A Bayesian network to simulate macroinvertebrate responses to multiple stressors in lowland streams.

Aquatic ecosystems are affected by multiple environmental stressors across spatial and temporal scales. Yet the nature of stressor interactions and stressor-response relationships is still poorly understood. This hampers the selection of appropriate restoration measures. Hence, there is a need to understand how ecosystems respond to multiple stressors and to unravel the combined effects of the individual stressors on the ecological status of waterbodies. Models may be used to relate responses of ecosystems to environmental changes as well as to restoration measures and thus provide valuable tools for water management. Therefore, we aimed to develop and test a Bayesian Network (BN) for simulating the responses of stream macroinvertebrates to multiple stressors. Although the predictive performance may be further improved, the developed model was shown to be suitable for scenario analyses. For the selected lowland streams, an increase in macroinvertebrate-based ecological quality (EQR) was predicted for scenarios where the streams were relieved from single and multiple stressors. Especially a combination of measures increasing flow velocity and enhancing the cover of coarse particulate organic matter showed a significant increase in EQR compared to current conditions. The use of BNs was shown to be a promising avenue for scenario analyses in stream restoration management. BNs have the capacity for clear visual communication of model dependencies and the uncertainty associated with input data and results and allow the combination of multiple types of knowledge about stressor-effect relations. Still, to make predictions more robust, a deeper understanding of stressor interactions is required to parametrize model relations. Also, sufficient training data should be available for the water type of interest. Yet, the application of BNs may now already help to unravel the contribution of individual stressors to the combined effect on the ecological quality of water bodies, which in turn may aid the selection of appropriate restoration measures that lead to the desired improvements in macroinvertebrate-based ecological quality.

Sensors in the Stream: The High-Frequency Wave of the Present.

New scientific understanding is catalyzed by novel technologies that enhance measurement precision, resolution or type, and that provide new tools to test and develop theory. Over the last 50 years, technology has transformed the hydrologic sciences by enabling direct measurements of watershed fluxes (evapotranspiration, streamflow) at time scales and spatial extents aligned with variation in physical drivers. High frequency water quality measurements, increasingly obtained by in situ water quality sensors, are extending that transformation. Widely available sensors for some physical (temperature) and chemical (conductivity, dissolved oxygen) attributes have become integral to aquatic science, and emerging sensors for nutrients, dissolved CO2, turbidity, algal pigments, and dissolved organic matter are now enabling observations of watersheds and streams at time scales commensurate with their fundamental hydrological, energetic, elemental, and biological drivers. Here we synthesize insights from emerging technologies across a suite of applications, and envision future advances, enabled by sensors, in our ability to understand, predict, and restore watershed and stream systems.

Long-term predictions of ecosystem acidification and recovery.

This paper considers the long-term (500year) consequences of continued acid deposition, using a small forested catchment in S. England as an example. The MAGIC acidification model was calibrated to the catchment using data for the year 2000, and run backwards in time for 200years, and forwards for 500. Validation data for model predictions were provided by various stream and soil measurements made between 1977 and 2013. The model hindcast suggests that pre-industrial stream conditions were very different from those measured in 2000. Acid Neutralising Capacity (ANC) was +150µeqL(-1) and pH7.1: there was little nitrate (NO3). By the year 2000, acid deposition had reduced the pH to 4.2 and ANC to c. -100µeqL(-1), and NO3 was increasing in the stream. The future state of the catchment was modelled using actual deposition reductions up to 2013, and then based on current emission reduction commitments. This leads to substantial recovery, to pH6.1, ANC +43µeqL(-1), though it takes c. 250years. Then, however, steady acidification resumes, due to continued N accumulation in the catchment and leaching of NO3. Soil data collected using identical methods in 1978 and 2013 show that MAGIC correctly predicts the direction of change, but the observed data show more extreme changes - reasons for this are discussed. Three cycles of forest growth were modelled - this reduces NO3 output substantially during the active growth phase, and increases stream pH and ANC, but acidifies the soil which continues to accumulate nitrogen. The assumptions behind these results are discussed, and it is concluded that unmanaged ecosystems will not return to a pre-industrial state in the foreseeable future.

An analysis of long-term trends, seasonality and short-term dynamics in water quality data from Plynlimon, Wales.

This paper examines two hydrochemical time-series derived from stream samples taken in the Upper Hafren catchment, Plynlimon, Wales. One time-series comprises data collected at 7-hour intervals over 22 months (Neal et al., 2012-this issue), while the other is based on weekly sampling over 20 years. A subset of determinands: aluminium, calcium, chloride, conductivity, dissolved organic carbon, iron, nitrate, pH, silicon and sulphate are examined within a framework of non-stationary time-series analysis to identify determinand trends, seasonality and short-term dynamics. The results demonstrate that both long-term and high-frequency monitoring provide valuable and unique insights into the hydrochemistry of a catchment. The long-term data allowed analysis of long-term trends, demonstrating continued increases in DOC concentrations accompanied by declining SO(4) concentrations within the stream, and provided new insights into the changing amplitude and phase of the seasonality of the determinands such as DOC and Al. Additionally, these data proved invaluable for placing the short-term variability demonstrated within the high-frequency data within context. The 7-hour data highlighted complex diurnal cycles for NO(3), Ca and Fe with cycles displaying changes in phase and amplitude on a seasonal basis. The high-frequency data also demonstrated the need to consider the impact that the time of sample collection can have on the summary statistics of the data and also that sampling during the hours of darkness provides additional hydrochemical information for determinands which exhibit pronounced diurnal variability. Moving forward, this research demonstrates the need for both long-term and high-frequency monitoring to facilitate a full and accurate understanding of catchment hydrochemical dynamics.