Impacts of topography and land use changes on the air surface temperature and precipitation over the central Peruvian Andes

Journal Pre-proof Impacts of topography and land use changes on the air surface temperature and precipitation over the central Peruvian Andes

Miguel Saavedra, Clementine Junquas, Jhan-Carlo Espinoza, Yamina Silva PII:

S0169-8095(19)30499-5

DOI:

https://doi.org/10.1016/j.atmosres.2019.104711

Reference:

ATMOS 104711

To appear in:

Atmospheric Research

Received date:

26 April 2019

Revised date:

4 October 2019

Accepted date:

19 October 2019

Please cite this article as: M. Saavedra, C. Junquas, J.-C. Espinoza, et al., Impacts of topography and land use changes on the air surface temperature and precipitation over the central Peruvian Andes, Atmospheric Research(2018), https://doi.org/10.1016/ j.atmosres.2019.104711

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© 2018 Published by Elsevier.

Journal Pre-proof

Impacts of topography and land use changes on the air surface temperature and precipitation over the central Peruvian Andes Miguel Saavedraa,b,*, Clementine Junquasc , Jhan-Carlo Espinozac , Yamina Silvaa a b c

Instituto Geofísico del Perú, Calle Badajoz 169, Mayorazgo IV Etapa, Ate, Lima 15012, Perú Facultad de Ciencias Físicas, Universidad Nacional Mayor de San Marcos, Lima 15081, Perú Univ. Grenoble Alpes, IRD, CNRS, Grenoble INP, IGE, F-38000 Grenoble, France

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*Corresponding author: [email protected]

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ABSTRACT

This paper focuses on the representation of the air surface temperature and precipitation

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using high spatiotemporal simulations (3 km-1 hour) of the WRF3.7.1 model in the central

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Peruvian area. It covers, from east to west, the coastal zone, the western slope of the Andes, the Andean Mantaro basin (500-5000 masl), and the Andes-Amazon transition

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region in the eastern Andes. The study covers the January months from 2004 to 2008.

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Three experiments were conducted using different topography and land use data sources: (1) a control simulation using the default WRF topography and land use datasets from the

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United States Geological Survey (USGS); (2) a simulation changing only the topography by using the SRTM topography dataset; and (3) a simulation changing the land use data of (2) by a new dataset adapted from Eva et al. (2004). SRTM topography performed better than the control simulation for representing the actual altitudes of 57 meteorological stations that were used for precipitation and surface air temperature data. As a result, the simulations of experiments (2) and (3) produced lower bias values than that of (1). Topography change (experiment (2)) showed improvements in temperature bias that were directly associated with linear modifications of 5.6 and 6.7 ºC.km -1 in minimum and maximum temperature, respectively. Increasing (decreasing) precipitation with topography

Journal Pre-proof or land use change (experiment (3)) was clearly controlled by changes in the moisture flux patterns and its convergence in the Andes-Amazon transition. On the western slope, precipitation increase could be associated with the increase in easterly flow by the smaller altitudes of the Andes mountains in SRTM topography and by increasing evaporation with new land use. Inside the Mantaro Basin, low level moisture flux seems to control the rainfall changes. Overall, relative changes (positive or negative) in precipitation due to

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topography or land use change could reach values above 25%.

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Keywords: modeling; WRF; land use

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1. Introduction

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It is well known that topography and land use cover data are key to the performance of regional climate models (RCMs). Topography is particularly important in cloud formation

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and the distribution of temperature with altitude via atmospheric dynamics such as local

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and planetary boundary layer (PBL) processes. On the other hand, land use cover data play an important role in the distribution of the energy balance of the surface via variables,

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such as albedo, roughness height and emissivity, and the local to regional atmospheric

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circulation, among other factors. In this respect, various studies have shown that improving the representation of these features by using updated land use data and high-resolution topography is important to improve the performance of the Weather Research and Forecasting (WRF) model outputs (e.g., Pitman et al., 2004; Grossman et al., 2005; Grossman-Clarke et al., 2010; Lee and Beberly, 2012; Cheng et al., 2013; De Meij and Vinuesa, 2014; Teixeira et al., 2014; Schicker et al., 2015; Jiménez-Esteve et al., 2018). In particular, improving the modeling of air surface temperature and precipitation at high resolution is especially useful in complex mountain regions such as the tropical Andes as forcing inputs to hydrological and glaciological models (e.g., Moure et al., 2016; Heredia et al., 2018).

Journal Pre-proof The Peruvian central Andes (PCA) is characterized by a very complex climate, with a strong zonal gradient from the west dry region close to the Pacific Ocean toward the humid Amazonian plains to the east (e.g., Garreaud, 1999; Killeen et al., 2007), including a maximum rainfall zone in the eastern slope of the Andes, also called “rainfall hotspot”, where annual precipitation reaches approximately 6000 mm.year-1 (Espinoza et al., 2015; Chavez and Takahashi, 2018). Previous studies have used the WRF model at high

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resolution in the PCA region and have identified strong WRF precipitation biases (up to 300%) using different parameterization schemes (e.g., Mourre et al., 2016; Junquas et al.,

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2017). The major model precipitation biases were generally found over the eastern slope

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of the Andes. Studies that analyzed surface temperature biases in WRF simulations are

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scarce in the PCA region and need to be addressed. In the Ecuadorian Andes, Ochoa et al. (2016) found strong biases (up to 8 ºC). These works have used the same datasets of

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topography and land cover from the United States Geological Survey (USGS), which are

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default datasets in the WRF model. Therefore, in this study, we aim to evaluate the WRF model performance in the PCA region in terms of precipitation and air surface temperature

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by using a more up-to-date land use cover developed by Eva et al. (2004), and by using a

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different Digital Elevation Model (DEM) with 30 arc-second spatial resolution (or approximately 1 km) from the Shuttle Radar Topography Mission (SRTM30 product; Farr et al., 2007).

As previously mentioned, several studies have shown that modeling can be sensitive to the representation of both topography and land use cover datasets. Some authors have performed experiments with the WRF model using different datasets. For example, Teixeira et al. (2014) modeled precipitation using the SRTM as topography forcing. They showed differences of up to 500 m between USGS topography data and SRTM in the Portuguese Madeira Island. They also found changes in precipitation patterns associated

Journal Pre-proof with the change in topographic forcing, with increases in the mountains and decreases in the valleys. Cheng et al. (2013) found enhancements in air temperature using a land use dataset built from SPOT (Satellites Pour l’Observation de la Terre) satellite images in the Taiwan area. This SPOT product gave better results than the two land use datasets provided by WRF, USGS and a product obtained from MODIS (Moderate Resolution Imaging Spectroradiometer). Using the European CORINE (Coordination of Information on

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the Environment) land cover, De Meij and Vinuesa (2014) showed improvements in the simulation of the temperature and air quality by changing the land use in zones that were

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converted to urban zones. Grossman et al. (2005) stated that changes in land use affect

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the turbulent fluxes and planetary boundary layer (PBL) height. In addition, Kim et al.

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(2013) found that the impact of using updated land cover could show a larger impact on the simulated meteorological variables than changing the parameterization of PBL

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schemes. Other studies conducted by Schicker et al. (2015), Grossman-Clarke et al.

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(2010), Lee and Beberly (2012) and Pitman et al. (2004) also showed changes in

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temperature or precipitation when modifications of land use were taken into account.

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Studies using high resolution WRF modeling over the Andes, as mentioned before, have not considered changes of topography and land use cover and how these changes could affect the WRF results. In this work, we evaluate these changes and how they could impact the local atmospheric circulation, precipitation and air surface temperature during the January months from 2004 to 2008, corresponding to the core of the wet season in the PCA region (e.g., Segura et al., 2018). Our study area covers the PCA region from 10°S14.25°S in latitude and 78°W-73°W in longitude. This area also covers the Mantaro basin (hereafter called the “MB”), one of the largest basins in the Peruvian Andes (between 500 and 5500 masl) and one whose agricultural activity provides products to Lima, the 9.5 million-population capital of Peru. The study area also includes the coastal front of the

Journal Pre-proof Pacific Ocean and the Amazon-Andes transition region, which are located west and east of the Andes Mountains, respectively.

This study is organized as follows. Data and methods are discussed in Section 2. Section 3 covers the WRF simulations with updated land use and topography datasets. This section also evaluates the performance of the experiments that considered the observed

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daily data of rainfall and minimum and maximum temperature from in situ meteorological stations. Then, a general discussion is given in Section 4. Finally, in Section 5, conclusions

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are presented based on the results and some suggestions are made about future

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atmospheric simulations for the Andes.

2. Data and methodology

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Fig. 1a and 1b show the study region and its location in the western border of tropical

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South America, respectively. In addition to the MB, Fig. 1a also shows three additional regions analyzed in this work: the Along the Coast (AC) region, which is separated by the

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2000 masl contour line from the High Western Slope of the Andes (HWSA), which extends

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to the western border of the MB. The region from the eastern border of the MB to the lowland Amazon (Andes-Amazon transition region) is called the Eastern Slope of the Andes (ESA). We also point out the Central Mantaro Valley, which is important for agriculture, and the Eastern Mantaro Valley, which connects the MB basin with the Amazonian region. Fig. 1b presents in advance the nested domains (spatial resolution) considered for modeling with the WRF model: D01 (27 km), D02 (9 km), and D03 (3 km). We evaluate the model with the outputs of D03, which coincides with the study region.

Journal Pre-proof The data used are divided into two groups. The first group considers the inputs and outputs from the WRF model. The other group includes observed data from meteorological in situ stations. Data for both are the January months from 2004 to 2008.

2.1. Inputs and outputs for modeling To run the WRF model, boundary and initial conditions were the Final Operational Global

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Analysis data (FNL) provided by the National Center for Environmental Prediction (NCEP), which have 1° x 1° spatial resolution and 27 vertical levels at a frequency of six hours. Sea

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surface temperature (SST) conditions were provided from 0.5° real-time global SST

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analyses (Thiébaux, et al., 2003). To evaluate how the modeling outputs are impacted due

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to topography and land use change in D03, two datasets of each kind of data are

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considered.

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We define hgt_USGS (Fig. 2a) and lu_USGS (Fig. 2d) as the corresponding topography and land use data at 3-km spatial resolution obtained in the preprocessing module of

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WRF. In the same way, we define hgt_SRTM (Fig. 2b) and lu_ENEW (Fig. 2e) at 3-km

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spatial resolution as the topography and land use obtained from SRTM and from the data generated by Eva et al. (2004), respectively. The new topography (hgt_SRTM) and land use information (lu_ENEW) were implemented in the WRF model with a resolution of 30 arc-seconds (approximately 1 km; resampled to 3 km during the WRF preprocessing) since it was also the resolution of the WRF default USGS datasets. The documentation of the hgt_SRTM data implemented in WRF at 1 km can be found at https://dds.cr.usgs.gov/srtm/version2_1/SRTM30/srtm30_documentation.pdf. The seventyfour categories considered by Eva et al. (2004) were homogenized to twenty-four according to the USGS definition. The large number of categories in Eva et al. (2004) is

Journal Pre-proof due to the subclassification of the most common ones. For example, there are more than 40 subcategories within the forest denomination.

2.1.1. Topography datasets The most substantial differences between hgt_SRTM and hgt_USGS are located around the northern and eastern border of the MB and in the ESA (Fig. 2c). hgt_SRTM shows

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lower altitudes than hgt_USGS, with differences of more than 1000 m in the eastern border of the MB between 11.2ºS and 12.2ºS. These differences are also present with less

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spatial coverage, approximately 73.4ºW,12.2ºS; 77.2ºW,10.8ºS and 75.8ºW,10.2ºS.

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Conversely, higher altitudes in hgt_SRTM with respect to hgt_USGS are present in the

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ESA, for example, the mountain at approximately 73.8ºW,11.6ºS in hgt_SRTM, which does not appear in hgt_USGS. Other cases in the ESA are near 73.6ºW,12.6ºS and

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75.5ºW,11.2ºS or near 76.0ºW, 11.8ºS. Lower differences in the study region are in the

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2.1.2. Land use datasets

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AC, the southern MB and the Amazonian plains.

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There are important changes in the study zone when lu_USGS is replaced by lu_ENEW (Fig. 2d and e, and Supplementary Table S1). Along the coast there is a reduction of barren or sparsely vegetated (category 19) of 4.66% due to the appearance of Mixed shrubland/grassland (category 9), with 3.63%. Here, there is also an obvious increase of the urban/built-up land (category 1) at 12˚S, which includes the Lima metropolitan city. Wooded tundra (category 21) disappears above 2000 masl in the HWSA but deciduous broadleaf forest (category 11) appears sparsely and covers 5.66%. Cropland/grassland mosaic (category 5) increases from 0.36 to 3.48%, with a major presence along the MB. There is also a reduction of cropland/woodland mosaic (category 6). Finally, in the Amazon region there is an increase of evergreen broadleaf forest (category 13) of 5.02%,

Journal Pre-proof which replaces wooded tundra, cropland and shrubland categories. These last changes appear in the Amazon-Andes mountain region, where hgt_USGS also shows strong differences when compared with hgt_SRTM (see previous section). Peruvian rivers showed in Fig. 2a and 2b are not considered in lu_ENEW; however, some of the broadest ones located in the Amazon side are taken into account in lu_USGS. In addition, Fig. 2c-e show subregions (with boxes) to evaluate the appearance of the forest category in the

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HWSA (A), topography and land use change in the Central Mantaro Valley of the MB (B)

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and the appearance of mountain and forest in the ESA (C).

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The main outputs we will use are the precipitation and air surface temperatures at an

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hourly time step. Surface features will be described by Leaf Index Area (LAI), albedo, soil moisture, and by surface energy fluxes. Rain mixing ratio (rw), surface moisture flux and

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the vertically integrated moisture flux of simulations and its respective divergence are

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assessed to explore the atmospheric process associated with the spatial pattern of precipitation (van Doyle and Barros, 2002; Banacos and Schultz, 2005; and Zomeren and

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van Delden, 2007).

2.2. Observed data

Fig. 3a and Table 1 show data from fifty-seven meteorological stations from Servicio Nacional de Meteorología e Hidrología (SENAMHI - Peru, www.senamhi.gob.pe) that were obtained to assess the performance of the model. Similarly, Fig. 3a and Table 2 show hourly data from seven stations for rainfall and air surface temperature (blue dots) also used to evaluate the model performance in simulating the diurnal cycle, especially for the hours with maximum precipitation. The number of stations with daily data of cumulated precipitation, minimum and maximum temperature are 44, 48 and 53, respectively; and availability (absence) is stated with 1 (0). After eliminating the outliers outside of the 3.5

Journal Pre-proof standard deviation, each variable contains more than 80% of the data in the study period. Table 1 specifies the location of the stations, their actual altitude and the overestimation or underestimation of hgt_USGS and hgt_SRTM datasets when compared with the real station altitude. The classification by regions is also shown according to the location of the stations: eleven are in the AC region below 1500 masl, twelve are in the HWSA between 2000 and 5000 masl, twenty-four are in the MB in the interval of 2000 to 5000 masl, and

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ten are on the ESA below 4000 masl up to 500 masl.

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On average, hgt_USGS (hgt_SRTM) gives higher (lower) overestimation of the actual

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altitude of most stations, with mean values of 210 (170), 216 (156), and 447 (158) m for

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the AC, MB and ESA, respectively. In contrast, hgt_USGS (hgt_SRTM) gives lower (higher) overestimation in the HWSA, with a value of 270 (369) m. Land use in the stations

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also varies when lu_ENEW is replaced with lu_USGS. For example, the predominant

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category in AC stations is barren or sparsely vegetated in lu_ENEW, with two more stations than in lu_USGS. In the HWSA, shrubland and wooded tundra categories

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predominate in lu_USGS, and deciduous broadleaf forest and cropland/grassland

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predominate in lu_ENEW. In the MB stations, grassland dominates in lu_USGS, and shrubland or cropland/grassland mosaic in lu_ENEW. In the ESA, stations above 2000 masl show grassland and shrubland in both datasets, whereas below 2000 masl, the prevailing evergreen broadleaf forest in lu_USGS is replaced by savanna or cropland/woodland in lu_ENEW.

Fig. 3b-d shows the mean observed values of precipitation, minimum and maximum temperature for the months of January from 2004 to 2008. Precipitation in the AC is not considered because it is scarce, with mean values below 3 mm.month-1 (Garreaud, 2009; Garreaud et al., 2009; Lavado-Casimiro et al., 2012; Rau et al., 2017). For the HWSA, MB

Journal Pre-proof and ESA, mean rainfall ranges of 1.0-5.0, 2.0-6.5 and 1.5-8.0 mm.day-1 are found, respectively, consistent with other studies (e.g., Rau et al., 2007; Silva et al., 2008; Espinoza et al., 2009; Lavado et al., 2013; Zubieta et al., 2017). Altitudes and mean daily values give a significant trend of 1.2 mm.km -1 in the HWSA, and -1.3 mm.km -1 is found in the ESA, which is consistent with reports by Espinoza et al. (2009) and Lavado et al. (2013). On the other hand, minimum (maximum) temperatures decrease with altitude and

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show ranges of 15-20 (26-33), 0-15 (10-25), 0-11 (10-21), and 5-22 (13-33) ºC for the AC, HWSA, MB and ESA, respectively. Minimum and maximum temperatures show significant

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trends between -5.0 and -5.8 ºC.km -1 in the subregions other than the AC, in which a

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higher value of -2.9 ºC.km -1 is shown for the minimum, and not significant value of 0.2 is

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2.3. Modeling and performance

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shown for the maximum.

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For modeling, all Januaries from 2004 to 2008 were simulated with the Weather Research and Forecasting (WRF) model version 3.7.1 (Skamarock et al., 2008), and the outputs in

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the domain of interest (D03, Fig. 1b) were stored for each hour. The model was configured

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with the following schemes: the NOAH land surface model (Chen and Dudhia, 2001), the Yonsei University planetary boundary layer scheme (Hong et al., 2006), the Dudhia short wave radiation scheme (Dudhia, 1989), the Rapid Radiative Transfer Model longwave radiation scheme (Iacono et al., 2008), the single-moment 6-class microphysics scheme WSM6 (Hong and Lim, 2006) and the Kain-Fritsch cumulus scheme (Kain, 2004), which is deactivated for D03.

Using this configuration, three experiments were performed. The first one was considered as the control simulation (CTRL) and admits hgt_USGS and lu_USGS. Here, we note that since the release of WRF version 3.8, a new default topography dataset has been

Journal Pre-proof implemented in this model based on the SRTM database and other satellite data with 1-km spatial resolution, called GMTED2010 (Danielson and Gesh, 2011). There were not substantial differences when hgt_SRTM was compared with a similar 3-km spatial resolution product obtained from GMTED2010 (Supplementary Fig. S1). This implies that the WRF3.7.1 simulations with hgt_SRTM should be similar to simulations using WRF3.8 and its default topography, as shown by Nunalee et al. (2015). The second simulation

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(SIM01) was performed by changing only the topography database, using hgt_SRTM instead of hgt_USGS. The last simulation (SIM02) used hgt_SRTM and lu_ENEW instead

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of lu_USGS.

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The objective of these experiments was to analyze the changes of the precipitation and the air surface temperature when (1) only the topography is changed (from CTRL to

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SIM01) and (2) when only the land use is changed (from SIM01 to SIM02). To do this, we

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analyzed the daily means of cumulative rainfall and the maximum and minimum of air surface temperature for each experiment. To support the main changes in these variables,

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the other outputs already mentioned in Section 2.1 were also analyzed by considering the

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differences between simulations.

The performance of the experiments was assessed by regions, using the means of the following standard statistical indicators: bias, the root mean square error (RMSE) and the Pearson’s correlation coefficient. The variables to evaluate were the minimum and maximum temperature and rainfall. Additionally, to evaluate the model’s capacity to reproduce the daily distribution of rainfall, we computed the skill score used by Perkins et al. (2007), which measures the overlap of the observed and simulated probability density functions (PDFs), with the value of 1 meaning similar PDF and 0 meaning totally distinct PDFs. Finally, the diurnal cycles of observed precipitation and temperature of the

Journal Pre-proof automatic stations were compared with experiments to find the hours when precipitation was strong or made the greatest contribution.

3. Assessment of experiments 3.1. Surface parameters LAI in CTRL or SIM01 (Fig. 4a) increases eastward with the appearance of vegetation

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from the barren or sparsely vegetated category in the AC to the forest categories in the ESA. There is a predominant increase of LAI when comparing SIM02 with SIM01 due to

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the appearance of shrubland/grassland in the AC, forest approximately 3500 masl in the

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HWSA, cropland/grassland in the MB (especially in the Central Mantaro Valley), and forest

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in the ESA. In this respect, changes in LAI are significant in subregions A, B and C (Table 3). In an opposite way, albedo in the CTRL increases westward, from most vegetated in

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the ESA to lower ones in the AC (Fig. 4c). Fig. 4d shows increasing (decreasing) albedo

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associated with increasing (decreasing) altitudes, explained by more (less) recurrent snowfall on highlands (lowlands) (not shown). Increasing (decreasing) albedo of SIM02

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with respect to SIM01 (Fig. 4e) is controlled by the corresponding decreasing (increasing)

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LAI as shown in subregions A and B (Table 3). Overall, the soil moisture increases eastward (Fig. 4f), which indicates a relationship with the amount of precipitation as shown by Junquas et al. (2017) and Mourre et al. (2016). In Fig. 4g, places with higher (lower) altitudes in SIM01 than in CTRL seem to be associated with increasing (decreasing) moisture. In Fig. 4h, soil moisture values of SIM02 with respect to SIM01 show an increase at approximately 3500 masl in the HWSA and in the Central and Eastern Mantaro Valley, and the changes are significant near these first two places, as shown by mean values in subregions A and B (Table 3). In subregion C, the increasing moisture is also significant when the topography is changed (Table 3). In the ESA, the trend is dipole-like, with a negative (positive) pole in the northwest (southeast).

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Changes in surface parameters can significantly impact the surface energy fluxes when lu_ENEW is considered instead of lu_USGS (Table 3). For example, the significant decrease (increase) of reflected shortwave radiation is associated with decreasing (increasing) albedo in A and C (B). Regarding latent heat, although increasing moisture occurs in A and B, the decreasing (increasing) albedo determines the increase (decrease)

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of latent heat in A (B) by more (less) retention of solar energy. On the other hand, topography directly change the impacts on the outgoing longwave radiation in B, and C

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because of the Stefan-Boltzmann law approximation and decreasing temperature with

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altitude. Incoming longwave radiation seems to also be affected by altitude change by

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3.2. Effects on precipitation

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increasing or decreasing the column of water vapor.

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In Fig. 5a-c, we show the mean daily precipitation of the CTRL, SIM01 and SIM02 experiments. Overall, a similar spatial pattern of precipitation is displayed by all

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simulations, with increasing rainfall toward the east. Scarce rainfall or less than 1 mm.day-1

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occurs in the AC. Over the HWSA, the rainfall goes up to 10 mm.day-1. Here, the highest values are over the mountaintops between the valleys at approximately the 3500 masl contour. In the MB, large extensions of relative maximum precipitation between 5 and 10 mm.day-1 can be found over the north and south of this region. In the ESA, the rainfall is over 5 mm.day-1 and can reach values above 30 mm.day-1 below 1500 masl.

3.2.1. With change of topography (CTRL  SIM01) Fig. 5d and 5g show the precipitation change and its relative values of SIM01 regarding CTRL, respectively. Additionally, the scatterplot of changes of rainfall vs. altitude for all grid points in the study area are shown in Fig. 5f. There are no changes above 1 mm.day-1

Journal Pre-proof in the AC, and changes in the HWSA and MB are below 4 mm.day-1. More substantial variations are found in the ESA, with differences above 15 mm.day-1.

Increasing (decreasing) altitude is associated with increasing (decreasing) rainfall in the HWSA and MB, which is noticeable at approximately the ±500 m contour line and for subregion A and B with altitude and rainfall changes less than 1 km and 5 mm.day-1, respectively (Fig. 5f). This relationship is less clear in the ESA, as found in subregion C

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with corresponding maximum altitude and rainfall changes of 2 km and 20 mm.day-1.

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Therefore, the relationship in the study zone states a rate of change of 4 mm.day-1.km -1.

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Relative changes between 25 and 75% mean approximate absolute changes between

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0.25 and 3.0 mm.day-1 in the MB, 1.0 and 4.0 mm.day-1 in the HWSA; and between 5 and 15 mm.day-1 in the ESA. Moreover, the most recurring rainfall change in the study area

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varies from 10 to 50%, although HWSA and MB values above 50% are conditioned to

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altitude changes higher than 500 m and are confined to the north part. This condition is not necessarily satisfied in the ESA, especially in lowlands. Additionally, rainfall changes

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between 100% and 200% can be reached at the southeast part of the MB near the

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Eastern Mantaro Valley.

3.2.2. With change of land use (SIM01  SIM02) The absolute and relative rainfall variations of SIM02 with regard to SIM01 are shown in Fig. 5e and 5h, respectively. The absolute values state that strong positive and negative changes occur in the lowland Amazon basin. Here, a dipole pattern with up to 15 mm.day-1 can be observed between the southeast (positive pole) and the northwest (negative pole); however, it is less clear with relative changes since most of these are below 30%. In the HWSA and MB, increasing values between 1 and 4 mm.day-1 correspond to relative changes from 30 to 70%. These increases occur close to the 3500 masl contour level in

Journal Pre-proof the HWSA, in the Central Mantaro Valley and in the northern MB. There is also a rainfall reduction of 10 to 30% is observed in the southern 12ºS of the HWSA and out of the southern MB.

3.2.3. Comparison with observed precipitation Table 4 shows the mean of skill parameters for the precipitation. All simulations

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underestimate the precipitation in the HWSA and overestimate it in the MB and ESA; however, performance is worst by CTRL and there is negligible contrast between SIM01

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and SIM02 for all regions. In other words, SIM01 and SIM02 reduced the bias with CTRL

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by up to 29% (less than 0.2 mm.day-1) in the HWSA and MB, and in the ESA, the reduction

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can reach 50% (6.0 mm.day-1). RMSE values show similar behavior to that of bias, with similar values performed by SIM01 and SIM02 and larger (smaller) values in the ESA

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(HWSA and MB). Low values of the correlation coefficient indicate that the daily variability

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of precipitation is not reproduced by the simulations. Similarities between observed and all simulated PDFs are above 0.66, but SIM01 and SIM02 obtain better scores in the HWSA

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and the ESA.

According to Fig. 6, the diurnal cycle phase is well simulated by all experiments in the HWSA and MB stations of Table 2, with maxima at approximately 16 and 19 LT, respectively. In Carania, Huaros and Huayao, underestimation is observed in the maximum rainfall, which also explains the underestimation of the cumulative values. On the other hand, in Tarma and Oxapampa (ESA), although there is no clearly-defined observed diurnal cycle, all simulations perform well the rainfall occurrence during all-night and morning but with clear overestimation. In Acora, the only automatic station of the AC, all simulations give the maximum rainfall four hours earlier (at 15 LT).

Journal Pre-proof Although more stations are necessary to extrapolate the hours with maximum precipitation, a first approximation can be produced by the simulations. In this way, the maximum is found between 13-18 LT in the HWSA and between 16-18 LT in most areas of the MB, except in the Eastern Mantaro valley where the maximum occurs between 22-03 LT or in the Central Mantaro valley with occurrences between 16-21 LT. In the ESA, the maximum occurs during early morning (01-09 LT) (Fig. 7a-c). In addition, according Fig.

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7d-f, precipitation seems to be more concentrated in few (many) hours in the HWSA and in the lowland region of the ESA (central-eastern MB and highlands of the ESA above 1500

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masl). This approximation is also observed in the subregions (Fig. 7g-i).

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We have found key changes when considering a new topography (hgt_SRTM) in SIM01. This modifies the results of CTRL that use hgt_USGS and tends to increase (decrease)

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the rainfall in those places where the altitude increases (decreases). This basically

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happens in the HWSA and the MB. The ESA region is also modified because topography databases show strong contrasts. On the other side, the increase in rainfall considering

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the new land use (SIM02-SIM01) is more homogeneous in most of the study area. The

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increase in HWSA is notable between 1500 and 3500 masl and is associated with the appearance of the forest category, and in the ESA there is a dipolar-like pattern of rainfall with increasing rainfall in the south and decreasing rainfall in the north.

3.3. Effects on minimum and maximum temperature The minimum and maximum temperatures modeled with CTRL are shown in Fig. 8a and 8d, respectively, which show a clear decreasing with altitude that is also represented in SIM01 and SIM02 (not shown). The Amazon side below 1500 masl shows the warmest places in the study zone, with minimum temperatures between 20 and 25 ºC and the maximum above 25 ºC. Intermediate values are present along the coastal zone below

Journal Pre-proof 1500 masl where minimum temperature are between 15 and 20 ºC, and maximum temperatures are between 20 and 30 ºC. Lowest temperatures in the study zone are present in the highlands, where values between -10 and 5 ºC (0 and 15 ºC) are modeled above 3500 masl for minimum (maximum) temperatures.

3.3.1. With change of topography (CTRL SIM01)

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Fig. 8b and 8e show the relative changes of the minimum and maximum temperatures of SIM01 regarding CTRL. Moreover, Fig. 8g and 8h show that changes in temperature are

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linearly related to differences in topography, with a gradient of -6.6 and -5.6 ºC.km -1 for

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maximum and minimum temperatures, respectively. In other words, the greatest changes

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of temperatures (as above 6 ºC in the northern and eastern MB) are observed when hgt_SRTM is considered instead of hgt_USGS. In addition, most large (small) variations of

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temperature in the study zone are found in the ESA (HWSA and MB), which contains the

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subregion C (A and B).

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3.3.2. With change of land use (SIM01  SIM02)

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As shown in Fig. 8c and 8f, there are no noticeable changes in the minimum or maximum temperatures when lu_ENEW replaces lu_USGS. However, this replacement can produce noticeable alterations in specific areas. For example, the growth of the Lima metropolitan area considered in lu_ENEW is associated with increasing minimum (maximum) temperature of 2.0 ºC (1.0 ºC). The appearance of ice and snow in northern and western MB reduces the minimum (maximum) temperature in 3.0 ºC (1.5 ºC). In the lowland ESA, the consideration of forest in lu_ENEW, instead of a river in lu_USGS, generates a maximum temperature rise of more than 2 ºC. During the night, the minimum temperature does not show essential variations with the change of surface moisture, as shown by Dai et al. (1999) and Herb et al. (2008).

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3.3.3. Comparison with observed air surface temperature Table 5 shows the skill parameters for minimum and maximum temperature. In all regions, the three simulations produce negative biases or underestimations of the minimum and maximum temperature, except in the AC for minimum temperatures where there is a slight overestimation. However, better scores are found with SIM01 and SIM02, except for the

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HWSA where CTRL gives less underestimation. It is also identified that the larger improvement by SIM01 and SIM02 is performed in the ESA with reductions of 0.5 and 2.5

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ºC for minimum and maximum temperature, respectively. RMSE reflects most of the

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corresponding bias values in most simulations, with smaller (larger) values in SIM01 and

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SIM02 (CTRL). Finally, all simulations produce correlation coefficients between 0.50 and 0.54 for minimum and maximum temperatures in most stations in the HWSA. Other

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significant but lower values are found for maximum temperature in the MB and the ESA.

Fig. 9 shows the diurnal cycle of the air surface temperature in the stations listed in Table

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2. As with minimum and maximum temperatures, for the seven automated stations, all

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simulations underestimate the hourly temperatures and are better simulated with SIM01 and SIM02, which present similar values. This improvement is better observed in stations located in the ESA (Oxapampa and Tarma), where there is also a better adjustment of the diurnal range temperature. Reduction of bias is also observed in Lircay in the MB. Additionally, similarities between diurnal cycles with SIM01 and SIM02 can also be observed in subregions A, B and C, as well as their strong difference with respect to CTRL in the ESA represented by subregion C (Fig. 10a-c).

3.4. Surface and vertically integrated moisture flux

Journal Pre-proof Mean vertically integrated moisture flux in CTRL and its alteration with topography and land use change are shown in Fig. 11. In addition, the profile of the moisture flux and rw over the subregions are shown in Fig. 12. Fig. 11a, 12f and 12i show features of the atmospheric system in South America during DJF: strong southward moisture fluxes over the ESA, predominance of easterlies over the Andes Mountains and offshore northward winds (Garreaud, 2009; Marengo et al., 2004). In addition, Fig. 12c shows high values of rw

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below 4500 m.

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As with precipitation changes in the ESA (Fig. 5d and e), a similar pattern of the vertically

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integrated moisture flux divergence change (with opposite sign) is found in the

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southeastward direction. It is a decreasing-increasing-decreasing divergence pattern in SIM01-CTRL (Fig. 11b) and a dipole increase-decrease pattern in SIM02-SIM01 (Fig.

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11c). In addition, rw for region C shows a decrease (increase) associated with increasing

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(decreasing) divergence when topography (land use) change occurs. Increases in southeastward fluxes are also observed below 1500 masl. These changes will be

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commented in the Discussion section.

Patterns in other regions are more easily described as a function of the profiles in subregions A and B. For example, in B, the decrease in rw below 4500 masl seems to be associated with the weakening of northward and westward fluxes after topography change (Fig. 12b, e and h). In addition, rw and westward flux increases above 4500 masl. Moreover, increasing rw after land use change (Fig. 12b) is related to increasing southward flux above 4500 masl and the appearance of a slight eastward flux below this level (Fig. 12e and h). In A, after topography change, decreasing rw is accompanied by the enhancing and weakening of easterly and southward moisture fluxes, respectively (Fig. 12a, d and g). Consideration of lu_ENEW shows that rising rw is mainly associated with increasing

Journal Pre-proof westward flux. These two subregions also show a notorious surface moisture convergence (or negative divergence) that coincides with maximum precipitation (Fig. 13a and b).

4. Discussion 4.1. Impacts on rainfall It was found that daily rainfall outputs of SIM01 and SIM02, or those simulations that

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consider SRTM topography, showed fewer differences with the observed rainfall values. This is illustrated by the improvement of statistics skills. In other words, the improvement

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was mainly produced by considering the SRTM database. These findings were obtained

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using 57 meteorological stations and the nearest grid points in the simulations (CTRL,

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SIM01 and SIM02). However, important changes were observed in the spatial patterns of simulations that are not captured by a station-grid points analysis. We think that this

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occurred because stations are usually located in places with easy human accessibility,

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such as valleys, instead of mountaintops.

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In subregion C, it is interesting to note that the precipitation change is not explained by the

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divergence, but by more mechanical processes. What happens in subregion C is mainly a change of orientation of the orographic barrier. As already shown in Espinoza et al. (2015), the orientation of the orography with respect to the direction of the moisture flow can control the spatial pattern of precipitation. The perpendicular moisture flow (Fig. 11a) on the topography configuration in CTRL induces local maximum rainfall upstream of the flow (to the northwest, Fig. 5a) by a topographic blocking process. In SIM01 and SIM02, the shape of the mountain is modified and is parallel to the flow. Precipitation decreases upstream due to the suppression of the topographic blocking of the flow. In addition, a cyclonic circulation anomaly is generated locally (Fig. 11b), which favors maximum rainfall along the mountain ridge and on its east side (Fig. 5d).

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The decreasing altitudes in the mountains near the MB allow a better channelization of the easterly flux above the Andes Mountains. This can be the primary cause of an increase in rainfall in the Eastern Mantaro Valley and in the southern 12ºS in the HWSA. In addition, increasing (decreasing) rainfall also seems to be generated by increasing (decreasing) altitude in the HWSA and the MB, which enhances (inhibits) the thermal forcing during the

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afternoon, as shown by Choudhury et al. (1994), Al-Kaisi et al. (1989), and Li et al. (2005). Particularly for the MB, a decrease in rainfall can be due to less availability of moisture

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coming from the north. For the Central Mantaro Valley, circulation below 5000 masl seems

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to be important when the change in the topography is performed. Here, decreasing

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precipitation is associated with weakening of northward and westward moisture fluxes. Increasing rainfall after the introduction of new land use does not seem to be consistent

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with decreasing latent heat (Table 3), but it does appear consistent with slight increases of

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eastward (southward) moisture fluxes at low (high) levels.

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Overall, new land use produces more vegetation as shown by LAI. This reduces the

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albedo to retain more solar energy and thus generates more evaporation or latent heat. This occurs in the HWSA and, together with the slight increase of easterly moisture flows, can explain the increasing rainfall in this zone by the availability of more water vapor. In the ESA, evaporation can also be a source for atmospheric vapor where increasing vegetation occurs. Finally, in almost all of the study region, the peak of maximum precipitation in the three simulations are consistent with observations and also accord with other works that used data from models, Tropical Rainfall Measuring Mission (TRMM) and cloudy conditions obtained by the Geostationary Operational Enviroment Satellite (GOES) imageries (Garreaud, 1999; Mourre et al., 2016; Chavez and Takahashi, 2017; Junquas et al., 2017).

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4.2. Impacts on air surface temperature Underestimation of the temperatures (minimum, maximum and hourly) is produced by the overestimation of the actual altitudes of stations in both topography databases (hgt_USGS and hgt_SRTM). However, simulations with hgt_SRTM generate less bias than those with hgt_USGS, and the greatest improvement is performed in the Andes-Amazon transition.

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Spatially, a clear relationship is found between increasing (decreasing) altitude and decreasing (increasing) temperatures, with mean rates of change of -5.7 and -6.6 ºC.km -1

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in minimum and maximum temperature, respectively.

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The means of bias values in all regions are almost similar in simulations with lu_USGS or lu_ENEW. However, spatial differences can be notorious in some specific zones. For

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example, there is an increase of temperatures by the heat island effect in urban zones,

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decreasing temperatures by the appearance of surface with snow/ice, or by consideration of forests instead of rivers increasing temperatures due to increasing sensible heat and

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reduction of latent heat. Unfortunately, there are not enough stations to evaluate the bias

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of simulations for each category of land use. In this respect, it would be interesting to have more stations in the cropland area of the MB and to assess the physical processes associated with extreme minimum temperatures.

5. Concluding remarks and recommendations In the present study, high spatial resolution simulations were conducted using the WRF model version 3.7.1 by changing its topography database from hgt_USGS (default) to hgt_SRTM (implemented) and by changing the land use from lu_USGS (default) to lu_ENEW (implemented). The changes of the precipitation, the air surface temperature

Journal Pre-proof and the local atmospheric circulation were evaluated according to changes of the topography and land use databases. The observed data of meteorological stations were also taken into account to evaluate the performance of the simulations.

The mean values of daily rainfall, minimum and maximum temperatures were better represented with simulations that consider SRTM topography in contrast with the USGS

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topography used as the default. The improvement can be generated due to changes in the circulation at high levels because of the greater transport of humidity by the easterly flux or

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due to variations in the moisture flux at levels near the soil surface, as occurs in the

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Central Mantaro Valley. The improvement was larger in the eastern slope of the Andes or

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the Andes-Amazon transition where the precipitation bias could be reduced by up to 50%. With respect to land use change, although this change does not impact the mean values of

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temperatures, except when urban zones increase or water bodies or ice occur, land use

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derived from Eva et al. (2004) introduces more vegetation (LAI), which generates more latent heat to generate more precipitation. This process seems to be clear in the Western

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rainfall can increase.

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slope of the Andes and the Andes-Amazon transition, where together with moisture fluxes,

These results provide a first analysis of WRF simulations in relation to changes of the topography and land surface configurations over the central Peruvian Andes. Further improvements in atmospheric modeling over the tropical Andes is particularly important regarding the impacts of climate change already observed over this region, such as the increase of air and surface temperatures and glacier retreat (Vuille et al., 2015, 2018; Aguilar-Lome, 2019; Rabatel et al., 2013). Because of these reasons, we encourage researchers to use the SRTM topography database instead of the USGS database in WRF versions prior to version 3.8 to perform numerical simulations in the very complex

Journal Pre-proof topography of the Andes, especially for those involving simulations on the eastern slope of the Andes.

Acknowledgments This research was supported by the project "Study of the physical processes that control the superficial fluxes of energy and water for the modeling of frosts, intense rains and

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evapotranspiration in the central Andes of Peru" (contract: 400-PNICP-PIBA-2014) funded by the “Programa Nacional de Innovación para la Competitividad y Productividad”

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(Innovate-Peru). Jhan-Carlo Espinoza and Clémentine Junquas were partially supported

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by the French AMANECER-MOPGA project funded by ANR and IRD (ref. ANR-18-MPGA-

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0008). Simulations were possible thanks to the project “Sistema Computacional de Alto Rendimiento para la Simulación de Fluidos Geofísicos Computacional”, funded by

1.

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List of Figures

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Fig. 1. (a) Study area and (b) its location in South America. (a) also shows regions of interest: Along the Coast (AC), High Western Slope of the Andes (HWSA), Mantaro Basin

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(MB) and Eastern Slope of the Andes (ESA). The capital of Peru (Lima), the coastal line, the 2000 masl contour line, delimitation of MB and the position of the Central and Eastern

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Mantaro Valleys are also indicated. In (b), the domains D01, D02 and D03 are indicated,

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which are considered for modeling with WRF with resolutions of 27 km, 09 km and 03 km, respectively. The topography belongs to SRTM.

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Fig. 2. Topography of 3-km spatial resolution for D03 used in (a) CTRL (hgt_USGS) and in

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(b) SIM01 and SIM02 (hgt_SRTM); (c) shows the difference hgt_SRTM-hgt_USGS. The

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river system is represented with blue lines. (d) and (e) show the land use of 3-km spatial resolution for D03 used in (d) CTRL (lu_USGS) and in (e) SIM01 and SIM02 (lu_ENEW).

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Categories of land use are indicated according to USGS definitions. Details about percentage changes in land use can be found in Table S1. Specific subregions are indicated by boxes A, B and C.

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Fig. 3. (a) Location of the 57 meteorological stations with numbers corresponding to Table

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1. Blue dots indicate the location of automatic stations in Table 2. Thin and wide black lines correspond to the 2000 masl contour of topography and the limits of the MB. (b)

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Mean of precipitation. (c) Mean of minimum temperature. (d) Mean of maximum temperature. The averages correspond to January months from 2004 to 2008. Empty black circles indicate no data.

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Fig. 4. (Left column) (a) LAI, (c) albedo and (f) soil moisture for CTRL. Dashed and continuous lines show 1500 and 3500 m contour levels of hgt_USGS. (Center column) Differences in (d) albedo and (g) soil moisture of SIM01 and CTRL. Blue and red lines are -500 and +500 m contour levels of hgt_SRTM-hgt_USGS. (Right column) Differences in (b) LAI, (e) albedo and (h) soil moisture of SIM02 and SIM01. Dashed and continuous lines are 1500 and 3500 m contour levels of hgt_SRTM. From (a) to (h), the limits of MB are represented with thick black lines.

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Fig. 5. Mean of daily precipitation for (a) CTRL (b) SIM01 and (c) SIM02. (d) shows differences in the precipitation between SIM01 and CTRL and (e) shows the difference in the precipitation between SIM02 and SIM01. (f) shows the scatterplot between differences in the precipitation of SIM01 and CTRL vs. differences in the altitudes of the topography of SIM01 and CTRL for each grid point. Blue, green and red dots correspond to subregions

Journal Pre-proof A, B and C and with linear regression of 4 mm.km -1 (p-value<0.05) for all grid points (gray dots). (g) and (h) show the relative amounts of values shown in (d) and (e), respectively. The thick black line shows MB borders. In (a) and (b) dashed and continuous lines are the 1500 and 3500 m contour levels of hgt_USGS, and in (c), (e) and (h) these levels correspond to hgt_SRTM. In (d) and (g), blue and red lines are the -500 and +500 m

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contour levels of hgt_SRTM - hgt_USGS.

Fig. 6. Diurnal cycle of precipitation for the seven stations in Table 2. Green, black, blue and red colors indicate values for observations, CTRL, SIM01, and SIM02, respectively. Hourly values are normalized to mm.day-1. Horizontal lines show the daily cumulated rainfall. Contour levels of 2000 and 3500 masl and delimitation for the Mantaro basin are also shown.

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Fig. 7. (Top) Hour of maximum precipitation in the diurnal cycle of (a) CTRL, (b) SIM01 and (c) SIM02. (Middle) Relative cumulated rainfall respect to the total accumulated precipitation in the six hours nearest the maximum rainfall of (d) CTRL, (e) SIM01 and (f) SIM02. (Bottom) Diurnal cycle for subregions (g) A, (h) B and (i) C. Daily cumulative values are also indicated. Subregions are indicated with boxes in panels a to f.

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Fig. 8. (Top) (a) Mean minimum temperature for CTRL, (b) difference of the mean minimum temperature between SIM01 and CTRL, and (c) between SIM02 and SIM01. (Middle) (d) Mean maximum temperature for CTRL, (e) difference of the mean maximum temperature between SIM01 and CTRL, and (f) between SIM02 and SIM01. The dashed and solid lines represent the 1500 and 3500 m contour levels with hgt_USGS in (a) and (d) and with hgt_SRTM in (c) and (f). (b) and (e) show the contour lines of -500 (blue) and 500 m (red) of hgt_SRTM-hgt_USGS. (Bottom) (g) Scatterplot between differences in the minimum temperatures of SIM01 and CTRL vs. the differences in the altitudes of the

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regression coefficients for gray dots in (g) and (h) are -5.7 and -6.6 ºC.km -1, respectively.

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Figure 9. As in Figure 6 but with air surface temperature.

Fig. 10. Diurnal cycle of air surface temperature (T2) produced by CTRL, SIM01 and SIM02 in the subregions (a) A, (b) B and (c) C. Mean values and lines with black, blue and red colors are CTRL, SIM01 and SIM02 simulations, respectively.

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Journal Pre-proof Fig. 11. (a) Mean vertically integrated moisture flux (arrows) and its divergence (shaded) for CTRL. (b) Difference of the mean vertically integrated moisture flux and its divergence between SIM01 and CTRL. (c) Difference of the mean vertically integrated moisture flux and its divergence between SIM02 and SIM01. The dashed and solid magenta lines represent the 1500 and 3500 m contour levels according hgt_USGS for (a), hgt_SRTM-

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hgt_USGS for (b) and hgt_SRTM for (c).

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Fig. 12. Mean vertical profiles of (a) rain mixing ratio, (d) zonal moisture flux and (g) meridional moisture flux in subregion A. The corresponding variables for subregion B are shown in (b), (e) and (h), and those for subregion C are displayed in (c), (f) and (i). Black, blue and red lines correspond to CTRL, SIM01 and SIM02, respectively.

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Fig. 13. Diurnal cycle of surface moisture flux convergence (Div) produced by CTRL (black line), SIM01 (blue line) and SIM02 (red line) in subregions (a) A, (b) B and (c) C. Mean

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values are shown with black, blue and red colors.

List of Tables

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12 13 14 15 16 17 18 19 20 21 22 23

Cajatambo P icoy Canta Matucana Vilca Carania Yauyos SJ Yanac Huachos Tunel cero Challaca Sgo Cochorvos

-10.478 -10.917 -11.467 -11.839 -12.115 -12.35 -12.492 -13.22 -13.22 -13.254 -13.783 -13.833

-76.999 -76.733 -76.617 -76.378 -75.826 -75.867 -75.911 -75.796 -75.542 -75.085 -75.383 -75.251

3405 2990 2974 2431 3864 3875 2294 2540 2598 4475 1951 2700

24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44

Yanahuanca Cerro P asco Marcapomacoc ha La Oroya

-10.49 -10.694 -11.405 -11.576 -11.783 -11.881 -12.004 -12.04 -12.125 -12.163 -12.199 -12.252 -12.366 -12.406 -12.393 -12.407 -12.576 -12.556 -12.779 -12.864 -12.983

-76.508 -76.254 -76.325 -75.966 -75.479 -75.288 -75.221 -75.321 -75.432 -75.234 -74.786 -75.355 -75.059 -75.085 -74.866 -74.678 -75.247 -74.536 -75.034 -74.559 -74.729

3190 4260 4479 4007 3360 3422 3302 3322 3650 3186 3275 3831 3675 3650 3240 2920 3890 3370 3770 3356 3513

45 46 47

La Quinua Vilcashuaman Andahuaylas

48 49 50 51 52 53 54 55 56 57

San Rafael Huasahuasi Tarma Ricran Runatullo Comas Oxapampa P ichanaky Satipo P to Ocopa

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Jauja Ingenio Sta Ana Huayao SJ Jarpa Viques Salcabamba Laive Acostambo P ilchaca P ampas Colcabamba Huancalpi P aucarbamaba Huancavelica Acobamba Lircay

DUSGS +112 +26 +204 +636 +338 +345 -10 +476 +41 +134 +10 +210 +626 -189 +491 +596 +140 -334 +502 +27 +597 +50 +295 +433 +270 +209 -39 +42 -130 +390 +504 +581 -15 +341 +25 +75 +82 +237 +128 +762 +169 +241 +521 +342 -151

DSRTM +69 -31 +155 +534 +284 +359 -14 +442 +19 +49 +3 +170 +525 +532 +220 +606 +395 -71 +898 +72 +502 +80 +271 +392 +369 +453 +79 -34 +66 +21 +148 +49 +14 +372 +173 +309 +44 +92 +98 +337 +294 +272 +465 +286 -168

-13.034 -13.644 -13.657

-74.135 -73.949 -73.371

3316 3394 2866

+484 +169 -60 +277 +216 -40 -382 +1105 +797 +1030 +1314 +1104 -8 -20 -435 +447

+20 +42 -55 +244 +151 0 +474 +256 +470 -70 +448 +153 +47 +4 -205 +158

-10.322 -11.254 -11.397 -11.542 -11.593 -11.749 -10.593 -10.955 -11.22 -11.136

-76.169 -75.627 -75.69 -75.525 -75.051 -75.129 -75.39 -74.832 -74.627 -74.254

3060 2737 3200 3687 3475 3640 1814 526 588 830

lu_USG S 8 8 19 19 19 19 21 19 21 19 21

lu_E NEW 19 7 19 19 8 19 5 19 19 19 19

19/21 8 8 6 8 21 8 21 19 21 7 21 8

19 5 11 5 11 7 11 8 7 5 5 8 11

8/21 7 6 7 21 7 7 7 7 7 7 13 7 7 7 7 7 7 7 7 7

11/5 8 7 8 7 5 8 8 5 11 5 8 5 5 7 5 5 7 8 7 8

7 7 7 6

8 8 8 8

7 7 8 8 7 8 8 13 13 13 13 13/8

8/5 8 8 8 7 7 7 10 6 6 13 7/8

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Altitude 120 180 523 442 721 312 60 844 294 1060 398

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Longitude -77.55 -77.237 -76.839 -76.493 -76.055 -76.195 -76.136 -75.678 -75.966 -75.593 -75.728

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Latitude -11.05 -11.467 -11.989 -12.522 -12.862 -13.029 -13.474 -13.627 -13.763 -13.847 -14.068

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Station Alcantarilla Donoso Nana La Capilla P acaran Socsi Fonagro Huancano Hda Bernales Huamani San Camilo

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1 2 3 4 5 6 7 8 9 10 11

PP 0 0 0 0 0 0 0 0 0 0 0

Tmin 1 1 1 1 1 1 1 1 1 1 1

Tmax 1 1 1 1 1 1 1 1 1 1 1

Region AC AC AC AC AC AC AC AC AC AC AC

1 1 0 1 1 1 1 1 1 1 0 1

1 1 1 1 0 0 1 1 1 1 1 1

1 1 1 1 0 0 1 1 1 1 1 1

HWSA HWSA HWSA HWSA HWSA HWSA HWSA HWSA HWSA HWSA HWSA HWSA

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 0

0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

MB MB MB MB MB MB MB MB MB MB MB MB MB MB MB MB MB MB MB MB MB

1 1 1

1 1 1

1 1 1

MB MB MB

1 1 1 1 1 1 1 1 1 1

1 1 0 1 0 1 0 1 1 1

1 1 1 1 0 1 1 1 1 1

ESA ESA ESA ESA ESA ESA ESA ESA ESA ESA

Journal Pre-proof Table 1 Name and location (latitude, longitude, altitude) of 57 meteorological stations in the study zone. DUSGS and DSRTM denote overestimation or underestimation in hgt_USGS and hgt_SRTM databases, respectively. lu_USGS and lu_ENEW are land use categories for each station. Available Precipitation data (PP), minimum temperature (Tmin) and maximum temperature (Tmax) are labeled with 1 or 0 when there is no data. Stations are

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also grouped according to the region: Along the Coast (AC), High Western Slope of the Andes (HWSA), Mantaro Basin (MB) and the Eastern Slope of the Andes (ESA). Values in

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bold mean the average of DUSGS and DSRTM, and predominant land use categories of

Latitude

Longitude

Altitude

Acora

-13.784

-75.367

1845

2

Carania

-12.350

-75.867

3

Huaros

-11.407

-76.576

4

Lircay

-12.983

-74.729

5

Huayao

-12.034

-75.339

3322

6

T arma

-11.397

-75.692

7

Oxapampa

-10.593

-75.390

DSRTM

+571

+528

lu_USGS

lu_ENEW

Wooded T undra (21)

3875

-334

-71

Shrubland (08)

Shrubland (08)

3573

+235

Decid. Broadl. (11)

+197

Cropl./Woodl. (06)

Shrubland (08)

3372

+625

+161

Grassland (07)

Shrubland (08)

-15

+14

Grassland (07)

Cropl./Grassl. (05)

3030

+1275

+426

Shrubland (08)

Shrubland (08)

1850

+1068

+117

Everg. Broadl. (13)

Savanna (10)

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Table 2.

DUSGS

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Station 1

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each subregion.

Name and location of seven automatic weather stations (with hourly data) of air surface temperature and precipitation in the study zone. DUSGS and DSRTM denote overestimation or underestimation in hgt_USGS and hgt_SRTM databases, respectively. lu_USGS and lu_ENEW are land use categories according to USGS database and the obtained from Eva et al., (2004).

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SubReg .

B

C

LAI

Albedo ---

LH

SH

SWd

SWu

LWd

LWu

m3m-3

Wm-2

Wm-2

Wm-2

Wm-2

Wm-2

Wm-2

375

SM

mm.day-1

CTRL

3.1

0.6

0.26

0.29

24

69

269

78

286

SIM01

3.2

0.6

0.26

0.29

24

68

264

77

287

375

SIM02

3.9

1.1

0.27

0.26

26

69

253

66

289

373

CTRL

4.4

2.0

0.30

0.23

64

61

278

62

282

366

SIM01

3.7

0.22

66

62

283

62

286

372

SIM02

4.2

2.5

0.30

0.23

63

62

284

65

285

372

CTRL

26.8

4.0

0.37

0.16

104

34

218

34

395

438

SIM01

22.6

4.0

0.37

0.16

36

207

31

383

425

SIM02

27.1

5.3

0.36

0.12

94 102

37

205

25

384

425

0.29

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Table 3.

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outgoing (LWu) longwave radiation in regions A, B and C. The mean values are shown for

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CTRL, SIM01, and SIM02. Highlighted values in bold for SIM01 (SIM02) indicate

HWSA

MB

ESA

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Sim.

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significant differences between SIM01 and CTRL (SIM02 and SIM01).

BIAS (mm.day -1) CTRL

-1.0

SIM01

-0.8

SIM02

-0.7

HWSA

MB

ESA

HWSA

RSME (mm.day -1)

MB

ESA

Corr. Coef.

HWSA MB ESA Perkins skill score

0.7

11.6

5.3

7.3

23.4

0.08

0.15

0.15

0.76

0.83

0.66

0.5

5.9

5.1

7.0

16.5

0.13

0.15

0.13

0.80

0.83

0.74

0.6

6.1

5.2

7.2

16.1

0.15

0.15

0.20

0.80

0.83

0.74

Table 4. Mean values of bias, root mean square error (RMSE), correlation coefficient, and Perkins skill score for daily precipitation. The mean values were calculated according to the available data in the HWSA, MB, and ESA stations.

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Variable

Sim.

AC

HWSA

MB

ESA

AC

HWSA

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BIAS ( C) Minimum Temperature

ESA

AC

HWSA

RSME ( C)

MB

ESA

Corr. Coef.

CTRL

0.0

-1.7

-1.5

-2.3

1.6

2.5

2.6

3.5

0.17

0.54

0.19

0.14

SIM01

0.2

-2.0

-0.9

-1.8

1.6

2.5

2.3

2.6

0.19

0.50

0.19

0.15

SIM02

0.3

-2.1

-0.9

-1.8

1.6

2.5

2.4

2.6

0.16

0.50

0.20

0.12

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BIAS ( C)

RSME ( C)

Corr. Coef.

CTRL

-3.6

-5.3

-3.7

-6.0

4.1

5.7

4.4

6.6

0.14

0.50

0.33

0.32

SIM01

-3.4

-6.1

-2.9

-3.5

3.9

6.4

3.7

4.3

0.13

0.52

0.36

0.25

SIM02

-3.4

-6.0

-2.9

-3.4

3.8

6.2

3.7

4.2

0.13

0.52

0.35

0.25

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Maximum Temperature

MB o

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Table 5.

Mean of bias, RMSE and correlation coefficient for minimum temperature and maximum

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temperature. The mean values were calculated according to the available data in the AC,

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HWSA, MB, and ESA stations. Highlighted values in bold indicate significative correlation

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(p-value<0.05) in more than half of the stations in each region.

Supplementary material

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Figure S1. (a) Implemented SRTM topography used by WRF3.7.1 for domain D03. (b) Default topography used by WRF3.8 for domain D03. (c) Difference between (b) and (a).

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lu_USGS

LU_ENEW

lu_ENEW

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(%)

(#)

(%)

12 179 11 97 1077 5127 4133 0 551 0 5361 2 5962 2879 0 1824 0

0.04 0.66 0.04 0.36 3.96 18.84 15.19 0 2.02 0 19.7 0.01 21.91 10.58 0 6.7 0

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Urban and Built-up Land Dryland Cropland and Pasture Irrigated Cropland and Pasture Cropland/Grassland Mosaic Cropland/Woodland Mosaic Grassland Shrubland Mixed Shrubland/Grassland Savanna Deciduous Broadleaf Forest Evergreen Broadleaf Mixed Forest Water Bodies Barren or Sparsely Vegetated Herbaceous Tundra Wooded Tundra Snow or Ice

66 0 53 948 203 5506 3325 989 50 1540 7000 0 5878 1612 0 0 46

0.24 0 0.19 3.48 0.75 20.23 12.22 3.63 0.18 5.66 25.72 0 21.6 5.92 0 0 0.17

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1 2 3 5 6 7 8 9 10 11 13 15 16 19 20 21 24

Name of category

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Category

Table S1.

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Number and percentage of pixels (for domain D03) of each category of land use according

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to lu_USGS and lu_ENEW. Categories follow the definition used by the default land use in

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WRF3.7.1 model.

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 Simulations are performed at complex terrain in the Peruvian central Andes during the rainiest month.  Differences between SRTM and USGS topography databases can reach more than 1km in the Andes of Peru.  Major impacts on rainfall and temperature are found in the eastern slope of the Andes.  Better representation of topography reduces the bias of rainfall and temperature.

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The authors declare that they have no conflict of interest.