Process-based Modeling Approach Research Articles

Mapping and assessment of marine ecosystem services supply in the Baltic Sea

Coastal and marine ecosystems supply multiple Ecosystem Services (ES). Nevertheless, these ecosystems are among the most impacted by human activities, harming the ES sustainable supply. Since ES are a spatial phenomenon, mapping can contribute to understand ES supply. For this, we use quantitative spatio-temporal frameworks to map and assess the supply of one provisioning (food from fisheries) and two regulating ES (nursery habitats and nutrient regulation), considering two periods: Baltic Sea Holistic Assessment (HOLAS) 2 (2011–2016) and 3 (2016–2021). The ES supply was assessed following a process-based modelling approach, using bio-physical indicators as proxies. The three ES models were applied and validated, showing moderate results. For fisheries and nursery ES the results showed a significantly higher supply in HOLAS 3 than in 2, and for nutrient ES the opposite. This indicates that the assessed ES changed due to environmental activities. The Anselin Local Moran's results showed that most ES index values aggregate in the High-High cluster; Moran's I and semi-variogram results showed a clustered pattern; and the Getis Ord* analysis showed that hot and cold spots corresponded to high and low supply areas. For fisheries, high ES supply areas were located in the central-southern part of the Baltic Sea, while low-supply regions were located in the northern part. For nursery ES, high supply areas were located in the southwestern Finnish and western Estonian coasts. For nutrient ES, high supply areas occurred in the central- and eastern-southern parts close to the coast. Correlations showed a statistically significant negative correlation between fisheries and nursery ES and a significant positive correlation between fisheries and nutrient ES. No statistically significant correlations were observed between nursery and nutrient ES supply. The results obtained are essential to support coastal and marine management and planning in the Baltic Sea as well as international environmental policies and directives.

Evaluating dynamic tree-species-shifting and height development caused by ungulate browsing in forest regeneration using a process-based modeling approach

Ungulate browsing can prevent the successful regeneration and development of forest-owner-intended and climate-resilient admixed tree species, compromising the future provision of multiple ecosystem services. A reduction of the height increment of palatable tree species caused by ungulate browsing results in a competition shift in favor of less palatable species. While this key process of losing admixed tree species is well understood, forest growth models have not yet captured its full complexity. Therefore, our objectives were to simulate (i) the loss of susceptible tree species from different browsing regimes and its impact on stand development, (ii) changes in the height increment of saplings and trees, and (iii) the shifts in social status during stand development without pre-defined limits a sapling can resist browsing. To address these objectives, we used the process-based model iLand to simulate the exposure of Norway spruce, Silver fir, and European beech stands to varying browsing densities. We successfully reproduced expected dynamic shifts in species composition and height development 24 years after establishment, depending on ungulate pressure and species-specific competitive strength. Stand composition and growth differences already occur with slight changes in ungulate density. When saplings remained in stand structure, independent of the number being browsed, our results led to more individuals with substantially smaller dimensions. We found that dynamic tree species shifting can be simulated realistically by a process-based modeling approach, which can be used to show the strong impact of ungulates on stand development.

Wave setup estimation at regional scale: Empirical and modeling-based multi-approach analysis in the Mediterranean Sea

Wave setup is a physical process that induces a temporal increase of the mean water level due to wave dissipation by bottom friction and breaking in the surf zone, extending over tens to hundreds of meters in the cross-shore direction. Wave setup contribution to coastal sea level solely induced by wind and atmospheric effects can increase by more than 100% under extreme events and conditions favoring its formation. It is therefore crucial to consider this phenomenon when assessing sea-level-related coastal hazards. Previous studies estimated the wave setup effect by means of numerical modeling and empirical formulations at regional and global scale. Such analyses require either high computational capacity to implement high-resolution numerical models over large domains, and/or accurate information on coastal morphological features from global or regional databases. Although the Mediterranean Sea is a fetch-limited environment, waves generated from extra-tropical cyclones are powerful enough for wave setup to develop, and subsequently for a potential significant wave setup contribution to extreme coastal sea level. Through the use of both numerical and empirical methods, we investigate the uncertainty associated to wave setup representation on the frequency and magnitude of coastal extreme sea levels occurring on sandy beaches in the Mediterranean Sea. Wave setup values are compared at beach scale between process-based modeling and empirical approaches, showing highly variable results. We also quantify the impact of wave setup on return levels of coastal sea level extremes using reconstructed sea levels. We employ various methods to calculate the wave setup component. Results show high spatial dispersion, with clear differences between the numerical and empirical approaches, especially in regions prone to the development of energetic waves. The total inter-method dispersion of 100-year return levels is often higher than 30 cm for average values of 62.4 cm. We emphasize the important limitations related to wave setup modeling (i.e., its underestimation) at large scale, and call for caution when applying empirical formulations (generally developed from local studies) at regional to global scale, which can lead to unrealistic wave setup values.

Learning vs. understanding: When does artificial intelligence outperform process-based modeling in soil organic carbon prediction?

In recent years, machine learning (ML) algorithms have gained substantial recognition for ecological modeling across various temporal and spatial scales. However, little evaluation has been conducted for the prediction of soil organic carbon (SOC) on small data sets commonly inherent to long-term soil ecological research. In this context, the performance of ML algorithms for SOC prediction has never been tested against traditional process-based modeling approaches. Here, we compare ML algorithms, calibrated and uncalibrated process-based models as well as multiple ensembles on their performance in predicting SOC using data from five long-term experimental sites (comprising 256 independent data points) in Austria. Using all available data, the ML-based approaches using Random forest and Support vector machines with a polynomial kernel were superior to all process-based models. However, the ML algorithms performed similar or worse when the number of training samples was reduced or when a leave-one-site-out cross validation was applied. This emphasizes that the performance of ML algorithms is strongly dependent on the data-size related quality of learning information following the well-known curse of dimensionality phenomenon, while the accuracy of process-based models significantly relies on proper calibration and combination of different modeling approaches. Our study thus suggests a superiority of ML-based SOC prediction at scales where larger datasets are available, while process-based models are superior tools when targeting the exploration of underlying biophysical and biochemical mechanisms of SOC dynamics in soils. Therefore, we recommend applying ensembles of ML algorithms with process-based models to combine advantages inherent to both approaches.

Toward interpretable LSTM-based modeling of hydrological systems

Abstract. Several studies have demonstrated the ability of long short-term memory (LSTM) machine-learning-based modeling to outperform traditional spatially lumped process-based modeling approaches for streamflow prediction. However, due mainly to the structural complexity of the LSTM network (which includes gating operations and sequential processing of the data), difficulties can arise when interpreting the internal processes and weights in the model. Here, we propose and test a modification of LSTM architecture that is calibrated in a manner that is analogous to a hydrological system. Our architecture, called “HydroLSTM”, simulates the sequential updating of the Markovian storage while the gating operation has access to historical information. Specifically, we modify how data are fed to the new representation to facilitate simultaneous access to past lagged inputs and consolidated information, which explicitly acknowledges the importance of trends and patterns in the data. We compare the performance of the HydroLSTM and LSTM architectures using data from 10 hydro-climatically varied catchments. We further examine how the new architecture exploits the information in lagged inputs, for 588 catchments across the USA. The HydroLSTM-based models require fewer cell states to obtain similar performance to their LSTM-based counterparts. Further, the weight patterns associated with lagged input variables are interpretable and consistent with regional hydroclimatic characteristics (snowmelt-dominated, recent rainfall-dominated, and historical rainfall-dominated). These findings illustrate how the hydrological interpretability of LSTM-based models can be enhanced by appropriate architectural modifications that are physically and conceptually consistent with our understanding of the system.

Enhancing Streamflow Prediction Physically Consistently Using Process-Based Modeling and Domain Knowledge: A Review

Streamflow prediction (SFP) constitutes a fundamental basis for reliable drought and flood forecasting, optimal reservoir management, and equitable water allocation. Despite significant advancements in the field, accurately predicting extreme events continues to be a persistent challenge due to complex surface and subsurface watershed processes. Therefore, in addition to the fundamental framework, numerous techniques have been used to enhance prediction accuracy and physical consistency. This work provides a well-organized review of more than two decades of efforts to enhance SFP in a physically consistent way using process modeling and flow domain knowledge. This review covers hydrograph analysis, baseflow separation, and process-based modeling (PBM) approaches. This paper provides an in-depth analysis of each technique and a discussion of their applications. Additionally, the existing techniques are categorized, revealing research gaps and promising avenues for future research. Overall, this review paper offers valuable insights into the current state of enhanced SFP within a physically consistent, domain knowledge-informed data-driven modeling framework.

Intensifying rice production to reduce imports and land conversion in Africa

Africa produces around 60% of the rice the continent consumes, relying heavily on rice imports to fulfill the rest of the domestic demand. Over the past 10 years, the rice-agricultural area increased nearly 40%, while average yield remained stagnant. Here we used a process-based crop simulation modelling approach combined with local weather, soil, and management datasets to evaluate the potential to increase rice production on existing cropland area in Africa and assess cropland expansion and rice imports by year 2050 for different scenarios of yield intensification. We find that Africa can avoid further increases in rice imports, and even reduce them, through a combination of cropland expansion following the historical trend together with closure of the current exploitable yield gap by half or more. Without substantial increase in rice yields, meeting future rice demand will require larger rice imports and/or land conversion than now.

Improving Laio’s model for investigating soil water response to vegetation restoration in the middle Yellow River Basin, China

Over the last few decades, vegetation–soil water interactions in the semiarid middle Yellow River basin have been a topic of interest for many researchers, especially since the implementation of the Grain-to-Green Project (GTGP) in 1999. The probabilistic states of soil water and water stress on vegetation development provide interactions from a perspective that is different from process-based modelling approaches. The Laio’s model, a widely used probabilistic soil water model was applied in the study. To include the infiltration-excess runoff, co-exist with the saturation-excess runoff, or even be dominant in the study region, a parameter Δ2 was incorporated to reflect mean surface runoff of a rainfall event under current theoretical framework of Laio’s model. The modified model was evaluated against the observations, and the effectiveness of the modification was proved in the study region. Based on the modified model, the soil water response to vegetation restoration was studied with remote-sensing based land use and leaf area index in the 2000 s and 2010 s. Vegetation was restored in most parts of the region, resulting in soil water reduction in approximately 83 % of the area, and the soil water stress increased by 49 % on average. The reduction in soil water and increase in soil water stress were more serious in areas that had converted vegetation and loess soil than those had non-converted vegetation and non-loess soil. These findings might imply that vegetation greening will reach an upper limit due to the negative soil water balance and increased water stress on plant growth, which is meaningful for a better understanding of vegetation–soil water interactions and ecosystem management in semi-arid regions.

Resolving experimental biases in the interpretation of diffusion experiments with a user-friendly numerical reactive transport approach

The reactive transport code CrunchClay was used to derive effective diffusion coefficients (De), clay porosities (ε), and adsorption distribution coefficients (KD) from through-diffusion data while considering accurately the influence of unavoidable experimental biases on the estimation of these diffusion parameters. These effects include the presence of filters holding the solid sample in place, the variations in concentration gradients across the diffusion cell due to sampling events, the impact of tubing/dead volumes on the estimation of diffusive fluxes and sample porosity, and the effects of O-ring-filter setups on the delivery of solutions to the clay packing. Doing so, the direct modeling of the measurements of (radio)tracer concentrations in reservoirs is more accurate than that of data converted directly into diffusive fluxes. While the above-mentioned effects have already been described individually in the literature, a consistent modeling approach addressing all these issues at the same time has never been described nor made easily available to the community. A graphical user interface, CrunchEase, was created, which supports the user by automating the creation of input files, the running of simulations, and the extraction and comparison of data and simulation results. While a classical model considering an effective diffusion coefficient, a porosity and a solid/solution distribution coefficient (De–ε–KD) may be implemented in any reactive transport code, the development of CrunchEase makes it easy to apply by experimentalists without a background in reactive transport modeling. CrunchEase makes it also possible to transition more easily from a De–ε–KD modeling approach to a state-of-the-art process-based understanding modeling approach using the full capabilities of CrunchClay, which include surface complexation modeling and a multi-porosity description of the clay packing with charged diffuse layers.

Understanding groundwater storage and drainage dynamics of a high mountain catchment with complex geology using a semi-distributed process-based modelling approach

Mountainous groundwater systems are usually characterized by great geological complexity and highly heterogeneous hydraulic properties. For that reason, proper system characterization, monitoring and modelling remain challenging. In this study, we investigated a geologically complex alpine catchment (from 1400 to 2900 m asl) in the Dolomites (Italian Alps) by combining hydrogeological field investigation, hydrological monitoring and modelling. A semi-distributed process-based hydrological model was applied to simulate the continuous measured catchment discharge in a period of three years, which covers a large variation of hydrodynamic conditions. The model structure couples the sequential hydrogeological units within the studied catchment: (1) the fractured dolomitic rocks as bedrock aquifer and 2) the unconsolidated deposits accumulating on the slopes and at the valley floor as porous aquifer. In order to evaluate the model structure and parameterization in depth, we applied a multi-step evaluation approach considering both parameter sensitivity and uncertainty. The modelling results demonstrate that the newly developed model can reproduce most discharge behavior of aquifers. The model indicates a seasonal dynamic linkage between surface and subsurface storage units during different flow conditions. Besides the matrix and conduit flow in fractured dolomitic aquifer, it highlights the important role of unconsolidated sediments (porous aquifer) to the storage and discharge behavior of the entire groundwater system. Furthermore, with the comprehensive model evaluation we learned the model performance deficit during extreme high flow conditions and proposed a more detailed hydrogeological conceptual model. We believe that the proposed modelling approach can be transferred to other high mountain catchments with similar hydro(geo)logical characteristics to characterize the collective behavior of aquifer systems, explore conceptual model weakness and optimize catchment monitoring work.

Incorporation of carbon dioxide production and transport module into a Soil-Plant-Atmosphere continuum model

Carbon dioxide release from agricultural soils is influenced by multiple factors, including soil (soil properties, soil-microbial respiration, water content, temperature, soil diffusivity), plant (carbon assimilation, rhizosphere respiration), atmosphere (climate, atmospheric carbon dioxide), etc. Accurate estimation of the carbon dioxide (CO2) fluxes in the soil and soil respiration (CO2 flux between soil and atmosphere) requires a process-based modeling approach that accounts for the influence of all these factors. In this study, a module for CO2 production via root and microbial respiration and diffusion-based carbon dioxide transport is developed and integrated with MAIZSIM (a process-based maize crop growth model that accounts for detailed soil and atmospheric processes) based on a modularized architecture. The developed model simulates root respiration based on root mass, root age, soil water content, and temperature. Microbial respiration is based on the soil microbial processes by accounting for the carbon dynamics in the litter, humus, and organic fertilizer pools as moderated by the soil water content, temperature, microbial synthesis, humification, and decomposition of the carbon pools. Case studies presented include scenarios with different soil, climate, and carbon pools that simulated the soil respiration with an average index of agreement of 0.73 and root mean squared error of 11.4 kg carbon ha−1 between the measured and simulated soil respiration. The modular architecture used in the model development facilitates easy integration with other existing crop models and future modifications.

Quantifying the Nitrogen-Removal Performance of a Constructed Wetland Dominated by Diffuse Agricultural Groundwater Inflows Using a Linked Catchment–Wetland Model

Nitrogen loading from diffuse agricultural sources is a major water-quality problem worldwide. Constructed wetlands have been increasingly used to treat runoff and drainage from agricultural lands. However, the diffuse nature of nitrogen loading from farmlands often makes it challenging to trace flow pathways and measure the direct input loading to wetlands, and assess their nutrient-reduction performance. The Owl Farm wetland, Cambridge, New Zealand, receives inputs mainly from a subsurface drain and groundwater seepage. As it was not possible to directly measure wetland inflows, we used the Soil and Water Assessment Tool (SWAT) to estimate and partition the wetland inflow and nitrogen loading from the drain and seepage. A dynamic first-order tanks-in-series wetland model was linked with SWAT to evaluate the wetland capacity for nitrogen removal over a four-year period. The linked catchment–wetland model could simulate flow and nitrate load at the wetland outlet reasonably well with a Nash–Sutcliffe efficiency (NSE) of 0.7 and 0.76, respectively, suggesting that it provides a good representation of the hydrological and nitrogen processes in the upland catchment and the constructed wetland. We used two approaches, a mixed measurement-and-modelling-based approach and a process-based modelling approach to estimate the wetland efficiency of nitrogen removal. In both approaches, we found that the percentage load removal for nitrate-N and total N was related exponentially to the wetland outflow rate. Based on the process-based model estimates, the Owl Farm constructed wetland is very effective in removing nitrate-N with annual estimates of 55–80% (average 61%) removal. However, this capacity is very dynamic depending on the inflow from the catchment. The removal efficiency is very high at low flow and reduces when flow increases but is still maintained at around 20–40% during higher-flow periods. However, actual nitrogen-load removal in the wetland is greatest during high-flow periods when input loads are elevated. This study illustrates how a linked catchment–wetland modelling approach can be used to partition and quantify diffuse nitrogen input loads into wetlands from different types of runoff and to evaluate their subsequent reduction rates. The tool is particularly useful for situations where diffuse groundwater inflows, which are difficult to measure, are important nutrient sources.

Spatial heterogeneity of global forest aboveground carbon stocks and fluxes constrained by spaceborne lidar data and mechanistic modeling.

Forest carbon is a large and uncertain component of the global carbon cycle. An important source of complexity is the spatial heterogeneity of vegetation vertical structure and extent, which results from variations in climate, soils, and disturbances and influences both contemporary carbon stocks and fluxes. Recent advances in remote sensing and ecosystem modeling have the potential to significantly improve the characterization of vegetation structure and its resulting influence on carbon. Here, we used novel remote sensing observations of tree canopy height collected by two NASA spaceborne lidar missions, Global Ecosystem Dynamics Investigation and ICE, Cloud, and Land Elevation Satellite 2, together with a newly developed global Ecosystem Demography model (v3.0) to characterize the spatial heterogeneity of global forest structure and quantify the corresponding implications for forest carbon stocks and fluxes. Multiple-scale evaluations suggested favorable results relative to other estimates including field inventory, remote sensing-based products, and national statistics. However, this approach utilized several orders of magnitude more data (3.77 billion lidar samples) on vegetation structure than used previously and enabled a qualitative increase in the spatial resolution of model estimates achievable (0.25° to 0.01°). At this resolution, process-based models are now able to capture detailed spatial patterns of forest structure previously unattainable, including patterns of natural and anthropogenic disturbance and recovery. Through the novel integration of new remote sensing data and ecosystem modeling, this study bridges the gap between existing empirically based remote sensing approaches and process-based modeling approaches. This study more generally demonstrates the promising value of spaceborne lidar observations for advancing carbon modeling at a global scale.

Structural diversity: a digital revolution

One of the fundamental rules in biology and ecology is that structure determines function. At a microscale, the transmissibility of COVID-19 is related to the unique structure of the novel coronavirus's trimeric spike protein. At a macroscale, the success of early human settlement along the west coast of North America during the Holocene is likely associated with the rich resources provided by the diverse structures of kelp forests. Until recently, research on ecosystem structure and diversity has lagged, due primarily to a lack of tools to measure these components effectively and accurately. For instance, scientists studying deciduous forests have relied on stem size and leaf area as proxies of forest structure to predict ecosystem functions. However, these traditional, proxy-based metrics are laborious to collect, logistically challenging to assess at large scales, and fail to capture other critical aspects of structural diversity such as canopy depth and complexity, both of which provide better predictions of ecosystem functions and processes as compared to their conventional counterparts. Fortunately, innovations in digital technology and data science are converging to yield a wide range of new research capabilities. As demonstrated by the papers in this Special Issue, ecologists are starting to embrace these promising advances, unlocking opportunities to understand structural diversity. We now have a robust suite of sensors that can be deployed on different platforms and used to map and measure three-dimensional (3D) structures in both terrestrial and aquatic systems, across a wide range of scales and resolutions. For example, modern single-photon lidar can provide wall-to-wall, high-resolution, 3D point cloud data in less than one hour for an area of over 10,000 ha, allowing individual tree-level measurement. Spanning large geographic regions, these unprecedented 3D data will enable investigations of fundamental ecological questions (eg macroscale patterns, scaling) that traditional plot-based approaches cannot. As with other innovations, however, these structural data also bring unencountered challenges. To take full advantage of ecology's digital advances, we argue that the following challenges must be addressed. The first is the necessity to ensure truly transdisciplinary collaboration. Although recognized for decades, the demand for such collaboration has never been greater in 3D structural diversity enabled ecology, especially at its current early stages of development. Most of the skillsets essential to harnessing and realizing the potential of these new technologies currently lie in disciplines beyond ecology and the environmental sciences. We need expertise from aviation technology for stable and safe data acquisition, engineering solutions for sensor integration, geomatics for data registration, computer science for data integration and segmentation, graphic design for visualization, information science for managing data and ensuring fair access, and social science for understanding societal implications and unintended consequences, to name a few. The quickest way for ecologists and environmental scientists to incorporate these research skills into their own work is through collaboration. The second challenge is embracing novel computational platforms (eg super computers, cloud computing) and approaches (eg machine learning and artificial intelligence). When covering large spatial extents, these 3D data often range from gigabytes to terabytes to petabytes in size. Therefore, advanced computing power and algorithms are required to handle them. Third, to better utilize these data, investigators will have to shift from traditional hypothesis-driven approaches to data-driven approaches or to a hybrid of the two. As these 3D data can generate hundreds of variables measuring different aspects of ecosystem structure, making sense of the data, especially when combined with other climatic and geophysical datasets, can be daunting or nearly impossible. Such massively multivariate datasets can quickly overwhelm traditional process-based modeling approaches. When deployed by knowledgeable analysts, machine learning approaches – whether data driven or domain knowledge guided – may reveal insights into ecosystem patterns and processes. Lastly, there is a pressing need to educate our next generation to embrace these digital advances. New data-oriented skills such as acquisition, visualization, analysis, and management of large datasets must become essential parts of ecological training. Consideration of structural diversity is foundational to our understanding of ecological systems. Consequently, advances in 3D data acquisition and analytics will likely reshape our understanding of ecology. Adopting these emerging digital technologies will enable the next generation of ecologists to gracefully operate a fleet of sensors to measure ecosystems and swim freely in the resulting ocean of data.

Additive manufacturing in green supply chains: A parametric model for life cycle assessment and cost

The use of additive manufacturing (AM) can improve the green performance of supply chains. However, adopting a centralized or a decentralized supply chain remains an open issue. A decision support model, that integrates Life Cycle Cost (LCC) and Life Cycle Assessment (LCA) methodologies, is proposed in this paper. The lifecycle of a printed product is parametrized depending on strategic decisions that impact economic and environmental sustainability at once, i.e., the production strategy, the degree of outsourcing and production decentralization. Capital and variable costs have been modelled by adopting the process-based cost modelling (PBCM) approach. The monetary valuation of life cycle assessment (MLCA) has been used to aggregate different environmental impact categories. The green-ability of compared production scenarios is measured through a dimensionless-aggregated metric that, besides the scenario performance, also consider the weights the decision maker(s) should assign to the sustainability dimensions. A numerical application is carried out by comparing three different scenarios (i.e., centralized, decentralized, and mixed). Regarding the used data, results show that fully decentralizing printed product production by renting production capacity and outsourcing post-processing activities is the greenest solution regardless of the weights given to the economic and environmental dimensions.

Synergistic contributions of climate and management intensifications to maize yield trends from 1961 to 2017

Understanding contributions of climate and management intensifications to crop yield trends is essential to better adapt to climate changes and gauge future food security. Here we quantified the synergistic contributions of climate and management intensifications to maize yield trends from 1961 to 2017 in Iowa (United States) using a process-based modeling approach with a detailed climatic and agronomic observation database. We found that climate (management intensifications) contributes to approximately 10% (90%), 26% (74%), and 31% (69%) of the yield trends during 1961–2017, 1984–2013, and 1982–1998, respectively. However, the climate contributions show substantial decadal or multi-decadal variations, with the maximum decadal yield trends induced by temperature or radiation changes close to management intensifications induced trends while considerably larger than precipitation induced trends. Management intensifications can produce more yield gains with increased precipitation but greater losses of yields with increased temperature, with extreme drought conditions diminishing the yield gains, while radiation changes have little effect on yield gains from management intensifications. Under the management condition of recent years, the average trend at the higher warming level was about twice lower than that at the lower warming level, and the sensitivity of yield to warming temperature increased with management intensifications from 1961 to 2017. Due to such synergistic effects, management intensifications must account for global warming and incorporate climate adaptation strategies to secure future crop productions. Additional research is needed to understand how plausible adaptation strategies can mitigate synergistic effects from climate and management intensifications.

Adaptation measures under the impacts of climate and land-use/land-cover changes using HSPF model simulation: Application to Gongola river basin, Nigeria

Recently, there is an upsurge in flood emergencies in Nigeria, in which their frequencies and impacts are expected to exacerbate in the future due to land-use/land cover (LULC) and climate change stressors. The separate and combined forces of these stressors on the Gongola river basin is feebly understood and the probable future impacts are not clear. Accordingly, this study uses a process-based watershed modelling approach – the Hydrological Simulation Program FORTRAN (HSPF) (i) to understand the basin's current and future hydrological fluxes and (ii) to quantify the effectiveness of five management options as adaptation measures for the impacts of the stressors. The ensemble means of the three models derived from the Coupled Model Intercomparison Project Phase 5 (CMIP5) are employed for generating future climate scenarios, considering three distinct radiative forcing peculiar to the study area. Also, the historical and future LULC (developed from the hybrid of Cellular Automata and Markov Chain model) are used to produce the LULC scenarios for the basin. The effective calibration, uncertainty and sensitivity analyses are used for optimising the parameters of the model and the validated result implies a plausible model with efficiency of up to 75 %. Consequently, the results of individual impacts of the stressors yield amplification of the peak flows, with more profound impacts from climate stressor than the LULC. Therefore, the climate impact may trigger a marked peak discharge that is 48 % higher as compared to the historical peak flows which are equivalent to 10,000-year flood event. Whilst the combine impacts may further amplify this value by 27 % depending on the scenario. The proposed management interventions such as planned reforestation and reservoir at Dindima should attenuate the disastrous peak discharges by almost 36 %. Furthermore, the land management option should promote the carbon-sequestering project of the Paris agreement ratified by Nigeria. While the reservoir would serve secondary functions of energy production; employment opportunities, aside other social aspects. These measures are therefore expected to mitigate feasibly the negative impacts anticipated from the stressors and the approach can be employed in other river basins in Africa confronted with similar challenges.

Forest cover resilience to climate change over India using the MC2 dynamic vegetation model.

It is imperative to understand the climate change impact on the forest ecosystem to develop appropriate mitigation and management strategies. We have employed a process-based dynamic vegetation modeling (MAPSS-CENTURY: MC) approach to project change in vegetation life forms under projected climate conditions that attained 81% overall accuracy. The present and projected climate conditions suggested highly resilient/stable forest covers in wet climate regimes and moderately resilient in dry semi-arid regions. Several forested grids in the seasonally dry tropical forest in the Eastern Ghats and dry Deccan peninsula regions are estimated to be less resilient, which may experience a regime shift toward scrub and grassland. The future prediction demonstrated an upward temperature shift in the Western Himalayas and trans-Himalaya, which may facilitate forest spread at higher elevations. Although the forest cover resilience may increase in future climate conditions, the disturbances in several regions in the Deccan Peninsula and the Eastern Ghats may trigger forest to scrub and grassland transition. The inaccuracy in model simulation in the Western Himalayas could be attributed to coarse resolution grids (0.5°) failing to resolve the narrow climate niches. The spatially explicit model simulation provides opportunities to develop long-term climate change adaptation and conservation strategies.

Stratigraphic Forward Modeling Assists Carbonate-Reservoir Characterization

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 202775, “Application of Stratigraphic Forward Modeling to Carbonate-Reservoir Characterization: A New Paradigm From the Albion Research and Development Project,” by Jean Borgomano, Aix-Marseille University, and Gérard Massonnat and Cyprien Lanteaume, TotalEnergies, et al. The paper has not been peer reviewed. _ Improving carbonate-reservoir prediction, field development, and production forecasts, especially in zones lacking data, requires novel reservoir-modeling approaches, including process-based methods. Classical geostatistic modeling methods alone cannot match this challenge, particularly if subtle stratigraphic architectures or sedimentary and diagenetic geometries not directly identified as properties with well data control the reservoir heterogeneity. Stratigraphic forward-modeling approaches can provide pertinent information to carbonate-reservoir characterization. The complete paper describes a modeling package tested and calibrated with high-resolution stratigraphic outcrop models. It allows valid prediction of carbonate facies associations mimicking the spatial distribution mapped along the Urgonian platform transects. Background Classical carbonate-reservoir characterization protocols rely mainly on 3D geostatistical models based on well data, allowing the realization of 3D numerical grids of reservoir properties. These geostatistic property models are supported by deterministic geological interpretations such as stratigraphic well correlations that are commonly based on sequenced stratigraphic concepts and carbonate sedimentological interpretations. The stratigraphic framework obtained from these deterministic interpretations has a critical effect on further static and dynamic reservoir models because it constrains the spatial stationarity of the geostatistic property simulations or imposes discrete flow units or barriers. These deterministic carbonate sequence stratigraphic and associated sedimentological interpretations, however, introduce significant biases, uncertainties, and imprecisions in reservoir models and furthermore are not validated by process-based modeling approaches as one should expect from any scientific protocol. This lack of validation represents a fundamental scientific gap in classical reservoir-characterization work flows that is generally avoided in other scientific domains such as physics by iterations combining experimentation and process-based models to verify deterministic interpretations and hypothesis. The paradox is that this virtuous scientific method is applied at the ultimate stage of the reservoir flow modeling with the classical “flow history matching,” implying the following strong hypothesis (Fig. 1a): If the dynamic model obtained from the upscaled static model matches the dynamic history and the flow records of the studied field and carbonate reservoir, then the geological model, including the deterministic stratigraphic and sedimentary interpretations, is validated. Reservoir flow and dynamic behavior certainly are controlled by initial geological conditions, but those are not dependent on flow processes. According to fundamental scientific principles, geological interpretations and deterministic models must be validated by geological process-based models. To fill this scientific gap in the presented carbonate-reservoir characterization approach, the authors introduce process-based stratigraphical and sedimentological models that are calibrated on pertinent, well-studied outcrop analogs.

An effective process-based modeling approach for predicting hypoxia and blue tide in Tokyo Bay

ABSTRACT Hypoxia and blue tide are the most significant environmental issues in Tokyo Bay as they have been damaging fisheries for a long time. Although studies on modeling these two associated phenomena have been conducted for decades, the scarcity of relevant observational datasets has greatly hindered the progress, and no study has successfully reproduced the entire processes of blue tide or predicted the time and place of its outbreak. To address the problems from limited data, this study proposed a relatively conventional benthic flux model and developed a novel method that converts the total organic carbon content into the fluxes of sediment oxygen consumption and sulfide release to represent the spatial differences in benthic fluxes. A pelagic sulfur model with only three key chemical reactions of blue tide that includes the disproportionation of elemental sulfur was proposed. The method significantly improved the results of dissolved oxygen in bottom water, as seen by the root mean square error decreasing by 15.9% and 18.9% in two simulations with largely different forcings. The sulfur model also accurately predicted the outbreaks of blue tides in each simulation, which is significant to the stakeholders as it facilitates the forecast of blue tides in Tokyo Bay.