Importance of Water Quantity on Modeling Using a Vulnerability Index for Northern Ontario Watersheds
The 2007 Brisbane Declaration and the Intergovernmental Panel on Climate Change have made recommendations for assessing the water resources of a hydrologic system as an integral part of hydrologic and hydraulic modeling. Considering these recommendations, it is necessary to understand the flow regime and available water resources. This paper describes the data sources and methods used to create a water quantity vulnerability index for the Southwestern Hudson Bay and Nelson watersheds in Ontario, Canada. The index was generated for 71 gauged watersheds of the Water Survey of Canada across the project area with an impact matrix being constructed from a set of 27 scale weighted streamflow and climate impact variables. Variables were specifically selected to represent the dimensions of water quantity exposure by describing the magnitude, frequency, duration and timing of flows. Vulnerability index values ranged between 21 and 24 with hydrologic regions of Far North West being lowest at 21. A database of water quantity statistics, impact matrix variables and estimated vulnerability indices has been created to serve water policy, planning and management staff.
Since 2010, the Spatial Data Infrastructure (SDI) unit of the Ontario Ministry of Natural Resources and Forestry (OMNRF) has produced a series of hydrology and climate statistics datasets for the Southwestern Hudson Bay and Nelson River watershed systems. It is necessary to enhance the synthesized information on flow regime for modeling by assigning a vulnerability index (VI) based on a suite of streamflow and climate variables for each of the selected gauged watersheds. This information is pivotal to hydrologic modeling and land use planning, including climate change, for managing water resources in the Far North region of Ontario.
There are two key definitions from the following international bodies. They are:
- The Brisbane Declaration (2007) defines environmental flows as:
Environmental flows describe the quantity, timing, and quality of water flows required to sustain freshwater and estuarine ecosystems and the human livelihoods and well-being that depend on these ecosystems.
- The Intergovernmental Panel on Climate Change (IPCC) defines vulnerability as:
The degree to which a system is susceptible to, and unable to cope with, adverse effects of climate change, including climate variability and extremes. Vulnerability is a function of the character, magnitude and rate of climate change and the variation to which a [social-ecological] system is exposed, its sensitivity and its adaptive capacity (Parry et al. 2007).
This study assists with identifying the water quantity components of the environmental flows of the Brisbane Declaration and the dimension of exposure associated with climate change by describing the magnitude, frequency, duration and timing of flows. For any modeling project, one or more of these measures are used.
The objectives of this project were to generate a VI for each of The Water Survey of Canada’s HYDAT stream gauges of the Southwestern Hudson Bay and Nelson River watershed systems. As the stream gauge density and the length of record are very sparse in Northern Ontario, this study is not as exhaustive as one that would be done in Great Lakes Watershed system of Ontario. This study is important for understanding the water resources, the science gap and the vulnerabilities. This study is initiated as part of the Ontario Government’s Far North Land Use Planning Initiative. The reader may consider these limitations while reading this paper.
2.1 Data Source
The study used original, derived and analysed data from different sources (see Table 1). This data is available for free download from Land Information Ontario. For simplifying the analysis of VI, the broader units of analysis are the watershed regions in the Ontario Integrated Hydrology (OIH) data package: Far North West (FNW), Far North Central 2 (FNC2), Far North Central 1 (FNC1), Far North East (FNE), and North West (NW). The data and the application tool were assessed through the metadata management tool of Land Information Ontario. Figure 1 shows the extent of these regions, the location of streamflow, level, climate stations and the Far North communities. From Figure 1 the sparse density of monitoring stations is very clear. Within the stream network, the magnitude of streamflow shows spatial (upstream–downstream) autocorrelation. This relationship for the statistics generated (flood, low flow, mean annual flow) was checked for upstream–downstream flow gauges.
Table 1 Data source information.
|Type of Data||Source|
|Baseline Hydrology Statistics||Land Information Ontario
Baseline Hydrology: For the Southwestern Hudson Bay and Nelson River Watershed systems, March 2014
|Climate Data||Environment Canada’s Climate Data|
|Extreme Flow Statistics||Land Information Ontario
Flood Flow and Low Flow Statistics: For the Southwestern Hudson Bay and Nelson River Watershed Systems, February 2013
|Ontario Flow Assesment Tool (OFAT)||Land Information Ontario
Watershed flow assessment tool, 2015
|Ontario Integrated Hydrology Data||Land Information Ontario
Ontario Integrated Hydrology Data, 2015
|Streamflow||Environment Canada’s Hydrometric Data for WSC HYDAT gauges|
Figure 1 Ontario Integrated Hydrology (OIH) package with the gauges used for analysis.
2.2 Steps in Estimating the Vulnerability Index
Three steps were involved in assigning the VI as given below:
- Develop the impact matrix (IM) for water quantity of the watershed;
- Classify each of the impact matrices described in step 1 into three to five levels; and
- Generate the VI ( the ratio of the sum of the weighted scale to the total scale).
Each variable was given a nominal classification number or level (eg.1 to 5), for comparison. The level varies depending on the variable. A small level, say 1, indicates least vulnerable and the vulnerability increases with the value. The classification basis for each variable is referenced as the last sentence in each of the subsections of Section 2.3.
In this document, the VI is referenced as a relative term and the value obtained will lie between the minimum (all VI variables that score the least score) and the maximum (all VI variables that score the highest scores). This ranking can identify potentially sensitive watersheds where mitigation measures are required.
2.3 Impact Matrix Variables
In total 27 impact matrices were developed to best describe the hydrological, ecological or geomorphological condition of the watershed. This study utilizes the available streamflow data, so not all IM variables are complete for all the watersheds. A short description of each of the impact matrices is given in the following paragraphs.
Natural and Regulated Watersheds
Environment Canada classifies stream gauges as natural or regulated based on whether the system is in its pristine (natural) condition or in a modified condition. The modification of watersheds occurs because of watershed development, the presence of reservoirs, abstractions, or other human induced modifications. Natural systems are less resilient to changes than modified systems.
The IM is based on Environment Canada’s classification of stream gauges as natural or regulated.
Drainage Area of the Watershed
The watershed is the basic geographical unit of all hydrologic analyses and designs. The horizontal projection of the area of a watershed, the drainage area of a stream at that cross section, reflects the volume of water generated from a rainfall event. The salient feature is that a watershed has volume. The drainage area influences its hydrological response. Headwaters are the areas where channel formation begins and these areas are more sensitive to hydrological changes.
The IM is classified based on the classification given by Tiburan et al. (2010).
The coefficient of variation is the ratio of the standard deviation to the mean annual streamflow, which describes the natural variability of streamflow (Hurd et al. 1999). Relatively high ratios indicate regions of extreme variability and therefore higher vulnerability to hydrologic change.
The IM is based on the classification given by Hurd et al. 1999.
Ratio of the Annual Maximum to the Annual Minimum Streamflow
The underlying geologic structure of the watershed controls the ratio of the annual maximum to the annual minimum stream-flow. Summer low flow is the measure of the system’s capacity to absorb and retain the water that the watershed receives as precipitation. On the other hand, the maximum streamflow is the quick response of the watershed. The ratio helps to understand the influence of direct runoff and ground water storage. The variability of the ratio for the study area is quite high, mainly due to the extent of the gauged watersheds that are located on the Canadian Shield.
The IM is based on the range of values for each level.
Flow Duration Curve
A flow duration curve (FDC) is a versatile analytical tool used in hydrology and water related projects. It depicts the magnitude of streamflow as a function of percentage exceedance and is often used to study ground water influence and streamflow variability in the watershed. The shape of the curve reflects the composite effects of the physiography, including geologic and climatic influences. High water velocity considerations at low exceedance (e.g. 10%) and low water depth considerations at higher exceedance (90%), expressed as the ratio 10:90, are used to study ecosystem function and watershed storage capacity. A previous study by Singer and Cheng (2002) used the square root of (Q25/Q75) as a measure of ground water. The previous values reported are comparable to this study and are given in Table 2.
The IM is based on the 10:90 exceedance ratio and the square root of (Q25/Q75).
Table 2 Comparison of flow duration values obtained from previous study.
|Watershed||HYDAT||Sqrt(Q25/Q75) (2002)||Sqrt(Q25/Q75) (2015)|
Base Flow Index
Base flow index (BFI) is the ratio of the base flow to the total streamflow and expressed as a value ranging from zero to one. This index is used to understand the relative contribution of ground water or base flow (Caissie et al. 2009). Rudra et al. (2015) classified Ontario streams based on the base flow index as slow response (>0.5) and rapid response (<0.5). A summary of the BFI values for the study area are given in Figure 2.
The IM is based on the classification given by Rudra et al. (2015).
Figure 2 Summary of base flow index with the latitude of the gauges.
From the Extreme Flow Statistics data package, characteristics of the flood for each of the selected gauged watersheds were analysed. The IM selected are:
- sample coefficient of variation;
- extreme volatility index;
- flood envelope curve of the system; and
The coefficient of variation gives the ratios of the large floods to the mean flood. It is the relative variation from the mean or, in other words, the degree of dispersion. If this ratio is small, then the flood magnitude variability is marginal.
The concept of return periods is used to describe the likelihood of occurrence of a flood with its magnitude. The 2 y flood value is a measure of the first moment (measure of central tendency). The ratio of Q100/Q2 y flood is a measure of the second moment (measure of dispersion) which is called the extreme volatility ratio (Fuller 2006). The normalized measure is called the extreme volatility index (EVI) and ranges between zero and one. The ratio depicts the volatility of the floods.
In 1941, Creager developed an envelope curve. The flood envelope curve was plotted by either taking peak discharge or discharge per unit area against the drainage area. The watersheds that form the (x, y) points on the envelope curve are important as these watersheds have experienced the extreme of extremes for their respective drainage areas.
Data was tested using the Spearman rank order correlation coefficient test for trend. The null hypothesis for the test at significance levels of 5% and 1% was tested to see whether the computed test value lies within the region of rejection or not. Monotonic upward or downward trend was tested for the consecutive annual instantaneous maximum flow for each watershed to determine whether there has been an increase or decrease in flows. The premise behind this test is that gradual land use changes and climate changes in a watershed may affect the magnitude of annual flood (Pilon and Harvey 1994).
The IM for flood flow is based on the four flood flow variables.
Similar to flood flows, the data was extracted from the Extreme Flow Statistics data package. The IM chosen were:
- sample coefficient of variation;
- ratio of 7Q20/7Q2;
- low flow envelope curve of the system;
- stationarity; and
- value of 7Q20.
The first four matrices are similar to the flood flow matrices except in reference to the low flow. The lower envelope curve provides the availability of water in the watershed (Burn et al. 2008).
To maintain water quality, a receiving water body has a definable dilution, dispersion or assimilation capacity for receiving waste discharges. Similarly, there is a minimum environmental streamflow required for water abstraction. In Ontario, the 7Q20 value is recommended for both permits to take water (MOE 2007) and approval of sewage works (MOE 2010) guidelines. This value should not reach zero in order to sustain the minimum environmental flow. 05PB014 (Pipestone River above Rainy Lake) is the only gauge with this value equal to zero.
The IM for low flow is based on the five variables stated above.
Timing of Maximum and Minimum Flows
The timings (month) of the annual minimum and maximum streamflows were plotted as histograms based on the counts for the entire period of record. This helps to understand the month of occurrence of the events and gives insights into the type and cause of flood or drought (winter, summer, mixed). The analysis was completed in the Baseline Hydrology package.
The IM is based on the classification given by Hulley et al. (2013).
The extremal index for flood or low flow analysis above or below a threshold streamflow value was taken from the Baseline Hydrology package. It is common that hydrological events take place in clusters and the index gives the short term dependency of either flood flow or low flow. The extremal index is a measure of the degree of clustering and is the reciprocal of the mean of the cluster size (Smith and Wiseman 1994). The extremal index is found by dividing the estimated number of clusters by the total number of exceedances over the threshold streamflow, with the value ranging between zero and one. In an ideal case, in order for the events (flood or low flow) to be independent and identically distributed, the theoretical value of cluster length should be 1.
The IM is based on whether extremal index is 1 or not.
Centre of Timing of Streamflow
The centre of timing (CT) is a timing measure representing the centre of mass of the streamflow curve (Stewart et al. 2004). The governing equation is:
|t||=||Julian date, days, and|
CT is now becoming widely used in understanding ecosystem functions. CT allows environmental cues to trigger life cycles in the ecosystem. If the shift in the CT is not coordinated, then coevolution within the ecosystem will disturb the life cycle sequences (Geddes-Osborne 2010). Further to that, earlier CT caused by snowmelt poses flooding and difficulties for water supply management. Earlier runoff timing means redistribution of the fraction of monthly flow from the historic streamflow (Stewart et al. 2004).
CT is sensitive to the snow–rain transition and decreasing snowpack, and is correlated to air temperature, elevation and latitude (Geddes-Osborne 2010). Usually, snowmelt is either single or double peaked (modularity). This study did not evaluate the changes in structure or modularity in the system.
The CT for each of the years is estimated and data is plotted with the CT in the y–axis and the corresponding year in the x–axis. A trend line is drawn to see whether there is a positive or negative slope. A negative slope implies that CT is happening earlier in the calendar year. The CT for 04MF001 (North French River near the Mouth) is shown in Figure 3. Modeled CT did occur earlier for some watersheds, but showed no pronounced spatial clustering.
The IM for CT is based on whether there is positive or negative slope in the trend.
Figure 3 Central timing for the period of record.
Double Mass Curve
Anthropogenic activities and climate change in the watershed will cause changes in the annual volumes of streamflow. Streamflow change can be analysed with the use of a double mass curve. A double mass curve is an analytical tool used on the principle that when each recorded data comes from the same population, they are consistent. The double mass curve is used for checking the consistency of streamflow over time. If the relationship remains constant, the curve plots as a straight line. A break in the slope of the curve indicates that the watershed condition has changed. A curve or bend to the right indicates less streamflow whereas a curve to the left indicates more streamflow. Double mass curves do not provide information on the cause of the change.
The cumulative volume of streamflow for each year is plotted against the cumulative years. Double mass curves of 04MF001, with the equality line, are shown in Figure 4. It can be seen that over the period of record, annual volume of streamflow for 04MF001 watershed has not changed greatly.
The IM is based on the R2 value obtained in the trend line.
Figure 4 Double mass curve.
The ratios of runoff to precipitation (energy limited system) and potential evapotranspiration to precipitation (water limited system) are used to characterise water balance in the watersheds. The latter ratio is called the dryness ratio and is similar to the aridity index or Budyko number.
The dryness ratio is the share of total average annual precipitation that is lost through evapotranspiration. The ratio is found using the equation:
|P||=||precipitation, mm, and|
|Q||=||runoff depth, mm.|
The ratio identifies areas where the availability of water is less. A small change in either the temperature or precipitation will drastically affect the magnitude of water stress for watersheds that are dry. It describes the relationship of climate to water partitioning within the ecosystem (Geddes-Osborne 2010). The summary of precipitation with respect to the gauge latitude is shown in Figure 5.
The IM classification for each level is based on Hurd et al. (1999).
Figure 5 Annual precipitation with the latitude of the gauges.
Rainfall to Snowfall Ratio
Rainfall to snowfall ratio is given as one of the watershed exposure indicators in the Tools for Climate Change Vulnerability Assessment for Watersheds report (Nelitz et al. 2013). Air temperature affects the form of precipitation (snowfall or rainfall) and the rate of snowpack ablation. A higher temperature increases the rain to snowfall ratio.
The ratio of rainfall to snowfall for the period of record was obtained from Environment Canada’s climate data.
The IM is based on whether the ratio is >2 or <2.
Number of Days of Ice Affected Flow
The presence of river ice cover is influenced by climate parameters of the watershed (Beltaos 1995). Due to snowfall during the winter months, ice is present in rivers in Ontario. In general, the presence of ice in the river systems within the study area was continuous to nearly-continuous. The relative amount of precipitation falling as rain or snow is the main cause of the length of ice affected river flow (Hodgkins et al. 2005). The annual dates of ice affected dates (start date, end date, and the annual number of days with the presence of ice) was reported in the Baseline Hydrology Statistics data package.
The IM is based on the median number of days with ice affected flow.
Mean Annual Air Temperature
Mean annual air temperature is one of the most important climate variables for hydrologic modeling. Mean annual air temperature is estimated for each of the watersheds using the OMNRF–OFAT (2015) tool. It is seen that the mean temperature drops below 0 °C in most of the watersheds where the Severn, Winisk, Ekwan and Attawapiskat river systems lie. This indicates that the number of days with active temperature (>0 °C) is <365 d.
The temperature in the higher latitudes is <0 °C and the relationship between the gauge latitude and the mean annual temperature is given in Figure 6. The slope of the line suggests that there is a decrease in annual temperature of 0.7 °C for unit increase in latitude.
The IM is classified based on >2.5 °C, 0 °C to 2.5 °C and <0 °C, with the highest score if the temperature is <0 °C (no active temperature).
Figure 6 Summary of annual temperature with the latitude of the gauges.
Maximum Water Temperature
Maximum water temperature is also given as an indicator similar to the rain:snow ratio by Nelitz et al. (2013). During fall and winter the air temperature plummets, hence the water temperature will be low. On the other hand, during the summer, the high air temperature in addition to the low water level increases the water temperature.
Stream water temperature is an important factor for all aspects of stream ecology. The factors that affect water temperature are air temperature, amount of shade, soil erosion increasing turbidity, pollution from anthropogenic activities, and ground water contribution. At the same time, water temperature affects the solubility of dissolved oxygen, rate of aquatic plant growth, metabolic rate of organisms, spawn timing, distribution of the organisms within the ecosystem, and density and viscosity of water (Manning 1997; Chu et al. 2009; Cassie et al. 2014).
As water temperature increases, density (>4 °C) and viscosity decrease. An increase in 0.5 °C in temperature will boost the flow rate by ~1.5% because of the decreased viscosity (Manning 1997).
In order not to impair the water quality of the natural environment, the maximum water temperature of a receiving water body, at any point in the thermal plume, should be ≤30 °C. This applies to cooling water abstraction for industrial use (Environment Canada 2014).
Water temperature data with respect to period records and the number of gauges is very sparse. Only 26 streamflow gauges and 14 lake level gauges have data. The summary on the water temperature for stream gauges is given in Figure 7.
The IM is based on the classification given by Credit Valley Conservation Authority (2013).
Figure 7 Summary of water temperature with the latitude of the gauges.
Besides streamflow, lake level information is also gathered. For lake levels, only coefficients of variation of the level and water temperature are taken. There are 47 level gauges; of those, fourteen locations have temperature data. Data from four level gauges that are diverted and drain to the Great Lakes–St. Lawrence System is also included. The information for the lakes is for reference only.
2.4 Classification Table for Impact Matrix
Table 3 shows the classification and the scale for the 27 IM used for generating the VI. The maximum score of the impact matrix is 74 for streamflow gauges and 6 for lake level gauges. Not all gauges have all 27 IM variables, because of the lack of data, so a zero value has been assigned in these cases. The summary of the maximum score of the impact is given in Figure 8. VI is estimated by taking the weighted average of the impact matrix scale because of the variability of the scale.
The VI is found using the equation given below:
|n||=||number of IM variables.|
The summary of the VI for each of the data packages with streamflow gauges is given in Table 3.
Table 3 Summary of the impact matrix classification.
|Impact Matrix Variables||Level 1||Level 2||Level 3||Level 4||Level 5||Max_Value|
|Base Flow Index||>0.5||<0.5||2|
|Centroid Timing (d/month)||Positive||Negative||2|
|Double Mass Curve R2 Value||99–100||95–99||<95||3|
|Drainage Area_OFAT_2015 (km2)||>16 153||2 241–16 153||311–2 241||43–311||<43||5|
|Flood Flow _EVI||<0.5||0.5–0.7||>0.7||3|
|Flow Duration Curve Sqrt(Q25/Q75)||0–1||1–3||>3||3|
|Flow Duration Curve 10:90 Ratio||0–10||10–30||>30||3|
|Low Flow 7Q20/7Q2||<0.3||0.3–0.7||>0.7||3|
|Low Flow_7Q20 = 0||Yes||No||2|
|Low Flow_ Envelope||Yes||No||2|
|Maximum Extremal Index||1||<1||2|
|Maximum Flow Timing||Summer||Winter||Mixed||3|
|Maximum Flow/Minimum Flow||<100||100–4 000||>4 000||3|
|Maximum Water Temperature(˚C)||<26||26–28||28–30||3|
|Mean Annual Air Temperature (˚C)||>2.5||0–2.5||<0||3|
|Median Ice Days (d)||<120||120–150||>150||3|
|Minimum Extremal Index||1||<1||2|
|Minimum Flow Timing||Summer||Winter||Mixed||3|
|Total Value of the Impact Matrix||74|
Figure 8 Summary of distribution of the gauges based on the maximum scale.
Table 4 gives an example of the estimation for 04FC001 (Attawapiskat River below Muketei River). The VI for the example is 21. If the watershed is in its pristine stage and all the IM scales are 1, then the VI will be 14. On the other extreme, if the watershed scored the highest scale then the VI will be 37.
Table 4 Example of estimating the VI.
|Impact Matrix (IM) Variables||Max Scale||IM Values||IM Scale||Weighted Average|
|Flood_Trend||3||1 & 5||1||0.33|
|Low Flow 7Q20/7Q2||3||0.7||2||0.67|
|VI (15.03/71) = 21||21.00|
The summary of the results is given in Table 5. It is seen that the standard deviation in the index values is small. Vulnerability index values ranged between 21 and 24 with the hydrologic regions of Far North West, which include the Hayes and Severn watersheds, being lowest at 21; Far North East, which includes such systems as the Moose, Missinaibi-Mattagami, Abitibi and Harricanaw watersheds, was the highest ranking with a value of 24 (see also Figure 9).
Table 5 Summary of the results of VI.
|Data Package||Number of Gauges||Low VI||High VI||Present VI||Standard Deviation|
Figure 9 Summary of the vulnerability index for the Northern Ontario watersheds.
As an example of the variability across the study area, two watersheds with the highest and lowest index values were further evaluated from sixteen watersheds where all IM variables were populated with a maximum possible score of 74. The highest VI value was estimated for 04DB001 (Asheweig River at Straight Lake) in the Winisk River with 05RC001 (Berens River above Berens Lake) in the Nelson River achieving the lowest VI value. In order to understand this variability the IM variables were analysed. Both watersheds are natural and have the same drainage area and mean annul flow coefficient of variation IM scale. However, variables of mean annual temperature, median ice days with the presence of ice, extreme volatility index of the flood flows, the ratio of the 7Q20/7Q2 of the low flows and the shift in the centroid timing result in a higher variability score. From this we can infer that the Asheweig River is more prone to flood, low flow, mean annual temperature, median ice days and centroid timing than the Berens River. Figure 10 shows the comparison of the two watersheds based on individual IM variables.
Figure 10 Comparison of two watersheds with the all IM variables, maximum variability in VI.
The index enables planners and managers to assess the state of the watershed, identify and monitor the state of the watershed over time, and allow watersheds to be compared and contrasted to assess water security (Dunn and Bakker 2011).
An accompanying Microsoft Access geodatabase data package was generated for this study. It contains all the estimated values and results of this study. Some results are based on the data products previously mentioned in Section 2.1, as well as the updated and new flow statistics. Three feature classes are included which describe the locations of the climate, streamflow and lake level stations. The project geodatabase and user guide will be published online (public unrestricted) through Land Information Ontario.
4 Data Use and Considerations
4.1 Data Use
The scope of the study is limited to selecting and populating the matrix variables and assigning a VI to gauged watersheds as an initial step. This data can be used as a forward link for many business applications. Some of the applications are:
- providing a framework and baseline information on the present conditions of the watersheds, to monitor and assess watershed performance as a function of time in the future;
- understanding the sensitive impact matrix variables that cause vulnerability, and improving the watershed health prior to any development;
- evaluating project specific impact matrix variables and environmental flows;
- performing watershed vulnerability assessment (e.g. flood and low flow sensitivity) using multiple variables;
- VI can be used to compare or contrast watersheds within or between hydrologic regions;
- science gaps can be identified to append and update the impact matrix variables;
- modeling future developments and projecting future climate change scenarios;
- evaluating overlapping variables (e.g. water quantity and water quality) and integrated variables (surface water and ground water); and
- extending the present study of exposure (water quantity) with water quality, and proceeding to sensitivity analysis, and adaptive capacity for climate change vulnerability.
4.2 Data Considerations
- The flow values in the regulated gauges are not converted to natural flows;
- the estimated values are only for the HYDAT gauge locations, not for ungauged locations of a river reach;
- the HYDAT gauge locations and coordinates are snapped to the river network in (OMNRF–OFAT 2015): drainage area calculation obtained from the WSC and from (OMNRF–OFAT 2015) may differ slightly;
- the study utilizes the available streamflow data, so all the VIs for the selected gauges are not complete;
- lake level information is only for reference as the VI variables are only two: coefficient of variation and water temperature; and
- snow water equivalent is not included in the study because of the lack of monitoring.
The Far North Branch provided support for this project to the Mapping and Information Resources Branch within the Ontario Ministry of Natural Resources and Forestry as part of the Information and Knowledge Management project portfolio.
- Beltaos, S. 1995. River Ice Jams. Highlands Ranch, CO: Water Resources Publications.
- Brisbane Declaration. 2007. The Brisbane Declaration: Environmental flows are essential for freshwater ecosystem health and human well-being. In 10th International River Symposium, Brisbane, Australia (pp. 3-6).
- Burn, D. H., J. M. Buttle, D. Caissie, G. MacCulloch, C. Spence and K. Stahl. 2008. The Processes, Patterns and Impacts of Low Flows Across Canada. Canadian Water Resources Journal 33 (2): 107–24.
- Caissie, D. and S. Robichaud. 2009. Towards a Better Understanding of the Natural Flow Regimes and Streamflow Characteristics of Rivers of the Maritime Provinces. Moncton, New Brunswick: Department of Fisheries and Oceans. Canadian Technical Report of Fisheries and Aquatic Sciences 2843. http://www.dfo-mpo.gc.ca/Library/337317.pdf.
- Caissie, D., N. El-Jabi and N. and Turkkan. 2014. Stream Water Temperature Modeling under Climate Change Scenarios B1 & A2. Moncton, New Brunswick: Department of Fisheries and Oceans. Canadian Technical Report of Fisheries and Aquatic Sciences 3106 http://publications.gc.ca/collections/collection_2014/mpo-dfo/Fs97-6-3106-eng.pdf.
- Chu, C., N. E. Jones, A. R. Piggott and J. M. Buttle. 2009. “Evaluation of a Simple Method to Classify the Thermal Characteristics of Streams Using a Nomogram of Daily Maximum Air and Water Temperatures.” North American Journal of Fisheries Management 29 (6): 1605–19.
- Credit Valley Conservation Authority. 2013. “Water Temperature.” In Credit River Watershed Health Report, 2013, ch 13. http://www.creditvalleyca.ca/watershed-science/watershed-monitoring/credit-river-watershed-health-report/chapter-13-water-temperature/.
- Dunn, G. and K. Bakker. 2011. “Fresh Water-Related Indicators in Canada: An Inventory and Analysis.” Canadian Water Resources Journal, 36 (2):135–48. https://doi.org/10.4296/cwrj3602815.
- Environment Canada, 2014 Guidance document: Environmental effects assessment of freshwater thermal discharge.
- Fuller, C. T., A. Sabesan, S. Khan, G. Kuhn, A. R. Ganguly, D. Erickson and G. Ostrouchov. 2006. “Quantification and Visualization of the Human Impacts of Anticipated Precipitation Extremes in South America.” Eos Transactions, AGU (American Geophysical Union) 87 (52 Fall Meet. Suppl.): Abstract GC44A-03. http://abstractsearch.agu.org/meetings/2006/FM/GC44A-03.html.
- Geddes-Osborne, A. 2010. Indices of Hydrologic Vulnerability: A Model-based, Landscape-scale Method for Assessing Impacts of Climate Change on Aquatic Resources. Davis, CA: University of California, Davis.
- Hodgkins, G. A., R. W. Dudley and T. G. Huntington. 2005. “Changes in the Number and Timing of Days of Ice-Affected Flow on Northern New England Rivers, 1930–2000.” Climatic Change 71 (3): 319–40.
- Hulley, M., C. Clarke and E. Watt. 2013. “Occurrence and Magnitude of Low Flows for Canadian Rivers: An Ecozone Approach.” Canadian Journal of Civil Engineering, 41 (1): 1–8.
- Hurd, B., N. Leary, R. Jones and J. Smith. 1999. “Relative Regional Vulnerability of Water Resources to Climate Change.” Journal of the American Water Resources Association 35 (6): 1399–410.
- Manning, J. C. 1997. Applied Principles of Hydrology. Upper Saddle River, NJ: Prentice Hall.
- MOE (Ministry of the Environment). 2007. Guide to Permit to Take Water Application Form. https://dr6j45jk9xcmk.cloudfront.net/documents/929/3-4-2-guide-to-permit-to-take-water-en.pdf.
- MOE (Ministry of the Environment). 2010. Guide for Applying for Approval of Sewage Works. http://www.ontla.on.ca/library/repository/mon/24006/302534.pdf.
- Nelitz, M., Boardley, S. and Smith, R., 2013. Tools for Climate Change Vulnerability Assessments for Watersheds.
- OMNRF-OFAT. (2015). Ontario Flow Assessment Tool (OMNRF-OFAT). Toroto: Queen’s Printer for Ontario. http://www.gisapplication.lrc.gov.on.ca/webapps/OFAT/Viewer/Viewer.html?lang=en-US
- Parry, M. L., O. F. Canziani, J. P. Palutikof, P. J. van der Linden and C. E. Hanson (editors). 2007. Climate Change 2007: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press.
- Pilon, P. J. and K. D. Harvey. 1994. Consolidated Frequency Analysis (CFA), Version 3.1 Reference Manual. Ottawa: Environment Canada.
- Rudra, R., I. Ahmed, A. A. Khan, K. G. Singh, P. K. Goel, M. Khayer and T. Dickinson. 2015. “Use of Baseflow Indices to Delineate Baseflow Dominated and Rapid Response Flow Dominated Watersheds.” Canadian Biosystems Engineering 57 (1): 1–11. https://doi.org/10.7451/CBE.2015.57.1.1.
- Singer, S. N. and C. K. Cheng. 2002. An Assessment of the Groundwater Resources of Northern Ontario. Hydrogeology of Ontario Series, Report 2. Toronto: Ontario Ministry of the Environment, Environmental Monitoring and Reporting Branch.
- Smith, R. L. and I. Weissman. 1994. “Estimating the Extremal Index.” Journal of the Royal Statistical Society, Series B (Methodological) 56 (3): 515–28.
- Stewart, I. T., D. R. Cayan and M. D. Dettinger. 2004. “Changes In Snowmelt Runoff Timing in Western North America Under a ‘Business As Usual’ Climate Change Scenario.” Climatic Change 62 (1–3): 217–32.
- Tiburan, C., I. Saizen, K. Mizuno and S. Kobayashi. 2010. “Geospatial-Based Vulnerability Assessment of Watersheds in the Philippines.” USM R&D Journal 18 (2): 161–9.