Addressing the incorrect usage of wavelet-based hydrological and water resources forecasting models for real-world applications with best practices and a new forecasting framework - Quilty, John; Adamowski, Jan (Journal of Hydrology, 2018)

A stochastic wavelet-based data-driven framework for forecasting uncertain multiscale hydrological and water resources processes - Quilty, John; Adamowski, Jan (Environmental Modelling & Software, 2020)

Use of digital elevation models for flood susceptibility assessment via a hydrogeomorphic approach: A case study of the Basento River in Italy - Albano, Raffaele; Adamowski, Jan (Natural Hazards, 2025)
Abstract Floods are the most common and threatening natural risk for many countries in the world. Flood risk mapping is therefore of great importance for managing socio-economic and environmental impacts. Several researchers have proposed low-complexity and cost-effective flood mapping solutions that are useful for data scarce environments or at large-scale. Among these approaches, a line of recent research focuses on hydrogeomorphic methods that, due to digital elevation models (DEMs), exploit the causality between past flood events and the hydraulic geometry of floodplains. This study aims to compare the use of freely-available DEMs to support an advanced hydrogeomorphic method, Geomorphic Flood Index (GFI), to map flood-prone areas of the Basento River basin (Italy). The five selected DEMs are obtained from different sources, are characterized by different resolutions, spatial coverage, acquisition process, processing and validation, etc., and include: (i) HydroSHEDS v.1.1 (resolution 3 arc-seconds), hydrologically conditioned, derived primarily from STRM (NASA) and characterized by global coverage; (ii) ASTER GDEM v.3 with a res. of around 30 m (source: METI and NASA) and global coverage; (iii) EU-DEM v. 1.1 (res. 1 arc-second), Pan-European and combining SRTM and ASTER GDEM, customized to obtain a consistency with the EU-Hydro and screened to remove artefacts (source: Copernicus Land Monitoring Service); (iv) TinItaly DEM v. 1.1, (res. 10 m-cell size grid) and produced and distributed by INGV with coverage of the entire Italian territory; (v) Laser Scanner DEM with high resolution (5 m cell size grid) produced on the basis of Ground e Model Keypoint and available as part of the RSDI geoportal of the Basilicata Region with coverage at the regional administrative level. The effects of DEMs on the performance of the GFI calibration on the main reach of the Basento River, and its validation on one of its mountain tributaries (Gallitello Creek), were evaluated with widely accepted statistical metrics, i.e., the Area Under the Receiver Operating Characteristics (ROC) curve (AUC), Accuracy, Sensitivity and Specificity. Results confirmed the merits of the GFI in flood mapping using simple watershed characteristics and showed high Accuracy (AUC reached a value over 0.9 in all simulations) and low dependency on changes in the adopted DEMs and standard flood maps (1D and 2D hydraulic models or three return periods). The EU-DEM was identified as the most suitable data source for supporting GFI mapping with an AUC > 0.97 in the calibration phase for the main river reach. This may be due in part to its appropriate resolution for hydrological application but was also due to its customized pre-processing that supported an optimal description of the river network morphology. Indeed, EU-DEM obtained the highest performances (e.g., Accuracy around 98%) even in the validation phase where better results were expected from the high-resolution DEM (due to the very small size of Gallitello Creek cross-sections). For other DEMs, GFI generally showed an increase in metrics performance when, in the calibration phase, it neglected the floodplains of the river delta, where the standard flood map is produced using a 2D hydraulic model. However, if the DEMs were hydrologically conditioned with a relatively simple algorithm that forced the stream flow in the main river network, the GFI could be applied to the whole Basento watershed, including the delta, with a similar performance.
Keywords: Infolettre; Infolettre_Fevrier_2025

Participatory mapping of ecosystem services to understand stakeholders’ perceptions of the future of the Mactaquac Dam, Canada - Reilly, Kate; Adamowski, Jan; John, Kimberly (Ecosystem Services, 2018)

Integrated assessment of localized SSP–RCP narratives for climate change adaptation in coupled human-water systems - Alizadeh, Mohammad Reza; Adamowski, Jan; Inam, Azhar (Science of The Total Environment, 2022)

Hybrid artificial intelligence-time series models for monthly streamflow modeling - Mehdizadeh, Saeid; Fathian, Farshad; Adamowski, Jan F. (Applied Soft Computing, 2019)

Applying the Theory of Reliability to the Assessment of Hazard, Risk and Safety in a Hydrologic System: A Case Study in the Upper Sola River Catchment, Poland - Ozga-Zielinski, Bogdan; Adamowski, Jan; Ciupak, Maurycy (Water, 2018)

Comparison of wavelet-based hybrid models for the estimation of daily reference evapotranspiration in different climates - Araghi, Alireza; Adamowski, Jan; Martinez, Christopher J. (Journal of Water and Climate Change, 2020)
Abstract Reference evapotranspiration (ETo) is one of the most important factors in the hydrologic cycle and water balance studies. In this study, the performance of three simple and three wavelet hybrid models were compared to estimate ETo in three different climates in Iran, based on different combinations of input variables. It was found that the wavelet-artificial neural network was the best model, and multiple linear regression (MLR) was the worst model in most cases, although the performance of the models was related to the climate and the input variables used for modeling. Overall, it was found that all models had good accuracy in terms of estimating daily ETo. Also, it was found in this study that large numbers of decomposition levels via the wavelet transform had noticeable negative effects on the performance of the wavelet-based models, especially for the wavelet-adaptive network-based fuzzy inference system and wavelet-MLR, but in contrast, the type of db wavelet function did not have a detectable effect on the performance of the wavelet-based models.

The role of place meanings in opposition to water-related infrastructure projects: the case of the Mactaquac Dam, New Brunswick, Canada - Reilly, Kate H.; Adamowski, Jan F.; John, Kimberly (Ecology and Society, 2019)

A statistical approach for the estimation of water quality characteristics of ungauged streams/watersheds under stationary conditions - Khalil, Bahaa; Adamowski, Jan; Abdin, Alaa; Elsaadi, Aiman (Journal of Hydrology, 2019)

Insights from socio-hydrological modeling to design sustainable wastewater reuse strategies for agriculture at the watershed scale - Jeong, Hanseok; Bhattarai, Rabin; Adamowski, Jan; Yu, David J. (Agricultural Water Management, 2020)

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)

Regionalization of flood magnitudes using the ecological attributes of watersheds - Jabbarian Amiri, Bahman; Baheri, Bahareh; Fohrer, Nicola; Adamowski, Jan Franklin (Geocarto International, 2020)

Temporal and depth variation of water quality due to thermal stratification in Karkheh Reservoir, Iran - Noori, Roohollah; Berndtsson, Ronny; Franklin Adamowski, Jan; Rabiee Abyaneh, Maryam (Journal of Hydrology: Regional Studies, 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.

Using bootstrap ELM and LSSVM models to estimate river ice thickness in the Mackenzie River Basin in the Northwest Territories, Canada - Barzegar, Rahim; Ghasri, Mahsa; Qi, Zhiming; Quilty, John; Adamowski, Jan (Journal of Hydrology, 2019)

A participatory system dynamics modeling approach to facilitate collaborative flood risk management: A case study in the Bradano River (Italy) - Perrone, Antonio; Inam, Azhar; Albano, Raffaele; Adamowski, Jan; Sole, Aurelia (Journal of Hydrology, 2020)

Using the Mann–Kendall test and double mass curve method to explore stream flow changes in response to climate and human activities - Pirnia, Abdollah; Golshan, Mohammad; Darabi, Hamid; Adamowski, Jan; Rozbeh, Sajad (Journal of Water and Climate Change, 2019)
Abstract Analyzing intra-annual stream flow can reveal the main causes for runoff changes and the contributions of climate variability and human activities. For this purpose, the Mann–Kendall and cumulative rank difference (CRD) tests, and the double mass curve method, were applied to a time series of hydro-meteorological variables from 1971 to 2010 in the Tajan River basin in Iran. Results indicated that runoff changes in the wet and dry seasons after 1999 had significant respective decreasing and increasing trends, at the 0.01 confidence level, due to dam construction. In the pre-dam period (1991–1998), the results of the double mass curve method showed that climate variability and human activities contributed 57.76% and 42.24%, respectively, to the runoff decrease during the wet season. For the post-dam period (1999–2010), climate variability and anthropogenic activities contributed 24.68% and 75.32%, respectively, to the wet season runoff decrease of 116.55 mm. On the other hand, in the same period during the dry season, climate variability contributed −30.68% and human activities contributed 130.68% to the runoff increase of 41.45 mm. It is evident that runoff changes in both wet and dry seasons were mainly due to human activities associated with dam construction to meet water supply demands for agriculture.

Comparative assessment of time series and artificial intelligence models to estimate monthly streamflow: A local and external data analysis approach - Mehdizadeh, Saeid; Fathian, Farshad; Safari, Mir Jafar Sadegh; Adamowski, Jan F. (Journal of Hydrology, 2019)

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.

Flood scenario spatio-temporal mapping via hydrological and hydrodynamic modelling and a remote sensing dataset: A case study of the Basento river (Southern Italy) - Albano, Raffaele; Limongi, Carmine; Dal Sasso, Silvano Fortunato; Mancusi, Leonardo; Adamowski, Jan (International Journal of Disaster Risk Reduction, 2024)
Keywords: Infolettre; Infolettre_Septembre_2024

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).

Improving GALDIT-based groundwater vulnerability predictive mapping using coupled resampling algorithms and machine learning models - Barzegar, Rahim; Razzagh, Siamak; Quilty, John; Adamowski, Jan; Kheyrollah Pour, Homa; Booij, Martijn J. (Journal of Hydrology, 2021)

Interregional imbalance in the Yellow River Basin: Insights from flood-sediment transport, socioeconomic, and environmental subsystems - Hu, Hao; Tian, Guiliang; Adamowski, Jan F.; Albano, Raffaele; Liu, Yixue; Jing, Xiaodong; Zhao, Qiuya (Journal of Environmental Management, 2024)
Keywords: Infolettre; Infolettre_Decembre_2024

Contributions of climate, elevated atmospheric CO2 concentration and land surface changes to variation in water use efficiency in Northwest China - Yang, Linshan; Feng, Qi; Wen, Xiaohu; Barzegar, Rahim; Adamowski, Jan F; Zhu, Meng; Yin, Zhenliang (CATENA, 2022)

Large Scale Flood Risk Mapping in Data Scarce Environments: An Application for Romania - Albano, Raffaele; Samela, Caterina; Crăciun, Iulia; Manfreda, Salvatore; Adamowski, Jan; Sole, Aurelia; Sivertun, Åke; Ozunu, Alexandru (Water, 2020)
Large-scale flood risk assessment is essential in supporting national and global policies, emergency operations and land-use management. The present study proposes a cost-efficient method for the large-scale mapping of direct economic flood damage in data-scarce environments. The proposed framework consists of three main stages: (i) deriving a water depth map through a geomorphic method based on a supervised linear binary classification; (ii) generating an exposure land-use map developed from multi-spectral Landsat 8 satellite images using a machine-learning classification algorithm; and (iii) performing a flood damage assessment using a GIS tool, based on the vulnerability (depth–damage) curves method. The proposed integrated method was applied over the entire country of Romania (including minor order basins) for a 100-year return time at 30-m resolution. The results showed how the description of flood risk may especially benefit from the ability of the proposed cost-efficient model to carry out large-scale analyses in data-scarce environments. This approach may help in performing and updating risk assessments and management, taking into account the temporal and spatial changes in hazard, exposure, and vulnerability.

Soil Erosion in the United States. Present and Future (2020-2050) - Shojaeezadeh, Shahab Aldin; Al-Wardy, Malik; Nikoo, Mohammad Reza; Mooselu, Mehrdad Ghorbani; Alizadeh, Mohammad Reza; Adamowski, Jan Franklin; Moradkhani, Hamid; Alamdari, Nasrin; Gandomi, Amir H. (2022)
Soil erosion is a significant threat to the environment and long-term land management around the world. Accelerated soil erosion by human activities inflicts extreme changes in terrestrial and aquatic ecosystems, which is not fully surveyed/predicted for the present and probable future at field-scales (30-m). Here, we estimate/predict soil erosion rates by water erosion, (sheet and rill erosion), using three alternative (2.6, 4.5, and 8.5) Shared Socioeconomic Pathway and Representative Concentration Pathway (SSP-RCP) scenarios across the contiguous United States. Field Scale Soil Erosion Model (FSSLM) estimations rely on a high resolution (30-m) G2 erosion model integrated by satellite- and imagery-based estimations of land use and land cover (LULC), gauge observations of long-term precipitation, and scenarios of the Coupled Model Intercomparison Project Phase 6 (CMIP6). The baseline model (2020) estimates soil erosion rates of 2.32 Mg ha 1 yr 1 with current agricultural conservation practices (CPs). Future scenarios with current CPs indicate an increase between 8% to 21% under different combinations of SSP-RCP scenarios of climate and LULC changes. The soil erosion forecast for 2050 suggests that all the climate and LULC scenarios indicate either an increase in extreme events or a change in the spatial location of extremes largely from the southern to the eastern and northeastern regions of the United States.
Keywords: Infolettre; Infolettre_Ete_2024

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