Adapting water resources systems to climate change requires identifying hydroclimatic signals that reliably indicate long-term transitions to vulnerable system states. While recent studies have classified the conditions under which vulnerability occurs (i.e., scenario discovery), there remains an opportunity to extend such methods into a dynamic planning context to design and assess early warning signals

Modelling climate is complex due to multi-scale interactions and strong nonlinearities. However, climate signals are typically quasi-periodical and are likely to depend on exogenous-variables. Motivated by this insight, we propose a strategy to circumvent modelling complexity based on the following ideas. 1) The observed signals can be decomposed into non-stationary trends and quasi-periodicities through

Large-scale computational investigations of groundwater levels are proposed to accelerate science delivery through a workflow spanning database assembly, statistics, and information synthesis and packaging. A water-availability study of the Mississippi River alluvial plain, and particularly the Mississippi River Valley alluvial aquifer (MRVA), is ongoing. Software (visGWDBmrva) has been released as

Interbasin Water Transfer (IWT) is often a complex decision-making process that depends on factors ranging from hydro-meteorological conditions to socio-economic pressures. Hydrologic modelling is particularly challenging under these circumstances, requiring accurate quantitative information which may not always be available. This study proposes a methodological framework to simulate IWT flow contributions

Decision makers need an accurate understanding of aquifer storage trends to effectively manage groundwater resources. Groundwater is difficult to monitor and quantify since the data collected from monitoring wells are often available only at irregular and infrequent intervals. We present an open-source web application (app) to visualize groundwater data over time and automatically calculate changes

The SWAT model is a well-documented hydrologic model. However, some studies report that the existing SWAT auto-irrigation methods are unable to represent actual irrigation management, particularly in intensively irrigated regions. In the U.S. Great Plains, the SWAT model does not reproduce the management allowed depletion (MAD) irrigation scheduling commonly used by researchers and producers. To this

Wave attenuation is a key process that impacts activities at the coastal land margin and is an ecosystem service provided by many natural landscapes. Traditional modeling tools for wind wave attenuation require advanced expertise to apply. We present an alternative, GIS-based option for estimating wave attenuation, the Wave Attenuation Toolbox (WATTE). The outputs are a map of wave height transmission

Real-time flood forecasting computational frameworks that can dynamically integrate oceanic, coastal and estuarine processes are becoming essential to provide accurate and timely information for emergency response and planning in largely populated estuaries during extreme events. This study presents a newly developed real-time total water flood guidance system that is fully automated based on the coupled

Operational ecological forecasting is an emerging field that leverages ecological models in a new, cross-disciplinary way – using a real-time or nearly real-time climate forecast to project near-term ecosystem states. These applications give decision-makers lead time to anticipate and manage state changes that degrade ecosystem functions or directly impact humans. The Everglades Forecasting model (EverForecast)

Many aspects of land-use management and policy making require information regarding how and where land cover and land-uses will change in the future. In this research, we propose a method for modeling and predicting developed land expansion using the idea of pixel-wise semantic segmentation through deep convolutional neural networks. This analysis is done on a watershed scale with a focus on where

Accurate streamflow forecasting has always been a challenge. Although there were many novel approaches using deep learning models, accuracy of these models is often limited to a short lead time. This study proposes a new deep recurrent neural network approach, Neural Runoff Model (NRM), which has been applied on 125 USGS streamflow gages in the State of Iowa for predicting the next 120 h. We use a

A novel ArcGIS toolbox that applies the Mapping Evapotranspiration with Internalized Calibration model was developed and tested in a semi-arid environment. The tool, named METRIC-GIS, facilitates the pre-processing operations and the automatic identification of potential calibration and pixels review. The energy balance components obtained from METRIC-GIS were contrasted with those from the original

There has been increasing interest in using automated bioacoustics analysis to monitor the environment. This involves using computational approaches to identify animals and other environmental phenomena from the sounds that they generate. The volume of data being recorded for bioacoustics analyses is increasing, as the scale of environmental surveys is increasing. This presents significant computational

Out of many approaches to landscape connectivity, we choose patch-mosaic, flow-neutral framework for modelling. Our new software system GraphScape transforms a vector mosaic map into landscape graph, finds shortest paths and combines them into minimum spanning tree. The result is an optimal network of connections for a given set of patches. To capture mobile agent properties, we use two types of resistance

The increasing availability of remotely sensed soil moisture data offers new opportunities for data-driven modelling approaches as alternatives for process-based modelling. This study presents the applicability of transfer function-noise (TFN) modelling for predicting unsaturated zone conditions. The TFN models are calibrated using SMAP L3 Enhanced surface soil moisture data. We found that soil moisture

The effect of changing environmental conditions on the operation of ecosystem-based pond aquaculture has been analyzed with long-term dynamic simulations. The applied non-conventional framework of Programmable Process Structures supports the implementation of the trans-disciplinary functionalities in a transparent structure. The developed model of medium complexity encompasses the interacting physical

This study aimed to develop an ecological habitat suitability model (EHSM) for Zacco platypus by integrating hydraulic (HHS) and physiologic (PHS) habitat suitability. Study sites (117 stations) were selected throughout the five major watersheds in South Korea, where hydraulic, water temperature, and fish survey data (2008–2015) were available. The HHS was determined using preference curves for water

Increasing volumes of data allow environmental scientists to use machine learning to construct data-driven models of phenomena. These models can provide decision-relevant predictions, but confident decision-making requires that the involved uncertainties are understood. We argue that existing frameworks for characterizing uncertainties are not appropriate for data-driven models because of their focus

The prediction and control of river sediment yield (SY) are critical but challenging tasks. Erosion and sediment transfer in river catchments are controlled by different processes, whose relative importance varies in space and time. We thus put forward that SY can be estimated more efficiently by using explicitly the information contained in the similarity within groups. To test this hypothesis, we

Despite the growing acknowledgment of reproducibility crisis in computational science, there is still a lack of clarity around what exactly constitutes a reproducible or replicable study in many computational fields, including environmental modelling. To this end, we put forth a taxonomy that defines an environmental modelling study as being either 1) repeatable, 2) runnable, 3) reproducible, or 4)

There are a many reasons for creating quantitative models of deforestation, supported by a variety of modelling techniques for doing so. We examine the suitability of four different modelling techniques for predicting deforestation; Bayesian networks (BNs), artificial neural networks (ANNs), Gaussian processes (GPs), and generalised linear mixed models (GLMMs). The analysis is provided in the context

Despite the plethora of methods available for uncertainty quantification, their use has been limited in the practice of water quality (WQ) modeling. In this paper, a decision support tool (DST) that yields a continuous time series of WQ loads from sparse data using streamflows as predictor variables is presented. The DST estimates uncertainty due to residual errors using a relevance vector machine

The Soil and Water Assessment Tool (SWAT) simplified the hydrological processes in large patches of paddy fields. Based on the original SWAT code, the main equations are modified and validated with the long-term field observations in freezing-thawing watershed. The original SWAT and SWAT with paddy module (SWAT-P) both perform well in the simulation of stream flow at the basin scale (NSE ≥ 0.7). SWAT-P

Evapotranspiration (ET) is a central flux in the hydrological cycle. Various approaches to compute ET via energy balance models exist, but their handling is often complex and challenging. We developed QWaterModel as an easy-to-use tool to make ET predictions available to broader audiences. QWaterModel is based on the DATTUTDUT energy balance model and uses land surface temperature maps as an input

Practitioners often employ diverse, though not always thoroughly validated, numerical models to directly or indirectly estimate wave overtopping (q) at sloping structures. These models, broadly classified as either phase-resolving or phase-averaged, each have strengths and limitations owing to the physical schematization of processes within them. Models which resolve the vertical flow structure or

Hydrological GIS preprocessing tools are used to spatially relate different datasets for producing topologically connected watersheds and deriving attributes for model parameterization. Obtaining such information requires disparate data-sources from various websites with lengthy processing requirements. This research describes the development of HMS-PrePro, an open-source GIS preprocessing tool that

Governments and social benefit organisations are expected to consider evidence in decision-making. In development and sustainability, evidence spans disciplines and methodological traditions and is often inconclusive. Graphical models are widely promoted to organize interdisciplinary evidence and improve decision-making by considering mediating variables. However, the reproducibility, objectivity and

Seagrass ecosystems are increasingly subjected to multiple interacting stressors, making the consequent trajectories difficult to predict. Here, we present a new process-based model of seagrass decline in response to cumulative light and temperature stress. The model is calibrated to laboratory datasets for Great Barrier Reef seagrasses using Bayesian inference. Our model, which is fit to both physiological

Web based applications, web services, and online data and model sharing technology are becoming increasingly available to support hydrologic research. This promises benefits in terms of collaboration, computer platform independence, and reproducibility of modeling workflows and results. In this research, we designed an approach that integrates hydrologic modeling web services with an online data sharing

Australian and Queensland Government's Reef 2050 Water Quality Improvement Plan has set targets for improving the water quality entering the Great Barrier Reef lagoon. Given the large public investment and the deficit of data linking on-farm land management to changes in environmental outcomes, there is a need for a robust and efficient methods of quantifying links between land management and water

Various methods have been developed for the calibration of cellular automata (CA), which can produce a plausible cell-by-cell fit between modeling results and observed land-use data. However, traditional cell-based CA models still fail to characterize the aggregate landscape patterns of multiple land-use changes. To address this problem, we introduced a landscape-driven multiple CA model that can consider

Recently, a stochastic data-driven framework was introduced for forecasting uncertain multiscale hydrological and water resources processes (e.g., streamflow, urban water demand (UWD)) that uses wavelet decomposition of input data to address multiscale change and stochastics to account for input variable selection, parameter, and model output uncertainty (Quilty et al., 2019). The former study considered

There is a growing need to address water pollution that demands advanced tools to predict the fate and transport of water quality constituents. Existing stream solute transport models use simple first-order kinetics to evaluate nutrient loss, however these ignore biochemical reactions and lack a user-friendly interface. To address this shortcoming, we integrated the One-dimensional Transport with Inflow

Mechanistic phytoplankton functional group (PFG) models are used to develop water quality targets designed to mitigate cyanobacteria blooms, but it remains unclear whether PFG models adequately simulate cyanobacteria dynamics as most are evaluated against observations of chlorophyll-a instead of PFG biomass. To address this challenge, we analyzed an application of CE-QUAL-ICM, a 3D mechanistic PFG

The Great Barrier Reef is a UNESCO World Heritage Area that has been assessed as having a very poor outlook and needing urgent intervention. The eReefs hydrodynamic-biogeochemical models are used to complement monitoring, facilitate data interpretation and support policy decisions. Management and policy for the Great Barrier Reef are politically contentious, so a high standard of model evaluation is

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