Browsing by Author "Hannaford, Jamie"

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    Coping with drought and water scarcity: lessons for the agricultural sector
    (Cranfield University, 2021-10-15 09:33) Holman, Ian; Knox, Jerry; Hess, Tim; McEwen, Lindsey; Salmoral Portillo, Gloria; Rey Vicario, Dolores; Hannaford, Jamie; Grove, Ivan; Thompson, Jill; Quinn, Nevil
    This report, an output from the UKRI-funded Drought and Water Scarcity Programme, synthesis the insights for the agricultural sector. It considers how drought and water scarcity affect different types of agriculture; whether we can forecast drought and its impacts and how drought and water scarcity impacts on agriculture be reduced?
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    Indicator-to-impact links to help improve agricultural drought preparedness in Thailand
    (EGU: European Geophysical Union, 2023-07-06) Tanguy, Maliko; Eastman, Michael; Magee, Eugene; Barker, Lucy J.; Chitson, Thomas; Ekkawatpanit, Chaiwat; Goodwin, Daniel; Hannaford, Jamie; Holman, Ian P.; Pardthaisong, Liwa; Parry, Simon; Rey Vicario, Dolores; Visessri, Supattra
    Droughts in Thailand are becoming more severe due to climate change. Developing a reliable drought monitoring and early warning system (DMEWS) is essential to strengthen a country's resilience to droughts. However, for a DMEWS to be valuable, the drought indicators provided to stakeholders must have relevance to tangible impacts on the ground. Here, we analyse drought indicator-to-impact relationships in Thailand, using a combination of correlation analysis and machine learning techniques (random forest). In the correlation analysis, we study the link between meteorological drought indicators and high-resolution remote sensing vegetation indices used as proxies for crop yield and forest growth impacts. Our analysis shows that this link varies depending on land use, season and region. The random forest models built to estimate regional crop productivity allow a more in-depth analysis of the crop- and region-specific importance of different drought indicators. The results highlight seasonal patterns of drought vulnerability for individual crops, usually linked to their growing season, although the effects are somewhat attenuated in irrigated regions. Integration of the approaches provides new, detailed knowledge of crop- and region-specific indicator-to-impact links, which can form the basis of targeted mitigation actions in an improved DMEWS in Thailand and could be applied to other parts of Southeast Asia and beyond.
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    The influence of catchment characteristics on river flow variability
    (Cranfield University, 2015-08-05) Chiverton, Andrew; Hannaford, Jamie; Holman, Ian P.; Prudhomme, Christel; Hess, Tim M.; Bloomfield, John
    Hydrology is yet to fully understand the role that catchment characteristics have in determining a river’s response to precipitation variability. This thesis assesses the influence that catchment characteristics have on modulating a river’s response to changes in precipitation throughout the UK. Central to this aim is the concept of the precipitation- to-flow relationship (the transformation of precipitation into river flow), which is characterised using the Variogram, a way of indexing temporal dependence (i.e. the average relationship between river flow on a given day and river flow on the previous days). Firstly, 116 catchments were grouped into four clusters, based on the shape of their variogram, which significantly differed in their catchment characteristics demonstrating that catchment characteristics control how, on average, precipitation is transformed into river flow. Furthermore, over 70% of un-gauged catchments could be clustered correctly using information about their soil type, slope and the percentage of arable land. Secondly, a new method which identifies the changes in the variogram parameters over 5-year overlapping moving windows was developed to investigate temporal changes in the variogram parameters. This method was successfully demonstrated to detect changes in multiple aspects of artificially perturbed river flow time series (e.g. seasonality, linear changes and variability). On average >70% of the variability in the catchment variogram parameters was explained by the precipitation characteristics, although there was large variability between catchments. Finally, the influence that the catchment characteristics have on the temporal changes in the variogram parameters was analysed, demonstrating that rivers in relatively impermeable upland catchments have a relationship with precipitation which is closer to linear and less variable than lowland, permeable catchments. This thesis contributes significant new knowledge that can be used for both assessing how individual catchments are likely to respond to projected changes in precipitation and in informing data transfer to un-gauged catchments.
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    Using variograms to detect and attribute hydrological change
    (European Geosciences Union (EGU) - Copernicus Publications, 2015-05-12) Chiverton, Andrew; Hannaford, Jamie; Holman, Ian P.; Corstanje, Ronald; Prudhomme, Christel; Hess, Tim M.; Bloomfield, J. P.
    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.
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    Which catchment characteristics control the temporal dependence structure of daily river flows?
    (John Wiley & Sons, Ltd, 2014-12-31T00:00:00Z) Chiverton, Andrew; Hannaford, Jamie; Holman, Ian P.; Corstanje, Ronald; Prudhomme, Christel; Bloomfield, John; Hess, Tim M.
    Hydrological classification systems seek to provide information about the dominant processes in the catchment to enable information to be transferred between catchments. Currently, there is no widely agreed-upon system for classifying river catchments. This paper develops a novel approach to classifying catchments based on the temporal dependence structure of daily mean river flow time series, applied to 116 near-natural ‘benchmark' catchments in the UK. The classification system is validated using 49 independent catchments. Temporal dependence in river flow data is driven by the flow pathways, connectivity and storage within the catchment and can thus be used to assess the influence catchment characteristics have on moderating the precipitation-to-flow relationship. Semi-variograms were computed for the 116 benchmark catchments to provide a robust and efficient way of characterising temporal dependence. Cluster analysis was performed on the semi-variograms, resulting in four distinct clusters. The influence of a wide range of catchment characteristics on the semi-variogram shape was investigated, including: elevation, land cover, physiographic characteristics, soil type and geology. Geology, depth to gleyed layer in soils, slope of the catchment and the percentage of arable land were significantly different between the clusters. These characteristics drive the temporal dependence structure by influencing the rate at which water moves through the catchment and/or the storage in the catchment. Quadratic discriminant analysis was used to show that a model with five catchment characteristics is able to predict the temporal dependence structure for un-gauged catchments. This method could form the basis for future regionalisation strategies, as a way of transferring information on the precipitation-to-flow relationship between gauged and un-gauged catchments.