HIND, OOMN, SDN, SPUB, SROT, BGBI

KRUPNIK 0.867 SPEC, BGBI, OIOX, NITR!

IIWQC 0.878 SPEC, BGBI, OIOX, NITRI, AMON

0.9 OOMN, EVNS, SROT, BGBI, OIOX, AMON 0.934 HIND, EVNS, SROT, BGBI, DIOX, AMON

MARINO POLE 0.597

mox

IWQC 0.58 NUMB, OOMN, BGBI, POXY,NTRl

WQC= water quahty class

RESULTS & DISCUSSION

Using the discrete data series (stretch by stretch), the results obtained were much more satisfactory. There is strong correlation between cenotic parameters and the flow characteristics (i.e. the module). The very specific feature of these relations was the fact that this correlation had different direction (positive or negative) for each site/stretch depending mostly on the water quality/category. In cases of higher pollution (saprobity) level, the larger discharge retlected in worse community parameters. In more stable and moderate saprobic conditions lower discharge caused restructuring the conununity to figures relevant to lower water quality class.

The possible conclusion is that the relations between the discharge and the community parameters were not one digit determined and many factors played role in forming the biotic responses to the dynamic of hydrological situation at a given site/stretch. The multiple regression analysis showed the participation ofall cornmunity parameters (Tables 4) (Razhdavitsa)) responsible for assessment of both water quality and its biological sufficiency. Amongst them, ail three groups of indices widely used for water quality assessment (see Knoben at al., 1995) are presented: saprobic index (SPUB, SROT), species diversity indices (SDN, HIND, EVNS, OOMN) and biotic index (BGBI). The total R²grew up to 0.822 (for the site of Pernik) and 0.910 (for the site of Razhdavitsa) when only biotic indices were used. In case we included sorne hydrochemical parameters such as dissolved oxygen (OIOX), anunonia ions (AMON) and biochemical oxygen demand (B005), the same figures for R² grew up to 0.991 and 0.911 respectively. This is a subject of further discussion whether it is possible to reduce the number of biotic indices in use for water quality assessment while sorne of them are deri vative (like EVNS from HIND) or analogous (like SPUB and SROT) or reciprocal (like OOMN and EVNS). This is still unclear what kind of measure are biotic indices (like BGBI) used in most of the EU countries inspite of their mode of calculation.

Based on these relations, it looks possible to calculate the minimum water discharge (low tlow) for each site/stretch in terms of protection of the biologicallspecies diversity and conununity integrity as formed at a given (planned or actual) water quality class/category.

The results of calculation the ecologically admissible low tlow show that the value of tlow II water quality class is lower than the respective on the I and III. It is possible to give the follow explanations:

1. There is a non-linear regression between water discharge and ecosystem parameters.

2. The best ecosystem quality (in terms both of biodiversity and integrity) is II water quality class, where the rates of the organic matter production and destruction are the same (i.e.P/R= 1).

3. Target ecological status should be a natural equilibrium of the reverine ecosystems, characterized by maximum biodiversity, best community integrity and stable saprobic conditions.

4. Shift from l to II water quality class is relatively easy, but back from

ru

to II class needs much of water for reaching the desirable ecological state of the communities.

CONCLUSIONS

The main conclusions of the preliminary results represented might be:

1. There is significant correlation between cenotic parameters (used for water quality assessment) and the water discharge at a given level of pollution in terrns of water quality classes.

2. At relatively stable water quality (level of pollution) these relations are weil expressed for each of the sites/stretchesalong the river under study.

3. The results discussed above are very supportive in looking for a possible methodic for assessment and determination/calculation of such ecologically friendly low flow values which do not change the cenotic parameters of the river communities and protect the biological diversity within the national standards for each of the water quality classes.

4. The integral assessments of the ecological status of streamflow show sorne improvements for the last few years.

ACKNOWLEDGMENTS

The present report is a part of the proj ect No 288-732911999 sponsored by the Ministry of Environment &

Water.

REFERENCES

DAKOVA Sn., UZUNOV Y, MANDADJIEV D. Low flow - the river's ecosystem limiting factor, Ecological Engineering, Elsevier (in print)

KNOBEN, R.A.E., C.RooS, M.C.M.van OIRSCHOT. 1995. Biological Assessment Methods for Watercourses. - UNIECE Task Force on Monitoring& Assessment, vol. 3, 86 p.

MATTHEWS, R.A., A.L. BUIKEMA, 1. CAIRNS, J.H. RODGERS. 1982. Biological monitoring: Part lIa: Receiving system functional methods, relationships and indices, Water Res., 16: 129-139.

SAATY, T. 1990. Multicriterial decision making: the analytical hierarchy process, ARO Series, RWS, v. 1, 502 pp.

SAATY,T. 1996. Decision making with dependence and feedback: the analylic network process, RWS, 386 pp.

UZUNOV, Y, L. PENEV, S. KOVACHEV, P. BAEV. 1998. Bulgarian Biotic Index (BGBI) . an Express Method for Bioassessment of the Quality of Running Waters - Comp.Rend.Bulg. Acad.Sci., 51, No 11-12: 117-120.

VACHEV B. 1993, Research Procedure and Criteria for Analysis and Choice of Variants for Construction of National Radioactive Wastes Depository, In: IAEA regional project for technical assistance PET/9/0/0.

Recommendations on Management of Radioactive Wastes from VVER, 22-26 Feb., 1993, Sofia, 23-40.

***

Ordinance No 7 of the Council of Ministers on standards and indices for assessment of the water quality of superficial running waters, Official Gazette, 96, 1986.

METHODOLOGY FOR REGIONAL LOW FLOW ANALYSIS AND AN APPLICATION

Onoz B., Bayazlt M., Oguz B.

Division of Hydraulics, Istanbul Technical University, 80626 Istanbul, Turkey

ABSTRACT

Regional analysis of low flows helps to increase the information used in the frequency analysis over that supplied by the at-site data. A methodology is presented for trus analysis. Selection of the sites to be included, choice of the probability distribution function and parameter estimation method, goodness of fit testing, treatrnent of zero values are discussed. On an example it is found that power distribution with parameters estimated by LL-moments has the best fit to regional low flow data among the. 2-parameter probability distributions.

KEYWORDS

Regional analysis, low flows, frequency analysis

INTRODUCTION

Estimation of the characteristics of low flows of a stream is important in water supply planning, water quality management, navigation and for determining release policies from storage reservoirs in dry periods. Low flows are usually characterized by the annual minimum average discharge for a certain duration, such as 7 days. The probability distribution of the 7-day minimum flow should be known to estimate its value for a given return period, e.g. 10 years. The selected distribution function should have a good fit to the lower tail of the data.

Inmany cases available data are not of sufficient length to make reliable estimates on the basis of at-site observations only. Regional analysis may be helpful in such a case where data from a nearby homogeneous region are considered in determining the probability distribution function.

Regional analysis has been used extensively in flood studies but only a few studies have been found in literature conceming low flows. By regional analysis it should be possible to improve the quality of low flow estima tes over the at-sÎte analysis and to obtain estima tes at ungaged sites.

Most of the work on regional procedures in low flow analysis is restricted to those estimating low flow statistics by regression upon basin characteristics, which are shown to have large prediction errors (Stedinger et al., 1993). Gustard and Gross (1989) adopted the 2-parameter Weibull distribution as the regional probability distribution of low flows. Vukmirovic et al.(1998) discussed the steps in regional statistical analysis of low flows, such as regional homogeneity investigation, regression with basin area and determination of the probability distribution function. Durrans and Tomic (1996) analyzed the low flows observed in the State of Alabama and concluded that the log Pearson Type 3 distribution is a suitable candidate for modeling them, when the shape parameter is estimated on a regional basis.

In this study methodology for perforrning regional low flow analysis is presented. First step is to select the flow gaging stations to be included in the study and to apply discordancy analysis with the aim of detecting the sites in the region that are not homogeneous with the others. Simple and multiscaling analysis is then made to decide whether simple relationships hold between the statistical parameters and drainage basin areas of various sites in the region.

Having found that non-dimensional low flows of the sitesin the region follow a single distribution, the best-fit probability distribution function is chosen from among the candidates.

2- and 3-parameter probability distribution functions have been used for low flows. This study is confined to the investigation of 2-parameter distributions to reduce the sampling errors in parameter estimation. 1t is found that at sorne sites data at the lower tail follow a different distribution, in which case parameters should be estimated to give weight to lower flows.

Insorne regions low flow data at certain sites may have sorne zero observations. 1t is discussed how these sites should be treated in regional analysis.

The application of the method developed in the study is explained on an example.

Dans le document « Hydrologie des Régions Méditerranéennes » (Page 37-41)