High-Resolution Interoperable Human-Friendly Naming System for Hydrographic Features and Model Elements (HRI-HydroName)

Spatial resolution considerations for urban hydrological modelling

The complexity of watersheds requires engineers to use computer models for planning purposes. The models are a reflection of the available data, software, and subjective judgment of the designer. Assumptions and simplifications may be made in characterizing hydrologic and hydraulic properties such that subcatchments are aggregated and small conduits are ignored. For this study, two models were developed for an urban watershed with different levels of spatial resolution using EPA SWMM. The high resolution model depicted each surface type (backyard, building, sidewalk, and street) as individual hydrological response units. The low resolution model represented the entire domain as a single hydrological response unit. This comparative study of calibrated models at different spatial resolution was undertaken in order to assess the effect of resolution on representing low impact development strategies for an urban watershed. The study consists of two components: the model development and the evaluation of Low Impact Development (LID) retrofits. Although hydrologic response unit resolution was the focus on the study, uncertainty in the observations and model structure was addressed and incorporated in the methodology for calibrating the models. A multi-objective optimization procedure was used to identify pareto solutions, i.e. sets of model parameters that yield model identical predictions. Statistical measures for comparing predictions to observations (e.g. percent volume difference and root mean square error) indicated that the low resolution model had a slightly better fit than the high resolution model to the observed flow regardless of whether the model was calibrated or uncalibrated. An examination of the pareto parameter sets for the two models revealed another inference; the uncalibrated low resolution model was the most physically representative. The calibrated low resolution model exhibited equifinality in the parameter values while the calibrated high resolution had comparative values to the uncalibrated model. Calibration did not greatly enhance the prediction performance of either the low or high resolution model since the initial parameters were informed from an intensive data review. When simulating LID within the models, the low resolution models predicted greater percent reduction in both peak flow rates and total volume flows than the high resolution model. Furthermore, the outcome is highly dependent on the specific storms events considered. Analysis of LID is considered preliminary until performance measures can be validated from field measurements. Even with physical data, models have inherent uncertainty which limit their predicative ability.

Abstract. There is a clear need for the development of modelling frameworks for both climate change and air quality to help inform policies for addressing these issues simultaneously. This paper presents an initial attempt to develop a single modelling framework, by introducing a greater degree of consistency in the meteorological modelling framework by using a two-step, one-way nested configuration of models, from a global composition-climate model (GCCM) (140 km resolution) to a regional composition-climate model covering Europe (RCCM) (50 km resolution) and finally to a high (12 km) resolution model over the UK (AQUM). The latter model is used to produce routine air quality forecasts for the UK. All three models are based on the Met Office's Unified Model (MetUM). In order to better understand the impact of resolution on the downscaling of projections of future climate and air quality, we have used this nest of models to simulate a 5-year period using present-day emissions and under present-day climate conditions. We also consider the impact of running the higher-resolution model with higher spatial resolution emissions, rather than simply regridding emissions from the RCCM. We present an evaluation of the models compared to in situ air quality observations over the UK, plus a comparison against an independent 1 km resolution gridded dataset, derived from a combination of modelling and observations, effectively producing an analysis of annual mean surface pollutant concentrations. We show that using a high-resolution model over the UK has some benefits in improving air quality modelling, but that the use of higher spatial resolution emissions is important to capture local variations in concentrations, particularly for primary pollutants such as nitrogen dioxide and sulfur dioxide. For secondary pollutants such as ozone and the secondary component of PM10, the benefits of a higher-resolution nested model are more limited and reasons for this are discussed. This study highlights the point that the resolution of models is not the only factor in determining model performance – consistency between nested models is also important.

A continuous and multi-decadal surface water extent (SWE) record is vital for water resources management, flood risk assessment, and comprehensive climate change impact studies. The advancements in remote sensing technologies offer a valuable tool for monitoring surface water with high temporal and spatial resolution. However, challenges persist due to image gaps resulting from sensor issues and adverse weather conditions during data collection. To address this issue, one way to fill the gaps is by leveraging in situ measurements such as streamflow discharges (SFDs). We investigate the relationship between SFDs and Landsat-derived SWE in the New England region watersheds (eight-digit hydrological unit code (HUC)) on a monthly scale. While previous studies indicate the relationship exists, it remains elusive for larger domains. Recent research suggests using monthly average SFD data from a single stream gage to fill the gaps in SWE. However, as SWE represents a monthly maximum value, relying on a single gage with average values may not capture the complex dynamics of surface water. Our study introduces a novel approach by replacing the monthly average SFD with the maximum day streamflow discharge anomaly (SFDA) within a month. This adjustment aims to better reflect extreme scenarios, and we explore the relationship using ridge regression, incorporating data from all stream gages in the study domain. The SWE and SFDA are both transformed to stabilize the variance. We found that there is no discernible correlation between the magnitude of the correlation and the size of the basins. The correlations vary based on HUC and display a wide range, indicating the variances of the importance of stream gages to each HUC. The maximum correlation is found when the stream gage is located outside of the target HUC, further verifying the complex relationship between SWE and SFDA. Covering over 30 years of data across 45 HUCs, the imputing technique using ridge regression shows satisfactory performance for most of the HUCs analyzed. The results show that 41 out of 45 HUCs achieve a root-mean-square error (RMSE) of less than 10, and 44 out of 45 HUCs exhibit a normalized root-mean-square error (NRMSE) of less than 0.1. Of 45 HUCs, 42 have an R-squared (R2) score higher than 0.7. The Nash–Sutcliffe efficiency index (Ef) shows consistent results with R2, with the relative bias ranging from –0.02 to 0.03. The established relationship serves as an effective imputing technique, filling gaps in the time series of SWE. Moreover, our approach facilitates the identification and visualization of the most significant gages for each HUC, contributing to a more refined understanding of surface water dynamics.

The combined impacts of climate change, river basin management, planning and development on the components of the Water-Energy-Food-Environment (WEFE) nexus are different depending on the specific scale and location where they are assessed. This implies that a wide variety of natural and anthropogenic components should be modelled together to obtain a comprehensive picture of the inter-connections and tradeoffs among the WEFE components. The main goal of the high-resolution WEFE modelling framework we will present is to provide a quantitative framework for evaluating nexus indicators at a range of locations and at various space and timescales within a river basin. The framework can then be subjected to different scenarios of projected climate, land-use, and socio-economic developments as well as infrastructure operation policies to aid in robust planning and development for an uncertain future.The concept developed makes use of a two-level modelling approach to quantify the impacts:Firstly, based on a concise description of the basin and bulk infrastructures, the strategic Multi-Objective Robust Decision Making model produces basin operating rules (policies) through optimization with respect to several key WEFE objectives. This allows a subsequent screening analysis to assess tradeoffs among the policies and objectives set for the different sectors.In a follow up phase, a high-resolution WEFE simulation model, is used to quantify in greater detail the impact on a broader set of evaluation indicators. This is done by implementing the basin infrastructure developments and policies in the high-resolution model and simulating under various future climate and socio-economic development scenarios. The high-resolution WEFE model is based on a detailed description of the river basin and relevant infrastructures and simulates the WEFE nexus at high spatial and temporal resolution using the optimized policies coming from the strategic model. The goal is to compute an extended set of WEFE evaluation indicators.We will present an outline of the above methodology and selected results of the framework implementation in the transboundary Zambezi river basin, as part of the European Union funded GoNEXUS project.

Net radiation (Rn) is a key component of the Earth’s energy balance. With the rise of deep learning technology, remote sensing technology has made significant progress in the acquisition of large-scale surface parameters. However, the generally low spatial resolution of net radiation data and the relative scarcity of surface flux site data at home and abroad limit the potential of deep learning methods in constructing high spatial resolution net radiation models. To address this challenge, this study proposes an innovative approach of a multi-scale transfer learning framework, which assumes that composite models at different spatial scales are similar in structure and parameters, thus enabling the training of accurate high-resolution models using fewer samples. In this study, the Heihe River Basin was taken as the study area and the Rn products of the Global Land Surface Satellite (GLASS) were selected as the target for coarse model training. Based on the dense convolutional network (DenseNet) architecture, 25 deep learning models were constructed to learn the spatial and temporal distribution patterns of GLASS Rn products by combining multi-source data, and a 5 km coarse resolution net radiation model was trained. Subsequently, the parameters of the pre-trained coarse-resolution model were fine-tuned with a small amount of measured ground station data to achieve the transfer from the 5 km coarse-resolution model to the 1 km high-resolution model, and a daily high-resolution net radiation model with 1 km resolution for the Heihe River Basin was finally constructed. The results showed that the bias, R2, and RMSE of the high-resolution net radiation model obtained by transfer learning were 0.184 W/m2, 0.924, and 24.29 W/m2, respectively, which was better than those of the GLASS Rn products. The predicted values were highly correlated with the measured values at the stations and the fitted curves were closer to the measured values at the stations than those of the GLASS Rn products, which further demonstrated that the transfer learning method could capture the soil moisture and temporal variation of net radiation. Finally, the model was used to generate 1 km daily net radiation products for the Heihe River Basin in 2020. This study provides new perspectives and methods for future large-scale and long-time-series estimations of surface net radiation.

Generation Expansion Planning (GEP), electricity market, and grid operation of the power system require a high spatial and temporal resolution model. However, limited data availability is the main obstacle to building a power system model with high resolution. This paper presents a method of constructing an open-source test power system with a high spatial resolution for transmission line topology and generation infrastructures and temporal resolution for variable renewable energy (VRE) sources and electricity demand. In addition, investment and operating cost information of existing infrastructures are summarized. A high-resolution open-source test power system representing China is created only by using publicly available data. The open-source Chinese power system can be used for the GEP model to investigate the low-carbon transition of the power system.

Air temperature and precipitation are two important meteorological factors affecting the earth’s energy exchange and hydrological process. High quality temperature and precipitation forcing datasets are of great significance to agro-meteorology and disaster monitoring. In this study, the accuracy of air temperature and precipitation of the fifth generation of atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ERA5) and High-Resolution China Meteorological Administration Land Data Assimilation System (HRCLDAS) datasets are compared and evaluated from multiple spatial–temporal perspectives based on the ground meteorological station observations over major land areas of China in 2018. Concurrently, the applicability to the monitoring of high temperatures and rainstorms is also distinguished. The results show that (1) although both forcing datasets can capture the broad features of spatial distribution and seasonal variation in air temperature and precipitation, HRCLDAS shows more detailed features, especially in areas with complex underlying surfaces; (2) compared with the ground observations, it can be found that the air temperature and precipitation of HRCLDAS perform better than ERA5. The root-mean-square error (RMSE) of mean air temperature are 1.3 °C for HRCLDAS and 2.3 °C for ERA5, and the RMSE of precipitation are 2.4 mm for HRCLDAS and 5.4 mm for ERA5; (3) in the monitoring of important weather processes, the two forcing datasets can well reproduce the high temperature, rainstorm and heavy rainstorm events from June to August in 2018. HRCLDAS is more accurate in the area and magnitude of high temperature and rainstorm due to its high spatial and temporal resolution. The evaluation results can help researchers to understand the superiority and drawbacks of these two forcing datasets and select datasets reasonably in the study of climate change, agro-meteorological modeling, extreme weather research, hydrological processes and sustainable development.

Urban sustainable development and improved resilience to geohazards requires an exhaustive understanding of the geological structure, physical properties and dynamics of the shallow subsurface underneath urbanized areas at the sub-kilometer scale. Yet, our current understanding of the urban subsurface is limited by our ability to image its structure and temporal variations at high resolution using classical geophysical approaches. Recently, the application of conventional ambient noise interferometry analysis to dynamic strain data recorded using Distributed Acoustic Sensing (DAS) deployed on unused telecommunication fiber-optic cables (dark fibers) has emerged as an attractive alternative for cost-efficient, regional scale (10’s of km) seismic imaging and monitoring at high spatial and temporal resolution. Still, its application to urban environments remains vastly underutilized. One of the most significant hurdles is the lack of adequate and efficient data exploration and processing tools to address and harness the unique challenges associated with DAS-based urban seismic noise recordings, which include the complexity of the noise field, unconventional array geometries and non-uniform coupling conditions, and increasingly massive data volumes. The InDySE project (Interrogating the Dynamic Shallow Earth) aims at addressing these challenges to develop and validate the next-generation of subsurface imaging and monitoring platforms in urban areas based on the combination of existing fiber-optic networks and high-frequency infrastructure noise. The project comprises (1) developing high-performance computational tools, advanced processing workflows and machine learning approaches for efficient data exploration, selection and processing using existing datasets, (2) field experiments in target areas to retrieve high-resolution velocity models and monitor changes in seismic velocities, (3) integrating the resultant high-resolution seismic models with complementary datasets such as deformation maps derived from InSAR measurements. One of the selected study areas is the highly populated metropolitan area of Istanbul (Turkey), where understanding the structure, properties and dynamics of the shallow subsurface at high-resolution is critical for evaluating geohazard exposure. Among our objectives will be illuminating potential hidden faults underneath the city, obtaining high-resolution maps of geological materials and subsurface properties that can be translated into maps of local site response to large earthquakes, and tracking seismic velocity changes linked to hydrological dynamics that are known to be responsible for ground movements such as landsliding and subsidence. Ultimately, InDySE aims at developing efficient approaches for using dark fiber and ambient noise in urban subsurface investigations with implications in geohazard assessment.

Abstract. We present the CarbonTracker Europe High-Resolution (CTE-HR) system that estimates carbon dioxide (CO2) exchange over Europe at high resolution (0.1 × 0.2∘) and in near real time (about 2 months' latency). It includes a dynamic anthropogenic emission model, which uses easily available statistics on economic activity, energy use, and weather to generate anthropogenic emissions with dynamic time profiles at high spatial and temporal resolution (0.1×0.2∘, hourly). Hourly net ecosystem productivity (NEP) calculated by the Simple Biosphere model Version 4 (SiB4) is driven by meteorology from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th Generation (ERA5) dataset. This NEP is downscaled to 0.1×0.2∘ using the high-resolution Coordination of Information on the Environment (CORINE) land-cover map and combined with the Global Fire Assimilation System (GFAS) fire emissions to create terrestrial carbon fluxes. Ocean CO2 fluxes are included in our product, based on Jena CarboScope ocean CO2 fluxes, which are downscaled using wind speed and temperature. Jointly, these flux estimates enable modeling of atmospheric CO2 mole fractions over Europe. We assess the skill of the CTE-HR CO2 fluxes (a) to reproduce observed anomalies in biospheric fluxes and atmospheric CO2 mole fractions during the 2018 European drought, (b) to capture the reduction of anthropogenic emissions due to COVID-19 lockdowns, (c) to match mole fraction observations at Integrated Carbon Observation System (ICOS) sites across Europe after atmospheric transport with the Transport Model, version 5 (TM5) and the Stochastic Time-Inverted Lagrangian Transport (STILT), driven by ECMWF-IFS, and (d) to capture the magnitude and variability of measured CO2 fluxes in the city center of Amsterdam (the Netherlands). We show that CTE-HR fluxes reproduce large-scale flux anomalies reported in previous studies for both biospheric fluxes (drought of 2018) and anthropogenic emissions (COVID-19 pandemic in 2020). After applying transport of emitted CO2, the CTE-HR fluxes have lower median root mean square errors (RMSEs) relative to mole fraction observations than fluxes from a non-informed flux estimate, in which biosphere fluxes are scaled to match the global growth rate of CO2 (poor person's inversion). RMSEs are close to those of the reanalysis with the CTE data assimilation system. This is encouraging given that CTE-HR fluxes did not profit from the weekly assimilation of CO2 observations as in CTE. We furthermore compare CO2 concentration observations at the Dutch Lutjewad coastal tower with high-resolution STILT transport to show that the high-resolution fluxes manifest variability due to different emission sectors in summer and winter. Interestingly, in periods where synoptic-scale transport variability dominates CO2 concentration variations, the CTE-HR fluxes perform similarly to low-resolution fluxes (5–10× coarsened). The remaining 10 % of the simulated CO2 mole fraction differs by >2 ppm between the low-resolution and high-resolution flux representation and is clearly associated with coherent structures (“plumes”) originating from emission hotspots such as power plants. We therefore note that the added resolution of our product will matter most for very specific locations and times when used for atmospheric CO2 modeling. Finally, in a densely populated region like the Amsterdam city center, our modeled fluxes underestimate the magnitude of measured eddy covariance fluxes but capture their substantial diurnal variations in summertime and wintertime well. We conclude that our product is a promising tool for modeling the European carbon budget at a high resolution in near real time. The fluxes are freely available from the ICOS Carbon Portal (CC-BY-4.0) to be used for near-real-time monitoring and modeling, for example, as an a priori flux product in a CO2 data assimilation system. The data are available at https://doi.org/10.18160/20Z1-AYJ2 (van der Woude, 2022a).

Initialisation using high spatial, temporal and spectral resolution satellite observations

A high-resolution, 3D groundwater-surface water simulation of the contiguous US: Advances in the integrated ParFlow CONUS 2.0 modeling platform

Accurate representation of terrestrial Essential Climate Variables (tECVs) is crucial for practically understanding the Earth's climate system and supporting policy decisions. This study initiates benchmarking practices within the Land Surface/Hydrologic Model (LSM/HM) communities by integrating high-resolution data with hyper-resolution hydrological modelling. The European Space Agency (ESA)-funded 4DHydro project employs six advanced LSM/HMs: Community Land Model (CLM), GEOfram, mesoscale Hydrologic Model (mHM), PCRaster Global Water Balance (PCR-GLOBWB), TETIS, and wflow_sbm.We benchmark, calibrate, and analyze scalability using consistent EMO1 precipitation forcings, focusing on 1 km spatial resolution. We introduce a novel multi-basin (MB) calibration technique based on streamflow data from the Po, Rhine, and Tugela River basins, highlighting its impact on model performance. Scalability analysis evaluates computational trade-offs and performance improvements at higher resolutions while ensuring flux matching. The study includes 34 simulations addressing water balance closure to enhance tECVs.Key findings explore the advantages of high-resolution modelling, introducing a reference benchmark dataset of 1 km hydrological simulations, optimal gauge selection for MB calibration, and comparative performance of different LSMs and HMs in flux matching across spatial scales. These insights contribute to advancing the integration of high-resolution data with hydrological modelling, promoting consistent and accurate terrestrial ECVs at regional and continental scales.

<p>This study examines the climatology and structure of precipitation associated with tropical cyclones based on the Atmospheric Model Intercomparison Project (AMIP) runs of the Process-based climate simulation: advances in high resolution modelling and European climate risk assessment (Primavera) Project during 1979-2014. We assess the role of spatial resolution in shaping tropical cyclone precipitation along with inter-model variability by evaluating climate models with a variety of dynamic cores and parameterization schemes. AMIP runs that prescribe historical sea surface temperatures and radiative forcings can well reproduce the observed spatial pattern of tropical cyclone precipitation climatology, with high-resolution performing better than low-resolution ones in the first order. Overall, the AMIP runs can also reproduce the fractional contribution of tropical cyclone precipitation to total precipitation in observations. Similar to tropical cyclone precipitation climatology, the factional contrition is better simulated by high-resolution models. All the models in the AMIP runs underestimate the observed composite tropical cyclone rainfall structure over both land and ocean, and we identify differences in this factor between high-resolution and low-resolution models. The underestimation of rainfall composites by the AMIP runs are also supported by the radial profile of tropical cyclone precipitation. This study shows that the high-resolution climate models can reproduce well the spatial pattern of tropical cyclone climatology and underestimate the composite rainfall structure, with increased spatial resolution that overall improves the performance of simulation.</p>

Large-scale, high-resolution agricultural systems modeling using a hybrid approach combining grid computing and parallel processing