Assessing the impacts of climate change on river flows in England using the UKCP09 climate change projections

This paper presents an assessment of the effects of climate change on river flow regimes in representative English catchments, using the UKCP09 climate projections. These comprise a set of 10,000 coherent climate scenarios, used here (i) to evaluate the distribution of potential changes in hydrological behaviour and (ii) to construct relationships between indicators of climate change and hydrological change. The study uses six catchments, and focuses on change in average flow, high flow (Q5) and low flow (Q95). There is a large range in hydrological change in each catchment between the plausible UKCP09 climate projections, with differences between catchments largely due to differences in catchment geology and baseline water balance. The range in change between the UKCP09 projections is in most catchments smaller than the range between changes with scenarios based on the CMIP3 ensemble of climate models, and earlier UK scenarios produce changes that tend towards the lower (drier) end of the UKCP09 range. The difference between emissions scenarios is small compared to the range across the 10,000 scenarios. Changes in high flows are largely driven by changes in winter precipitation, whilst changes in low flows are determined by changes in summer precipitation and temperature.

Challenges of operational river forecasting

Skillful and timely streamflow forecasts are critically important to water managers and emergency protection services. To provide these forecasts, hydrologists must predict the behavior of complex coupled human–natural systems using incomplete and uncertain information and imperfect models. Moreover, operational predictions often integrate anecdotal information and unmodeled factors. Forecasting agencies face four key challenges: 1) making the most of available data, 2) making accurate predictions using models, 3) turning hydrometeorological forecasts into effective warnings, and 4) administering an operational service. Each challenge presents a variety of research opportunities, including the development of automated quality-control algorithms for the myriad of data used in operational streamflow forecasts, data assimilation, and ensemble forecasting techniques that allow for forecaster input, methods for using human-generated weather forecasts quantitatively, and quantification of human interference in the hydrologic cycle. Furthermore, much can be done to improve the communication of probabilistic forecasts and to design a forecasting paradigm that effectively combines increasingly sophisticated forecasting technology with subjective forecaster expertise. These areas are described in detail to share a real-world perspective and focus for ongoing research endeavors.

Characterising phosphorus and nitrate inputs to a rural river using high-frequency concentration-flow relationships

The total reactive phosphorus (TRP) and nitrate concentrations of the River Enborne, southern England, were monitored at hourly interval between January 2010 and December 2011. The relationships between these high-frequency nutrient concentration signals and flow were used to infer changes in nutrient source and dynamics through the annual cycle and each individual storm event, by studying hysteresis patterns. TRP concentrations exhibited strong dilution patterns with increasing flow, and predominantly clockwise hysteresis through storm events. Despite the Enborne catchment being relatively rural for southern England, TRP inputs were dominated by constant, non-rain-related inputs from sewage treatment works (STW) for the majority of the year, producing the highest phosphorus concentrations through the spring–summer growing season. At higher river flows, the majority of the TRP load was derived from within-channel remobilisation of phosphorus from the bed sediment, much of which was also derived from STW inputs. Therefore, future phosphorus mitigation measures should focus on STW improvements. Agricultural diffuse TRP inputs were only evident during storms in the May of each year, probably relating to manure application to land. The nitrate concentration–flow relationship produced a series of dilution curves, indicating major inputs from groundwater and to a lesser extent STW. Significant diffuse agricultural inputs with anticlockwise hysteresis trajectories were observed during the first major storms of the winter period. The simultaneous investigation of high-frequency time series data, concentration–flow relationships and hysteresis behaviour through multiple storms for both phosphorus and nitrate offers a simple and innovative approach for providing new insights into nutrient sources and dynamics.

Satellite-supported flood forecasting in river networks: a real case study

Satellite-based (e.g., Synthetic Aperture Radar [SAR]) water level observations (WLOs) of the floodplain can be sequentially assimilated into a hydrodynamic model to decrease forecast uncertainty. This has the potential to keep the forecast on track, so providing an Earth Observation (EO) based flood forecast system. However, the operational applicability of such a system for floods developed over river networks requires further testing. One of the promising techniques for assimilation in this field is the family of ensemble Kalman (EnKF) filters. These filters use a limited-size ensemble representation of the forecast error covariance matrix. This representation tends to develop spurious correlations as the forecast-assimilation cycle proceeds, which is a further complication for dealing with floods in either urban areas or river junctions in rural environments. Here we evaluate the assimilation of WLOs obtained from a sequence of real SAR overpasses (the X-band COSMO-Skymed constellation) in a case study. We show that a direct application of a global Ensemble Transform Kalman Filter (ETKF) suffers from filter divergence caused by spurious correlations. However, a spatially-based filter localization provides a substantial moderation in the development of the forecast error covariance matrix, directly improving the forecast and also making it possible to further benefit from a simultaneous online inflow error estimation and correction. Additionally, we propose and evaluate a novel along-network metric for filter localization, which is physically-meaningful for the flood over a network problem. Using this metric, we further evaluate the simultaneous estimation of channel friction and spatially-variable channel bathymetry, for which the filter seems able to converge simultaneously to sensible values. Results also indicate that friction is a second order effect in flood inundation models applied to gradually varied flow in large rivers. The study is not conclusive regarding whether in an operational situation the simultaneous estimation of friction and bathymetry helps the current forecast. Overall, the results indicate the feasibility of stand-alone EO-based operational flood forecasting.

Modelling primary producer interaction and composition: an example of a UK lowland river

Nutrient enrichment and drought conditions are major threats to lowland rivers causing ecosystem degradation and composition changes in plant communities. The controls on primary producer composition in chalk rivers are investigated using a new model and existing data from the River Frome (UK) to explore abiotic and biotic interactions. The growth and interaction of four primary producer functional groups (suspended algae, macrophytes, epiphytes, sediment biofilm) were successfully linked with flow, nutrients (N, P), light and water temperature such that the modelled biomass dynamics of the four groups matched that of the observed. Simulated growth of suspended algae was limited mainly by the residence time of the river rather than in-stream phosphorus concentrations. The simulated growth of the fixed vegetation (macrophytes, epiphytes, sediment biofilm) was overwhelmingly controlled by incoming solar radiation and light attenuation in the water column. Nutrients and grazing have little control when compared to the other physical controls in the simulations. A number of environmental threshold values were identified in the model simulations for the different producer types. The simulation results highlighted the importance of the pelagic–benthic interactions within the River Frome and indicated that process interaction defined the behaviour of the primary producers, rather than a single, dominant driver. The model simulations pose interesting questions to be considered in the next iteration of field- and laboratory based studies.

Characterization of para-Nitrophenol-degrading bacterial communities in river water by using functional markers and stable isotope probing

Microbial degradation is a major determinant of the fate of pollutants in the environment. para-Nitrophenol (PNP) is an EPA listed priority pollutant with a wide environmental distribution, but little is known about the microorganisms that degrade it in the environment. We studied the diversity of active PNP-degrading bacterial populations in river water using a novel functional marker approach coupled with [13C6]PNP stable isotope probing (SIP). Culturing together with culture-independent terminal restriction fragment length polymorphism analysis of 16S rRNA gene amplicons identified Pseudomonas syringae to be the major driver of PNP degradation in river water microcosms. This was confirmed by SIP-pyrosequencing of amplified 16S rRNA. Similarly, functional gene analysis showed that degradation followed the Gram-negative bacterial pathway and involved pnpA from Pseudomonas spp. However, analysis of maleylacetate reductase (encoded by mar), an enzyme common to late stages of both Gram-negative and Gram-positive bacterial PNP degradation pathways, identified a diverse assemblage of bacteria associated with PNP degradation, suggesting that mar has limited use as a specific marker of PNP biodegradation. Both the pnpA and mar genes were detected in a PNP-degrading isolate, P. syringae AKHD2, which was isolated from river water. Our results suggest that PNP-degrading cultures of Pseudomonas spp. are representative of environmental PNP-degrading populations.

The risk of river flooding

This section of the report outlines the effect of different levels of climate change on exposure to river flood risk, at national and watershed scales.