Coastal water supply infrastructure systems are exposed to saltwater intrusion exacerbated by sea-level rise stressors. To enable assessing the long-term resilience of these systems to the impact of sea-level rise, this study developed a novel hazards-humans-infrastructure nexus framework that enables the integrated modeling of stochastic processes of hazard scenarios, decision-theoretic elements of adaptation planning processes of utility agencies, and dynamic processes of water supply infrastructure performance. Using the proposed framework and data collected from South Miami-Dade service area, a multi-agent simulation model was created to conduct exploratory assessments of the long-term resilience of water supply infrastructure under various sea-level rise scenarios and adaptation approaches. The results showed the capability of the proposed model for scenario landscape generation to discover robust adaptation pathways for enhanced infrastructure resilience under uncertainty. The analysis results could provide actionable scientific information to water infrastructure managers to improve their adaptation planning and investment decision-making processes.

The coupling of a geographic information system (GIS) and nonpoint source (NPS) models has significantly promoted NPS modeling and result visualization. This study reported an approach using R to develop a prototype system that fully integrated a GIS and NPS models and to build an interactive web interface. A case study in a semiarid northern China subwatershed showed that the proposed method was feasible, flexible and effective. Our experiences demonstrated that developing a fully-coupled GIS-NPS model system in R could simplify NPS modeling across computation platforms, promote modeling efficiency, implement dynamic simulations, enhance model inputs/outputs display and provide readily interactivity. We envision that the experiences could provide a promising option for NPS modeling.

A new generalised water resource network modelling Python library, Pywr, is presented. The model uses a low-level interface to existing linear programming solvers for fast optimisation-driven simulation of complex water systems. The library uses an object based system for users to provide input data and record simulation outputs. A novel multi-scenario simulation method provides an almost 4-fold improvement in model run-times and supports calculating robustness metrics across scenarios. A flexible interface to specify multi-objective optimisation formulations as part of a model’s input file is included. These features enable analysts to apply advanced water planning approaches, such as robust decision making and robust optimisation, to real systems. The library is available under the GPLv3 open source licence, includes several examples and a regression test suite.

Multi-spectral spaceborne sensors with different spatial resolutions produce Earth observation (EO) time series (TS) with global coverage. The interactive visualization and interpretation of TS is essential to better understand changes in land-use and land-cover and to extract reference information for model calibration and validation. However, available software tools are often limited to specific sensors or optimized for application-specific visualizations. To overcome these limitations, we developed the EO Time Series Viewer, a free and open source QGIS plugin for user-friendly visualization, interpretation and labeling of multi-sensor TS data. The EO Time Series Viewer (i) combines advantages of spatial, spectral and temporal data visualization concepts that are so far not available in a single tool, (ii) provides maximum flexibility in terms of supported data formats, (iii) minimizes the user-interactions required to load and visualize multi-sensor TS data and (iv) speeds-up labeling of TS data based on enhanced GIS vector tools and formats.

Smart meters installed at the user-level provide a new data source for managing water infrastructure. This research explores the use of machine learning methods, including Random Forests (RFs), Artificial Neural Networks (ANNs), and Support Vector Regression (SVR) to forecast hourly water demand at 90 accounts using smart-metered data. Demands are predicted using lagged demand, seasonality, weather, and household characteristics. Time-series clustering is applied to delineate data based on the time of day and days of the week, which improves model performance. Two modeling approaches are compared. Individual models are developed separately for each meter, and a Group model is trained using a data set of multiple meters. Individual models predict demands at meters in the original data set with lower error than Group models, while the Group model predicts demands at new meters with lower error than Individual models. Results demonstrate that RF and ANN perform better than SVR across all scenarios.

As the resolution of DEMs is becoming higher, both the efficiency and accuracy of channel head recognition are important for drainage network extraction. In this paper, a D8-compatible high-efficient channel head recognition method is proposed. For each potential channel, this method first calculates a geomorphologic parameter series along the flow path, and then determines the channel head by detecting the change point in the series. Instead of directly using one threshold value for a whole region that is still commonly used in D8-based methods, the proposed method recognizes channel heads by their local geomorphology one by one. The proposed method is applied with high resolution DEMs for different terrains, and the identified channel heads and extracted drainage networks show good agreement with observations. In comparison with a popular software with state-of-the-art channel head recognition method, the proposed one shows similar accuracy but much higher computational efficiency.

Physically based distributed hydrologic models require geospatial and time-series data that take considerable time and effort in processing them into model inputs. Tools that automate and speed up input processing facilitate the application of these models. In this study, we developed a set of web-based data services called HydroDS to provide hydrologic data processing ‘software as a service.’ HydroDS provides functions for processing watershed, terrain, canopy, climate, and soil data. The services are accessed through a Python client library that facilitates developing simple but effective data processing workflows with Python. Evaluations of HydroDS by setting up the Utah Energy Balance and TOPNET models for multiple headwater watersheds in the Colorado River basin show that HydroDS reduces the input preparation time compared to manual processing. It also removes the requirements for software installation and maintenance by the user, and the Python workflows enhance reproducibility of hydrologic data processing and tracking of provenance.

This paper describes and illustrates new functionality for fitting spatially varying coefficients models in the spBayes (version 0.4–2) R package. The new spSVC function uses a computationally efficient Markov chain Monte Carlo algorithm and extends current spBayes functions, that fit only space-varying intercept regression models, to fit independent or multivariate Gaussian process random effects for any set of columns in the regression design matrix. Newly added OpenMP parallelization options for spSVC are discussed and illustrated, as well as helper functions for joint and point-wise prediction and model fit diagnostics. The utility of the proposed models is illustrated using a PM10 analysis over central Europe.

This paper presents a MATLAB framework for forecasting optimal flow releases in a multi-storage system for flood control. This framework combines four widely-used models intended for (1) performing hydrologic analysis for a watershed and for level-pool routing in the storage systems, (2) simulating river inundation, (3) solving the optimization problem of determining hourly optimal flow releases in a multi-storage system, and (4) data management and plotting. The integration of all software is performed in MATLAB, which is a state-of-the-art and an easy-to-use environment for integrating computation, visualization, and programming. This paper focuses on (1) presenting the MATLAB scripts for interfacing the aforementioned four software, (2) describing the rationale for the objective function of the optimization, and (3) demonstrating the practical use of the MATLAB framework by applying it to the operation of a hypothetical eight-pond system in the Cypress Creek watershed in Houston, Texas. The results of the aforementioned application show that this framework could help in mitigating flooding.

Ecological niche models (ENMs) is a popular method in ecology, mostly due to its broad applicability and the fact that required data is simple and easily accessible from digital databases. Nevertheless, there is an underlying methodological complexity, often overlooked by many scientists that rely on ENMs to achieve other objectives. We present here the package ENMTML, an Open Source R package. The main purpose of this package is to assemble all this methodological complexity spread over several papers and bring it into the spotlight in a simple way for people not used to the details of ENMs. The package contains several alternatives to different methodological steps, e.g., pseudo-absence allocation and accessible area delimitation, formulated within a single function, to make it accessible for people not used to the programming environment.

The Agricultural Water Use (AG) Package was developed for simulating demand-driven and supply-constrained agricultural water use in MODFLOW and GSFLOW models. The AG Package uses pre-existing hydrologic simulation provided by MODFLOW and GSFLOW. Three options are available for simulating water use for agriculture: (1) user-specified demands, (2) demands determined by a user-specified irrigation trigger value that is compared to the ratio of the simulated actual to potential evapotranspiration (ET), and (3) demands determined by minimizing the difference between potential and actual ET. The latter two approaches use energy and soil-water balance to determine crop-water demands. Irrigation withdrawals are diverted into canals and routed to fields using the MODFLOW SFR Package, or irrigation water is provided/supplemented by groundwater. Combined with MODFLOW or GSFLOW, the AG Package can simulate dynamic water use by agriculture in developed basins while providing flexibility to represent a range of irrigation practices.

Estimating the impact of environmental processes on vertical reef development in geological time is a very challenging task. pyReef-Core is a deterministic carbonate stratigraphic forward model designed to simulate the key biological and environmental processes that determine vertical reef accretion and assemblage changes in fossil reef drill cores. We present a Bayesian framework called Bayesreef for the estimation and uncertainty quantification of parameters in pyReef-Core that represent environmental conditions affecting the growth of coral assemblages on geological timescales. We demonstrate the existence of multimodal posterior distributions and investigate the challenges of sampling using Markov chain Monte-Carlo (MCMC) methods, which includes parallel tempering MCMC. We use synthetic reef-core to investigate fundamental issues and then apply the methodology to a selected reef-core from the Great Barrier Reef in Australia. The results show that Bayesreef accurately estimates and provides uncertainty quantification of the selected parameters that represent environment and ecological conditions in pyReef-Core. Bayesreef provides insights into the complex posterior distributions of parameters in pyReef-Core, which provides the groundwork for future research in this area.

Regression models (e.g. Land-Use Regression) are currently the most popular way to estimate retrospective exposures to air pollution. However, these models lack important features of atmospheric dispersion. We developed a new non-linear air quality regression model which is based on the physical grounds of the well-established and commonly applied Gaussian dispersion model. This was achieved through parametrization of the basic Gaussian model (including its standard deviations) and optimizing the parameters to provide a least-squares fit with ambient measurements at each individual time-point. The new model (GaussODM) outperformed both a simpler regression model and a benchmark interpolation model in predicting spatial ambient nitrogen oxides (NOx) concentrations. The GaussODM enables a deeper understanding of the relationship between air pollution and adverse health effects. This is partly because it is better adapted at incorporating meteorological data and the effects of elevated emissions compared with previously available air pollution regression models.

The Utah Energy Balance (UEB) model supports gridded simulation of snow processes over a watershed. To enhance computational efficiency, we developed two parallel versions of the model, one using the Message Passing Interface (MPI) and the other using NVIDIA's CUDA code on Graphics Processing Unit (GPU). Evaluation of the speed-up and efficiency of the MPI version shows that the effect of input/output (IO) operations on the parallel model performance increases as the number of processor cores increases. As a result, although the computation kernel scales well with the number of cores, the efficiency of the parallel code as a whole degrades. The performance improves when the number of IO operations is reduced by reading/writing larger data arrays. The CUDA GPU implementation was done without major refactoring of the original UEB code, and tests demonstrated that satisfactory performance could be obtained without a major re-work of the existing UEB code.

Coupled fire-atmospheric modeling tools are increasingly used to understand the complex and dynamic behavior of wildland fires. Multiple research tools linking combustion to fluid flow use Navier-Stokes numerical solutions coupled to a thermodynamic model to understand fire-atmospheric feedbacks, but these computational fluid dynamics approaches require high-performance computing resources. We present a new simulation tool called QUIC-Fire to rapidly solve these feedbacks by coupling the mature 3-D rapid wind solver QUIC-URB to a physics-based cellular automata fire spread model Fire-CA. QUIC-Fire uses 3-D fuels inputs similar to the CFD model FIRETEC, allowing this tool to simulate effects of fuel structure on local winds and fire behavior. Results comparing fire behavior metrics to the computational fluid dynamic model FIRETEC show strong agreement. QUIC-Fire is the first tool intended to provide an opportunity for prescribed fire planners to compare, evaluate and design burn plans, including complex ignition patterns and coupled fire atmospheric feedbacks.

Prediction in data-scarce regions is one of the challenging issues in environmental problems. In hydrology, this issue is commonly addressed by utilizing regression or similarity-based regionalization techniques. The core of similarity-based regionalization techniques is a physical/climatic similarity metric that is typically predetermined from the knowledge about the physics of the problem and study area. The purpose of this paper is to: (1) reduce the subjectivity that exists in the selection of the physical/climatic similarity metric by establishing a systematic approach, and (2) propose a generic similarity-based regionalization framework that estimates the parameters of environmental models in data-scarce regions. The efficacy of the proposed framework is evaluated for the regionalization of a statistical model that creates probabilistic floodplain maps in data-scarce regions. Results show a trained Support Vector Machine (SVM) with ten basin descriptors and accuracy of 86% is an appropriate physical/climatic similarity metric that creates reliable floodplain maps in the Arkansas-White-Red region.

Most of the commonly available sensitivity analysis methods cannot reliably compute the interaction effect. Even though the Sobol’ type methods that use Monte Carlo simulation can evaluate the interaction effect, the result is either inaccurate or requires an extraordinary number of model runs to obtain a reasonable estimate. In this study, we evaluate the sparse polynomial chaos (SPC) method as a reasonable way to estimate the interaction effect. This method is evaluated on two mathematical test functions (Ishigami and Sobol’ G) and two hydrologic models (HBV-SASK and SAC-SMA). Our results show the SPC method needs about a sample size of 30 to 70 times the number of dimensions of the parameter space to evaluate the interaction effects of hydrologic models. Our findings are significant for hydrologic simulation and model calibration, as we aim to improve the understanding of complex interactions among model components and to reduce model uncertainty.

The DynACof model was designed to model coffee agroforestry systems and study the trade-offs to e.g. optimize the system facing climate changes. The model simulates net primary productivity (NPP), growth, yield, mortality, energy and water balance of coffee agroforestry systems according to shade tree species and management. Several plot-scale ecosystem services are simulated by the model, such as production, canopy cooling effect, or potential C sequestration. DynACof uses metamodels derived from a detailed 3D process-based model (MAESPA) to account for complex spatial effects, while running fast. It also includes a coffee flower bud and fruit cohort module to better distribute fruit carbon demand over the year, a key feature to obtain a realistic competition between sinks. The model was parameterized and evaluated using a highly comprehensive database on a coffee agroforestry experimental site in Costa Rica. The fluxes simulated by the model were close to the measurements over a 5-year period (nRMSE = 26.27 for gross primary productivity; 28.22 for actual evapo-transpiration, 53.91 for sensible heat flux and 15.26 for net radiation), and DynACof satisfactorily simulated the yield, NPP, mortality and carbon stock for each coffee organ type over a 35-year rotation.

Rainfall-triggered shallow landslides are widespread natural hazards around the world, causing many damages to human lives and property. In this study, we focused on predicting landslides in a large region by coupling a 1 km-resolution hydrological model and a 90 m-resolution slope stability model, where a downscaling method for soil moisture via topographic wetness index was applied. The modeled hydrological processes show generally good agreements with the observed discharges: relative biases and correlation coefficients at three validation stations are all <20% and >0.60, respectively. The derived scaling law for soil moisture allows for near-conservative downscaling of the original 1-km soil moisture to 90-m resolution for slope stability assessment. For landslide prediction, the global accuracy and true positive rate are 97.2% and 66.9%, respectively. This study provides an effective and computationally efficient coupling method to predict landslides over large regions in which fine-scale topographical information is incorporated.

The coupled carbon (C) cycle across terrestrial and aquatic environments at the watershed scale has been identified as an important, but poorly constrained component of the global carbon budget. Here, we extended Soil and Water Assessment Tool (SWAT) with coupled riverine particulate organic carbon (POC) and dissolved organic carbon (DOC) modules (referred to as SWAT-C hereafter). Results show that SWAT-C reproduced daily POC and DOC fluxes well in two watersheds in the Northeastern United States. We found that SWAT-C tended to underestimate high flows and peak DOC and POC fluxes. Uncertainty analysis indicated flux uncertainties associated with POC and DOC simulations were larger than those for flow simulation. Sensitive parameters controlling POC and DOC biogeochemical processes were identified along with how these parameters influence mechanisms underlying C cycling. We anticipate that the tool developed and applied here will inform C related ecosystem services in watershed assessment and planning.

Karstification is considered as one of the most common reasons for interbasin groundwater flow (IGF). IGF in some karst areas could be significant such that it must be accounted for in hydrologic modelling. In this study, the Soil and Water Assessment Tool (SWAT) was modified to explicitly account for IGF in karst areas. The modified model uses two conceptual models to simulate hydrologic processes in karst and non-karst regions. The modified model was applied in the karst-dominated region in the southwest Harz Mountains, Germany. Multisite streamflow data and satellite-derived actual evapotranspiration (ETa) were used for model calibration. Results show that (1) the modified model can be satisfactorily calibrated and validated for streamflow and ETa (2) the model performance for ETa and streamflow at some gauging stations are highly correlated, and (3) the use of satellite-derived ETa does not affect the model performance.

Nitrate contamination in groundwater was evaluated using the concept of integrated aquifer assessment by combining groundwater characterization and risk analysis with tiered approaches for land and surface runoff contamination by soil chemicals and leaching of contamination to groundwater in the Upper White River Watershed (UWRW) in Indiana. Integrated aquifer vulnerability assessment was conducted using an integration of a distributed watershed model (Soil and Water Assessment Tool [SWAT]) and a machine learning technique (Geospatial-Artificial Neural Network [Geo-ANN]). The results indicate that integrated aquifer vulnerability assessment performed well based on the model performance (NSE/R2/PBIAS = 0.66/0.70/0.07). Thus, the overall assessment of aquifer vulnerability can be performed using the integrated aquifer vulnerability assessment technique provided in this study. Moreover, this approach provides an efficient guide for managing groundwater resources for policy makers and groundwater-related researchers.

Air pollution problems have a severe effect on the natural environment and public health. The application of machine learning to air pollutant data can result in a better understanding of environmental quality. Of these methods, the deep learning method has proven to be a very efficient and accurate method to forecast complex air quality data. This paper proposes a deep learning model based on an auto-encoder and bidirectional long short-term memory (Bi-LSTM) to forecast PM2.5 concentrations to reveal the correlation between PM2.5 and multiple climate variables. The model comprises several aspects, including data preprocessing, auto-encoder layer, and Bi-LSTM layer. The performance of the proposed model was verified based on a real-world air pollution dataset, and the results indicated this model can improve the prediction accuracy in an experimental scenario.

We present in this paper the development of a new, open-source MATLAB toolbox SRS-GDA that aims to provide random spatial sampling of grid-based hydro-climatic datasets for environmental change studies. This toolbox addresses the needs of quantifying how hydro-climatic responses, which are often driven by grid-based forcing datasets such as climate model projections, vary with location and scale. The toolbox can be used to carry out random spatial sampling of grid-based quantities with various constraints: shape, size, location, dominant orientation and resolution. A case study of a large dataset, the GEAR rainfall dataset is supplied to demonstrate the typical uses case of this toolbox. The provision of the toolbox for downloading together with the sample data are also presented.

We developed a new global model to predict biogeochemical cycling of mercury in the ocean. We describe and evaluate the model, and discuss mercury levels, distribution, and budgets based on a simulation with a total time span of 260 years. The model is based on a fully coupled atmosphere-ocean chemical transport model, and considers methylated mercury production in the water column, followed by biotransfer to lower-order marine organisms including spatial and temporal variations in partitioning properties. Model validation shows that we can simulate total dissolved mercury (HgT) concentrations in the surface ocean with model data differences at a maximum of one order of magnitude. The simulated oceanic HgT content is currently (2010) 1.6–16.9 times larger than previously modeled estimates. The estimated overall turnover time of oceanic HgT determined by our model is 320 years, which is shorter than suggested by previous modeling studies.

Geohazards, e.g. floods and landslides, are increasingly resulting in loss of life and economic damage. Process models can help the understanding of the frequency and occurrence of these hazards, reducing their impact. However, models require significant resources to develop and, ideally, should be made widely available. Further, integrated modelling demands that process models are discoverable, available and, ultimately, linkable. Metadata standards are defined for data, but the equivalent for process models are not yet available as an international standard or even as accepted best practice. Requirements have been gathered for model metadata using a questionnaire and a metadata schema has been developed. The resulting schema split model code (software) and model instance (application) given that model codes are used multiple times. International data metadata standards, e.g. ISO19115, have been used for the core of the standard. The schema has been implemented via the PURE portal (www.pureportal.org) which makes models available and forms the basis of an international standard for model metadata.

Coupled wave – 3D-hydrodynamics model runs are performed to investigate thermal discharge release to coastal areas by means of including nearshore effects of wave-current dynamics. The study area comprises the vicinity of a power plant at Cerano, in South Italy, where cooling industrial waters are released to the sea. The implemented model is calibrated by using temperature measurements and sensitivity analyses are carried out for various relevant drivers and input parameters. Afterwards, the effect of thermal discharge is investigated through distinct hypothetical scenarios for a combination of metocean conditions and operational features of the power plant (modifying water discharge and temperature at its outlet). The model results of this representative array of conditions are intercompared and evaluated on the basis of heat dispersion rate and areas of influence, providing with useful insights on the numerical simulation of the process and the potential effects for the specific coastal area.

Eliciting stakeholders' mental models is an important participatory modelling (PM) tool for building systems knowledge, a frequent challenge in natural resource management. Therefore, mental models constitute a valuable source of information, making it imperative to document them in detail, while preserving the integrity of stakeholders’ beliefs. We propose a methodology, the Rich Elicitation Approach (REA), which combines direct and indirect elicitation techniques to meet these goals. We describe the approach in the context of the effects of climate change on Baltic salmon. The REA produced holistic depictions of mental models, with more variables and causal relationships per diagram than direct elicitation alone, thus providing a solid knowledgebase on which to begin PM studies. The REA was well received by stakeholders, and fulfilled the substantive, normative, instrumental, and educational functions of PM. However, motivating stakeholders to confirm the accuracy of their models during the verification stage of the REA was challenging.

Uncertainty and sensitivity analysis (UA/SA) aid in assessing whether model complexity is warranted and under what conditions. To support these analyses software tools have been developed to provide UA/SA methods and approaches in an accessible manner. This paper applies a hybrid bibliometric approach using Web of Science data to identify software packages for UA/SA within the environmental sciences and general trends in environmental UA/SA. We find an increased interest in uncertainty management, particularly improving the reliability and effectiveness of UA/SA. Although available software is typically open-source and freely available, uptake of software tools is apparently slow or otherwise their use under-reported. We also see a prevalence for self-implemented UA/SA tools and general usability is an ongoing concern. An overview of available software is provided to aid modelers in choosing an appropriate software tool for their purposes. Code and representative data used for this analysis can be found at https://github.com/frog7/uasa-trends (10.5281/zenodo.3406946).

Hydrodynamic models are commonly used to understand flood risk and inform flood management decisions. However, their high computational cost can impose practical limits on real-time flood forecasting and uncertainty analysis which require fast modelling response or many model runs. Emulation models have the potential to reduce simulation times while still maintaining acceptable accuracy of the estimates. In this study, we propose an artificial neural networks (ANNs) based emulation modelling framework for flood inundation modelling. We investigate the suitability of ANNs as flood inundation models using a river segment in Queensland, Australia. Our results show that ANNs can model the time series behaviour of flood inundation and significantly reduce the simulation times required, which facilitates their use in applications requiring fast model response or a large number of model runs. Based the model development process and results, the major challenges and future research directions are discussed.

Quantifying Populus growth and the impacts on hydrology and water quality are important should it be widely planted. Soil and Water Assessment Tool (SWAT) tree growth algorithms and parameters for hybrid poplar in Midwestern US and cottonwood in Southern US were improved. Tree growth representation led to SWAT2012 code changes including a new leaf area parameter (TREED), new leaf area index algorithm, and leaf biomass algorithm. Simulated hybrid poplar LAI and aboveground woody biomass (PBIAS: 34 - 5%, NSE: 0.51–0.99, and R2: 0.72–0.99), and cottonwood aboveground biomass, runoff, sediment, and nitrate-N (PBIAS: 39 - 11%, NSE: 0.86–0.99, and R2: 0.93–0.99) from the modified SWAT were satisfactory. Improved algorithms, and parameter values and potential ranges for Populus were reasonable. Thus, the modified SWAT can be used for Populus biofeedstock production modeling and hydrologic and water quality response to its growth.

This paper presents SWAT-MODFLOW-RT3D, a model that couples the semi-distributed watershed model SWAT (Soil and Water Assessment Tool) with the groundwater flow model MODFLOW and the groundwater solute reactive transport model RT3D to simulate nitrate (NO3) fate and transport in a watershed system. The model is based on a recently developed SWAT-MODFLOW model, with RT3D now called as a subroutine within the MODFLOW code to provide a single, stand-alone model code. RT3D uses NO3 concentration of deep percolation water from SWAT and groundwater heads and flows from MODFLOW to simulate spatially-varying groundwater NO3 concentration and NO3 loading to/from streams, with the latter used by SWAT to route NO3 mass through the network. Model use is demonstrated through an application to the Sprague River Watershed (4100 km2) in Oregon. Other chemical species of interest can be included in the RT3D reaction module in applications of the model to other watersheds.

This study presents an integrated wetland-watershed modeling approach that capitalizes on inundation maps and geospatial data to improve spatial prediction of wetland inundation and assess its prediction uncertainty. We outline problems commonly arising from data preparation and parameterization used to simulate wetlands within a (semi-) distributed watershed model. We demonstrate how wetland inundation can be better captured by the wetland parameters developed from remotely sensed data. We then emphasize assessing model prediction using inundation maps derived from remotely sensed data. This integrated modeling approach is tested using the Soil and Water Assessment Tool (SWAT) with an improved riparian wetlands (RWs) extension, for an agricultural watershed in the Mid-Atlantic Coastal Plain, US. This study illustrates how spatially distributed information is necessary to predict inundation of wetlands and hydrologic function at the local landscape scale, where monitoring and conservation decision making take place.

The ability to simulate algal systems is critical for watershed-scale models. The objective of this study was to develop and evaluate a modified algal module that simulates the dynamics of three major algal groups (cyanobacteria, green algae, and diatoms) in a stream using variables available in the Soil and Water Assessment Tool. The proposed module 1) models the dynamics of the three algal groups while accounting for nutrients from algal die-off and 2) has temperature multipliers that consider the effect of temperature changes on kinetic rates. Data to test the module were collected from a forest-dominated watershed over two years. The modified module was efficient in predicting seasonal variations in algal group biomass and simulated the regeneration of nutrients after algal die-off. This module will be useful in predicting the dynamics of the three studied algal groups and evaluating the best management practices for algal blooms in watersheds.

The uptake of soil-applied pesticide by runoff was evaluated at the field scale (0.06–3.6 ha) by examining data from previous field studies and using two popular chemical-uptake models: a uniform mixing cell, in which runoff mixes uniformly to a set depth, and a non-uniform uptake model, where chemical uptake decreases exponentially with soil depth. Both uptake models were implemented through the field-scale Pesticide Root Zone Model (PRZM5), which includes an update for adjusting runoff uptake parameters. For the nonuniform model, we conceptualized runoff as a distribution of subsurface flow, which assisted in revealing that a large amount (81%) of runoff must bypass soil interaction to adequately simulate the field data, with the remaining runoff acting over about a 3-cm depth. Similarly, the mixing cell required a large amount (84%) of runoff bypass for proper simulation, with the remaining portion acting over a 0.75-cm depth.

Predicting flood risk is important for climate change adaptation. We quantify fluvial flood risk due to changing climate in a mixed-use watershed in Michigan, USA. We apply two approaches to project future climate change: an ensemble of temperature and precipitation perturbations on the historical record and an ensemble of global and regional climate models. We incorporate climate projections into the Soil and Water Assessment Tool (SWAT) to estimate daily streamflow, then quantify flood risk using indices related to flood probability, duration, magnitude, and frequency. Results indicate rising temperatures may counteract small increases in precipitation, likely due to increased evapotranspiration. Climate model data without bias correction used in SWAT produced reasonable future streamflow changes—similar to the perturbation of historical climate—therefore retaining the predicted change in the flood frequency distribution. This work advances the application of climate models in SWAT for flood risk evaluation at watershed scales.

An efficient model to store and retrieve watershed boundaries using graph-theoretic approaches is proposed. Our approach utilizes three algorithms and accepts as input standard Digital Elevation Model DEM derived flow direction grids (D8) or stream reach catchment boundaries and outputs derived watershed boundaries. This technique was applied to the ≈ 36,000 km2 Delaware River Watershed using the National Hydrography Plus Version 2 Dataset. This technique is showed to provide significant reductions in processing (98–99%), storage (81–82%), and retrieval complexity (95–96%) for polygons and (80–86%) for nodes when compared to existing and hypothetical data models that are used to create watershed boundaries.

Integrated model systems that simulate land use and land cover change (LUCC) and associated changes of ecosystem services (ES) are increasingly important for supporting policies and management decisions. However, only few model frameworks exist that consider policy options as drivers of future LUCC and ES simultaneously. We present a modeling procedure that predicts policy-induced LUCC and ES through a combination of cellular automata (CA) and the Soil and Water Assessment Tool (SWAT). We employed this procedure to assess the efficiency of alternative policy instruments including direct payments and command-and-control regulations in mountain watersheds of South Korea. Our approach successfully captures spatial patterns of LUCC, hydrological processes, and the associated gains and losses in ES as well as potential negative externalities (“leakage” effects). Our modeling procedure provides an informative and robust basis for the development of decision support systems for mountain watersheds, where water provision and regulation are of particular concern.

Representations of land use change in hydrologic impact assessment studies mostly rely on static land use information of two points in time, even though the availability of dense time series of land use data allows for the incorporation of dynamic land use changes. We compare the hydrologic impacts of dynamic land use change assessments to those of static land use change assessments. These effects are illustrated with the help of two land use scenarios applied to a hydrologic model of a rapidly developing meso-scale (2036 km2) catchment upstream of Pune, India. The results show that a linear dynamic land use development could be better approximated with the static approach than a non-linear development. An analysis of the impact of the frequency of land use updates indicates that the prediction of non-linear land use change impacts already improves substantially when frequent land use information every five to nine years is used.

We propose an adjustable modelling platform (KarstMod) for both the simulation of spring discharge at karst outlets and analysis of the hydrodynamics of the compartments considered in the model. KarstMod provides a modular, user-friendly modelling environment for educational, research and operational purposes. It can reproduce the structure of most conceptual lumped models of karst systems in the literature. The modularity of the platform allows to compare different hydrosystems within the same methodological approach. To promote good modelling practices, the platform provides a variety of graphs and tools that facilitate improved understanding and insights in the behaviour of the models, and that detect possible flaws in structure and parameterization. The model and users manual are freely downloadable from the SNO Karst website (www.sokarst.org).

Urban drainage is being affected by Climate Change, whose effects are likely to alter the intensity of rainfall events and result in variations in peak discharges and runoff volumes which stationary-based designs might not be capable of dealing with. Therefore, there is a need to have an accurate and reliable means to model the response of urban catchments under extreme precipitation events produced by Climate Change. This research aimed at optimizing the stormwater modelling of urban catchments using Design of Experiments (DOE), in order to identify the parameters that most influenced their discharge and simulate their response to severe storms events projected for Representative Concentration Pathways (RCPs) using a statistics-based Climate Change methodology. The application of this approach to an urban catchment located in Espoo (southern Finland) demonstrated its capability to optimize the calibration of stormwater simulations and provide robust models for the prediction of extreme precipitation under Climate Change.

The watershed health, vulnerability and recovery potential for determining protection and restoration priorities were assessed for the Han River basin (34,148 km2) in South Korea. Six components, including the watershed landscape, stream geomorphology, hydrology, water quality, aquatic habitat conditions, and biological conditions, were used to evaluate the watershed health (basin natural capacity). The Soil and Water Assessment Tool (SWAT) was utilized to examine the hydrology and water quality components in the study basin, which includes 237 sub-watersheds, three multipurpose dams, one hydroelectric dam, and three multifunction weirs. Four components, namely, the future impervious area, future climate conditions, future water use, and recent land cover changes were used to evaluate the watershed vulnerability to artificial stressors. We determined the protection and restoration priorities by evaluating and comparing the health and vulnerability of each sub-watershed. Sixty-seven sub-watersheds out of 237 were classified to have restoration priorities with high recovery potentials.

Earth observation (EO) systems play a significant role in environmental monitoring and the prediction of natural disasters. These systems generate a massive amount of heterogeneous data stored in different formats. The exploitation of this data is still limited while, in most cases, data are not linked, and sources are not interoperable. Hence, data cannot be exploited as an interoperable global knowledge graph to have more in-depth analyzes of environmental phenomena. Ontology, as a knowledge representation formalism, is a promising solution for the semantic interoperability between this data. In this work, we present a modular ontology for environmental monitoring developed based on an original agile methodology. The so-called MEMOn (Modular Environmental Monitoring Ontology) aims to support semantic interoperability, data integration, and linking of heterogeneous data collected through a variety of observation techniques and systems. We also present real use case studies to show the usefulness of the proposed ontology.

The hydrology of cold regions has been studied for decades with substantial progress in process understanding and prediction. Simultaneously, work on nutrient yields from agricultural land in cold regions has shown much slower progress. Advancement of nutrient modelling is constrained by well-documented issues of spatial heterogeneity, climate dependency, data limitations and over-parameterization of models, as well as challenges specific to cold regions due to the complex (and often unknown) behaviour of hydro-biogeochemical processes at temperatures close to and below freezing where a phase change occurs. This review is a critical discussion of these issues by taking a close look at the conceptual models and methods behind used catchment nutrient models. The impact of differences in model structure and the methods used for the prediction of hydrological processes, erosion and biogeochemical cycles are examined. The appropriateness of scale, scope, and complexity of models are discussed to propose future research directions.

Percentile-Range Indexed Mapping and Evaluation (PRIME) is a new tool to visualize and quantifying spatio-temporal dynamics of long-term dataset. PRIME is based on categorical partitioning of magnitude based on user defined indices assigned to ranges of percentile and mapping subsets of data at selected percentiles of long-term data. Indices can reflect attributes such as sea-level and water management decisions, tolerable range of water quality to a species, ecological risk, response to and recovery from disturbance, and values of ecosystem services. PRIME provides visual and robust datascapes and flexibility to evaluate variability in space and time for long-term environmental assessment. Here, we demonstrate the utility of PRIME using 16 years of hydrologic and salinity data from 14 sites representing three unique hydrological systems in the Florida Coastal Everglades (FCE). The resulting PRIME datascapes reveal interaction between water management and sea-level rise to drive salinity levels in the FCE.

Open-source research software is an important element of open science. While the number of software packages made available by researchers is increasing, there has been little analysis about their subsequent uptake. We collect basic information about prospective users when sharing our open-source sensitivity analysis toolbox. This enabled us to carry out a user survey to assess adoption success – beyond simply counting download numbers. Survey results confirm the key role of extensive documentation to ensure adoption, to enhance learning and to enable research implementation. We found that workflows are an effective tool to guide users to tailor methods to their problems. However, workflows also need to include guidance for interpretation of results, otherwise sophisticated functionalities are overlooked as their value is unclear. Developing effective documentation requires significant time investment but is essential if the ultimate aim of open research software is to promote the adoption of scientific methodologies and best practices.

Surface mining irreversibly disturbs the landscape. A first order priority is to establish an erosionally stable landscape. Soil and surface material particle size has a strong influence on soil erosion and a number of erosion models have been developed based on the relationship with particle size. Here we highlight the practicalities of assessing material particle size for a post-mining landscape. In particular, the CAESAR-Lisflood landscape evolution model (the focus here) requires a defined material particle size as input. A key feature which differentiates CAESAR-Lisflood (the model used here) is the ability to apply particle size data at the same resolution as the digital elevation model (DEM) representing the landform surface. Here we develop particle size distributions influence erosion for a potential post-mining landform. Field data from the site demonstrates that material particle size distribution changes little over a ten year period and yet a strong influence on erosion rates.

The global water gap, water demand minus water supply, is expected to increase through 2100, negatively affecting agriculture, industry and households. Adaptation measures are necessary, but projections on their effectiveness and costs are currently unavailable. Here, we present an adaptation evaluation framework aimed at closing the water gap, which is applied offline at the global scale, and made available for regional decision making as a web service. It includes climate change and socioeconomic scenarios over the 21st century as drivers for global projections of water supply and demand. The transient water gap was calculated for 1604 water provinces globally and we determined the water gap reduction that could be achieved by three increasingly involved sets of adaptation measures. The median annual adaptation costs amount to 1.4–1.6% of the GDP per affected water province. The interactive web-based simulation allows users to include information that is not available at the global scale.

The Denitrification Decomposition model (DNDC) has known limitations for simulating soil hydrology which can strongly influence biogeochemical processes. For this study, DNDC's soil hydrological framework was enhanced by including a new sub-model for mechanistic tile drainage, improved water flux, root growth dynamics, and a deeper and heterogeneous soil profile. Comparisons were then conducted against the Root Zone Water Quality Model (RZWQM2), using measurements of soil water storage, runoff, and drainage in eastern Canada and the US Midwest. Simulation of soil water storage (DNDC 0.81 ≤ d ≤ 0.90; RZWQM2 0.76 ≤ d ≤ 0.84), daily water flow (DNDC 0.76 ≤ d ≤ 0.88; RZWQM2 0.77 ≤ d ≤ 0.90) and nitrogen loading to tile drains were improved post-development. DNDC was able to capture the observed differences in water and N losses between conventional drainage and controlled drainage management with sub-irrigation. The enhancements to DNDC's hydrological framework should enable the development of improved biogeochemical processes.

Numerous organisations collect data in the Great Barrier Reef (GBR), but they are rarely analysed together due to different program objectives, methods, and data quality. We developed a weighted spatio-temporal Bayesian model and used it to integrate image-based hard-coral data collected by professional and citizen scientists, who captured and/or classified underwater images. We used the model to predict coral cover across the GBR with estimates of uncertainty; thus filling gaps in space and time where no data exist. Additional data increased the model's predictive ability by 43%, but did not affect model inferences about pressures (e.g. bleaching and cyclone damage). Thus, effective integration of professional and high-volume citizen data could enhance the capacity and cost-efficiency of monitoring programs. This general approach is equally viable for other variables collected in the marine environment or other ecosystems; opening up new opportunities to integrate data and provide pathways for community engagement/stewardship.

Nonpoint source (NPS) pollution is a common problem faced in many places around the world. On-site evaluation of NPS pollution is helpful in deciding how serious the pollution is and where a controlling practice can potentially be implemented with the best effectiveness. In this study, a web GIS interface for the Agricultural Policy Environmental eXtender (APEX) model named GeoAPEXOL was developed. The interface serves as a tool to automatically setup the APEX model for a watershed or field and conduct a rapid evaluation of the NPS pollution. A background database including DEM, landuse, soil, climate, and agricultural management was prepared for automating the process of model setup. One important feature of the interface is that NPS pollution can be evaluated for a field boundary considering contribution from upstream areas, which may be located outside the field boundary and is generally ignored by traditional NPS evaluation methods at the field scale.

Input data acquisition and preprocessing is time-consuming and difficult to handle and can have major implications on environmental modeling results. US EPA's Hydrological Micro Services Precipitation Comparison and Analysis Tool (HMS-PCAT) provides a publicly available tool to accomplish this critical task. We present HMS-PCAT's software design and its use in gathering, preprocessing, and evaluating precipitation data through web services. This tool simplifies catchment and point-based data retrieval by automating temporal and spatial aggregations. In a demonstration of the tool, four gridded precipitation datasets (NLDAS, GLDAS, DAYMET, PRISM) and one set of gauge data (NCEI) were retrieved for 17 regions in the United States and evaluated on 1) how well each dataset captured extreme events and 2) how datasets varied by region. HMS-PCAT facilitates data visualizations, comparisons, and statistics by showing the variability between datasets and allows users to explore the data when selecting precipitation datasets for an environmental modeling application.