Evidence for changes in groundwater drought in temperate environments associated with climate change

There is currently a significant gap in our understanding of the effect of anthropogenic warming on groundwater drought. This is due to a number of factors including the limited availability of long groundwater level time series suitable for analysis, the low signal-to-noise ratios characteristic of many hydrological systems, and the infrequent nature of episodes of groundwater drought in temperate systems. Formal attribution of groundwater droughts due to anthropogenic warming is also challenging because of the potentially confounding influences of land use change and groundwater abstraction on groundwater drought. In the present study, we have not attempted to formally attribute groundwater droughts to climate change. Instead, we investigate how known centennialscale anthropogenic warming may be modifying the nature of groundwater droughts when other factors are discounted, and address the following question: how has the occurrence, duration, magnitude and intensity of groundwater drought, as expressed by changes in monthly Standardised Groundwater level Index (SGI) and in episodes of groundwater drought changed since 1891 under anthropogenic warming? Standardised indices of monthly groundwater levels (SGI), precipitation (SPI) and temperature (STI) are analysed, using two long, continuous monthly groundwater level data sets from the UK, for the period 1891 to 2015. Precipitation deficits are the main control on groundwater drought formation and propagation. However, long-term changes in groundwater drought include increases in the frequency and intensity of individual groundwater drought months, and increases in the frequency, magnitude and intensity of episodes of groundwater drought, are shown to be associated with anthropogenic warming over the study period. These is a transition from coincidence of episodes of groundwater and precipitation droughts at the end of the 19th century, to an increasing coincidence groundwater droughts with both precipitation droughts and with hot periods in the early 21st century. In the absence of long-term changes in precipitation deficits, it is inferred that the changing nature of groundwater droughts is due to changes in evapotranspiration (ET) associated with anthropogenic warming. Given the extent of shallow groundwater globally, anthropogenic warming may widely effect changes to groundwater drought characteristics in temperate environments

Changes in groundwater drought associated with anthropogenic warming

Here we present the first empirical evidence for changes in groundwater drought associated with anthropogenic warming in the absence of long-term changes in precipitation. Analysing standardised indices of monthly groundwater levels, precipitation and temperature, using two unique groundwater level data sets from the Chalk aquifer, UK, for the period 1891 to 2015, we show that precipitation deficits are the main control on groundwater drought formation and propagation. However, long-term changes in groundwater drought are shown to be associated with anthropogenic warming over the study period. These include increases in the frequency and intensity of individual groundwater drought months, and increases in the frequency, magnitude and intensity of episodes of groundwater drought, as well as an increasing tendency for both longer episodes of groundwater drought and for an increase in droughts of less than 1 year in duration. We also identify a transition from a coincidence of episodes of groundwater drought with precipitation droughts at the end of the 19th century, to an increasing coincidence with both precipitation droughts and with hot periods in the early 21st century. In the absence of long-term changes in precipitation deficits, we infer that the changing nature of groundwater droughts is due to changes in evapotranspiration (ET) associated with anthropogenic warming. We note that although the water tables are relatively deep at the two study sites, a thick capillary fringe of at least 30 m in the Chalk means that ET should not be limited by precipitation at either site. ET may be supported by groundwater through major episodes of groundwater drought and, hence, long-term changes in ET associated with anthropogenic warming may drive long-term changes in groundwater drought phenomena in the Chalk aquifer. Given the extent of shallow groundwater globally, anthropogenic warming may widely effect changes to groundwater drought characteristics in temperate environments

CO2 storage monitoring: leakage detection and measurement in subsurface volumes from 3D seismic data at Sleipner

Demonstrating secure containment is a key plank of CO2 storage monitoring. Here we use the time-lapse 3D seismic surveys at the Sleipner CO2 storage site to assess their ability to provide robust and uniform three-dimensional spatial surveillance of the Storage Complex and provide a quantitative leakage detection tool. We develop a spatial-spectral methodology to determine the actual detection limits of the datasets which takes into account both the reflectivity of a thin CO2 layer and also its lateral extent. Using a tuning relationship to convert reflectivity to layer thickness, preliminary analysis indicates that, at the top of the Utsira reservoir, CO2 accumulations with pore volumes greater than about 3000 m3 should be robustly detectable for layer thicknesses greater than one metre, which will generally be the case. Making the conservative assumption of full CO2 saturation, this pore volume corresponds to a CO2 mass detection threshold of around 2100 tonnes. Within the overburden, at shallower depths, CO2 becomes progressively more reflective, less dense, and correspondingly more detectable, as it passes from the dense phase into a gaseous state. Our preliminary analysis indicates that the detection threshold falls to around 950 tonnes of CO2 at 590 m depth, and to around 315 tonnes at 490 m depth, where repeatability noise levels are particularly low. Detection capability can be equated to the maximum allowable leakage rate consistent with a storage site meeting its greenhouse gas emissions mitigation objective. A number of studies have suggested that leakage rates around 0.01% per year or less would ensure effective mitigation performance. So for a hypothetical large-scale storage project, the detection capability of the Sleipner seismics would far exceed that required to demonstrate the effective mitigation leakage limit. More generally it is likely that well-designed 3D seismic monitoring systems will have robust 3D detection capability significantly superior to what is required to prove greenhouse gas mitigation efficacy

RS-Del: Edit Distance Robustness Certificates for Sequence Classifiers via Randomized Deletion

Randomized smoothing is a leading approach for constructing classifiers that are certifiably robust against adversarial examples. Existing work on randomized smoothing has focused on classifiers with continuous inputs, such as images, where $\ell_p$-norm bounded adversaries are commonly studied. However, there has been limited work for classifiers with discrete or variable-size inputs, such as for source code, which require different threat models and smoothing mechanisms. In this work, we adapt randomized smoothing for discrete sequence classifiers to provide certified robustness against edit distance-bounded adversaries. Our proposed smoothing mechanism randomized deletion (RS-Del) applies random deletion edits, which are (perhaps surprisingly) sufficient to confer robustness against adversarial deletion, insertion and substitution edits. Our proof of certification deviates from the established Neyman-Pearson approach, which is intractable in our setting, and is instead organized around longest common subsequences. We present a case study on malware detection--a binary classification problem on byte sequences where classifier evasion is a well-established threat model. When applied to the popular MalConv malware detection model, our smoothing mechanism RS-Del achieves a certified accuracy of 91% at an edit distance radius of 128 bytes.Comment: To be published in NeurIPS 2023. 36 pages, 7 figures, 12 tables. Includes 20 pages of appendice

Spatial scaling of CO2 efflux in a temperate grazed grassland

Understanding CO2 efflux from soil at different scales is important when up-scaling CO2 measurements from plot to larger scales, but there have been few studies investigating spatial CO2 efflux in temperate environments. We conducted a nested analysis of variation to explore how the CO2 efflux variation occurs between different spatial scales. Ninety-six manual dynamic chamber flux measurements of CO2 were undertaken during three, four hour surveys within seven grouped sites, each containing an optimised nested design with lag distances of 0.3m, 1m, 3m and 9m across six hectares of grazed hillslope grassland. This design also included continuous logging soil moisture sensors (plus conductivity and temperature) at 10cm soil depth. A previous study showed at this site that the variation of soil moisture is divided relatively equally between the four spatial scales 9m. The proportion of large-scale (>9m) variation increased after rainfall. In contrast in the three surveys analysed to date, the vast majority of the variation in CO2 flux occurred over the two smallest scales. No significant correlation between CO2 and soil moisture was observed over any of the spatial scales. All of these three surveys were conducted on relatively dry soils. We also investigated whether there were significant temporal variations in CO2 efflux over a period of three weeks using an automated soil flux system. These data showed there was no significant temporal variability between 10:00 to 16:00 hrs during late summer. There has recently been substantial rainfall at the field site and we are now conducting additional surveys to examine how the total CO2 fluxes and their spatial variation is effected by these wetter conditions

Efficient sampling for geostatistical surveys

A geostatistical survey for soil requires rational choices regarding the sampling strategy. If the variogram of the property of interest is known then it is possible to optimize the sampling scheme such that an objective function related to the survey error is minimized. However, the variogram is rarely known prior to sampling. Instead it must be approximated by using either a variogram estimated from a reconnaissance survey or a variogram estimated for the same soil property in similar conditions. For this reason, spatial coverage schemes are often preferred, because they rely on the simple dispersion of sampling units as uniformly as possible, and are similar to those produced by minimizing the kriging variance. If extra sampling locations are added close to those in a spatial coverage scheme then the scheme might be broadly similar to one produced by minimizing the total error (i.e. kriging variance plus the prediction error due to uncertainty in the covariance parameters). We consider the relative merits of these different sampling approaches by comparing their mean total error for different specified random functions. Our results showed the considerable benefit of adding close‐pairs to a spatial coverage scheme, and that optimizing with respect to the total error generally gave a small further advantage. When we consider the example of sampling for geostatistical survey of clay content of the soil, an optimized scheme based on the average of previously reported clay variograms was fairly robust compared to the spatial coverage plus close‐pairs scheme. We conclude that the direct optimization of spatial surveys was only rarely worthwhile. For most cases, it is best to apply a spatial coverage scheme with a proportion of additional sampling locations to provide some closely spaced pairs. Furthermore, our results indicated that the number of observations required for an effective geostatistical survey depend on the variogram parameters

How is Baseflow Index (BFI) impacted by water resource management practices?

Water resource management (WRM) practices, such as abstractions and discharges, may impact baseflow. Here the CAMELS-GB large-sample hydrology dataset is used to assess the impacts of such practices on baseflow index (BFI) using statistical models of 429 catchments from Great Britain. Two complementary modelling schemes, multiple linear regression (LR) and machine learning (random forests, RF), are used to investigate the relationship between BFI and two sets of covariates (natural covariates only and a combined set of natural and WRM covariates). The LR and RF models show good agreement between explanatory covariates. In all models, the extent of fractured aquifers, clay soils, non-aquifers, and crop cover in catchments, catchment topography and aridity are significant or important natural covariates in explaining BFI. When WRM terms are included, groundwater abstraction is significant or the most important WRM covariate in both modelling schemes and discharge to rivers is also identified as significant or influential, although natural covariates still provide the main explanatory power of the models. Surface water abstraction is a significant covariate in the LR model but of only minor importance in the RF model. Reservoir storage covariates are not significant or are unimportant in both the LR and RF models for this large-sample analysis. Inclusion of WRM terms improves the performance of some models in specific catchments. The LR models of high BFI catchments with relatively high levels of groundwater abstraction show the greatest improvements, and there is some evidence of improvement in LR models of catchments with moderate to high discharges. However, there is no evidence that the inclusion of the WRM covariates improves the performance of LR models for catchments with high surface water abstraction or that they improve the performance of the RF models. These observations are used to formulate a conceptual framework for baseflow generation that incorporates WRM practices. It is recommended that information on WRM, particularly groundwater abstraction, should be included where possible in future large-sample hydrological data sets and in the analysis and prediction of BFI and other measures of baseflow