water Article Reconstructing Spatiotemporal Dynamics in Hydrological State Along Intermittent Rivers Michael Eastman 1, * , Simon Parry 1 , Catherine Sefton 1 , Juhyun Park 2,3 and Judy England 4   Citation: Eastman, M.; Parry, S.; Sefton, C.; Park, J.; England, J. Reconstructing Spatiotemporal Dynamics in Hydrological State Along Intermittent Rivers. Water 2021, 13, 493. https://doi.org/10.3390/ w13040493 Academic Editors: Stephanie Kampf, Kristin Jaeger and Fritz Ken Received: 10 December 2020 Accepted: 4 February 2021 Published: 14 February 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 UK Centre for Ecology & Hydrology, Maclean Building, Benson Lane, Crowmarsh Gifford, Wallingford, Oxfordshire OX10 8BB, UK; spar@ceh.ac.uk (S.P.); catsef@ceh.ac.uk (C.S.) 2 ENSIIE & LaMME, University of Paris-Saclay, 91025 Evry, France; juhyun.park@ensiie.fr 3 Department of Mathematics and Statistics, Lancaster University, Lancaster, Lancashire LA1 4YF, UK 4 Environment Agency, Wallingford, Oxfordshire OX10 8BD, UK; judy.england@environment-agency.gov.uk * Correspondence: miceas@ceh.ac.uk Abstract: Despite the impact of flow cessation on aquatic ecology, the hydrology of intermittent rivers has been largely overlooked. This has resulted in a lack of monitoring projects, and conse- quently, datasets spanning a period of sufficient duration to characterise both hydrological extremes. This report documents an investigation into the potential for statistical modelling to simulate the spatiotemporal dynamics of flowing, ponded and dry hydrological states in an internationally rare hydrological state dataset. The models presented predict unrecorded hydrological state data with performance metrics exceeding 95%, providing insights into the relationship between ponding preva- lence and the performance of statistical simulation of this ecologically important intermediate state between drying and flowing conditions. This work demonstrates the potential for hydrological intermittence to be simulated in areas where hydrological state data are often sparse, providing opportunities for quality control and data infilling. This further understanding of the processes driving intermittence will inform future water resource assessments and the influence of climate change on hydrological intermittence. Keywords: temporary streams; ephemeral streams; chalk streams; Chilterns; low flows; network contraction; ordinal regression; cumulative logit model 1. Introduction Temporary flow cessation is common, with more than 50% of the global river network estimated to be intermittent [1]. Their dynamic hydrological behaviour, their prevalence across a range of physical and chemical conditions, and spatiotemporal variability in their habitat structure, mean that intermittent rivers and ephemeral streams (IRES) support diverse biological communities [2–7]. Despite this ecological importance and the ecosystem services they provide [8], they have been largely overlooked in governance and policy, resulting in a lack of legislative protection [9–11]. Processes driving hydrological regime in IRES include those relating to climate, ge- ology and land cover [12]. Identification of drivers—and quantification of the relative sensitivity of the hydrological regime to each—is therefore an essential step towards the as- sessment of a river’s response to hydrological extremes and to abstraction, land-use change and climate change pressures [1,13]. Such evidence would further our understanding of the ecological sensitivity of IRES and underpin the development of suitable management strategies for their protection. However, the extent of the monitored IRES network is constrained both spatially and temporally, limiting the understanding that can be acquired from investigations into these datasets alone. Simulation of the hydrological regime of these rivers enables in- sights into the conditions beyond the monitored record, as well as inferences about the factors driving intermittence. Physically based modelling of intermittent headwaters Water 2021, 13, 493. https://doi.org/10.3390/w13040493 https://www.mdpi.com/journal/water