The effect of chalk representation in land surface modelling

Abstract. Modelling and monitoring of hydrological processes in the unsaturated zone of the chalk, which is a porous medium with fractures, is important to optimize water resources assessment and management practices in the United Kingdom (UK). However, efficient simulations of water movement through chalk unsaturated zone is difficult mainly due to the fractured nature of chalk, which creates high-velocity preferential flow paths in the subsurface. Complex hydrology in the chalk aquifers may also influence land surface mass and energy fluxes because processes in the hydrological cycle are connected via non-linear feedback mechanisms. In this study, it is hypothesized that explicit representation of chalk hydrology in a land surface model influences land surface processes by affecting water movement through the shallow subsurface. In order to substantiate this hypothesis, a macroporosity parameterization is implemented in the Joint UK Land Environment Simulator (JULES), which is applied on a study area encompassing the Kennet catchment in the Southern UK. The simulation results are evaluated using field measurements and satellite remote sensing observations of various fluxes and states in the hydrological cycle (e.g., soil moisture, runoff, latent heat flux) at two distinct spatial scales (i.e., point and catchment). The results reveal the influence of representing chalk hydrology on land surface mass and energy balance components such as surface runoff and latent heat flux via subsurface processes (i.e., soil moisture dynamics) in JULES, which corroborates the proposed hypothesis. </jats:p

Opportunities and challenges in using catchment-scale storage estimates from cosmic ray neutron sensors for rainfall-runoff modelling

Acknowledgements We thank the Macaulay Development Trust and School of Geosciences, University of Aberdeen for KDPs scholarship. JG would like to acknowledge funding from the Royal Society and the Carnegie Trust for the Universities of Scotland (project 70112). JG and LV acknowledge funding from the UK Natural Environment Research Council (project NE/N007611/1 and CC13_080). MW was supported by the Rural & Environment Science & Analytical Services Division of the Scottish Government. RR received funding from the Natural Environment Research Council (projects NE/M003086/1, NE/R004897/1 and NE/T005645/1) and from the International Atomic Energy Agency of the United Nations (IAEA/UN) (project CRP D12014). Special thanks to Carol Taylor, Jessica Fennell, Alice Poli and many more for assistance with fieldwork. Finally, we would like to acknowledge Kenneth Loades for providing us with essential equipment for soil sampling and thank David Finlay and his team for enabling land access in Elsick.Peer reviewedPostprin

Ground truthing global-scale model estimates of groundwater recharge across Africa

Groundwater is an essential resource for natural and human systems throughout the world and the rates at which aquifers are recharged constrain sustainable levels of consumption. However, recharge estimates from global-scale models regularly disagree with each other and are rarely compared to ground-based estimates. We compare long-term mean annual recharge and recharge ratio (annual recharge/annual precipitation) estimates from eight global models with over 100 ground-based estimates in Africa. We find model estimates of annual recharge and recharge ratio disagree significantly across most of Africa. Furthermore, similarity to ground-based estimates between models also varies considerably and inconsistently throughout the different landscapes of Africa. Models typically showed both positive and negative biases in most landscapes, which made it challenging to pinpoint how recharge prediction by global-scale models can be improved. However, global-scale models which reflected stronger climatic controls on their recharge estimates compared more favourably to ground-based estimates. Given this significant uncertainty in recharge estimates from current global-scale models, we stress that groundwater recharge prediction across Africa, for both research investigations and operational management, should not rely upon estimates from a single model but instead consider the distribution of estimates from different models. Our work will be of particular interest to decision makers and researchers who consider using such recharge outputs to make groundwater governance decisions or investigate groundwater security especially under the potential impact of climate change

Translating seasonal climate forecasts into water balance forecasts for decision making

Seasonal rainfall forecasts support early preparedness. These forecasts are typically disseminated at Regional Climate Outlook Forums (RCOFs), in the form of seasonal tercile probability categories—above normal, normal, below normal. However, these categories cannot be related directly to impacts on terrestrial water stores within catchments, since they are mediated by non-linear hydrological processes occurring on fine spatiotemporal scales, including rainfall partitioning into infiltration, evapotranspiration, runoff and groundwater recharge. Hydrological models are increasingly capable of capturing these processes, but there is no simple way to drive such models with a specific RCOF seasonal tercile rainfall forecast. Here we demonstrate a new method, “Quantile Bin Resampling” (QBR), for producing seasonal water forecasts for a drainage basin by integrating a tercile seasonal rainfall forecast with a hydrological model. QBR is based on mapping historical quantiles of basin-average rainfall to historical simulations of the water balance, and circumvents challenges associated with using climate model output to drive impact models directly. We evaluate QBR by generating 35 years of seasonal reforecasts for various water balance stores and fluxes for the Upper Ewaso Ng’iro basin in Kenya. Hindcasts indicate that when input tercile rainfall forecasts have skill, QBR provides accurate water forecasts at kilometre-scale resolution, which is relevant for anticipatory action down to village level. Pilot operational experimental water forecasts were produced for this basin using QBR for the 2022 March-May rainfall season, then disseminated to regional stakeholders at the Greater Horn of Africa Climate Outlook Forum (GHACOF). We discuss this initiative, along with limitations, plans and future potential of the method. Beyond the demonstrated application to water-related forecasts, QBR can be easily adapted to work with any rainfall-driven impact model. It can translate objective tercile climate probabilities into impact-relevant water balance forecasts at high spatial resolution in an efficient, transparent and flexible way