High-resolution Grid Research Articles

Assessing water resource vulnerability based on remote sensing data-enhanced SWAT+ and High-Resolution precipitation data

Climate change and human activities have significantly impacted the global water cycle, increasingly threatening water security across various sectors. By improving the performance of hydrological models in simulating the water balance, including precipitation and evapotranspiration, we can better support decision-making for regional water resource management and the deployment of climate change adaptation measures. This study aims to enhance the simulation performance of the vegetation growth module in the SWAT + model and reduce its uncertainty by dynamically updating the model’s data files with satellite-derived leaf area index and phenology data. Additionally, this study modified the weather station allocation mechanism in SWAT + to fully utilize the high-resolution meteorological data grids. The simulation results show that the enhanced SWAT + model achieved a Nash-Sutcliffe Efficiency (NSE) between 0.84 and 0.90 and a PBIAS of less than 6 % for daily streamflow simulations at four hydrological stations in the Weihe River Basin. The enhanced SWAT + model’s water balance simulation results for the Weihe River Basin were used to analyze the vulnerability of water resources within the basin. The results indicate that the Qinling region, benefiting from its well-developed forest ecosystems, has the lowest green water vulnerability. The overall blue water vulnerability of the basin reached 1.12, suggesting that during periods of insufficient precipitation or intense evaporation, blue water resources may not meet the demands of agriculture, industry, ecology, and residential use. Among these, agricultural water use exhibits the highest vulnerability. Efficient irrigation technologies can be employed to improve irrigation water use efficiency, and the use of treated reclaimed water for agricultural irrigation can be promoted to reduce the demand for fresh blue water resources.

A three-quantile bias correction with spatial transfer for the correction of simulated European river runoff to force ocean models

Abstract. In ocean or Earth system model applications, the riverine freshwater inflow is an important flux affecting salinity and marine stratification in coastal areas. However, in climate change studies, the river runoff based on climate model output often has large biases on local, regional, or even basin-wide scales. If these biases are too large, the ocean model forced by the runoff will drift into a different climate state compared to the observed state, which is particularly relevant for semi-enclosed seas such as the Baltic Sea. To achieve low biases in riverine freshwater inflow in large-scale climate applications, a bias correction is required that can be applied in periods where runoff observations are not available and that allows spatial transferability of its correction factors. In order to meet these requirements, we have developed a three-quantile bias correction that includes different correction factors for low-, medium-, and high-percentile ranges of river runoff over Europe. Here, we present an experimental setup using the Hydrological Discharge (HD) model and its high-resolution (1/12°) grid. First, bias correction factors are derived at the locations of the downstream stations with available daily discharge observations for many European rivers. These factors are then transferred to the respective river mouths and mapped to neighbouring grid boxes belonging to ungauged catchments. The results show that the bias correction generally leads to an improved representation of river runoff. Especially over northern Europe, where many rivers are regulated, the three-quantile bias correction provides an advantage compared to a bias correction that only corrects the mean bias of the river runoff. Evaluating two NEMO (Nucleus for European Modelling of the Ocean) model simulations in the German Bight indicated that the use of the bias-corrected discharges as forcing leads to an improved simulation of sea surface salinity in coastal areas. Although the bias correction is tailored to the high-resolution HD model grid over Europe in the present study, the methodology is suitable for any high-resolution model region with a sufficiently high coverage of river runoff observations. It is also noted that the methodology is applicable to river runoff based on climate hindcasts, as well as on historical climate simulations where the sequence of weather events does not match the actual observed history. Therefore, it may also be applied in climate change simulations.

Hydrokinetic energy site selection under high seasonality: The i-IHE index

A novel index is developed to select sites for hydrokinetic energy exploitation in coastal areas with high resource seasonality – the typical case being estuaries whose hydrodynamics are highly influenced by variable river discharges. The intra-annual Integrated Hydrokinetic Energy (i-IHE) index is constructed by means of a new penalty function that modifies the Integrated Hydrokinetic Energy (IHE) index. The i-IHE index thus defined is applied to a case study: the Miño Estuary (NW Iberian Peninsula), with seasonal variations of up to 1000 % in the riverine discharge. The hydrodynamics are modelled by the state-of-the-art Delft3D-FLOW code using a high-resolution 2DH grid. The results confirm a strong variability in the resource, with time periods shorter than seasons. The results obtained for the penalty function throughout the Miño Estuary, with values ranging between 0.5 and 0.75, show the relevance of the i-IHE index in order to not overestimate the potential of coastal areas subject to large intra-annual variability. Compared to the conventional IHE index, the novel i-IHE index highlights smaller areas with greater energy potential. The larger exploitable energy in these areas, along with the lower costs and fewer restrictions for hydrokinetic energy operation, will be of interest to developers and stakeholders.

The role of the electricity grid in operation-induced greenhouse gas emissions by a residential building: A multi-year retrospective simulation study

In view of the growing shares of renewables in the electricity grid in combination with the electrification of hvac (heating, ventilation, air conditioning) systems in residential buildings, the grid intensity (in terms of carbon dioxide emissions per unit of electric energy) becomes increasingly sensitive to weather conditions, and synchronicity between weather and the grid becomes a more critical aspect in building performance assessment. Using building performance simulation techniques to seek robust building designs requires awareness about the uncertainties in circumstantial factors that affect performance. This 2016 – 2022 retrospective study highlights the effects of using low or high temporal resolution grid emissions intensity data on projected operation-induced carbon dioxide emissions for a terraced dwelling in the Netherlands. Building fabric quality, the occupant profile, and systems configurations (i.e., hvac and photovoltaics) are varied to investigate the effects of the applied grid model resolution. This study shows that ignoring high-resolution grid intensity data is getting increasingly problematic; applying low resolution (annual) instead of high-resolution (15-minute) grid intensity data leads to an increasingly unjustified optimistic assessment both for net and gross emissions (either or not allowing for carbon displacement by feeding locally generated electricity into the grid).

Modeling the Impact of Wind Drag Coefficient on Wind-Driven Currents in Lake Taihu, China

The wind drag coefficient, Cd, has a great influence on the numerical results obtained from shallow lakes. To analyze the modeling impacts of Cd on wind-driven currents, a series of numerical simulations of Lake Taihu were conducted at three grid resolutions (800 m × 800 m, 400 m × 400 m, and 100 m × 100 m) using the empirical formulae of Flather (F76), Large and Pond (LP81), Large and Yeager (LY04), Andreas (A12), and Gao (G20). The G20 formula produced the optimum results of all the formulae for both the water level and velocity simulations; however, the grid resolution was found to have a significant influence on simulation in G20 cases. Thus, the G20 formula is only recommended when using a high-resolution grid to meet the accuracy requirements of analyzing wind-driven currents in the numerical modeling of Lake Taihu. A combination of the A12 formula and a coarse grid is preferred when taking computational efficiency into consideration.

Estimation of tidal energy potential in the Vietnam East Sea: A comprehensive analysis using semi-empirical tide models

This study assesses the tidal energy potential in the Vietnam East Sea (VES), a region with complex hydrodynamics and significant interactions with the Western Pacific Ocean. A rigorous validation of six prominent semi-empirical global ocean tide models (FES2014, DTU16, EOT20, GOT4.10c, HAMTIDE12, OSU12.V1) was conducted against an extensive network of 34 tide gauge stations in the VES region. The validation identified FES2014 as the superior model, exhibiting the closest agreement with observations due to its advanced data assimilation techniques, high-resolution grids, and robust hydrodynamic representation influenced by the complex VES bathymetry. Leveraging the FES2014 model, the influential role of the semi-diurnal component (M2) in dictating energy distribution is prominently evident. Evaluation results of tidal amplitudes revealed three emerging hotspots: the Batang Lupar estuary (Malaysia), Anpu Gang (China), and Bac Lieu (Vietnam), with tidal amplitudes up to 326.2 cm. The Anpu Gang exhibited the highest theoretical potential tidal energy of 14.90 Wh/m2, making it the most promising site for localized tidal power development. Although moderate compared to globally renowned sites, the identified VES hotspots merit consideration for small-to-medium scale projects tailored to local conditions. While limited to potential energy assessments, this study provides a crucial baseline for the VES region, highlighting opportunities for sustainable tidal energy exploitation.

Recent developments in tornado theory and observations.

This article critically reviews research on tornado theory and observations over the last decade. From the theoretical standpoint, the major advances have come through improved numerical-simulation models of supercell convective storms, which contain the tornado's parent circulation. These simulations are carried out on a large domain (to capture the supercell's circulation system), but with high grid resolution and improved representations of sub-grid physics (to capture the tornado). These simulations offer new insights into how and why tornadoes form in some supercells, but not others. Observational advances have come through technological improvements of mobile Doppler radars capable of rapid scanning and dual-polarization measurements, which offer a much more accurate view of tornado formation, tornado structure, and the tornado's place within its parent supercell.

Accelerating the clean-SC and CMF beamforming deconvolution methods using neural gridcompression

Deconvolution algorithms can considerably improve the spatial resolution of sound sources when applied to beamforming techniques. However, they are often associated with additional computation time, which can be prohibitive for real-time applications, especially when using high-resolution scanning grids. Additionally, depending on the size of the problem, full deconvolution may not even be possible due to memory constraints. Recently, it has been shown that a neural network approach to compress the possible source directions can speed up the results of the DAMAS deconvolution algorithm by orders of magnitude. In this paper, this neural grid compression preprocessing is evaluated for the CLEAN-SC and CMF deconvolution techniques, and it is shown that it is possible to achieve dramatic speedups without significant loss of accuracy and even improve the results in some examples, especially for the CLEAN-SC technique.

Adjusting Manning coefficients to simulate tsunami propagation over porous coral reef

This study investigates the effect of porous coral reef on the tsunami propagation in terms of experimental and numerical modelling. It aims at quantifying the influence of several input parameters on the wave attenuation and at adjusting Manning coefficients to reproduce experimental results. The density and the surface of individual reefs are fixed as well as the width and length of the coral barrier. Results show that the reef height is the most sensitive parameter. This latter affects the tsunami propagation with an attenuation of the first wave reaching 15 % compared to the case with a smooth reef. Wave breaking occurs on the reef flat for each test but, as expected, its location depends greatly on the reservoir depths difference. Numerical simulations show that the Manning coefficient must be adjusted both by considering the coral reef height and the spatial grid resolution. It varies from 0.01 (for lowest reef with highest grid resolution) to 0.058 (for higher reefs with coarsest grid resolution).

Numerical investigations on seasonal variation of waves in the Cat Ba – Ha Long coastal area (Vietnam) in 2021

The Delft3D model, equipped with a high-resolution grid, was employed to simulate wave conditions in the Cat Ba-Ha Long coastal area by combining hydrodynamic and wave modules. The model was calibrated and validated using the measurement data at Hon Dau station, and demonstrated a good match between the data and simulation results (NSE = 0.55–0.69). In 2021, the simulation results revealed that wind waves are the primary type of waves in the Cat Ba-Ha Long coastal area. The lowest monthly-averaged wave heights were found in the January-February period (about 0.4 m), followed by a gradual increase, reaching its peak in the May-June period (1.18 m), and subsequent fluctuations throughout the rest. Wave heights ranging from 0.2 to 0.6 m were frequently observed in the majority of months, including January, February, March, April, July, November, and December. The wave heights ranging 0.6–0.8 m are primarily recorded during the months of August and October. The occurrence of high-altitude waves of 1.0–2.0 m in height was predominantly seen during the months of May and June. The areas with elevated wave heights (>1 m) are primarily located in the southern part of Cat Ba and the southeast of Ha Long, accounting for around 30 % of the region. The areas characterized by gentle undulations are primarily concentrated in Ha Long Bay, where wave heights range from 0.2 to 0.6 m, accounting for 70 % of the region. The variations in wave height primarily result from the southern region of Cat Ba and the southeastern area of Ha Long Bay, which are more exposed and less influenced by the topography, hence facilitating the development of wind waves. Simultaneously, the Ha Long area is encircled by small islands, characterized by shallow depths, with waves that are diffused in direction and exhibit modest heights. In this area, the dominant wave directions are south-southeast (32.9 %) and east (29.7 %).

Numerical simulation of seasonal underground hydrogen storage: Role of the initial gas amount on the round-trip hydrogen recovery efficiency

Subterranean structures such as aquifers and depleted gas reservoirs (DGRs) offer a scalable, high-pressure, secure, cost-efficient, and ecologically friendly means of hydrogen (H2) storage. Underground H2 storage (UHS) has emerged as a potential solution to alleviate the imbalance between the fluctuating renewable energy generation and the demand for a constant energy supply. Quantifying the recovery efficiency is of paramount importance in the effort toward large-scale UHS. In this numerical simulation study, we employed a high-resolution grid for the discretization of a synthetic (but realistic) heterogeneous anticline intended as a H2 storage facility, and we assessed the H2 recovery efficiency, and the purity of produced gas associated with UHS in natural reservoirs involving different pre-existing gases and their quantities. All of these studies included an initial phase of cushion gas injection, followed by 4 cycles of H2 storage operations composed of injection-idle-withdrawal periods. The simulation results indicated that the H2 round-trip recovery efficiency RH increased (a) with an increasing number of storage cycles, routinely exceeding 90% and providing evidence of a self-enhancement or self-optimization H2 recovery mechanism and (b) with an increasing amount of pre-existing gas in the storage zone prior to the H2 injections — at the end of the 4-cycle test, the highest RH is associated with a DGR with a pre-existing gas consisting of residual CH4 and additional injected N2, and the lowest RH corresponds to an aquifer with no cushion gas. Conversely, the H2 mass fraction of the produced gas FHQ (a) increased with an increasing number of storage cycles, but (b) decreased rapidly with an increasing amount of pre-existing gas. The presence of a pre-existing gas inevitably leads to severe contamination of the produced H2, which never exceeds 40% in the produced gas, can be as low as 4% in early cycles, and cannot be used as a H2 fuel without gas separation. Aquifer storage without a cushion gas may exhibit the lowest H2 recovery (about 75% in the long run), but yields practically pure H2 that does not require further processing before use. A positive conclusion is that, under the conditions of this study, total H2 losses (including H2 escaping into the caprock, and inaccessible H2 dissolved in the aqueous phase of the formation or remaining in the gas storage zone) are limited and manageable, indicating the technical feasibility of geologic H2 storage.

Efficient large-scale scene representation with a hybrid of high-resolution grid and plane features

Existing neural radiance fields (NeRF) methods for large-scale scene modeling require days of training using multiple GPUs, hindering their applications in scenarios with limited computing resources. Radiance field models based on explicit dense or hash grid features have been proposed for fast optimization, but their effectiveness has mainly been demonstrated in the context of object-scale scene representation. In this paper, we point out that the low feature resolution in explicit representation is the bottleneck for large-scale unbounded scene representation. To address this problem, we introduce a new and efficient hybrid feature representation for NeRF that fuses the 3D hash-grids and high-resolution 2D dense plane features. Compared with the dense-grid representation, the resolution of a dense 2D plane can be scaled up more efficiently. Based on this hybrid representation, we propose a fast optimization NeRF variant, called GP-NeRF, that achieves better rendering results while maintaining a compact model size. Extensive experiments on multiple large-scale unbounded scene datasets show that our model can converge in 1.5 h using a single GPU while achieving results comparable to or even better than the existing method that requires about one day’s training with 8 GPUs. Our code can be found at https://zyqz97.github.io/GP_NeRF/.

Numerical Modeling of Extreme Sea Levels on the Laptev Sea Coast

The present study is devoted to the analysis of extreme sea level oscillations of the Laptev Sea using the ADCIRC model. The numerical modeling is performed on a high-resolution grid and verified for sea level observations from three tide gauges. We have revealed regional characteristics of extreme sea level oscillations for different parts of the Laptev Sea coast. The maximum total sea level range was 544 cm in Ebelyakh Bay, while the minimum was 267 cm in Khatanga Bay, where maximum tidal ranges were obtained. Some areas in Khatanga Bay and Anabar Bay had maximum tidal ranges exceeding 200 cm. The study provided an estimation of the possible magnitude of coastal flooding by calculating the extreme total and residual sea levels for different return periods: 1, 2, 5, 10, 20, 50, and 100 years. The amplitude of extreme surges calculated for the 100-year return period can exceed 300 cm for several sections of the Laptev Sea coast, with the maximum sea level range being about 680 cm for Anabar and Ebelyakh Bays.

Baroclinic responses of Qiongzhou Strait throughflow to different forcings

The Qiongzhou Strait (QS) throughflow is affected by the seasonal circulation in both the northern South China Sea (SCS) shelf and the Beibu Gulf. The water masses with distinct properties between these two regions signify the baroclinic processes in the QS. In this study, a high-resolution unstructured-grid Finite Volume Community Ocean Model (FVCOM) was used to investigate the features and dynamic mechanisms of baroclinic-induced currents (BCC) in the QS. Different forcings were considered such as tides, river discharges, winds, and remote forcings (low-frequency sea surface level and current variations). The model results indicate that the volume transport of the BCC contributes to 31.26% of the total in the QS, determining the transport direction in summer. The baroclinic response to low-frequency remote forcings is most pronounced during the summer-autumn season, but less than winds during the winter-spring season. This finding underscores the importance of low-frequency remote forcings on the QS throughflow, which has often been overlooked in previous studies. Momentum balance suggests that the BCCs are predominantly driven by both the barotropic and baroclinic pressure gradient forces. Decrease (increase) in either of these two forces will lead to an intensified eastward (westward) flow. The contribution of the horizontal advection is relatively weak but cannot be ignored. This study highlights the baroclinic responses of QS throughflow to different forcings, which is important when investigating the circulation in the northern SCS, especially in the Beibu Gulf.

Wave Downscaling Approach with TCN model, Case Study in Bengkulu, Indonesia

When conducting marine operations that rely on wave conditions, such as maritime trade, the fishing industry, and ocean energy, accurate wave downscaling is important, especially in coastal locations with complicated geometries. Traditional approaches for wave downscaling are usually obtained by performing nested simulations on a high-resolution local grid from global grid information. However, this approach requires high computation resources. In this paper, to downscale global wave height data into a high-resolution local wave height with less computation resources, we propose a machine learning-based approach to downscaling using the Temporal Convolutional Network (TCN) model. To train the model, we obtain the wave dataset using the SWAN model in a local domain. The global datasets are taken from the ECMWF Reanalysis (ERA-5) and used to train the model. We choose the coastal area of Bengkulu, Indonesia, as a case study. The results of TCN are also compared with other models such as LSTM and Transformers. It showed that TCN demonstrated superior performance with a CC of 0.984, RMSE of 0.077, and MAPE of 4.638, outperforming the other models in terms of accuracy and computational efficiency. It proves that our TCN model can be alternative model to downscale in Bengkulu’s coastal area.

Runoff simulation modeling method integrating spatial element dynamics and neural network for remote sensing precipitation data

Effective management of water resources amidst global climate change necessitates advancements in runoff simulation techniques. This study introduces a novel methodology that integrates a spatial element dynamic model with a neural network designed for satellite precipitation product (SPP) and remote sensing data to address the complex dynamics of precipitation-runoff processes. Implemented in the Songhua River basin of China, the methodology features a runoff estimation module that operates across both high-resolution and SPP grid scales. This module synergizes spatial attributes with fuzzy classification for refined confluence optimization, and yielding simulations through long short-term memory network. The results indicate that the proposed multi-scale production-confluence scheme achieved Nash-Sutcliffe efficiency (NSE) scores of 0.87 and 0.84 during calibration (2011–2015) and validation (2017 and 2018) periods, respectively, representing an 8.15% improvement in peak percentage error (PPE10%) over conventional hydrologic models. Additionally, the scheme demonstrated superior simulation performance at tributary sites set compared to main channel sites set, with differences of 0.08 and 0.07 in NSE and Kling-Gupta efficiency (KGE), respectively. The introduction of the surface Ⅱ operator for adjusting precipitation intensity effectively improved runoff estimation accuracy, reducing the average error by 25.48% across both site sets. This study provides a scientific foundation for applying SPP in hydrological modeling and offers decision support for managing water resources.

Direct and mediated impacts of mixed forests on Norway spruce infestation by European bark beetle Ips typographus

Climate change-induced windfalls and droughts exacerbate bark beetle outbreaks, severely impacting forest ecosystems. Despite extensive research on bark beetle infestation patterns, the question regarding the optimal spatial grid (SG) size for analyzing this phenomenon remains unresolved. The protective potential of natural forest complexity against bark beetles has been underestimated. We used remotely sensed data to fit Generalized Linear Models and structural equation models to explore the direct and environment-mediated effects of mixed forest cover (MFC) on Norway spruce forest infestation in central Europe. We assessed the effectiveness of various spatial grids (from 100 m to 1 km) for studying infestation by Ips typographus. We found a strong non-linear decline in infestation with increasing MFC across different spatial scales. The relationship between infestation and temperature was positive, while elevation had a negative effect on infestation, with higher infestation rates observed below 900 m. Direct effects of environmental predictors on infestation were significant at SGs of 100 m, 300 m, 400 m, and 500 m, but insignificant at 200 m and 1000 m SGs. Slope positively influenced infestation at 300 m and 400 m SGs. MFC exhibited significant indirect effects on infestation mediated by elevation, temperature, potential evapotranspiration, slope, and heat load index. Landscape variables played a significant role in the models at high-resolution spatial grids, whereas climate variables were influential in models at lower spatial grids of infestation data. We argue that mixed forest facilitates cooling, preserves water, enriches symbiotic fungal community, alleviating tree drought stress and mitigating infestation risk. Broad-leave trees’ non-host volatiles presumably disrupt olfactory signals, impeding beetles’ ability to locate host trees effectively. Our results underscore the potential of increasing mixed forest cover as a self-sustainable silvicultural measure for forest protection against I. typographus and for mitigating the effects of climate change.

Near-Complete Sampling of Forest Structure from High-Density Drone Lidar Demonstrated by Ray Tracing

Drone lidar has the potential to provide detailed measurements of vertical forest structure throughout large areas, but a systematic evaluation of unsampled forest structure in comparison to independent reference data has not been performed. Here, we used ray tracing on a high-resolution voxel grid to quantify sampling variation in a temperate mountain forest in the southwest Czech Republic. We decoupled the impact of pulse density and scan-angle range on the likelihood of generating a return using spatially and temporally coincident TLS data. We show three ways that a return can fail to be generated in the presence of vegetation: first, voxels could be searched without producing a return, even when vegetation is present; second, voxels could be shadowed (occluded) by other material in the beam path, preventing a pulse from searching a given voxel; and third, some voxels were unsearched because no pulse was fired in that direction. We found that all three types existed, and that the proportion of each of them varied with pulse density and scan-angle range throughout the canopy height profile. Across the entire data set, 98.1% of voxels known to contain vegetation from a combination of coincident drone lidar and TLS data were searched by high-density drone lidar, and 81.8% of voxels that were occupied by vegetation generated at least one return. By decoupling the impacts of pulse density and scan angle range, we found that sampling completeness was more sensitive to pulse density than to scan-angle range. There are important differences in the causes of sampling variation that change with pulse density, scan-angle range, and canopy height. Our findings demonstrate the value of ray tracing to quantifying sampling completeness in drone lidar.

Optimisation of Bridge Pier Winding Flow Numerical Simulation Scheme Based on Delft3D

The majority of existing numerical simulations of the effect of bridge piers on water’s movement are based on a limited number of bridge piers at a laboratory scale. Furthermore, some 2D numerical simulations for actual bridge projects have deficiencies, including the use of overly large meshes and an inadequate treatment of bridge piers. In this study, we compare three methods (structural mesh encryption, suspension mesh, and non-structural mesh) based on Delft3D, and we apply the optimisation scheme to a real bridge project. It is demonstrated that optimal results can be achieved by utilising a grid size comparable to the pier diameter (Dp) in the region away from the pier. In the vicinity of the pier, the grid cell size should be no larger than 1/9 Dp. The suspended grid technique (DD Boundary) can yield results consistent with those obtained using a full-area high-resolution grid, provided that the total grid number can be reduced and the computational time is considerably reduced. In this study, the unstructured mesh (Delft3D Flexible Mesh) scheme was unable to capture the oscillations in the wake flow behind the bridge piers. However, the application of the optimised scheme in bridge engineering demonstrated its practical value. The findings of this study on mesh resolution and suspension mesh schemes can be applied to the Delft3D software and are also useful for other numerical simulation work.

Reproduction of the Current Climatic State of the Lake Ladoga Ecosystem

A three-dimensional ecohydrodynamic model of Lake Ladoga based on the St. Petersburg Baltic Eutrophication Model (SPBEM) is proposed. Unlike existing models of the Lake Ladoga ecosystem, the proposed model is implemented on a high-resolution spherical grid (horizontal grid size ≈1 km), contains a benthic layer module and describes the cycles of nitrogen and phosphorus in the water column and bottom sediments. A run of the seasonal and interannual variability of the state of Lake Ladoga in the period 1979–2018 was carried out when setting as forcing the atmospheric influence and runoff of rivers flowing into Lake Ladoga for the hydrothermodynamic module and the supply of nutrients from the atmosphere and from land for the biogeochemical module. A comparison of the results of calculating the current climatic state of Lake Ladoga with the available satellite andexpeditionary observation data showed that the model correctly reproduces the climatic seasonal variation of the surface temperature field, its vertical distribution, average values and range of changes in the main characteristics of the lake’s ecosystem. The proposed model can be used to study the influence of external natural and anthropogenic factors on biogeochemical processes and the functioning of the Lake Ladoga ecosystem.