Browsing by Author "Waine, Toby"
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Item Open Access A Method to Assess the Performance of SAR-derived Surface Soil Moisture Products(Cranfield University, 2021-04-13 09:13) Beale, John; Waine, Toby; Corstanje, Ronald; Evans, JonathanA Method to Assess the Performance of SAR-derived Surface Soil Moisture Products John Beale, Toby Waine, Jonathan Evans, Ronald Corstanje This study brought together existing research data obtained from a number of different sources, some of which were upon request and subject to licence restrictions. Full details of how these data may be obtained are in this documentItem Open Access Assessing n-alkane and neutral lipid biomarkers as tracers for land-use specific sediment sources(Elsevier, 2023-03-28) Wiltshire, C.; Waine, Toby; Grabowski, Robert C.; Meersmans, Jeroen; Thornton, B.; Addy, S.; Glendell, M.Sediment fingerprinting (SF) methods using taxonomic-specific biomarkers such as n-alkanes have been successfully used to distinguish sediment sources originating from different land uses at a catchment scale. In this study, we hypothesise that using a combination of soil biomarkers of plant, fungal and bacterial origin may allow greater discrimination between land uses in SF studies. Furthermore, we assess if the inclusion of short chain (shorter than C22) neutral lipid fatty acids (SC-NLFA) improves land use discrimination, considering the Loch Davan catchment (34 km2) in Scotland as a case study. Fatty acids are commonly used to measure abundance and diversity of soil microbial and fungal communities. The spatial distribution of these soil communities has been shown to depend mainly on soil properties and, therefore, soil types and land management practices. The n-alkane and SC-NLFA concentrations and their compound specific stable isotope signatures (CSSI) in four land cover classes (crop land, pasture, forest, and moorland) were determined and their contribution to six virtual sediment mixture samples was modelled. Using a Bayesian un-mixing model, the performance of the combined n-alkane and SC-NLFA biomarkers in distinguishing sediment sources was assessed. The collection of new empirical data and novel combinations of biomarkers in this study found that land use can be distinguished more accurately in organic sediment fingerprinting when combining n-alkanes and SC-NLFA or using SC-NLFA and their CSSI alone. These results suggest that fingerprinting methods using the output of unmixing models could be improved by the use of multiple tracer sets if there is a commensurate way to determine which tracer set provides the “best” capacity for land use source discrimination. This new contribution to the organic sediment fingerprinting field highlights that different combinations of biomarkers may be required to optimise discrimination between soils from certain land use sources (e.g., arable-pasture). The use of virtual mixtures, as presented in this study, provides a method to determine if addition or removal of tracers can improve relative error in source discrimination. Combining biomarkers from different soil communities could have a significant impact on the identification of recent sources of sediment within catchments and therefore on the development of effective management strategies.Item Open Access Assessing the source and delivery of organic carbon at a catchment scale using a combined sediment fingerprinting and carbon loss modelling approach(EGU: European Geophysical Union, 2022-05-27) Wiltshire, Catherine; Waine, Toby; Grabowski, Robert C.; Glendell, Miriam; Thornton, Barry; Addy, Steve; Meersmans, JeroenQuantifying land use sources and understanding the dynamics of organic carbon (OC) in river catchments is essential to reduce both on-site and off-site impacts of soil OC erosion. The lake area of Loch Davan, located in Aberdeenshire, Scotland, has been significantly reduced over the last century due to sediment inputs and, in this study, we aimed to identify the primary source(s) and delivery of OC to the loch’s main feeder stream, Logie Burn and its major tributaries.Item Open Access Assessing the source and delivery processes of organic carbon within a mixed land use catchment using a combined n-alkane and carbon loss modelling approach(Springer, 2022-04-08) Wiltshire, Katy; Glendell, M.; Waine, Toby; Grabowski, Robert C.; Meersmans, JeroenPurpose: Understanding fluxes of soil organic carbon (OC) from the terrestrial to aquatic environments is crucial to evaluate their importance within the global carbon cycle. Sediment fingerprinting (SF) is increasingly used to identify land use-specific sources of OC, and, while this approach estimates the relative contribution of different sources to OC load in waterways, the high degree of spatial heterogeneity in many river catchments makes it challenging to precisely align the source apportionment results to the landscape. In this study, we integrate OC SF source apportionment with a carbon loss model (CLM) with the aim of: (i) reducing ambiguity in apportioning OC fluxes when the same land use exists in multiple locations within a catchment; and (ii) identifying factors affecting OC delivery to streams, e.g., buffer zones. Methods: Two main approaches were used in this study: (i) identification of the sources of freshwater bed sediment OC using n-alkane biomarkers and a Bayesian-based unmixing model; and (ii) modelling and analysis of spatial data to construct a CLM using a combination of soil OC content modelling, RUSLE soil erosion modelling and a connectivity index. The study was carried out using existing OC and n-alkane biomarker data from a mixed land use UK catchment. Results: Sediment fingerprinting revealed that woodland was the dominant source of the OC found in the streambed fine sediment, contributing between 81 and 85% at each streambed site. In contrast, CLM predicted that arable land was likely the dominant source of OC, with negligible inputs from woodland. The areas of the greatest OC loss in the CLM were predicted to be from arable land on steeper slopes surrounding the stream channels. Results suggest extensive riparian woodland disconnected upslope eroded soil OC and, concomitantly, provided an input of woodland-derived OC to the streams. It is likely the woodland contribution to streambed OC is derived from litter and leaves rather than soil erosion. Conclusion: This study demonstrates how location-specific OC sources and delivery processes can be better determined using sediment fingerprinting in combination with CLM, rather than using sediment fingerprinting alone. It highlights that, although wooded riparian buffer strips may reduce the impact of upslope, eroded soil OC on waterways, they could themselves be a source of OC to stream sediments through more direct input (e.g., organic litter or leaf debris). Characterising this direct woodland OC as a separate source within future fingerprinting studies would allow the contributions from any eroded woodland soil OC to be better estimated.Item Open Access Atmospheric rivers and associated extreme rainfall over Morocco(Wiley, 2022-05-13) Khouakhi, Abdou; Driouech, Fatima; Slater, Louise; Waine, Toby; Chafki, Omar; Chehbouni, Abdelghani; Raji, OtmaneAtmospheric rivers (ARs) are long, narrow, and transient corridors of enhanced water vapour content in the lower troposphere, associated with strong low-level winds. These features play a key role in the global water cycle and drive weather extremes in many parts of the world. Here, we assessed the frequency and general characteristics of landfalling ARs over Morocco for the period 1979–2020. We used ECMWF ERA5 reanalysis data to detect and track landfalling ARs and then assessed AR association with rainfall at the annual and seasonal scales, as well as extreme rainfall events (defined as a daily precipitation amount exceeding the 99th percentile threshold of the wet days) at 30 gauging stations located across Morocco. Results indicate that about 36 ARs/year make landfall in Morocco. AR occurrence varies spatially and seasonally with highest occurrences in the autumn (SON) and Winter (DJF) in the northern part of the country and along the Atlantic across northern regions. AR rainfall climatology indicates up to 180 mm·year−1 recorded in stations located in the northwest. High fractional contributions (~28%) are recorded in the north and the Atlantic regions, with the driest regions of the south receiving about a third of their annual rainfall from ARs. For extreme rainfall, the highest AR contributions can attain over 50% in the southern dry regions and along the Atlantic north coast and Atlas highlands.Item Open Access Evaluating MODIS dust-detection indices over the Arabian Peninsula(MDPI, 2018-12-08) Albugami, Sarah; Palmer, Steven; Meersmans, Jeroen; Waine, TobySand and dust storm events (SDEs), which result from strong surface winds in arid and semi-arid areas, exhibiting loose dry soil surfaces are detrimental to human health, agricultural land, infrastructure, and transport. The accurate detection of near-surface dust is crucial for quantifying the spatial and temporal occurrence of SDEs globally. The Arabian Peninsula is an important source region for global dust due to the presence of extensive deserts. This paper evaluates the suitability of five different MODIS-based methods for detecting airborne dust over the Arabian Peninsula: (a) Normalized Difference Dust Index (NDDI); (b) Brightness Temperature Difference (BTD) (31–32); (c) BTD (20–31); (d) Middle East Dust Index (MEDI) and (e) Reflective Solar Band (RSB). We derive detection thresholds for each index by comparing observed values for ‘dust-present’ versus ‘dust-free’ conditions, taking into account various land cover settings and analyzing associated temporal trends. Our results suggest that the BTD (31–32) method and the RSB index are the most suitable indices for detecting dust storms over different land-cover types across the Arabian Peninsula. The NDDI and BTD (20–31) methods have limitations in identifying dust over multiple land-cover types. Furthermore, the MEDI has been found to be unsuitable for detecting dust in the study area across all land-cover types.Item Open Access Extreme weather associated with atmospheric rivers over Morocco(EGU: European Geophysical Union, 2021-04-30) Khouakhi, Abdou; Driouech, Fatima; Slater, Louise; Waine, Toby; Chafki, Omar; Raji, OtmaneItem Open Access From field to stream: Tracing streambed organic carbon origins at a catchment scale(EGU: European Geophysical Union, 2021-04-30) Wiltshire, Katy; Glendell, Miriam; Waine, Toby; Grabowski, Robert C.; Thornton, Barry; Meersmans, JeroenItem Open Access Improved soil moisture estimation with Sentinel-1 for arable land at the field scale(EGU: European Geophysical Union, 2021-04-30) Beale, John; Waine, Toby; Corstanje, Ron; Evans, JonathanItem Open Access Indicators of soil quality - Physical properties (SP1611). Final report to Defra(Defra, 2012-09-30) Rickson, R. Jane; Deeks, Lynda K.; Corstanje, Ronald; Newell-Price, Paul; Kibblewhite, Mark G.; Chambers, B.; Bellamy, Patricia; Holman, Ian P.; James, I. T.; Jones, Robert; Kechavarsi, C.; Mouazen, Abdul; Ritz, K.; Waine, TobyThe condition of soil determines its ability to carry out diverse and essential functions that support human health and wellbeing. These functions (or ecosystem goods and services) include producing food, storing water, carbon and nutrients, protecting our buried cultural heritage and providing a habitat for flora and fauna. Therefore, it is important to know the condition or quality of soil and how this changes over space and time in response to natural factors (such as changing weather patterns) or to land management practices. Meaningful soil quality indicators (SQIs), based on physical, biological or chemical soil properties are needed for the successful implementation of a soil monitoring programme in England and Wales. Soil monitoring can provide decision makers with important data to target, implement and evaluate policies aimed at safeguarding UK soil resources. Indeed, the absence of agreed and well-defined SQIs is likely to be a barrier to the development of soil protection policy and its subsequent implementation. This project assessed whether physical soil properties can be used to indicate the quality of soil in terms of its capacity to deliver ecosystem goods and services. The 22 direct (e.g. bulk density) and 4 indirect (e.g. catchment hydrograph) physical SQIs defined by Loveland and Thompson (2002) and subsequently evaluated by Merrington et al. (2006), were re-visited in the light of new scientific evidence, recent policy drivers and developments in sampling techniques and monitoring methodologies (Work Package 1). The culmination of these efforts resulted in 38 direct and 4 indirect soil physical properties being identified as potential SQIs. Based on the gathered evidence, a ‘logical sieve’ was used to assess the relative strengths, weaknesses and suitability of each potential physical SQI for national scale soil monitoring. Each soil physical property was scored in terms of: soil function – does the candidate SQI reflect all soil function(s)? land use - does the candidate SQI apply to all land uses found nationally? soil degradation - can the candidate SQI express soil degradation processes? does the candidate SQI meet the challenge criteria used by Merrington et al. (2006)?This approach enabled a consistent synthesis of available information and the semi-objective, semi-quantitative and transparent assessment of indicators against a series of scientific and technical criteria (Ritz et al., 2009; Black et al., 2008). The logical sieve was shown to be a flexible decision-support tool to assist a range of stakeholders with different agenda in formulating a prioritised list of potential physical SQIs. This was explored further by members of the soil science and soils policy community at a project workshop. By emphasising the current key policy-related soil functions (i.e. provisioning and regulating), the logical sieve was used to generate scores which were then ranked to identify the most qualified SQIs. The process selected 18 candidate physical SQIs. This list was further filtered to move from the ‘narrative’ to a more ‘numerical’ approach, in order to test the robustness of the candidate SQIs through statistical analysis and modelling (Work Package 2). The remaining 7 physical SQIs were: depth of soil; soil water retention characteristics; packing density; visual soil assessment / evaluation; rate of erosion; sealing; and aggregate stability. For these SQIs to be included in a robust national soil monitoring programme, we investigated the uncertainty in their measurement; the spatial and temporal variability in the indicator as given by observed distributions; and the expected rate of change in the indicator. Whilst a baseline is needed (i.e. the current state of soil), it is the rate of change in soil properties and the implications of that change in terms of soil processes and functioning that are key to effective soil monitoring. Where empirical evidence was available, power analysis was used to understand the variability of indicators as given by the observed distributions. This process determines the ability to detect a particular change in the SQI at a particular confidence level, given the ‘noise’ or variability in the data (i.e. a particular power to detect a change of ‘X’ at a confidence level of ‘Y%’ would require ‘N’ samples). However, the evidence base for analysing the candidate SQIs is poor: data are limited in spatial and temporal extent for England and Wales, in terms of a) the degree (magnitude) of change in the SQI which significantly affects soil processes and functions (i.e. ‘meaningful change’), and b) the change in the SQI that is detectable (i.e. what sample size is needed to detect the meaningful signal from the variability or noise in the signal). This constrains the design and implementation of a scientifically and statistically rigorous and reliable soil monitoring programme. Evidence that is available suggests that what constitutes meaningful change will depend on soil type, current soil state, land use and the soil function under consideration. However, when we tested this by analysing detectable changes in packing density and soil depth (because data were available for these SQIs) over different land covers and soil types, no relationships were found. Schipper and Sparling (2000) identify the challenge: “a standardised methodology may not be appropriate to apply across contrasting soils and land uses. However, it is not practical to optimise sampling and analytical techniques for each soil and land use for extensive sampling on a national scale”. Despite the paucity in data, all seven SQIs have direct relevance to current and likely future soil and environmental policy, because they can be related (qualitatively) to soil processes, soil functions and delivery of ecosystem goods and services. Even so, meaningful and detectable changes in physical SQIs may be out of time with any soil policy change and it is not usually possible to link particular changes in SQIs to particular policy activities. This presents challenges in ascertaining trends that can feed into policy development or be used to gauge the effectiveness of soil protection policies (Work Package 3). Of the seven candidate physical SQIs identified, soil depth and surface sealing are regarded by many as indicators of soil quantity rather than quality. Visual soil evaluation is currently not suited to soil monitoring in the strictest sense, as its semi-qualitative basis cannot be analysed statistically. Also, few data exist on how visual evaluation scores relate to soil functions. However, some studies have begun to investigate how VSE might be moved to a more quantified scale and the method has some potential as a low cost field technique to assess soil condition. Packing density requires data on bulk density and clay content, both of which are highly variable, so compounding the error term associated with this physical SQI. More evidence is needed to show how ‘meaningful’ change in aggregate stability affects soil processes and thus soil functions (for example, using the limited data available, an equivocal relationship was found with water regulation / runoff generation). The analysis of available data has given promising results regarding the prediction of soil water retention characteristics and packing density from relatively easy to measure soil properties (bulk density, texture and organic C) using pedotransfer functions. Expanding the evidence base is possible with the development of rapid, cost-effective techniques such as NIR sensors to measure soil properties. Defra project SP1303 (Brazier et al., 2012) used power analyses to estimate the number of monitoring locations required to detect a statistically significant change in soil erosion rate on cultivated land. However, what constitutes a meaningful change in erosion rates still requires data on the impacts of erosion on soil functions. Priority cannot be given amongst the seven SQIs, because the evidence base for each varies in its robustness and extent. Lack of data (including uncertainty in measurement and variability in observed distributions) applies to individual SQIs; attempts at integrating more than one SQI (including physical, biological and chemical SQIs) to improve associations between soil properties and processes / functions are only likely to propagate errors. Whether existing monitoring programmes can be adapted to incorporate additional measurement of physical SQIs was explored. We considered options where one or more of the candidate physical SQIs might be implemented into soil monitoring programmes (e.g. as a new national monitoring scheme; as part of the Countryside Survey; and as part of the National Soil Inventory). The challenge is to decide whether carrying out soil monitoring that is not statistically robust is still valuable in answering questions regarding current and future soil quality. The relationship between physical (and other) SQIs, soil processes and soil functions is complex, as is how this influences ecosystem services’ delivery. Important gaps remain in even the realisation of a conceptual model for these inter-relationships, let alone their quantification. There is also a question of whether individual quantitative SQIs can be related to ecosystem services, given the number of variables.Item Open Access Investigating optimal unmanned aircraft systems flight plans for the detection of marine ingress(Elsevier, 2022-03-08) Mcilwaine, Ben; Rivas Casado, Monica; Waine, TobyFrom the shutting down of coastal tourism industries, the mass destruction of aquaculture, to the clogging of power station water intakes, marine ingress events have the potential to cause widespread disruption along our coastlines. To gain the ability to respond to such events, efforts are being made to advance the understanding of bloom events which predominantly present as large aggregations of jellyfish, or detached aquatic macroalgaes in the water column. This paper investigates the optimal flight search patterns with a focus on marine ingress bloom detection from unmanned aircraft systems (UAS). The detection performance of four flight search patterns are examined against five different bloom shapes. Monte-Carlo simulations are deployed to assess probable performance of flight search pattern against variable bloom shapes. A total of 50,000 simulated flights were conducted, offering a maximum of 500 million marine ingress objects for possible detection. A two phased flight approach is proposed, with first phase flights conducted as area search strategies, and second phase flights as datum searches for scenarios where some information of possible bloom location is available. Parallel sweep was found to be the best performing generalist flight search pattern, closely followed by the phase two search pattern expanding square. Crossing barrier was found to be competitive but appeared to lend itself towards specific detection scenarios with sector search being a consistently poor performing flight search pattern. This paper also investigates the comparative performance of visual line of sight (VLOS), extended visual line of sight (EVLOS), and beyond visual line of sight (BVLOS) operations. Increase of total survey area was found to increase bloom detection frequency, with BVLOS operations the highest performer successfully increasing bloom detection by a factor of 3.7. This paper exhibits the first assessment of flight search patterns within the context of drone-based detection of marine ingress bloom events. This should facilitate the development of an early warning detection system that can provide reliable warning to coastal industries prior to a marine ingress event occurring.Item Open Access Maritime vessel classification to monitor fisheries with SAR: demonstration in the North Sea(MDPI, 2019-02-11) Snapir, Boris; Waine, Toby; Biermann, LaurenIntegration of methods based on satellite remote sensing into current maritime monitoring strategies could help tackle the problem of global overfishing. Operational software is now available to perform vessel detection on satellite imagery, but research on vessel classification has mainly focused on bulk carriers, container ships, and oil tankers, using high-resolution commercial Synthetic Aperture Radar (SAR) imagery. Here, we present a method based on Random Forest (RF) to distinguish fishing and non-fishing vessels, and apply it to an area in the North Sea. The RF classifier takes as input the vessel’s length, longitude, and latitude, its distance to the nearest shore, and the time of the measurement (am or pm). The classifier is trained and tested on data from the Automatic Identification System (AIS). The overall classification accuracy is 91%, but the precision for the fishing class is only 58% because of specific regions in the study area where activities of fishing and non-fishing vessels overlap. We then apply the classifier to a collection of vessel detections obtained by applying the Search for Unidentified Maritime Objects (SUMO) vessel detector to the 2017 Sentinel-1 SAR images of the North Sea. The trend in our monthly fishing-vessel count agrees with data from Global Fishing Watch on fishing-vessel presence. These initial results suggest that our approach could help monitor intensification or reduction of fishing activity, which is critical in the context of the global overfishing problem.Item Open Access A method for monthly mapping of wet and dry snow using Sentinel-1 and MODIS: Application to a Himalayan river basin(Elsevier, 2018-10-01) Snapir, Boris; Momblanch, Andrea; Jain, Sanjay K.; Waine, Toby; Holman, Ian P.Satellite Remote Sensing, with both optical and SAR instruments, can provide distributed observations of snow cover over extended and inaccessible areas. Both instruments are complementary, but there have been limited attempts at combining their measurements. We describe a novel approach to produce monthly maps of dry and wet snow areas through application of data fusion techniques to MODIS fractional snow cover and Sentinel-1 wet snow mask, facilitated by Google Earth Engine. The method is demonstrated in a 55,000 km2 river basin in the Indian Himalayan region over a period of ∼2.5 years, although it can be applied to any areas of the world where Sentinel-1 data are routinely available. The typical underestimation of wet snow area by SAR is corrected using a digital elevation model to estimate the average melting altitude. We also present an empirical model to derive the fractional cover of wet snow from Sentinel-1. Finally, we demonstrate that Sentinel-1 effectively complements MODIS as it highlights a snowmelt phase which occurs with a decrease in snow depth but no/little decrease in snowpack area. Further developments are now needed to incorporate these high resolution observations of snow areas as inputs to hydrological models for better runoff analysis and improved management of water resources and flood risk.Item Open Access A method to assess the performance of SAR-derived surface soil moisture products(IEEE, 2021-04-06) Beale, John; Waine, TobySynthetic aperture radar (SAR) is a remote sensing technique for mapping of soil moisture with high spatial resolution. C -band SAR can resolve features at field scale, or better, but responds to moisture only within the top 1 to 2 cm of the soil. When validating SAR-derived soil moisture products against standard in situ measurements at 5 to 10 cm depth, the greater moisture variability at the soil surface may be inaccurately categorized as measurement error. An alternative method was developed where the C -band SAR product is validated against soil moisture simulated at 2 cm depth by the HYDRUS-1D model. This reproduces soil moisture depth profiles from daily meteorological observations, leaf area index, and soil hydraulic parameters. The model was fitted at 13 COSMOS-UK sites so that the model output at 10 cm depth closely reproduced the cosmic ray neutron sensor data. At ten of the sites studied, there was an improvement of up to 8% in root-mean-squared difference by validating the Copernicus surface soil moisture (SSM) product at 2 cm compared to 10 cm. This suggests that Copernicus SSM and other C -band SAR surface soil moisture algorithms may be more accurate than have hitherto been acknowledged.Item Open Access A multi sensor data fusion approach for creating variable depth tillage zones(Cambridge University Press, 2017-06-01) Mouazen, Abdul Mounem; Waine, Toby; Whattoff, DavidIn this research a multi-sensor and data fusion approach was developed to create variable depth tillage zones. Data collected with an electromagnetic sensor was fused with measurements taken with a hydraulic penetrometer and conventionally acquired soil bulk density (BD) and moisture content (MC) measurements. Packing density values were then calculated for eight soil layers to determine the need to cultivate or not. From the results 62% of the site required the deepest tillage at 38 cm, 16% required tillage at 33 cm and 22% required no tillage at all. The resultant maps of packing density were shown to be a useful approach to map layered soil compaction and guide VDT operations.Item Open Access Opium yield estimates in Afghanistan using remote sensing(International Institute for Sustainable Development, 2016-10-31) Simms, Daniel M.; Waine, TobyAccurate estimates of opium production are essential for informing counter-narcotics policy in Afghanistan. The cultivated area of opium poppy is estimated remotely by interpretation or digital classification of very high resolution (VHR) satellite imagery at sample locations. Obtaining an accurate estimate of average yield is more challenging as poor security prevents access to a sufficient number of field locations to collect a representative sample. Previous work carried out in the UK developed a regression estimator methodology using the empirical relationship between the remotely sensed normalised difference vegetation index (NDVI) and the yield indicator mature capsule volume. The application of the remote sensing approach was investigated in the context of the existing annual opium survey conducted by the United Nations Office on Drugs and Crime and Afghanistan’s Ministry of Counter Narcotics (UNODC/MCN) and indicated the potential for bias correction of yield estimates from a small targeted field sample. In this study we test the approach in Afghanistan using yield data and VHR satellite imagery collected by the UNODC/MCN surveys in 2013 and 2014. Field averaged measurements of capsule volume were compared to field averaged NDVI extracted using visual interpretation of poppy fields. The study compares the empirical relationships from the UK field trials with the Afghanistan data and discusses the challenges of developing an operational methodology for accurate opium yield estimation from the limited sample possible in Afghanistan.Item Embargo Spatial representation of faecal pollution in unsewered urban catchments(Cranfield University, 2023-12) Sultana, Mst Sufia; Tyrrel, Sean; Waine, TobyIn many secondary cities in Bangladesh and other economically developing regions in Asia, Africa and Latin America, urban sanitation is dependent on individually constructed and maintained decentralised sanitation technologies, e.g., septic tanks operating in the absence of a city-wide support system. In such urban areas, wastewater is transported through a network of storm drains which were not designed for this purpose. The release of wastewater runs the risk of imperfect containment and high risk of exposure to faecal pathogens. Effective methods to identify the sources and movement pathways of faecal matter within cities are currently lacking. Here, a Sanitation Infrastructure and Faecal Flow (SanIFFlow) approach is introduced, representing a novel methodology that utilises open-source data to map the sanitation infrastructure and the faecal matter sources and movement pathways. This approach is first demonstrated through a prototype sub-catchment model within Rajshahi city, Northwest Bangladesh. The sub-catchment model identifies and characterises the sources, pathways, and movement of faecal matter. To refine and validate the method, an uncertainty analysis was conducted, supplemented by a field study, to assess the reliability of the approach. Sensitivity analysis identified five key factors influencing the spatial pattern of faecal flow: septic tank emptying, soak pit use, sludge removal from drains, variations in faecal matter production, and the absence of toilets in some buildings. While each factor might have a negligible impact individually, in combination the factors showed almost 50% faecal matter cannot reach the outlet point. Further insights from the uncertainty analysis and fieldwork suggest that, although the sub-catchment model has potential for individual building level sanitation management, the existing ward-level management system, being the smallest administrative unit in the case study city, calls for a model at that spatial scale as a more practical approach. Building upon this, the SanIFFlow approach has been deployed to develop a city-scale model built from ward-level subunits, tailored for practical application in unsewered cities like Rajshahi. This approach holds promise for global applicability, given the widespread availability of open-source data.Item Open Access Stepwise model parametrisation using satellite imagery and hemispherical photography: tuning AquaCrop sensitive parameters for improved winter wheat yield predictions in semi-arid regions(Elsevier, 2024-03-08) Oulaid, Bader; Milne, Alice E.; Waine, Toby; El Alami, Rafiq; Rafiqi, Maryam; Corstanje, RonCrop models are complex with many parameters, which has limited their application. Here we present an approach which both removes the model complexity through reducing the parameter dimensionality through sensitivity analysis, and presents a subsequent efficient approach to model parameterisation using swarm optimisation. We do this for two key model outputs, crop canopy and yield, and for two types of observational data, hemispheric photographs and Landsat7 imagery. Importantly we compare the usefulness of these two sources of data in terms of accurate yield prediction. The results showed that the dominant model parameters that predict canopy cover were generally consistent across the fields, with the exception of those related water stress. Although mid-season canopy cover extracted from Landsat7 was underestimated, good agreement was found between the simulated and observed canopy cover for both sources of data. Subsequently, less accurate yield predictions were achieved with the Landsat7 compared to the hemispherical photography-based parametrizations. Despite the small differences in the canopy predictions, the implications for yield prediction were substantial with the parametrization based on hemispherical photography providing far more accurate estimates of yield. There are, however, additional resource implications associated with hemispherical photography. We evaluate these trade-offs, providing model parametrization sets and demonstrating the potential of satellite imagery to assist AquaCrop, particularly on large scales where ground measurements are challenging.Item Open Access SusHi-Wat - Monthly maps of snow cover(Cranfield University, 2018-02-06 09:13) Snapir, Boris; Waine, Toby; Momblanch Benavent, Andrea; Holman, IanThese data were generated for the project Sustaining Himalayan Water Resources in a Changing Climate (SusHi-Wat), which aims at improving our understanding on how water is stored in, and moves through, a Himalayan river system in northern India. The data set contains a list of images (GeoTIFF format) corresponding to monthly maps of dry snow and wet snow for a Himalayan river basin. The maps were obtained by combining satellite remote sensing data from Sentinel-1 and the Moderate Resolution Imaging Spectroradiometer (MODIS). The image resolution is about 500m. The coordinate system is EPSG:4326 The possible pixel values are: 0: no snow 1-100: wet snow cover fraction 101-200: dry snow cover fraction with an offset of 100 240: missing Sentinel-1 data 250: pixel wrongly identified as wet snow by sentinel-1 (false positives) 255: fill valueItem Open Access Towards a new spatial representation of faecal sources and pathways in unsewered urban catchments using open-source data(IWA Publishing, 2023-03-14) Sultana, Mst Sufia; Waine, Toby; Bari, Niamul; Tyrrel, SeanSpatial representation of sanitation infrastructure and service coverage is essential for management planning and prioritising services. The provision of sanitation services in developing countries is inherently unequal because the sanitation infrastructure is lacking, and onsite sanitation is managed individually. Here, we developed a prototype method for creating a spatial representation of faecal sources and movement in a small area in Rajshahi city in northwest Bangladesh, which is representative of 60 other such secondary cities. We demonstrate an approach to estimate spatial variability in faecal production at the building scale by combining widely accessible buildings, ground elevation, and population data. We also demonstrate an approach to attribute potential faecal movement pathways by integrating drainage data, and faecal production at the building scale. We made use of free and open-source data and provide answers to the broader topic of spatial representation of faecal mobility in unsewered urban settings which has implications in a similar setting in developing countries.