Model Spatial Resolution Research Articles

Future climate change projections in the agroecological, bioclimatic, biogeographical and altitudinal vegetation zones of Morocco

AbstractMorocco is located in a region vulnerable to the impacts of climate change, which can have profound effects on its social, economic and environmental systems. This makes studies aimed at forecasting these impacts in future using climate models particularly important. However, the generally coarse spatial resolution of models, combined with a large number of models, imposes a limitation on the models, allowing the selection of the most appropriate ones for climate change impact assessments in a specific region. In this study, 38 GCMs and GCM‐RCMs from CMIP5 ensemble and CORDEX project were downscaled and bias‐corrected for use in projecting climate change over Morocco under the RCP4.5 and RCP8.5 scenarios. A three‐step sequential process was adopted, involving in that order the selection of models based on: (i) projection of climate means; (ii) projection of climate extremes; and (iii) ability of the models to simulate the baseline climate. Climate projections show precipitation decreases of up to 10% by the beginning of the century, with decreases of more than 20% under RCP8.5 projected by 2100, with the central and northern mountainous regions of the country being the most affected. Seasonal projections showed autumn months likely to experience the greatest decline in precipitation, up to 36.56% by the end of the century. Temperature projections revealed an upward trend in mean, maximum and minimum temperatures, with increases of up to 3°C predicted by mid‐century over most of the country, particularly in the winter months. Our results point to a concerning future, with impacts related to decreased precipitation and increased temperatures expected to be many and varied across the country. Nevertheless, they can help provide a knowledge base for efforts to mitigate and adapt to expected changes.

Variable effects of spatial resolution on modeling of nitrogen oxides

Abstract. The lifetime and concentration of nitrogen oxides (NOx) are susceptible to nonlinear production and loss and to the resolution of a chemical transport model (CTM). This is due to the strong spatial gradients of NOx and the dependence of its own chemical loss on such gradients. In this study, we use the GEOS-Chem CTM in its high-performance implementation (GCHP) to investigate NOx simulations over the eastern United States across a wide range of spatial model resolutions (six different horizontal grids from 13 to 181 km). Following increasing grid size, afternoon surface NOx mixing ratios over July 2015 generally decrease over the Great Lakes region (GL) and increase over the southern states of the US region (SS), yielding regional differences (181 km vs. 13 km) of −16 % (in the GL) to 7 % (in the SS); meanwhile, hydrogen oxide radicals (HOx) increase over both regions, consistent with their different chemical regimes (i.e., NOx-saturated in the GL and NOx-limited in the SS). Nighttime titration of ozone by surface nitric oxide (NO) was found to be more efficient at coarser resolutions, leading to longer NOx lifetimes and higher surface mixing ratios of nitrogen dioxide (NO2) over the GL in January 2015. The tropospheric NO2 column density at typical afternoon satellite overpass time has spatially more coherent negative biases (e.g., −8 % over the GL) at coarser resolutions in July, which reversed the positive biases of surface NOx over the SS. The reduced NOx aloft (>1 km altitude) at coarser resolutions was attributable to the enhanced HOx that intrudes into the upper troposphere. Application of coarse-resolution simulations for interpreting satellite NO2 columns will generally underestimate surface NO2 over the GL and overestimate surface NO2 over the SS in summer, but it will uniformly overestimate NOx emissions over both regions. This study significantly broadens understanding of factors contributing to NOx resolution effects and the role of fine-resolution data in accurately simulating and interpreting NOx and its relevance to air quality.

Topological Relationship‐Based Flow Direction Modeling: Mesh‐Independent River Networks Representation

AbstractRiver networks are important features in surface hydrology. However, accurately representing river networks in spatially distributed hydrologic and Earth system models is often sensitive to the model's spatial resolution. Specifically, river networks are often misrepresented because of the mismatch between the model's spatial resolution and river network details, resulting in significant uncertainty in the projected flow direction. In this study, we developed a topological relationship‐based river network representation method for spatially distributed hydrologic models. This novel method uses (a) graph theory algorithms to simplify real‐world vector‐based river networks and assist in mesh generation; and (b) a topological relationship‐based method to reconstruct conceptual river networks. The main advantages of our method are that (a) it combines the strengths of vector‐based and DEM raster‐based river network extraction methods; and (b) it is mesh‐independent and can be applied to both structured and unstructured meshes. This method paves a path for advanced terrain analysis and hydrologic modeling across different scales.

Improving interpretation of sea-level projections through a machine-learning-based local explanation approach

Abstract. Process-based projections of the sea-level contribution from land ice components are often obtained from simulations using a complex chain of numerical models. Because of their importance in supporting the decision-making process for coastal risk assessment and adaptation, improving the interpretability of these projections is of great interest. To this end, we adopt the local attribution approach developed in the machine learning community known as “SHAP” (SHapley Additive exPlanations). We apply our methodology to a subset of the multi-model ensemble study of the future contribution of the Greenland ice sheet to sea level, taking into account different modelling choices related to (1) numerical implementation, (2) initial conditions, (3) modelling of ice-sheet processes, and (4) environmental forcing. This allows us to quantify the influence of particular modelling decisions, which is directly expressed in terms of sea-level change contribution. This type of diagnosis can be performed on any member of the ensemble, and we show in the Greenland case how the aggregation of the local attribution analyses can help guide future model development as well as scientific interpretation, particularly with regard to spatial model resolution and to retreat parametrisation.

Downhole density estimation using multielement geochemistry and machine learning

Machine learning (ML) was used to estimate bulk density from multielement geochemistry. At the Wheeler River site, host to the Phoenix and Gryphon uranium deposits, multielement geochemistry data were acquired for 829 exploration holes. Of those holes, 41 were logged with a downhole dual-spaced density probe during several mobilizations between 2009 and 2019. Density measurements were collected to provide constraints for inversions of airborne gravity data. To improve the density model's spatial resolution, ML models were trained to estimate bulk density from collocated multielement geochemistry data. Two geochemical laboratory methods were used (251 holes for the old method and 578 holes for the new method); therefore, two separate models were trained. Leave-one-hole-out cross-validation mean absolute error (MAE) results from the old and new geochemistry models showed similar scores of 0.027 g/cm3 and 0.025 g/cm3, respectively. Eight test holes were removed from the training data and used for final evaluation once the model was trained. Test hole results showed MAE scores of 0.026 g/cm3 for the old geochemistry model and 0.043 g/cm3 for the new geochemistry model. A unique aspect of this data set was the presence of repeat logs for multiple boreholes over a decade-long logging campaign. This provided the opportunity to assess the measurement uncertainty across time, density probes, operators, and boreholes conditions. The process of estimating downhole density from multielement geochemistry data could be used for many exploration projects to help generate better starting density models for use in geophysical inversions and other applications.

Effect of data length, spin-up period and spatial model resolution on fully distributed hydrological model calibration in the Moselle basin

ABSTRACT Subjective decisions in hydrologic model calibration can have drastic impacts on our understanding of basin processes and simulated fluxes. Here, we present a multicase calibration approach to determine three pillars of an appropriate hydrological model configuration, i.e. calibration data length, spin-up period, and spatial resolution, using a spatially distributed meso-scale hydrological model (mHM) together with a dynamically dimensioned search (DDS) algorithm and Nash-Sutcliffe efficiency (NSE) for the Moselle basin. The results show that a 10-year calibration data length, 2-year spin-up period, and 4-km model resolution are appropriate for the Moselle basin to reduce the computational burden while simulating streamflow with a decent performance. Although the calibration data length and spatial resolution are related to the extent and quality of the data, and the spin-up period is basin dependent, analysing the combined effects further allowed us to understand the interactions of these three usually overlooked pillars in the mHM configuration.

MODELING COMPLEX FLOW INDUCED BY WATER WAVES PROPAGATION OVER SUBMERGED SQUARE OBSTACLES

Submerged breakwaters are efficient structures used for shore protection. Many design features of these structures are captured upon modeling wave propagation over submerged square obstacles. The presence of separation vortices and large free surface deformations complicates the problem. A multiphase turbulent numerical model is developed using ANSYS commercial package. Careful domain discretization is done employing suitable mesh clustering to capture high gradients. Various numerical model parameters are provided, including grid size and time step. Special attention is directed towards clarifying turbulence initial conditions. Stable simulation results are obtained within acceptable computational time. Numerical results are validated quantitatively using subsurface measurements. Comparison along continuous horizontal and vertical velocity profiles is provided. Temporal and spatial model resolutions are illustrated for three test cases. The effect of wave period and height is well focused. The unsteady vortical structure is visualized. The incident wave energy is calculated and validated against theoretical values. The wave energy dissipation characteristics are briefly explained.

Food Desert Assessment: An Analytical Framework for Comparing Utility of Metrics and Indices; Case Study of Key Factors, Concurrences, and Divergences

Background: Low accessibility to healthy and nutritious food has been hypothesized to increase health disparities. Low-accessibility areas, called food deserts, are particularly commonplace in lower-income neighborhoods. However, indices for modeling food desert intensity are subjectively defined, and there is little agreement in the literature on their validity or relative strength. Methods: We first propose an assessment framework for objectively defining and comparing the utility of food desert indices using machine learning models. We introduce the concept of food desert index utility score, based on which we can compare the strength of indices for describing the food environment. We then explore the effect of the geographic spatial resolution of models and the impact of neighborhood-level income on the utility of food desert indices. Results: We observe the following: 1) Including healthy food suppliers only within a half-a-mile distance (as opposed to 1, 10, or 20 miles) from residents and discarding other food source variables leads to models of food environment with the highest utility scores. 2) The widely-accepted distinction between rural and urban areas in modeling food deserts adds little value to the utility score of the resulting models in our study region (Metro Atlanta). 3) The highest utility food desert index identified in our study region used a combination of access to food suppliers and the share of residents with low income and low access to vehicles. This approach achieved a utility score of 93% for identifying food deserts.

Marine plastic debris in Indonesia: Baseline estimates (2010-2019) and monitoring strategies (2021-2025)

Indonesia set the mission to reduce marine plastic debris by 70% between 2018-2025 with a global significance to support the UN Sustainable Development Goal 14.1. This short communication assesses marine debris baseline estimates in Indonesia before 2020 from available contributions and provides recommendations for monitoring marine debris mitigation between 2021-2025. Widely ranging model estimates of plastic debris released into seas highlight the roles of data source, the spatial resolution of models, and in situ data to provide representative baseline values. Recognizing the strengths and uncertainties of available contributions, model outputs converge on a baseline value of 0.52 ± 0.36 million tons (Mt) per year prior to 2020 in Indonesia, therefore setting a targeted reduced number of 0.16 Mt of marine debris releases in 2025. The Indonesian Institute of Sciences showed a preliminary value of plastic debris accumulation in beaches at 113.58 ± 83.88 g/m2 monthly or equivalent to 0.40 Mt/year by assuming plastic debris is most pervasive within 3 meters from Indonesia’s 99,093 km-long coastlines. It is important to distinguish that while river monitoring data informs land-based plastic debris releases, stranded beach debris represents a fraction of debris that is not present in the water column and bottom sediments. Moving forward, monitoring initiatives to mitigate marine debris should leverage on nationwide municipality-level model estimates (e.g., the source to leakage route framework of the National Plastic Action Partnership) as well as in situ river and coastal particularly but not limited to sites co-identified in previous monitoring studies (i.e., Medan, Batam-Bintan, Padang, Jakarta-Seribu Islands, Semarang, Pontianak, Bali, Lombok, Makassar, Manado, Bitung). The latter should be conducted at least seasonally, considering evidence of monsoonal variations of marine debris release and accumulation in Indonesia. Indonesia's vastness and regional diversity require coordination among stakeholders (government agencies, research institutions, universities, NGOs, citizen scientists) to monitor progress in the environments.

Plateaus and jumps in the atmospheric radiocarbon record – potential origin and value as global age markers for glacial-to-deglacial paleoceanography, a synthesis

Abstract. Changes in the geometry of ocean meridional overturning circulation (MOC) are crucial in controlling past changes of climate and the carbon inventory of the atmosphere. However, the accurate timing and global correlation of short-term glacial-to-deglacial changes of MOC in different ocean basins still present a major challenge. The fine structure of jumps and plateaus in atmospheric and planktic radiocarbon (14C) concentration reflects changes in atmospheric 14C production, ocean–atmosphere 14C exchange, and ocean mixing. Plateau boundaries in the atmospheric 14C record of Lake Suigetsu, now tied to Hulu Cave U∕Th model ages instead of optical varve counts, provide a stratigraphic “rung ladder” of up to 30 age tie points from 29 to 10 cal ka for accurate dating of planktic oceanic 14C records. The age differences between contemporary planktic and atmospheric 14C plateaus record the global distribution of 14C reservoir ages for surface waters of the Last Glacial Maximum (LGM) and deglacial Heinrich Stadial 1 (HS-1), as documented in 19 and 20 planktic 14C records, respectively. Elevated and variable reservoir ages mark both upwelling regions and high-latitude sites covered by sea ice and/or meltwater. 14C ventilation ages of LGM deep waters reveal opposed geometries of Atlantic and Pacific MOC. Like today, Atlantic deep-water formation went along with an estuarine inflow of old abyssal waters from the Southern Ocean up to the northern North Pacific and an outflow of upper deep waters. During early HS-1, 14C ventilation ages suggest a reversed MOC and ∼1500-year flushing of the deep North Pacific up to the South China Sea, when estuarine circulation geometry marked the North Atlantic, gradually starting near 19 ka. High 14C ventilation ages of LGM deep waters reflect a major drawdown of carbon from the atmosphere. The subsequent major deglacial age drop reflects changes in MOC accompanied by massive carbon releases to the atmosphere as recorded in Antarctic ice cores. These new features of MOC and the carbon cycle provide detailed evidence in space and time to test and refine ocean models that, in part because of insufficient spatial model resolution and reference data, still poorly reproduce our data sets.

The end of travel time matrices: Individual travel times in integrated land use/transport models

To reduce inaccuracies due to insufficient spatial resolution of models, it has been suggested to use smaller raster cells instead of larger zones. Increasing the number of zones, however, increases the size of a matrix to store travel times, called skim tables in transport modeling. Those become difficult to create, to store and to read, while most of the origin-destination pairs are calculated and stored but never used. At the same time, such approaches do not solve inaccuracies due to lack of temporal resolution. This paper analyzes the use of personalized travel times at the finest spatial resolution possible (at x/y coordinates) and a detailed temporal resolution for synthetic agents. The approach is tested in the context of an existing integrated land use/transport model (ILUT) where travel times affect, among others, household relocation decisions. In this paper, person-level individual travel times are compared to traditional skim-based travel times to identify the extent of errors caused by spatial and temporal aggregation and how they affect relocation decisions in the model. It was shown that skim-based travel times fail to capture the spatial and temporal variations of travel times available at a microscopic scale of an agent-based ILUT model. Skims may provide acceptable averages for car travel times if a dense network and small zones are used. Transit travel times, however, suffer from temporal and spatial aggregation of skims. When analyzing travel-time-dependent relocation decisions in the land use model, transit captive households tend to react more sensitively to the transit level of service when individual travel times are used. The findings add to the existing literature a quantification of spatial biases in ILUT models and present a novel approach to overcome them. The presented methodology eliminates the impact of the chosen zone system on model results, and thereby, avoids biases caused by the modifiable spatial unit problem.

Main directions in modern research of interaction between climate and land use/land cover changes

The purpose of the research is to analyse and assess existing approaches in investigation of interconnections between climate and underlying surface. Land use/land cover (LULC) influences climate formation via physical and chemical properties (albedo, roughness, height, chemical composition etc.). Climate in its turn affects land cover by means of meteorological parameters (air temperature and humidity, precipitation, wind etc.) and causes both cyclic and irreversible changes in land cover. In addition, anthropogenic factors exacerbate surface-climate interactions through? for example, LULC change that usually causes an additional release of chemical compounds. The paper distinguishes three main directions of the “climate - LULC” interactions research that is conducted mainly with application of satellite monitoring products, observation dataset, geographic information systems (GIS) and numerical modelling. The first direction implies monitoring and research of cyclic changes and transformation of LULC influenced by natural and anthropogenic factors, using different GIS-based satellite and surface meteorological observation databases. Despite significant technical progress and great amount of studies conducted for detecting dynamics of LULC change for different time intervals, the problems of dealing with cloudiness and shadows from orographic and other objects still remain. The second direction investigates the influence of LULC change on the chemical composition in the atmospheric boundary layer and on the regional climate. Numerous researches were dedicated to the influence of different kinds of surface such as forests, grasslands, croplands, urban areas etc. on climate characteristics and also on fluxes, for example, CO2. The effect of midlatitude forests on climate remains to be one of the challenging and urgent issues. The third direction relates to LULC change modelling and regional climate modelling. For the last decade a spatial resolution of models was considerably increased and, as a result, representation of interaction between atmosphere and land improved. Online integrated numerical atmospheric models are found as the most promising ones. They include "meteorological parameters – atmospheric chemical composition" feedbacks and can consider LULC on global and regional scales. However, some issues still need improvement, namely radiative transfer, cloud microphysics, cloud-aerosol-precipitation interactions, as well as parametrizations of some types of land and their interaction with the atmospheric boundary layer.

Hindcasting and forecasting of regional methane from coal mine emissions in the Upper Silesian Coal Basin using the online nested global regional chemistry–climate model MECO(n) (MESSy v2.53)

Abstract. Methane is the second most important greenhouse gas in terms of anthropogenic radiative forcing. Since pre-industrial times, the globally averaged dry mole fraction of methane in the atmosphere has increased considerably. Emissions from coal mining are one of the primary anthropogenic methane sources. However, our knowledge about different sources and sinks of methane is still subject to great uncertainties. Comprehensive measurement campaigns and reliable chemistry–climate models, are required to fully understand the global methane budget and to further develop future climate mitigation strategies. The CoMet 1.0 campaign (May to June 2018) combined airborne in situ, as well as passive and active remote sensing measurements to quantify the emissions from coal mining in the Upper Silesian Coal Basin (USCB, Poland). Roughly 502 kt of methane is emitted from the ventilation shafts per year. In order to help with the flight planning during the campaigns, we performed 6 d forecasts using the online coupled, three-time nested global and regional chemistry–climate model MECO(n). We applied three-nested COSMO/MESSy instances going down to a spatial resolution of 2.8 km over the USCB. The nested global–regional model system allows for the separation of local emission contributions from fluctuations in the background methane. Here, we introduce the forecast set-up and assess the impact of the model's spatial resolution on the simulation of methane plumes from the ventilation shafts. Uncertainties in simulated methane mixing ratios are estimated by comparing different airborne measurements to the simulations. Results show that MECO(3) is able to simulate the observed methane plumes and the large-scale patterns (including vertically integrated values) reasonably well. Furthermore, we obtain reasonable forecast results up to forecast day four.

Downscaling projections of climate change in Sao Tome and Principe Islands, Africa

Sao Tome and Principe is a small insular African country extremely vulnerable to rising sea levels and impacts such as inundation, shore line change, and salt water intrusion into underground aquifers. Projections of climate change have considered coarse model resolutions. The objective of this work is to dynamically downscale the global model projections to 4-km resolution and to assess the climate change in the Sao Tome and Principe islands. The global climate projections are provided by the Canadian Earth System Model under two Representative Concentration Pathways greenhouse gas scenarios, RCP4.5 and RCP8.5. The downscaling is produced by the Eta regional climate model. The baseline period is taken between 1971 and 2000, and the future climate period is taken between 2041 and 2070. The 2-m temperature simulations show good agreement with station data. The model simulates temperature more accurately than precipitation. The precipitation simulations systematically show underestimation and delay of the rainy and the dry seasons by about 1 month, a feature inherited from the global climate model. In the middle of the 21st century, projections show the strongest warming in the elevated parts of the Sao Tome Island, especially in February under RCP8.5. Warmer nights and warmer days become more frequent in the islands when compared with those in the present. While under RCP4.5, precipitation increases in the islands; under RCP8.5, it decreases everywhere in both islands. Heavy precipitation rates should increase, especially in the south-southwestern parts of the Sao Tome islands. Detailed spatial variability of the temperature and precipitation changes in the islands can only be revealed at very high spatial model resolution. Implications for the potential energy production from two major river basins are assessed in this work.

Event selection and two-stage approach for calibrating models of green urban drainage systems

Abstract. The calibration of urban drainage models is typically performed based on a limited number of observed rainfall–runoff events, which may be selected from a larger dataset in different ways. In this study, 14 single- and two-stage strategies for selecting the calibration events were tested in calibration of a high- and low-resolution Storm Water Management Model (SWMM) of a predominantly green urban area. The two-stage strategies used events with runoff only from impervious areas to calibrate the associated parameters, prior to using larger events to calibrate the parameters relating to green areas. Even though all 14 strategies resulted in successful model calibration (Nash–Sutcliffe efficiency; NSE >0.5), the difference between the best and worst strategies reached 0.2 in the NSE, and the calibrated parameter values notably varied. The various calibration strategies satisfactorily predicted 7 to 13 out of 19 validation events. The two-stage strategies reproduced more validation events poorly (NSE <0) than the single-stage strategies, but they also reproduced more events well (NSE >0.5) and performed better than the single-stage strategies in terms of total runoff volume and peak flow rates, particularly when using a low spatial model resolution. The results show that various strategies for selecting calibration events may lead in some cases to different results in the validation phase and that calibrating impervious and green-area parameters in two separate steps in two-stage strategies may increase the effectiveness of model calibration and validation by reducing the computational demand in the calibration phase and improving model performance in the validation phase.

From ENSEMBLES to CORDEX: Evolving climate change projections for Upper Danube River flow

Regional Climate Models (RCMs) generate past and future climate simulations that serve as input for subsequent modelling of impact projections. The most recent coordinated regional climate modelling initiative, CORDEX, provides RCM data for Europe with increased spatial resolution (12.5 km for the CORDEX EUR-11 ensemble) and based on the new Representative Concentration Pathways (RCPs). RCM data from the previous initiative ENSEMBLES had a spatial resolution of 25 km and was based on the SRES emission scenarios. In this contribution we explore the development from ENSEMBLES to CORDEX in a hydrological impact study for the Upper Danube, focussing on the representation of past climate and projections of the future for climate and resulting river discharge. We replicated a hydrological modelling framework, including RCM downscaling and bias correction, that used data of 21 ENSEMBLES RCMs under SRES A1B emission scenario (Kling et al., 2012), and now applied CORDEX EUR-11 data of 32 RCM simulations under RCP4.5 and RCP8.5 emission scenarios. Results with CORDEX show a small improvement in the representation of historical precipitation, but a decline in the accuracy of historical temperature simulations, despite the increase in spatial model resolution. The tendency of ENSEMBLES climate projections is reproduced with the CORDEX RCMs, albeit with slightly higher precipitation in the CORDEX data, yielding a less pronounced reduction in future mean annual discharge for the Upper Danube at Vienna. The previously identified change in discharge seasonality – increasing winter discharge and decreasing summer discharge – is confirmed with the new CORDEX data. Updating climate impact simulations in the presented rigorous replication framework allows analysing specific new information in the latest generation climate model data as well as crystallizing essential expectable patterns of change that need to be addressed in adaptation strategies.

Application of the GNSS-R in tomographic sounding of the Earth atmosphere

Reflected GNSS signals offer a great opportunity for detecting and monitoring of water level variation, land surface roughness and the atmosphere around the Earth. The application type intensely depends on satellites’ geometry and the topography of study area. GNSS-R can be used in sounding the water vapor as one of the most important parameters in troposphere. In view of temporal and spatial changes, retrieval of this parameter is complicated. GNSS tomography is a common approach for this purpose. Considering the dependency of this inverse approach to the number of stations and satellites’ coverage at study area, tomographic reconstruction of water vapor is an ill-posed problem. Additional constraints are usually used to find a solution. In this research reflected signals known as GNSS-R are offered for the first time to resolve the rank deficiency of this problem. This has been implemented to a tomographic model which has been already developed for modeling the water vapor in the North West of Iran. In view of low number of GPS stations in this area, the design matrix of the model is rank deficient. Simulated results demonstrate that the rank deficiency of this matrix can be reduced by implementing appropriate number of GNSS-R stations when the spatial resolution of model is optimized. Resolution matrix is used as a measure for analyzing the efficiency of the proposed method. Results from DOY 300 and 301 in year 2011 show that the applied method can even remedy the rank deficiency of the design matrix. The satellites’ constellation and the time response of the model are the effective parameters in this respect. On average the rank deficiency of the design matrix is improved more than 90% when the reflected signals are used. This is easily seen in terms of the resolution matrix of the model. Here, the mean bias and RMSE of reconstructed image are 0.2593 and 1.847 ppm, respectively.

Sensitivity of the Baltic Sea level prediction to spatial model resolution

Abstract The three-dimensional hydrodynamic model of the Baltic Sea (M3D) and its new parallel version (PM3D), developed at the Institute of Oceanography, University of Gdansk in Poland, was tested to establish a grid resolution adequate for the Baltic Sea level prediction. Four outputs of the M3D/PM3D, calculated with spatial resolution varying from 3 NM to 0.5 NM, were validated by comparing the results with hourly sea level readings collected at 9 Baltic gauges in 2010–2015. The spatial resolution of 1 NM applied to the Baltic Sea resulted in a distinct improvement of agreement between the calculated and observed distributions of data. An increase in the resolution to 0.5 NM in the southern Baltic Sea improved the model quality further, as indicated by the lowest variability, the highest correlation and the highest percentage of water level simulations within the range of ± 0.15 m difference relative to readings. The increase in horizontal resolution allowed to improve the fit between the observed water levels and those calculated by the PM3D in the cases of rapid sea level fluctuations, such as those registered in January 2012. The model performed slightly worse for stations with larger ranges of water level oscillations. As parallel calculations were used in the PM3D, the time necessary for computing the simulations was significantly reduced, which allowed to apply the high-resolution grid also to the operational version of the model.

Large-watershed flood forecasting with high-resolution distributed hydrological model

Abstract. A distributed hydrological model has been successfully used in small-watershed flood forecasting, but there are still challenges for the application in a large watershed, one of them being the model's spatial resolution effect. To cope with this challenge, two efforts could be made; one is to improve the model's computation efficiency in a large watershed, the other is implementing the model on a high-performance supercomputer. This study sets up a physically based distributed hydrological model for flood forecasting of the Liujiang River basin in south China. Terrain data digital elevation model (DEM), soil and land use are downloaded from the website freely, and the model structure with a high resolution of 200 m × 200 m grid cell is set up. The initial model parameters are derived from the terrain property data, and then optimized by using the Particle Swarm Optimization (PSO) algorithm; the model is used to simulate 29 observed flood events. It has been found that by dividing the river channels into virtual channel sections and assuming the cross section shapes as trapezoid, the Liuxihe model largely increases computation efficiency while keeping good model performance, thus making it applicable in larger watersheds. This study also finds that parameter uncertainty exists for physically deriving model parameters, and parameter optimization could reduce this uncertainty, and is highly recommended. Computation time needed for running a distributed hydrological model increases exponentially at a power of 2, not linearly with the increasing of model spatial resolution, and the 200 m × 200 m model resolution is proposed for modeling the Liujiang River basin flood with the Liuxihe model in this study. To keep the model with an acceptable performance, minimum model spatial resolution is needed. The suggested threshold model spatial resolution for modeling the Liujiang River basin flood is a 500 m × 500 m grid cell, but the model spatial resolution with a 200 m × 200 m grid cell is recommended in this study to keep the model at a better performance.

ML-Space: Hybrid Spatial Gillespie and Particle Simulation of Multi-Level Rule-Based Models in Cell Biology

Spatio-temporal dynamics of cellular processes can be simulated at different levels of detail, from (deterministic) partial differential equations via the spatial Stochastic Simulation algorithm to tracking Brownian trajectories of individual particles. We present a spatial simulation approach for multi-level rule-based models, which includes dynamically hierarchically nested cellular compartments and entities. Our approach ML-Space combines discrete compartmental dynamics, stochastic spatial approaches in discrete space, and particles moving in continuous space. The rule-based specification language of ML-Space supports concise and compact descriptions of models and to adapt the spatial resolution of models easily.