A5029640828- Hydrology and Watershed Management Studies
- Hydrological Forecasting Using AI
- Water resources management and optimization
- Water-Energy-Food Nexus Studies
- Flood Risk Assessment and Management
- Hydrology and Drought Analysis
- Plant Water Relations and Carbon Dynamics
- Soil and Unsaturated Flow
- Transboundary Water Resource Management
- Groundwater flow and contamination studies
- Climate variability and models
- Soil Moisture and Remote Sensing
- Energy and Environment Impacts
- Tree-ring climate responses
- Soil erosion and sediment transport
- Climate change and permafrost
- Irrigation Practices and Water Management
- Cryospheric studies and observations
- Neural Networks and Applications
- Environmental Impact and Sustainability
- Hydrology and Sediment Transport Processes
- Climate Change Policy and Economics
- Hydropower, Displacement, Environmental Impact
- Water Quality and Pollution Assessment
- Complex Systems and Time Series Analysis
University of Saskatchewan
2016-2025
Global Institute for Water Security
2015-2024
International Institute for Applied Systems Analysis
2023-2024
Charles River Laboratories (Netherlands)
2024
John Wiley & Sons (United States)
2016-2020
City University of New York
2014
Syncrude (Canada)
2006
University of Kentucky
2001-2002
ASCE Foundation
2000
Lakehead University
2000
Abstract. Recently, deep learning (DL) has emerged as a revolutionary and versatile tool transforming industry applications generating new improved capabilities for scientific discovery model building. The adoption of DL in hydrology so far been gradual, but the field is now ripe breakthroughs. This paper suggests that DL-based methods can open up complementary avenue toward knowledge hydrologic sciences. In avenue, machine-learning algorithms present competing hypotheses are consistent with...
Abstract Machine learning (ML) applications in Earth and environmental sciences (EES) have gained incredible momentum recent years. However, these ML largely evolved ‘isolation’ from the mechanistic, process‐based modelling (PBM) paradigms, which historically been cornerstone of scientific discovery policy support. In this perspective, we assert that cultural barriers between PBM communities limit potential ML, even its ‘hybridization’ with PBM, for EES applications. Fundamental, but often...
Abstract The notion of convergent and transdisciplinary integration, which is about braiding together different knowledge systems, becoming the mantra numerous initiatives aimed at tackling pressing water challenges. Yet, transition from rhetoric to actual implementation impeded by incongruence in semantics, methodologies, discourse among disciplinary scientists societal actors. Here, we embrace “integrated modeling”—both quantitatively qualitatively—as a vital exploratory instrument advance...
Abstract. A comprehensive data driven modeling experiment is presented in a two-part paper. In this first part, an extensive data-driven proposed. The most important concerns regarding the way (DDM) techniques and were handled, compared, evaluated, basis on which findings conclusions drawn are discussed. concise review of key articles that comparisons among various DDM presented. Six techniques, namely, neural networks, genetic programming, evolutionary polynomial regression, support vector...
Abstract. In this second part of the two-part paper, data driven modeling (DDM) experiment, presented and explained in first part, is implemented. Inputs for five case studies (half-hourly actual evapotranspiration, daily peat soil moisture, till two rainfall-runoff datasets) are identified, either based on previous or using mutual information content. Twelve groups (realizations) were randomly generated from each dataset by sampling without replacement original dataset. Neural networks...
"Panta Rhei – Everything Flows" is the science plan for International Association of Hydrological Sciences scientific decade 2013–2023. It founded on need improved understanding mutual, two-way interactions occurring at interface hydrology and society, their role in influencing future hydrologic system change. calls strategic research effort focused delivery coupled, socio-hydrologic models. In this paper we explore synthesize opportunities challenges that socio-hydrology presents...
Abstract This study investigates the capability of sequence-to-sequence machine learning (ML) architectures in an effort to develop streamflow forecasting tools for Canadian watersheds. Such are useful inform local and region-specific water management flood related activities. Two powerful deep-learning variants Recurrent Neural Network were investigated, namely standard attention-based encoder-decoder long short-term memory (LSTM) models. Both models forced with past hydro-meteorological...
Abstract Cold regions provide water resources for half the global population yet face rapid change. Their hydrology is dominated by snow, ice and frozen soils, climate warming having profound effects. Hydrological models have a key role in predicting changing but are challenged cold regions. Ground‐based data to quantify meteorological forcing constrain model parameterization limited, while hydrological processes complex, often controlled phase change energetics. River flows impacted poorly...
Abstract Evapotranspiration constitutes one of the major components hydrological cycle and hence its accurate estimation is vital importance to assess water availability requirements. This study explores utility genetic programming (GP) model evapotranspiration process. An important characteristic GP that both structure coefficients are simultaneously optimized. The method applied in modelling eddy-covariance (EC)-measured latent heat (LE) as a function net radiation (NR), ground temperature...
Key Points Climate change causes shifts in flow regime The can be reconstructed Canadian prairies is vulnerable to the
Huang, M., Barbour, S. L., Elshorbagy, A., Zettl, J. D. and Si, B. C. 2011. Infiltration drainage processes in multi-layered coarse soils. Can. Soil Sci. 91: 169–183. soils are complicated by contrasting hydraulic properties. The objective of this study was to evaluate the performances hysteretic non-hysteretic models simulate infiltration from three different natural soil profiles containing as many 20 texturally layers. Hydraulic properties were estimated textures using pedotransfer...
Abstract Natural proxy records of hydroclimatic behavior, such as tree ring chronologies, are a rich source information past climate‐driven nonstationarities in hydrologic variables. In this study, we investigate chronologies that demonstrate significant correlations with streamflows, the objective identifying spatiotemporal patterns and extents climate hydrology, which essentially representations “climate changes.” First second‐order particular interest study. As prerequisite, develop...
This paper introduces a modeling framework for the analysis of real and virtual water flows at national scale. The has two components: (1) model that simulates agricultural, industrial municipal uses, available land resources; (2) an international trade captures exports imports related to in crops animal products. National Water, Food & Trade (NWFT) is applied Egypt, water-poor country world's largest importer wheat. Egypt's food gaps country's (virtual water) are estimated over baseline...
We explore how to address the challenges of adaptation water resources systems under changing conditions by supporting flexible, resilient and low-regret solutions, coupled with on-going monitoring evaluation. This will require improved understanding linkages between biophysical social aspects in order better anticipate possible future co-evolution society. also present a call enhance dialogue foster actions governments, international scientific community, research funding agencies...
Adapting agriculture to climate change without deteriorating natural resources (e.g., water and energy) is critical sustainable development. In this paper, we first comprehensively evaluate six agricultural adaptations in response (2021–2050) through the lens of water-energy-food (WEF) nexus Saskatchewan, Canada, using a previously developed model—WEF-Sask. The involve agronomic measures (early planting date, reducing soil evaporation, irrigation expansion), genetic improvement (cultivars...
Spring runoff prediction in the Red River Valley, southern Manitoba, Canada, is an important issue because of devastating effect flood 1997 that area. Increasing accuracy process a practical necessity. This study looks at artificial neural networks (ANN) technique and compares it to linear nonlinear regression techniques. The advantages disadvantages three modeling techniques are discussed. To fill predictive evaluation gap left by mean squared error relative error, modified statistic,...
The mining of oil sands in northern Alberta leaves behind large open pits, tailings, and overburden piles which the surface subsurface hydrology has been completely disrupted. Extensive reclamation work is required to reconstruct entire landscape reestablish various elements hydrologic cycle. Syncrude Canada Ltd. established a series small instrumented watersheds reclaimed pile at Mildred Lake mine Alberta, Canada, test sustainability different strategies. purpose these field sites assess...
Abstract The complexity of the evapotranspiration process and its variability in time space have imposed some limitations on previously developed models. In this study, two data‐driven models: genetic programming (GP) artificial neural networks (ANNs), statistical regression models were compared for estimating hourly eddy covariance (EC)‐measured actual (AET) using meteorological variables. utility investigated was also with that HYDRUS‐1D model, which makes use conventional Penman–Monteith...