The data requirements of many socio-environmental system (SES) modelling studies have increased substantially in recent years. This has made the already challenging task of data compilation, retrieval, and sharing progressively more difficult. Recognising the current lack of best practice for knowledge management in SES modelling studies, we propose using SES wikis as a means of addressing these challenges

The use of data-driven techniques such as artificial neural network (ANN) models for outdoor air pollution forecasting has been popular in the past two decades. However, research activity on uncertainty surrounding the development of ANN models has been limited. Therefore, this review outlines the approaches for addressing model uncertainty according to the steps for building ANN models. Based on 128 articles

Numerical prediction of coastal inundation can be complex due to the multiple physical processes involved and typically requires two-dimensional numerical model extents, particularly in areas with complex along-shore morphology. Such model domains often incur relatively high computational expense. Recent extreme inundation studies for Wellington, New Zealand, were executed using the numerical tool

Widely-used term-weighting schemes and machine learning (ML) classifiers with default parameter settings were assessed for their performance when applied to environmental big data analysis. Five term-weighting schemes [term frequency (TF), TF–inverse document frequency (TF-IDF), Best Match 25 (BM25), TF–inverse gravity moment (TF-IGM), and TF–IDF–inverse class frequency (TF-IDF-ICF)] and five different

In light of the rapidly increasing regulation of rivers due to planned and constructed reservoirs, monitoring reservoir operations has become very crucial. The Reservoir Assessment Tool (RAT) framework was developed to monitor reservoir operations globally, using hydrological modeling and satellite observations. With feedback from stakeholders, improvements in the RAT framework are demonstrated in

In this study, we evaluated the use of synthetic aperture radar (SAR) and multispectral data to detect aquaculture waterbodies in Southern Bangladesh to quantify fish production on a national scale. For this purpose, we developed an object-based framework comprised of three sequential stages: 1) water detection, 2) feature segmentation, and 3) feature classification. Techniques such as Edge-Otsu for

Complex physical models are the most advanced tools available for producing realistic simulations of the climate system. However, such levels of realism imply high computational cost and restrictions on their use for policymaking and risk assessment. Two central characteristics of climate change are uncertainty and that it is a dynamic problem in which international actions can significantly alter

Understanding the internal structure of permeable and impermeable sediments (e.g. point-bars and tidal-flat deposits) generated by the evolution of meandering tidal channels is essential for accurate modeling of groundwater flow and contaminant transport in coastal areas. The detailed reconstruction of stratal geometry and hydraulic properties from measurements must be accompanied by depositional history

The Soil & Water Assessment Tool (SWAT) is a useful model for evaluating socio-ecological tradeoffs and analyzing coupled natural-human system dynamics in agricultural watersheds. However, reservoir operating options in SWAT are limited. This study advances the representation of reservoir operations in SWAT by adding an option for closed-loop, multi-reservoir operating policies that can be optimized

This paper introduces HydroLang, an open-source and integrated community-driven computational web framework for hydrology and water resources research and education. HydroLang employs client-side web technologies and standards to carry out various routines aimed at acquiring, managing, transforming, analyzing, and visualizing hydrological datasets. HydroLang consists of four major high-cohesion low-coupling

The recording and simulation data of forest landscapes are massive, high-dimensional, and abstract, requiring intuitive representation. 3-D visualization is an efficient tool to comprehend possible landscape changes generated by real-world or forest landscape models. Based on current advantages of game engines (realism and convenience), we developed a platform for 3-D visualization of forest landscapes

This paper describes a simplified flood model based on cellular automata in order to be implemented in applications, which require the simulation of a great number of flood scenarios at regional scale. The model is based on a top-down evaluation of the flow process through the cells of the DEM. According to the conditions in a Moore Neighbourhood, the cells are classified in a dynamic process in four

We develop a regional hydrological model that applies to arid and semi-arid regions, by explicitly considering the effect of irrigation on the hydrological processes. A new irrigation module is here integrated into the recently introduced Atmospheric and Hydrological Modelling System (AHMS) for the quantitative assessment of basin-scale hydrological response to climate change and the impact of anthropogenic

Agent-based models (ABMs) are an increasingly popular choice for simulating large systems of interacting components, and have been applied across a wide variety of natural and environmental systems. However, ABMs can be incredibly disparate and often opaque in their formulation, implementation, and analysis. This can impede critical assessment and re-implementation, and jeopardize the reproducibility

A multivariate polynomial regression modeling (MPR) framework is developed to estimate total dissolved solids (TDS) in stormwater runoffs from rangelands in the Colorado River Basin in the Southwestern United States. An accurate TDS estimation model is needed to simulate terrestrial and aquatic salt transport processes on rangelands, identify critical source areas, and manage these sources effectively

Complex environmental model outputs used to inform decisions often have systematic errors and are of inappropriate resolution, requiring downscaling and bias correction for local applications. Here we provide a new interpretation of dasymetric modelling (DM) as a spatial bias correction framework useful in environmental modelling. DM is based on areal interpolation where estimates of some variable

PRISM, a new forest management decision support system, was developed for United States national forest planning at the same time existing tools were deprecated. PRISM has a friendly graphical user interface to facilitate model development, database management, sensitivity analysis, and data visualization. It integrates SQLITE engine for database storage and exploration. It supports both classic forest

This study evaluates an emerging capability to monitor high spatial (pixel size = 3.7 m) and temporal (daily to sub-daily imagery) resolution coastal change using PlanetScope cubesats. A new toolkit (CoastSat.PlanetScope) is presented that enables users to map shorelines from PlanetScope imagery, using a workflow of image co-registration, segmentation, thresholding, shoreline detection and elevation

Forest landscapes pattern and development are affected by environment and disturbance. Disentangling their effects is important to understanding current landscape and predicting future changes. Such studies are limited by short-term observation and sparse disturbance-history data. Spatially-explicit forest landscape modeling represents a solution to these limitations. Here, we reconstructed the 300-year-time-series

The Palmer Drought Severity Index (PDSI) ranges from −10 to 10 and is used for monitoring drought extent and severity. PDSI is a monthly global gridded data set with partial global coverage from 1850 through 1947 and full global coverage from 1948 through 2018. PDSI updates are infrequent. We present a method to extend PDSI using Global Land Data Assimilation System (GLDAS) data. We provide an updated

Sand ripples are geomorphic features on the seafloor that affect bottom boundary layer dynamics including wave attenuation and sediment transport. We present a new equilibrium ripple predictor using a machine learning approach that outputs a probability distribution of wave-generated equilibrium wavelengths and statistics including an estimate of ripple height, the most probable ripple wavelength,

Despite their frequent use, there are few simple and readily accessible tools to help guide the logistical planning of tracer injections in streams and rivers. We combined the widely used advection-dispersion-reaction equation, peak concentration estimates based on a meta-analysis of hundreds of tracer injections carried out in streams and rivers, and simple mass balances in a dynamic Excel Workbook

Groundwater model data assimilation (DA) aims to reduce uncertainty in simulated outcomes of interest to resource management while minimizing the potential for predictive bias. Sequential DA, which can estimate model states along with properties and stresses dynamically in time, offers a potentially powerful alternative to batch DA (i.e., history matching) for reducing bias in decision-relevant predictions

Earth Observation (EO) technologies have played an increasingly important role in monitoring the Sustainable Development Goals (SDG). These technologies often combined with Machine Learning (ML) models provide efficient means for achieving the SDGs. The great progress of this combination is also demonstrated by the large number of software, web tools and packages that have been made available for free

Prediction of wildfire propagation plays a crucial role in reducing the impacts of such events. Various machine learning (ML) approaches, namely Support Vector Regression (SVR), Gaussian Process Regression (GPR), Regression Tree, and Neural Networks (NN), were used to understand their applicability in developing models to predict the rate of spread of grassfires. A dataset from both wildfires and experimental

Climate matching allows comparisons of climatic conditions between different locations to understand location and species range climatic suitability. The approach may be used as part of horizon scanning exercises such as those conducted for invasive species. We implemented the CLIMATCH algorithm into an R package, climatchR. The package allows automated and scripted climate matching exercises across

Coastal and riverine flooding is one of the most common environmental hazards that affect billions of people worldwide. A coupled hydrologic and coastal storm surge simulation is required to develop an improved understanding of the individual and collective mechanisms that can cause flooding within watersheds. These simulations are dependent on an accurate digital elevation model (DEM); however, it

Climate is an essential component of environmental models. Over the last two decades, many weather generators have been presented in the literature. Although their implementation into software has been of great help to environmental modellers, their lack of integration into modelling frameworks still represents a challenge for end users. In many cases, end users have to retrieve the climate variables

Complex systems simulations can support collaborative water planning by allowing stakeholders to jointly see hidden effects of land- and water-use decisions on groundwater flow. We adopted a participatory modeling progression where stakeholders learned to modify and use increasingly sophisticated models to assess policy impacts on groundwater levels. Stakeholders' shared understanding of the problem

The Soil and Water Assessment Tool (SWAT) is one of the most widely used and well-tested eco-hydrological models. However, parameter calibration, sensitivity analysis and uncertainty analysis remain among the most challenging tasks. Existing SWAT parameter calibration, sensitivity analysis, and uncertainty analysis tools are either commercial products or free tools with limited options. This study

Concerns about water resource availability and preservation have grown over recent decades due to increased levels of pollution stemming mainly from economic activities and uncontrolled population growth. In this context, the present study seeks to identify and quantify point pollution sources released from river margins. A two-dimensional transient advection–diffusion equation is proposed to model

Abstract not available

Models are widely used for investigating cause-effect relationships in complex systems. However, often different models yield diverging causal claims about specific phenomena. Therefore, critical reflection is needed on causal insights derived from modeling. As an example, we here compare ecological models dealing with the dynamics and collapse of cod in the Baltic Sea. The models addressed different

We investigated the ability of the process-based, 1-dimensional hydrodynamic-ecosystem lake model GOTM-WET to reproduce the fluctuating dynamics and shifts between turbid and clear water states following restoration in temperate, shallow Lake Arreskov, Denmark. The lake model was calibrated on a comprehensive 12-year dataset with a multiple single-model ensemble approach to address model parameter

The advent of detailed hydrodynamic model simulations of urban flooding has not been matched by improved capabilities in flood exposure analysis which rely on validation against observed data. This work introduces a generic, building-level flood exposure analysis tool applying high resolution flood data and building geometries derived from hydrodynamic simulations performed with the 2D hydrodynamic

The aims of this study are to characterize river network patterns and identify whether the slope of preexisting surfaces relatively unaffected by modern landscape processes influences the formation of networks on the peninsula. The distributional properties of the tributary junction angles by the beta distribution are examined, and support vector machines based on the parameter estimates of the distribution

Live fuel moisture content (LFMC) is an important environmental indicator used to measure vegetation conditions and monitor for high fire risk conditions. However, LFMC is challenging to measure on a wide scale, thus reliable models for estimating LFMC are needed. Therefore, this paper proposes a new deep learning architecture for LFMC estimation. The architecture comprises an ensemble of temporal

Calibration and validation are standardized practices to establish the credibility of biogeochemical models for understanding agroecosystem nutrient dynamics. We evaluated three automatic calibration approaches, including simultaneous, sequential, and separate, for the calibration of model parameters of the biogeochemical DeNitrification DeComposition (DNDC) model through inverse modeling using PEST

Strategic land conservation investments that prioritize stakeholder priorities show promise for protection of ecological integrity and associated socioeconomic benefits of land conservation. However, despite many known benefits, decisions on where and what to conserve are often made without effective ecological and socioeconomic considerations. Ideally, multiple measurable attributes relevant to the

Snow cover is a key hydrological variable, critical to understanding water cycles and informing management decisions around resource extraction and recreational activities. Remote sensing open-access data and cloud-based computing platforms are two innovative tools for snow cover estimation. In this paper, we present SnowWarp, a processing framework that uses Google Earth Engine and the R programming

Land Use Land Cover (LULC) change is widely recognised as one of the most important factors impacting the hydrological response of river basins. SWAT+, the latest version of the Soil and Water Assessment Tool, has been used extensively to assess the hydrological impacts of LULC change. However, the process of making and assessing such changes in SWAT+ is often cumbersome and non-intuitive, thereby

Urban flooding has become an increasingly frequent and fatal natural hazard and numerical modeling techniques play a vital role in its prediction and management. We review urban flood numerical simulations by systematically summarizing the calculation methods of surface runoff, drainage systems, and coupled models. Following the review, accuracy and computational efficiency are found to be the two

Fragmentation affects river ecosystems worldwide by dampening the movement and dispersal of aquatic organisms and material (such as sediment, water, nutrients) across the river network. In this paper, we develop river connectivity indices to explain biodiversity patterns, prioritize reaches that need habitat restoration and barriers that need improvement. We provide a general framework for calculating

This paper presents an integrated modeling framework aiming at accurate predictions of flood hazard from heavy rainfalls. The accuracy of such predictions generally depends on the complexity and resolution of the employed model components. We propose an integration of complementary models in one framework that facilitates GPUs to improve accuracy and simulation time. The spatially distributed runoff

Recently, the conceptual data-driven approach (CDDA) was proposed to correct residuals of ensemble hydrological models (HMs) using data-driven models (DDMs), followed by the stochastic CDDA (SCDDA) that used HM simulations as input to DDMs within a stochastic framework - both approaches improved ensemble HMs' simulations. Here, a new SCDDA is introduced where CDDA uncertainty is estimated (instead

Forest microclimate can buffer biotic responses to summer heat waves, which are expected to become more extreme under climate warming. Prediction of forest microclimate is limited because meteorological observation standards seldom include situations inside forests. We use eXtreme Gradient Boosting ‒ a Machine Learning technique ‒ to predict the microclimate of forest sites in Brandenburg, Germany

With the advent of increasingly powerful computational architectures, scientists use these possibilities to create simulations of ever-increasing size and complexity. Large-scale simulations of environmental systems require huge amounts of resources. Managing these in an operational way becomes increasingly complex and difficult to handle for individual scientists. State-of-the-art simulation infrastructures

Fire is a dominant disturbance in temperate and boreal biomes, and increasing burned area with climate change may fundamentally alter forests. Improved information about how fire-induced changes to forests may feedback to affect subsequent burning at regional scales could inform forest management and climate-mitigation strategies. However, fire is simplistically represented in Earth System Models,

In this paper, a space-time model is developed to support spatial planning. The temporal nature of the model allows the variables that condition land use to evolve periodically. In each time-step, land use allocation is optimized considering sustainability criteria. The model combines system dynamics techniques to simulate the evolution and interactions of economic and biophysical drivers using a multi-objective

The identification of landscape classes facilitates the implementation of planning strategies. Although landscape patterns are key distinctive features of landscape classes, existing unsupervised clustering techniques for clustering landscapes rely on categorical input data to quantify such patterns and consider only a limited number of pattern metrics. To unlock the great potential of continuous spatial

Fire simulation models are useful to advance fire research and improve landscape management. However, a better understanding of these tools is crucial to increase their reliability and expansion into research fields where their application remains limited (e.g., ecosystem services). We evaluated several components of the BFOLDS Fire Regime Module and then tested its ability to simulate fire regime

We present an operational system for multi-sensor data fusion implemented at the Finnish Environment Institute. The system uses Ensemble Kalman filter and smoother algorithms, which are often used for probabilistic analysis of multi-sensor data. Uncertainty and spatial and temporal correlations present in the available observation data are accounted for to obtain accurate and realistic results. To