape metrics for patches of parks were calculated using landscapemetrics R library and related to neighbourhood distances over built-up land use (LU). Results Urban parks with homogenous cores and less complex shape provide distinctly higher cooling of neighbouring built-up LU of circa 2.55 °C over the distance of 18 m from park boundaries. Four metrics: contiguity index (CONTIG), core area index (CAI), fractal dimension index (FRAC) and perimeter-area ratio (PARA) represent significant relationship between spatial configuration of parks and their cooling distance. No cooling capacity of parks regardless of their shape and core was observed beyond the distance of 18 m, which remained constant with small fluctuations in the range of 0.5 °C up to the distance of 600 m. Conclusions The study concludes that cooling distance of urban parks in their neighbourhood extends up to 18 m, which is shorter than suggested by other studies

The importance of non-stationary multiannual periodicities in the North Atlantic Oscillation index for forecasting water resource drought

Drought forecasting and early warning systems for water resource extremes are increasingly important tools in water resource management in Europe where increased population density and climate change are expected to place greater pressures on water supply. In this context, the North Atlantic Oscillation (NAO) is often used to indicate future water resource behaviours (including droughts) over Europe, given its dominant control on winter rainfall totals in the North Atlantic region. Recent hydroclimate research has focused on the role of multiannual periodicities in the NAO in driving low frequency behaviours in some water resources, suggesting that notable improvements to lead-times in forecasting may be possible by incorporating these multiannual relationships. However, the importance of multiannual NAO periodicities for driving water resource behaviour, and the feasibility of this relationship for indicating future droughts, has yet to be assessed in the context of known non-stationarities that are internal to the NAO and its influence on European meteorological processes. Here we quantify the time–frequency relationship between the NAO and a large dataset of water resources records to identify key non-stationarities that have dominated multiannual behaviour of water resource extremes over recent decades. The most dominant of these is a 7.5-year periodicity in water resource extremes since approximately 1970 but which has been diminishing since 2005. Furthermore, we show that the non-stationary relationship between the NAO and European rainfall is clearly expressed at multiannual periodicities in the water resource records assessed. These multiannual behaviours are found to have modulated historical water resource anomalies to an extent that is comparable to the projected effects of a worst-case climate change scenario. Furthermore, there is limited systematic understanding in existing atmospheric research for non-stationarities in these periodic behaviours which poses considerable implications to existing water resource forecasting and projection systems, as well as the use of these periodic behaviours as an indicator of future water resource drought