Influence of hydro-meteorological data spatial aggregation on streamflow modelling

Abstract. Backtrajectory differences and clustering sensitivity to the meteorological input data are studied. Trajectories arriving in Southeast Spain (Elche), at 3000, 1500 and 500 m for the 7-year period 2000–2006 have been computed employing two widely used meteorological data sets: the NCEP/NCAR Reanalysis and the FNL data sets. Differences between trajectories grow linearly at least up to 48 h, showing faster growing after 72 h. A k-means cluster analysis performed on each set of trajectories shows differences in the identified clusters (main flows), partially because the number of clusters of each clustering solution differs for the trajectories arriving at 3000 and 1500 m. Trajectory membership to the identified flows is in general more sensitive to the input meteorological data than to the initial selection of cluster centroids.

Abstract. Evapotranspiration (ET) is a crucial flux of the hydrological water balance, commonly estimated using (semi-)empirical formulas. The estimated flux may strongly depend on the formula used, adding uncertainty to the outcomes of environmental studies using ET. Climate change may cause additional uncertainty, as the ET estimated by each formula may respond differently to changes in meteorological input data. To include the effects of model uncertainty and climate change and facilitate the use of these formulas in a consistent, tested, and reproducible workflow, we present PyEt. PyEt is an open-source Python package for the estimation of daily potential evapotranspiration (PET) using available meteorological data. It allows the application of 20 different PET methods on both time series and gridded datasets. The majority of the implemented methods are benchmarked against literature values and tested with continuous integration to ensure the correctness of the implementation. This article provides an overview of PyEt's capabilities, including the estimation of PET with 20 PET methods for station and gridded data, a simple procedure for calibrating the empirical coefficients in the alternative PET methods, and estimation of PET under warming and elevated atmospheric CO2 concentration. Further discussion on the advantages of using PyEt estimates as input for hydrological models, sensitivity and uncertainty analyses, and hindcasting and forecasting studies (especially in data-scarce regions) is provided.

Potassium (K) is a critical nutrient for crops, as it is a major constituent in fertilizer formulations. With increasing concentrations of K in agricultural soil, it is necessary to understand its movement and retention in the soil. Sub-surface modeling is an alternative method to overcome the exhausting and uneconomical methods to study and determine the actual concentration of K in soil. HYDRUS-1D is considered an effective finite-element model which is suitable for sub-surface modeling. This model requires the input of ground-station meteorological (GM) data taken at a daily timestep for the simulation period. It can be a limiting factor in the absence of ground stations. The study compares K predictions in surface and sub-surface soil layers under Boro rice cultivation obtained with the usage of different meteorological datasets. Thus, the main hypothesis of the study was to validate that, in the absence of GM data, satellite-based meteorological data could be utilized for simulating the K concentration in soil. The two meteorological datasets that are considered in the study included the GM and satellite-derived NASA-Power (NP) meteorological datasets. The usage of a satellite meteorological product at a field scale may help in applying the method to other regions where GM data is not available. The numerical model results were validated with field experiments from four experimental fields which included varied K doses. The concentration in soil was assessed at the regular depths (0–5, 5–10, 10–15, 15–30, 30–45 and 45–60 cm), and at various stages of crop growth, from bare soil and sowing, to the tillering stages. The concentration of K was measured in the laboratory and also simulated through the optimized model. The modeled values were compared with measured values statistically using relative root mean square error (RMSER) and Nash–Sutcliffe modeling efficiency (E) for simulating K concentration in the soil for the Boro rice cropping pattern with both GM data and NP data. The model was found most suitable for the 0–30 cm depth on all days and for all treatment variations.

Deep learning has become the de facto standard for streamflow simulation. While there are examples of deep learning based streamflow forecast models (e.g., 1-5), the majority of the development and research has been done with hindcast models. The primary challenge in using deep learning models for forecasting (e.g., flood forecasting) is that the meteorological input data are drawn from different distributions in hindcast vs. forecast. The (relatively small) amount of research that has been done on deep learning streamflow forecasting has largely used an encoder-decoder approach to account for forecast distribution shifts. This is, for example, what Google’s operational flood forecasting model uses [4]. In this work we show that the encoder-decoder approach results in artifacts in forecast trajectories that are not detectable with standard hydrological metrics, but which can cause forecasts to have incorrect trends (e.g., rising when they should be falling and vice-versa).  We solve this problem using regularized embeddings, which remove forecast artifacts without harming overall accuracy. Perhaps more importantly, input embeddings allow for training models on spatially and/or temporally incomplete meteorological inputs, meaning that a single model can be trained using input data that does not exist everywhere or does not exist during the entire training or forecast period. This allows models to learn from a significantly larger training data set, which is important for high-accuracy predictions. It also allows large (e.g., global) models to learn from local weather data. We demonstrate how and why this is critical for state-of-the-art global-scale streamflow forecasting.  Franken, Tim, et al. An operational framework for data driven low flow forecasts in Flanders. No. EGU22-6191. Copernicus Meetings, 2022. Kao, I-Feng, et al. "Exploring a Long Short-Term Memory based Encoder-Decoder framework for multi-step-ahead flood forecasting." Journal of Hydrology 583 (2020): 124631. Liu, Darong, et al. "Streamflow prediction using deep learning neural network: case study of Yangtze River." IEEE access 8 (2020): 90069-90086. Nevo, Sella, et al. "Flood forecasting with machine learning models in an operational framework." Hydrology and Earth System Sciences 26.15 (2022): 4013-4032. Girihagama, Lakshika, et al. "Streamflow modelling and forecasting for Canadian watersheds using LSTM networks with attention mechanism." Neural Computing and Applications 34.22 (2022): 19995-20015.  

Impact of spatial and temporal aggregation of input parameters on the assessment of irrigation scheme performance

A dispersion modelling system SILAM and its evaluation against ETEX data

Eulerian air quality models that require gridded meteorological inputs have to adapt to recent advances in meteorological models for fully compressible atmosphere. When the input meteorological data are recast with a robust fully compressible governing set of equations, chemistry-transport models can follow the dynamic and thermodynamic descriptions of the meteorological data closely. For evaluating mass consistency in meteorological data, one may take advantage of the characteristics of the governing set of equations applicable for a specific vertical coordinate system. This paper discusses how the data from meteorological models should be used in air quality simulations. It proposes a general methodology to conserve mass of trace species in air quality models by maintaining consistency in the wind and air density fields. Limitations of several simplifying assumptions on atmospheric dynamics are also discussed. In summary, it attempts to bridge the information gap between dynamic meteorologists ...

Reference evapotranspiration is a key element in agricultural management, particularly in a changing global environment, and represents an important requirement for the effective planning, monitoring, and management of water resources. However, accurate evapotranspiration estimation requires spatially well-distributed continuous meteorological data to capture regional variations, and reanalysis datasets are valuable tools for this purpose. In this context, this study aimed to assess the performance of the Copernicus European Regional ReAnalysis (CERRA) dataset in the western Iberian Peninsula, focusing on Portugal and Galicia (Spain). Meteorological data (air temperature, relative humidity, solar radiation, and wind speed) from several surface stations were used to analyze the differences between the observations and CERRA hindcasts. The reference evapotranspiration ( ET o ) was then computed for both datasets to estimate CERRA’s consistency and accuracy. The results revealed that CERRA data strongly correlated with the observational data, accurately capturing the spatial and temporal atmospheric patterns. Daily air temperature was the most accurately represented variable, followed by relative humidity, solar radiation, and wind speed. ET o estimates from the CERRA dataset were closely aligned with observations. The high spatial resolution of CERRA enabled an accurate representation of the regional climatic variations, addressing the weaknesses found in other reanalysis datasets, particularly in coastal areas influenced by land‒sea interactions. The findings of this study indicate that CERRA is a highly valuable database for climate studies to validate the results of regional climate models with high resolution. These models are essential for developing effective adaptation and mitigation strategies to address agricultural planning and management in response to climate-related challenges. Graphical Abstract In this study, the performance of the Copernicus European Regional ReAnalysis (CERRA) dataset in replicating atmospheric variables and reference evapotranspiration (ET o ) for agrometeorological applications in the western Iberian Peninsula was assessed. CERRA hindcasts were compared with meteorological observations (minimum and maximum air temperature, relative humidity, wind speed, and solar radiation) from surface stations using Taylor diagrams, box plots, scatter plots, and the Kling–Gupta efficiency ( KGE ) metric for validation. The ET o was subsequently computed for both datasets. The results indicate strong correlations between CERRA and observational data, with CERRA effectively reproducing spatial and temporal patterns. ET o estimates from the CERRA dataset closely align with observations. This study emphasizes the ability of CERRA to accurately represent regional climatic variations because of its high spatial resolution, overcoming the limitations of other reanalysis datasets, particularly in coastal zones. The results suggest that CERRA is a valuable asset for climate studies, validation of high-resolution regional climate models, water resource management, and agricultural planning.

<strong class="journal-contentHeaderColor">Abstract.</strong> Increasing temperatures due to global warming will influence building, heating, and cooling practices. Therefore, this data set aims to provide formatted and adapted meteorological data for specific users who work in building design, architecture, building energy management systems, modelling renewable energy conversion systems, or others interested in this kind of projected weather data. These meteorological data are produced from the regional climate model MAR (Modèle Atmosphérique Régional in French) simulations. This regional model, adapted and validated over Belgium, is forced firstly, by the ERA5 reanalysis, which represents the closest climate to reality and secondly, by three Earth system models (ESMs) from the Sixth Coupled Model Intercomparison Project database, namely, BCC-CSM2-MR, MPI-ESM.1.2, and MIROC6. The main advantage of using the MAR model is that the generated weather data have a high resolution (hourly data and 5 km) and are spatially and temporally homogeneous. The generated weather data follow two protocols. On the one hand, the Typical Meteorological Year (TMY) and eXtreme Meteorological Year (XMY) files are generated largely inspired by the method proposed by the standard ISO15927-4, allowing the reconstruction of typical and extreme years, while keeping a plausible variability of the meteorological data. On the other hand, the heatwave event (HWE) meteorological data are generated according to a method used to detect the heatwave events and to classify them according to three criteria of the heatwave (the most intense, the longest duration, and the highest temperature). All generated weather data are freely available on the open online repository Zenodo (<a href="https://doi.org/10.5281/zenodo.5606983">https://doi.org/10.5281/zenodo.5606983</a>, Doutreloup and Fettweis, 2021) and these data are produced within the framework of the research project OCCuPANt (<span class="uri">https://www.occupant.uliege.be/</span> (last access: 24 June 2022)​​​​​​​ – ULiège).

The thermal structure of Sau Reservoir (NE: Spain): a simulation approach

Modelling the spatial and temporal variations of the water balance for the Weser catchment 1965–1994

Hygrothermal simulations have become essential for sustainable and resilient building design because moisture is the major cause of problems in buildings. Appropriate meteorological input data are important to obtain meaningful simulation results. Therefore, this article reviews different methods to create Hygrothermal Reference Years (HRY) as severe or average climate inputs. The current standards define HRYs solely based on outdoor temperature, although moisture problems are caused by a combination of climate parameters, including driving rain and other loads. Therefore, there are also methods considering several impact parameters. The existing methods can be classified into two categories: construction-independent and construction-dependent methods. The former determines HRY based on a weather data analysis and is useful for large-scale parametric studies comprising many climatic parameters acting on buildings. The latter is based in addition to computer simulations to verify the HRY also in the context of specific construction types. It is a more comprehensive approach since the moisture responses of constructions are the decisive outcome for performance predictions. The advantages and disadvantages of the different methods are summarized and compared. Lastly, further research questions and simplifications aimed at practitioners are pointed out to arrive at reliable hygrothermal building performance predictions.

Lagrangian models are fundamental tools to study atmospheric transport processes and for practical applications such as dispersion modeling for anthropogenic and natural emission sources. However, conducting large-scale Lagrangian transport simulations with millions of air parcels or more can become numerically rather costly. In this study, we assessed the potential of exploiting graphics processing units (GPUs) to accelerate Lagrangian transport simulations. We ported the Massive-Parallel Trajectory Calculations (MPTRAC) model to GPUs using the open accelerator (OpenACC) programming model. The trajectory calculations conducted within the MPTRAC model were fully ported to GPUs, i.e., except for feeding in the meteorological input data and for extracting the particle output data, the code operates entirely on the GPU devices without frequent data transfers between CPU and GPU memory. Model verification, performance analyses, and scaling tests of the MPI/OpenMP/OpenACC hybrid parallelization of MPTRAC were conducted on the JUWELS Booster supercomputer operated by the Jülich Supercomputing Centre, Germany. The JUWELS Booster comprises 3744 NVIDIA A100 Tensor Core GPUs, providing a peak performance of 71.0 PFlop/s. As of June 2021, it is the most powerful supercomputer in Europe and listed among the most energy-efficient systems internationally. For large-scale simulations comprising 108 particles driven by the European Centre for Medium-Range Weather Forecasts' ERA5 reanalysis, the performance evaluation showed a maximum speedup of a factor of 16 due to the utilization of GPUs compared to CPU-only runs on the JUWELS Booster. In the large-scale GPU run, about 67 % of the runtime is spent on the physics calculations, conducted on the GPUs. Another 15 % of the runtime is required for file-I/O, mostly to read the large ERA5 data set from disk. Meteorological data preprocessing on the CPUs also requires about 15 % of the runtime. Although this study identified potential for further improvements of the GPU code, we consider the MPTRAC model ready for production runs on the JUWELS Booster in its present form. The GPU code provides a much faster time to solution than the CPU code, which is particularly relevant for near-real-time applications of a Lagrangian transport model.

Abstract. Lagrangian models are fundamental tools to study atmospheric transport processes and for practical applications such as dispersion modeling for anthropogenic and natural emission sources. However, conducting large-scale Lagrangian transport simulations with millions of air parcels or more can become rather numerically costly. In this study, we assessed the potential of exploiting graphics processing units (GPUs) to accelerate Lagrangian transport simulations. We ported the Massive-Parallel Trajectory Calculations (MPTRAC) model to GPUs using the open accelerator (OpenACC) programming model. The trajectory calculations conducted within the MPTRAC model were fully ported to GPUs, i.e., except for feeding in the meteorological input data and for extracting the particle output data, the code operates entirely on the GPU devices without frequent data transfers between CPU and GPU memory. Model verification, performance analyses, and scaling tests of the Message Passing Interface (MPI) – Open Multi-Processing (OpenMP) – OpenACC hybrid parallelization of MPTRAC were conducted on the Jülich Wizard for European Leadership Science (JUWELS) Booster supercomputer operated by the Jülich Supercomputing Centre, Germany. The JUWELS Booster comprises 3744 NVIDIA A100 Tensor Core GPUs, providing a peak performance of 71.0 PFlop s−1. As of June 2021, it is the most powerful supercomputer in Europe and listed among the most energy-efficient systems internationally. For large-scale simulations comprising 108 particles driven by the European Centre for Medium-Range Weather Forecasts' fifth-generation reanalysis (ERA5), the performance evaluation showed a maximum speed-up of a factor of 16 due to the utilization of GPUs compared to CPU-only runs on the JUWELS Booster. In the large-scale GPU run, about 67 % of the runtime is spent on the physics calculations, conducted on the GPUs. Another 15 % of the runtime is required for file I/O, mostly to read the large ERA5 data set from disk. Meteorological data preprocessing on the CPUs also requires about 15 % of the runtime. Although this study identified potential for further improvements of the GPU code, we consider the MPTRAC model ready for production runs on the JUWELS Booster in its present form. The GPU code provides a much faster time to solution than the CPU code, which is particularly relevant for near-real-time applications of a Lagrangian transport model.

A probabilistic framework for incorporating uncertainty in air quality models is described. The quantitative dependence of the uncertainty in calculated air quality concentrations on the uncertainty in the input meteorological data is illustrated using a simple Second-order Closure integrated Model Plume in combination with the EPRI Plume Model Validation and Development Data Set. Evaluation of the model results demonstrate that even though individual hourly samples cannot be deterministically predicted downwind of a powerplant stack, statistical representations of the observed cumulative distribution of the sample values are quite predictable. We discuss the data needed to improve the definition of the range of meteorological uncertainty within an ensemble of flows defined by given meteorological data, and thus provide for improvements in predictability models of the type illustrated. We argue that attempts to collect the data needed to define more precisely the variance within the ensemble of compatible flows will prove more productive than attempts to eliminate meteorological uncertainties in given datasets.