Although the complexity of physically-based models continues to increase, they still need to be calibrated. In recent years, there has been an increasing interest in using new satellite technologies and products with high resolution in model evaluations and decision-making. The aim of this study is to investigate the value of different remote sensing products and groundwater level measurements in the temporal calibration of a well-known hydrologic model i.e., Hydrologiska Bryåns Vattenbalansavdelning (HBV). This has rarely been done for conceptual models, as satellite data are often used in the spatial calibration of the distributed models. Three different soil moisture products from the European Space Agency Climate Change Initiative Soil Measure (ESA CCI SM v04.4), The Advanced Microwave Scanning Radiometer on the Earth Observing System (EOS) Aqua satellite (AMSR-E), soil moisture active passive (SMAP), and total water storage anomalies from Gravity Recovery and Climate Experiment (GRACE) are collected and spatially averaged over the Moselle River Basin in Germany and France. Different combinations of objective functions and search algorithms, all targeting a good fit between observed and simulated streamflow, groundwater and soil moisture, are used to analyze the contribution of each individual source of information.

The aim of this study is to model the hydrodynamic processes of the Istanbul Strait with its stratified flow characteristics, and calibrate the most important parameters using local and global search algorithms. For that, two open boundary conditions are defined, which are in the northern and southern parts of the Strait. Observed bathymetric, hydrographic, meteorological, and water-level data are used to set up the Delft3D-FLOW model. First, the sensitivities of the model parameters on the numerical model outputs are assessed using Parameter EStimation Tool (PEST) toolbox. Then, the model is calibrated based on the objective functions, focusing on the flow rates of the upper and lower layers. The salinity and temperature profiles of the strait are only used for model validation. The results show that the calibrated model outputs of the Istanbul Strait are reliable and consistent with the in situ measurements. The sensitivity analysis reveals that the spatial low-pass filter coefficient, horizontal eddy viscosity, Prandtl–Schmidt number, slope in log–log spectrum, and Manning roughness coefficient are most sensitive parameters affecting the flow rate performance of the model. The agreement between observed salinity profiles and simulated model outputs is promising, whereas the match between observed and simulated temperature profiles is weak, showing that the model can be improved, particularly for simulating the mixing layer.

Although the complexity of physically based models continues to increase, they still need to be calibrated. In recent years, there has been an increasing interest in using new satellite technologies and products with high resolution in model evaluations and decision-making. The aim of this study is to investigate the value of different remote sensing products and groundwater level measurements in the temporal calibration of a well-known hydrologic model i.e. HBV. This has been rarely done for conceptual models as satellite data are often used in spatial calibration of the distributed models. Three different soil moisture products from ESA CCI SM v04.4, AMSR-E and SMAP, and total water storage anomalies from GRACE are collected and spatially averaged over the Moselle River Basin in Germany and France. Different combinations of objective functions and search algorithms all targeting a good fit between observed and simulated streamflow, groundwater and soil moisture are used to analyse the contribution of each individual source of information. Firstly, the most important parameters are selected using sensitivity analysis and then, these parameters are included in a subsequent model calibration. The results of our multi-objective calibration reveal substantial contribution of remote sensing products to the lumped model calibration even if their spatially distributed information is lost during the spatial aggregation. Inclusion of new observations such as groundwater levels from wells and remotely sensed soil moisture to the calibration improves the model’s physical behaviour while it keeps a reasonable water balance that is the key objective of every hydrologic model.

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Applying pressure management reduces lost water and excessive hydraulic pressures in water distribution networks (WDNs). There are currently four different types of pressure management in the literature, i.e. fixed outlet, time modulated, flow modulated, and remote node modulated. The primary device used in pressure management is the pressure reducing valve (PRV) that dynamically controls the outlet pressure by moving up and down its main valve element. In this study, we firstly introduce the dynamic PRV model with four different pressure management types to the source code of EPANET v3.1 software and assess the effect of different valve opening schemes on pressure graphs and leakage quantities. The results showed that dynamic PRV significantly reduces lost water amounts and excessive hydraulic pressures in the WDN when valve opening is continuously adjusted. Our smart PM extension implemented into EPANET v3.1 software is publicly available in Zenodo repositories (https://zenodo.org/record/6243078).