The hydrological response of the Ourthe catchment to climate change as modelled by the HBV model

The Meuse is an important river in Western Europe, which is almost exclusively rain-fed. Projected changes in precipitation characteristics due to climate change, therefore, are expected to have a considerable effect on the hydrological regime of the river Meuse. We focus on an important tributary of the Meuse, the Ourthe, measuring about 1600 km2. The well-known hydrological model HBV is forced with three high-resolution (0.088°) regional climate scenarios, each based on one of three different IPCC CO2 emission scenarios: A1B, A2 and B1. To represent the current climate, a reference model run at the same resolution is used. Prior to running the hydrological model, the biases in the climate model output are investigated and corrected for. Different approaches to correct the distributed climate model output using single-site observations are compared. Correcting the spatially averaged temperature and precipitation is found to give the best results, but still large differences exist between observations and simulations. The bias corrected data are then used to force HBV. Results indicate a small increase in overall discharge, especially for the B1 scenario during the beginning of the 21st century. Towards the end of the century, all scenarios show a decrease in summer discharge, partially because of the diminished buffering effect by the snow pack, and an increased discharge in winter. It should be stressed, however, that we used results from only one GCM (the only one available at such a high resolution). It would be interesting to repeat the analysis with multiple model

Climatology of daily rainfall semi-variance in The Netherlands

Rain gauges can offer high quality rainfall measurements at their locations. Networks of rain gauges can offer better insight into the space-time variability of rainfall, but they tend to be too widely spaced for accurate estimates between points. While remote sensing systems, such as radars and networks of microwave links, can offer good insight in the spatial variability of rainfall they tend to have more problems in identifying the correct rain amounts at the ground. A way to estimate the variability of rainfall between gauge points is to interpolate between them using fitted variograms. If a dense rain gauge network is lacking it is difficult to estimate variograms accurately. In this paper a 30-year dataset of daily rain accumulations gathered at 29 automatic weather stations operated by KNMI (Royal Netherlands Meteorological Institute) and a one-year dataset of 10 gauges in a network with a radius of 5 km around CESAR (Cabauw Experimental Site for Atmospheric Research) are employed to estimate variograms. Fitted variogram parameters are shown to vary according to season, following simple cosine functions. Semi-variances at short ranges during winter and spring tend to be underestimated, but semi-variances during summer and autumn are well predicted

Multi-level optical signal generation using a segmented-electrode InP IQ-MZM with integrated CMOS binary drivers

We present a segmented-electrode InP IQ-MZM, capable of multi-level optical signal generation (5-bit per I/Q arm) by employing direct digital drive from integrated, low-power (1W) CMOS binary drivers. Programmable, multi-level operation is demonstrated experimentally on one MZM of the device

Mapping (dis)agreement in hydrologic projections

Hydrologic projections are of vital socio-economic importance. However, they are also prone to uncertainty. In order to establish a meaningful range of storylines to support water managers in decision making, we need to reveal the relevant sources of uncertainty. Here, we systematically and extensively investigate uncertainty in hydrologic projections for 605 basins throughout the contiguous US. We show that in the majority of the basins, the sign of change in average annual runoff and discharge timing for the period 2070–2100 compared to 1985–2008 differs among combinations of climate models, hydrologic models, and parameters. Mapping the results revealed that different sources of uncertainty dominate in different regions. Hydrologic model induced uncertainty in the sign of change in mean runoff was related to snow processes and aridity, whereas uncertainty in both mean runoff and discharge timing induced by the climate models was related to disagreement among the models regarding the change in precipitation. Overall, disagreement on the sign of change was more widespread for the mean runoff than for the discharge timing. The results demonstrate the need to define a wide range of quantitative hydrologic storylines, including parameter, hydrologic model, and climate model forcing uncertainty, to support water resource planning

726.7-Gb/s 1.5-µm single-mode VCSEL discrete multi-tone transmission over 2.5-km multicore fiber

A 107Gb/s net-rate DMT optical signal was generated using a single-mode longwavelength VCSEL with a modulation bandwidth of 23GHz. We experimentally demonstrated a total net-rate up to 726.7Gb/s at 1.5μm over 2.5km 7-core dispersion-uncompensated MCF.</p

Machine learning for real-time reservoir operation simulation: comparing input variables and algorithms for the Sirikit Reservoir, Thailand

Machine learning (ML) models offer advantages over process-based models for real-time reservoir operation modelling, yet the impact of input variable selection (IVS) and data pre-processing on model performance remains underexplored. This study investigates various input variables for simulating daily reservoir outflow, using the Sirikit reservoir in Thailand as a case study. The datasets include daily Sirikit storage and inflow, outflow of Bhumibol (neighbouring reservoir), downstream discharge, and temporal factors (month and day of the week). Time series decomposition and correlation analyses were used to assess data relationships. We tested seven ML models: multiple linear regression, support vector machine, K-nearest neighbour, classification and regression tree, random forest, multi-layer perceptron, and recurrent neural network (RNN). The optimal input set comprised the previous day’s storage, inflow from 2 days before to 2 days after, and month. With these inputs, all ML models simulated outflow adequately (KGEtraining = 0.42–1.0 and KGEtesting = 0.46–0.56), with RNN showing the most potential for improvement. Input scaling significantly enhanced model performance, reducing RMSEtraining by 44 m3 s-1 and RMSEtesting by 14 m3 s-1. This study’s novelty lies in its comprehensive insights of IVS and data scaling, highlighting their critical roles in enhancing ML model application for operational reservoir simulations

Wind-driven ventilation improvement with plan typology alteration: a CFD case study of traditional Turkish architecture

Aligned with achieving the goal of net-zero buildings, the implementation of energy-saving techniques in minimizing energy demands is proving more vital than at any time. As practical and economic options, passive strategies in ventilation developed over thousands of years have shown great potential for the reduction of residential energy demands, which are often underestimated in modern building’s construction. In particular, as a cost-effective passive strategy, wind-driven ventilation via windows has huge potential in the enhancement of the indoor air quality (IAQ) of buildings while simultaneously reducing their cooling load. This study aims to investigate the functionality and applicability of a common historical Turkish architectural element called “Cumba” to improve the wind-driven ventilation in modern buildings. A case study building with an archetypal plan and parameters was defined as a result of a survey over 111 existing traditional samples across Turkey. Buildings with and without Cumba were compared in different scenarios by the development of a validated CFD microclimate model. The results of simulations clearly demonstrate that Cumba can enhance the room’s ventilation rate by more than two times while harvesting wind from different directions. It was also found that a flexible window opening strategy can help to increase the mean ventilation rate by 276%. Moreover, the room’s mean air velocity and ventilation rate could be adjusted to a broad range of values with the existence of Cumba. Thus, this study presents important findings about the importance of plan typology in the effectiveness of wind-driven ventilation strategies in modern dwellings