This document discusses using the PESERA model to analyze soil erosion in various locations: 1) In Portugal, the PESERA model results matched fieldwork showing low erosion in mature forests but higher erosion in areas disturbed by wildfires. Prescribed burning increased erosion slightly less than wildfires. 2) In Crete, Greece, PESERA results were comparable to measured erosion rates and reflected catchment conditions. 3) Data preparation for running PESERA in other locations, like Morocco and Russia, is discussed. Maps, soil data, and climate data were used as inputs to the model.

1. BOTSWANA

2. Boteti: basis for modelling
alternative uses of biomass to for
fuel
Biomass
Cattle
Fodder Wood
Manure
Soil Organic
Matter
Fertilisatio
n
Soil
Moisture
retention
Collection
for Biogas
Collection
for Fuel
Grazing
pressure
Waste
slurry

3. PORTUGAL

4. 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

5. 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 15 202.5
Kilometers
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
0 5 10 15 202.5
Kilometers
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
0 5 10 15 202.5
Kilometers
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
Low :
0 5 10 15 202.5
Kilometers

8. 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.
▶ All physical data used as input to PESERA model are easily obtained from
regular existing soil, climate, topographic and vegetation data base.

9. CRETE

10. 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).
▶ 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 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).
▶ All physical data used as input to PESERA model are easily obtained
from regular existing soil, climate, topographic and vegetation data base.

11. 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.

14. 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

15. 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).
▶ 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 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).
▶ All physical data used as input to PESERA model are easily obtained
from regular existing soil, climate, topographic and vegetation data base.

16. MOROCCO
Data for running PESERA model

17. 90m DEM
for elevation

18. Land use
classes

19. Soil
Classes

20. Degradatio
n classes

21. Conservation
practices

22. Analysis of precipitation at best available
site

23. NW Africa: Annual precipitation (mm)
10 minute
(15-20 km)
Interpolated
data from CRU.
Monthly
averages
for1960-1990

24. RUSSIA

25. WaterErosionModel
Dzhanybek(Russia)
25

26. 26
Объект исследования – Палласовский
район
Географическое положение
Климатические условия
По 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 снимок
2000 год

27. 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.
a
CA
L KLBLW ⋅= −⋅−− ))12,0exp((1
)1,0(100
Уравнение для определения смыва почв талыми водами
(по Г. А. Ларионову), т/га/год
N
PhB =
Z
ThSA )( +=
)( h
MeR
FhC
−
−
=
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. 28
А B
C L
Ka
WL
Картографическое моделирование водной
эрозии
Структура ArcGIS
Схема подготовки и обработки данных
a
CA
L KLBLW ⋅= −⋅−− ))12,0exp((1
)1,0(100 (5)

29. 29
, -Сбор подготовка и обработка исходных данных для ГИС
проекта
Цифровая модель рельефа
Оцифровка 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. 30
, -Сбор подготовка и обработка исходных данных для ГИС
проекта
Примеры слоев данных с посчитанным
коэффициентом склонового стока (h) в Палласовском
районе с разрешениями
90 м (слева); 1 км (справа)
Склоновый сток (h)
E
HDih =
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 данных
с коэффициентом агрофона
(разрешение 1 км)
Слой данных,
показывающий тип землепользования
(разрешение 1 км)
Табл. 1
Часть таблицы для определения коэффициента агрофона
Коэффициент агрофона (Ka)
Табл. 2
Значение параметров D и E
(6)

31. 31
, -Сбор подготовка и обработка исходных данных для ГИС
проекта
Коэффициенты A, B, C из уравнения (1)
Оцифрованная почвенная карта
Палласовского района
(М 1 : 2 000 000)
N
PhB =
Z
ThSA )( +=
)( h
MeR
FhC
−
−
=
(8)
(7)
(9)
Слои данных построенных по коэффициентам А, В, С
Табл. 3
Рассчитанная смываемость почвы
A (разрешение 1 км) B (разрешение 90 м) C (разрешение 50 м)
Оцифрованная почвенная карта
ОАО «Ромашки»
(М 1 : 25 000)
Табл. 4
Рассчитанная смываемость почвы

32. 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
незначительны
й
слабы
й
средний
сильны
й
очень
сильны
й
Риск смыва почвы
Проценты
Разрешение 1км
Разрешение 90м
Карта оценки риска водной эрозии почв (разрешение 90 м)

33. 33
Результаты моделирования водной
эрозии
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. 34
Результаты моделирования водной
эрозии
Сценарий №2. Моделирование смыва почв при измененном севообороте
Карта 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
незначительны
й
слабы
й
средний
сильны
й
очень
си
льны
й
Риск смыва почвы
Проценты
Сценарий № 1
Сценарий № 2
Карта оценки риска водной эрозии почв для ОАО «Ромашки» с
разрешением 50 м (сценарий № 2)
Границы
противоэрозионных
мероприятий
Границы
противоэрозионных
мероприятий

35. 35
Результаты моделирования водной
эрозии
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
незначительны
й
слабы
й
средний
сильны
й
очень
сильны
й
Риск смыва почвы
Проценты
Сценарий № 1
Сценарий № 3
а)
б)
Границы
противоэрозионных
мероприятий

36. 36
Результаты моделирования водной
эрозии
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
незначительны
й
слабы
й
средний
сильны
й
очень
сильны
й
Риск смыва почвы
Проценты
Сценарий № 1
Сценарий № 4

37. 37
Результаты моделирования водной эрозии
Эффективность различных сценариев с точки зрения уменьшения негативного антропогенного
воздействия вызывающего водную эрозию для ОАО «Ромашки»
100
SSЭ
×
−
=
М
М
Э
S
Э SЭ - площадь эрозионно-опасных земель без мероприятий, [га];
- площадь эрозионно-опасных земель с противоэрозионными мероприятиями, [га];
SМ - площадь земель задействованных в противоэрозионных мероприятиях, [га]
М
ЭS
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)
Риск смыва почвы
Проценты
Сценарий № 1
Сценарий № 2
Сценарий № 3
Сценарий № 4
0
10
20
30
40
50
60
Эффективность,%
2 3 4
№ Сценария
Эффективность сценариев с
противоэрозионными мероприятиями

38. WaterErosionModel
Novy(Russia)
38

39. 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 мм
400-500 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
температуравоздуха,°С
Среднемесячная температура воздуха
Среднегодовое распределение
осадков
Снимок со спутника

40. 40
Классификация
Смыв 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
Слабосмы
тые
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
Классификация эрозии почв, предложенная М.Н.Заславским
Классификация водной эрозии
Виды водной эрозии в Марксовском районе

41. 41
Создание ГИС-проекта: входные данные
Цифровая модель рельефа
http://edcdaac.usgs.gov/gtop
o30/gtopo30
ftp://e0srp01u.ecs.nasa.gov/s
rtm/version1/
Данные метеорологической
станции г.Маркс
Данные о растительности
Global 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. 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(,)1.35
9
s
(x)m
22.1
(=
λ
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

43. 43
Слой 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
nj
jj 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 0,1-0,001
e – 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. 44
Оценка риска водной эрозии для современного состояния
Сравнение оценок риска водной эрозии в текущем
состоянии с различными разрешениями
0,17 0 0 0
17,92
2,46 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
Площадь,Га
Разрешение 1км
Разрешение 90 м
Разрешение 1км Разрешение 90 м
Современное состояние
Площадь, тыс. Га / процент от общей площади, %
незначи
тельный
слабый средний сильный
очень
сильный
∑S
Разрешение 1 км
259,52 0,17 - - - 259,69
99,93 0,07 - - - 100
Разрешение 90м
238,59 17,92 2,46 0,62 0,10 259,69

45. 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
Площадь,тыс.Га
Текущее состояние
(Сценарий 1)
Моделирование
противоэрозионных
мероприятий (Сценарий 2)
Сценарий 1
Сравнение
Площадь, тыс. Га / процент от общей площади, %
незначит
ельный
слабый средний сильный
очень
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
247,13 12,13 0,42 0,01 - 259,69
Современное состояние

46. 46
Описание исследуемого участка
Параметры исследовательского участка
1.Орошаемое поле, тип орошения
бороздковый
2.Выращиваемая культура: томаты
3.Площадь исследовательского участка: 0,6 га.
4.Почва: темно-каштановая
5.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. 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 2
3
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 -

48. Botswana
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49. 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_zone34S
Linear Unit: Meter (1.000000)
Angular Unit: Degree
False_Easting: 500000
False_Northing: 10 000 000.00
Central_Meridian: 27
Scale_Factor: 0.9996
Latitude_Of_Origin: 0
Datum: D_WGS_1984
Extent: Left Bottom 254019. 991039; 7629744.989911
Extent: 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.

50. Crete
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51. masked_DEM080.png
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.
Introduction
Crete 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 Preparation
1 – Climate
Meteorological 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 use
Land 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 – Soil
A 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_Area
Linear Unit: Meter (1.000000)
Angular Unit: Degree (0,017453292519943299)
False_Easting: 0
False_Northing: 0
Central_Meridian: -9
Latitude_Of_Origin: 48
Datum: D_User_Defined
Extent: 1440901, -1294005, 1463901, -1281005
Columns 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)
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).
▶ 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 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).
▶ All physical data used as input to PESERA model are easily obtained from regular existing soil, climate, topographic and vegetation data base.
4 – Topography
5 – EconomicsDESMICE 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
Initial output/results
The 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/results
The 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.

52. Morocco
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53. masked_DEM080.png
Zeuss-Koutine watershed (Médenine-Tunisia)- Data preparation and
the application of PESERA-DESMICEIn 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 Preparation
1 – 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 – Topography
The 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_S: Additional maps Y
IS_S: Transport details Y
IS_S: Production costs & benefits Y
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