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. 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 a 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 behavior, while it keeps a reasonable water balance that is the key objective of every hydrologic model.

In recent decades, renewable energy projects have required careful consideration of environmental factors. This study investigates the impact of a mid-latitude cyclone on planned floating photovoltaic (FPV) facilities in Antalya, Turkey, focusing on the severe thunderstorm events that brought heavy rainfall and tornadoes in January 2019. Synoptic analysis reveals a deep cut-off low over the Genoa Gulf, causing trough formation and vertical cloud development due to moisture convergence. Warm air advection pushed an unstable thunderstorm system northward along an occluded front. Using the Weather Research and Forecast (WRF) model, sensitivity analysis is conducted, highlighting regional variations in wind speeds. The model outputs are compared with observations, identifying the best configuration using statistical indicators. The Mellor–Yamada–Janjic (MYJ) planetary boundary layer (PBL) scheme and the Milbrandt microphysics scheme produced better results in the western and central regions. The model output of the best configurations is used to calculate regional wave characteristics with a modified Shore Protection Manual (SPM) method for water reservoirs. These findings offer invaluable insights for future FPV projects, providing a better understanding of how to address challenges posed by extreme weather conditions and how to enhance system safety and reliability.

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.

Precipitation extremes are commonly linked with land use types. The UKCP18 Convection-Permitting Model (CPM) Projections at 5km high resolution simulation provide opportunities to investigate probable relationships between precipitation extremes and land use types. Changes in the duration and severity of extreme precipitation events can be linked to landscape characteristics, which affect the risk of rapid and local hydrological hazards.Based on publicly accessible data and a standard approach, Local Climate Zones (LCZs) provide coherent descriptions of the form and function of urban landscapes. From the World Urban Database and Access Portal, the LCZ is used to translate relevant land attributes to urban canopy parameters for climate and weather modelling applications at appropriate scales. Using the Severn River Basin as a case study, we use LCZ data to calculate urban fractions to investigate the roles of urban land types to the extreme distribution parameters.In conjunction with the LCZ data, the Corine Land Cover (CLC) and the Moderate Resolution Imaging Spectroradiometer (MODIS) datasets are used to benchmark how future changes in rainfall intensities and seasonal patterns might be related to land use. The results are used to generate possible hypotheses to run different CPM models based on the LCZ data.Based on these findings, we present a novel land-use-based approach for water hazard management addressing hydrological risk connected to regional climate resilience. For management authorities and infrastructure owners, precipitation extreme risk related to land use is critical for their long-term investment planning. The proposed methodology would be advantageous to many UK water regulators and stakeholders in generating more informative precipitation extreme estimations based on land use, for the high greenhouse gas emissions scenario RCP8.5.

Increasing spatial resolution to kilometre scales allows the deactivation of deep convection parameterisation schemes. As a result of various global initiatives for the next generation of climate studies, continental convection-permitting model (CPM) simulations are now accessible. Nonstationary local extremes, like heatwaves and intense precipitation, are probabilistically linked to regional circulation through scaling relationships. However, these relationships have not been extensively explored in the new simulations available in the early 2020s. Hourly time series data were extracted from the UK Climate Science for Service Partnership (CSSP) and the US South America Affinity Group (SAAG) CPM simulations to compare extreme characteristics of precipitation and temperature for 39 stations in a region of São Paulo, Brazil. Compared to reanalysis and satellite data, which exhibit lower variance in hourly time series, these two sets of CPM simulations have precipitation that is more similar to station observations than the ERA5 data and the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) data.The cross-correlation structures of the time series are investigated to quantify temporal dependence and reveal patterns between temperature and precipitation at an hourly timescale. Within a higher-dimensional probability space for joint risk, the cross-correlation structures between temperature and precipitation at different lags demonstrate the "memory" of these variables, indicating the influence of past values on future behaviour across multiple time points. Their forecasting power for these two variable based on each other is also explored to offer insights into the physical processes within the evolving simulated dynamic system.Overall, the results underscore the added value of convection-permitting models in providing more realistic simulations of local dynamics of extremes. The identified cross-correlation structures from the CPMs are valuable for exploring opportunities to design AI engines based on weather generator algorithms that use stochastic differential equations. Using CPM simulations, these weather generators can be employed to develop AI approaches for rapid decision support tools aimed at stakeholders facing extreme weather events related to compound risks of temperature and precipitation.

Convection-permitting model outputs offer significant opportunities for training statistical downscaling approaches. The Coordinated Regional Climate Downscaling Experiment (CORDEX) on the urban environment and regional climate change ensemble simulations provide valuable insights into the uncertainties of numerical atmospheric models. Traditional weather generators, based on the Maximum Likelihood for the Generalised Linear Model approach, have been instrumental in modelling precipitation occurrence and amount. This study advances the statistical downscaling method by integrating Generative AI approaches, using deep learning to create stochastic precipitation ensembles.Compared to deterministic simulations, this new probabilistic approach allows for an exploration of the nonstationary statistical properties influenced by regional climate conditions through more feasible nonlinear representation for the weather generator parameters by deep learning. Emphasis is placed on the importance of probabilistic and agnostic methods in exploring, interpreting, and explaining uncertainties.Findings related to temperature variations for daily precipitation extremes attribute the roles of sensible and latent heat, which are further interpreted through regional processes. The integration of generative AI highlights the stochastic uncertainties in weather generators, emphasising the need for consistency between deterministic convection-permitting model outputs and observational data. By examining scaling relationships, the interpretability and explainability of model outputs, particularly concerning energy balance processes, are demonstrated.Through interpretable and explainable statistical downscaling, the approach to modelling precipitation extremes based on maximum likelihood theory fosters international collaboration in the Climate Collaboratorium* project (IIRCC; ‘Exploring climate solutions with interactive theatre’)This includes contributions from Canada, Germany, the UK, and the US, aimed at providing accessible science that can inform climate decisions in partnership with social science/arts and humanities researchers, tailored to place-based user needs. Advocacy for responsible AI in atmospheric and water sciences facilitates interdisciplinary climate adaptation and mitigation with Taiwanese and Brazilian communities. This approach promotes transparency and fairness through explainable and interpretable climate scenarios. By incorporating immersive experiences and smart decision-making processes, the pathway for human oversight remains central to fair climate action to achieve Sustainable Development Goal 13.*https://www.ukri.org/publications/international-science-partnerships-fund-iircc-initiative-funded-projects/international-joint-initiative-for-research-in-climate-change-adaptation-and-mitigation-project-overview/