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• Higher fluoride in drinking water is slow poison with irrecoverable consequences. • The paper discusses the natural factors behind occurrences of high fluorosis globally. • The Global Fluoride Belts are geographically connected and geologically similar with natural factors. • In situ process of fluoride removal techniques, their removal capacity, advantages and disadvantages are discussed. Fluorosis is an endemic disease that is now a global challenge. In this review paper we have classified and described the global fluoride belts (GFB) geologically and geographically connecting different countries. A proper map of the global fluoride belts showing the inland connections between each belt is presented. Several natural factors like the rock type, soil, tectonics, hydrogeology and climate of a terrain which are majorly responsible for engrafting higher fluoride in drinking water are described. A detailed uniform description of each belts with respect to geology, geochemistry are discussed. The minor factors like altitude, phosphate mining etc. on fluorosis are also discussed including countries like Canada, Norway etc. which are potentially affected by fluorosis. The correlation of different ions like Cl−, Ca2+, etc; pH, electrical conductivity, TDS etc are compared and reviewed in detailed with respect to different GFB. The genesis and sources of fluoride contamination are also supported by different isotopic evidences. In this article, the in situ methods involving natural minerals and materials which can adsorb fluoride effectively and are being used globally are exhaustively assessed. The methods which are highly sustainable with minimum waste generation and cost effective are also evaluated. [ABSTRACT FROM AUTHOR]

• Groundwater level prediction for a long period of time is considered in this paper. • We proposed a graph-based machine learning using which the spatial relationships are taken into account. • Our results indicate that spatial correlations are helpful when one deals with geospatial data. • The proposed method is compared with standard techniques. Accurate prediction of groundwater level (GWL) over a period of time is of great importance for groundwater resources management. Machine learning techniques due to their great performance have recently been used for this problem. Previous methods, however, did not consider the spatial relationships between wells due to the difficulty to handle unstructured well location data. In this paper, a graph neural network (GNN) is used to forecast groundwater dynamics where it can represent each well as a node in the graph. The spatial information is, thus, extracted from an interconnected network using graph convolution layers with a self-adaptive adjacency matrix. The temporal features of the sequence are obtained by gated temporal convolutional networks. The model was applied and evaluated for wells in the southwest area of British Colombia in Canada using data about 11 years (2010–2020). The proposed model performs better in terms of all the defined evaluation metrics, when compared with two baseline models: long short-term memory (LSTM) and gated recurrent units (GRU). Moreover, when the spatial dependencies are completely unknown, the model can still learn them from the data and obtain comparable performance. Furthermore, the proposed model has a high efficiency since it can simultaneously model GWL change for all monitoring wells in the system. We also demonstrated that the spatial dependencies between each well could be intuitively interpreted from the learned adjacency matrix. [ABSTRACT FROM AUTHOR]

Summary: Artificial neural network ensemble (ANN ensemble) prediction is a technique in which the outputs of a set of separately trained ANNs are combined to form one unified prediction. ANN ensemble models are developed in this paper to improve the results of single artificial neural network (single ANN) for the estimation of the ice thickness in a number of selected Canadian lakes during the early winter ice growth period. An effective ensemble consists of a set of ANNs that may not be highly performing when they are used separately, but have their prediction errors greatly reduced once combined. This paper evaluates the effectiveness of a number of ensemble techniques including randomization, bagging and boosting for creating members of an ensemble, then averaging and stacking techniques for combining ensemble members. The experiments show that, in the context of estimation of lake ice thickness, boosting is much better than randomization, and sometimes better than bagging. Stacking was found to be more competitive than averaging. Overall, ANN ensemble models for the estimation of ice thickness proved to be more accurate than single ANN models, especially when boosting is used for combining ensemble members and when stacking is used to combine the outputs from individual members. ANN ensembles achieve the best generalization performance when the ensemble size is increased to around 20. [Copyright &y& Elsevier]

Many probability distributions have been developed to model the annual maximum rainfall series (AMS). However, there is no general agreement as to which distribution should be used due to the lack of a suitable evaluation method. This paper presents hence a general procedure for assessing systematically the performance of ten commonly used probability distributions in rainfall frequency analyses based on their descriptive as well as predictive abilities. This assessment procedure relies on an extensive set of graphical and numerical performance criteria to identify the most suitable models that could provide the most accurate and most robust extreme rainfall estimates. The proposed systematic assessment approach has been shown to be more efficient and more robust than the traditional model selection method based on only limited goodness-of-fit criteria. To test the feasibility of the proposed procedure, an illustrative application was carried out using 5-min, 1-h, and 24-h annual maximum rainfall data from a network of 21 raingages located in the Ontario region in Canada. Results have indicated that the GEV, GNO, and PE3 models were the best models for describing the distribution of daily and sub-daily annual maximum rainfalls in this region. The GEV distribution, however, was preferred to the GNO and PE3 because it was based on a more solid theoretical basis for representing the distribution of extreme random variables. [ABSTRACT FROM AUTHOR]

• Snow, glaciers, wetlands, frozen ground and permafrost needed in hydrological models. • Water quality export by coupling biochemical transformations to cold regions processes. • Hydrological sensitivity to land use depends on cold regions processes. • Strong cold regions hydrological sensitivity to climate warming. Cold regions involve hydrological processes that are not often addressed appropriately in hydrological models. The Cold Regions Hydrological Modelling platform (CRHM) was initially developed in 1998 to assemble and explore the hydrological understanding developed from a series of research basins spanning Canada and international cold regions. Hydrological processes and basin response in cold regions are simulated in a flexible, modular, object-oriented, multiphysics platform. The CRHM platform allows for multiple representations of forcing data interpolation and extrapolation, hydrological model spatial and physical process structures, and parameter values. It is well suited for model falsification, algorithm intercomparison and benchmarking, and has been deployed for basin hydrology diagnosis, prediction, land use change and water quality analysis, climate impact analysis and flood forecasting around the world. This paper describes CRHM's capabilities, and the insights derived by applying the model in concert with process hydrology research and using the combined information and understanding from research basins to predict hydrological variables, diagnose hydrological change and determine the appropriateness of model structure and parameterisations. [ABSTRACT FROM AUTHOR]

The water content of wetlands represents a key driver of their hydrological services and it is highly dependent on short- and long-term weather conditions, which will change, to some extent, under evolving climate conditions. The impact on stream flows of this critical dynamic component of wetlands remains poorly studied. While hydrodynamic modelling provide a framework to describe the functioning of individual wetland, hydrological modelling offers the opportunity to assess their services at the watershed scale with respect to their type ( i.e. , isolated or riparian). This study uses a novel approach combining hydrological modelling and limited field monitoring, to explore the effectiveness of wetlands under changing climate conditions. To achieve this, two isolated wetlands and two riparian wetlands, located in the Becancour River watershed within the St Lawrence Lowlands (Quebec, Canada), were monitored using piezometers and stable water isotopes (δD – δ18O) between October 2013 and October 2014. For the watershed hydrology component of this study, reference (1986–2015) and future meteorological data (2041–2070) were used as inputs to the PHYSITEL/HYDROTEL modelling platform. Results obtained from in-situ data illustrate singular hydrological dynamics for each typology of wetlands ( i.e., isolated and riparian) and support the hydrological modelling approach used in this study. Meanwhile, simulation results indicate that climate change could affect differently the hydrological dynamics of wetlands and associated services ( e.g., storage and slow release of water), including their seasonal contribution ( i.e., flood mitigation and low flow support) according to each wetland typology. The methodological framework proposed in this paper meets the requirements of a functional tool capable of anticipating hydrological changes in wetlands at both the land management scale and the watershed management scale. Accordingly, this framework represents a starting point towards the design of effective wetland conservation and/or restoration programs. [ABSTRACT FROM AUTHOR]

Summary The Canadian Precipitation Analysis (CaPA) is a data analysis system used operationally at the Canadian Meteorological Center (CMC) since April 2011 to produce gridded 6-h and 24-h precipitation accumulations in near real-time on a regular grid covering all of North America. The current resolution of the product is 10-km. Due to the low density of the observational network in most of Canada, the system relies on a background field provided by the Regional Deterministic Prediction System (RDPS) of Environment Canada, which is a short-term weather forecasting system for North America. For this reason, the North American configuration of CaPA is known as the Regional Deterministic Precipitation Analysis (RDPA). Early in the development of the CaPA system, weather radar reflectivity was identified as a very promising additional data source for the precipitation analysis, but necessary quality control procedures and bias-correction algorithms were lacking for the radar data. After three years of development and testing, a new version of CaPA–RDPA system was implemented in November 2014 at CMC. This version is able to assimilate radar quantitative precipitation estimates (QPEs) from all 31 operational Canadian weather radars. The radar QPE is used as an observation source and not as a background field, and is subject to a strict quality control procedure, like any other observation source. The November 2014 upgrade to CaPA–RDPA was implemented at the same time as an upgrade to the RDPS system, which brought minor changes to the skill and bias of CaPA–RDPA. This paper uses the frequency bias indicator ( FBI ), the equitable threat score ( ETS ) and the departure from the partial mean ( DPM ) in order to assess the improvements to CaPA–RDPA brought by the assimilation of radar QPE. Verification focuses on the 6-h accumulations, and is done against a network of 65 synoptic stations (approximately two stations per radar) that were withheld from the station data assimilated by CaPA–RDPA. It is shown that the ETS and the DPM scores are both improved for precipitation events between 0.2 mm and 25 mm per 6-h, and that the FBI is unchanged. [ABSTRACT FROM AUTHOR]

Summary Across the southern Canadian Prairies, annual precipitation is relatively low (200–400 mm) and periodic water deficits limit economic and environmental productivity. Rapid population growth, economic development and climate change have exposed this region to increasing vulnerability to hydrologic drought. There is high demand for surface water, streamflow from the Rocky Mountains in particular. This paper describes the application of dendrohydrology to water resource management in this region. Four projects were initiated by the sponsoring organizations: a private utility, an urban municipality and two federal government agencies. The fact that government and industry would initiate and fund tree-ring research indicates that practitioners recognize paleohydrology as a legitimate source of technical support for water resource planning and management. The major advantage of tree-rings as a proxy of annual and seasonal streamflow is that the reconstructions exceed the length of gauge records by at least several centuries. The extent of our network of 180 tree-ring chronologies, spanning AD 549–2013 and ∼20° of latitude, with a high density of sites in the headwaters of the major river basins, enables us to construct large ensembles of tree-ring reconstructions as a means of expressing uncertainty in the inference of streamflow from tree rings. We characterize paleo-droughts in terms of modern analogues, translating the tree-ring reconstructions from a paleo-time scale to the time frame in which engineers and planners operate. Water resource managers and policy analysts have used our paleo-drought scenarios in their various forms to inform and assist drought preparedness planning, a re-evaluation of surface water apportionment policy and an assessment of the reliability of urban water supply systems. [ABSTRACT FROM AUTHOR]

Summary: This paper investigates the effects of model structure and parameter equifinality on the uncertainty related to hydrological modelling in climate change impact studies. The study is conducted on a snow-dominated watershed located in the southern part of the province of Quebec (Canada). Hydrological model structure uncertainty is examined through the use of two very different simulation tools, one lumped conceptual model and one spatially-distributed physically-based model. Parameter equifinality is examined by performing multiple automatic calibrations with both hydrological models. The analysis is first carried out under recent past climate and then under modified climate conditions following two contrasted projections that are analysed separately. The delta change approach is used to build the two climate projections. Overall, this study reveals that the impact of hydrological model structure uncertainty is more significant than the effect of parameter uncertainty, under recent past climate as well as future climate conditions. Ultimately, the use of hydrological models with different levels of complexity should be considered as part of the global uncertainty related to hydrological model structure. [ABSTRACT FROM AUTHOR]

Summary: Ensemble techniques are used in regression/classification tasks with considerable success. Due to the flexible geometry of artificial neural networks (ANNs), they have been recognized as suitable models for ensemble techniques. The application of an ensemble technique is divided into two steps. The first step is to create individual ensemble members, and the second step is the appropriate combination of outputs of the ensemble members to produce the most appropriate output. This paper deals with the techniques of both generation and combination of ANN ensembles. A new performance function is proposed for generating neural network ensembles. Also a probabilistic method based on the K-nearest neighbor regression is proposed to combine individual networks and to improve the accuracy and precision of hydrological forecasts. The proposed method is applied on the peak discharge forecasting of the floods of Red River in Canada as well as the seasonal streamflow forecasting of Zayandeh-rud River in Iran. The study analyses the advantages of the proposed methods in comparison with the conventional empirical methods such as conventional artificial neural networks, and K-nearest neighbor regression. The utility of the proposed method for forecasting hydrological variables with a conditional probability distribution is demonstrated. The results of this study show that the application of the ensemble ANNs through the proposed method can improve the probabilistic forecast skill for hydrological events. [ABSTRACT FROM AUTHOR]

Summary: Uncertainty estimation of climate change impacts has been given a lot of attention in the recent literature. It is generally assumed that the major sources of uncertainty are linked to General Circulation Models (GCMs) and Greenhouse Gases Emissions Scenarios (GGES). However, other sources of uncertainty such as the choice of a downscaling method have been given less attention. This paper focuses on this issue by comparing six downscaling methods to investigate the uncertainties in quantifying the impacts of climate change on the hydrology of a Canadian (Quebec province) river basin. The downscaling methods regroup dynamical and statistical approaches, including the change factor method and a weather generator-based approach. Future (2070–2099, 2085 horizon) hydrological regimes simulated with a hydrological model are compared to the reference period (1970–1999) using the average hydrograph, annual mean discharge, peak discharge and time to peak discharge as criteria. The results show that all downscaling methods suggest temperature increases over the basin for the 2085 horizon. The regression-based statistical methods predict a larger increase in autumn and winter temperatures. Predicted changes in precipitation are not as unequivocal as those of temperatures, they vary depending on the downscaling methods and seasons. There is a general increase in winter discharge (November–April) while decreases in summer discharge are predicted by most methods. Consistently with the large predicted increases in autumn and winter temperature, regression-based statistical methods show severe increases in winter flows and considerable reductions in peak discharge. Across all variables, a large uncertainty envelope was found to be associated with the choice of a downscaling method. This envelope was compared to the envelope originating from the choice of 28 climate change projections from a combination of seven GCMs and three GGES. Both uncertainty envelopes were similar, although the latter was slightly larger. The regression-based statistical downscaling methods contributed significantly to the uncertainty envelope. Overall, results indicate that climate change impact studies based on only one downscaling method should be interpreted with caution. [Copyright &y& Elsevier]

Summary: The ability to quantify and predict discharge and nutrient export patterns from agricultural catchments is confounded by the effects of antecedent hydrologic conditions (AHC) in upland areas. This paper examines the impacts of AHC on storm hydrologic response and nutrient export for 59 events measured over a 2-year period (year-round). Event-related discharge and nutrient (nitrate, phosphate) export were highly temporally variable. Runoff ratios ranged from 0 to 0.99 over the study period, with the lowest median runoff ratios being observed during the summer and early autumn period and the highest median runoff ratios being observed in March following snowmelt. Runoff ratios were positively correlated with AHC indices such as pre-event stream discharge (1-day) and antecedent (14-day) stream discharge although strong predictive relationships could not be generated (p >0.01). In general, runoff ratios increased throughout successive events as AHC became wetter; however trends observed among suites of events with having similar AHC did not produce similar patterns of hydrologic or nutrient export response, which clearly demonstrates the non-linear responses to variable AHC in the watershed. Basin nitrate (), total phosphorus (TP) and soluble reactive phosphorus (SRP) export varied with AHC in the basin, and flow-weighted mean nutrient concentrations (FWMC) ranged considerably (mean, range: 8, 1–18mgL−1 NO3–N; 18, 3–161μgL−1 SRP; 75, 15–274μgL−1 TP). Nutrient export increased with successive events when the basin was on a wetting trend, but decreased with successive events when the basin was on an overall drying trend. These patterns were linked to peak water table position during events and timing of fertilizer application. Overall, this work demonstrates that watershed hydrologic and nutrient export responses to precipitation and AHC are complex and cannot be predicted using simple linear relationships. This study also demonstrates the need to collect larger data sets when examining relationships between watershed hydrologic and nutrient export response. [Copyright &y& Elsevier]

Summary: In recent years, climate change impacts on water resources sectors have been extensively documented. Anticipated changes range from more severe storms to more frequent floods and drought at regional scale. Pressure on water resources and hence on the environment will probably increase with the need of quality data for sustainable design of water resources projects. Therefore, for an optimal network design, hydrometric networks are to be reviewed periodically based on the information needs of the various end users and the perspective for future water resources development. This paper evaluates hydrometric networks to identify essential streamflow stations and critical areas (highly poor network density) within major watersheds across the different provinces of Canada using entropy theory. The method is applied to daily streamflow data and information coefficients such as marginal entropy, joint entropy and transinformation index are used for the identification of important stations as well as critical areas in the basin. The analysis results show that almost all Canadian main watersheds contain deficient hydrometric networks. The most deficient streamflow networks are identified in Alberta (North Saskatchewan, Oldman, and Red Deer basins), Northern Ontario (Hudson Bay basin), and the Northwest Territories. The information might prove to be helpful for decision makers to undertake cost-benefit analyses for hydrometric network updating in each region. [Copyright &y& Elsevier]

Summary: This paper proposes a non-linear model to describe streamflow recessions. A model of combining power-law and exponential functions is derived from a non-linear differential equation associating outflow change rate and flow itself with a time-dependent decreasing decay rate. The decay rate is approximated by the first three terms in a Taylor series expansion. The solution of the resulting differential equation includes three components of the expansion determining two exponential functions and a power-law function, respectively. It is demonstrated analytically and empirically that the introduction of a non-linear differential equation comprising a mixture of power-law and exponential forms is superior to using an ordinary differential equation with exponential solution, or a non-linear equation with power-law solution, for modeling the decay patterns of flow in river systems. Analytical properties of the mixing model are discussed and the model is subsequently validated by characterizing the relationships between decay of peak flow as a function of time after the initial occurrence of flow peak in 10 datasets of flow events chosen from five gauging stations in the Oak Ridges Moraine (ORM) area, Canada. [Copyright &y& Elsevier]

Summary: Regional flood frequency analysis (FFA) is commonly used to estimate flood risk at a particular site where little or no information is available on peak flows. This approach requires the assumption of flood stationarity. In this paper, we present a method to perform regional FFA at ungauged sites when the assumption of stationarity is not valid. Non-stationary at-site models are used to estimate flood risk at gauged sites. This information can then be used along with meteorological and drainage basin characteristics to define a hydrologic neighborhood of the ungauged site by means of canonical correlation analysis (CCA). A multiple regression model is then developed within the hydrologic neighborhood. Feasibility of regional FFA at ungauged sites is illustrated with a group of river flow gauging stations located in southeastern Canada and northeastern United States and which show a signal of non-stationarity. Results indicate that the development of a multiple regression model using 2–4 explanatory variables (including basin drainage area) leads to efficient estimation of the non-stationary regional flood quantiles Q 5(t) and Q 100(t) for the end of the historic observation period (relative root mean square error (RMSEr) of 38.2% and 60.8%, respectively). The use of CCA for the definition of a hydrologic neighborhood did not lead to better results. This is explained by the fact that the total number of sites (29) is small and, consequently, the size of the hydrologic neighborhoods is too low to develop efficient regression models within each of them. Comparison with the stationary results show that ignoring a trend in the hydrologic regime of an ungauged site can lead to serious under- or overestimation of the quantile estimates for that site. [Copyright &y& Elsevier]

Summary: The immobilization of water in the form of deposited ice on the reservoir’s embankments during winter operations has received little attention in hydrological literature. In Nordic countries like Canada, this phenomenon is likely to have significant negative impacts on reservoir operation. In a previous work (Seidou, O., Ouarda, T.B.M.J., Bilodeau, L., Hessami, M., St-Hilaire, A., Bruneau, P., 2006. Modeling ice growth on Canadian lakes using artificial neural metworks. Water Resources Research 42. doi: doi:10.1029/2005WR004622); the authors developed a global artificial neural network model to predict ice thickness growth in unmonitored Canadian lakes and reservoirs. This model is used in this paper to compute a time series of ice thickness on the Gouin reservoir (Saint-Maurice River, Quebec, Canada) and the Outardes 4 reservoir (Aux Outardes River, Quebec, Canada). This and the relationship between surface water area and water level were then used to evaluate the volume of ice left on the banks of the two reservoirs during winter operation, as its water level is progressively lowered in order to release water for hydroelectric production at downstream power dams. Results show that the ratio of frozen water left on the banks to the active storage varies from 2% to 8% at the Gouin reservoir, and from 0.41% to 1.15% at the Outardes 4 reservoir depending on the water usage policy and the geometry of the reservoir. [Copyright &y& Elsevier]

Abstract: Estimation of sediment and nutrient loads is of crucial interest for a good assessment of water pollution. This paper proposes an overview of existing estimation methods and a framework to select the most suited one given available streamflow and concentration data. Correlations between contaminant concentration and streamflow should first be checked to generate missing concentration values by regression. However, correlations are not always strong, in which case the ratio estimator method is more appropriate. Given a 6-year data set (1989–1995) from the Beaurivage River (Québec, Canada) with, at best, a weekly sampling, the ratio estimator method was selected to estimate annual and seasonal loads of sediments and nutrients (N and P). Results show relatively steady annual loads (on average 8.1 and 1.1kghayr−1 for total dissolved N and total P, respectively) and a low erosion rate (0.23thayr−1). The results also confirm that nutrient and sediment transport via runoff is essentially a springtime process in this region, and they indicate that dissolved P represents the bulk of the total P load, most likely due to artificial subsurface drainage systems in the watershed. These results are compared to the results obtained by using averaging methods and to several other sources of information from literature, confirming the order of magnitude but highlighting some remaining uncertainties. Finally, some research avenues are proposed to improve the proposed framework and to investigate other estimation methods adapted to data characteristics. [Copyright &y& Elsevier]

• Flow partitioning based model-wrapper significantly improves the simulation results. • Model wrapper helps identify redundant models for multi-modelling. • Signature-based multi-criteria auto-calibration helps parameter identifiability. Multi-modelling aims to make use of the strengths of single hydrologic models to improve the accuracy of simulating the watershed system behavior. Considering hydrological signatures such as the flow duration curve segmentation in the calibration of each hydrologic model leads to a better parameter identifiability. In this study, a novel weighted average model-wrapper based on flow duration curve segmentation is introduced to aggregate the calibrated models into a multi-model. The proposed framework is applied to develop a model-wrapper of the Upper Assiniboine River Basin for flood forecasting upstream of the Shellmouth reservoir in the Prairie region of Canada. The HEC-HMS, HBV-EC, HSPF, and WATFLOOD hydrologic models that are being used at the Hydrologic Forecast Centre of Manitoba Infrastructure for operational inflow forecasting are calibrated using signature-based multi-objective optimization. These models have significantly different structural complexities. The calibration of each of these models is set up as three simulation-optimization problems with different objective functions to balance the model capability in simulating multiple important hydrological signatures. Results show that the model-wrapper outperforms each of the single calibrated models that are of operational use at Manitoba Infrastructure, e.g. NSE improved from 0.44 for the best individual model to 0.76 for the model-wrapper in the calibration period. Moreover, the weights associated with each hydrologic model component indicate the contribution rate of the individual models to the model-wrapper in high-flow, mid-flow, and low-flow portions of streamflow time series. Quantifying the contribution of each model component provides a deeper insight into model selection strategy, especially when a component has minimal or no contribution, e.g. HEC-HMS and HBV-EC in this paper, to the model-wrapper performance in all ranges of streamflow simulation compared to other model components. [ABSTRACT FROM AUTHOR]

• Two problems of the T-V method in determining soil moisture index are proved. • A new long-term T-V scatter plot is proposed. • A new temperature-vegetation index is proposed. • A new soil moisture index is designed which is named SM iSEE. • SM iSEE is studied on two networks and compared with three other well-known indices. The estimation of soil moisture content on the global scale is a key research issue in the field of remote sensing and, to date, a range of methods have been developed to achieve this. The temperature-vegetation (T-V) technique, which is perhaps one of the most common and successful, suffers from being inaccurate in some cases, especially when the daily T-V scatter plots are not able to define the wet and dry edges in a reliable way. A further challenge is the scale inconsistency between the results obtained on different days, which is a serious concern when monitoring soil moisture changes over periods of time. To address these restrictions, this paper introduces a new soil moisture index, called SM iSEE , driven by a new long-term T-V scatter plot that is established from data covering a 1-year period. The normalized NDVI is set as the horizontal axis and three different temperature factors are suggested as the vertical axis in the proposed 1-year scatter plot. From these three temperature factors, the results proved that "LST day -Ta day (10:30 am)" is the most appropriate choice. In contrast to most linear co-distance indexes reported in the literature, the proposed SM iSEE is a novel non-linear soil moisture index that defines the locus of co-moisture points in the scatter plot as unequal distance curves. To investigate the proposed method, two different soil moisture observation networks with different climate and vegetation conditions, namely, SMAPVEX12 in Canada and REMEDHUS in Spain, were applied. The results demonstrated that the efficiency of the proposed 1-year scatter plot, which was even applicable to, and improved upon, other indexes in the literature. In addition, SM iSEE outperformed SEE, iTVDI and φas the most recent similar indexes, achieving correlation coefficients of 0.65 and 0.74 for the SMAPVEX12 and REMEDHUS networks, respectively. These results appear to be promising, especially for the vegetated area in the SMAPVEX12 network. [ABSTRACT FROM AUTHOR]