Climate patterns are natural processes that drive climate variability in the short, medium, and long term. Characterizing the patterns behind climate variability is essential to understand the functioning of the regional atmospheric system. Since investigations typically reveal only the link and extent of the influence of climate patterns in specific regions, the magnitude of that influence in meteorological records usually remains unclear. The central Peruvian Andes are affected by most of the common climate patterns of tropical areas, such as Intertropical Convergence Zone (ITCZ), Sea Surface Temperature (SST), solar irradiance, Madden Julian Oscillation (MJO), Pacific Decadal Oscillation (PDO), and El Niño Southern Oscillation (ENSO). They are also affected by regional processes that are exclusive from South America, such as the South American Low-Level Jet (SALLJ), South American Monsoon System (SAMS), Bolivian High (BH), and Humboldt Current. The aim of this research is to study the climate variability of precipitation, maximum and minimum temperature records over Cordillera Blanca (Peru), and its relationship with the intensity and periodicity of the common climate patterns that affect this region. To achieve this aim, a spectral analysis based on Lomb’s Periodogram was performed over meteorological records (1986–2019) and over different climate pattern indexes. Results show a coincidence in periodicity between MJO and SALLJ, with monthly cycles for precipitation and temperature (27-day, 56-day, and 90-day cycles). Moreover, the most intense periodicities, such as annual (365 days) and biannual (182 and 122 days) cycles in meteorological variables, possibly would be led by ITCZ and ENSO together, as well as a combination of the Humboldt Current and SALLJ. Additionally, interannual periodicities (3-year, 4.5-year, 5.6–7-year and 11-year cycles) would have coincidence with the ENSO–solar combination, while the longest cycles (16 years) could match PDO variability.

<p>The Peruvian coast is one of the driest in the world, but it is continuously affected by extraordinary rains associated with El Niño and/or La Niña phenomenon. During these periods of intense rainfall, high flow rates are registered and gravitational processes are reported along the valleys, such as: landslides, debris flow, rock falls, avalanches, among others.</p><p>This work presents the first estimation of the Stream Power, relationship between the energy, the flow, the slope of the channel and the density of the flow of the Chancay - Lambayeque basin, with the objective of determining the energy of the main rivers in the basin and relating with gravitational processes and damage to infrastructures.</p><p>We use two softwares: LSDTopoTools and ArcSWAT (version for ArcGIS 10.6). Using high resolution Digital Elevation Models (Alos Palsar, 12.5 m) we delimit the basin, its drainage area, water network and slope using LSDTopoTools. Subsequently, we use the SWAT program.</p><p>First, the sub-basins were delimited. Second, the Hydrological Response Units (HRU) were obtained, applying the Land Use data and the FAO base guide on soil types updated by the Ministry of Agriculture and Irrigation of Peru (MINAGRI). Third, we process data on temperature, wind speed, humidity, solar radiation and rainfall from 1970 - 2018 from five meteorological stations distributed in the study basin, whose data were provided by the National Meteorology and Hydrology Service of Peru (SENAMHI). Next, we include in the analyzes the flow data from the Tinajones reservoir (6° 38´S, 79° 29´W). Finally, the annual flow rates (Hm<sup>3</sup>/s) were simulated and adjusted using SWATCup.</p><p>The results show an average flow for the year 2018 that varies from 13 Hm<sup>3</sup>/s - 49 Hm<sup>3</sup>/s. This means that the Stream Power varies from 1.3x10<sup>12</sup>Kw-4.8x10<sup>12</sup>Kw, the maximum power coinciding with the location of the Tinajones reservoir in the middle basin.</p><p>These results have allowed us to identify that 73% of the critical zones (zones with presence of gravitational processes) are in the sections where the rivers register high Stream Power; and in the same way in these sections geological dangers predominate such as flows and rock falls. In addition, infrastructures were located that may be susceptible to being damaged (e.g. three bridges, where flows range between ~22-35 Hm<sup>3</sup>/s) and/or may compromise the health of the inhabitants (e.g. five mining deposits located along the basin, considered high risk).</p><p>And to conclude, because the Tinajones reservoir is reaching its maximum capacity, a possible area was identified where a new reservoir can be housed (complying with all technical conditions), whose location would be 20 km to the east, in the province of Chumbil Alto (Cajamarca - Peru).</p>

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<p>The Equilibrium Line Altitude (ELA, m) is a good indicator for the impact of climate change on tropical glaciers , because it varies in time and space depending on changes in temperature and/or precipitation.The estimation of the ELA and paleoELA using the Area x Altitude Balance Ratio method (AABR; Osmaston, 2005) requires knowing the surface and hypsometry of glaciers or paleoglaciers (Benn et al. 2005) and the Balance Ratio (BR) correct (Rea, 2009).</p><p>In the Llanganuco basin (~ 9°3´S; 77°37´W) there are very well preserved moraines near the current glaciers front. These deposits provide information to reconstruct the extent of paleoglaciers since the Little Ice Age (LIA) and deduce some paleo-climatic variables.</p><p>The goal of this work has been to reconstruct the paleotemperature (°C) during LIA, deduced from the difference between ELA AABR<sub>2016</sub> and paleoELA AABR<sub>LIA</sub>.</p><p>The paleoclimatic reconstruction was carried out in 6 phases: Phase 1) Development of a detailed geomorphological map (scale 1/10,000), in order to  identify glacial landforms (advance moraines and polished rocks) which, due to their geomorphological context, can be considered of LIA, so palaeoglaciers can be delimited. Current glacial extension was done using dry season, high resolution satellite images. Phase 2) Glacial bedrock Reconstruction from glacier surface following the GLABTOP methodology (Linsbauer et al 2009). Phase 3) 3D reconstruction of paleoglacial surface using GLARE tool, based on bed topography and flow lines for each defined paleoglacial (Pellitero et al., 2016). As perfect plasticity model does not reflect the tension generated by the side walls of the valley, form factors were calculated based on the glacier thickness, lateral moraines and the geometry of the valley following the equation proposed by Nye (1952), adjusting the thicknesses generated in the paleoglacial front. Phase 4) Calculation of BR in a reference glacier (Artesonraju; 8° 56’S; 77º38’W), near to the study area, using the product BR = b • z • s, where BR= Balance Ratio; b= mass balance measured in fieldwork 2004-2014 (m); z= average altitude (meters) and s= surface (m<sup>2</sup>) of each altitude band of the glacier (with intervals of 100 m altitude). A value BR = 2.3 was estimated. Phase 5) Automatic reconstruction of the ELA  AABR<sub>2016 </sub>and paleoELA AABR<sub>LIA</sub> using ELA Calculation tool (Pellitero et al. 2015) after 3D reconstruction of the glacial and paleoglacial surface in phases 2 and 3. Phase 6) Estimation of paleotemperature during LIA by solving the equation of Porter et al. (1995): ∆T (°C)= ∆ELA • ATLR, where ∆T= air temperature depression (ºC); ∆ELA = variation of ELA AABR 2016-LIA and ATLR = Air Temperature Lapse Rate, using the average global value of the Earth (0.0065 °C/m), considered valid for tropics.</p><p>The results obtained were: ELA AABR<sub>2016</sub>= 5260m, paleoELA AABR<sub>LIA</sub>= 5084m, and ∆T = 1.1 °C. The reconstruction of air paleotemperature is consistent with different studies that have estimated values between 1–2 °C colder than the present, with intense rainfall (Matthews & Briffa, 2005; Malone et al., 2015).</p>