cn 9 wed13_all_wb5_posters

BOTSWANA

Boteti: basis for modelling alternative uses of biomass

to for fuelBiomas

s

Cattle

Fodder Wood

Manure

Soil Organic Matter

Fertilisation

Soil Moistu

re retenti

onCollection for Biogas

Collection for Fuel

Grazing

pressure

Waste

slurry

PORTUGAL

Wildfires have been a major concern for the Portuguese stakeholders for several years now. Steps have to be taken now in order to prevent further site degradation. The DESIRE project may aid in this sense, studying and applying innovative techniques to minimize wildfire impact on the soil.

MAÇÃO (PORTUGAL) - Data preparation and the application of PESERA-DESMICE

7°50'0"W

7°50'0"W

8°0'0"W

8°0'0"W

39°40'0"N 39°40'0"N

39°30'0"N 39°30'0"N

±

Legend

msrf1_spet

Value

High : 0.597

Low : 0.422

0 5 10 15 202.5Kilometers

Climate Summary: annual rainfall / annual PET

7°50'0"W

7°50'0"W

8°0'0"W

8°0'0"W

39°40'0"N 39°40'0"N

39°30'0"N 39°30'0"N

±

Legend

use

400

334

330

310

210

100


Land use: PESERA classifications

7°50'0"W

7°50'0"W

8°0'0"W

8°0'0"W

39°40'0"N 39°40'0"N

39°30'0"N 39°30'0"N

±

Legend

merod

5.15

5

3

2.8


Soil Erodibility: drived from European Soil Database

7°50'0"W

7°50'0"W

8°0'0"W

8°0'0"W

39°40'0"N 39°40'0"N

39°30'0"N 39°30'0"N

±

Legend

msrtm

Value

High : 637

Low : 23


Main conclusions: Issues arising from results / data preparation▶ The PESERA results comply with the fieldwork results from the study site, in the sense that the areas with mature, unburned forest show little or no erosion rates (it is expected that the soil will improve under forest), but increase sharply

(up to as much as 120 t/ha/yr in the areas disturbed by forest fires. ▶ In this respect, the prescribed burning techniques increase slightly erosion rate, but are well below the values presented by areas burned by wildfires.

▶ All physical data used as input to PESERA model are easily obtained from regular existing soil, climate, topographic and vegetation data base.

Main conclusions: Issues arising from results / data preparation▶ The PESERA results comply with the fieldwork results from the study site, in the sense that the areas with mature, unburned forest show little or no erosion rates (it is expected that the soil will improve under forest), but increase sharply

(up to as much as 120 t/ha/yr in the areas disturbed by forest fires. ▶ In this respect, the prescribed burning techniques increase slightly erosion rate, but are well below the values presented by areas burned by wildfires.


Data preparation and the application of PESERA-DESMICE in

Mesara Valley – Crete Greece

Main conclusions: Issues arising from results / data preparation▶ The obtained results with the PESERA model are comparable with the measured soil erosion rates in the monitoring sites (WB4).

▶ Soil sediment losses corresponds to part of Messara valley catchment conditions. (we need the new version of PESERA).





Executive summary

Soil erosion is considered as the main threat of land degradation and desertification in the study site of Crete. Techniques on land management ensuring adequate plant cover of the soil surface will greatly contribute to combat desertification.

DESMICE Data Category Available info

IS_S: Additional maps Y

IS_S: Transport details Y

IS_S: Production costs & benefits Y

IS_T: Applicability limitations Y

IS_T: Spatial variation in investmestment/maintenance

N

IS_T: Change in production and production costs

Y

MOROCCO

Data for running PESERA model

90m DEM for

elevation

Land use

classes

Soil Classe

s

Degradation

classes

Conservation

practices

Analysis of precipitation at best available site

NW Africa: Annual precipitation (mm)

10 minute (15-20 km)Interpolated data from CRU.Monthly averages for1960-1990

RUSSIA

WaterErosionModel

Dzhanybek(Russia)

25

26

Объект исследования – Палласовский район

Географическое положение

Климатические условияПо данным агрометеостанции

Данная цель была поставлена и решалась в рамках международного проекта GOCE-2007-037046-DESIRE «Уменьшение воздействия опустынивания и восстановление

земель: глобальный подход для локальных решений», реализуемого совместно с 28 партнерами из разных стран.

Почвенная карта М 1 : 2 000 000

данные 1967 года

Спутниковый снимок2000 год

27

Водная эрозия

Виды эрозии по морфологическим признакам:а) поверхностная плоскостная эрозия (во время снеготаяния);

б) поверхностная струйчатая эрозия; в) линейная эрозия.

Эрозия почвы - совокупность взаимосвязанных процессов отрыва, переноса и отложения почвы поверхностным стоком временных водных потоков и ветром.

aCA

L KLBLW ))12,0exp((1 )1,0(100

Уравнение для определения смыва почв талыми водами (по Г. А. Ларионову), т/га/год

NPhB

ZThSA )(

)( hMeRFhC

WL – смыв почвы талыми водами, [т/га/год];

A, B, C - эмпирические коэффициенты;L – длина склона, [м] (топографический фактор); Ka – коэффициент агрофона (биогенный фактор)

h – слой склонового стока, [мм] (климатический фактор);P, N, S, T, Z, F, R, M – эмпирические коэффициенты, зависящие от смываемости почвы (почвенные факторы).

(1)

(3)

(2)

(4)

28

А BC L

KaWL

Картографическое моделирование водной эрозии

Структура ArcGIS

Схема подготовки и обработки данных

aCA

L KLBLW ))12,0exp((1 )1,0(100 (5)

29

Сбор, подготовка и обработка исходных данных для ГИС-проекта

Цифровая модель рельефаОцифровка данных

топографической съемки

Цифровая модель рельефа для ОАО «Ромашки»с разрешением 50 м

Дистанционное зондирование отметок земной поверхности

Окрестности озера Эльтон с разрешением 1 км

Цифровая модель рельефа для ОАО «Ромашки»с разрешением 90 м

Окрестности озера Эльтон с разрешением 90 м

30


Примеры слоев данных с посчитанным коэффициентом склонового стока (h) в Палласовском

районе с разрешениями 90 м (слева); 1 км (справа)

Склоновый сток (h)

EHDih H - запас воды в снеге, [мм] (зависит от толщины снега; толщина снега принималась равной 40 см - среднегодовое значение)D, E – коэффициенты, зависящие от типа почвы;i – уклон поверхности земли, %

Слой данныхс коэффициентом агрофона

(разрешение 1 км)

Слой данных, показывающий тип землепользования

(разрешение 1 км)

Табл. 1Часть таблицы для определения коэффициента агрофона

Коэффициент агрофона (Ka)

Табл. 2Значение параметров D и E

(6)

31


Коэффициенты A, B, C из уравнения (1)Оцифрованная почвенная карта

Палласовского района (М 1 : 2 000 000)

NPhB

ZThSA )(

)( hMeRFhC

(8)

(7)

(9)

Слои данных построенных по коэффициентам А, В, С

Табл. 3Рассчитанная смываемость почвы

A (разрешение 1 км) B (разрешение 90 м) C (разрешение 50 м)

Оцифрованная почвенная картаОАО «Ромашки»

(М 1 : 25 000)

Табл. 4Рассчитанная смываемость почвы

32

Результаты моделирования водной эрозии

Сценарий №1. Текущая ситуация в Палласовском районе Карта оценки риска водной эрозии почв (разрешение 1 км)

Гистограмма сравнения риска смыва почвы при различных разрешениях

0

10

20

30

40

50

Риск смыва почвы

Пр

оц

ен

ты

Разрешение 1км

Разрешение 90м

Карта оценки риска водной эрозии почв (разрешение 90 м)

33


Сценарий №1. Текущая ситуация в ОАО «Ромашки»

Карта оценки риска водной эрозии почв для ОАО «Ромашки» с разрешением 50 м

Карта землепользования для ОАО «Ромашки» на ноябрь 2003 года

Спутниковый снимок пос. Ромашки

34


Сценарий №2. Моделирование смыва почв при измененном севооборотеКарта оценки риска водной эрозии почв для ОАО «Ромашки» с разрешением 50 м (сценарий № 1)

Гистограмма сравнения риска почвы смыва при различных сценариях

0

10

20

30

40

50


Пр

оц

ен

ты

Сценарий № 1


Карта оценки риска водной эрозии почв для ОАО «Ромашки» с разрешением 50 м (сценарий № 2)

Границы противоэрозионных мероприятий

Границы противоэрозионных мероприятий

35


Сценарии №3. «Моделирование смыва почв при посадке кустарников на склонах»

Карты эрозии почв для ОАО «Ромашки» с разрешением 50 м по: а) первому сценарию; б) третьему сценарию

Гистограмма сравнения риска смыва почвы при различных сценариях

0

10

20

30

40

50


Пр

оц

ен

ты



а)

б)Границы противоэрозионных мероприятий

36


Сценарии №4. «Моделирование смыва почв при обваловании склонов»Карты эрозии почв для ОАО «Ромашки» с разрешением 50 м/пиксель по: а) первому сценарию; б) по четвертому сценарию

а)

б)Границы противоэрозионных мероприятий

Гистограмма сравнения риска смыва почвы при различных сценариях

0

10

20

30

40

50


Пр

оц

ен

ты



37

Результаты моделирования водной эрозииЭффективность различных сценариев с точки зрения уменьшения негативного антропогенного

воздействия вызывающего водную эрозию для ОАО «Ромашки»

100SSЭ

М

МЭ

SЭ SЭ - площадь эрозионно-опасных земель без мероприятий, [га];

- площадь эрозионно-опасных земель с противоэрозионными мероприятиями, [га];SМ - площадь земель задействованных в противоэрозионных мероприятиях, [га]

МЭS

Гистограмма сравнения риска смыва повы при различных сценариях

0

10

20

30

40

50

60

70

В переделах допустимого (<1.5 т/га/год) Выше допустимого (>1.5 т/га/год)


Про

цент

ы

Сценарий № 1Сценарий № 2Сценарий № 3Сценарий № 4

0

10

20

30

40

50

60

Эф

фе

кти

вно

сть,

%

2 3 4

№ Сценария

Эффективность сценариев с противоэрозионными мероприятиями

WaterErosionModelNovy(Russia)

38

Объект исследования

Тип почв %

Каштановые с солонцами 12,8

Лугово-болотные и иловатые 0,7

Темно-каштановые остаточно-луговатые 7,8

Аллювиальные дерново-насыщенные 3,6

Черноземы южные 0,1

Каштановые 0,5

Темно-каштановые 73,1

Черноземы 1,4

Климатические данные

Географическое расположение

Почвенная карта

Почвы Марксовского района

500-580 мм400-500 мм

375-425 мм

Среднемесячная температура воздуха, °С

-15

-10

-5

0

5

10

15

20

25

1 2 3 4 5 6 7 8 9 10 11 12

месяцы

тем

пер

ату

ра в

озд

уха,

°С

Среднемесячная температура воздуха

Среднегодовое распределение осадков

Снимок со спутника

40

КлассификацияСмыв почв

т/га в год мм/год

Незначительный до 0,5 до 0,05

Слабый 0,5-2 0,05-0,2

Средний 2-5 0,2-0,5

Сильный 5-10 0,5-1

Очень сильный более 10 более 1

Среднегодовой предельно допустимый смыв почв, по Г.П.Сурмачу (т/га)

Почвы

Степень смытости

Слабосмытые

Средне-смытые

Сильно-смытые

Дерново-подзолистые, светло-серые лесные на лессовых и других рыхлых породах

2,0 1,5 1,0

Серые и темно-серые лесные, черноземные и темно-каштановые

2,0 2,0 1,5

Каштановые, светло-каштановые, сероземы 1,5 1,5 1,0

Почвы, сформировавшиеся на опоках и мелах 1,0 0,5 0,5

Классификация эрозии почв, предложенная М.Н.Заславским

Классификация водной эрозии

Виды водной эрозии в Марксовском районе

41

Создание ГИС-проекта: входные данные

Цифровая модель рельефаhttp://edcdaac.usgs.gov/

gtopo30/gtopo30ftp://e0srp01u.ecs.nasa.gov/

srtm/version1/

Данные метеорологической станции г.Маркс

Данные о растительностиGlobal Map Version 1.2 Specifications 2005г.http://www.iscgm.org

Почвенная карта. Эколого-ресурсный атлас

Саратовской области. 1996г.

Снимки со спутника Landsat. 24.09.2000г.

42

Расчет фактора рельефа (P)

Слой фактор рельефа Слой фактора рельефа

Слой длин склона Слой длин склонаСлой уклонов Слой уклонов

Цифровая модель рельефа http://edcdaac.usgs.gov/gtopo30/gtopo30 Цифровая модель рельефа

ftp://e0srp01u.ecs.nasa.gov/srtm/version1

Разрешение 1 км Разрешение 90 м

)3(,)1.359s

( x )m22.1

( =

P

λ – длина склона;S – Крутизна склона (уклон); m – коэффициент зависящий от крутизны склона:

m S,%0.2 <1

0.3 1-3

0.4 3-5

0.5 >5

43Слой эрозионного

потенциала дождевых осадков Слой эродируемости почвы

Слои эрозионного индекса культур а) Разрешение 1км (Global Map Version 1.2 Specifications) б) Разрешение 90м (классификация полученная при помощи программы ENVI)

Смываемость почв (П)Эрозионный индекс растительности (С)

Эрозионный потенциал дождевых осадков (Д)

)5(,}])h64I8.94(lg2.3[24.73{01.0= 30

nj

njjj IД

Ij – интенсивность дождя за j-ый интервал времени; hj – слой дождя за j – ый интервал времени; I30 – максимальная 30-минутная интенсивность осадков.

Расчет факторов водной эрозии

f – содержание фракции 0,1-0,001e – содержание фракции <0,001 a – содержание гумуса,b – класс структуры почвы; c – класс водопроницаемости почвы; d – поправочный коэффициент на каменистость почвы.

П={16,67*10-6*[f*(100-e)]1,14*(12-a) +0,25*(b-2)+0,193(4-c)}*d (4)

44

Оценка риска водной эрозии для современного состояния

Сравнение оценок риска водной эрозии в текущем состоянии с различными разрешениями

0,17 0 0 0

17,922,46 0,62 0,1

259,52

238,59

0

50

100

150

200

250

Незначительный слабый средний сильный Очень сильный

Смыв почвы, т/Га год

Пл

ощ

адь

, Га

Разрешение 1км

Разрешение 90 м

Разрешение 1км Разрешение 90 м

Современное состояние

Площадь, тыс. Га / процент от общей площади, %

незначительный

слабый средний сильныйочень

сильный∑S

Разрешение 1 км259,52 0,17 - - - 259,69

99,93 0,07 - - - 100

Разрешение 90м238,59 17,92 2,46 0,62 0,10 259,69

91,87 6,90 0,95 0,24 0,04 100

45

Оценка риска водной эрозии с применением противоэрозионных мероприятий

Сравнение сценариев 1 и 2

2,46 0,62 0,10,42 0

238,59

17,92

0,01

247,13

12,13

0

50

100

150

200

250

Незначителный Слабый Средний Сильный Очень сильный

Смыв почв, т/Га год

Пл

ощ

ад

ь,

ты

с.

Га

Текущее состояние(Сценарий 1)

Моделированиепротивоэрозионныхмероприятий (Сценарий 2)

Сценарий 1

Сравнение

Площадь, тыс. Га / процент от общей площади, %

незначительный

слабый средний сильныйочень

сильный∑S

Современное состояние 238,59 17,92 2,46 0,62 0,10 259,69

91,87 6,90 0,95 0,24 0,04 100

С применением противоэрозионных мероприятий (сценарий3)

247,13 12,13 0,42 0,01 - 259,69

95,166 4,67 0,16 0,004 - 100

Современное состояние

46

Описание исследуемого участка

Параметры исследовательского участка1.Орошаемое поле, тип орошения

бороздковый2.Выращиваемая культура: томаты3.Площадь исследовательского участка: 0,6 га.4.Почва: темно-каштановая5.Уклон резко выраженный ~ 0,013

На территории, для которого характерен сильный смыв почвы, производилось орошение по бороздам, что добавляло к водной эрозии под влиянием ливневых осадков так же и ирригационную эрозию, возникшую при орошении по бороздам.

47

Оценка риска водной эрозии при орошении по бороздам

Для расчета оценки опасности эрозии почв при поливе по бороздам применялась разработанная М.С.Кузнецовым модель водной эрозии почв:

)5(,10 23

xxx tBqQ

Q - смыв почвы с участка длиной х за время полива t; qx ‑ интенсивность выноса почвы потоком на расстоянии х от головной части борозды ;Вх - суммарная ширина потоков воды в створе х, замыкающем снизу площадь в 1 га;t2x - длительность транзита воды через створ х за один полив.

Применением противоэрозионных мероприятий:•Изменение способа орошения•Обработка почвы в направлении горизонталей

Оценка риска водной эрозии для текущего

состояния

Оценка риска водной эрозии с применением

противоэрозионных мероприятий

Вид эрозии почв Смыв почв

Текущее состояние

Дождевая эрозия 5,1 т/га год

Орошение по бороздам 45 т/га за полив

Противоэрозионные мероприятия

Дождевая эрозия 1,2 т/га год

Орошение по бороздам -

Botswana (switch to Portrait

mode)

masked_DEM080.png

Boteti study site - Data preparation and the application of PESERA-DESMICE

Biogas technology is a very welcome technology in the urbanizing world, as it consumes food waste, chicken droppings and sewage. In Botswana, the technology is experiencing resurgence, with more plants in the near future. It is clear that biogas technology will add value to MDGs, sustainability, V2016, mitigating climate change (CDMs, REDDs).

The Boteti study area has overgrazing as the degradation challenge. The extent of overgrazing in pastures, woodland and settlements is fragile to critical. Firewood collection adds to the range degradation, thus alternative energy in the form of biogas has been proposed by the stakeholders.

1 – Climate▶Results of the climate include rainfall data trend across the years; and wind rose diagram to show the wind conditions in the study area. ▶ Many stakeholders, attribute the drought years (low rainfall trends in the chart) as causes land degradation / desertification. They point out that, once the rains come back, the range and livelihoods bounce back.

2 – Land use▶A map of degradation arising out of the various land uses was developed.

▶ Many areas suffered varying extents of degradation – with pastures, settlements and woodlands having some fragile to critical conditions.

3 – Energy ▶Based on energy survey studies in the area, almost all villagers use fuel wood.

▶Wood availability has declined around villages; hence long distances covered to collect wood and thus more costly.

Projection, coordinates and extent of the data layers Spatial Ref: WGS_1984_UTM_zone34SLinear Unit: Meter (1.000000)Angular Unit: DegreeFalse_Easting: 500000False_Northing: 10 000 000.00Central_Meridian: 27Scale_Factor: 0.9996Latitude_Of_Origin: 0Datum: D_WGS_1984Extent: Left Bottom 254019. 991039; 7629744.989911Extent: Top right 299112.162678; 768088.403962

Main conclusions: Issues arising from results / data preparation▶ The biogas technology dissemination was hindered by lack of policies i.e. no policies for its efficient dissemination.▶ The biogas technology was expensive compared to other technologies, coupled with poor maintenance. ▶ A cost benefit analysis based on IPCC (Clean Development Mechanism) indicates that biogas is viable at a large scale, and may qualify for CDM funding under climate change. Thus, as waste is generated by urbanization, biogas uses this waste as energy, hence clean technology.▶ Biogas technology has many benefits: protects forests/ecosystems; less bronchial problems (no smoke); reduces women’s workload; thus the technology enhances sustainability, adds value to MDGs, fights desertification and mitigates climate change. The research adds value to the Decade of Education for Sustainable Development (2005-2012).

Main conclusions: Issues arising from results / data preparation▶ The biogas technology dissemination was hindered by lack of policies i.e. no policies for its efficient dissemination.▶ The biogas technology was expensive compared to other technologies, coupled with poor maintenance. ▶ A cost benefit analysis based on IPCC (Clean Development Mechanism) indicates that biogas is viable at a large scale, and may qualify for CDM funding under climate change. Thus, as waste is generated by urbanization, biogas uses this waste as energy, hence clean technology.▶ Biogas technology has many benefits: protects forests/ecosystems; less bronchial problems (no smoke); reduces women’s workload; thus the technology enhances sustainability, adds value to MDGs, fights desertification and mitigates climate change. The research adds value to the Decade of Education for Sustainable Development (2005-2012).

4 – Topography Insert map

▶Mopipi, Boteti sub-district in Botswana.

▶Current commercial biogas plants: Cumberland Hotel – uses food waste. Richmark poultry – uses chicken dropping and has solar water heater that elevates the temperature of the mixture (source).Currently 2 individuals own biogas plants, one has fitted purifier. Food waste, cow dung used.Two new plants expected in Kgalagadi, to use sewage from schools.

Key issues – biogas technology in Botswana•Biogas first started in 1980s in Botswana, with about 10 plants, supported by govt. •The plants in 1980s were used for borehole water pumping (mainly), followed by cooking uses and bakery. Syndicates/institutions and in some cases, individuals were the owners. Most have since been abandoned.•Biogas types tested included: floating drum digester (Indian); Fixed dome digester (Chinese) and Plug flow digester (S. Africa).•The average per capita consumption of firewood (cooking, heating, boiling water) in rural areas is 3kg of wood per day. This daily capita = 13KWh, and this can be covered by a 2m3 biogas plant (Somolekae, 2009).•A biogas with a volume of 2.8m3 can save 0.12ha of woodland each year (Green Power, India). This counters degradation/desertification.Study Site/Stakeholders model/output evaluation,•There is keen interest on the project.•Based on analysis, the up-scaling should focus on building larger biogas plants, as opposed to several smaller units, the cost benefit analysis indicates the larger units to be more viable.

Key issues – biogas technology in Botswana•Biogas first started in 1980s in Botswana, with about 10 plants, supported by govt. •The plants in 1980s were used for borehole water pumping (mainly), followed by cooking uses and bakery. Syndicates/institutions and in some cases, individuals were the owners. Most have since been abandoned.•Biogas types tested included: floating drum digester (Indian); Fixed dome digester (Chinese) and Plug flow digester (S. Africa).•The average per capita consumption of firewood (cooking, heating, boiling water) in rural areas is 3kg of wood per day. This daily capita = 13KWh, and this can be covered by a 2m3 biogas plant (Somolekae, 2009).•A biogas with a volume of 2.8m3 can save 0.12ha of woodland each year (Green Power, India). This counters degradation/desertification.Study Site/Stakeholders model/output evaluation,•There is keen interest on the project.•Based on analysis, the up-scaling should focus on building larger biogas plants, as opposed to several smaller units, the cost benefit analysis indicates the larger units to be more viable.

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Crete (switch to Portrait

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SITE NAME - Data preparation and the application of PESERA-DESMICE in Mesara Valley – Crete Greece

Executive summary

Soil erosion is considered as the main threat of land degradation and desertification in the study site of Crete. Techniques on land management ensuring adequate plant cover of the soil surface will greatly contribute to combat desertification.

IntroductionCrete is one of the most important areas of Greece subjected to high desertification risk. Olive groves and pastures are widely expanded in the area subjected to various degrees of soil erosion and desertification due to the different applied land management practices. The purpose of this study is to apply the PESERA model for assessing soil erosion rates under the existing land uses and land management practices.

Data availability/Source

Soil, vegetation, and climate data have been collected for the study area for the purposes of DESIRE project. Topography data have been provided by the Greek Geographical Army Service. Data required by DESMICE have been collected from the local Institutes.

Results of the Data Preparation1 – ClimateMeteorological data were measured by an automatic meteo station installed for the purpose of this project in the study area. Rainfall , wind speed, solar radiation, air temperature, relative humidity were recorded every three minutes and average on hourly and daily basis. ETo was estimated using the modified Penman equation and using an open pan evaporation meter. Mean annual rain fall is 570 mm , while mean annual ETo is estimated at 1300 mm.

2 – Land useLand uses were described by air-photo analysis and field observations conducted for the study area. The main land use cover type is olives covering 56% of the area. The following important land uses are arable land, vineyards, pastures, grassland, heterogeneous (agricultural and natural vegetation) covering a percentage of 3%, 5%, 13%, and 23% respectively.

3 – SoilA soil survey was conducted in the area using existing soil survey systems. Soil mapping units were drawn on ortho-photo maps in the scale of 1:30.000. Soils of the area are mainly well drained, moderately fine-textured, moderately deep to shallow, slightly sloping to very steep, formed mainly on marl, plysh, conglomerates and limestone parent materials.

Projection, coordinates and extent of the data layers

Spatial Ref: Lambert_Azimuthal_Equal_AreaLinear Unit: Meter (1.000000)Angular Unit: Degree (0,017453292519943299)False_Easting: 0False_Northing: 0Central_Meridian: -9Latitude_Of_Origin: 48Datum: D_User_DefinedExtent: 1440901, -1294005, 1463901, -1281005Columns 460 Rows 260 Cellsize__X._Y 50, 50m

Variable 1960 1970 1980 1990 2000 2010

Daily Rainfall

Daily Temperature

Monthly PET

or Monthly Rainfall (CTRU CL 2.0)

or Monthly Temperature (CTRU CL 2.0)







4 – Topography

5 – EconomicsDESMICE Data Category Available info




IS_T: Applicability limitations Y

IS_T: Spatial variation in investmestment/maintenance N

IS_T: Change in production and production costs Y

Initial output/resultsThe applied old version of PESERA model provides as output only the soil sediment loss rates. Under the existing land management practices, the dominant class of soil erosion rates are lees than 1 tone per hectare per year. The next important classes are 2-5, 10-20 and 20-50 t/ha/year. Similar erosion rates have been measured in the monitoring sites (WB4) existing on this area.Soil erosion rates estimated or measured corresponds to less than 1 mm/year, while tillage erosion rates are ranging from 2-13 mm/year.Local stakeholders are very much interested for having the rest of outputs such as surface water runoff rates, financial assessments of soil erosion rates on plant production.

Initial output/resultsThe applied old version of PESERA model provides as output only the soil sediment loss rates. Under the existing land management practices, the dominant class of soil erosion rates are lees than 1 tone per hectare per year. The next important classes are 2-5, 10-20 and 20-50 t/ha/year. Similar erosion rates have been measured in the monitoring sites (WB4) existing on this area.Soil erosion rates estimated or measured corresponds to less than 1 mm/year, while tillage erosion rates are ranging from 2-13 mm/year.Local stakeholders are very much interested for having the rest of outputs such as surface water runoff rates, financial assessments of soil erosion rates on plant production.

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Morocco(switch to Portrait mode)

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Zeuss-Koutine watershed (Médenine-Tunisia)- Data preparation and the application of PESERA-DESMICE

In order to evaluate the effects of measures to mitigate land degradation, we used on the context of desire project a new and innovative approaches for modeling at regional scale. The PESERA model offers an explicit theoretical response based on erosion model, making use of land-use, topographic, soil and climatic data. Hydrology and vegetation biomass are run to equilibrium. Runoff is estimated. From the components, the model estimates water and sediment delivered to stream channels.

Desertification constitute a major concern of countries in the Sahelian region of North Africa and specially in Tunisia. This phenomenon is responsible for the degradation of the natural habitat and for the arable land disappearance. Therefore, it’s important to study this phenomenon at spatial and temporal scales and analyze the interaction between the various elements of the environment in relation to soil dynamics and human activity. Data availability/Source

PESERA:• six classes are presented on theLand use map. •Fourteen climate station are available in an around the catchment area of Zeuss-koutine• The soil map is extracted from the soil map of the region. We have eight calasses according to the French soil system.• Topography data are extracted from the digital elevation model (dem90) and topographic maps.Results of the Data Preparation1 – Climate

▶ The climatic data are collected from different origin ( station around study area, CRDA, INM, rapports, web site…).

▶ Many parameters are calculated by using empiric equation, for example PET have been calculated with Penman –Monteith equation.

▶ In the case of PESERA model, we prepared a monthly climate data related to rainfall, PET, temperature,…

2 – Land use▶The fruit trees are mainly olives and are found on jessour and tabias only. The cereals (winter barley and wheat) are grown episodically during wet years.

▶The natural vegetation (ranges) was divided into three classes: mountain, plain, and halophyte, because of their different phenology and grazing practices

3 – Soil▶The soil map of the study watershed was extracted from the soil map of the region. It made by use, analyse and interpretation of provided imagery data, the soil map was elaborated according to the French soil classification (CPCS, 1967),

▶The soils are developed on a calcareous substratum in the upstream area and gypsum or gypsum to calcareous in the downstream area. The soil horizons are generally shallow, stony, unstructured with sandy to fine sandy texture.

DESMICE

1.A study site information (physical and socio-economic data) are available.

2.A technology information should be completed for each technology to be included in the model assessment.

Variable 1960 1970 1980 1990 2000 2010

Daily Rainfall

Daily Temperature

Monthly PET

or Monthly Rainfall (CTRU CL 2.0)

or Monthly Temperature (CTRU CL 2.0)

Main conclusions

▶ Olives and trees on jessour is the most representative of catchment conditions. ▶ Erosion, water deficit and runoff layers were more representative of catchment conditions. ▶ The model needs to be calibrated with field investigation, historical data and stakeholder knowledge. ▶ The historic spatio-temporal data related to vegetation are difficult to obtain.

Main conclusions

▶ Olives and trees on jessour is the most representative of catchment conditions. ▶ Erosion, water deficit and runoff layers were more representative of catchment conditions. ▶ The model needs to be calibrated with field investigation, historical data and stakeholder knowledge. ▶ The historic spatio-temporal data related to vegetation are difficult to obtain.

4 – TopographyThe study area covered the watersheds of wadi Oum Zessar and wadi El Halouf which are localized in southeast Tunisia (north west of the city of Médenine). It has an area of 1226 km2 and stretches from the upstream area of Béni Khédache to the downstream area of sebkhat Oum Zessar.

5 – Economics

The additional DESMICE data requests are under preparation.

DESMICE Data Category Available info




IS_T: Applicability limitations N

IS_T: Spatial variation in investmestment/maintenance N

IS_T: Change in production and production costs Y

Initial output/results

According to the map, annual soil water deficits increase from the up stream (less than 300 mm) to the middle and rich a value of 380 mm on the down stream. Due to quality of soil, land use, climate change and topography of the study area, the mean annual erosion is significant on the up stream. PESERA outputs of interest: erosion, runoff, soil moisture, biomass/productivity, vegetation cover, wind erosion, nutrient status, OM; DESMICE out puts of interest: spatial financial feasibility, and scenario analyses of policy choices and cost-effectiveness), stakeholders evaluation.

Initial output/results

According to the map, annual soil water deficits increase from the up stream (less than 300 mm) to the middle and rich a value of 380 mm on the down stream. Due to quality of soil, land use, climate change and topography of the study area, the mean annual erosion is significant on the up stream. PESERA outputs of interest: erosion, runoff, soil moisture, biomass/productivity, vegetation cover, wind erosion, nutrient status, OM; DESMICE out puts of interest: spatial financial feasibility, and scenario analyses of policy choices and cost-effectiveness), stakeholders evaluation.

Land use map Soil map

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Education

pesera results

results data preparation

pesera model

physical data

interpolated data

morocco data

soil classes

portugal data preparation