The Multi-Basin DYnamic Reservoir Simulation Model (MB-DYRESM) has been developed to bridge the gap between the computationally cheap, one-dimensional lake models and the computationally expensive, three-dimensional lake models. It was extended from the classic one-dimensional lake model DYRESM by incorporating a horizontal water exchange algorithm to simulate gravitational mass transport between adjacent

The freeware DiscoverFramework provides new tools to build spatial and temporal data visualization applications accessible to stakeholders, policy makers, scientists, and educators. By focusing on environmental data and supporting applications accessible via laptops, tablets, and cell phones, the DiscoverFramework can be used to increase public awareness and inspire responsible use of complex environmental

Environmental models involve inherent uncertainties, the understanding of which is required for use by practitioners. One method of uncertainty quantification is global sensitivity analysis (GSA), which has been extensively used in environmental modeling. The suitability of GSA methods depends on the model, implementation, and computational complexity. Thus, we present a comparative analysis of different

The increasing pressure on Earth's ecosystems due to climate change is becoming more and more evident and the impacts of climate change are especially visible on coral reefs. Understanding how climate change interacts with the physical environment of reefs to impact coral growth and reef development is critically important to predicting the persistence of reefs into the future. In this study, a biophysical

Land use regression (LUR) is a widely used method to develop prediction models in environmental sciences. However, the process of creating and applying LUR models is repetitive and time-consuming. The XLUR tool was developed to automate this process, while at the same time providing a detailed log of the model building process for reproducibility, and providing evaluation metrics to assess model quality

Scientific visualizations are the foundation for communicating results and findings to a variety of audiences. As the creation of novel and large environmental datasets has grown, this has necessitated new schemes and recommendations for creating effective visualizations. In this overview, we review the foundations of scientific visualization and considerations for visualization of large datasets within

In free-surface turbulent flows, large amount of air may be entrapped and advected in the water current. The resulting air-water flows are frequently observed in natural water systems, where they are also relevant to water quality, ecological sustainability and integrated assessment within such systems. Herein, a review of physical and numerical modelling of air-water flows is developed, providing

A Richards-based soil water model was implemented in the APEX and EPIC terrestrial ecosystem models to improve their hydrologic modeling capabilities. The Richards model together with two existing soil water models were calibrated and evaluated to assess their performance for simulating watershed-level hydrology under scenarios of landscape conversion to bioenergy crop production. The Richards model

Traditional approaches to inundation modelling are computationally intensive and thus not well suited to assessing the uncertainty involved in estimating flood inundation surfaces for planning, design and forecasting purposes. In this study, a rapid flood inundation modelling framework is developed, consisting of a novel spatial reduction and reconstruction (SRR) approach and a deep learning (DL) modelling

In the era of ubiquitous data collection and generation, demands are high to make these data accessible as widely as possible, with as little effort and as much power and flexibility as ever possible. On Earth data, this holds in particular for pixel data and point clouds, some of the main “Big Data” today. Coverages represent a unifying concept for space/time-varying data, especially for spatio-temporal

The wildland-urban interface (WUI) is defined as a geographic area where human developments and flammable vegetation merge in a wildfire-prone environment. Losses due to wildfire have been rising in the past decade, attributed to changes in vegetation growth, fuel availability, and increased land developments in WUI. This paper studies the process of wildfire spread inside WUI communities. The fire

Strategic decision-making on long-term drought risk management can be supported by integrated assessment models to explore uncertain future conditions and potential policy actions. Such models have to meet many – sometimes conflicting – requirements posed by policy-makers, model developers, and stakeholders. This paper discusses the case of the National Water Model (NWM) that is applied for national

Accurate estimates of water volumes are crucial for water management. This study applies an automated methodology to detect small-scale on-farm dams and develops a novel application of Bayesian inference to jointly simulate volumes and uncertainties. A linear relationship was assumed between flooded pixel area images derived from LiDAR datasets and water index rasters derived from Sentinel 2 images

Worldwide, billions of people rely on fresh groundwater reserves for their domestic, agricultural and industrial water use. Extreme droughts and excessive groundwater pumping put pressure on water authorities in maintaining sustainable water usage. High-resolution integrated models are valuable assets in supporting them. The Netherlands Hydrological Instrument (NHI) provides the Dutch water authorities

APEX (Agricultural Policy/Environmental eXtender) is an oft-used agroecosystem model but has limited use in groundwater-driven watersheds due to a simplistic representation of groundwater processes. This paper presents the linkage of APEX and the groundwater flow model MODFLOW into a single modeling code. The mapping of recharge, groundwater head, and groundwater-surface water interactions are handled

Integrated Assessment Models (IAMs) are important tools to analyse cross-sectoral interdependencies and the use of global resources. Most current tools are highly detailed and require expert knowledge and proprietary software to generate scenarios and analyse their insights. In this paper, the complementary Global Least-cost User-friendly CLEWs Open-Source Exploratory (GLUCOSE) model is presented as

In environmental participatory modeling (PM), both computer and non-computer-based modeling techniques are used to aid participatory problem description, solution, and decision-making actions in environmental contexts. Although many PM case studies have been published, few efforts have sought to systematically describe and understand dominant PM processes or establish best practices for PM. As a first

Modelling river physical processes is of critical importance for flood protection, river management and restoration of riverine environments. Developments in algorithms and computational power have led to a wider spread of river simulation tools. However, the use of two-dimensional models can still be hindered by complexity in the setup and the high computational costs. Here we present the freeware

To facilitate understanding and decision making in the food-energy-water (FEW) nexus context, we develop an integrated technology-environment-economics model (ITEEM) at a watershed scale. ITEEM is built as an integration of various models, including models for grain processing, drinking water treatment, and wastewater treatment (technology); a watershed model for hydrology, water quality, crop production

Common methods for spatial distribution, such as hydrologic response units, are subjective, time-consuming, and fail to capture the full range of basin attributes. Recent advances in statistical-learning techniques allow for new approaches to this problem. We propose the use of Gaussian Mixture Models (GMMs) for spatial distribution of hydrologic models. GMMs objectively select the set of modeling

To aid decision making about environmental systems under deep uncertainty, robustness metrics are commonly used to represent system performance over a number of scenarios. However, there are many robustness metrics and many ways of generating scenarios, making it difficult to know which to choose in order to quantify system robustness and to make robust decisions. To address this shortcoming, we introduce

Ocean waves are widely estimated using physics-based computational models, which predict how energy is transferred from the wind, dissipated, and transferred spatially across the ocean. Machine learning methods offer an opportunity to predict these data with significantly reduced data input and computational power. This paper describes a novel surrogate model developed using the random forest method

Ensemble-based Data Assimilation (EDA) has been effectively applied to estimate model parameters through inverse modeling in subsurface flow and transport problems. To facilitate the management of EDA workflow and lower the barriers for adopting EDA-based parameter estimation in subsurface science, we develop a software framework linking the Data Assimilation Research Testbed (DART) with a massively

Process-based crop models are popular tools to analyze and simulate the response of agricultural systems to weather, agronomic, or genetic factors. They are often developed in modeling platforms to ensure their future extension and to couple different crop models with a soil model and a crop management event scheduler. The intercomparison and improvement of crop simulation models is difficult due to

Skillful hydrological prediction is highly valuable for optimizing water resources operation and planning. This study proposes a hybrid Bayesian vine copula (BVC) model for daily and monthly water level prediction. To achieve this goal, we first build a conditional vine copula (VC) prediction framework by utilizing the flexible vine copula as the baseline technique. Then, a Bayesian-based model averaging

Since the 2010 Deepwater Horizon (DWH) oil spill, the Gulf of Mexico Research Initiative (GOMRI) has studied the oil spill from the perspectives of ocean environment, ecosystems, socioeconomics and human health. As GOMRI sunsets in its tenth year after the DWH oil spill, synthesis efforts recently took place to assess the accomplishments of the program. In this paper, we report on DWH modeling as part

The complexity associated with water quality models (WQMs) has increased owing to the introduction of numerous physical and biological mechanisms in the models. Sensitivity analysis (SA) is conducted to identify influential parameters in these mechanisms. However, enormous computational power and time are required to obtain numerical solutions from thousands of model simulations. Therefore, a cloud-based

Climate change adaptation requires understanding of complex social ecological systems (SESs). One source of uncertainty in complex SESs is ambiguity, defined as the range and variety of existing perceptions in and of an SES, which are considered equally valid, resulting in a lack of a unique or single system understanding. Current modelling practices that acknowledge the presence of ambiguity in SESs

Wildfire behavior predictions typically suffer from significant uncertainty. However, wildfire modeling uncertainties remain largely unquantified in the literature, mainly due to computing constraints. New multifidelity techniques provide a promising opportunity to overcome these limitations. Therefore, this paper explores the applicability of multifidelity approaches to wildland fire spread prediction

A new open source multi-GPU 2D flood model called TRITON is presented in this work. The model solves the 2D shallow water equations with source terms using a time-explicit first order upwind scheme based on an Augmented Roe's solver that incorporates a careful estimation of bed strengths and a local implicit formulation of friction terms. The scheme is demonstrated to be first order accurate, robust

This study employs a distributed eco-hydrological-landslide model, the tRIBS-VEGGIE-Landslide, to evaluate the influence of terrain resolution on the hydro-geomorphological processes involved in slope stability analysis. The model implements a Triangulated Irregular Network (TIN) to describe the topography starting from a grid-DEM. Five grid-DEM resolutions of the case study basin, i.e., 10, 20, 30

Watershed system models are important tools for understanding complex watershed systems and integrated river basin management. However, there are very few models that can represent the coevolution of water, ecology and socioeconomic system at the river basin scale. In this study, a watershed system model was developed using the case of the Heihe River basin (HRB), a typical endorheic river basin in

This study introduces the California Food-Energy-Water System (CALFEWS) simulation model to describe the integrated, multi-sector dynamics that emerge from the coordinated management of surface and groundwater supplies throughout California's Central Valley. The CALFEWS simulation framework links the operation of state-wide, interbasin transfer projects (i.e., State Water Project, Central Valley Project)

This study develops a novel reservoir regulation routine, incorporated into a continental-scale hydrologic model in the Nelson, Churchill, Yenisey, Ob, and Lena basins. This regulation routine is integrated into the Hydrological Predictions for the Environment (HYPE) hydrologic model, used for continental-scale applications. Applying this daily timestep regulation routine at 19 reservoirs in the Arctic

Monitoring and understanding the dissolved organic matter (DOM) cycle in a drinking water reservoir is crucial to water authorities, since most water treatment practices aim to remove DOM to prevent the formation of potentially harmful disinfection by-products. A vertical profiling system (VPS) installed in reservoirs can continuously detect the fluorescent DOM (fDOM) and determine the fDOM transport

Reservoir control policies provide a flexible option to adapt to the uncertain hydrologic impacts of climate change. This challenge requires robust policies capable of navigating scenarios that are wetter, drier, or more variable than anticipated. While a number of prior studies have trained robust policies using large scenario ensembles, there remains a need to understand how the properties of training

Water resources are under growing pressure globally. Sustainable water management requires an understanding of how much water is available, where it is stored and used, and how future developments may impact availability. Understanding this requires investment in data, models, and experts. These investments are labour intensive, with considerable effort spent in three main areas: collecting and transforming

A new method named cluster-based GSA is proposed to enhance the sensitivity analysis of models with temporal or spatial outputs. It is based on a tight coupling between Global Sensitivity Analysis (GSA) and clustering procedures. Clustering is introduced to characterize the different behaviors of the model outputs by grouping them into clusters. The cluster-based GSA produces variance-based indices

Data-driven modelling with machine learning (ML) is already being used for predictions in environmental science. However, it is less clear to what extent data-driven models that successfully predict a phenomenon are representationally accurate and thus increase our understanding of the phenomenon. Besides empirical accuracy, we propose three criteria to indirectly assess the relationships learned by

Under the impacts of hydrologic extremes, there is a growing need for integrated frameworks for flood inundation mapping. In this study, we introduce a framework: the Coupled Routing and Excess Storage for inundation MApping and Prediction (CREST-iMAP). It utilizes a highly parallelized and fully integrated hydrologic-hydraulic model and aims to improve flood prediction. To highlight the advantages

Spatially distributed process models represent soil-water-plant-nutrient interactions within a watershed. We investigated spatial patterns of water balance components and nitrate transport simulated using the fully distributed AgES (Agricultural Ecosystems Services) and the semi-distributed SWAT (Soil and Water Assessment Tool, 2012) models in the Big Dry Creek Watershed, a mixed agricultural and suburban

As coastal circulation models have evolved to predict storm-induced flooding, they must include progressively more overland regions that are normally dry, to where now it is possible for more than half of the domain to be needed in none or only some of the computations. While this evolution has improved real-time forecasting and long-term mitigation of coastal flooding, it poses a problem for parallelization

Daily real-time nowcasts (current conditions) and 2-day forecasts of environmental conditions in the Chesapeake Bay have been continuously available for 4 years. The forecasts use a 3-D hydrodynamic-biogeochemical model with 1–2 km resolution and 3-D output every 6 h that includes salinity, water temperature, pH, aragonite saturation state, alkalinity, dissolved oxygen, and hypoxic volume. Visualizations

Dam construction in developing nations is on the rise. Monitoring these dams is essential to understanding downstream hydrologic impacts and for better planning and management of water resources. Satellite observations and advancements in information technology now present a unique opportunity to overcome the traditional limitations of reservoir monitoring. In this study, a global reservoir monitoring

To mitigate the unavailability of accurate river channel terrain data for flood modeling, this work develops a new approach to construct channel terrain based on available global coarse digital elevation models (DEMs). Compared to other established methods, the approach constructs the river surface using the sparse cross-section data from available coarse DEMs and not measurements. For a 134 km river

CASSIE is an open-source web tool for automatic shoreline mapping and analysis using satellite imagery (www.cassiengine.com). This tool was built in JavaScript, using Google Earth Engine (GEE) API, and can be applied to any coastal region on Earth where a boundary between water and land exists. CASSIE use the Landsat and Sentinel-2 satellite imagery, freely available from GEE, and implements an automatic

Quantarctica (https://www.npolar.no/quantarctica) is a geospatial data package, analysis environment, and visualization platform for the Antarctic Continent, Southern Ocean (>40oS), and sub-Antarctic islands. Quantarctica works with the free, cross-platform Geographical Information System (GIS) software QGIS and can run without an Internet connection, making it a viable tool for fieldwork in remote

Computer models of environmental systems routinely inform decision making for water resource management. In this context, quantifying uncertainty in the important simulated outputs, and reducing uncertainty through assimilating historic system-state observations, is as important as the numerical model. However, implementing high-dimensional and stochastic workflows are challenging, often requiring

Distributed hydrological models predict the spatial variability in processes that govern observed mass and energy fluxes. A challenge associated with the use of these models is the computational burden associated with representing the Earth’s (sub)surface via millions of computational elements. This burden is exacerbated as more complex process representations are included because their parameterizations

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

Relying on solar energy alone to power water monitoring stations limits the ability to monitor water resources in low-light locations, such as for streams with dense tree canopy cover. To address this limitation, water monitoring stations could have a supplementary micro hydro-turbine to harvest kinetic energy from streamflow. To explore this possibility, we simulated the energy harvesting potential

The existing bias correction (BC) methods used in impact studies are routinely based on a fixed model structure and often ignore the nature and magnitude of biases, and their variations into the future. As a calibrated model is applied to bias correct the future time series, there is no feedback mechanism to assess the impact of model complexity on the model performance in the future. In this paper