Optimising hydropower operations to balance economic profitability and support functioning ecosystem services is integral to river management policy. In this article, we propose a multi-objective optimization framework for small hydropower plants (SHPs) to evaluate trade-offs among environmental flow scenarios. Specifically, we examine the balance between short-term losses in hydropower generation and the potential for compensatory benefits in the form of revenue from recreational ecosystem services, irrespective of the direct beneficiary. Our framework integrates a fish habitat model, a hydropower optimization model, and a recreational ecosystem service model to evaluate each environmental flow scenario. The optimisation process gives three outflow release scenarios, informed by previous streamflow realisations (dam inflow), and designed environmental flow constraints. The framework is applied and tested for the river Kuusinkijoki in North-eastern Finland, which is a habitat for migratory brown trout and grayling populations. We show that the revenue loss due to the environmental flow constraints arises through a reduction in revenue per generated energy unit and through a reduction in turbine efficiency. Additionally, the simulation results reveal that all the designed environmental flow constraints cannot be met simultaneously. Under the environmental flow scenario with both minimum flow and flow ramping rate constraints, the annual hydropower revenue decreases by 16.5%. An annual increase of 8% in recreational fishing visits offsets the revenue loss. The developed framework provides knowledge of the costs and benefits of hydropower environmental flow constraints and guides the prioritizing process of environmental measures.

The Gotvand dam was built on the most important Iranian river to support a number of populated cities with freshwater, provide irrigation water for million hectares of fertile farmlands, and meet water demand for the country’s hub industrial zones. This dam is known as one of the worst engineering failures in Iran’s history because its impoundment submerged the enormous salty unit of Gachsaran evaporite formation (GEF) outcropped in the reservoir, leading to reservoir water salinization in deep layers up to several times greater than that of in the high-seas. Given the failed practical application of direct intervention strategies to control the salinity crisis, we suggested a low-cost salinity management strategy based on the reservoir operation to mitigate the dam outlet salinity and preserve the downstream environment from the salinity hazards. The three-dimensional MIKE3 model, was run to calculate the GEF dissolution rate, accumulated salt in the reservoir, and the dam outlet salinity. Then, we ran the model considering different outlet salinity levels to explore the best reservoir operation strategy to prohibit the accumulated salt in the reservoir and keep the safe salinity for downstream irrigation-use. Simulation results suggested that the GEF dissolution rate varied from 0.5 to 7 cm/hr, mainly due to incremental submergence of the GEF during multi-stage impoundment of the reservoir. Considering the final dissolution rate of 0.5 cm/hr and inlet salinity from the upstreams, salt accumulation inside the reservoir can be gradually prevented by setting the outlet salinity to its maximum historical downstream level, i.e., 1400 µmhos/cm.

The role of hydropower as a renewable and balancing power source is expected to significantly increase in a Net Zero Emissions by 2050 scenario. As a common phenomenon in hydropower plants, hydropeaking will become more prominent, resulting in additional stresses on the ecological status of rivers. Here we propose a novel approach to design and operate auxiliary reservoirs called re-regulation reservoirs that aims to mitigate the adverse impacts of hydropeaking on rivers. A re-regulation reservoir aims at smoothing flow fluctuations caused by hydropeaking by diverting and retaining parts of high flows and returning them back to river corridors during low flows. The regulatory performance of re-regulation reservoirs is a function of its geometry and volume availability. It is defined (and optimized) by restricting various flow components thresholds. Using actual data from a hydropeaking-influenced river system, the operation and efficiency of potential re-regulation reservoir have been investigated by employing a range of thresholds for hydropeaking mitigation. A methodology and an open-access algorithm to operate re-regulation reservoirs, by establishing a hierarchy of conditions to restrict peak flow, minimum flow, up-ramping rates, and down-ramping rates was developed. Our calculations show clear theoretical possibilities for regulating hydropeaking with re-regulation reservoirs, while offering several advantages, including greater flexibility and adaptability to changing environmental conditions, power, and water demand without increasing the operational cost of power systems.

In the last decade, recognizing and reducing uncertainties in hydrological forecasting has shown renewal interest. However, from a modeler’s perspective, a unified code of practice is always needed to handle the various facets of uncertainty in hydrological forecasting. Pappenberger and Beven, (2006) suggested nine codes of practice for handling uncertainties in hydrological modelling. In this paper, we have revisited those principles and added new insights to yield seven key principles for accounting and reducing uncertainties in catchment related hydrological forecasting tasks: (1) objectives define the need for uncertainty, (2) exploring the Catchment Puzzle, (3) selection of models is key, (4) choices of the method for quantifying uncertainties and calibration (5) finding the sources of uncertainties (6) advancements are a critical choice (7) prioritizing End User Needs for Reliable Forecasting Services. We derive these principles as a summary of understanding how modelers across the world have approached uncertainty handling from the analysis of recent literature on reducing uncertainties in hydrological forecasting. The triangulated interdependence and uncertainty contributions between the hydrological processes, epistemic uncertainties, and model development inevitably impact the forecast. Yet, the mapping of these principles provided in this study can assist the modelers in developing an improved framework for hydrological forecasting. Further, this work calls for discussions among the hydrological science community to establish these principles.

One of the negative effects of hydropower on river environment includes rapid changes in flow and habitat conditions. Any sudden flow change could force fish to move towards a refuge area in a short period of time, causing serious disturbances in the life cycle of the fish. A probability-based multiscale model was developed to quantify the impact of hydropeaking on habitat suitability for two fish species. The model used habitat preference curves, river flow and depth to develop the suitability maps. The suitability index maps reveal that habitat suitability deteriorates as flow increases in this part of the river. The probability model showed that, on average, suitability indices are higher for adult grayling than juvenile trout in hydropeaking events in the studied area. In addition, the life stages of fish determine their response to the sudden flow change. The method developed shows the potential to be used in river management and the evaluation of hydropeaking impacts in river systems affected by hydropower.

Smart drainage management to limit summer drought damage in Nordic agriculture under the circular economy conceptSyed Md Touhidul Mustafa 1, *, Kedar Ghag1, Anandharuban Panchanathan2, Bishal Dahal 1, Amirhossein Ahrari1, Toni Liedes 3, Hannu Marttila1, Tamara Avellán1, Mourad Oussalah2, Björn Klöve 1, & Ali Torabi Haghighi11Water, Energy and Environmental Engineering Research Unit, University of Oulu, P.O. Box 4300, FIN90014, Oulu, Finland2Center for Machine Vision and Signal Analysis, University of Oulu, Finland3Intelligent Machines and Systems, University of Oulu, PO Box 4200, 90014 Oulu, Finland