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Abstract. Chalk aquifers are an important source of drinking water in the UK. Due to their properties, they are particularly vulnerable to groundwater-related hazards like floods and droughts. Understanding and predicting groundwater levels is therefore important for effective and safe water management. Chalk is known for its high porosity and, due to its dissolvability, exposed to karstification and strong subsurface heterogeneity. To cope with the karstic heterogeneity and limited data availability, specialised modelling approaches are required that balance model complexity and data availability. In this study, we present a novel approach to evaluate simulated groundwater level frequencies derived from a semi-distributed karst model that represents subsurface heterogeneity by distribution functions. Simulated groundwater storages are transferred into groundwater levels using evidence from different observations wells. Using a percentile approach we can assess the number of days exceeding or falling below selected groundwater level percentiles. Firstly, we evaluate the performance of the model when simulating groundwater level time series using a spilt sample test and parameter identifiability analysis. Secondly, we apply a split sample test to the simulated groundwater level percentiles to explore the performance in predicting groundwater level exceedances. We show that the model provides robust simulations of discharge and groundwater levels at three observation wells at a test site in a chalk-dominated catchment in south-western England. The second split sample test also indicates that the percentile approach is able to reliably predict groundwater level exceedances across all considered timescales up to their 75th percentile. However, when looking at the 90th percentile, it only provides acceptable predictions for long time periods and it fails when the 95th percentile of groundwater exceedance levels is considered. By modifying the historic forcings of our model according to expected future climate changes, we create simple climate scenarios and we show that the projected climate changes may lead to generally lower groundwater levels and a reduction of exceedances of high groundwater level percentiles.

Floods are a natural hazard that affect communities worldwide, but to date the vast majority of flood hazard research and mapping has been undertaken by wealthy developed nations. As populations and economies have grown across the developing world, so too has demand from governments, businesses, and NGOs for modeled flood hazard data in these data-scarce regions. We identify six key challenges faced when developing a flood hazard model that can be applied globally and present a framework methodology that leverages recent cross-disciplinary advances to tackle each challenge. The model produces return period flood hazard maps at ∼90 m resolution for the whole terrestrial land surface between 56°S and 60°N, and results are validated against high-resolution government flood hazard data sets from the UK and Canada. The global model is shown to capture between two thirds and three quarters of the area determined to be at risk in the benchmark data without generating excessive false positive predictions. When aggregated to ∼1 km, mean absolute error in flooded fraction falls to ∼5%. The full complexity global model contains an automatically parameterized subgrid channel network, and comparison to both a simplified 2-D only variant and an independently developed pan-European model shows the explicit inclusion of channels to be a critical contributor to improved model performance. While careful processing of existing global terrain data sets enables reasonable model performance in urban areas, adoption of forthcoming next-generation global terrain data sets will offer the best prospect for a step-change improvement in model performance.

Floods are a natural hazard that affect communities worldwide, but to date the vast majority of flood hazard research and mapping has been undertaken by wealthy developed nations. As populations and economies have grown across the developing world, so too has demand from governments, businesses, and NGOs for modeled flood hazard data in these data‐scarce regions. We identify six key challenges faced when developing a flood hazard model that can be applied globally and present a framework methodology that leverages recent cross‐disciplinary advances to tackle each challenge. The model produces return period flood hazard maps at ∼90 m resolution for the whole terrestrial land surface between 56°S and 60°N, and results are validated against high‐resolution government flood hazard data sets from the UK and Canada. The global model is shown to capture between two thirds and three quarters of the area determined to be at risk in the benchmark data without generating excessive false positive predictions. When aggregated to ∼1 km, mean absolute error in flooded fraction falls to ∼5%. The full complexity global model contains an automatically parameterized subgrid channel network, and comparison to both a simplified 2‐D only variant and an independently developed pan‐European model shows the explicit inclusion of channels to be a critical contributor to improved model performance. While careful processing of existing global terrain data sets enables reasonable model performance in urban areas, adoption of forthcoming next‐generation global terrain data sets will offer the best prospect for a step‐change improvement in model performance.

Diffuse water pollution from agriculture (DWPA) is a major environmental concern, with significant adverse impacts on both human and ecosystem health. However, without an appropriate understanding of the multiple factors impacting on water, mitigation measures cannot be targeted. Therefore, this paper addresses this gap in understanding, reporting the hydrochemical monitoring evidence collected from the UK Government's Demonstration Test Catchments (DTC) programme including contrasting chalk and clay/mudstone catchments. We use data collected at daily and sub-daily frequency over multiple sites to address: (1) How does the behaviour of the full range of nitrogen (N) species and phosphorus (P) fractions vary? (2) How do N species and P fractions vary inter- and intra-annually? (3) What do these data indicate about the primary pollution sources? And (4) which diffuse pollution mitigation measures are appropriate in our study landscapes? Key differences in the rates of flux of nutrients were identified, dependent on catchment characteristics. Full N speciation and P fractionation, together with dissolved organic carbon (DOC) enabled identification of the most likely contributing sources in each catchment. Nitrate (NO3-N) was the dominant N fraction in the chalk whereas organic and particulate N comprised the majority of the load in the clay/mudstone catchments. Despite current legislation, orthophosphate (PO4-P) was not found to be the dominant form of P in any of the catchments monitored. The chalk sub-catchments had the largest proportion of inorganic/dissolved organic P (DOP), accompanied by episodic delivery of particulate P (PP). Contrastingly, the clay/mudstone sub-catchments loads were dominated by PP and DOP. Thus, our results show that by monitoring both the inorganic and organic fractions a more complete picture of catchment nutrient fluxes can be determined, and sources of pollution pin-pointed. Ultimately, policy and management to bring nutrient impacts under control will only be successful if a multi-stressor approach is adopted.

The Demonstration Test Catchments (DTC) project is a UK Government funded initiative to test the effectiveness of on-farm mitigation measures designed to reduce agricultural pollution without compromising farm productivity. Three distinct catchments in England have been chosen to test the efficacy of mitigation measures on working farms in small tributary sub-catchments equipped with continuous water quality monitoring stations. The Hampshire Avon in the south is a mixed livestock and arable farming catchment, the River Wensum in the east is a lowland catchment with predominantly arable farming and land use in the River Eden catchment in the north-west is predominantly livestock farming. One of the many strengths of the DTC as a national research platform is that it provides the ability to investigate catchment hydrology and biogeochemical response across different landscapes and geoclimatic characteristics, with a range of differing flow behaviours, geochemistries and nutrient chemistries. Although numerous authors present studies of individual catchment responses to storms, no studies exist of multiple catchment responses to the same rainfall event captured with in situ high-resolution nutrient monitoring at a national scale. This paper brings together findings from all three DTC research groups to compare the response of the catchments to a major storm event in April 2012. This was one of the first weather fronts to track across the country following a prolonged drought period affecting much of the UK through 2011–2012, marking an unusual meteorological transition when a rapid shift from drought to flood risk occurred. The effects of the weather front on discharge and water chemistry parameters, including nitrogen species (NO3-N and NH4-N) and phosphorus fractions (total P (TP) and total reactive P (TRP)), measured at a half-hourly time step are examined. When considered in the context of one hydrological year, flow and concentration duration curves reveal that the weather fronts resulted in extreme flow, ...

Abstract. This paper discusses how epistemic uncertainties are currently considered in the most widely occurring natural hazard areas, including floods, landslides and debris flows, dam safety, droughts, earthquakes, tsunamis, volcanic ash clouds and pyroclastic flows, and wind storms. Our aim is to provide an overview of the types of epistemic uncertainty in the analysis of these natural hazards and to discuss how they have been treated so far to bring out some commonalities and differences. The breadth of our study makes it difficult to go into great detail on each aspect covered here; hence the focus lies on providing an overview and on citing key literature. We find that in current probabilistic approaches to the problem, uncertainties are all too often treated as if, at some fundamental level, they are aleatory in nature. This can be a tempting choice when knowledge of more complex structures is difficult to determine but not acknowledging the epistemic nature of many sources of uncertainty will compromise any risk analysis. We do not imply that probabilistic uncertainty estimation necessarily ignores the epistemic nature of uncertainties in natural hazards; expert elicitation for example can be set within a probabilistic framework to do just that. However, we suggest that the use of simple aleatory distributional models, common in current practice, will underestimate the potential variability in assessing hazards, consequences, and risks. A commonality across all approaches is that every analysis is necessarily conditional on the assumptions made about the nature of the sources of epistemic uncertainty. It is therefore important to record the assumptions made and to evaluate their impact on the uncertainty estimate. Additional guidelines for good practice based on this review are suggested in the companion paper (Part 2).