Modeling sustainability visions: A case study of multi-scale food systems in Southwestern Ontario - Halbe, Johannes; Adamowski, Jan (Journal of Environmental Management, 2019)

Empowering marginalized communities in water resources management: Addressing inequitable practices in Participatory Model Building - Butler, Cameron; Adamowski, Jan (2015)

Assessing the benefits of serious games to support sustainable decision-making for transboundary watershed governance - Furber, Alison; Medema, Wietske; Adamowski, Jan (Canadian Water Resources Journal / Revue canadienne des ressources hydriques, 2018)

Multi-objective decision-making for green infrastructure planning (LID-BMPs) in urban storm water management under uncertainty - Raei, Ehsan; Reza Alizadeh, Mohammad; Reza Nikoo, Mohammad; Adamowski, Jan (Journal of Hydrology, 2019)

Fuzzy-based conflict resolution management of groundwater in-situ bioremediation under hydrogeological uncertainty - Taravatrooy, Narges; Nikoo, Mohammad Reza; Adamowski, Jan Franklin; Khoramshokooh, Nafiseh (Journal of Hydrology, 2019)

An Entropy-Based Approach to Fuzzy Multi-objective Optimization of Reservoir Water Quality Monitoring Networks Considering Uncertainties - Pourshahabi, Shokoufeh; Nikoo, Mohammad Reza; Raei, Ehsan; Adamowski, Jan Franklin (Water Resources Management, 2018)

Spatio-temporal analysis of rainfall trends in Chhattisgarh State, Central India over the last 115 years - Nema, Manish K.; Khare, Deepak; Adamowski, Jan; Chandniha, Surendra K. (Journal of Water and Land Development, 2018)
Abstract A quantitative and qualitative understanding of the anticipated climate-change-driven multi-scale spatio-temporal shifts in precipitation and attendant river flows is crucial to the development of water resources management approaches capable of sustaining and even improving the ecological and socioeconomic viability of rain-fed agricultural regions. A set of homogeneity tests for change point detection, non-parametric trend tests, and the Sen’s slope estimator were applied to long-term gridded rainfall records of 27 newly formed districts in Chhattisgarh State, India. Illustrating the impacts of climate change, an analysis of spatial variability, multi-temporal (monthly, seasonal, annual) trends and inter-annual variations in rainfall over the last 115 years (1901–2015 mean 1360 mm·y −1 ) showed an overall decline in rainfall, with 1961 being a change point year (i.e., shift from rising to declining trend) for most districts in Chhattisgarh. Spatio-temporal variations in rainfall within the state of Chhattisgarh showed a coefficient of variation of 19.77%. Strong inter-annual and seasonal variability in regional rainfall were noted. These rainfall trend analyses may help predict future climate scenarios and thereby allow planning of effective and sustainable water resources management for the region.

Probabilistic Event Based Rainfall-Runoff Modeling Using Copula Functions - Abdollahi, Sajjad; Akhoond-Ali, Ali Mohammad; Mirabbasi, Rasoul; Adamowski, Jan Franklin (Water Resources Management, 2019)

Coupling the maximum overlap discrete wavelet transform and long short-term memory networks for irrigation flow forecasting - Mouatadid, Soukayna; Adamowski, Jan F.; Tiwari, Mukesh K.; Quilty, John M. (Agricultural Water Management, 2019)

The Potential of Serious Games to Solve Water Problems: Editorial to the Special Issue on Game-Based Approaches to Sustainable Water Governance - Medema, Wietske; Mayer, Igor; Adamowski, Jan; Wals, Arjen E.J.; Chew, Chengzi (Water, 2019)
In this editorial, the authors (and guest editors) introduce the Special Issue titled Understanding Game-based Approaches for Improving Sustainable Water Governance: The Potential of Serious Games to Solve Water Problems. The authors take another look at the twelve contributions, starting from the subtitle question: what is the potential? The authors summarize the insights and give directions for future research.

Regionalizing Flood Magnitudes using Landscape Structural Patterns of Catchments - Amiri, Bahman Jabbarian; Junfeng, Gao; Fohrer, Nicola; Mueller, Felix; Adamowski, Jan (Water Resources Management, 2018)

A comparative study of fuzzy logic-based models for groundwater quality evaluation based on irrigation indices - Vadiati, Meysam; Nalley, Deasy; Adamowski, Jan; Nakhaei, Mohammad; Asghari-Moghaddam, Asghar (Journal of Water and Land Development, 2019)
Abstract Groundwater quality modelling plays an important role in water resources management decision making processes. Accordingly, models must be developed to account for the uncertainty inherent in the modelling process, from the sample measurement stage through to the data interpretation stages. Artificial intelligence models, particularly fuzzy inference systems (FIS), have been shown to be effective in groundwater quality evaluation for complex aquifers. In the current study, fuzzy set theory is applied to groundwater-quality related decision-making in an agricultural production context; the Mamdani, Sugeno, and Larsen fuzzy logic-based models (MFL, SFL, and LFL, respectively) are used to develop a series of new, generalized, rule-based fuzzy models for water quality evaluation using widely accepted irrigation indices and hydrological data from the Sarab Plain, Iran. Rather than drawing upon physiochemical groundwater quality parameters, the present research employs widely accepted agricultural indices (e.g., irrigation criteria) when developing the MFL, SFL and LFL groundwater quality models. These newly-developed models, generated significantly more consistent results than the United States Soil Laboratory (USSL) diagram, addressed the inherent uncertainty in threshold data, and were effective in assessing groundwater quality for agricultural uses. The SFL model is recommended as it outperforms both MFL and LFL in terms of accuracy when assessing groundwater quality using irrigation indices.

Correcting Satellite Precipitation Data and Assimilating Satellite-Derived Soil Moisture Data to Generate Ensemble Hydrological Forecasts within the HBV Rainfall-Runoff Model - Ciupak, Maurycy; Ozga-Zielinski, Bogdan; Adamowski, Jan; Deo, Ravinesh C; Kochanek, Krzysztof (Water, 2019)
An implementation of bias correction and data assimilation using the ensemble Kalman filter (EnKF) as a procedure, dynamically coupled with the conceptual rainfall-runoff Hydrologiska Byråns Vattenbalansavdelning (HBV) model, was assessed for the hydrological modeling of seasonal hydrographs. The enhanced HBV model generated ensemble hydrographs and an average stream-flow simulation. The proposed approach was developed to examine the possibility of using data (e.g., precipitation and soil moisture) from the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility for Support to Operational Hydrology and Water Management (H-SAF), and to explore its usefulness in improving model updating and forecasting. Data from the Sola mountain catchment in southern Poland between 1 January 2008 and 31 July 2014 were used to calibrate the HBV model, while data from 1 August 2014 to 30 April 2015 were used for validation. A bias correction algorithm for a distribution-derived transformation method was developed by exploring generalized exponential (GE) theoretical distributions, along with gamma (GA) and Weibull (WE) distributions for the different data used in this study. When using the ensemble Kalman filter, the stochastically-generated ensemble of the model states generally induced bias in the estimation of non-linear hydrologic processes, thus influencing the accuracy of the Kalman analysis. In order to reduce the bias produced by the assimilation procedure, a post-processing bias correction (BC) procedure was coupled with the ensemble Kalman filter (EnKF), resulting in an ensemble Kalman filter with bias correction (EnKF-BC). The EnKF-BC, dynamically coupled with the HBV model for the assimilation of the satellite soil moisture observations, improved the accuracy of the simulated hydrographs significantly in the summer season, whereas, a positive effect from bias corrected (BC) satellite precipitation, as forcing data, was observed in the winter. Ensemble forecasts generated from the assimilation procedure are shown to be less uncertain. In future studies, the EnKF-BC algorithm proposed in the current study could be applied to a diverse array of practical forecasting problems (e.g., an operational assimilation of snowpack and snow water equivalent in forecasting models).

Serious games as a catalyst for boundary crossing, collaboration and knowledge co-creation in a watershed governance context - Jean, Steven; Medema, Wietske; Adamowski, Jan; Chew, Chengzi; Delaney, Patrick; Wals, Arjen (Journal of Environmental Management, 2018)

Multi-household grazing management pattern maintains better soil fertility - Cao, Jianjun; Xu, Xueyun; Deo, Ravinesh C.; Holden, Nicholas M.; Adamowski, Jan F.; Gong, Yifan; Feng, Qi; Yang, Shurong; Li, Mengtian; Zhou, Junju; Zhang, Jian; Liu, Minxia (Agronomy for Sustainable Development, 2018)

Flood Spatial Modeling in Northern Iran Using Remote Sensing and GIS: A Comparison between Evidential Belief Functions and Its Ensemble with a Multivariate Logistic Regression Model - Tien Bui, Duie; Khosravi, Khabat; Shahabi, Himan; Daggupati, Prasad; Adamowski, Jan F.; M.Melesse, Assefa; Thai Pham, Binh; Pourghasemi, Hamid Reza; Mahmoudi, Mehrnoosh; Bahrami, Sepideh; Pradhan, Biswajeet; Shirzadi, Ataollah; et al. (Remote Sensing, 2019)
Floods are some of the most dangerous and most frequent natural disasters occurring in the northern region of Iran. Flooding in this area frequently leads to major urban, financial, anthropogenic, and environmental impacts. Therefore, the development of flood susceptibility maps used to identify flood zones in the catchment is necessary for improved flood management and decision making. The main objective of this study was to evaluate the performance of an Evidential Belief Function (EBF) model, both as an individual model and in combination with Logistic Regression (LR) methods, in preparing flood susceptibility maps for the Haraz Catchment in the Mazandaran Province, Iran. The spatial database created consisted of a flood inventory, altitude, slope angle, plan curvature, Topographic Wetness Index (TWI), Stream Power Index (SPI), distance from river, rainfall, geology, land use, and Normalized Difference Vegetation Index (NDVI) for the region. After obtaining the required information from various sources, 151 of 211 recorded flooding points were used for model training and preparation of the flood susceptibility maps. For validation, the results of the models were compared to the 60 remaining flooding points. The Receiver Operating Characteristic (ROC) curve was drawn, and the Area Under the Curve (AUC) was calculated to obtain the accuracy of the flood susceptibility maps prepared through success rates (using training data) and prediction rates (using validation data). The AUC results indicated that the EBF, EBF from LR, EBF-LR (enter), and EBF-LR (stepwise) success rates were 94.61%, 67.94%, 86.45%, and 56.31%, respectively, and the prediction rates were 94.55%, 66.41%, 83.19%, and 52.98%, respectively. The results showed that the EBF model had the highest accuracy in predicting flood susceptibility within the catchment, in which 15% of the total areas were located in high and very high susceptibility classes, and 62% were located in low and very low susceptibility classes. These results can be used for the planning and management of areas vulnerable to floods in order to prevent flood-induced damage; the results may also be useful for natural disaster assessment.
Keywords: Site_internet_RIISQ