report

CONTENTS

1. Project overview ................................................................................................................... 1

1.1 Introduction ...................................................................................................................... 1

1.2 Project goal, geographic area and time schedule ............................................................. 1

1.3 Technical background ...................................................................................................... 1

1.4 Data .................................................................................................................................. 1

1.5 Working group ................................................................................................................. 1

1.6 Results .............................................................................................................................. 1

2. Task introduction ................................................................................................................. 2

2.1 Problem analysis .............................................................................................................. 2

2.2 Goal of work ..................................................................................................................... 3

3. Methodology ......................................................................................................................... 3

3.1 Geographic area - watershed Bystrianka .......................................................................... 3

3.2 Original model of snow accumulation and melting ......................................................... 4

3.3 Digital elevation model of the water basin ....................................................................... 5

3.4. Implementation of model into GIS .................................................................................. 6

3.5 Application’s functionality and methodology of precision evaluation ............................ 7

4. Analysis of results ................................................................................................................. 9

4.1 Technical solution ............................................................................................................ 9

4.2 List and description of modules ....................................................................................... 9

4.4 Detailed list of modules .................................................................................................. 10

4.5 Functionality and precision of application ..................................................................... 12

5. Annexes ............................................................................................................................... 14

5.1 List of used literature ...................................................................................................... 14

5.2 List of tables ................................................................................................................... 14

5.3 List of pictures ................................................................................................................ 14

1. Project overview

1.1 Introduction

One of the tasks of Slovak Environmental Agency (SEA) is creation of Environmental Action

Plans (EAP) for areas (known as threatened in Slovakia) with bad environmental conditions.

Development of EAP includes the evaluation of the environment and setting goals for achieving better or at least preserving the current status. Process of EAP development should contain also tools for simulation of quality changes of individual environmental components.

1.2 Project goal, geographic area and time schedule

The goal of working group was the development of software application for simulation and prediction of quantity and quality of runoff by use of tools contained in geographic information systems ArcInfo and ArcView. Geographic area was the basin of Bystrianka stream - picture No.1. Work started in June 98, time schedule is outlined in table No.13.

1.3 Technical background

Application with working name Bystrianka was developed for two environments. Basic version specifically designed for watershed Bystrianka was programmed in GIS ArcInfo using Arc Macro Language. GIS ArcInfo v.7.1.1 was used on workstation SUN Sparc Ultra 1 with operating system Solaris 2.6. Advanced version of application was designed to work with data from any water basin. It was programmed in object programming language Avenue, which is part of GIS ArcView. GIS ArcView v.3.0.a was used with extensions Spatial Analyst v.1.0 and Dialog Designer v.1.0 on PC Fujitsu Lifebook 675Tx with operating system

Windows 98.

1.4 Data

Input data included digital elevation model created by Slovak Environmental Agency (SEA), daily average temperatures, daily totals of precipitation and water equivalents of snow (WES) observed by Slovak Hydro-Meteorological Institute (SHMI).

1.5 Working group

Mr. Anton Bartko, Mr. Rudolf Navrátil - SEA Banská Bystrica

Mrs. Gabriela Babiaková - SHMI Bratislava

Mr. Ján Tuček, Mr. Erich Pacola - Technical University Zvolen

1.6 Results

Logic of implemented algorithms was approved in both water quantity and quality at chosen water gauge stations. Acceptable coincidence of calculated and measured values was achieved. Results from simulation of seasonal snow cover quality were not acceptable.

Advanced version of the application can by used to forecast runoff and concentration of sulphates at chosen stream water gauge stations in areas where weather and geographical conditions are similar to ones observed in the basin of Bystrianka.

- 1 - Copyright@SEA and US EPA, 1999

2. Task introduction

2.1 Problem analysis

The most serious consequences of acid atmospheric deposition are the deterioration of soil and consequently the surface and ground water quality, forests and other environmental components. These consequences are into large extent dependent upon physical and chemical quality of soil cover and development of snow cover accumulation in the mountain conditions. These areas are characteristic by typical development of snow cover in the course of long winter periods whereupon deposition and washout of the contaminants are directly related to its accumulation and melting. Processes of snow accumulation and melting on the other hand significantly influence runoff in mountain watersheds.

Problems related to course of snow cover had been solved by specialists from SHMI and

Institute of Hydrology (IH) of the Slovak Academy of Sciences (SAS) for a long time.

Research work at these institutes is concentrated on number of tasks such as simulation of relationship between meteorological factors and depth of snow cover or its water equivalent, chemical composition of the snow, runoff from melting, influence of water on damages in streams, accumulation and transport of contaminants or their influence on soils, forests, etc.

Large number of papers concentrated on these issues was published. Several specific projects were carried out especially with aim to utilize database of IH for streams Bystrianka and

Jalovecky potok. Systematic long-term measurements exist mainly for watershed of

Bystrianka. Results were thoroughly analyzed and repeatedly published. Three studies by

Babiaková, Bodiš (1985), Babiaková, Bodiš, Palkovič (1988) and Babiaková, Bodiš, Palkovič

(1990) were taken into account in this project. These studies were chosen with respect to presented completeness of discussed issues.

Mentioned publications completely solve meteorological, hydrological and emission problems but because of time of their completion they don’t utilize GIS technology. Potential use of GIS in discussed field demonstrate projects of:

A.

United States Environmental Protection Agency: ESTAT - Ecological Sensitivity Targeting and Assessment Tool is designed for simulation, analysis and mapping of surface water contamination by PROUTE subsystem and air by ISCLT2 subsystem. Application is created in GIS ArcInfo environment by means of internal programming language AML and works with Oracle RDBMS data. BASINS - Better Assessment Science Integrating Point and

Nonpoint Sources is tool designed for simulation of water quality including euthrophication

(subsystem QUAL2E), simulation of contaminants transfer in river network (subsystem

TOXIROUTE) and estimation of water contamination from nonpoint sources (subsystem

NPSM_HSPF). Individual subsystems are programmed in languages C and C++. Integration, visualization and user interface is provided by GIS ArcView.

B.

Slovak Environment Agency: MINDER - is tool originally created by Water Research

Center in Swindon UK but substantially changed and accommodated for simulation of surface water contamination by phosphorus from point and nonpoint sources. System was created in

GIS ArcInfo environment by means of AML language.

Copyright@SEA and US EPA, 1999

- 2 -

2.2 Goal of work

Goal of work is adaptation of snow accumulation and melting model for Bystrianka water basin created by SHMI and IH and its implementation into GIS. Besides its basic functionality, model contains also part intended for simulation of contaminants accumulation and their washout from the snow cover.

Results (calculated values) produced by original model are nearly identical with measured values and thus it was assumed that model exhibits logical correctness. Spatial application of original method within ArcInfo for whole area of basin was based on the assumption outlined in previous sentence. New application was created with purpose to be used in two ways. First way is simulation of system behavior based on measured values of input and output factors.

Second way is prediction of WES and runoff based on various scenarios of meteorological situation.

Both parts (simulation and prediction) enable to calculate described output values for every smallest element of the space. Simulation part enables to test correct behavior of the model.

Prediction part enables to develop scenarios which answers the questions like: „What if“.

Model also contains apparatus for solving spatial contamination of seasonal snow cover and contamination of water at watershed's outlet.

3. Methodology

3.1 Geographic area - watershed Bystrianka

Detailed characteristics of chosen watershed are included in above-mentioned studies. Water basin of Bystrianka stream is situated in Low Tatra Mountains. Allocation of Bystrianka water basin within Slovakia is shown in pic. No.1. It is considered to be representative for the whole Low Tatra Mountains with respect to formation of snow supplies. Area of basin is 23.4 km

2

. It is typical rough mountain relief where elevation varies from 700 m to 2043 m above sea level. Spruce forests cover about 60 % of the area at elevations-1500 - 1550 m above sea level. They continually change into mixed ones at elevations 1100 - 1150 with major representation of beach at lower elevations. Soil profile is represented by brown forest soil with nonsaturated sorptive complex. Its depth varies with elevation. Generally, it decreases with increasing elevation and from 1850 m appears only parent rock. Detailed relief analysis can be performed from DEM, which was created for project needs (see later).

Typical snow accumulation period in our latitudes is months December - February. Majority of water contained in snow is created during these months. The same applies for accumulation of contaminants in the snow cover. However, examined period was expanded from November to April with respect to the presence of higher elevations within Bystrianka basin. Climatic and meteorological measurements were taken from relevant meteorological stations Brezno

(570 m) and Chopok (2043 m). Allocation of the stations relatively to the area of the basin is convenient, station Chopok is situated on its territory (see picture No.2).


3.2 Original model of snow accumulation and melting

Original model methodology and results were repeatedly published as is mentioned above.

Study published by Babiaková, Bodiš, Palkovič (1988) was the most suitable for achievement the goal of this project. Study presents model as simulation and prediction tool for factors like

WES and concentration of sulphates in the system atmosphere/snow cover. Idea of the study is that process of snow supplies formation and changes of sulphates content in the snow cover are connected. Model is composed of hydrologic and sulphate subroutines.

Hydrologic subroutine describes circulation of water within the system. It uses input data about precipitation and air temperature. Output data are WES and snow melting expressed in millimeters as daily water discharge. Amount of melted water is calculated by method of degrade factor FI (intensity of thawing in mm/+ o

C/day). FI is considered to be integral expression of effect caused by multiple meteorological factors e.g. air temperature, albedo, radiation, moisture, wind, etc. Values of FI vary in the course of winter period depending on mean day temperatures.

It was not possible to take into account thorough geographic variability of temperatures and precipitation in original model. Only three elevation zones were used. They were created on the basis of prevailing type of vegetation cover:

elevation zone up to 1150 m with prevailing mixed forests on the area of 5.53 km

2

elevation zone from 1150 m up to 1550 m with prevailing spruce forests on the area of

11.66 km 2

elevation zone above 1550 m with incoherent stands of dwarf pine on the area of 6.45 km

2

Temperature estimates for centers of elevation zones were calculated by linear interpolation from data measured at stations Brezno and Chopok. Estimates were carried out in one-hour intervals and resulting mean day temperature was calculated. One-hour time interval was used because of need to study the influence of air temperature on the snow cover, which on the other hand influences concentrations of sulphates. Mean day value smoothes eventual extremes and fluctuations. That is why this approach was used for calculation of snow melting. However, daily water discharge and sulphates concentrations were calculated in onehour interval.

Distribution of precipitation into space of the basin from measured point values was serious problem because of complex mountain conditions. Several methods were used. Method of elevation gradient, dependence of precipitation on elevation expressed by polynomial and methods of historic analogy. Despite of known drawbacks and limitations, method of partial zonal gradients was used.

Calculation of snow accumulation/melting in individual elevation zones represented by mean height values was carried out by means of application of degrade factor FI. It enters formula as variable parameter dependent on mean day temperature and month. Representative curves of factor FI are explained in the study by Babiaková (1985). Every curve represents interval

2,5 o

C of the mean day temperature.

If air temperature is lower than 0 o

C and precipitation does not occur, WES does not change.

If there is precipitation during subzero temperature precipitation is accumulated in existing snow cover i.e. WES increases. If air temperature is above 0 o

C snow melts. In this case the


- 4 -

amount of water in the snow also increases until water discharge occurs. If there is precipitation during above-zero temperature water discharge is even higher.

Simulation of runoff is solved by means of empirical - regression model derived explicitly for chosen watershed. Model works with 24 hours interval. It is described in detail in study by

Babiaková, Bodiš, Palkovič (1990). Amount of runoff is empirically calculated by means of two components. Decreasing component of the hydrogram takes into account diminishing value of runoff from previous day. Increasing component of the hydrogram contains water, which originates either from snow melting process or precipitation or both also from previous day. This amount of water enters empirical formula as mean of modeled values for three elevation zones.

Simulation of snow cover contamination by sulphates is described in study by Babiaková,

Bodiš, Palkovič (1990). The same applies for contamination of water at watershed's outlet.

Concentration of sulphates in snow cover is calculated by functional relationship. It expresses dependency of concentration on the amount of water coming from snow melting process expressed in % from total snow amount. Real values of sulphates washout from snow cover were found out by experiments. Results are described in the study by Babiaková, Bodiš,

Palkovič (1988).

Calculation of SO

4

concentration at watershed's outlet was based on the assumption that the changes, which took place in the stream represented the processes of "concentration and dilution". It was found out from the observation that in the period without any SO

4

coming from basin the change in the flow corresponds to the outflow curve equation. With the discharge increase, which follows from snowmelt or precipitation an increase of sulphates content occurs. The increase/decrease of SO

4

content depends on the amount of discharge and the duration of the contribution episode.

Model described above was tested. Calculated and measured values were compared for winter periods 84/85, 85/86 (Babiaková, Bodiš, Palkovič 1988) and 86/87, 88/89, 89/90 (Babiaková,

Bodiš, Palkovič 1990). Results of testing were in both cases i.e. amount of water and concentration of sulphates in snow cover as well as water at watershed's outlet evaluated as acceptable.

3.3 Digital elevation model of the water basin

The whole process of simulation was carried out on DEM of Bystrianka basin. Hydrologically correct DEM was created by means of tools of TOPOGRID, which is part of ArcInfo. Basic input data used for DEM creation were contours and stream network. These data were obtained from basic maps of Slovak Republic at scale 1 : 10 000 by means of vectorization.

Better delineation of relief was achieved by use of many points from triangulation and nivelation networks. Watershed boundary was automatically generated by use of ArcInfo

GRID function - watershed.

Parameters of used DEM: pixel (cell) size number of rows number of columns format coordinate system

10 x 10 meters

892

1040 grid / Arc/Info

S-JTSK


3.4. Implementation of model into GIS

Sun Ultra 1 with the following configuration was used: graphics Creator 3D, 128 MB RAM,

10GB HDD with OS Solaris version 2.6. Application was developed for ArcInfo version

7.1.1. Majority of algorithms uses tools of GRID i.e. works with discrete raster structure.

Tools of map algebra are interconnected by means of AML routines. Application has its own graphical user interface (GUI) which is derived form ArcTools menu system. Coding standards for creation of ArcInfo utility tools were kept during GUI development.

Application GUI is activated from ArcInfo - arc prompt. It is composed of modules designed for simulation, prediction and visualization of output rasters and graphs. All modules are accessible from dialog windows. These are started from main menu.

Original model was not changed during its implementation into ArcInfo. Changes of environmental parameters can be explained in more detail when these are studied within GIS environment. GIS allows thorough geographic description of environmental parameters. If they enter into model as input data we can expect more precise output values. What is more,

GIS enables the simulation of output parameters for every portion of considered space.

Original model calculated only some output values in one-hour interval. Developed application calculates all output parameters in 24 hours interval.

Simulation part of the application uses measured input data about precipitation and air temperature from meteorological stations Brezno and Chopok. In the case of precipitation it is possible to use also data from stations Jaraba and Myto pod Dumbierom. Input data was compiled into ASCII file in the form: date mean_day_temperature sum_of_day_precipitation.

One file for every considered meteorological station is required. This file has to contain above-mentioned data for expected simulation period. Simulation does not need to be processed in one turn. Dialog window allows entering dates of first and last days of the simulation. Spatially defined source of input parameters is DEM of water basin. It defines elevations of relief divided into regular elements - cells. Calculations of all derived parameters and output values are performed for all cells.

Spatial distribution of temperature and precipitation over the basin is solved by linear regression model from values measured at mentioned meteorological stations. Results of these map algebra functions are rasters of temperatures and precipitation. Raster of temperatures together with date information is variables defining the values of degrade factor FI for every spatial cell of the basin. Curves defining the change of FI based on mean day temperatures and months are applied. Final calculations of WES and water discharge are carried out again for every cell by use of individual values of temperature, factor FI and precipitation for every day defined in time row of input ASCII file between first and last days of simulation.

Model core is outlined in flow chart in picture No.3. Algorithm exactly matches the approach used in original model (see chapter 2.2). Outputs from model are rasters defining values of

WES and water discharge for every spatial cell. Raster of WES is modified on every day of the simulation. Raster of water discharge from every day of the simulation is used to calculate the value of runoff at gauging profile of the watershed's outlet. Calculation of runoff is carried out according to the same methodology as was used in original model. Value of runoff for every day of the simulation is written into INFO database table FLOW[date].DAT.


- 6 -

One of the outputs is point coverage, which defines positions of individual snow control points. This coverage helps to process the results of control testing. It is used for extraction of pixel values from any raster, e.g. raster of WES or raster of water discharge. Extracted values are written into INFO database table VCOV[date].PAT. INFO table can be exported to other formats that are more suitable for further processing e.g. comparison with measured values, graphs construction or statistical analysis.

Application’s part designed for prediction of WES, water discharge and runoff uses similar principles. Input data is in this case interactively entered date, expected values of air temperature and precipitation for meteorological stations Brezno and Chopok. If prediction for longer period is required initial files containing expected values of temperature and precipitation for every considered station and day of prediction must be constructed in the same way as it is described in simulation part of the application. Outputs are the same as in the case of simulation.

Hydrologic part of the application is composed of two models - runoff model and sulphate model. These models are activated on demand directly from simulation or prediction dialog windows. Prediction of runoff is based on entered value of measured runoff from previous day. Output (predicted) value is written into INFO database table, which can be exported to other file formats for further processing.

Input data for sulphate model come from monitoring of dry and wet depositions. This data is observed at meteorological station Chopok and is used for modeling of SO

4

concentrations in snow cover. Concentrations of SO

4

at watershed's outlet are calculated from following input data: measured concentration of SO

4

in water from previous day and calculated value of runoff for actual day. Outputs of SO

4

model are: raster[year_month_day] with calculated values of SO

4

concentrations in snow cover, point coverage defining localities of snow control points and INFO table FLOW[year_month_day] with item FLOW_MODEL which contains calculated values of SO

4

concentration in water at watershed's outlet. Concentrations of SO

4

in snow cover are extracted at snow control points by means of above mentioned point coverage and are written into individual INFO database table CCOV[year_month_day].PAT.

Values are then compared with field data.

3.5 Methodology of precision evaluation

Functionality of individual modules was gradually examined in the process of application development. Internal tools of AML language controlled logic and quantitative correctness.

Unpredictable faults are managed by directive SEVERITY and standard routines BAILOUT and EXIT. All input parameters are controlled with respect to their structure and range of used values. If incorrect value is entered the system queries the user to check the value.

Simulation part of the application enables to test its logic and quantitative correctness of modeling process. This type of application is possible if required input data are available and also measured values of output parameters i.e. WES measured at different elevations (snow control points), runoff data measured at watershed's outlet, concentrations of SO

4

in the snow cover at different locations (snow control points) and concentrations of SO

4

in water at watershed's outlet, all for different time periods.

All required information was available with respect to detailed documentation about original model. Standard meteorological data for stations Brezno and Chopok as well as runoff data for gauging station Tale exists in archive of SHMI.


Limiting factor is data about snow cover i.e. its height, WES and chemical composition at different locations. IH of SAS carried out systematic measurements of these parameters at 30 snow control points representatively distributed over the whole area of the basin. Selection of their positions takes into account variability of snow supplies in the basin (see picture No.4).

Snow control point measurements from five winter periods (87/88 - 91/92) were used. Two or three measurements were available for every snow control point and winter period. Initial results of the measurements - WES - are listed in table No.1. Control of correct functionality of the application was carried out for five mentioned winter periods.

Example of commonly monitored data about temperature and precipitation at meteorological stations Brezno and Chopok are listed in table No.2. This data was obtained from SHMI in

MS Excel format. Data was processed in MS Excel and mean temperatures for every day were calculated. Compiled tables were exported into ASCII format. Example of its content for station Brezno and month November is in table No.3. It has to be mentioned that during testing of presented application files contained data for the whole winter period.

Simulation can be executed for any time period if commented input files containing data from this period exist. Only first and last dates of simulation have to be entered and simulation can start. Testing of application was carried out by entering last dates for which measured data from snow control points existed (see table No.1). Tests were carried out for 13 simulation periods.

Individual values of WES were extracted from grid of accumulated WES for each snow control point and each control date. Extracted values and point‘s IDs were written into ASCII file. File was marked by control date i.e. date when snow samples were taken in the field. All files (one file for each control date) were imported into MS Excel for further processing, comparison and analysis.

First of all, mean values of WES for individual elevations were calculated (see table No.1).

Network of snow control points was dense enough so that there were always several points at the same elevation. Comparison of individual values of WES at snow control points in some cases showed that measured values were considerably higher than calculated ones. That is why the sum of precipitation (accumulated value) resulting from time span of simulation period was added into graphs as control value. It has to be pointed out that used method of precipitation distribution into space from point values (meteorological stations) substantially influences the process of simulation and its results.

Next step was the calculation of differences between measured and calculated values of WES.

Differences were expressed in % from measured values. Measured and calculated values were finally plotted in XY graphs as values dependent on elevation.

ASCII files with calculated values of runoff for individual days of simulation were imported into MS Excel for comparison with measured values. Structure of both files was the same

(date value_of_water_flow). Comparison was carried out in similar way as in the case of

WES i.e. differences between measured and modeled values were calculated and expressed in

% from measured values. Finally, results were visualized in graphs.

The runoff was calculated by means of the same method as was used in original model and also by other methods. One of them was the derivation of regression relationship where runoff is function of daily water discharge from water basin area on actual day or several latest days


- 8 -

and runoff of previous day or other factors. Values of runoff calculated by application were in all cases arranged and evaluated in the same way as described above.

Concentrations SO

4

were calculated from snow samples taken at above mentioned snow control points. This data from 88/89 winter was used to test functionality and precision of SO

4 model.

Procedure of results evaluation was similar as with parameters mentioned so far.

Concentrations of SO

4

were extracted from resulting raster at snow control points for every control day into INFO table CCOV[year_month_day].PAT. This table was exported into dBase format and consequently imported into MS Excel for comparison with measured values. Differences were calculated and expressed as fractions of measured values. All results were summarized in table and plotted in graphs.

Concentration of sulphates in water at watershed's outlet was found out by means of standard procedure. Measured values for winter periods 87/88, 88/89 and 89/90 were used to test correct functionality of sulphate model. Test procedure was the same as with runoff model i.e. methodology of SHI was used.

4. Analysis of results

4.1 Technical solution

One of the previous chapters describes how was the original model implemented

(programmed) into ArcInfo. Information about technical solution provided in this chapter is rendered in easy way so that it is understandable to ordinary user without thorough programming skills or GIS background. Individual parts of the application are described in more detail. Functionality of modules as well as relationship among them is explained. Exact names of modules are also stated. Logical part of the chapter would be source codes of individual modules. However, it is so specific and large that it was not included into this report.

System of modules (application) enables the user to carry out tasks described in chapter 3.4.

Further functionality includes means for simple but powerful visualization of results (rasters, graphs and tables). These are also briefly described in the following chapters.

4.2 List and description of modules

BYSTRIANKA.AML and BYSTRIANKATOOL.AML - core modules. They activate main menu and environment variables.

Visualization utilities:

DISPLAY_THEME.AML - visualization of rasters with modeled values of snow water capacity, daily water discharge and SO

4

concentrations.

GRAPH_THEME.AML - visualization of point and line graphs for modeled and measured values of snow water capacity, water flow and SO

4

concentrations at water basin outlet.


Utilities designed for work with ARC/INFO data structures:

COVER_MNGR.AML - copying, renaming, deleting and description of vector layers.

GRID_MNGR.AML - copying, renaming, deleting and description of raster layers.

TABLE_MNGR.AML - copying, renaming, deleting and description of INFO database tables.

JOIN_INFO.AML - joins two INFO tables. It is used to join table containing measured values with table containing modeled values. Join is carried out by means of identical columns

(relational items). These are IDs of snow control points.

DBDOINFO.AML - conversion of dBase table into INFO table and vice versa.

Function modules:

BYSTRSIMUL.AML - calculation of output values from assumed (or calculated) input data

BYSTRREAL.AML - calculation of output values from measured input data

4.3 Modules sorted hierarchically

BYSTRIANKA.AML

BYSTRIANKATOOL.AML

BYSTRIANKA.MENU

DISPLAY_THEME.AML COVER_MNGR.AML BYSTSIMUL.AML

COVER_MNGR.MENU BYSTRSIMUL.MENU DISPLAY_THEME.MENU

GRAPH_THEME.AML BYSTRREAL.AML

BYSTRREAL.MENU GRAPH_THEME.MENU

GRID_MNGR.AML

GRID_MNGR.MENU

TABLE_MNGR.AML

TABLE_MNGR.MENU

JOIN_FILE.AML

JOIN_FILE.MENU

DBDOINFO.AML

DBDOINFO.MENU

4.4 Detailed list of modules

DISPLAY_THEME getgrid.aml getgrid.menu davka2.rmt davka.key


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davka_text.txt so2.rmt so.key so_text.txt vhs2.rmt vhs.key vhs_text.txt

GRAPH_THEME getgrid.aml getgrid.menu gettable.aml gettable.menu linesymbol.aml, .menu markersymbol.aml, .menu shadesymbol.aml, .menu textsymbol.aml, .menu graph_theme_props.menu getrealte.aml, .menu

COVER_MNGR get_routines.aml copycov.aml copycov.menu renamecov.aml renamecov.menu

GRID_MNGR get_routines.aml copygrid.aml, .menu renamegrid.aml, .menu

TABLE_MNGR copytable.aml, .menu renametable.aml, .menu

JOIN_ITEM gettable.aml, .menu

BYSTRSIMUL getroutines.aml getfile.aml, .menu gettable.aml, .menu getitem.aml, .menu getitem_depozit.aml, .menu sira_model.aml

BYSTRREAL getroutines.aml


msresponse3.aml, .menu

4.5 Functionality and precision of application

As it is mentioned in chapter 3.5 great attention has been paid to logical as well as quantitative and qualitative correctness of the application. Simulation and prediction parts of the application were tested using measured data from five winter periods (87/88 - 91/92).

Coincidence of calculated and measured values was evaluated in the case of simulation part.

Prediction was based on alternative i.e. expected or real input parameters. Logical and functional correctness was approved in both cases. Acceptable coincidence of calculated and measured values was achieved. Functionality tests of modules for visualization of results were also successful.

Majority of evaluation work was dedicated to simulation part of the application. Only this evaluation provided the possibility to compare calculated values with measured ones. Good results of this evaluation provide the basic assumption for possible use of the application for prediction and they enable users to consider precision and limits of obtained output values.

Snow samples were taken at snow control points shown in picture No.4. WES data obtained from these samples are in tables No. 4 and 8. All data were sorted in time. Influence of season

(month of sampling) on results was considered. Since this influence was not proved only some tables with data sorted in time were included into this report. However, summaries of all measurements were sorted according to years in table No.9 and months in table No.10.

Values in tables show that deviations of modeled values from measured ones are not systematic. Year or month of sampling does not influence them. Positive and negative deviations are relatively balanced although underestimated modeled values slightly prevail.

Average values of deviations (expressed absolutely) for whole set of measurements carried out in one time reach from 2.2% to 36% with individual minimum differences equal to 0% and one maximum difference equal to 188%.

For better transparency were results plotted in graphs and arranged according to years - pictures No.5 - 9 and months - pictures No.10 - 13.

Measured value of WES was in comparison considered as correct one. However, it is important to mention that height of snow cover as well as processes of its accumulation and melting are in reality influenced by many other factors and not only elevation which presented application takes into account. Other factors might include horizontal transfer of snow and rain precipitation, influence of aspect and forest cover, etc. Major problem of presented application arises from applied simple linear method of precipitation distribution into space from point values. It is hard to consider the improvement of this part of the application.

Problems with geographic modeling of precipitation distribution in general were mentioned in chapter describing original model. This drawback is also depicted in graphs (pictures No. 5 -

13) by lines symbolizing sums of precipitation as calculated by application for individual snow control points i.e. elevations. It is obvious that amount of snow precipitation at meteorological station Chopok (2043 meters above sea level) is considerably lower than are real measured values. This difference between snow precipitation measured on day n and


- 12 -

WES expected at the same site on day n + 10 is caused by wind. Snow is blown from mountain ridges down. In this case the gradient of precipitation should be higher or precipitation should be distributed into space by polynomial function with maximum at elevation 1600 - 1700 meters. This type of improvement is possible only if there is much more data from measurements carried out at mentioned elevations.

Establishment of new meteorological stations or at least lateral precipitation points is not real at this time. Effort should be concentrated on use of existing data. Improved estimates of vertical change of snow precipitation can be achieved by use of data from meteorological stations Jaraba and Myto pod Dumbierom. However, these stations are outside of considered watershed and are located at lower elevations.

Despite of mentioned problems it can be stated that application achieves results comparable with above cited works by Babiakova, Bodis and Palkovic. Improvement of modeled values can be achieved by further modifications of chosen parameters. This approach requires more resources and activities in the area of basic research.

Runoff values were calculated mainly by means of original method used at SHMI. One of the other applied methods calculated runoff as function of water discharge from the area of the basin on actual day or several latest days. Method of Multiple Linear Regression and method of Distributed Lags Analysis known from analysis of two connected time rows were also used. The aim was to express the changes in runoff on the basis of casual factors without need to use the measured value of runoff from previous day.

Comparisons of measured and calculated values of runoff in graphs are shown in pictures:

No.14 - multiple linear regression where (runoff = f(water discharge on actual day..water discharges of 5 days backward)) and No.15 - distributed lags analysis with 5 days shift backward. Summarized comparison of deviations obtained from all used methods is in table

No.11.

Results show that changes of runoff are rather dynamic i.e. are influenced by other factors.

Acquisition of information about these factors is problematic so that determination of runoff without use of measured value of runoff from previous day is not precise enough. Measured value of runoff from previous day is used in original method of SHMI. This method is multiple linear regression where runoff on actual day = f(runoff from previous day, water discharge on actual day..water discharges of two days backward). Results of comparison for this method are shown in pictures No.16a, 16b, 16c. Results of another (slightly different) statistical analysis are in picture No.17.

The most problematic part related to implementation of original model into GIS was the simulation of SO

4

concentration in the snow cover. Acceptable results comparable with outputs from original model were not achieved despite of various modifications and changes applied in algorithms. Deviations of modeled values from measured ones were very high so they are not included in this report.

Simulation of SO

4

concentration in water at watershed's outlet matches the original model.

Graphical representation of results and comparison of calculated and measured values are shown in pictures No.18 for winter period 87/88, No.19 for winter period 88/89 and No.20 for winter period 89/90. Summarized quantification of deviations is shown in table No.12.


Both issues commented in last two paragraphs need further testing related to possible use of other known methods and work in the field of basic research.

5. Annexes

5.1 List of used literature

1. Babiaková, Bodiš, Palkovič: Contribution to simulation of quantity and quality of seasonal snow cover, Vodohospodársky časopis, 36, 1988, No. 4, p. 361-375

2. Babiaková: Accumulation of snow supplies in mountain water basins and calculation of water flow hydrogram, Vodohospodársky časopis, 33, 1985, No. 1, p. 23-45

Babiaková, Bodiš, Palkovič: Hydrological and hydrochemical response of water basin,

Vodohospodársky časopis, 38, 1990, No. 4, p. 427-452

5.2 List of tables

1.

Locations of snow control points, WES in millimeters

2.

Example of commonly observed data at meteorological stations

3.

Example of input file for simulation in ASCII format (station Brezno, November 1988)

4.

WES - evaluation of deviations between measured and calculated values in 1988

5.


6.


7.


8.


9.

WES - comparison of deviations between measured and calculated values according to years - summary

10.

WES - comparison of deviations between measured and calculated values according to months - summary

11.

Summarized evaluation of accuracy achieved in runoff calculations (calculated values)

12.

Summarized evaluation of accuracy achieved in sulphates concentration calculations in water of Bystrianka at Tale profile

13.

Time schedule

5.3 List of pictures

1.

Location of Bystrianka basin in Slovakia

2.

Locations of considered meteorological stations

3.

Flow chart of implemented model

4.

Location of snow control points in the basin

5.

WES - graphical comparison of measured and calculated values in 1988

6.


7.


8.


9.


10.

WES - graphical comparison of measured and calculated values in January

11.

WES - graphical comparison of measured and calculated values in February

12.

WES - graphical comparison of measured and calculated values in March


- 14 -

13.

WES - graphical comparison of measured and calculated values in April

14.

Runoff - graphical comparison of measured and calculated values - multiple linear regression based on water discharge values of five days backward

15.

Runoff - graphical comparison of measured and calculated values - distributed lags analysis - five days

16.

Runoff - graphical comparison of measured and calculated values in 87/88 - original method of SHMI

17.


18.


19.

Runoff - graphical comparison of measured and calculated values - multiple linear regression based on measured value of runoff from previous day and water discharge values of two days backward

20.

Sulphates concentration in water - graphical comparison of measured and calculated values in 87/88

21.


22.



report

1. Project overview

2. Task introduction

3. Methodology

4. Analysis of results

5. Annexes

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report

1. Project overview

2. Task introduction

3. Methodology

4. Analysis of results

5. Annexes

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