1. E. Sustainable watershed management E1 - Erosion Control KEY – NOTE SPEECH “Ss. Cyril and Methodius” University in Skopje Faculty of forestry – Skopje, Macedonia Dept. of Land and Water http://www.sf.ukim.edu.mk THE THIRD WORLD CONFERENCE OF WORLD ASSOCIATION OF SOIL AND WATER CONSERVATION NEW CHALLENGES AND STRATEGIES OF SOIL AND WATER CONSERVATION IN THE CHANGING WORLD SUSTAINABLE MANAGEMENT OF SOIL AND WATER RESOURCES August 22-26, 2016 Belgrade/ Serbia Prof. D-r Ivan BLINKOV

2. Recognizing the close interrelation between forests and water. Concerned that climate change will have severe effects on the frequency, scale and intensity of natural hazards such as floods, debris flow, avalanches, storms, and droughts and will have an impact on forest and water resources and their management. Stressing the role of forests and forest management in protecting water quality, managing water resources for the quantity of all waters, flood alleviation, combating desertification and soil protection as well as the importance of mountain forests in the reduction of land slides, erosion and effects of avalanches. Emphasising that the full economic value of forests has to be adequately recognised and in particular the value of providing ecosystem services …………….. ------------------------- ----------------------

3. FOREST EUROPE ( Ministerial Conference for Protection of Forests in Europe)  On the 5 th Ministerial conference of Forest Europe held in Warszawa in 2007, were adopted joint declaration and 2 Ministerial resolutions:  The Warsaw Declaration recognizes the role of forests and their sustainable management in climate change mitigation and highlights the need to ensure adaptation of forests and forest management to climate change.  Warsaw Resolution 1 (Forests, Wood and Energy) urges the countries and the European Community to enhance the contributions of the forest sector to energy production and mobilization of wood resources.  Warsaw Resolution 2 (Forests and Water) called for action to coordinate policies on forests and water, as well as to promote the management of fresh water resources as part of sustainable forest management.

4. PROJECT  Taking in consideration our close cooperation with CNVP ( following the Warszawa resolutions, we prepared a proposal project - 'Study and Analysis of Innovative Financing for Sustainable Forest Management in the Southwest Balkans‘.  Taking in consideration our close cooperation with CNVP ( a legacy organization of SNV in the Balkans for forestry and rural development ) following the Warszawa resolutions, we prepared a proposal project - 'Study and Analysis of Innovative Financing for Sustainable Forest Management in the Southwest Balkans‘. - 2 pilot case studies:  In Albania focus was on Forest –water relations, while in Kosovo on wood biomass energy.  CNVP was responsible for overall project management.  World Bank – PROFOR financed the project with total duration of 36 months in 2 phases..

5. 'Study and Analysis of Innovative Financing for Sustainable Forest Management in the Southwest Balkans‘. - ALBANIA Case - CNVP - University in Skopje, Faculty of Forestry, Macedonia - Wageningen University, The Netherlands, - Regional Federation of Communal Forests and Pastures in Diber, Albania

6.  ALBANIA CASE STUDY – PROBLEM In the late 90’s of the XX century, significant part of the forest in the ULZA reservoir catchment was fully destroyed. It enabled high erosion processes and siltation of the UlZA reservoir. The main use of water from the reservoir is for energy production – Ulza HPP. Lower part of the forest in the reservoir catchment is under competences of the Communes and Communal forest associations. Forest cut is restricted. Citizens from the commune need fuel wood but they can not supply from their region. Beneficiary of cut restriction was State Energy Company of Albania. ( now ULZA HPP is sold) Because is restricted right of communes to use the forest with aim to protect the Ulza Reservoir from siltation, beneficiary should indemnify the Commune. The possible instrument for this is Payment for Ecosystem Service. PES could be calculated based on model for erosion and sedimentation  WB and the Government of Albania were in the process of finalization of the expected new ESP (Environmental Services Project). The ESP among others focus on specific environmental services and pilot for Payment for Environmental Services.  The study carried out in frame of this project on watershed management, demonstrated through sound scientific methodologies how payments for environmental services (PES) could benefit rural land owners and private dam operators while improving environmental sustainability.  The baseline data generated formed the basis for a local PES scheme.

7. Albania - Watershed case study Context  Over 50% of total surface is forest  Large areas of abandoned agricultural land  High levels of soil erosion  Mountainous terrain  Unsustainable forest and agricultural practice  Increased communal forest management Focus Ulza hydro-power dam and watershed, Mati river basin area Verification on forest management practices including community based resource management The environmental services obtained by downstream stakeholders UKIM – FFS – Skopje - erosion Wageningen University - PES

8. Albania Watershed Case study Erosion related issues – UKIM - FFS 1 – EROSION MONITORING AND MODELLING 2 – MAPPING EROSION AND CALCULATION SEDIMENT PRODUCTUON and SEDIMNET YIELD 3 – BATHYMETRY OF THE ULZA RESERVOIR 4 – CROSS CUTTING ANALYSIS

9. CNVP team Project management and field support Peter Kampen – CNVP – check reports and control activities Saso Petrovski – CNVP - check reports and control activities Voislav Todorov – CNVP, Hamit Salkurt – CNVP, Durim Kaba – CNVP - participate in field activities Communal Forest and Pasture association -Diber: Shkelkim Hasa and Malvina Shehi, laboratory analysis for erosion monitoring Farmers - observers, collecting data from field Faculty of Forestry team: Prof. d-r Ivan Blinkov, coordinator of all scientific-expert activities, preparation methodologies, responsible for erosion monitoring and modeling, checking and interpreting results from bathymetry and sedimentation, cross-cutting analysis and preparation all reports Prof. d-r Trendafilov Aleksandar – erosion mapping, sediment calculation, bathymetry, Ass.m-r Ivan Mincev – bathymetry, sediment calculation, all GIS calculations and modeling other collaborators

10. I phase – preliminary studies Analyze of basin characteristics - defined basin area, all topographic parameters as perimeter, slope distribution, exposure.. - analyzed lithology pattern, analyzed soil distribution - analyzed climatic elements, analyzed land cover distribution Analyze of reservoir characteristics - analyze of basic characteristics of the dam - reservoir parameters - analyze of previous bathymetry - analyze of all related documents (environmental etc) Analyze of forest and forestry in the area - defined forest distribution per type, age, cover, silvicultural type etc - analyze of forest practices in past Developed basic GIS layers.maps

11. Study region: Ulza reservoir basin – Mati river 120- 2245 m asl High roughness of the terrain A = 1244 km 2 Vl = 240 mil. m 3

12.  The Ulza Hydro Power Plant (HPP) is located on Mat River upstream from mouth of Fani River and near the village of Ulza and Burrel town. It is a 64 m high concrete gravity dam with straight axis with impounded volume of 240 mil m 3. The reservoir created serves as a head pond for the Mat river cascade.  Downstream from Ulza Dam is located Shkopeti dam that is 50 m high concrete gravity dam with impounded volume of 40 mil m 3.  Ulza dam has been constructed in the period of year 1952 to 1958. It is a concrete gravity dam with a straight axis. Water tightness of the dam foundation is achieved by a grout curtain.  The maximum height of the dam above foundation is 64.2 m and the crest length is 260 m. The crest elevation is at 131.7 m asl and the maximum water level in the reservoir is at 129.5 m asl (full supply level). Minimum foundation level is 67.5 m asl. The dam volume is 0.26 million m3.  The following are some basic characteristics of the Mat River:  Annual discharge volume: 3,250 million m³  Specific discharge: 40.1 l/s/km²  Ratio of wettest month (December) to driest month (August): 10  One in 10 years high flow: about 25 times the river module  Storage capacity of Ulza reservoir: 240 million m³ (about 15% of annual flow of the Mat River)  The average annual inflow into the reservoir is 1.170 million m 3.  The reservoir total storage is 240 million m3 and  The active storage volume is 124 million m3.  Maximum water level is 129.5 m asl, while  Maximum operation water level is little bit less, i.e. 128.5 m asl.  Minimum operation water level is 117 m asl.

13. Collection and analyze of existed data for the reservoir and national data

14. Develop of basic data for the study area Dissected mountain ranges with Internal vali related to "Leptosol Luvisol-region" with cambisols, Regosols, vertisols, Luvisols and Phaeozems Pliocene limestone hills and mountain ranges related to "Leptosol-Cambisol region" with vertisols, and Phaeozems Fluvisols (Zdruli p). Forest (43%), TW+SV (30%), G (13%), A+Pm (6%) CCP (6%) other (2%)

15. Analyze of forest and forest practices related data for the area

16. ALL PREVIOUS ANALYSIS WERE NECESSARY TO BE DEFINED RIGHT METHODOLOGY for further studing

17.  Erosion and runoff monitoring  established 48 different plots on 3 exp. Locations (Klos, Lis, Suc),  42 were regularly shaped and with uniform slope, having and area of 100 m 2.  6 irregular plots that simulate gully on bare land (first time)  Defined characteristics of each plot  Launched soil laboratory analyses  Defined correlation between R, S, and P  Defined correlation between R, S, and slope  Defined impact of land cover on R and S  Bathymetry  2 measurements were performed (1 on a lower water level, the second on maximal level)  defined quantity of deposed sediment into the reservoir  assessed lifespan of the reservoir I phase

18. II phase  Erosion and runoff monitoring  established 46 different plots on 2 exp. Locations (Klos, Lis, Suc),  42 Gavrilovic type, having and area of 100 m 2 (11 new plots)  4 plots for gully monitoring using pins  Defined characteristics of new plots  Lunched 6 months monitoring  Defined correlation between R, S, and P and slope  Defined impact of land cover on R and S  Bathymetry  1 measurement was performed  defined quantity of deposed sediment into the reservoir  assessed lifespan of the reservoir  Erosion and sediments calculation using EPM  On-filed erosion mapping and creation eroson map  Development all necessary data for calculation  Calculation produced and transported sediments Cross – cutting analysis Erosion monitoring and modelling vs erosion and sediment calculations vs bathymetry Comparative analysis of the results from the I and II phase results Final recommendations II Phase

19. EROSION MONITORING Average runoff and sediment load per LC type LCU[L]RI Slope %LCUdm 3 RI I-B125401,7140 - 40I-B4053,29 B122261,6623 – 23B3973,23 A115921,5812 – 12A3833,11 Py106021,4423 – 23Py3272,66 Pm98951,3537 – 22O2482,02 O93401,2722 – 37Pm2381,93 G92241,2521 – 21G2071,68 F7351139 - 39F1231

20.  Erosion mapping and sediments calculation  using EPM

21. Sediments calculation (EPM) CatchmentZFRnWWspGGsp km 2 m 3 /ym 3 /km 2.ym 3 /ym 3 /km 2.y 2 - Rreshanit0,3311,990,5110640,37887,275429,69452,76 4 - Lazit0,2913,650,3110645,86780,143297,78241,67 6 - Zalli i Tarit0,4487,680,59108541,901237,9263890,51728,67 8 - Kurvajt0,6067,180,79125786,871872,2799320,491478,33 10 - Karices0,3128,100,4822266,90792,5110787,65383,95 90,278,170,345335,57652,701816,21222,18 110,2623,200,4415147,89653,016624,72285,59 12 - Zalli Hotes0,4331,720,6438056,551199,8724259,73764,88 10,286,680,544846,72725,762596,49388,81 50,2716,280,4211350,74697,104808,49295,31 30,269,180,386101,55664,712318,92252,63 7 - Urakes0,54165,580,93233513,901410,28217996,721316,57 Mat0,58742,620,731233905,441661,57900320,111212,36 Average0,541212,020,721826140,271506,69 1108,45 Sum 1212,02 1 826 140,27 1 343 467,52 Min0,266,680,314846,72652,701816,21222,18 Max0,60742,620,931233905,441872,27900320,111478,33

22. COMPARISON OF RESULTS of erosion monitoring and bathymetry 2013 vs 2014 SEDIME NT 20132014 ratiomean slope ratiomean slope A 3,11122.2515,2 P 2,31231,7922,0 G 1,821,51,2529,9 TW 1,934,1 F 139156,2

23. Connections results form erosion monitoring to those from erosion mapping and sediment calculation and from bathymetry CONCLUSIONS and RECOMENDATIONS

24. CONTRIBUTION TO SOLVING EROSION INTENSITY ON A BASIN SCALE THROUGH MEASURING ON PLOT - paper - Ivan Blinkov (on behalf of the CNVP team)

25.  Preparatory works and preparation methodology: prof. dr Ivan Blinkov  Selection experimental plots on field and basic characteristics defining:  Prof.d-r Ivan Blinkov - UKIM-FFS  Hamit Salkurt – CNVP, M-r Voislav Todorov – CNVP M-r Saso Petrovski - CNVP Mr. Shkelqim Hasa – CFAD Doc d-r Ivan Mincev- UKIM - FFS  Field measuring (basic data collection including samples of runoff) - 10 observers:  Laboratory analyses of the collected sample of runoff and preparation row data, and gully erosion intensity calculation –  - Malvina Sheshi, Mr. Shkelqim Hasa,  Support in calculation – Martna Blinkova – UKIM - FNS  Calculations, modeling and preparation reports : prof. d-r Ivan Blinkov

26. Aims and objectives  The main Aim of this study is to define impact of various land cover/use and slope on runoff and erosion intensity.  Objectives of this study are:  to establish experimental on-field erosion and runoff monitoring plots,  to collect various data from the experimental plots,  to collect data about runoff intensity (depth of liquid into the barrels),  to collect samples from the liquid into the barrels,  to do laboratory analyses and to define percentage of solid material  to preprocess row data from on-filed measuring  - to calculate precipitations, runoff and sediment load and to do basic statistical analyses of this data arrays for each plot and to calculate runoff and sediment load for each plot tp define impact of slope on runoff and sediment load  to define final impact of various and cover/use types on runoff and sediment load 

27. Theory of monitoring erosion Various erosion monitoring methods exist. . Erosion monitoring in fact erosion measurement could be carried out with various methods. The list of erosion monitoring methods is wide:  method with erosion monitoring plots ;  method using erosion pins;  pins with sensors (PEPP);  earth photogrammetric;  decoding of aerial or satellite images;  LiDAR techniques etc. The type of method depend on :  type of erosion process that should be monitored i.e sheet erosion, gullies, mass movement, sedimentation etc.  influence of one factor  influence of more factors.

28. Erosion monitoring methods  Erosion pins   Remote sensing techniques  Use of Cesium 137 for defining erosion  Monitoring on plots

29. Monitoring plots – various shape and dimensions

30. Monitoring plots  The most spread method for erosion monitoring is using plots. Erosion plot studies were started at the University of Missouri in 1915. Later, network of 10 soil erosion experimental plots was established in 1928. The form of these plots was rectangle.  The dimension varies and the Wischmeyer establish standard dimensions 22,1x4 m.  If length of slope is longer then 300 m the output results are not satisfactory. Beside it, these plots are previously aimed for agriculture land where sheet and rill erosion processes are dominant.  Gavrilovic (Serbia) in 1970 established square formed erosion plots having an area of 100 m 2 (10x10m).

31. USAGE OF PLOTS - For demonstration, where the purpose is to demonstrate known facts, or to show that erosion is much less from a plot which has a good vegetative cover than from a bare plot. - In comparative studies, for example to test, or demonstrate, or get an approximate indication of the effect on runoff or erosion of a simple comparison such as with or without a surface cover, or effects fo various practices on runoff and sediment. - A third possible use is to obtain data which are to be used to construct or to validate a model or equation to predict runoff or soil loss. But the difficulties in collecting data of sufficient accuracy and reliability are so great and so numerous that only large experimental programmes conducted at great expense over a long period of time can really meet this objective. - There must be sufficient replications, that is exactly similar repetitions, to allow a measure of the variance within treatments. This is the experimental error caused by unknown and uncontrollable variations in the land cover, soil, and slope. There is no absolute mathematical answer to the number of replications because it is a question of what level of accuracy is expected from the experiment.

32. Measuring and defining erosion on a level of an area or reservoir Mathematically the most accurate approach is organizing of sample plots stored as square grid cells. Disadvantage of this method is it’s limitation in a relative uniform regions especially were dominate sheet and rill erosion i.e. for agriculture areas. In a case of terrain wit a lot of gullies, landslides etc., direct use of this approach could results with a lot of mistakes. - Erosion transect lines Grid

33. E.Roose  On the Ulza reservoir catchment were noticed various erosion processes by type (sheet erosion, rills, gullies, fluvial erosion processes, mass movement erosion) and intensity (low, mid, high to extreme).

34. The main idea in this research is that erosion monitoring plots should imitate conditions on the catchment as far as possible appropriate.

35.  Agricultural land –dominate sheet and rill erosion

36.  Forest and semi-natural area – hilly mountain and mountain regiom

37. YEAR Annual increase [m] lengthwidth 192213,24,7 1923-251,01,4 1925-267,80,2 19270,80,0 19284,90,5 19291,00,2 1930-326,50,1 For 10 years. 35,27,1

38.  FINAL METHODOLOGY  During the field visits, various soil types and subtypes were noticed on the field. Using soil type as first section criteria would result in very remote locations spread out on a large area. This created impossibilities for the farmers/observers to carry out regular and reliable monitoring.  After that it was decided experimental location to be set by geo-position: Klos, Lis, Suc. These locations were chosen since it fitted in the overall soil type map locations, have areas relatively easy accessible and in vicinity of villages where reliable farmers connected with the Federation could be found. On the same experimental location there are plots with various soil types/subtypes, depending on the micro conditions (slope, exposure, land cover/use etc). This resulted in the end that in the total set of plots the soil variety is assured.  In mountain areas - their shape, dimension and slopes depend on the terrain conditions and simulate micro catchment and gullies. With aim to be as consistent as possible with the planed methodology, experimental

39.  The second criterion (terrain slope) was followed as proposed, so there are plots on 3 classes of slope: 20%, 40% and 60%.  The third criteria - land cover/use was almost fully fulfilled. There were very slight changes. Somewhere it was not feasible to find a certain type of land cover (forest on 20% slope, or plantation on 60% slope). With aim to overcome the problem, other plot type was selected, but to comparable with the neighboring plot (various percentage of cover on transitional woodland or various shapes etc.)  The plot shape is regular for all plots.  Additionally a few irregular shaped plots were selected. The aim of irregular shaped plots is to monitor natural terrain conditions

40. FINAL METHDOOLOGY  PLOTS on DIFFERENT LAND COVER  PLOTS on DIFERENT SLOPE  I phase – 3 experimental location with 7 sub-locations  II phase – 2 experimental location with 5 sub-location  Total number of experimental plots 48 and 46  Regular shaped and irregular shaped plots  Gullies - I phase (plots), II phase - pins

41. Plot shape Regular shaped plot Irregular shaped plot  Every observer is responsible for:  Establishing, maintenance and repair of the plots as needed   Monitoring of the plots (collection of samples)  Registering the necessary data (water depth, precipitation) in the sheet and storage of the samples  To note eventual changes on the plots

42. Methodology for collecting samples and laboratory works  After each raining, farmer responsible for monitoring, visit the plot and do the following activities;  1 - Check water depth with special measuring stick  2 – Take samples (mix the content of fluid inside the barrel to be got homogenous liquid and take 3 bottles – 3 samples from each plot and put stickers with plot name and date)  3 – wash out the content of the barrel  4 - eventual repair destroyed fence etc  3 – Notice in the sheet data and comments (appearance of gully, damaging of the fence, pouring out of water (out from the plot, or out from the barrel in a case of very high intensity rainfalls. If 1 barrel is not enough to be set additional barrel) Table 3: An extract of sheet for meteorological monitoring Dat u m PT [ o C ] Win d COMME 115NoCloudy 213NoCloudy 314NoWarm 415NoCloudy 516NoCloudy 616NoWarm 717NoWarm 817NoWarm 918NoWarm 1018NoWarm 1130020YesRainy 1218Nocloudy 1318NoWarm 1416NoCloudy 1550017YesRainy 1650017YesRainy 1716YesRainy 1850017YesRainy DATUM123 12/06/201486 15/06/201486 16/06/201486

43. Field laboratory DATUM123455a678 12/06/2014-- 42 15/06/2014-- 25 33--25 16/06/2014-- - 33817 Sediment ml/l Collected data (samples of runoff from barrels) pre- treatment in field laboratory

45. Modeling runoff and sediment yield  Data preprocessing ( calculation precipitations, runoff and sediment load for each event )  Basic statistical analyses of the data arrays (Stdev, Covar..)  Correlation analyses R = f (P) ; S = f (P) ; S = f (R) Comparative analyses - comparison R and S depend on the slope and land cover Uncertainity analyses

46.  Study limitation: - No plots on beech or pine forests, - No plots on weathering, landslides and streamflow erosion - Study period short and interrupted

47. RESULTS and DISCUSSION:

48. Distribution of plots per Slope and Land cover 12 25 6

49. I phase Results  Period of monitoring was from November 2012 – August 2013 i.e 10 months. Three experimental locations were established in villages: Klos, Lis, Suc. Precipitation events varied from 7,5 mm up to 75 mm. According to the literature, as torrential rainfalls are accepted those higher than 30 mm. Total number of rainy days that cause runoff and sediment load is 52.  Generally during the monitoring period the rainiest month was March when there were 9 rainy days and all of them were torrential. February and January were rainy months too with 7 and 6 torrential days. In December there were 6 rainy days and the maximum precipitation were noticed on the 5th December 75,4 mm on all 3 experimental locations. All these rain events caused runoff. element meas ure XIXIIIIIIIIIVVVIVIIVIII Rainy events Nro6677957500 Torrential events Nro4367904000 Precipita tions from – to [mm] 7.5- 56.5 18.8- 75.4 18.8- 56.5 29.6- 56.5 37.7- 75.4 15.1- 24.9 18.8- 37.7 18.8- 22.6 00 Table 2 – Precipitation during the monitoring

50. NrCodeP mmR - lS - dm3 1 K4P45,720,25 40,84 2 K2O45,720,81 33,89 3 K2G45,722,37 27,11 4 K6P45,733,37 38,77 5 K2A45,725,23 37,05 6 K2O45,725,47 37,25 7 G1-K45,722,65 35,84 8 G2-K45,722,92 35,09 9 K2O45,719,00 35,96 10 K4G45,734,59 45,02 11 K6O45,735,16 46,63 12 K4O45,736,25 50,62 13 K4F45,732,67 45,72 14 K4F45,732,67 45,73 15 K4F45,732,42 54,95 16 K2P45,735,87 63,65 number16 average28,23 42,13 max36,25 63,65 minmin19,00 27,11 stdev6,37 9,08 Cv (%)22,5721,56

51. Correlation between, precipitations, runoff and sediment yield An example of correlation analyses for each plot Runoff = f (precipitations) Sediment load = f (precipitations) R2R2 correlationR2R2 A 0.19Mid correlation0.30Mid correlation P 0.21Mid correlation0.29Mid correlation G 0.26Mid correlation0.16Mid correlation O 0.19Mid correlation0.20Mid correlation F 0.23Mid correlation0.24Mid correlation

52.  For detail analysis  class P – Plantations, was divided in 2 subclasses – Py – young plantations on bare soil and Pm – mature plantation on meadow. Beside it, class Grassland was divided in 2 subclasses – O- overgrazed grassland and G – not grazed.  Bare land was divided in: overgrazed bare, transitional woodland/bare land and bare land /extremely degraded.  Sediment quantity is expressed in dm 3.  The final step of the calculation was to define ratio between average values of sediment yield per land cover type. Calculated values are presented in the following table. Forest covered plot is accepted as referent and all other are correlated with forest plot values  X – axis – precipitation [mm]  Y – axis – runoff or sediment [l; dm 3 ]  More series

53.  Comparison of sediment yield on plots O-G-Pm - low slope (10%,11%,12%)  Comparison of sediment load [dm3] on plots Land cover A-Ob-Om-G-F on slopes: 5, 14, 15, 11,23 Figure 39: Comparison of sediment yield on plots A-Ob-Om-G-F, slopes: 5, 14, 15, 11, 23% Precipitation = mm dm3 Figure 39: Comparison of sediment yield on plots A-Ob-Om-G-F, slopes: 5, 14, 15, 11, 23% dm3 Precipitation = mm

54. Figure 49: Comparison of sediment yield on plots (A-O-G) (slopes 18, 22, 22%) Figure 49: Comparison of sediment on plots (A-O-G) (slopes 18, 22, 22%) Figure 53: Comparison of sediment yield on plots (Pm-F) (slopes 60, 62%)

55. Depend on Slope Figure 61: Comparison of sediment yield on plots F- type (75, 62, 38%) F- type (75, 62, 38%) Figure 75: Comparison of runoff on Py-plots type, slope 32%, 22%, 16% on Py-plots type, slope 32%, 22%, 16%

56. General review of total runoff and sediment yield of all experimental plots

57. Although average slope of forest plots is much higher than others (except Pm value), the average sediment yield of other land cover types these values are much higher even more than 3 times compared to the referent value for forest. Green cover of the soil in mature plantation – Pm (plantations on meadows), reverses the slope handicap and average value is lower than for overgrazed land – O.. Irregularly shaped plots are not comparable to others but generally average values are the highest. [total annual]

59. II Phase results  Period of monitoring was from March 2014 – August 2014 i.e 6 months. Research was continued on two experimental locations in villages Klos and Lis. During the research period, there were 40 rainy days, out of them 38 were with appropriate measuring. Precipitation that cause runoff and sediment load vary from 10.5mm up to 52.5 mm and the average value of effective precipitations is 24.5 mm. During the research period monthly sum of precipitations varied form 66,5mm (in March on both experimental location) up to 241.5 mm (June – Lis) and 245 mm (June - Suç). 

60. Runoff = f (precipitations)Sediment load = f (precipitations) R2R2 correlationR2R2 A 0.94Extremely high correlation0.67High correlation Py 0.92Extremely high correlation0.90Extremely high correlation Pm 0.77High correlation0.23Mid correlation G 0.94Extremely high correlation0.68High correlation Tw 0.81High correlation0.68High correlation F 0.96Extremely high correlation0.78High correlation Table 4 – Runoff (R) and Sediment load (S) as a function of precipitations (P) – Phase II In the first phase each event, now the month sum

61. Table 5 - Sediment load per land cover class

62. F – Forest

63. Comparison of erosion monitoring activities in 2013 and 2014  2012/13  - established 48 monitoring plots in 3 el (Klos, Lis, Suc)  - established irregular shaped plots that simulate gully on bareland  10 month monitoring 2014 - established 46 monitoring plots (11 new) in 2 el Lis and Suc Not established plot on big gully on bareland Instead of this introduce gully measuring using pins Changed LC class of some plots (FAO definition) 6 month monitoring

64. Plot distribution per slope class and land cover

65. COMPARISON I and II phase  For comparison were used only results from the period March- August for both years 2013 and 2014. In the first and the second phase, 31 plots from experimental locations Suc and Lis were the same and were inters to analyze sediment load per plot too.  From the figure can be conclude that precipitation in 2013 are higher only in March, while in the other months were higher precipitations in 2014. Even in July and August 2013 there were no raining. Total sum of precipitations for the period March – October was 1,21 times higher in 2014.

66. Mean month sediment load of all plots is much higher in 2014, especially in April May and June. It is in correlation with precipitation regime.  Precipitation were 1,21 times higher in 2014  - Sediment load average for plots was  SUC - 8,2 vs 16,4 - 2 times more  LIS - 9,8 vs 14,1 - 1,44 times more  Total sediment load of both location - 8,97 m 3 /ha vs 14,4m3/ha - 1,6 times more  Only in March 2013 were measured higher values of runoff and sediment load.

67.  Definitely:  -  Higher values for 2013 were measured in the following plots:  Suc - 1, 2, 3 and 4 – all G – plots (grasslands); Lis – 1 (Pm), 6 (G), 7 (G-T), 8 (G- T), 9 (G-T-I), 11 (F), 16 (F).  8 out of 12 plots categorized as G – plots showed higher sediment load in 2013. . There is a significant correlation with the precipitation regime.

68. Comparison of sediment load ratio between land cover classes SEDIMENT Load 2013 2014 ratiomean slope ratiomean slope A 3,11 12.0 2.25 15,2 P 2,31 23.0 1,79 22,0 G 1,8 21,5 1,25 29,9 TW 1,9 34,1 F 1 39.0 1 56,2 Generally, slope difference is a very important parameter that influence runoff and especially sediment yield on so called “open land” i.e. arable land, bare land and young plantation. Even in a case of limited slope increase (up to 10%) results are showing high runoff and sediment yield values. In the case of grasslands/meadow the impact of slope gradient towards runoff and sediment yield is lower. The slope gradient makes a difference, but the differences in slopes should be higher than on “open land’ to show different values of runoff and sediment yield. In a case of forest covered plot, slope difference is not the crucial factor anymore for runoff and sediment yield because characteristics of the forest ecosystem (crown coverage, surface florae, uneven surface, litter etc.).

69. Erosion as a function of slope and land cover  Next step was preparation of a so called “mockup” diagram.  For this diagram were used data from the year 2013 and was increased 1,21 times to express one year result.  Because of absence of data on any slope was used method of correlation with aim interpolation and were fulfilled the empty places.  For arable land data was extrapolated for stepper slopes just to be shown on the diagram.

71. Erosion intensity on a basin level per land cover type For this anlyze were used data from the year 2013. Beside this as a base data was used CORINE land cover/use data. Then data from CORINE LCU – level 3, was grouped to be adjustable to the land cover classes defined during the research. For urbаn land and for lake was accepted 0-erosion intensity. Results show that forest although cover 43% of the basin contribute to the total produced sediments with 25,88%. Contribution to total produced sediments from bareland and arable is double higher then their area percentage. Land Cover Type Area Erosion intensity Total sediments ha%m 3 /ham3m3 % B - bareland 3325,02,7247,641584035,37 A - arable 14133,411,5445,9664957022,03 G - graslanad 17338,014,1628,8049933416,94 P - plantation 224,80,1833,6075530,26 TW - transitional woodland 32975,026,9326,4087054029,52 F - forest 52995,043,2914,4076312825,88 Urban land 612,00,500,000 lake 827,00,680,000 sum/average 122430,210024,082948528100,00

72. RESUME

73. Impact of slope gradient on runoff and sediment yield  Slope difference is very important parameter that influence runoff and especially sediment yield on so called “open land” i.e. arable land, bareland and young plantation. Even in a case of low slope difference (up to 10%) results show high runoff and sediment yield difference.  In a case of grasslands/meadow there is difference but slope difference should be higher then on “open land’ to be shown.  In a case of forest covered plot, slope difference is not the crucial factor because characteristics of forest ecosystem (crowns, surface florae, uneven surface, litter etc) reverse slope difference.

74. Impact of land cover/use on runoff and sediment yield  Average runoff values of arable land and bareland are 1,58 and 1,66 times higher then value for forest although slope of the forest was 2-3 times higher. Concerning the sediment yield, forest influence is higher so the average values of arable land and bareland were more then 3 times higher while average value for young plantation was 2,66 times higher.  Type of plowing, has significant influence. Typical example are plots Suc 8 and 12 where the crucial difference between them is plowing. On a plot nr.8 (i = 16%) where plowing is cross the contours, there are visible appearance of rills that increase runoff and sediment yield. Plot nr. 12 (22%) is set on a higher slope but along the contours and results are significantly lower.

75. Irregularly shaped plots on gully ( I phase )  Through this research, for the first time was carried out runoff and erosion monitoring on a 6 irregular shaped plots that simulate gully. Gullies are significant generator of sediment in the hilly and mountatin regions.  Irregularly shaped plots that simulate any gully according to the land cover are classified as bareland or transitional woodland. Although there were 2 barrels per plots, during the field measuring was noticed overflowing so the values should be much higher. However generally values of runoff and sediment yield are the highest but because of many differences they are not comparable to other. Coverness of the gully is very important factor.  Comparison between 2 similar irregularly shaped plots located on experimental location LIS having almost the same area and set one near by other where cover was different approve this. Runoff and sediment yield (12807 vs. 11502 liters; 377 vs 302 dm 3 ) on a plot nr. 14 (i=26%, very low cover, bareland) was significantly higher then those on a plot nr.15 ( i = 37%, dense bush cover, transitional woodland), where slope is significantly higher but it was annulated by the coverness.

76. Gully erosion monitoring EROSION MEASUREMENT EROSION MEASUREMENT OF THE SITES No.No. NurErosion mm No.Erosio n 1 315101214 25-11225- 361812425- 47-1312223 58-14 421 69-15 223 71020 81125 9133 Total81 31 EROSION MEASUREMENTEROSION MEASUREMENT OF THE SITES No.Number of piquet Erosio n mm 30 of Augus t April - Augu st No.Numb er of piquet Erosion cm April August MatjaErosMatjaEros 1 33823101214 19 6 2 55511225- 23 2 3 618-12425- 24 1 4 7--1312223 20 5 5 8--14 421 6 95515 223 3 7 1020- 8 1125- 9 1385 Totali119383139

77. For other gullies 78- 135 m 3 /ha  1-The calculation of erosion -April 2014  198 mm erosion/31 piquet = 6.4 mm/piquet  264 m 2 x 0.0064 m = 1.68 m 3.  10000 m 2 /264 m 2 = 37.8 x 1.68 m 3 =63.5 m 3 /ha  1-The calculation of erosion - August 2014  194 mm erosion /31 piquet= 6 mm/piquet  264 m 2 x 0.006 m = 1.58 m 3.  10000 m 2 /264 m 2 = 37.8 x 1.58 m 3 =60 m 3 /ha  Total for the period:1 march - 30 August 2014,  63.5 m 3 + 60 m 3 equal 123.5 m 3 /ha. Gully erosion monitoring

78. River basin Basin areaSediment yield km 2 t/ha.ann Drini597318,8 Mati24419,3 Fani107611,1 Erzeni76026,6 Shkumbini244423,7 Semani564920,9 Vjosa670613,5 Total average1834318,7 According to Zdruly et al. (2001) the estimated soil erosion values vary from 32 to 185 t/ha There are three areas where the annual erosion rate is more than 100 t/ha (two in Gjirokastër and one in Sarandë). Grazhdani (2006) stated that the maximum erosion intensity in Albania achieve 510 t/ha y in Gjirokaster Grazdani S. 2006 EROSION IN ALBANIA

80. WHY STILL HIGH EROSION INTENSITY in ULZA reservoir catchment

81. Ulza (Alb) vs Kalimanci (MKD)  2014 Z= 0,54  W = 1 862 000 m3/y  G = 1 331 741 m 3 /y 1983 Z=0,58 W = 900 000 m3/y G = 418 731 m 3 /y 2014 G = 330 000 m 3 /y Almost the same catchment area

83. Why decrease of sediment yield in the Kalimanci reservoir  Dry period 1987-2000 (lower precipitation, lower discharge…)  Consolidation >>> “effective sediment”  (permanent decrease per years based on measuring)  Erosion control works

84.  MASS AFFORESTATION

85. CHECK DAMS

86.  During the filed visit of the ULZA reservoir catchment hasn’t been noticed any check –dam.  All natural factors are worse then in Kalimanci catchment.  BUT Focus on erosion control was on agricultural land where are noticed terraces.  Beside it were noticed forest plantations.  But should be payed attention on hydraulic structures in the torrential beds too.

87. CONCLUSIONS  The original idea of this study was to perform monitoring of erosion that would inputs calibration parameter values of Z - factor – coefficient of erosion by Gavrilovik for Albanian conditions.   Also this research served for a comparative analysis of the impact of certain factors on the intensity of erosion. Beside other, this is an attempt to define the intensity of the erosion of the level of catchment areas based on land cover data and direct on-field measurements.   For accurate definition of erosion intensity on basin scale perennial continuous measuring are necessary.

88. SOIL Erosion  "The threat of nuclear weapons and man's ability to destroy the environment are really alarming. And yet there are other almost imperceptible changes –  I am thinking of the exhaustion of our natural resources, and especially of soil erosion - and these are perhaps more dangerous still, because once we begin to feel their repercussions it will be too late." ( p144 of The Dalai Lama's Little Book of Inner Peace: 2002, Element Books, London) Thank you for your attention!