Wildfire occurrence is expected to increase in future climate and Land Use Land Cover (LULC) change scenarios, especially in vulnerable areas as the European Mediterranean Basin. In this study future probability of wildfire occurrence was estimated for a 20-year time period (2041–2060, centered on 2050) by applying a statistically-based regression model using LULC-derived contact areas with the forest

Approximately 15% of global land is currently in some state of protection. Recent conservation research suggests the need for a drastic increase of protected lands by 2050. In order to reach this target, an additional 35% of lands need to be conserved or restored in a cost-effective and time-efficient manner in order to support the resiliency of our planet and its climate. While many individuals and

porousMedia4Foam is a package for solving flow and transport in porous media using OpenFOAM® - a popular open-source numerical toolbox. We introduce and highlight the features of a new generation open-source hydro-geochemical module implemented within porousMedia4Foam, which relies on micro-continuum concept and which makes it possible to investigate hydro-geochemical processes occurring at multiple

This paper compares the well-known genetic algorithm (GA) and pattern search (PS) optimization methods for forecasting optimal flow releases in a multi-storage system for flood control. The simulation models used by the optimization models include (a) a batch of scripts for data acquisition of forecasted precipitation and their automated post-processing; (b) a hydrological model for rainfall-runoff

Modeling the interactions between water and energy is crucial to managing holistically these resources. Here, we simulate water allocations and energy dispatch in the metropolitan region of Phoenix, Arizona in 2008-2017 using the WEAP and LEAP models under different spatiotemporal resolutions and coupling configurations. We find that increasing the temporal resolution from annual to monthly allows

The paper analyses how the Water-Energy-Food Nexus is treated in Computable General Equilibrium (CGE) models, discussing their design, importance and possible ways of improvement. The analysis of their structure is critical for evaluating their potential efficiency in understanding the Nexus, which will be particularly effective for gauging the importance of the topic, the reciprocal dependency of

In this paper we present the interactive Reservoir Operations Notebooks and Software (iRONS) toolbox for reservoir modelling and optimisation. The toolbox is meant to serve the research and professional community in hydrology and water resource management and contribute to bridge the gaps between them. iRONS is composed of a package of Python core functions and a set of interactive Jupyter Notebooks

This study evaluates a numerical weather prediction model as a tool for wind resource assessment in complex terrain and how the simulations are affected by the selection of initial and boundary conditions and various grid resolutions. Two global reanalyses, ERA-Interim and ERA5, and four grid resolutions, 27 km, 9 km, 3 km and 1 km, have been considered. The simulations have been compared to hub-height

With the increasing complexity of hydrological systems, hydrological modeling by modelers from different research areas has been regarded as an effective way to solve complex hydrological issues. As the first step of hydrological modeling, conceptual modeling plays an important role in supporting modeling idea communication among interdisciplinary modelers. Currently, conceptual modeling methods usually

Calibration of groundwater models is frequently hampered by limited availability of hydrogeological data. Non-intrusive geophysical methods are increasingly used to provide higher spatio-temporal resolution datasets for identification of hydrological processes and estimation of hydraulic properties. In groundwater model calibration performed through joint or coupled hydrogeophysical inversion, the

Regional climate models are expected to exhibit improved skill at finer spatial resolutions due to improved representation of land surface heterogeneity. However, at spatial scales between 1 and 10 km (grey scales), these improvements are often illusive due to the competing benefits from spatial resolution and cumulus parameterization. This study provides insights into the impact of model resolution

Pooled testing (also known as group testing), where diagnostic tests are performed on pooled samples, has broad applications in the surveillance of diseases in animals and humans. An increasingly common use case is molecular xenomonitoring (MX), where surveillance of vector-borne diseases is conducted by capturing and testing large numbers of vectors (e.g. mosquitoes). The R package PoolTestR was developed

The study of land change within social-ecological systems (SES) is of great interest and increasingly makes use of remote sensing (RS) imagery to scale inferences up through space and time. However, spatial analysis using dense time series of RS data poses technical hurdles for non-expert users. To broaden the community of SES researchers using RS, we present a simple tool for mapping land change at

This paper presents an innovative deep learning (DL) framework to (a) automatically identify river geometry and flood extent, and (b) predict river flooding depth. To do that, U-Net, an advanced convolutional neural network (CNN), was modified and given the designation of U-NetRiver. With the modification, the model received an input composite image with two bands of ground elevation and flooding discharge

To support equitable planning, model-based analyses can be used to explore inequality patterns arising from different scenarios. Scenario discovery is increasingly used to extract insights from ensembles of simulation. Here, we apply two scenario discovery approaches for unraveling inequality patterns and their drivers, with an application to spatial inequality of farms profitability in the Vietnam

Visualization of the outputs from sensitivity analyses of spatially explicit models can potentially enhance the perception of the uncertainty in model outputs. However, spatial global sensitivity analysis (GSA) produces multiple output maps, twice as many as the number of input parameters under investigation, which can result in an overwhelming visual load to interpret. In this research, we tested

Urbanization typically leads to erosion and instability in rivers, and many management and restoration strategies have been developed to dampen the worst impacts. Stream power, defined as the rate of energy expenditure in a river, is a promising metric for analyzing cumulative effects. In this paper we describe a spatial decision support system called the Stream Power Index for Networks (SPIN) toolbox

This study compares the performances of two sampling-based strategies for the simultaneous estimation of the first- and total-order variance-based sensitivity indices (a.k.a. Sobol’ indices). The first strategy corresponds to the current approach employed by practitioners and recommended in the literature. The second one was only recently introduced by the first and last authors of the present article

In this study, we investigate the implementation of a Proper Orthogonal Decomposition (POD) Polynomial Chaos Expansion (PCE) POD-PCE surrogate model for the propagation and quantification of the uncertainty in hydraulic modelling. The considered model consists of a system of multilayer shallow water equations with a mass exchange between the layers and over stochastic beds. As a numerical solver, we

This paper presents a novel transversal, agnostic-infrastructure, and generic processing model to build environmental big data services in the cloud. Transversality is used for building processing structures (PS) by reusing/coupling multiple existent software for processing environmental monitoring, climate, and earth observation data, even in execution time, with datasets available in cloud-based

Increased model complexity and data quantities have raised the computing power requirement for efficient evapotranspiration (ET) estimation. A cloud-based service is presented to encapsulate and publish the ETWatch modeling algorithms as web application programming interfaces (APIs) in a consistent style to provide extensible model calculation service and data storage service in a cloud platform for

A recently introduced multiscale model for representing the effects of small-scale hyporheic-zone biogeochemical processes is extended from the reach scale to river network scales. The model uses advection-dispersion-reaction equations for the channel network and one-dimensional advection-reaction subgrid models for the hyporheic zone. We summarize implementation in the integrated surface/subsurface

Gaussian processes (GPs) provide statistically optimal predictions in the sense of unbiasedness and maximal precision. Although the modern implementation of GPs as a machine learning technique is more capable and flexible than Kriging, their employment in environmental science is less routine. Their flexibility and capability as a spatial data interpolation technique are demonstrated by applying them

Sub-daily rainfall observations are vital to help us understand, model and adapt to changing climate extremes. However, gauge records often have quality issues, for example due to equipment malfunctions and recording errors. This paper presents a new, open-source quality control algorithm (GSDR-QC) to identify these issues in hourly rainfall data, along with an application of the algorithm to the Global

Entropy measures are standard tools in environmental and ecological sciences to describe the heterogeneity of data. This paper reviews a selection of spatial entropy indices, some of which are very recent, suitable to deal with spatial data on variables presenting a finite number of categories. A special focus is given on biodiversity data, but methods can be applied to any other environmental phenomena

Accurate prediction of stable alluvial hydraulic geometry, in which erosion and sedimentation are in equilibrium, is one of the most difficult but critical topics in the field of river engineering. Data mining algorithms have been gaining more attention in this field due to their high performance and flexibility. However, an understanding of the potential for these algorithms to provide fast, cheap

Owing to the heterogeneity of geo-analysis models, many scholars and researchers have designed and promulgated standards in an attempt to address this. However, models based on different standards still cannot be shared and reused easily among different model frameworks. For example, models based on the OpenMI, BMI and OpenGMS-IS standards have heterogeneous development styles and formats, so they

Propensity score matching (PSM) and distance-adjusted PSM enable estimation of causal effects from observational data by selecting controls that are similar to treated observations in terms of environmental covariates and spatial locations. Quantifying effects of natural disturbances such as wildfires often encounters limited availability of observational data due to the scarcity of ecological events

Hydrological modelling can be a relevant tool to support sustainable planning decisions. However, urban hydrological models usually rely on simplified underground representation that may limit their applicability in shallow groundwater environments. This paper introduces a set of modules designed to describe the numerous interactions between subsurface processes and surface hydrology that may occur

The presence of organic pollutants e.g. contaminants of emerging concern (CECs) in aquatic environments has been of great concern worldwide for years. Increasing numbers of different types of CECs detected in the aquatic environment requires action to a detailed understanding of the distribution and transport of these compounds in groundwater, to guarantee safe drinking water for people in the future

The reliability and applicability of hydrological models within ecohydrological frameworks are major concerns. Two multi-objective model calibration strategies were formulated to achieve a balanced representation of ecologically relevant hydrologic indices. Two approaches were employed: 1) a performance-based method constraining the targeted hydrologic indices and 2) an unconstrained signature-based

Previous criticisms of knowledge-based fuzzy logic modelling have identified some of its limitations and revealed weaknesses regarding the development of fuzzy sets, the integration of expert knowledge, and the outcomes of different defuzzification processes. We show here how expert disagreement and fuzzy logic mechanisms associated with the rule development and combinations can positively or adversely

Collecting and managing high temporal resolution residential water use data is challenging due to cost and technical requirements associated with the volume and velocity of data collected. We developed an open-source, modular, generalized architecture called Cyberinfrastructure for Intelligent Water Supply (CIWS) to automate the process from data collection to analysis and presentation of high temporal

Geographic models are becoming key methods for geographic environment research. In addition to the progress in model sharing and reusing, geographic simulation (geo-simulation) knowledge sharing and application are increasingly attracting much attention. This study aims to provide both models and geo-simulation knowledge simultaneously to support Web-based simulation. Firstly, we propose a geo-simulation

Recent breakthroughs in artificial intelligence (AI), and particularly in deep learning (DL), have created tremendous excitement and opportunities in the earth and environmental sciences communities. To leverage these new ‘data-driven’ technologies, however, one needs to understand the fundamental concepts that give rise to DL and how they differ from ‘process-based’, mechanistic modelling. This paper

In this study, a spatiotemporal estimation method based on Funk singular value decomposition (F-SVD) that considers the spatiotemporal correlation of rainfall is proposed to improve estimations from gauge observations. Hourly rainfall data of several flood events are selected to verify the proposed method by comparing with Inverse Distance Weighting (IDW) and Ordinary Kriging (OK) in Hanjiang basin

Digital soil mapping (DSM) techniques have provided soil information that has revolutionized soil management across multiple spatial extents and scales. DSM practitioners have been increasingly reliant on machine-learning (ML) techniques; yet, methods to generate uncertainty maps from ML predictions are limited. To address this issue, this study integrates ML-based DSM with quantile regression (QR)

Soil temperature is a key driver of several physical, chemical, and biological processes. The Environmental Policy Integrated Climate (EPIC) is a comprehensive ecosystem model that simulates soil temperature dynamics using a cosine function approach driven by daily air temperature and average annual soil temperature at damping depth, which may erroneously predict lower soil temperatures in winter.

Parallel computing is a primary way to increase computing efficiency of grid-based distributed hydrological models. This study proposed an automatic partition-based parallel algorithm (APPA) to approach the theoretical maximum speedup ratio (TMSR). Through a combination of flexible partition for the domain decomposition and the load balance of parallel simulation, APPA optimizes the parallelization

Faced with persistent flooding and water quality challenges, water managers are now seeking to build digital twins of surface water systems that combine sensor data with online models to better understand and control system dynamics. Towards this goal, this study presents pipedream—an end-to-end simulation engine for real-time modeling and state estimation in natural/urban drainage networks. The engine

Deterministic models have emerged as a widely accepted tool for investigations in lentic aquatic ecosystems. In this study, an extensive literature review is provided by an inventory of papers published over the last six years (2015–2020) to explore the recent progress of deterministic models applied to lakes and reservoirs. The current trends include the application of well-established models across

This paper proposes a novel optimization approach for multi-scenario multi-objective robust decision making, as well as an alternative way for scenario discovery and identifying vulnerable scenarios even before any solution generation. To demonstrate and test the novel approach, we use the classic shallow lake problem. We compare the results obtained with the novel approach to those obtained with previously

Agricultural Best Management Practices (BMPs) are popular approaches to reduce nonpoint source (NPS) pollutant losses. Hydrologic models that can simulate impacts of BMPs at the field-scale can help guide the selection of BMPs. Furthermore, high-performance computing techniques have significant potential for scaling spatial simulations and reducing model runtimes. In this study, a parallel modeling

Drainage network extraction is essential for different research and applications. However, traditional methods have low efficiency, low accuracy for flat regions, and difficulties in detecting channel heads. Although deep learning techniques have been used to solve these problems, different challenges remain unsolved. Therefore, we introduced distributed representations of aspect features to facilitate

Models that predict wildfire rate of spread (ROS) play an important role in decision-making during firefighting operations, including fire crew placement and timing of community evacuations. Here, we use a large set of remotely sensed wildfire observations, and explanatory data (focusing on weather), to demonstrate a Bayesian probabilistic ROS modelling approach. Our approach has two major advantages:

This study introduces the maximal overlap discrete wavelet packet transform (MODWPT) for forecasting hydrological variables that exhibit change over multiple timescales (e.g., rainfall, streamflow). The advantage of the MODWPT over other recent wavelet decomposition methods (à trous algorithm (AT) and the maximal overlap discrete wavelet transform (MODWT)) is that it can extract finer scale information

High-fidelity hydrological models are increasingly built and used to investigate the effects of management activities and climate change on water availability and quality for large areas with datasets of high spatial and temporal resolution. However, these advantages come at the price of greater computational demand and run time. This becomes challenging when modeling routines involve iterative model

Waterlogging on croplands has been a known problem for a long time, leading to adverse social, physical, economic and environmental issues. To better solve the problem, the complicated plant-soil-water dynamics system needs to be better understood. The challenge is to simulate the interactions between the components in the systems. There are models that simulate plant-soil-water system but either run