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.

The majority of lake temperature studies have investigated climate-induced changes occurring at the lake surface, primarily by analyzing detailed satellite images of surface water temperature. Whilst essential to observe long-term change, satellite images do not provide information on the thermal environment at depth, thus limiting our understanding of lake thermal responses to a warming world. Long-term in-situ observational data can fill some of the information gap, with depth-resolved field measurements providing a detailed view of thermal change throughout the water column. However, previous studies that have investigated multi-decadal changes in lake temperature, both at the surface and at depth, have typically focused on north temperate lakes. Relatively few studies have investigated temperature variations in lakes situated north of the Arctic circle, which is one of the most rapidly warming regions globally. Here, using a sixty-year (1961-2020) observational dataset of summer water temperature from Lake Inari (Finland), we investigate changes in the thermal environment of this pristine lake. Our analysis suggests a significant summer warming trend at the lake surface (+0.247 °C decade-1) and a marginal cooling trend (–0.027 °C decade-1) at depth. The contrasting thermal response of surface and bottom water temperatures to climatic warming has likewise resulted in a strengthening of summer stratification in this high latitude lake. Implications of the observed change in both temperature and stratification on the lake ecosystem will likely be extensive, including impacts on aquatic organisms in which this lake supports. Our work builds on ever-growing literature regarding lake thermal responses to climate change.

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