Increasing global demand for agricultural commodities has driven local land use/cover change (LUCC) and agricultural production across Brazil during the 21st Century. Modelling tools are needed to help understand the range of possible outcomes due to these ‘telecoupled’ global-to-local relationships, given future political, economic and environmental uncertainties. Here, we present CRAFTY-Brazil, a

Integration of Earth system data from various sources is a challenging task. Except for their qualitative heterogeneity, different data records exist for describing similar Earth system processes at different spatiotemporal scales. Data inter-comparison and validation are usually performed at a single spatial or temporal scale, which could hamper the identification of potential discrepancies in other

This paper proposes an Intelligent Decision Support (IDS) methodology based on the integration of a data-driven technique –Case Based Reasoning (CBR)– and model-driven technique –Rule Based Reasoning (RBR)– for control, supervision and decision support on environmental systems. Design stage of control and decision support tools for environmental systems tend to be somehow ad-hoc regarding to the nature

Although global circulation models (GCMs) have been used for the reconstruction of precipitation for selected geological time slices, there is a lack of a coherent set of precipitation models for the Mesozoic-Cenozoic period (the last 250 million years). There has been dramatic climate change during this time period capturing a super-continent hothouse climate, and continental breakup and dispersal

The 3D reproduction of dam-break floods plays a key role in emergency management and disaster information dissemination. However, existing systems for the simulation of floods have some deficiencies. First, the input data parameters are complex. Second, the simulation process is cumbersome and time consuming, and it lacks a complete workflow. In this paper, we develop a platform for the rapid 3D reproduction

Maritime activity is known to increase pollutant concentration levels in neighboring cities. In major touristic destinations, the singular need of cruise liners to keep supplying energy to on-board services and amenities while docked, has raised concerns about this industry contribution to pollutant emissions. To estimate the impact of port activities and that exclusively due to cruises, classical

Despite the critical role that replication plays in the advancement of science, its presence in modelling literature is rare. To encourage others to conduct replication and report success and challenges that facilitate or hinder replication, we present the replication of an agent-based model (ABM) of residential sprawl using the Replication Standard. Replication results achieved relational equivalence

Environmental modelling frameworks allow domain experts, rather than software developers, to implement and run numerical simulation models in earth and environmental sciences. Because of the need to use more detailed process representations or larger datasets as input to models, it may become infeasible to perform modelling studies, due to the increased amount of time it takes for models to calculate

Ensemble-tree machine learning (ML) regression models can be prone to systematic bias: small values are overestimated and large values are underestimated. Additional bias can be introduced if the dependent variable is a transform of the original data. Six methods were evaluated for their ability to correct systematic and introduced bias. Method performance was evaluated using four case studies of groundwater

Diagnostic testing is an oft-recommended use of sensitivity analysis to assess correctness or plausibility of model behavior. In this paper we demonstrate the use of sensitivity analysis as a complementary first-pass software test for the validation of model behavior. Typical testing processes rely on comparing model outputs to results known to be correct. Such approaches are limited to specific model

The main objective of this study is to develop a new modeling platform, which integrates all necessary modeling components for watershed simulation, including a pre-processing tool, a coupling of hydrologic and river transport models, and a calibration and uncertainty analysis tool. All components are connected and immersed into an open source non-proprietary GIS software. The modeling platform is

Assessing and modelling the coastal plume dispersion of nuclearized rivers is strategic in case of accidental releases, but taking into account the variation of main hydrodynamic forcings is challenging. This study uses fuzzy c-mean clustering of a 10 years series of discharge and wind speed at the Rhone River estuary (France) in order to explain the variability of its plume. The method allows to classify

Large-scale hydrological models are demanding both in term of memory allocation and CPU time, particularly when assessment of modeling uncertainty is required. High Performance Computing offers the opportunity to reach resolutions not achievable with standard serial coding. However, the advantages may be offset by poor scalability of the model due to components that have to be executed in series, such

Effective implementation of a scenario-neutral climate impact assessment relies on the integration of many modelling components at multiple stages: from the generation of appropriate climate boundary conditions that can be used to rigorously ‘stress-test’ a system, to the simulation, visualisation and interpretation of resulting system performance. For systems with complex temporal dynamics, the generation

Invasive plant and environmental pests can seriously impact environment, economy, health and amenity. It is challenging to form response policies given the diversity of pest species; complex spatiotemporal interplay between arrival, spread, surveillance, and control; and limited field data when pests are rare/absent. Models can provide useful decision support through the exploration of incursion pathways

Synthesizing and communicating knowledge on climate change to policymakers and stakeholders is often difficult due to the complexity and diversity of underlying research. As a “translation” instrument, we present the mipplot tool, an open-source R package that can be used to visualize data of long-term climate mitigation scenarios. With simple commands and under a user-friendly language platform, this

Surface water is an irreplaceable resource for human survival and environmental sustainability. Accurate, finely-detailed cartographic representations of hydrologic streamlines are critically important in various scientific domains, such as assessing the quantity and quality of present and future water resources, modeling climate changes, evaluating agricultural suitability, mapping flood inundation

Numerical models are essential tools for understanding the complex and dynamic nature of the natural environment. The ability to evaluate how well these models represent reality is critical in their use and future development. This study presents a combination of changepoint analysis and fuzzy logic to assess the ability of numerical models to capture local scale temporal events seen in observations

Recent technological advances in collecting data on emission sources, meteorological conditions and concentration of air pollutants in urban areas, offer invaluable opportunities for the better understanding of air quality problems. However, processing large sets of data to extract statistically valid evidence poses many challenges from both the conceptual and technical viewpoints. Air quality data

Agent-based models (ABMs) are widely used to analyze coupled natural and human systems. Descriptive models require careful calibration with observed data. However, ABMs are often not calibrated in a formal sense. Here we examine the impact of data record size and aggregation on the calibration of an ABM for housing abandonment in the presence of flood risk. Using a perfect model experiment, we examine

We present a Matlab toolbox to calculate hydrologic signatures, which are metrics that quantify streamflow dynamics. Signatures are widely used for catchment characterisation, hydrologic model evaluation, and assessment of instream habitat, but standardisation across applications and advice on signature selection is lacking. The toolbox provides accessible, standardised signature calculations, with

Reservoir construction and operation have significant impacts on catchment hydrology, flood risk and fluvial processes. However, few available hydrological modelling packages can simulate complex, dynamic, manually-operated reservoir control structures. We present SHETRAN-Reservoir, a physically-based spatially distributed modelling tool to simulate catchment hydrology, including reservoir operations

The study evaluates the biogeochemical model ForSAFE-2D, designed to simulate water and chemical transport from the forest to the stream, by simulating the hydrology and the transport of the chemical tracer chloride (Cl−) along a forest hillslope in Northern Sweden. The simulated Cl− exports were in balance with the simulated inputs but measurements suggested a net release of Cl− from the catchment

Forest disturbance regimes are changing around the globe. Of particular concern are biotic disturbance agents, as they respond strongly to climate warming and invade new ecosystems as alien pests and pathogens. To date, biotic disturbances are either ignored in simulations of vegetation dynamics or only a small number of common agents are considered explicitly. Here we present BITE, a general, process-based

The Piper diagram is the most widely used chart in groundwater hydrochemical studies to represent the chemical facies of a set of water samples. So far, most modifications of the original Piper diagram over time show a common problem: the Piper chart is not suitable for representing big data sets. When there are more than 40 or 50 analyses, the symbols overlap, and the data distribution becomes obscured

Spatial simulation and inversion problems are omnipresent in earth and environmental sciences. An open-source Python package (RMWSPy) for conditional spatial random field simulation and inversion based on a generalized implementation of the Random Mixing Whittaker-Shannon (RMWS) algorithm is presented in this paper. The RMWS algorithm has successfully been applied to a variety of environmental modelling

Soil erosion models, typically applied at basin and watershed scales, are rarely evaluated at agricultural field scales due to the lack of spatially-distributed time series data. A novel unmanned aerial vehicle (UAV) methodology was used to quantify farm-field soil erosion from nine UAV surveys and structure-from-motion (SfM). Using a semi-distributed approach, we evaluated soil erosion estimates from

The Variogram Analysis of Response Surfaces (VARS) has been proposed by Razavi and Gupta as a new comprehensive framework in sensitivity analysis. According to these authors, VARS provides a more intuitive notion of sensitivity and is much more computationally efficient than Sobol’ indices. Here we review these arguments and critically compare the performance of VARS-TO, for total-order index, against

Land subsidence provides important information about the spatial and temporal changes occurring in the subsurface (e.g. groundwater levels, geology, etc.). However, sufficient subsidence data are difficult to obtain using only one sensor or survey, often resulting in a tradeoff between spatial resolution and temporal coverage. This study aims to estimate the high spatio-temporal resolution land subsidence

Solute transport models are widely employed to the predict spatio-temporal fate of contaminants in rivers. However, most previous studies have assumed that contaminants spilled are unreactive; they disregard the inherent reactivity of contaminants in water. The Reactive Solute Transport Model (RSTM) (Gooseff et al., 2005) includes lumped decay terms to reflect the decayability of the chemicals, but

QES-Winds is a fast-response wind modeling platform for simulating high-resolution mean wind fields for optimization and prediction. The code uses a variational analysis technique to solve the Poisson equation for Lagrange multipliers to obtain a mean wind field and GPU parallelization to accelerate the numerical solution of the Poisson equation. QES-Winds benefits from CUDA dynamic parallelism (launching

Environmental monitoring has become of paramount importance for researchers and decision makers in order to better protect the environment and achieve sustainable development goals, especially with continuous population growth and rapid urbanization and industrialization processes that alter natural components of the environment and degrade the ecosystems. Hence, the use of Landsat data is of great

Evaluating distributed soil erosion models is challenging because of the uncertainty in models and measurements of system responses. Here, we present an approach to evaluate soil erosion and sediment delivery models, which incorporates sediment source fingerprinting and sediment-rating curve uncertainty into model testing. We applied the Generalized Likelihood Uncertainty Estimation (GLUE) methodology

Sensitivity analysis (SA) is en route to becoming an integral part of mathematical modeling. The tremendous potential benefits of SA are, however, yet to be fully realized, both for advancing mechanistic and data-driven modeling of human and natural systems, and in support of decision making. In this perspective paper, a multidisciplinary group of researchers and practitioners revisit the current status

Water exchange between the surface and subsurface is important for both water resource management and environmental protection. In this paper, we develop coupled surface and subsurface flow simulation capability in a parallel subsurface flow and reactive transport code PFLOTRAN. We sequentially couple the diffusion wave-based surface flow with the subsurface flow governed by the Richards equation in

In the majority of EU Member States, agricultural land is expected to decrease not only due to land-use changes in favour of urban expansion and afforestation but also to land abandonment processes. The knowledge on location and extent of agricultural land abandonment is relevant for estimating local external effects and adapting policy interventions. Currently, multi-level land-use models are able

Due to heterogeneous and inconsistent key performance indicators (KPIs) for the quantitative evaluation of a sustainable city's operational status, it is a great challenge to share multidimensional, multi-source and heterogeneous indicators. We propose a heterogeneous KPI capability representation model (HKPM) in our study. Based on the Meta Object Facility architecture, a nine-tuple multi-hierarchical

Assessing the effects of land disturbance on sediment yield requires quantification of erosion and sediment connectivity. Off-road vehicle (ORV) tracks represent sources of sediment pollution to coral reefs and coastal wetlands in a dry tropical setting of Southwestern Puerto Rico. This study assesses their contribution through a combined empirical and modeling approach. Rainfall simulation experiments

Interwave Analyzer is an open source software that provides detailed characterization of the dynamics of internal waves in lakes and reservoirs. It is based on well-established theories and empirical knowledge on internal waves and lake mixing and facilitates a general physical classification of lakes and reservoirs. As input data, the program requires time series of water temperature from various

Vrana et al. (2012a) presented the maximum-agreement-mean (MaxAgM) method, which aggregates expert opinions while focusing on points of agreement. Although MaxAgM can minimize Shannon entropy to optimize results, it has a high level of computational and conceptual complexity, yields difficult-to-interpret results, has no indicator of accuracy, and requires specialized software that is not widely available

Nonpoint source (NPS) particulate pollutants are a major source of urban surface pollution. It is essential to better understand and model the dynamic process of NPS pollutants to inform the design of effective strategies for urban stormwater management and pollution control. This work presents a novel coupled hydrodynamic and particle-based model to simulate the full-process dynamics of NPS particulate

Scenario-neutral climate impact assessments are being used increasingly to assess water resource system responses to possible climate changes. The purpose of such assessments is to identify system sensitivity to a range of plausible climate conditions, often including an evaluation of the joint effect of multiple climate stressors. Given the large number of climate variables and associated statistics

Wetlands play a key role in preserving biodiversity and preventing climate change. Their conservation poses an important and pressing challenge. In the Mediterranean region, one of the key threats to wetland survival is the lack of water due to competition for resources. The selection of the most sustainable water resources for wetland conservation is a complex elicitation problem. A novel Water Resources

The accurate understanding of the human-modified water cycle calls for a detailed representation of human and water systems, including relevant non-linearities, and of the feedback responses between them. This paper couples a microeconomic Positive Multi-Attribute Utility Programming model with a Hydrologic Modeling System (HEC-HMS) with the objective of incorporating the behavior and adaptive responses

Generalized additive models are increasingly used to identify and describe environmental trends. A major advantage of these models, as compared to simpler statistical tools such as linear regression or Mann-Kendall tests, is that they provide estimates of prevailing levels and trend magnitudes at any given point in time instead of an overall measure. For multiple time series, this versatility has to

The acceptance and usefulness of simulation models are often limited by the efficiency, transparency, reproducibility, and reliability of the modelling process. We address these issues by suggesting that modellers (1) “trace” the iterative modelling process by keeping a modelling notebook corresponding to the laboratory notebooks used by empirical researchers, (2) use a standardized notebook structure

It is common for model-based simulations to be reported using prediction interval estimates that characterize the lack of precision associated with the simulated values. When based on Monte-Carlo sampling to approximate the relevant probability density function(s), such estimates can significantly underestimate the width of the prediction intervals, unless the sample size is sufficiently large. Using

This paper describes a post-processing tool included in the globally used SWAT model for desktop assessment of environmental flows. The tool uses daily flow data from SWAT outputs and can apply different methodologies based on percentiles or moving averages to assess environmental flows. This tool, included in the visualization section of the SWAT model, is open source and can therefore be readily

A global sensitivity analysis of a lake ecosystem model (GOTM-FABM-PCLake) was undertaken to test the impacts of lake morphology on parameter sensitivity in three different lakes. The analysis was facilitated by the Parallel Sensitivity and Auto-Calibration tool (parsac) and included a screening step with the density-based Borgonovo's method followed by in-depth analysis with both Borgonovo's and the

Sequestering carbon into the soil can mitigate the atmospheric concentration of greenhouse gases, improving crop productivity and yield financial gains for farmers through the sale of carbon credits. In this work, we develop and evaluate advanced Bayesian methods for modelling soil carbon sequestration and quantifying uncertainty around predictions that are needed to fit more complex soil carbon models

Concerns regarding the impact of climate change, food price volatility, and weather uncertainty have motivated users of simulation models to consider uncertainty in their simulations. One way to do this is to integrate uncertainty components in the model equations, thus turning the model into a problem of numerical integration. Most of these problems do not have analytical solutions, and researchers

Accurate streamflow prediction largely relies on historical meteorological records and streamflow measurements. For many regions, however, such data are only scarcely available. Facing this problem, many studies simply trained their machine learning models on the region's available data, leaving possible repercussions of this strategy unclear. In this study, we evaluate the sensitivity of tree- and

As our understanding of the interactions present in socio-ecological systems advance, emulation modeling can help reduce the complexity and required computational resources of the models used to represent these systems. While emulation is commonly used in model meta-analyses and parameterization, it has been less explored in the context of environmental management. In this research, I analyze the reflections