The document analyzes satellite images from 1991, 1996, 2004, and 2011 to measure glacier areas in the Urubamba and Vilcabamba mountain ranges in Peru. It finds that over this period, all glaciers showed losses between 29.4-98.7% of their 1991 areas. For the Urubamba range, glacier coverage decreased from 64.8 km2 in 1991 to 29.4 km2 in 2011, while for the Vilcabamba range it decreased from 220.3 km2 to 129.4 km2. Losses were stronger for glaciers below 5400 meters. Analysis of precipitation data found no significant trends, suggesting losses are due to increasing air temperatures of 0.1-0.39°
Paleo environmental bio-diversity macro-evolutionary data mining and deep lea...Abdullah Khan Zehady
How are the environmental variables and marine evolution connected? Does astronomical forcing influence climate variation? Can we apply deep learning to classify index fossils?
In recent years, numerous studies have shown a growing concern about the effects of climate change on the hydrological cycle and hydrological extremes. In particular, statistical analyses on either long hydrological series or modelled data show conflicting trends in different areas of Europe. In addition, the absence of continuous observations and the significant alterations experienced by some watersheds makes difficult to quantify the effects of climate change. These critical issues are particularly felt in Southern Italy where hydrometric monitoring is often discontinuous, updated flow rating curves rarely exist, and territories underwent significant anthropogenic transformations. The present work aims to update flood time-series in Southern Italy, using direct and indirect measurements, over the period 1920-2021. The numerous missing data were reconstructed by means of specially defined flood rating curve or by using daily flow rates to derive equivalent flood flows through the empirical function by Fuller. The obtained series were, then, analysed using the nonparametric Mann-Kendall test in order to detect possible trends. The results of the present study provide preliminary indications of flood trends over the last 50 years in Southern Italy by integrating an information gap regarding this phenomenon and its dynamics.
Landslide distribution and susceptibility mapping are fundamental steps for landslide-related hazard and risk management activities. This is especially crucial in a country like
Ethiopia. It has mountainous terrain, heterogeneous rock units with varying degrees of
weathering, and complex hydrology that contribute to landslide initiation. Landslides can
result in loss of life, property damage, and infrastructure disruption. The primary purpose
of this study is to prepare a landslide susceptibility map for the Bichena and Yed Wiha area
using the Weight of Evidence (WoE) and Logistic Regression (LR) methods that use
continuous and discrete variables efficiently. Data were collected from different sources to
delineate landslide susceptibility zones in the study area to produce better results of a
landslide susceptibility map. Eight triggering factors were considered namely; Aspect,
Slope, Curvature, Distance from Stream, Distance from Lineament, Lithology, Land Use,
and Rainfall. Rainfall-induced landslides of different types frequently affect the hilly and
mountainous terrains of the highlands of Ethiopia and are weighted using the weight of
Evidence. The weight of each factor will be calculated and assigned in the Aeronautical
Reconnaissance Coverage Geographic Information System (Arc-GIS). To add these factors
to the Arc-GIS spatial analyst tools raster calculator, and produce a landslide susceptibility
map, the weighted contrast linear combination and coefficient of regression were used
between training landslide and landslide causative factors. To establish the relational
statistical correlation of various causative factors with past landslides in the area, landslide
inventory was made through Google Earth image interpretation. Further, the weight of
evidence and logistic regression, bivariate, and multivariate statistical analysis methods
were used to prepare the model. Statistical Package for the Social Science (SPSS) software
and MS Excel were used to check the validation of the overlay maps using Receiver
Operating Characteristic (ROC) and Landslide Density Index (LDI). Finally, a landslide
susceptibility map was prepared and classified into five classes: Very Low, Low, Medium,
High, and Very High. Finally, landslide mitigation measures were recommended for susceptible areas.
Spatial and Temporal Variation of Rainfall in IRAQIOSR Journals
Rainfall in Iraq is characterized by unorganized distribution of both spatial and temporal. The annual, seasonal and monthly mean rainfall varies considerly with years. The recorded rainfall quantity in the different meteorological stations varies from location to another according to sea surface elevation and the geographical position of meteorological stations.
Variation of rainfall with space and time were studied in Iraq for the period (1980-2010) using 22 meteorological stations. Mean monthly, seasonally and annually values of rainfall were found in different meteorological stations. Winter months represent about (42-56) % of total annual rainfall. The annual variability of rainfall in all these stations is high. Isohyetal method was used to estimate the mean monthly values of rainfall in Iraq. Simple and Multiple Regression Equations were found in Mosul, Baghdad and Basrah stations between rainfall and different meteorological elements.
Paleo environmental bio-diversity macro-evolutionary data mining and deep lea...Abdullah Khan Zehady
How are the environmental variables and marine evolution connected? Does astronomical forcing influence climate variation? Can we apply deep learning to classify index fossils?
In recent years, numerous studies have shown a growing concern about the effects of climate change on the hydrological cycle and hydrological extremes. In particular, statistical analyses on either long hydrological series or modelled data show conflicting trends in different areas of Europe. In addition, the absence of continuous observations and the significant alterations experienced by some watersheds makes difficult to quantify the effects of climate change. These critical issues are particularly felt in Southern Italy where hydrometric monitoring is often discontinuous, updated flow rating curves rarely exist, and territories underwent significant anthropogenic transformations. The present work aims to update flood time-series in Southern Italy, using direct and indirect measurements, over the period 1920-2021. The numerous missing data were reconstructed by means of specially defined flood rating curve or by using daily flow rates to derive equivalent flood flows through the empirical function by Fuller. The obtained series were, then, analysed using the nonparametric Mann-Kendall test in order to detect possible trends. The results of the present study provide preliminary indications of flood trends over the last 50 years in Southern Italy by integrating an information gap regarding this phenomenon and its dynamics.
Landslide distribution and susceptibility mapping are fundamental steps for landslide-related hazard and risk management activities. This is especially crucial in a country like
Ethiopia. It has mountainous terrain, heterogeneous rock units with varying degrees of
weathering, and complex hydrology that contribute to landslide initiation. Landslides can
result in loss of life, property damage, and infrastructure disruption. The primary purpose
of this study is to prepare a landslide susceptibility map for the Bichena and Yed Wiha area
using the Weight of Evidence (WoE) and Logistic Regression (LR) methods that use
continuous and discrete variables efficiently. Data were collected from different sources to
delineate landslide susceptibility zones in the study area to produce better results of a
landslide susceptibility map. Eight triggering factors were considered namely; Aspect,
Slope, Curvature, Distance from Stream, Distance from Lineament, Lithology, Land Use,
and Rainfall. Rainfall-induced landslides of different types frequently affect the hilly and
mountainous terrains of the highlands of Ethiopia and are weighted using the weight of
Evidence. The weight of each factor will be calculated and assigned in the Aeronautical
Reconnaissance Coverage Geographic Information System (Arc-GIS). To add these factors
to the Arc-GIS spatial analyst tools raster calculator, and produce a landslide susceptibility
map, the weighted contrast linear combination and coefficient of regression were used
between training landslide and landslide causative factors. To establish the relational
statistical correlation of various causative factors with past landslides in the area, landslide
inventory was made through Google Earth image interpretation. Further, the weight of
evidence and logistic regression, bivariate, and multivariate statistical analysis methods
were used to prepare the model. Statistical Package for the Social Science (SPSS) software
and MS Excel were used to check the validation of the overlay maps using Receiver
Operating Characteristic (ROC) and Landslide Density Index (LDI). Finally, a landslide
susceptibility map was prepared and classified into five classes: Very Low, Low, Medium,
High, and Very High. Finally, landslide mitigation measures were recommended for susceptible areas.
Spatial and Temporal Variation of Rainfall in IRAQIOSR Journals
Rainfall in Iraq is characterized by unorganized distribution of both spatial and temporal. The annual, seasonal and monthly mean rainfall varies considerly with years. The recorded rainfall quantity in the different meteorological stations varies from location to another according to sea surface elevation and the geographical position of meteorological stations.
Variation of rainfall with space and time were studied in Iraq for the period (1980-2010) using 22 meteorological stations. Mean monthly, seasonally and annually values of rainfall were found in different meteorological stations. Winter months represent about (42-56) % of total annual rainfall. The annual variability of rainfall in all these stations is high. Isohyetal method was used to estimate the mean monthly values of rainfall in Iraq. Simple and Multiple Regression Equations were found in Mosul, Baghdad and Basrah stations between rainfall and different meteorological elements.
Aporte a la construccion de una propuesta metodologica para la caracterizació...
Monitoring glacier variations in the Urubamba and Vilcabamba Mountain Ranges
1. 5300 5260 5235 5235 5211 5150 5150 5125 5100 5086 5053 5046 4949 4802
44.1 33.7 53.4 63.9 75.2 55.4 79.6 93.4 95.2 79.7 98.7 80.0 94.9 78.9
6271 5991 5991 5880 5771 5605 5500 5473 5450 5414 5400 5400 5400
47.2 69.6 29.4 50.9 41.2 37.0 47.0 47.6 50.3 46.3 50.8 67.2 70.1
Monitoring glacier variations in the Urubamba and Vilcabamba Mountain Ranges, Peru, using Landsat 5 images
SUAREZ Wilson1,3, CERNA Marco1, ORDÓÑEZ Julio1, FREY Holger2, GIRÁLDEZ Claudia2, HUGGEL Christian2.
REFERENCES
National Service of Meteorology and Hydrology of Peru (SENAMHI)1 , University of Zurich 2 , La Molina National Agrarian University of Peru (UNALM) 3
Correspondence to: Dirección General de Hidrología – SENAMHI Perú. Jr. Cahuide 785 Lima 11 Perú. dgh@senamhi.gob.pe
The Chains of Urubamba and Vilcanota Mountains are located in in the Cusco region in southern Peru, about 800 km from
the city of Lima. The mountain ranges constitute the boundary of the Andes and the jungle and have an elevation range from
800 to 6200 m a.s.l. Due to the difficult access to the glaciers of the mountain ranges, satellite-based monitoring is required.
Glaciers are grouped by main peaks (“nevado” in Spanish), which can drop different glacier tongues. The main map to the
right (Landsat 5, June 1996), shows the two mountain chains separated by the river Urubamba.
INTRODUCTION
DATAAND METHODOLOGY
The method used to characterize the ice is based on Silverio and Jacquet (2005) from the Cordillera Blanca, which is based
on the normalized difference snow index "NDSI”. Eight satellite scenes (=4 mosaics) of the dry months (southern winter)
were used from the years 1991, 1996, 2004 and 2011. Data from rainfall stations was used to identify precipitation-free
periods to avoid snow cover in the satellite images. A second analysis was performed with rain data (1966 - 2010) to
identify potential trends and breaks in precipitation patterns using nonparametric tests: Mann - Kendall for trends and
CUSUM for the breaks were applied (95% confidence).
Fig. 2: Glacier areas in the Urubamba and Vilcabamba mountain ranges in 1991, 2004, and 2011. The number in parentheses identifies its position in Fig.1. Text in pink background shows the peaks below 5400 m a.sl. Area loss (in %) refers to the period 1991-2011.
A
B
European Geosciences Union (EGU)
General Assembly 2013
Macchu Picchu, Incas City
11.02
7.37
3.27
11.55
8.67
6.74
2.06
1.86
5.18
1.72
2.78
1.29
1.13
0.22
8.13
5.20
1.93
7.11
5.25
3.74
0.83
0.81
1.91
0.60
0.86
0.24
0.20
0.02
6.94
4.11
1.70
5.91
4.42
2.80
0.64
0.52
1.40
0.32
0.46
0.13
0.07
0.01
0
2
4
6
8
10
12
14
(01)CCOLQUE
CRUZ
(02)CHICON
(03)MARCONI
(04)
HUAJAYHUILLCA
(05)VERONICA
(06)TERIJUAY
(07)SIRIHUANI
(08)
PUMAHUANJA
(09)
HALANCOMA
(10)CAPACSAYA
(11)BONANTA
(12)
PATACANCHA
(13)QUILLOC
(14)C.PADREYOC
Surface(km²)
Urubamba Mountains
1991 2004 2011
10.30
3.99
2.51
5.85
5.92
4.79
2.58
1.18
2.64
1.57
1.12
1.75
0.87
0.46
8.12
2.91
1.28
2.62
2.19
2.43
0.71
0.11
0.28
0.39
0.03
0.83
0.19
0.13
5.76
2.65
1.17
2.11
1.47
2.13
0.53
0.08
0.13
0.32
0.01
0.35
0.04
0.10
0
2
4
6
8
10
12
(28)
CHAUPIMAYO
(29)RUNASAYOC
(30)AMPAY
(31)AMPARAY
(32)
CHAUPILOMA
(33)Occoro
(34)MOYOC
(35)
CHOQUEZAFRA
(36)OCOBAMBA
(37)CHUCUITO
(38)PITUPACCHA
(39)CAYCO
(40)
MANDORCASA
(41)
QUENUAORCO
Surface(km²)
Vilcabamba Mountains
1991 2004 2011
ALTITUDE
(a.s.l.m) 5818 5530 5350 5361 5682 5264 5399 5246 5367 4962 5032 4982 4955 4863
LOST (%) 37.0 44.2 48.0 48.8 49.1 58.5 68.8 72.1 73.1 81.6 83.5 90.2 93.5 96.7
15.22
1.66
56.19
12.82
22.96
8.37
14.59
7.99
8.89
3.00
5.94
9.70
7.49
10.12
0.91
46.72
7.78
16.26
5.74
11.29
4.66
5.94
1.79
4.17
4.41
3.18
8.03
0.50
39.69
6.30
13.49
5.27
7.73
4.19
4.42
1.61
2.92
3.18
2.24
0
10
20
30
40
50
60
(15)SALCANTAY
(16)PUMASILLO
(17)SACSA
(18)TUCARHUAY
(19)PADREYOC
(20)PANTA
(21)
CHOQUETACARPO
(22)SOIROCOCHA
(23)HUAMANTAY
(24)SORAY
(25)
CORIHUAYNACHIN
A
(26)HUAYANAY
(27)PALJAY
Surface(km²)
Vilcabamba Mountains
1991 2004 2011
SACSA
RESULTS, CONCLUSIONS AND DISCUSSION
• Mark, B.G. ( 2002). Hot ice: glaciers in the tropics are making the press. Hydrological Processes 16, 3297–3302.
• Rabatel, A., Francou, B., Soruco, A., Gomez, J., Caceres, B.,Ceballos, J., Basantes, R., Vuille, M., Sicart, J.-E., Huggel, C., Scheel, M., Lejeune, Y.,
Arnaud, Y., Collet, M., Condom, T., Consoli, G., Favier, V., Jomelli, V., Galarraga, R., Ginot, P., Maisincho,, Mendoza, J., Menegoz, M., Ramirez, E.,
Ribstein, P., Suarez, W., Villacis, M., Wagnon, P. (2013). Current state of glaciers in the tropical Andes: a multi-century perspective on glacier
evolution and climate change, The Cryosphere 7, 81-102.
• Silverio, W. and Jaquet J.-M. (2005). Glacial cover mapping (1987–1996) of the Cordillera Blanca (Peru) using satellite imagery, Remote Sensing of
Environment 95, 342–350.
• Vuille, M., B. Francou, P. Wagnon, I. Juen, G. Kaser, B.G. Mark, and R.S. Bradley. (2008). Climate change and tropical Andean glaciers – Past,
present and future. Earth Science Reviews 89, 79-96.
VERONICA
SALKANTAY
LEGEND
GLACIER AREA LOST 1991-2011
White number underlined: 80 - 98.7%
Orange number underlined: 60 - 80%
White number : 40 - 60%
Orange number : 29.4- 40%
Yellow Letters: Precipitation Station
•In the last 20 years all glaciers showed a loss in area between 98.7% (Pitupaccha) and 29.4% (Sacsa). For the years 1991,
1996, 2004, 2011 glacier coverage of Urubamba was of 64.8 km ², 49.4 km ², 36.9 km ² and 29.4 km ², for Vilcabamba
220.3 km ², 183.8 km ², 145.2 km ² and 129.4 km ². It was observed that the area loss is stronger at mountains with
elevations below 5400 m a.s.l. (Fig. 2).
•Precipitation analysis showed no significant changes in the trend or breaks in the series.
•The analysis did not consider debris-covered glaciers, although these exist but are very rare (see example Salkantay) in
Fig. 1.
•According to Vuille et al. (2008) air temperature in the tropics has been increasing since 1930 at a rate of 0.1°C / decade,
and Mark (2002) indicates that in the tropics the temperature increased at a rate from 0.35 to 0.39°C / decade between
1951-1999. Together with the lack of variations in precipitation patterns this indicates, that glacier loss will not stop and
confirms the finding of Rabatel et al. (2013), that tropical glaciers below 5400 m a.s.l. are about to disappear in the coming
years.
Fig. 1: Mountain chains Vilcanota and Vilcabamba view from Landsat 5 June 1996
MacchuPicchu, Inca City