## 1 Introduction  
Studies on interactions between the land surface and atmosphere are fundamental to understanding the Earth's climate. In this context, the land surface and ecosystem models (LSEMs) play an important role to simulate fluxes and complex processes occur between the land surface and atmosphere [1, 2]. Currently, LSEMs are important tools of climate modeling, widely used in studies of carbon cycles, water availability, and land use and cover change (LUCC) effects [3, 4, 5]. Advances in representations of surface processes and vegetation dynamics by LSEMs have included complex parameterizations, increasing the number of parameters that describe biophysical and morphological characteristics of the land surface. The search for a good set of parameters that represent surface processes has become a challenge in LSEM. In general, to simplify the spatiotemporal representation of these processes, values of parameters are obtained for a single site that is characterized by a particular vegetation class or plant functional type (PFT) and extrapolated to an entire region with the same PFT [6,7]. This set of parameters is called default parameters. However, the use of default parameters in the LSEM can generate incorrect representations of the land surface in different regions [7, 8, 9]. Therefore, the LSEM models must be carefully calibrated to minimizing errors in different regions regarding the local complexity of the soil, vegetation, and climatology. Currently, multi-criteria calibration methodologies that involve simultaneously minimizing a vector with several objective functions have been widely used to calibrate LSEMs [10, 11, 12, 13]. These methodologies are a better approach to simultaneously optimize multiple model outputs. The Multi-Algorithm Genetically Adaptive Multi-objective (AMALGAM) calibration method developed by [11] is one of the most efficient algorithms for obtaining parameters and has been applied to several LESMs [14, 15, 16]. In this work, we used AMALGAM to estimate parameters that will improve the performance of the INLAND model to represent land surface processes of the natural vegetation in the Pampa biome of southern Brazil. The Integrated Model of Land Surface Processes (INLAND) is a version of the Integrated Biosphere Simulator (IBIS) model developed by [17]. Many works using the IBIS and INLAND have been conducted in recent years and have mainly focused on impacts of land use change and land cover [18, 19, 20, 21, 22, 23]. Ref. [24] describes the main characteristics of the Brazilian Global Atmospheric Model (BAM), the atmospheric component of the Brazilian Earth System Model (BESM), and the value of land surface scheme (IBIS) coupled to the atmospheric model. The evaluation studies of IBIS over the Amazon and central Brazil [18] and over Northeast Brazil [25] have shown the capability of this scheme to well represent the physical, physiological, and ecological processes occurring in vegetation and soils. However, most of these works focus on the Amazon and Northeastern Brazil, with no work using the Pampa biome from southern Brazil. The Pampa biome is characterized by natural grassland vegetation. According to [26], the terms savanna and steppe are inappropriate in classifying vegetation present in this biome. The appropriate term to classify the vegetation present in the Pampa is Campos, as discussed in [26]. Fauna and flora present are highly biodiverse and include more than 3000 species of grasses [27,28]. The vegetation in the Pampa biome is dominated by photosynthetic metabolism C3, but C4 species are also present. This biome has suffered over the last decades from extensive agricultural expansion and the intensive use of livestock, significantly reducing its original vegetation [27]. In this work, we establish a consistent set of parameters to represent the simulated surface processes of this unexplored biome that is dynamic, complex, and diverse in terms of plant varieties.