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  • View in gallery

    The WRF domains and the topography used in the study. Mother and nested domains have horizontal resolutions of 36 and 12 km, respectively. The contour of the LPB is shown in red. The contour intervals for topography (km) are indicated at the bottom.

  • View in gallery

    Land cover maps used for the control experiments for the (a) mother and (b) nested domains. The land cover types are defined on the right.

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    Three-month (SON 2002) averaged precipitation (mm day−1) from (a) gridded rain gauge observations, (b) TRMM, and (c) the CNTL experiment.

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    Three-month (SON 2002) averaged 2-m temperature (°C) from (a) surface observations (CRU), (b) the CNTL experiment, and (c) their difference. The contour intervals are indicated by the corresponding color bars.

  • View in gallery

    (a) LPB land cover changes when substituting dry cropland for savanna (region I), evergreen broadleaf forest (region II), and grasslands (region III). Corresponding changes in (b) albedo (%), (c) surface roughness length (cm), and (d) emissivity (%).

  • View in gallery

    (a),(c),(e) Mean diurnal cycle of surface fluxes for points A, B, and C in Fig. 5a. Solid and dotted lines denote CNTL and CROP, respectively. (b),(d),(f) Differences between CROP and CNTL for the same points. Black = net radiation, red = sensible heat flux, green = latent heat flux, and blue = ground heat flux.

  • View in gallery

    Three-month (SON 2002) averaged (a) sensible heat flux (W m−2) and (b) latent heat flux (W m−2) from (c),(d) the model CROP and CNTL simulations and their corresponding differences. The contour intervals are indicated by the color bars.

  • View in gallery

    As in Fig. 7 but for (a),(c) 2-m temperature (°C) and (b),(d) 2-m water vapor mixing ratio (g kg−1). The contours are indicated by the corresponding color bars.

  • View in gallery

    As in Fig. 7 but for maximum CAPE (J kg−1). The contour intervals are given by the corresponding color bars.

  • View in gallery

    As in Fig. 7 but for 10-m horizontal wind vectors (arrows) and their magnitude (shaded; m s−1). Regions above 1250 m and regions of weak wind below 0.1 m s−1 were masked out. The contour intervals are given in the corresponding color bars.

  • View in gallery

    (a) Three-month (SON 2002) average of vertically integrated moisture flux (kg m−1 s−1; shades represent the main features of the topography), (b) close up over the LPB of the vertically integrated moisture flux (arrows) and its convergence (shades), and (c) the CROP–CNTL differences; vectors smaller than 3.5 kg m−1 s−1 were masked out. The magnitude of the vectors is presented at the lower right of each panel.

  • View in gallery

    (a) Three-month (SON 2002) average of total precipitation (mm day−1) and (b) CROP–CNTL difference in total precipitation (mm day−1). The contour interval is 0.1 mm day−1.

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Land Cover Change Effects on the Climate of the La Plata Basin

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  • 1 Earth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, Maryland
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Abstract

Deforestation and replacement of natural pastures by agriculture have become a common practice in the La Plata River basin in South America. The changes in land cover imply changes in the biophysical properties of the land surface, with possible impacts on the basin’s hydroclimate. To help understand to what extent the climate could be affected, and through which processes, ensembles of seasonal simulations were prepared using the Weather Research and Forecasting Model for a control case and a scenario assuming an expansion of the agricultural activities to cover the entire basin. The La Plata River basin shows different climate responses to the land cover changes depending on the region. The northern part of the basin, where forests and savanna were replaced by crops, experiences an overall increase in albedo that leads to a reduction of sensible heat flux and near-surface temperature. A reduction of surface roughness length leads to stronger low-level winds that, in turn, favor a larger amount of moisture being advected out of the northern part of the basin. The result is a reduction of the vertically integrated moisture flux convergence (VIMFC) and, consequently, in precipitation. In the southern part of the basin, changes from grasslands to crops reduce the albedo and thus increase the near-surface temperature. The reduction in surface roughness length is not as large as in the northern sector, reducing the northerly moisture fluxes and resulting in a net increase of VIMFC and, thus, in precipitation. Notably, advective processes modify the downstream circulation and precipitation patterns over the South Atlantic Ocean.

Corresponding author address: Ernesto Hugo Berbery, Earth System Science Interdisciplinary Center/CICS, University of Maryland Research Park (M-Square), 5825 University Research Court, Suite 4001, College Park, MD 20740-3823. E-mail: berbery@essic.umd.edu

Abstract

Deforestation and replacement of natural pastures by agriculture have become a common practice in the La Plata River basin in South America. The changes in land cover imply changes in the biophysical properties of the land surface, with possible impacts on the basin’s hydroclimate. To help understand to what extent the climate could be affected, and through which processes, ensembles of seasonal simulations were prepared using the Weather Research and Forecasting Model for a control case and a scenario assuming an expansion of the agricultural activities to cover the entire basin. The La Plata River basin shows different climate responses to the land cover changes depending on the region. The northern part of the basin, where forests and savanna were replaced by crops, experiences an overall increase in albedo that leads to a reduction of sensible heat flux and near-surface temperature. A reduction of surface roughness length leads to stronger low-level winds that, in turn, favor a larger amount of moisture being advected out of the northern part of the basin. The result is a reduction of the vertically integrated moisture flux convergence (VIMFC) and, consequently, in precipitation. In the southern part of the basin, changes from grasslands to crops reduce the albedo and thus increase the near-surface temperature. The reduction in surface roughness length is not as large as in the northern sector, reducing the northerly moisture fluxes and resulting in a net increase of VIMFC and, thus, in precipitation. Notably, advective processes modify the downstream circulation and precipitation patterns over the South Atlantic Ocean.

Corresponding author address: Ernesto Hugo Berbery, Earth System Science Interdisciplinary Center/CICS, University of Maryland Research Park (M-Square), 5825 University Research Court, Suite 4001, College Park, MD 20740-3823. E-mail: berbery@essic.umd.edu

1. Introduction

The properties of land cover types (e.g., surface albedo, surface roughness length, root depth, stomatal resistance, and others) affect the land surface–atmosphere exchange of momentum, heat, and moisture. Changes in land cover will thus have an impact on those interactions, the overlying boundary layer, and eventually the processes that modulate precipitation (e.g., Pielke and Avissar 1990; Stohlgren et al. 1998; Pielke et al. 2007). The size, geographic location, and patchiness of an area where land cover changes take place may determine the extent to which they affect local, regional, and global climate (Marland et al. 2003). Smaller areas (e.g., on the order of 10 km) of land cover change can result in changes in the local pattern and intensity of precipitation. In tropical regions, where large thunderstorms are frequent, the effect will be larger with possible impacts even up to the global scales (Pielke 2001; Werth and Avissar 2002). One well-studied region with these characteristics is Amazonia, where the regional effects of deforestation imply changes in the surface water and energy budgets as well as in the low-level circulation (e.g., through changes in the surface roughness length). Deforestation may lead to a reduction of evapotranspiration and moisture flux convergence (MFC) and, consequently, of precipitation (Nobre et al. 1991). Deforestation also affects the intensity of precipitation and may produce shifts in the precipitation patterns due to, for example, advective processes (Hahmann and Dickinson 1997). Remote effects can also be expected, with Werth and Avissar (2002) reporting that Amazon deforestation could lead to changes in the annual cycle of precipitation in regions as far as the Gulf of Mexico, India, and the west Pacific.

The La Plata River basin (LPB; Fig. 1) in southeastern South America has also experienced considerable land cover change over recent decades. La Plata is the second largest river in South America and consists of three large tributaries—the Paraná, Paraguay, and Uruguay Rivers—each one with its own particular properties (Tucci and Clarke 1998; Berbery and Barros 2002; Berbery et al. 2005). From about 1950 to 1990, the intensification of agricultural production in the upper Paraná basin in Brazil led to decreases in forested area from 90% to less than 20%, with annual crops increasing from near zero to almost 60% of land coverage (Tucci and Clarke 1998). Significant deforestation has also occurred in both the Brazilian and Paraguayan portions of the Paraguay River basin—for example, the forested area in eastern Paraguay decreased from 45% to 15% (Mechoso et al. 2001, citing Bozzano and Weik 1992). Moreover, extensive changes in agricultural practices have been reported by Tucci and Clarke (1998) and Paruelo et al. (2005). These include transitions from coffee to soybeans and sugarcane. In Argentina, in the 15 years since the mid-1990s, soybean has become the main type of crop with an increase in coverage from 6 × 106 hectares (ha) to 18 × 106 ha (Paruelo et al. 2005; Argentine Ministry of Agriculture Livestock and Fisheries 2010).

Fig. 1.
Fig. 1.

The WRF domains and the topography used in the study. Mother and nested domains have horizontal resolutions of 36 and 12 km, respectively. The contour of the LPB is shown in red. The contour intervals for topography (km) are indicated at the bottom.

Citation: Journal of Hydrometeorology 13, 1; 10.1175/JHM-D-11-021.1

Changes in vegetation types can affect the hydrology of a region through changes in infiltration, root depth, soil moisture content, and evapotranspiration (e.g., Bosch and Hewlett 1982; Matheussen et al. 2000). For the second half of the twentieth century, increases have been observed in the streamflow of LPB subbasins, which led Tucci and Clarke (1998) to pose the question of whether those changes could be attributed to agricultural practices. This hypothesis was tested by Saurral et al. (2008) by means of an uncoupled hydrological model, and they concluded that the changes in streamflow should be attributed to increased precipitation rather than to land cover change. Uncoupled hydrologic models do not consider eventual feedbacks to precipitation, and a coupled land–atmosphere system is desirable to have a better understanding of the changes in those basins. Nevertheless, at present not much research can be found on the effects of land cover changes on the LPB hydroclimate. One exception is Beltrán-Przekurat et al. (2011), who performed regional numerical modeling experiments over southeastern South America to investigate the impacts of land cover changes on the near-surface atmosphere. According to their investigation, agriculture has a cooling effect when grasses (photosynthesis pathway C3) are replaced by crops, but a warming effect results when grasses (photosynthesis pathway C4) and wooded grasslands/trees are converted to crops. The cooling effect is similar to that found over the Great Plains by Baidya Roy et al. (2003) and Diffenbaugh (2009). Beltrán-Przekurat et al. (2011) also reported that experiments assuming afforestation gave increases in area mean precipitation, but the patterns exhibited heterogeneity, with patches of increased and decreased precipitation.

The objective of this study is to assess the potential regional impacts of the LPB land cover changes on precipitation and near-surface temperature during austral spring. Specifically, the study seeks to understand the mechanisms by which local and regional land cover changes may lead to variations in regional climate. Section 2 presents the model configuration and an evaluation of the model simulations. Section 3 discusses the effects on climate when idealized changes of regional land cover are assumed in model simulations. A summary of the results and the conclusions are presented in section 4.

2. Methodology and experimental design

a. The WRF Model and its configuration

This research is based on the analysis of model simulations performed with the Weather Research and Forecasting Model (WRF), in its Advanced Research WRF (ARW) version 2.2.1. The WRF is coupled with the Noah land surface model (LSM) to link the surface conditions with the atmosphere. The Noah LSM is a model of intermediate complexity for use in operational weather and seasonal prediction studies. Taken from the original Oregon State University (OSU) LSM (Mahrt and Pan 1984; Mahrt and Ek 1984; Pan and Mahrt 1987), the Noah LSM uses a Jarvis–Stewart canopy conductance approach and a linearized (noniterative) solution to the surface energy balance; it carries four soil layers with predicted states of soil moisture and temperature (using soil moisture diffusion and heat conduction equations, respectively) along with intercepted canopy water (see Chen et al. 1996; Ek et al. 2003). The canopy resistance is computed as a function directly proportional to the minimum stomatal resistance and inversely proportional to the leaf area index and the four Jarvis environmental functions for incoming solar radiation, air temperature, atmospheric humidity deficit, and soil moisture availability. The canopy resistance is then used with Penman potential evaporation and other terms to get the actual evapotranspiration (see Chen et al. 1996; Ek et al. 2003). The infiltration scheme follows that of Schaake et al. (1996) for the subgrid variability of precipitation runoff and soil moisture. Surface exchange coefficients (and thus surface fluxes) are determined via the surface layer parameterization described by Chen et al. (1997).

The WRF was run on a continental scale with two nested domains at 36 km and 12 km grid spacing. The vertical grid consists of 28 vertical levels from the surface up to 10 hPa, of which 11 are below 700 hPa. These levels range from 1 (lowest level) to 0 (top of the atmosphere) and are 1.000, 0.990, 0.978, 0.964, 0.946, 0.922, 0.894, 0.860, 0.817, 0.766, 0.707, 0.644, 0.576, 0.507, 0.444, 0.380, 0.324, 0.273, 0.228, 0.188, 0.152, 0.121, 0.093, 0.069, 0.048, 0.029, 0.014, and 0.000. Figure 1 presents the model domains and topography. The three major topographic features are 1) the Andes Mountains along the west coast with heights exceeding 4000 m, 2) the Brazilian Highlands with heights of about 1000–1500 m along the central–east coast of Brazil, and 3) the Guiana Highlands toward the northern part of the continent with heights also around 1500 m. Relatively low lands are present between the three high terrain features, and particularly over large expanses of the La Plata basin.

Land cover types are prescribed following 24 unique United States Geological Survey (USGS) categories, and each has assigned different physical properties, like surface albedo, surface roughness length, emissivity, and stomatal resistance, among others. The USGS land cover distribution (presented in Fig. 2a) is dominated by evergreen broadleaf forests over the Amazon basin, barren types over mountain regions, and shrublands over Patagonia. In the central region (Fig. 2b), and covering much of LPB, savanna, forests, croplands, and grasslands can be found. Large portions of LPB are among the most fertile regions in the Americas.

Fig. 2.
Fig. 2.

Land cover maps used for the control experiments for the (a) mother and (b) nested domains. The land cover types are defined on the right.

Citation: Journal of Hydrometeorology 13, 1; 10.1175/JHM-D-11-021.1

Numerical models are sensitive to their physical parameterizations, and a satisfactory combination of parameterizations for a certain region may not be as suitable for another. Numerous experiments were carried out to identify a model physics configuration that would work best for the austral springtime experiments over South America. The choice of parameterizations was done after an evaluation (not shown, but see Lee 2010) of the precipitation biases and a comparison against observations of the location and magnitude of the main precipitation centers. The final configuration, summarized in Table 1, uses the Dudhia (1989) shortwave scheme, the WRF Single-Moment 6-Class Microphysics scheme (WSM6) (Hong and Lim 2006), the Betts–Miller–Janjić (BMJ) cumulus scheme (Janjić 1994, 2000), the Mellor–Yamada–Janjić (MYJ) boundary layer scheme (Janjić 1990, 1996, 2002), the Monin–Obukhov–Janjić (MOJ) surface layer scheme (Janjić 1996, 2002), and the Noah land surface model (Chen and Dudhia 2001; Ek et al. 2003). Given that cumulus parameterizations depend on artificial separations of processes and scales (Arakawa 2004), it could be argued that they are not adequate for a horizontal grid spacing of 12 km. On the other hand, for several years the National Centers for Environmental Prediction (NCEP) has been successfully running the WRF operationally at 12 km using the BMJ cumulus scheme.

Table 1.

The specifications of the WRF system used in this study.

Table 1.

Sensitivity experiments were carried out to determine how vegetation types over the LPB may influence the surface and near-surface conditions, the boundary layer, and the processes that control the regional climate during austral spring [September–November (SON)]. The period is also the growing season for summer crops and the time when land and atmosphere processes become coupled and land–atmosphere interactions become large. Moisture recycling, which is considered a measure of coupling strength, reaches its peak during SON over the Amazon, while in the southern part of the LPB (the Pantanal and Pampas regions) it is only slightly smaller than during DJF, but still large (Dirmeyer and Brubaker 2007). Multiple 3-month simulations from 1 September to 30 November 2002 were performed. The year 2002 was chosen because it is a period that did not have large precipitation anomalies over the LPB. The austral spring of 2002 had area-average precipitation anomalies in the range of 0.0–0.2 mm day−1. The period was under a mild El Niño [El Niño 3.4 (5°N–5°S, 120–170°W) sea surface temperature (SST) index on the order of +1.2°C] and almost neutral north tropical Atlantic SSTs (+0.2°C), which is a combination that does not favor large precipitation anomalies in the LPB region (Mo and Berbery 2011). The choice of one specific year leaves out the analysis of interannual variability—an aspect that will be examined in future research.

The initial boundary conditions and the 6-h lateral boundary conditions for the larger domain were taken from the NCEP–National Center of Atmospheric Research (NCAR) global reanalysis data (Kalnay et al. 1996). A two-way interaction was used between the two regional domains. Initial conditions starting at different consecutive days are enough for generating ensembles with a measurable amount of dispersion (see, e.g., Hoffman and Kalnay 1983). As even small perturbations may produce some dispersion because of a strong nonlinearity of model physics (like convection and large-scale precipitation), it is assumed that the ensemble members are independent from each other. Four simulations for each set of experiments were done to assess the significance of the results.

The model domains, particularly the larger domain, cover ocean areas where values of sea surface temperatures (SSTs) are used at the lower boundary. The monthly reconstructed SSTs used in this study for the spring of 2002 are documented in Reynolds and Smith (1994) and Smith et al. (1996) and updated to 2002. The initial soil moisture is also taken from the NCEP–NCAR global reanalysis, and the interpolation from one grid to the other is done in the WRF preprocessing package. The soil moisture for each experiment is the one that corresponds to the initial time. Several studies have investigated the time it takes for a model to achieve thermal and hydrologic equilibrium between the land and the atmosphere. The spinup time depends on the initial state and depth of the layer. Near-surface layers adjust in a matter of days or weeks, while deeper layers will take longer, in some cases even years when starting from completely dry or saturated initial conditions (see Yang et al. 1995). However, Cosgrove et al. (2003) found that initialization with soil moisture taken from global reanalysis is more advantageous than a dry or saturated initialization, with important reductions in spinup time. According to Rodell et al. (2005), spinup time is also significantly reduced in humid regions, and given that freezing temperature is one of its main controls, a quicker equilibrium is expected in subtropical South America. An additional factor to be considered is that Zhang et al. (2011) have shown that the first layer (10 cm) of the Noah land surface model dominates the variability of the surface latent heat fluxes. We analyzed our results disregarding the first month and the conclusions are the same as reported here, indicating that spinup was not an issue.

b. Model evaluation

The model’s performance was evaluated in terms of precipitation and 2-m temperature. For the first one, a dataset of gridded observed precipitation (land only) produced by the NCEP’s Climate Prediction Center (Shi et al. 2000; Silva et al. 2007) was used. This product consists of daily rain gauge observations interpolated to a 1° × 1° latitude–longitude grid covering South America. The Tropical Rainfall Measurement Mission (TRMM) 3-h satellite data defined on a 0.25° × 0.25° grid (Huffman et al. 2007) was used to complement the rain gauge observations. The analysis of both datasets is desired as certain areas do not have enough rain gauge density to give a reliable spatial structure, and satellite products depend on calibrations with rain gauges to ensure a good quality product. Differences between the two observational datasets over land should be expected and give a sense of the existing uncertainties in measuring precipitation over South America.

Figure 3 presents the SON 2002 3-month averages for the two estimates of observed precipitation, as well as for the model ensemble control precipitation. The two observational datasets (Figs. 3a,b) have a close resemblance over land, with large values over the northwestern part of the continent, including Colombia and western Amazonia, the central–eastern LPB, and southern Chile. Differences in magnitude are noticed over Colombia where rain gauge precipitation is weaker than that estimated from TRMM (in agreement with previous studies; e.g., Nesbitt et al. 2004). It is not clear whether the rain gauges give a low estimate or TRMM too high, but some areas in Colombia qualify as the rainiest regions in the world (Poveda and Mesa 2000), therefore it is possible that the sparse rain gauges included in the observational dataset do not represent adequately the real precipitation over this complex terrain. TRMM precipitation over LPB has somewhat higher values and slightly more structure than the rain gauge–based product, probably because of its higher resolution.

Fig. 3.
Fig. 3.

Three-month (SON 2002) averaged precipitation (mm day−1) from (a) gridded rain gauge observations, (b) TRMM, and (c) the CNTL experiment.

Citation: Journal of Hydrometeorology 13, 1; 10.1175/JHM-D-11-021.1

The springtime precipitation field from the WRF simulations (Fig. 3c) captures the observed pattern and magnitude of the main observed precipitation centers: according to Figs. 3b,c, this is particularly true for southern Chile as well as the intertropical convergence zone (ITCZ) over land and ocean, except for the portion north of the Guianas. The precipitation center over LPB is reproduced as well, although with smaller magnitude. The model has more difficulty to reproduce the South Atlantic convergence zone (SACZ) and its extension over land. Although partial over or underestimation of precipitation magnitude is found, this is common to many other models as reported in the literature (e.g., Misra et al. 2002; Seth and Rojas 2003; Solman et al. 2008; Menéndez et al. 2010).

Given the emphasis of this research on surface processes, there was also interest in assessing the performance of the model’s 2-m temperature, for which the dataset of the University of East Anglia Climate Research Unit (CRU) version 3.0 was employed (Mitchell and Jones 2005; Brohan et al. 2006; University of East Anglia Climate Research Unit 2010). Stations measuring temperature are even sparser than those measuring precipitation, and this dataset may have unreliable values over large ungauged regions like Amazonia and mountain areas. Thus, the evaluation should not be more than qualitative. Figure 4 presents the 3-month (SON 2002) averaged 2-m temperature from CRU, the model ensemble, and their difference. The overall patterns of observed and model temperatures are similar in structure and northeast–southwest gradients, although the model simulations tend to exceed the observations by 1°–3°C over the central part of the continent and are slightly colder than observations near the coastlines and over the Andes Mountains (Fig. 4c). Notice that the regions with the largest biases are also the regions with the least observations. Within the LPB the differences are larger over the northwestern sector (up to 3°C) but decrease notably on the eastern and southern sectors, with values in the −1° to +1°C range. These results should be regarded positively as many previous articles have reported notably larger biases over Amazonia (e.g., Rauscher et al. 2007).

Fig. 4.
Fig. 4.

Three-month (SON 2002) averaged 2-m temperature (°C) from (a) surface observations (CRU), (b) the CNTL experiment, and (c) their difference. The contour intervals are indicated by the corresponding color bars.

Citation: Journal of Hydrometeorology 13, 1; 10.1175/JHM-D-11-021.1

c. Design of the land cover change experiments

Two sets of simulations for the period from September to November 2002 were performed. The first set of simulations was for control purposes and has been already discussed in the previous section. The control simulations employed the actual USGS vegetation types that represent the current land cover pattern (Fig. 2b) with cropland as well as natural vegetation (savanna, evergreen broadleaf forest, and grasslands in the upper, middle, and lower parts of LPB, respectively). This ensemble is identified as CNTL.

To assess the changes that could be expected on the regional climate of LPB because of land cover changes according to the WRF–Noah system, a set of idealized simulations was prepared. It assumes an extreme expansion of agricultural activity within LPB with all natural vegetation converted to dry croplands (this ensemble will be called CROP). For the CROP experiments, the three natural vegetation types within the La Plata basin (savanna, evergreen broadleaf forest, and grasslands) were replaced by dry croplands (rain-fed agriculture). For the CROP experiments, all changes were restricted to the LPB. It is well known that land cover changes have been extensive over the Amazon basin and other nearby areas that may also affect the climate of LPB, but these other factors are not part of the current study. Land cover changes imply changes of physical properties, like surface albedo, surface roughness length, emissivity, stomatal resistance, and root depth. Table 2, prepared from the model-fixed fields and an internal table packaged with the model, presents the specific values of several surface physical parameters used in the experiments.

Table 2.

Selected values of surface physical parameters adopted in the numerical model.

Table 2.

3. Analysis of the CROP experiment

This section will examine the changes experienced in the regional climate because of the assumptions made in the CROP experiments. The changes in selected physical properties resulting from the modified land cover types are presented in Fig. 5. In the places where savanna was changed to cropland (Fig. 5a; region I) there is almost no change in the albedo (Fig. 5b and Table 2), and a decrease of the surface roughness length is noted (Fig. 5c and Table 2). Larger changes occur over the region where evergreen broadleaf forest was replaced by cropland (region II), as in addition to the reduced surface roughness length there is also a noticeable increase in albedo. The area where grassland was replaced by cropland (region III) reveals a decreased albedo and a small reduction in surface roughness. Regions I and III do not have any changes in surface emissivity, while over region II the surface emissivity is decreased from 95% to 92% (Fig. 5d and Table 2).

Fig. 5.
Fig. 5.

(a) LPB land cover changes when substituting dry cropland for savanna (region I), evergreen broadleaf forest (region II), and grasslands (region III). Corresponding changes in (b) albedo (%), (c) surface roughness length (cm), and (d) emissivity (%).

Citation: Journal of Hydrometeorology 13, 1; 10.1175/JHM-D-11-021.1

a. The diurnal cycle of the surface energy balance

The local effects are investigated first by analyzing the simulated mean diurnal cycle of the surface energy budget for the three regions where natural vegetation was replaced by crops. (The results in Fig. 6 are presented for selected grid points representing each of the three regions, but computations for the area average of each region—not shown—were almost identical.) According to Fig. 6a, over the region covered by savanna (region I) the sensible heat flux compensates about two thirds of the net radiation, with latent heat flux and ground heat flux accounting for the remainder. These ratios are typical of semiarid regions where water availability is limited (e.g., Berbery et al. 2003). Figure 6b indicates that changes from savanna to dry cropland have a very small impact in the local surface energy balance, with changes in the latent and sensible heat fluxes in the range of ±10 W m−2 during daytime. Sensible heat flux experiences a slight increase while the latent and ground heat fluxes have a slight decrease. Changes in net radiation due to the land cover change (black line in Fig. 6b) peak at 2–3 W m−2 in magnitude and are close to zero during nighttime. The average over the diurnal cycle is less than 0.5 W m−2 as both the albedo and emissivity are barely affected by the change.

Fig. 6.
Fig. 6.

(a),(c),(e) Mean diurnal cycle of surface fluxes for points A, B, and C in Fig. 5a. Solid and dotted lines denote CNTL and CROP, respectively. (b),(d),(f) Differences between CROP and CNTL for the same points. Black = net radiation, red = sensible heat flux, green = latent heat flux, and blue = ground heat flux.

Citation: Journal of Hydrometeorology 13, 1; 10.1175/JHM-D-11-021.1

Compared to region I, region II (evergreen broadleaf forest) has a very different energy balance (Fig. 6c). The net radiation is similar to that over savanna, despite the fact that the albedo is smaller (12% versus 20%). The reason is that region II has a larger coverage of middle and low clouds (not shown) and therefore the cloud albedo has a larger effect. The balance is now achieved with a primary contribution of the latent heat flux and a smaller contribution of the sensible heat flux and ground heat flux. When evergreen broadleaf forest is replaced by crops, the effect on the energy balance becomes noticeable, with a reduction in net radiation and sensible heat flux and a small increase in latent heat flux (Fig. 6d). The net radiation depends on the effect of the albedo on the shortwave radiation and the effect of emissivity on the outgoing longwave radiation. During daytime, a reduction of the net radiation of about 40 W m−2 is the result of decreased absorption of incoming solar radiation at the surface due to the increased albedo (Fig. 5b). Table 2 shows that evergreen broadleaf forest has a surface emissivity of 95%, while the other three types of vegetation have a surface emissivity of 92%. Consequently, region II, which changes from evergreen broadleaf forest to dry cropland, has a slight increase in nighttime net radiation because of the decreased surface emissivity (Fig. 6d). The sensible and latent heat fluxes in region II are also affected by the changes in land cover: the daytime sensible heat flux decreases by about 60 W m−2 while the latent heat flux increases by 20 W m−2. Several studies have shown that forest evaporation is greater than crop evaporation, as expected from local processes only. This is not the case for region II, where nonlocal effects take place. Notice the relatively small area of the evergreen broadleaf forest (Fig. 5a) and that this region is downstream of the larger area where savanna was converted to dry cropland. Moreover, the two are in the path of the low-level jet (LLJ) east of the Andes that has a profound influence on the regional climate (e.g., Berri and Inzunza 1993; Berbery and Collini 2000; Salio et al. 2002).

According to Fig. 6e, the magnitude of the latent heat flux over the region covered with grassland (region III) is similar to that in region II, but the sensible heat flux is smaller, indicating an even smaller Bowen ratio. In other words, the relative contribution of the latent heat flux to the balance becomes more relevant than in the other regions at the expense of the sensible heat flux. The ground heat flux remains relatively small and is a minor factor in the energy balance. The changes from grassland to cropland result in an increase of the net radiation because of the reduced albedo (Fig. 5b) and increased absorption—equally balanced by the sensible and latent heat fluxes (Fig. 6f). In this case the changes in ground heat flux are nearly zero. Regions II and III experience increased latent heat fluxes but exhibit opposite changes in the net radiation and sensible heat fluxes (Figs. 6d,f).

In summary, over savanna the net radiation is largely balanced by the sensible heat flux while over forest and grassland, latent heat is the primary factor to balance the net radiation. The largest change in the surface energy balance occurs in the region transformed from evergreen broadleaf forest to dry cropland, and the smallest change occurs over the region changed from savanna to cropland.

b. Surface heat fluxes and near-surface atmospheric variables

So far the local effects have been described, but the changes tend to be widespread, as shown in Fig. 7, which presents the 3-month averaged fields of sensible and latent heat fluxes for the control run, CNTL, and the differences of CROP minus CNTL. To facilitate the analysis, two rectangular regions are considered. The northern LPB (NLPB) is defined as the box (28°–17°S, 66°–46°W), and the southern LPB (SLPB) as (37°–28°S, 65°–51°W). The latitude 28°S is conveniently located so that NLPB contains most of the changes from savanna and broadleaf forest to croplands (regions I and II), while SLPB encompasses most of the changes from grasslands to croplands (region III). Figure 7a indicates that sensible heat flux is large in mountain areas, while latent heat flux (Fig. 7b), which is proportional to evapotranspiration, has the larger values over the sea and the wetter low-altitude central part of LPB (large values are also noticed over the forests north of Bolivia). Note from Figs. 7a,b that areas where the sensible heat flux is large tend to have small latent heat flux, and vice versa. The CROP–CNTL differences show that the sensible heat flux decreases (in general) over NLPB with the largest changes in eastern Paraguay and with slight increases over SLPB (Fig. 7c). The latent heat flux (Fig. 7d) experiences the largest increase over eastern Paraguay and Uruguay. Although land cover changes were restricted to within the LPB, changes in latent heat flux are noticed outside the basin (parts of the Brazilian Highlands). In addition, sectors of the LPB basin with no vegetation changes also exhibit changes in surface fluxes. Both aspects indicate that the mechanisms that determine changes in surface fluxes in these areas are likely much more complicated than what can be explained from local effects alone. Nonlocal effects (e.g., advection) for the whole region will be discussed in section 3d.

Fig. 7.
Fig. 7.

Three-month (SON 2002) averaged (a) sensible heat flux (W m−2) and (b) latent heat flux (W m−2) from (c),(d) the model CROP and CNTL simulations and their corresponding differences. The contour intervals are indicated by the color bars.

Citation: Journal of Hydrometeorology 13, 1; 10.1175/JHM-D-11-021.1

The CNTL pattern of the near-surface (2 m) temperature is presented in Fig. 8a and the corresponding CROP–CNTL differences in Fig. 8c. The shape of the pattern of temperature change (Fig. 8c) resembles that of the sensible heat flux (Fig. 7c). The decreased sensible heat fluxes in NLPB correspond with the strong cooling of about to 1.2°C near the surface (Fig. 8c). Likewise, the warming of about 0.5°C in SLPB is associated with the increased sensible heat fluxes (Fig. 7c). The comparatively strong cooling and parts of the relatively weak warming over the SLPB are statistically significant at the 90% level. Beltrán-Przekurat et al. (2011) report that conversion from C3 grasslands to agriculture induces cooling, but if the conversion is from C4 grasslands, there is a slight warming. Our results are not directly comparable to those of Beltrán-Przekurat et al. (2011)—first, because the USGS land cover definition used in WRF is simpler and does not distinguish between different types of grass; and second, because the areas assumed with changes are different as well (recall that the case discussed here is an idealized one, while theirs follows estimated changes produced in recent years).

Fig. 8.
Fig. 8.

As in Fig. 7 but for (a),(c) 2-m temperature (°C) and (b),(d) 2-m water vapor mixing ratio (g kg−1). The contours are indicated by the corresponding color bars.

Citation: Journal of Hydrometeorology 13, 1; 10.1175/JHM-D-11-021.1

The CNTL 2-m water vapor mixing ratio and the corresponding difference between CROP and CNTL are presented in Figs. 8b,d. Changes in land cover result in increased water vapor content up to 0.4 g kg−1 over eastern Paraguay. These values are consistent with the increase in evapotranspiration, which ranges from 0.2 to 1.0 mm month−1 (corresponding to latent heat fluxes on the order of 2–10 W m−2 in Fig. 7d). The changes of water vapor mixing ratio outside the basin (between 10° and 15°S) are the consequence of nonlocal effects that play an additional role, as will be seen in section 3d.

c. Thermodynamic effects

The changes in the surface fluxes and surface energy balance are expected to influence the structure and stability of the overlying troposphere, which can be assessed using the convective available potential energy (CAPE). Following Emanuel (1994), CAPE is the total amount of potential energy of a parcel lifted from level i to the level of neutral buoyancy (n), and is represented by the equation
eq1
where αp is the air parcel specific volume, αa is the ambient environment specific volume, and the integral is performed between the pressures at the levels i and n. As noted in the equation, CAPE can be computed from the thermodynamic states at different starting levels (pi), thus giving a range of values at each grid point; here, the maximum CAPE for the column, as computed in the WRF postprocessing routines, will be discussed. CAPE is usually used in synoptic meteorology to assess when an environment can foster convection. A climatology of CAPE can also be used to provide information on regions that are prone to develop convection, or, as Riemann-Campe et al. (2009) note, to provide insight into the genesis and intensity of convection. In the case of a CAPE climatology, the averages include all days in the season (regardless if it rained or not), thus the magnitudes are much smaller than what would be expected for an individual storm.

Figure 9a shows that in general there is relatively large CAPE in the central and western parts of LPB. This region is well known for the development of mesoscale convective systems (MCSs) as discussed by Zipser et al. (2006) and Salio et al. (2007); therefore, conceptually the simulations are consistent with the observations. The CROP–CNTL differences (Fig. 9b) imply that the land cover changes on the whole act to increase CAPE between 30° and 24°S with a decrease in the northern and the southern sectors of LPB. The increases in CAPE indicate that the environment is more favorable for the development of convection, while the decreases in CAPE point to an environment less favorable for the development of convection. The fields in Fig. 9 represent local changes in the atmospheric column for each grid point; it will be shown next that changes are also detected in the low-level circulation, and thus other mechanisms that define the regional climate at larger scales may come into play.

Fig. 9.
Fig. 9.

As in Fig. 7 but for maximum CAPE (J kg−1). The contour intervals are given by the corresponding color bars.

Citation: Journal of Hydrometeorology 13, 1; 10.1175/JHM-D-11-021.1

d. Ten-meter winds

The CNTL 10-m wind displayed in Fig. 10a has the mark of the large-scale anticyclonic circulation over the South Atlantic Ocean with a generally westward flow. Changes in the surface properties—particularly the surface roughness length—are expected to induce changes in the 10-m winds, and this can be noticed in Fig. 10b. The general decrease of the surface roughness length inside the basin (Fig. 5c) results in a strengthening of the northerly–northeasterly winds, as seen in the difference between CROP and CNTL (Fig. 10b). The magnitude of the wind speed change reaches up to 1 m s−1 in the east–central region of the LPB including eastern Paraguay (Fig. 10b). Wind changes are also simulated over the ocean east of the LPB, suggesting indirect effects of the land cover changes.

Fig. 10.
Fig. 10.

As in Fig. 7 but for 10-m horizontal wind vectors (arrows) and their magnitude (shaded; m s−1). Regions above 1250 m and regions of weak wind below 0.1 m s−1 were masked out. The contour intervals are given in the corresponding color bars.

Citation: Journal of Hydrometeorology 13, 1; 10.1175/JHM-D-11-021.1

e. Large-scale horizontal moisture flows

The changes in the water vapor content and the low-level winds are expected to affect the moisture fluxes and their convergence. Different studies of Amazon deforestation have assigned different levels of importance to the moisture flux convergence (see Hahmann and Dickinson 1997, their Table 1). Those studies were done at lower resolutions, which may explain the differences as the moisture flux convergence is very sensitive to resolution (Berbery and Rasmusson 1999). Figure 11a presents the prevailing features of the CNTL ensemble mean vertically integrated moisture flux (VIMF), which is largely influenced by the lower troposphere. Large westward moisture fluxes associated with the trade winds can be noticed over the tropical Atlantic Ocean and into the Amazon basin. These moisture fluxes depict a counterclockwise rotation in the western Amazon and flow into the LPB over the relatively low lands between the Andes Mountains and the Brazilian Highlands. A closer look at this region (Fig. 11b) reveals the horizontal structure of the LLJ east of the Andes. Vertical cross sections of the moisture fluxes (not shown) confirm the LLJ structure of the flow. The vertically integrated moisture flux convergence (VIMFC) depicted in color shades in Fig. 11b shows a region of large convergence at the exit of the LLJ (on the western and southern parts of LPB) and further downstream over the ocean on the southern boundary of the South Atlantic anticyclone. As expected, the anticyclone itself exhibits divergence.

Fig. 11.
Fig. 11.

(a) Three-month (SON 2002) average of vertically integrated moisture flux (kg m−1 s−1; shades represent the main features of the topography), (b) close up over the LPB of the vertically integrated moisture flux (arrows) and its convergence (shades), and (c) the CROP–CNTL differences; vectors smaller than 3.5 kg m−1 s−1 were masked out. The magnitude of the vectors is presented at the lower right of each panel.

Citation: Journal of Hydrometeorology 13, 1; 10.1175/JHM-D-11-021.1

The CROP–CNTL differences in vertically integrated moisture fluxes and their convergence are presented in Fig. 11c. The northern sector of the basin, NLPB, exhibits a decrease of the moisture flux convergence (or increase of the divergence), while the southern sector shows an increase of the convergence. These results are consistent with the changes in surface roughness length. Figure 5c shows that the largest reduction of surface roughness length is found over the northwestern sector of the basin, so the magnitude of the low-level winds and moisture fluxes over that region are expected to increase (e.g., Stull 1997), inducing a reduction in the convergence as the one shown in Fig. 11c. The surface roughness length is only slightly reduced in SLPB, and thus a deceleration of the low-level winds (moisture fluxes) can be expected, increasing the moisture flux convergence, again as noted in Fig. 11c.

According to Fig. 11, the land cover changes within LPB produce not only climate-related changes in the interior of the basin but also over other regions, indicating that the effects of land cover change in the LPB are not strictly local. Advective or other processes modify the downstream circulation and precipitation patterns over the South Atlantic Ocean. One likely mechanism involves the increased moisture flux convergence once the now-accelerated flow over the basin reaches regions that maintained the original surface roughness. However, the significance of the results over the ocean will require further investigation.

f. Precipitation

Figure 12 presents the 3-month mean precipitation fields for the control ensemble and the difference between the CNTL and CROP ensembles. The precipitation in Fig. 12a is the same as in Fig. 3c, and is repeated here for easier reference. The CROP–CNTL precipitation differences (Fig. 12b) show that, as a result of the expansion of croplands, NLPB may experience a drier climate with a reduction in precipitation as large as −0.6 mm day−1. The precipitation increases in SLPB, thus suggesting changes in the north–south gradient of precipitation due to the land cover change. In general, a reduction of precipitation (Fig. 12b) is found in regions where the CROP–CNTL changes in moisture flux convergence are negative (divergent) as shown in Fig. 11c. Increased precipitation is found in regions where the moisture flux convergence has increased. The mechanisms are similar to those reported in the literature for other regions: there is an increase of moisture content due to increased evapotranspiration, which is compounded by changes in the moisture fluxes due to changes in surface roughness length. The two effects lead to increased moisture flux convergence and, in turn, in precipitation. The VIMFC is collocated with the precipitation anomaly, suggesting its primary role in modifying the patterns.

Fig. 12.
Fig. 12.

(a) Three-month (SON 2002) average of total precipitation (mm day−1) and (b) CROP–CNTL difference in total precipitation (mm day−1). The contour interval is 0.1 mm day−1.

Citation: Journal of Hydrometeorology 13, 1; 10.1175/JHM-D-11-021.1

While the effect of the LPB land cover change on the near-surface temperature is mainly limited to the interior of the basin, the effect on the precipitation is also found downstream over the South Atlantic Ocean. The precipitation pattern follows that of VIMFC, which was modified both by changes in low-level winds and moisture content. The results also indicate that considering the approximate moisture budget equation (P = VIMFC + E, where P is the precipitation and E is the evapotranspiration), the moisture flux convergence had a more relevant role than the evapotranspiration in defining the pattern of precipitation changes. Medvigy et al. (2011) also reported from their simulations that precipitation changes in the deforested regions of the Amazon basin were highly correlated with decreases of the moisture flux convergence (r 2 = 0.99), but not with evapotranspiration (r 2 = 0.08).

Observed precipitation has increased over the Uruguay River basin during the second half of the past century (Barros et al. 2000; Haylock et al. 2006). While it is believed that the observed precipitation increases respond to changes in circulation due to a widening of the tropical belt (e.g., Fu et al. 2006; Hu and Fu 2007; Seidel et al. 2008), the simulations suggest that land cover effects may have contributed as well. This finding may have an interesting implication associated with the study of Saurral et al. (2008): because their model was uncoupled, the changes in river discharge were attributed to changes of climate (rainfall) rather than land cover changes affecting river discharge. While this is the most likely reason, our results suggest that in a coupled model where land–atmosphere exchanges are possible, the river discharge increases could be at least partially explained by increases in precipitation resulting from the expansion of the cropland areas.

4. Summary and conclusions

The land cover of the La Plata basin has been subject to important changes because of agricultural practices that have modified the land surface properties of extensive areas. Simulations were performed with the WRF–Noah model to better understand the land surface–atmosphere feedbacks affected by land cover changes and their impacts on regional climate. To that end, we examined the mechanisms by which changes in land cover can affect near-surface variables, the boundary layer, the convective instability, the low-level moisture fluxes, and, ultimately, their manifestation on changes in precipitation. First, a set of control runs (with current land cover maps) was prepared. Then, an idealized scenario was assumed representing an expansion of the agricultural activities (i.e., replacement of all natural vegetation by croplands within LPB).

The control simulations reproduce closely all the main austral spring centers of observed precipitation, including southern Chile, the ITCZ, and particularly the precipitation center over the LPB, although with smaller magnitude. The model has more difficulty in representing the South Atlantic convergence zone. The general pattern and spatial gradients of the 2-m temperature obtained from the simulations are very similar to the observations, although differences in magnitude are obtained—particularly a slight cold bias near the coastal regions and high mountains and a warm bias over the central part of the continent. However, the biases should be considered qualitatively, as they are found over large portions of South America with scarce observations.

In the set of experiments that assumes an increase of agricultural practices within LPB, savanna and evergreen broadleaf forests over the northern sector were replaced by dry cropland. These changes lead to an overall increase in albedo and reduced surface friction, among other effects. The modified physical properties induce 1) a drop of sensible heat flux and near-surface temperature, 2) a reduction in convective instability, and 3) an acceleration of the moisture fluxes out of the region due to the large decrease of surface roughness, leading to a reduction of the convergence of moisture flux. All of these effects result in a reduction of precipitation.

In the basin’s southern sector, grassland and savanna were also replaced by dry croplands. In this case, the albedo is reduced and, consequently, the southern part of the basin exhibits the opposite behavior of the northern sector, with an increase of near-surface temperature. Since the reduction of surface roughness length is not as intense as in the northern sector, the low-level flow suffers a deceleration and the net effect is an increase of the moisture flux convergence and, hence, increased precipitation. Among the three replaced vegetation types, the largest change in surface climatic variables occurs in regions where dry cropland replaced the evergreen broadleaf forest. This suggests that, over those regions, potential future agricultural practices may have a larger impact than in the other regions. However, because of the diversity of processes involved, the impacts may reach other regions, even those not subject to land cover changes.

While the results are from idealized experiments, they are helpful to give a sense of the potential mechanisms that may be activated when changes in land surface occur within the LPB, as well as the possible changes in the regional climate. Given the large region under modification, it should be expected that effects will be found in regions outside the basin because of, for example, advective processes affecting downstream circulations and precipitation. The simulations suggest so, but further analysis will be needed to assess the reach of the land cover change effects.

Acknowledgments

The comments of René Laprise are appreciated. This research was supported by NSF Grant ATM0646856, NASA Grant NNX08AE50G, and the Inter American Institute for Global Change Research (IAI) through the Cooperative Research Network (CRN)-2094.

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