In hydrologic research, there is a need to manage, archive, and publish data in a discoverable way to increase data reuse, transparency, and reproducibility. Multidimensional space-time data are commonly used in hydrologic research, and systems are needed for sharing and exchanging such data. Simply exchanging files may result in loss of metadata information and can be challenging when files are large

This study proposes to jointly use six Budyko framework-based methods, hydrological simulation, sensitivity indicator method, and empirical statistics to build a comprehensive assessment framework for quantifying climatic and anthropogenic contributions to streamflow changes. To evaluate its effectiveness, we conducted a case study in a typical northern semi-arid basin named Guanting in China. On average

Lake Erie has experienced a resurgence of CHABs since the early 2000's dominated by Microcystis aeruginosa, which produce toxins known as microcystins. We develop an approach to predict the spatially- and temporally-resolved probability of exceeding a public health advisory (PHA) level (6 μg/L) of microcystins in the western basin of Lake Erie that would be suitable for use in a forecast system, consisting

Spatial discretization is the cornerstone of all spatially-distributed numerical simulations including watershed hydrology. Traditional square grid spatial discretization has several limitations including inability to represent adjacency uniformly. In this study, we developed a watershed delineation model (HexWatershed) based on the hexagon grid spatial discretization. We applied this model to two

The Piscine Stream Community Estimation System (PiSCES) provides users with a hypothesized fish community for any stream reach in the conterminous United States using information obtained from Nature Serve, the US Geological Survey (USGS), StreamCat, and the Peterson Field Guide to Freshwater Fishes of North America for over 1000 native and non-native freshwater fish species. PiSCES can filter HUC8-based

Google Earth Engine (GEE) is an ideal platform for large-scale geospatial agricultural and environmental modeling based on its diverse geospatial datasets, easy-to-use APIs, rich reusable library, and high-performance computational capacity. However, using GEE to prepare agricultural land use data for geospatial agricultural and environment modeling requires not only the programming skills of GEE APIs

The HIDROPIXEL high-resolution pixel-to-pixel distributed hydrological simulation approach was proposed based on digital elevation model (DEM) and adaptations of the curve number and unit hydrograph methods. This paper presents a reference evaluation of HIDROPIXEL, showing how sensible the model is to spatial resolution and assessing the effect of different DEM data sources. A small peri-urban Brazilian

The U.S. Pacific Northwest has a history of frequent and occasionally deadly landslides caused by various factors. Using a multivariate, machine-learning approach, we combined a Pacific Northwest Landslide Inventory with a 36-year gridded hydrologic dataset from the National Climate Assessment – Land Data Assimilation System to produce a landslide hazard indicator (LHI) on a daily 0.125-degree grid

In recent years, a family of approaches has emerged for supporting decision-making on complex environmental problems characterised by deep uncertainties and competing priorities. Many-Objective Robust Decision Making (MORDM), Multi-scenario MORDM and. Many-Objective Robust Optimization (MORO) differ with respect to the degree to which robustness is considered during the search for promising candidate

Land cover maps are key elements for understanding global climate and land use. They are often created by automatically classifying satellite imagery. However, inconsistencies in classification may be introduced inadvertently. Experts can reconcile classification discrepancies by viewing satellite and high-resolution images taken on the ground. We present and evaluate a framework to filter relevant

Recent advances in cloud-computing and social-networking are influencing how we communicate professionally, work collaboratively, and approach data-science tasks. Here we show how the groundwater modeling field is well positioned to benefit from these advances. We present a case study detailing a vertically-integrated, collaborative modeling framework jointly developed by participants at the American

Human exposure to threats from natural hazards is generally estimated using a static approach with the fixed number of people located in hazard-prone zones; however, in reality this number varies due to population mobility. This study proposes a human–hazard coupled City Model (HazardCM) for accurately calculating city spatiotemporal dynamic exposure to different hazards. It includes four components:

Although the Dublin principles of Integrated Water Resource Management (IWRM) are well-established, the third principle on gender is commonly missing in practice. We use gender mainstreaming to identify examples where gender-specific perspectives might influence water resource management modelling choices. We show how gender considerations could lead to different choices in all modelling phases, providing

Modeling the diameter distribution of trees in forest stands is a frequent application in ecology that supports key biologically and economically relevant management decisions. The choice of model used to represent the diameter distribution and how to estimate its parameters has received much attention in the forestry literature; however, accessible software that facilitates comprehensive comparison

Simulating karst spring discharge and land use change impacts in a recharge area is strategic for water resource management in many countries worldwide. We introduce a user-friendly modeling environment by integrating the recently proposed LuKARS (Land use change modeling in KARSt systems) model into the FREEWAT (FREE and Open Source Software Tools for WATer Resource Management) framework. LuKARS is

Deep uncertainty in future climate, socio-economic and technological conditions poses a great challenge to medium-long term decision making. Recently, several approaches have been proposed to identify solutions that are robust with respect to a large ensemble of deeply uncertain future scenarios. In this paper, we introduce ROSS (Robust Optimal Scenario Selection), a novel algorithm that uses an active

Since launching five years ago, SeaSketch has been used in approximately 30 large-scale projects and over 200 educational programs and research-based projects distributed over 40 countries. The study presented in this article engages SeaSketch developers, project administrators, and users through semi-structured interviews and surveys meant to inform designers on how to better incorporate the participatory

Optical remotely sensed data are typically discontinuous, with missing values due to cloud cover. Consequently, gap-filling solutions are needed for accurate crop phenology characterization. The here presented Decomposition and Analysis of Time Series software (DATimeS) expands established time series interpolation methods with a diversity of advanced machine learning fitting algorithms (e.g., Gaussian

The PAWN index is gaining traction among the modelling community as a sensitivity measure. However, the robustness to its design parameters has not yet been scrutinized: the size (N) and sampling (ε) of the model output, the number of conditioning intervals (n) or the summary statistic (θ). Here we fill this gap by running a sensitivity analysis of a PAWN-based sensitivity analysis. We compare the

Use of physically-motivated numerical models like groundwater flow-and-transport models for probabilistic impact assessments and optimization under uncertainty (OUU) typically incurs such a computational burdensome that these tools cannot be used during decision making. The computational challenges associated with these models can be addressed through emulation. In the land-use/water-quality context

This work presents a software platform to compute depth-integrated non-hydrostatic coastal and open channel flows. The software is based on the Vertically-Averaged and Moment (VAM) equations model. The VAM model uses the weighted residual method to account for the non-hydrostaticity and non-uniformity of flow. After presenting the governing equations, numerical scheme and structure of the graphical

Environmental models have a key role to play in understanding complex environmental phenomena in space and time. Although their inherent uncertainty and non-identifiability is being increasingly recognized with the development and application of various methods, a more holistic analysis of all sources of model uncertainty is warranted. This paper addresses sources of uncertainty from various types

A flexible Integrated Bayesian Multi-model Uncertainty Estimation Framework (IBMUEF) is presented to simultaneously quantify conceptual model structure, input and parameter uncertainty of a groundwater flow model. In this fully Bayesian framework, the DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm with a novel likelihood function is combined with Bayesian Model Averaging (BMA). Four alternative

This paper presents a version of SWAT + that uses MODFLOW to simulate groundwater flow and groundwater-surface interactions within a watershed system. The modeling code is applied to the 470 km2 Middle Bosque River Watershed (Texas, USA) to demonstrate accuracy and differences with SWAT+. The model is tested against field-measured stream discharge, groundwater levels and fluctuation patterns, runoff

Macroscale hydrological and land surface models increasingly rely on novel streamflow routing algorithms that explicitly account for the presence of engineered water storages, which largely affect river basin dynamics. In this short communication, we contribute to this growing field by presenting VIC-ResOpt, a software package for the representation and optimization of water reservoirs in the routing

Modelling coupled social and biophysical dynamics of water resources systems is increasingly important due to population growth and changes in the water cycle driven by climate change. Models that explicitly represent these coupled dynamics are challenging to design and implement, particularly given the complicated and cross-scale nature of water governance. Agent-based models (ABMs) can capture human

In the light of uncertainties, high initial costs, and temporal managerial flexibility, the real options approach has gained interest as a valuation tool for different types of natural resources management problems. Yet, neither real options valuation method excels under consideration of variability of resource endowments, returns-to-scale and predefined sizes of options. We fill the methodological

Bayesian Networks (BNs) are commonly used to model socio-ecological systems, as their graphical structure supports participatory modelling, they can integrate quantitative data and qualitative knowledge, and account for uncertainty. Although the spatial and temporal dimensions are important in socio-ecological systems, there is a lack of openly available and easy-to-use tools to run BNs with spatial

Streamflow forecasts are essential for water resources management. Although there are many methods for forecasting streamflow, real-time forecasts remain challenging. This study evaluates streamflow forecasts using a process-based model (Soil and Water Assessment Tool-Variable Source Area model-SWAT-VSA), a stochastic model (Artificial Neural Network -ANN), an Auto-Regressive Moving-Average (ARMA)

This paper introduces a polynomial feedforward neural network based on Chebyshev polynomials able to effectively model non-linear and highly complex environmental data. The data sets were cautiously selected from the fields of biology, ecology, climate, and environmental management, and economics as to represent a scientifically meaningful and consistent corpus of disparate domains of intensive focus

Model structure uncertainty is seldom calculated because of the difficulty and time required to perform such analyses. Here we explore how a coastal model using the Monod versus Droop formulations and a 6 km × 6 km versus 2 km × 2 km computational grid size predict primary production and hypoxic area in the Gulf of Mexico. Results from these models were compared to each other and to observations, and

Coastal shallow basins are often heavily anthropized and greatly exposed to environmental risk areas, thus requiring strict monitoring action by local authorities and stakeholders. Preventive measures against environmental degradation and early warning to hazards have been proved to benefit from the combined use of numerical models and field measurements. The present work sets out to show the potential

Variability (and extremes) in streamflow, wind speeds, temperatures, and solar irradiance influence supply and demand for electricity. However, previous research falls short in addressing the risks that joint uncertainties in these processes pose in power systems and wholesale electricity markets. Limiting challenges have included the large areal extents of power systems, high temporal resolutions

Forest Inventory and Analysis (FIA) is a US Department of Agriculture Forest Service program that aims to monitor changes in forests across the US. FIA hosts one of the largest ecological datasets in the world, though its complexity limits access for many potential users. rFIA is an R package designed to simplify the estimation of forest attributes using data collected by the FIA Program. Specifically

Automated and accurate wetland identification algorithms are increasingly important for wetland conservation and environmental planning. Deep learning for wetland identification is an emerging field that shows promise for advancing these efforts. Deep learning is unique to traditional machine learning techniques for its ability to consider the spatial context of object characteristics within a landscape

Systems models to improve ecosystems often identify flows to meet minimum instream flow requirements or minimize deviations from a predefined flow regime. Here, we present a new systems optimization model that determines when, where, and how much to allocate scarce water, financial resources, and revegetation efforts to improve aquatic, floodplain, and wetland habitat areas and quality. This optimization

Crop water production functions (quantifying crop yield as a function of irrigation rate) can help in the design of management systems that reduce the water footprint. We examined the role of parameter uncertainties in characterizing production functions using the DayCent agroecosystem model. A global sensitivity analysis was conducted to identify the model parameters associated with the greatest uncertainties

We present Hydroman, a flexible spatially explicit model coupling human and hydrological processes to explore shallow water tables and land cover interactions in flat agricultural landscapes, modeled after the Argentine Pampas. With fewer parameters, Hydroman aligned well with established hydrological models, and was validated with observed water table patterns and crop yield data. Simulations with

The aim of this paper is to create a user-friendly computational tool for analysis of wildland fire behavior and its effect on urban and other structures. A physics-based multiphase Computational Fluid Dynamics (CFD) model of wildfire initiation and spread has been developed and incorporated into the multi-purpose CFD software, PHOENICS. It accounts for all the important physicochemical processes:

Common software for land data assimilation is urgently needed to implement a wide variety of assimilation applications; however, a fast, easy-to-use, and multidisciplinary application-oriented assimilation platform has not been achieved. Therefore, we developed Common software for Nonlinear and non-Gaussian Land Data Assimilation (ComDA). ComDA integrates multiple algorithms (including diverse Kalman

We compiled information on 26 numerical models, which consider the terrestrial phosphorus (P) cycle and compared them regarding process description, model structure and applicability to different ecosystems and scales. We address the differences in their hydrological components and between their soil P routines, the implementation of a preferential flow component in soils, as well as whether the model

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

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

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

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

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

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

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

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

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