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HIGH-SPATIAL-RESOLUTION MONTHLY AND ANNUAL

CLIMATE MAPS FOR BOLIVIA

Journal: Journal of Maps

Manuscript ID: Draft

Manuscript Type: Original Article

Date Submitted by the Author: n/a

Complete List of Authors: Vicente-Serrano, Sergio M.; Instituto Pirenaico de Ecología, Consejo Superior de Investigaciones Científicas (IPE-CSIC), Departamento de Procesos Geoambientales y Cambio Global El Kenawy, Ahmed; Mansoura University, Azorin-Molina, Cesar; Pyrenean Institute of Ecology (Spanish Research Council), Geoenvironmental Processes and Global Change Chura, Orlando; SENAMHI, Trujillo, Félix; SENAMHI, Aguilar, Enric; 5Universidad Rovira i Virgili, Martín-Hernández, Natalia; Pyrenean Institute of Ecology (Spanish Research Council), Geoenvironmental Processes and Global Change López-Moreno, Juan Ignacio; Pyrenean Institute of Ecology (Spanish Research Council), Geoenvironmental Processes and Global Change Sanchez-Lorenzo, Arturo; Universidad Girina, Moran-Tejeda, Enrique; Pyrenean Institute of Ecology (Spanish Research Council), Geoenvironmental Processes and Global Change Ycaza, Pilar; CIIFEN, Friend, fanny; CIIFEN,

Keywords: precipitation, temperature, atmospheric water demand

URL: http://mc.manuscriptcentral.com/tjom

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HIGH-SPATIAL-RESOLUTION MONTHLY AND ANNUAL 1

CLIMATE MAPS FOR BOLIVIA 2

S.M. Vicente-Serrano1, A. El Kenawy2,3, C. Azorin-Molina1, O. Chura4, F. Trujillo4, E. 3

Aguilar5, N. Martín-Hernández1, J.I. López-Moreno1, A. Sanchez-Lorenzo1, E. Moran-4

Tejeda1, J. Revuelto1, P. Ycaza6, F. Friend6 5

6 1 Instituto Pirenaico de Ecología, Consejo Superior de Investigaciones Científicas 7

(IPE–CSIC), Zaragoza, Spain 2Hydrological Modeling & Earth Observation Group, 8

WDRC, King Abdullah University of Science and Technology, Saudi Arabia. 9 3Department of Geography, Mansoura University, Mansoura, Egypt

4Servicio Nacional 10

de Meteorología (SENAMHI), La Paz, Bolivia, 5Universidad Rovira i Virgili, 11

Tarragona, Spain, 6Centro Internacional para la Investigación del Fenómeno de El 12

Niño (CIIFEN), Guayaquil, Ecuador 13

14

* Corresponding author: [email protected] 15

16

17

Key-words: Precipitation, temperature, atmospheric water demand, regression-based 18

interpolation, water balance, Bolivia. 19

20

21

22

23

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Abstract: This study presents monthly and annual climate maps for relevant hydroclimatic 24

variables in Bolivia. We used the most complete network of precipitation and temperature 25

stations available in Bolivia, which passed a careful quality control and temporal 26

homogenization procedure. Monthly average maps at the spatial resolution of 1-km were 27

modeled by means of a regression-based approach using topographic and geographic variables 28

as predictors. The monthly average maximum and minimum temperatures, precipitation, and 29

potential exoatmospheric solar radiation under clear sky conditions are used to estimate the 30

monthly average atmospheric evaporative demand by means of the Hargreaves model. Finally, 31

the average water balance is estimated on a monthly and annual scale for each 1-km cell size by 32

means of the difference between precipitation and atmospheric evaporative demand. The digital 33

layers used to create the maps are available in the digital repository of the Spanish National 34

Research Council. 35

36

1. Introduction 37

Climate maps are essential tools for land planning, crop and water management. This is 38

especially critical in the countries whose economic activities are highly dependent on 39

climate variability (e.g., agriculture, livestock, hydropower production, etc.). The 40

development of methods for interpolating local data from meteorological stations has 41

allowed obtaining accurate maps of climate variables with high spatial resolution (e.g., 42

Daly et al., 2002; Marsico et al., 2012; Katsafados et al., 2012; Ninyerola et al., 2000). 43

There are some climatology maps covering the entire Earth (e.g., New et al., 2002; 44

Hijmans et al., 2005), but large-scale approaches commonly have problems related to 45

the number of stations used and the fact that models used may not capture the variability 46

of the climate conditions at the local scale. Moreover, to obtain accurate maps of 47

climate variables, it is essential to guarantee high quality of the data inputs, and 48

validation of the obtained results using independent data (Vicente-Serrano et al., 2003). 49

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Bolivia is expected to be one of the most affected countries by continental reductions in 50

water supplies as a consequence of climate change (Winters, 2012). This underscores 51

the need of employing the available climate information for better preparedness and 52

mitigation. Bolivia has very high climatic and environmental diversity as a consequence 53

of marked topographical gradients and diverse natural ecosystems. The north and east 54

Amazonian regions are characterized by evergreen equatorial forests (Navarro and 55

Ferreira, 2004), but the western Bolivian Altiplano (highlands) is dominated by dry 56

tropical forests and large cultivated areas. Geographic and topographic diversity, and the 57

influence of various atmospheric circulation mechanisms (Seiler et al., 2013) make the 58

Bolivian climate highly complex over space and time (García et al., 2007; Escurra et al., 59

2014). 60

An indication of the importance of water availability in the region is that 50% of the 61

active population of the Bolivian Altiplano is engaged in farming, with agricultural 62

production of bitter potato and quinoa being major economic outputs and export 63

commodities. Over a large part of the Altiplano the rainfall during the agricultural 64

season is less than half of the atmospheric water demand (Vacher and Imaña, 1987), 65

which reinforces negative agricultural impacts when a drought occurs (Vacher, 1998). In 66

addition, Bolivian tropical dry forests are also highly sensitive to droughts, with 67

secondary growth and net primary production being markedly reduced as a response to 68

long-lasting droughts (Mendivelso et al., 2014; Seiler et al., 2014). For these reasons, an 69

accurate evaluation of the climatology and the average water resources in the country 70

must be the necessary first step toward providing climate services in the country. 71

In this study, we developed and validated digital monthly and annual climate maps for 72

Bolivia at a spatial resolution of 1-km. National climate maps comprise precipitation 73

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and maximum and minimum air temperature as input variables, but also atmospheric 74

water demand and climatic water balance as derivate variables. 75

76

2. Methods 77

2.1 Climate data 78

From 102 monthly total precipitation (P) series and 26 monthly maximum (Tmax) and 79

minimum (Tmin) air temperature series from the National Meteorological & 80

Hydrological Service of Bolivia we selected those climate series having at least 25 years 81

within the period 1950-2000. Both precipitation and air temperature series were tested 82

for quality control and homogenization. The quality control procedure was based on 83

comparison of the rank of each data record with the average rank of the data recorded at 84

adjacent stations (Vicente-Serrano et al., 2010). Relative homogeneity methods are 85

commonly used to identify temporal homogeneity in climate series (Venema et al., 86

2012). For this purpose, we used a recently developed relative homogeneity software, 87

HOMER (Mestre et al., 2013), as a semi-automatic methodology that combines a fully 88

automatic joint segmentation (Pickard et al, 2011) with a partly subjective pairwise 89

comparison (Caussinus and Mestre, 2004). HOMER takes advantage of the results of 90

the benchmarking process conducted in the framework of the European Community 91

COST ACTION ES0601 (http://www.homogenisation.org/): "Advances in 92

homogenization methods of climate series: an integrated approach", and includes some 93

of the techniques recommended after an inter-comparison study of homogenization 94

procedures (Venema et al. 2012). Checking for inhomogeneities in each P, Tmax and 95

Tmin series was based on analysis involving the nearby 8 stations to each candidate 96

station. The segmentation analysis was based on annual data, and used ratios for 97

precipitation and temperature differences as a measure for comparisons. The method 98

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provides an estimation of break points in the time series relative to neighboring stations, 99

and identifies high probabilities of the presence of inhomogeneities. Thus, if a break 100

point was identified between a station and several of its neighboring stations for the 101

same year, it was considered probable that there was inhomogeneity in the series. When 102

a series presented an average ratio of one inhomogeneity every five years or larger, it 103

was discarded and was not used for mapping. A total number of 68 precipitation series 104

and 24 temperature series were finally available to obtain the monthly averages at each 105

station (Figure 1). 106

107

2.2 Climate mapping 108

Spatial interpolation of Tmax and Tmin and P were performed using regression-based 109

techniques. A number of different interpolation procedures were used to obtain climate 110

maps from punctual meteorological data: global, local and geostatistics techniques 111

(Borrough and McDonnell, 1998). The most widely used procedures are global methods 112

based on regression techniques. In these methods, different geographic and topographic 113

factors that control the spatial distribution of climate are used as independent variables 114

(Daly et al., 2002; Ninyerola et al., 2000), and dependence models are created between 115

the climate data and independent variables. The value of a climate variable in 116

unsampled points is obtained according to the following equation: 117

z(x) = b0 + b1P1 + b2P2 + ···+bnPn 118

where z is the predicted value of the climate variable at point x; b0 . . . bn are the 119

regression coefficients; and P1 . . .Pn are the values of the different independent 120

variables at point x. 121

The main advantage of this technique is that climate maps are compiled not only from 122

information from various weather stations, but also from auxiliary information that 123

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describes geographic and topographic variables; this approach improves the accuracy 124

and spatial detail of the resulting maps. We used independent variables at a spatial 125

resolution of 1-km. The independent variables were the elevation, latitude, longitude 126

and distance to the Atlantic and Pacific oceans. The elevation was obtained from the 127

Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) at a resolution of 128

7.5-arc-second (https://lta.cr.usgs.gov/GMTED2010), which was rescaled to 1-km of 129

spatial resolution in a latitude-longitude projection with datum WGS-84. The distance to 130

the Atlantic and Pacific oceans was obtained from the coastlines using the BUFFDIST 131

module of the MiraMon GIS (Pons, 2006). Low-pass filters with radii of 2.5, 5, 12.5 132

and 25-km were applied to elevation in order to measure the wider influence of this 133

variable on climate. Nevertheless, these filtered variables were only included for 134

modeling precipitation For temperature, we only included the raw elevation to avoid 135

that inclusion of elevation with different low-pass filter may cause that maximum 136

temperature to be lower than the minimum temperature given different variables 137

included in the model. Although some studies have also recommended to include the 138

effect that topographic barriers may produce on climatic variables, e.g. using the 139

maximum height in a wedge of given aspect and radius (Agnew and Palutikof, 2000), 140

we discarded its use since the strong topographic complexity in Bolivia can cause strong 141

artificial boundaries between adjacent areas with no climatological meaning. The 142

normality of each variable was tested by the Kolmogorov-Smirnoff test, and natural 143

logarithms were applied where necessary in order to fit a normal distribution more 144

closely. Correlation between independent variables leads to problems with colinearity. 145

To avoid this, we followed Hair et al. (1998) and applied a forward stepwise procedure 146

with ‘probability to enter’ set to 0.01 to retain only significant independent variables. 147

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The disadvantage of using regression based techniques to map climate data is that the 148

results are inexact because the predicted value of the climatic variable z(x) does not 149

coincide with the real data collected at weather stations; however, the error obtained at 150

each point is known (residual) and we performed a procedure to correct it. The residuals 151

(difference between the climatic variable measured at a weather station and that 152

predicted by the model) were spatially interpolated by the inverse distance weighting 153

algorithm, and the resulting “residual maps” were added to the regression maps, thus 154

refining the results (Ninyerola et al., 2000). 155

2.3. Calculation of the atmospheric water demand 156

A number of different methods are used to calculate atmospheric water demand (AED) 157

(Allen et al., 1998). The Food and Agriculture Organization of the United Nations 158

(FAO) have adopted the Penman-Monteith (PM) method (Allen et al., 1998) as the 159

standard for computing AED from climate data including a reference surface resistance 160

term based on standard crop (reference evapotranspiration -ETo-). Nevertheless, the PM 161

needs large amount of data, as it requires values of solar radiation, wind speed, and 162

relative humidity, which are not available in Bolivia. 163

Several authors have proposed the empirical Hargreaves (HG) equation (Hargreaves and 164

Samani, 1985) as the best alternative where data are scarce (e.g. Xu and Singh, 2001; 165

Martinez-Cob, 2002, Vicente-Serrano et al., 2014). This method only requires 166

information on Tmax and Tmin and extraterrestrial radiation (Ra). Because Ra can be 167

calculated theoretically (Droogers and Allen, 2002), the only variables required in this 168

method are observed air temperatures. Droogers and Allen (2002) modified the original 169

Hargreaves equation by including a rainfall term, on the assumption that monthly P can 170

represent relative levels of humidity and solar radiation. The ETo (mm day–1) is 171

calculated using the equation: 172

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ETo= 0.0013 Ra(T +17.0)(R− 0.0123 P)0.76

173

where P is the monthly total precipitation in mm. R is the difference between the 174

maximum and minimum temperatures (monthly averages; °C) and Ra is the 175

extraterrestrial solar radiation (mm day–1). Ra values are modeled from the Digital 176

Terrain Model with a cell size of 1-km. For this purpose, we used an algorithm that 177

considers the effects of terrain complexity (shadowing and reflection) and the daily 178

solar position (Pons and Ninyerola, 2008). Ra is provided in MJ m−2 day−1, which is 179

transformed to mm day−1 (1 MJ m−2 day−1 = 0.408 mm day−1). Vicente-Serrano et al. 180

(2007) showed that this approach provides more accurate estimations of ETo than 181

estimating Ra only according to the latitude and the day of the year. 182

We used the modified HG method to obtain monthly maps of ETo at a spatial resolution 183

of 1-km using the grids of P and Tmax and Tmin obtained by means of regression-based 184

interpolation and the Ra modeled by means of the DTM. 185

186

2.4 Calculation of the climatic water balance 187

We calculated a simple climatic water balance model by means of the difference 188

between P and ETo, monthly and annually (Thornthwaite, 1948). This measure, 189

although it cannot be considered as the water runoff potential in each grid cell, it can be 190

considered as a measure of the average long-term water deficit or surplus, which is very 191

useful as a measure of water stress and it is used to obtain robust drought indices 192

(Vicente-Serrano et al., 2010b). 193

194

2.5. Validation 195

In the absence of a sufficient number of observatories (68 precipitation and 24 196

temperature observatories for the whole country), the ‘jack-knifing’ method was 197

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adopted for validation, based on withholding, in turn, one station out of the network, 198

estimating climate at each point according to the procedure explained in section 2.2 199

from the remaining observatories and calculating the difference between the predicted 200

and observed value for each withheld observatory (Phillips et al., 1992). This method 201

has frequently been used in climatology (e.g., Daly et al., 1994, Holdaway, 1996, 202

Hofstra et al., 2008), particularly when low number of cases are available for validation. 203

The performance of each map is assessed by means of different validation statistics. 204

Combining the skill of a range of statistical estimators can provide a more rigorous 205

assessment of the model uncertainty. In this work we used three accuracy estimators, 206

including bias, the mean absolute error (MAE) and the D Willmott’s refined index of 207

agreement (Willmott et al., 2012). 208

The bias is calculated as a measure of the differences in values between the observed 209

(O) and model (P) data, and given as: 210

)(1

1i

N

i

i OPNBias −= ∑=

− 211

where N is the sample size, O is the observed value, P is the model value in the station i. 212

The MAE is calculated as the average of the absolute difference between observed and 213

predicted data: 214

∑=

−−=

N

i

ii OPNMAE1

1 215

216

The refined D Willmott statistic is a measure of agreement/disagreement between the 217

observed and modeled data, reflecting not only differences in their means, but also 218

differences in their standard deviations. This coefficient is dimensionless as it is 219

bounded between -1 (no skill) and 1 (perfect skill). According to Willmott et al. (2012), 220

the D statistic is given as: 221

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||1

1

1

∑

∑

=

=

−

−

−=n

i

i

i

n

i

i

OO

OP

D , when 222

∑∑==

−≤−n

i

ii

n

i

i OOOP11

|2|| 223

and 1||

||2

1

1 −

−

−

=

∑

∑

=

=

i

n

i

i

n

i

i

OP

OO

D , when 224

∑∑==

−>−n

i

ii

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i

i OOOP11

|2|| 225

226

3. Results 227

Table 1 shows the results for the regression model fit corresponding to the monthly P, 228

Tmax and Tmin using as predictors the topographic and geographic variables indicated 229

above. The fit is better for maximum and minimum temperature than for precipitation in 230

the majority of months. Minimum temperatures also show higher R2 values than 231

maximum temperature. There are no noticeable seasonal differences in the fit of the 232

different models. 233

Figures 2, 3 and 4 show the spatial distribution of Tmax, Tmin and P, respectively. 234

Precipitation shows strong geographical gradients and noticeable seasonality. The 235

humid season spans from November to March. Maximum precipitation is recorded in 236

north and northwest areas, which correspond to the Amazon basin and the slopes that 237

separate this area from the high elevation Altiplano. The Altiplano receives much less 238

precipitation, which is concentrated in the summer period. Maximum and minimum 239

temperature also show seasonality and noticeable spatial differences, mainly controlled 240

by the relief. 241

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The agreement between observed and estimated values obtained by means of the jack-242

knife approach is high (Figure 5, Table 2). The fit between observed and predicted data 243

is higher for minimum temperature than for maximum temperature. Moreover, 244

precipitation underestimation is observed in some meteorological stations. Bias and 245

MAE values show a clear seasonal pattern, which comes in agreement with 246

precipitation and temperature seasonality. In general precipitation shows a negative bias, 247

which means that predictions tend to underestimate observations, whereas minimum 248

temperature tends to be overestimated. Annual MAE is 141.1 mm for precipitation and 249

1.5 ºC for maximum and 1.6 ºC for minimum temperature. The D statistic, which allows 250

for comparison between months and variables, show high values (D> 0.9 for 251

precipitation between April and November). For temperature the D values are always 252

higher than 0.84, which means a good agreement between the magnitude and spatial 253

distribution of observed and predicted values. Annual D is around 0.9 in the three 254

modeled variables, which provides high confidence to the predicted values regarding to 255

the observed range of these variables. 256

Figure 6 shows the spatial distribution of monthly ETo that exhibits strong spatial 257

differences between neighbor areas are observed as a consequence of the complex 258

relief. The main contrast is recorded between southern and northern slopes during the 259

cold season. In winter, the ETo is mainly controlled by the available radiation, whereas 260

in summer the mean temperature and the daily temperature range play a major role in 261

ETo rates, explaining the reason for the lower topographical control of the ETo during 262

summer. 263

Figure 7 shows the spatial distribution of the climatic water balance (the difference 264

between precipitation and ETo). The different plots show strong seasonal differences 265

between the humid season (November-March), in which positive values are dominant, 266

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and the dry season (April-October), in which negative values are recorded, mainly in the 267

Altiplano region. 268

Climate averages showed in the main Annual Climate Map for Bolivia stresses the 269

strong contrasts of climate in the country and the clear separation between the 270

Amazonian basin (North) and the Altiplano (Southwest), the last one characterized by 271

limiting climate conditions for the development of the natural vegetation and 272

cultivations (i.e., low precipitation, cold temperatures and negative water balances). 273

274

4. Conclusions 275

We have created monthly and annual precipitation and maximum and minimum 276

temperature maps for Bolivia at a spatial resolution of 1-km using regression-based 277

modeling, with topographic and geographic variables as independent variables. The 278

different monthly climate maps have shown good agreement between observations and 279

predictions with an agreement index (D) around 0.9. Mean absolute error for annual 280

values is 141 mm for precipitation and around 1.5 ºC for both maximum and minimum 281

temperature. These errors can be considered low given the high precipitation and 282

temperature ranges in the country (>2000 mm, and >20ºC, for precipitation and 283

temperature, respectively). 284

We have also created climate maps for the atmospheric water demand based on 285

precipitation, temperature and the modeled exoatmospheric solar radiation derived from 286

the topography. The differences in the atmospheric water demand across Bolivia are 287

very important with approximately 2000 1800 mm year-1. In the south and southeast 288

areas the atmospheric water demand is higher than 2000 mm year-1. The behavior of the 289

precipitation and the atmospheric water demand explains that large areas of Bolivia 290

show a strong annual water deficit (>1000 mm year-1) and that humid areas are 291

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restricted to the northern part of the Amazonian basin and the transitional slopes to the 292

Altiplano. The 1-km monthly and annual digital layers of precipitation, maximum and 293

minimum temperature, ETo and climatic water balance are available in ArcGIS 294

ASCIIformat in the web repository of the Spanish National Research Council (CSIC) at 295

https://digital.csic.es/handle/10261/103342. 296

297

Acknowledgements 298

This work has been supported by research projects I-COOP H2O 2013CD0006: “Test 299

multisectorial y actividades demostrativa sobre el potencial desarrollo de sistemas de 300

monitorización de sequías en tiempo real en la región del oeste de Sudamérica” 301

financed by the Spanish National Research Council (CSIC), CGL2011-27574-CO2-02 302

financed by the Spanish Commission of Science and Technology and FEDER, 303

“Demonstration and validation of innovative methodology for regional climate change 304

adaptation in the Mediterranean area (LIFE MEDACC)” financed by the LIFE 305

programme of the European Commission. C. A-M received a postdoctoral fellowship# 306

JCI-2011-10263. A. S-L is supported by the “Secretaria per a Universitats i Recerca del 307

Departament d’Economia i Coneixement, de la Generalitat de Catalunya i del programa 308

Cofund de les Accions Marie Curie del 7è Programa marc d’R+D de la Unió Europea” 309

(2011 BP-B 00078) and the postdoctoral fellowship # JCI-2012-12508. 310

311

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Software 312

MiraMon v6.4 software was used for map modeling and generating the rater layers. 313

ArcGis 9.3 was used to generate the final cartographies. 314

315

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Como Herramienta de Gestión Ambiental, Cuadrat JM, Vicente y SM, Saz MA 358

(eds). Universidad de Zaragoza: Zaragoza; 117–122. 359

Mendivelso, H.A., Camarero, J.J., Gutiérrez, E. and Zuidema, P.A. (2014): Time-360

dependent effects of climate and drought on tree growth in a Neotropical dry 361

forest: Short-term tolerance vs. long-term sensitivity. Agricultural and Forest 362

Meteorology, 188: 13-23. 363

Mestre, O., Domonkos, P., Picard, F., Auer, I., Robin, S., Lebarbier, E., Böm, R., 364

Aguilar, E… (2013): HOMER: a homogenization software - methods and 365

applications. IDOJÁRÁS. Quarterly Journal of the Hungarian Meteorological 366

Service, 117: 47-67. 367

Navarro, G. and Fereira, W., (20049: Zonas de vegetación potencial de Bolivia: una 368

base para el análisis de vacíos de conservación. Rev. Bol. Ecol. 15: 40 pag. 369

New, M., Lister D, Hulme M, Makin I. 2002. A high-resolution data set of surface 370

climate over global land areas. Climate Research 21: 1–25. 371

Ninyerola, M., Pons, X., Roure, J.M. (2000): A methodological approach of 372

climatological modelling of air temperature and precipitation through GIS 373

techniques. International Journal of Climatology 20: 1823-1841 374

Phillips D.L., Dolph J, Marks D (1992) A comparison of geostatistical procedures for 375

spatial analysis of precipitation in mountainous terrain. Agric Meteorol 58:119–376

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Pons, X.. 2006. Manual of miramon. Geographic Information System and Remote 378

Sensing Software. Centre de Recerca Ecol`ogica i Aplicacions Forestals 379

(CREAF): Bellaterra, (http://www.creaf.uab.es/ miramon). 380

Pons, X., Ninyerola, M. (2008): Mapping a topographic global solar radiation model 381

implemented in a GIS and refined with ground data. International Journal of 382

Climatology 28: 1821-1834 383

Seiler, C., Hutjes, R.W.A., Kabat, P. (2013b) Likely ranges of climate change in 384

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in Bolivia, J. Geophys. Res. Biogeosci., 119, 758–775 388

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Environment 68: 99–108. 396

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interpolation methods in the middle Ebro valley (Spain): application to annual 398

precipitation and temperature. Climate Research 24: 161-180. 399

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procedures to map reference evapotranspiration using geographical information 401

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27: 1103-118. 403

Vicente-Serrano, S.M., Beguería, S., Juan I. López-Moreno, Miguel A. García-Vera y P. 404

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Vicente-Serrano S.M., Santiago Beguería, Juan I. López-Moreno, (2010b) A Multi-408

scalar drought index sensitive to global warming: The Standardized Precipitation 409

Evapotranspiration Index – SPEI. Journal of Climate 23: 1696-1718. 410

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Juan I. López-Moreno, José C. González-Hidalgo, Francisco Espejo (2014). 412

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Xu, C.Y., Singh VP. 2001. Evaluation and generalization of temperature based methods 422

for calculating evaporation. Hydrological Processes 15: 305–319. 423

424

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Table 1: Results of the monthly models to map precipitation, maximum and minimum 426

temperature. 427

428

429

430

431

432

433

434

435

436

437

438

439

440

441

442

443

444

445

446

447

448

449

450

451

452

R2 Adjusted R2 f p

Precipitation

January 0.66 0.64 38.9 0.049

February 0.64 0.63 47.2 0.042

March 0.67 0.66 53.5 0.003

April 0.89 0.88 124.4 0

May 0.84 0.83 103.3 0.01

June 0.58 0.57 36.9 0.006

July 0.70 0.67 34.4 0

August 0.64 0.63 48 0.004

September 0.71 0.69 63.8 0.002

October 0.80 0.80 163.2 0

November 0.85 0.95 156.4 0

December 0.75 0.73 57.4 0.003

Maximum temperature

January 0.94 0.94 279.7 0

February 0.94 0.93 274.1 0

March 0.92 0.92 236.5 0

April 0.89 0.89 157.2 0

May 0.86 0.85 222.8 0.002

June 0.85 0.84 68.8 0.002

July 0.87 0.86 83.4 0.001

August 0.90 0.90 334.1 0

September 0.91 0.91 190.9 0

October 0.91 0.91 189.5 0

November 0.91 0.90 177.3 0

December 0.92 0.92 219.0 0

Minimum temperature

January 0.98 0.95 927.9 0

February 0.98 0.95 1757.4 0

March 0.98 0.98 1635.2 0

April 0.98 0.96 1590.6 0

May 0.96 0.96 420.9 0

June 0.91 0.91 192.0 0

July 0.90 0.90 168.7 0.001

August 0.91 0.90 120.5 0

September 0.93 0.93 150.7 0

October 0.94 0.94 543.4 0

November 0.95 0.94 673.6 0

December 0.97 0.96 1073.3 0

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J F M A M J J A S O N D Annual

MA

E

(mm

) precip 25.169 23.990 22.708 11.463 8.346 7.073 5.456 6.382 8.784 11.872 15.337 23.241 141.154

Tmax 1.537 1.453 1.469 1.600 1.528 1.615 1.626 1.548 1.587 1.676 1.714 1.664 1.532

Tmin 0.913 0.920 0.995 1.394 1.968 2.281 2.366 2.309 1.999 1.655 1.495 1.195 1.624

MB

E

(mm

) precip -1.868 -1.848 -1.184 -0.046 -0.225 -0.010 0.129 -1.102 -0.665 -1.061 -1.175 0.794 -8.260

Tmax 0.063 0.045 0.004 -0.072 -0.050 -0.021 -0.006 0.009 0.039 0.031 0.053 0.047 0.012

Tmin 0.136 0.146 0.136 0.221 0.322 0.336 0.320 0.332 0.338 0.308 0.275 0.203 0.256

D

precip 0.749 0.752 0.800 0.947 0.969 0.976 0.982 0.978 0.967 0.944 0.904 0.772 0.901

Tmax 0.889 0.894 0.893 0.883 0.886 0.877 0.877 0.886 0.886 0.883 0.881 0.882 0.889

Tmin 0.938 0.937 0.931 0.900 0.855 0.858 0.862 0.844 0.853 0.882 0.895 0.918 0.889

Table 2: Error/accuracy statistics for the monthly precipitation and temperature maps

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Figure 1. Elevation and meteorological stations available for precipitation and temperature in Bolivia (circles: selected stations; triangles: discarded stations).

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Figure 2: Spatial distribution of monthly maximum temperature (ºC) obtained from regression-based modeling and local interpolation of residuals.

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Figure 3: Spatial distribution of monthly minimum temperature (ºC) obtained from regression-based modeling and local interpolation of residuals.

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Figure 4: Spatial distribution of monthly precipitation (mm) obtained from regression-based modeling and local interpolation of residuals.

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Figure 5: Relationship between observed and predicted monthly precipitation and temperature using the jacknife approach.

January

50 100 150 200 250 300 350P

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Dec.

10 15 20 25 30 35 40

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January

-5 0 5 10 15 20 25

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-5

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February

-5 0 5 10 15 20 25

-5

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Observed-15 -10 -5 0 5 10 15 20

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Observed-15 -10 -5 0 5 10 15 20

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PR

EC

IPIT

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ION

(m

m)

MA

XIM

UM

TE

MP

. (º

C)

MIN

IMU

M T

EM

P. (º

C)

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Figure 6: Spatial distribution of monthly ETo (mm) obtained from maximum and minimum temperature and the relief based-modeled solar radiation

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Figure 7: Spatial distribution of monthly climatic water balance (mm) by means of the difference between precipitation and ETo

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´

Oruro

Sucre

Cobija

Camiri

Tarija

Potosí

La Paz

Montero

Trinidad

Riberalta

Santa Cruz

Lllallagua

Cochabamba

Annual Climate Maps for Bolivia

PRECIPITATION (mm)

WATERBALANCE(mm)

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REFERENCEEVAPOTRANSPIRATION (mm)

Geographic Coordinate System: WGS84

0 200 400 600 800100Kilometers

Cell size for gridded data: 1 km.

0

Elevation (m):6000

3000

4500

1500

Meteorological station:

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Precip/Temp.

Precip.

S.M. Vicente-Serrano1, A. El Kenawy2,3, C. Azorin-Molina1, O. Chura4, F. Trujillo4, E. Aguilar5, N. Martín-Hernández1, J.I. López-Moreno1, A. Sanchez-Lorenzo1, E. Moran-Tejeda1, J. Revuelto1, P. Ycaza6, F. Friend6

1Instituto Pirenaico de Ecología, Consejo Superior de Investigaciones Científicas (IPE–CSIC), Zaragoza, Spain, 2Hydrological Modeling & Earth Observation Group, Saudi Arabia, 3Department of Geography, Mansoura University, Mansoura, Egypt, 4Servicio Nacional de Meteorología (SENAMHI), La Paz, Bolivia, 5Universidad Rovira i Virgili, Tarragona, Spain, 6Centro Internacional para la Investigación del Fenómeno de El Niño (CIIFEN), Guayaquil, Ecuador.

MAXIMUMTEMPERATURE (ºC)

MINIMUMTEMPERATURE (ºC)

0 250 500 750 1,000125Kilometers

Main citiesRoadsRivers

< -1300

-1100

-900

-700

-500

-300

-100

100

300

500

> 700

< 800

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1200

1400

1600

1800

2000

2200

2400

> 2600

< 200

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600

800

1000

1200

1400

1600

1800

> 2000

< 14

> 32

30

2826

24

22

20

18

16

< -1

17

15

13

11

9

7

5

3

1

> 21

19

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