Relationship of weather types on the seasonal and spatial variability of rainfall, runoff, and sediment yield in the western Mediterranean basin

atmosphere Article Relationship of Weather Types on the Seasonal and Spatial Variability of Rainfall, Runoff, and Sediment Yield in the Western Mediterranean Basin D. Peña-Angulo 1,*, E. Nadal-Romero 1, J.C. González-Hidalgo 2, J. Albaladejo 3, V. Andreu 4, H. Bahri 5, S. Bernal 6, M. Biddoccu 7, R. Bienes 8, J. Campo 4, M.A. Campo-Bescós9, A. Canatário-Duarte 10, Y. Cantón11,12 , J. Casali 9, V. Castillo 3, E. Cavallo 7, A. Cerdà13 , P. Cid 14 , N. Cortesi 15, G. Desir 16 , E. Díaz-Pereira 3, T. Espigares 17, J. Estrany 18 , J. Farguell 19, M. Fernández-Raga 20 , C.S. Ferreira 21 , V. Ferro 22, F. Gallart 23, R. Giménez 9, E. Gimeno 4, J.A. Gómez 24, A. Gómez-Gutiérrez 25 , H. Gómez-Macpherson 24, O. González-Pelayo 26 , O. Kairis 27, G.P. Karatzas 28, S. Keesstra 29,30 , S. Klotz 31, C. Kosmas 27, N. Lana-Renault 32 , T. Lasanta 1, J. Latron 23, R. Lázaro 33 , Y. Le Bissonnais 34, C. Le Bouteiller 31, F. Licciardello 35 , J.A. López-Tarazón36,37 , A. Lucía38 , V.M. Marín-Moreno 39, C. Marín16, M.J. Marqués40 , J. Martínez-Fernández 41 , M. Martínez-Mena 3, L. Mateos 24, N. Mathys 31, L. Merino-Martín42,43 , M. Moreno-de las Heras 23,44 , N. Moustakas 27, J.M. Nicolau 45 , V. Pampalone 46, D. Raclot 34, M.L. Rodríguez-Blanco 47, J. Rodrigo-Comino 13,48 , A. Romero-Díaz 49 , J.D. Ruiz-Sinoga 50 , J.L. Rubio 4, S. Schnabel 25, J.M. Senciales-González 50, A. Solé-Benet 33, E.V. Taguas 39, M.T. Taboada-Castro 51, M.M. Taboada-Castro 52, F. Todisco 53 , X. Úbeda 19 , E.A. Varouchakis 28, L. Wittenberg 54, A. Zabaleta 55 and M. Zorn 56 1Instituto Pirenaico de Ecología, IPE-CSIC, 22700 Zaragoza, Spain; [email protected] (E.N.-R.); [email protected] (T.L.) 2Departamento de Geografía, Universidad de Zaragoza, 50009 Zaragoza, Spain; [email protected] 3Soil and water conservation research group, CEBAS-CSIC, 30100 Murcia, Spain; [email protected] (J.A.); [email protected] (V.C.); [email protected] (E.D.-P.); [email protected] (M.M.-M.) 4Department of Environmental Quality and Soils, Desertification Research Centre-CIDE (CSIC, UV, GV), Moncada, 46113 Valencia, Spain; [email protected] (V.A.); [email protected] (J.C.); [email protected] (E.G.); [email protected] (J.L.R.) 5National Research Institute for Rural Engineering, Water, and Forestry (INRGREF), Carthage University, Carthage 1054, Tunisia; [email protected] 6Integrative Freshwater Ecology Group, Centre d’Estudis Avançats de Blanes (CEAB-CSIC), 17300 Blanes, Girona, Spain; [email protected] 7Institute for Agricultural and Earthmoving Machines (IMAMOTER), National Research Council of Italy (CNR), 10135 Torino, Italy; [email protected] (M.B.); [email protected] (E.C.) 8Departamento Investigación Aplicada y Extensión Agraria, Instituto Madrileño de Investigación y Desarrollo Rural, Agrario y Alimentario (IMIDRA), 28800 Madrid, Spain; [email protected]g 9Department of Engineering, ISFOOD Institute, Public University of Navarre, 31006 Pamplona, Spain; [email protected] (M.A.C.-B.); [email protected] (J.C.); [email protected] (R.G.) 10 Research Center for Natural Resources, Environment and Society (CERNAS), Polytechnic Institute of Castelo Branco, School of Agriculture, Castelo Branco, Portugal, Research Center GEOBIOTEC, UBI, 6201 Covilhã, Portugal; [email protected] 11 Department of Agronomy (Soil Science Area), University of Almeria. Engineering High School, 04120 Almeria, Spain; [email protected] 12 Centro de Investigación de Colecciones Científicas de la Universidad de Almería (CECOUAL), University of Almería, 04120 Almeria, Spain 13 Soil Erosion and Degradation Research Group, Department of Geography, University of Valencia, 46010 Valencia, Spain; [email protected] (A.C.); [email protected] (J.R.-C.) 14 Fall Creek Farm & Nursery, Tala 45300, Mexico; [email protected] 15 Department of Earth Sciences, Centro Nacional de Supercomputación, 08034 Barcelona, Spain; [email protected] Atmosphere 2020,11, 609; doi:10.3390/atmos11060609 www.mdpi.com/journal/atmosphere Atmosphere 2020,11, 609 2 of 20 16 Departamento de Ciencias de la Tierra, Universidad de Zaragoza, 50009 Zaragoza, Spain; [email protected] (G.D.); [email protected] (C.M.) 17 Departamento de Ciencias de la Vida, Unidad de Ecolog í a, Universidad de Alcal á , 28801 Alcal á de Henares, Madrid, Spain; mtiscar[email protected] 18 Department of Geography, Institute of Agroenvironmental and Water Economy Research -INAGEA, University of the Balearic Islands, 07122 Palma, Spain; [email protected] 19 Environmental Mediterranean Research Group (GRAM), Departament of Geography, University of Barcelona, 08007 Barcelona, Spain; [email protected] (J.F.); [email protected] (X.Ú.) 20 Department of Physics, IMARENAB, University of Leon, 24071 Leon, Spain; [email protected] 21 Research Center for Natural Resources, Environment and Society (CERNAS), Polytechnic Institute of Coimbra, Agrarian School of Coimbra, 1349 Coimbra, Portugal; [email protected] 22 Department of Earth and Marine Science, University of Palermo, 90133 Palermo, Italy; [email protected] 23 Surface Hydrology and Erosion group, Institute of Environmental Assessment and Water Research (IDAEA-CSIC), 08034 Barcelona, Spain; [email protected] (F.G.); jerome.latr[email protected] (J.L.); [email protected] (M.M.-d.l.H.) 24 Instituto de Agricultura Sostenible (CSIC), 14004 Córdoba, Spain; [email protected] (J.A.G.); [email protected] (H.G.-M.); [email protected] (L.M.) 25 INTERRA Research Institute, University of Extremadura, 06006 Cáceres, Spain; [email protected] (A.G.-G.); [email protected] (S.S.) 26 Department of Environment and Planning, Earth Surface Processes Team (ESP) Centre for Environmental and Marine Studies (CESAM), University of Aveiro Campus Universitario de Santiago, 3810 Aveiro, Portugal; [email protected] 27 Department of Natural Resources Management and Agricultural Engineering, Agricultural University of Athens, 3800 Athens, Greece; [email protected] (O.K.); [email protected] (C.K.); [email protected] (N.M.) 28 School of Environmental Engineering, Technical University of Crete, 70013 Chania, Greece; [email protected] (G.P.K.); [email protected] (E.A.V.) 29 Team Soil Water and Land Use, Wageningen Environmental Research, Wageningen UR, 6708 Wageningen, The Netherlands; [email protected] 30 Civil, Surveying and Environmental Engineering, The University of Newcastle, Callaghan 2308, Australia 31 University of Grenoble, INRAE, UR ETNA, 38000 Grenoble, France; [email protected] (S.K.); [email protected] (C.L.B.); [email protected] (N.M.) 32 Area of Geography, DCH, University of La Rioja, 26006 Logroño, Spain; [email protected] 33 Experimental Station of Arid Zones, EEZA-CSIC, 04120 Almería, Spain; [email protected] (R.L.); [email protected] (A.S.-B.) 34 LISAH, University Montpellier, INRAE, IRD, Institut Agro, 34 090 Montpellier, France; [email protected] (Y.L.B.); [email protected] (D.R.) 35 Department of Agriculture, Food and Environment, University of Catania, 95124 Catania, Italy; [email protected] 36 Fluvial Dynamics Research Group—RIUS, University of Lleida, 25003 Lleida, Spain; [email protected] 37 Institute for Environmental Science and Geography, University of Potsdam, 14476 Potsdam, Germany 38 Center for Applied Geosciences, Faculty of Science, Eberhard Karls Universität Tübingen, 72074 Tübingen, Germany; [email protected] 39 Departamento de Ingenier í a Rural, University of Cordoba—ETSIAM Campus Rabanales, Leonardo Da Vinci Building, 14014 Córdoba, Spain; [email protected] (V.M.M.-M.); [email protected] (E.V.T.) 40 Departamento de Geología y Geoquímica, Universidad Autónoma de Madrid, 28049 Madrid, Spain; [email protected] 41 Instituto Hispano Luso de Investigaciones Agrarias, University of Salamanca, 37008 Villamayor, Spain; [email protected] 42 University of Montpellier, AMAP, INRAE, CIRAD, CNRS, IRD, PS2 TA/A51, 34 398, Montpellier CEDEX 5, France; [email protected] 43 CEFE, University Montpellier, CNRS, EPHE, IRD, University Paul Valéry Montpellier 3, 34 398, Montpellier, France 44 Department of Ecology, Desertification Research Centre-CIDE (CSIC, UV, GV), Moncada, 46113 Valencia, Spain Atmosphere 2020,11, 609 3 of 20 45 Department of Agriculture and Environmental Sciences, University of Zaragoza, 50009 Zaragoza, Spain; [email protected] 46 Department of Agriculture, Food and Forest Sciences, University of Palermo, 90133 Palermo, Italy; [email protected] 47 History, Art and Geography Department, GEAAT Group, University of Vigo, Campus as Lagoas, 36310 Ourense, Spain; mr[email protected] 48 Physical Geography, Trier University, 54296 Trier, Germany 49 Departamento de Geografía, Universidad de Murcia, Campus de La Merced, 30100 Murcia, Spain; [email protected] 50 Department of Geography, University of Málaga, 29016 Málaga, Spain; [email protected] (J.D.R.-S.); [email protected] (J.M.S.-G.) 51 Faculty of Sciences, Centre for Advanced Scientific Research (CICA), University of A Coruna, 15001 A Coruña, Spain; [email protected] 52 ETSIIAA, Area of Soil Science and Soil Chemistry, University of Valladolid, 47002 Palencia, Spain; [email protected] 53 Agricultural and Biosystems Engineering Research Unit, Department of Agriculture-Food and Environmental Sciences, University of Perugia, 06121 Perugia, Italy; [email protected] 54 Department of Geography and Environmental Studies, University of Haifa, Haifa 3498838, Israel; [email protected] 55 Hydro-Environmental Processes Research Group, Science and Technology Faculty, University of the Basque Country, Leioa, Basque Country, 48940 Leioa, Spain; [email protected] 56 Geographical Institute, Research Centre of the Slovenian Academy of Sciences and Arts, 1000 Ljubljana, Slovenia; [email protected] *Correspondence: [email protected] Received: 8 May 2020; Accepted: 31 May 2020; Published: 9 June 2020   Abstract: Rainfall is the key factor to understand soil erosion processes, mechanisms, and rates. Most research was conducted to determine rainfall characteristics and their relationship with soil erosion (erosivity) but there is little information about how atmospheric patterns control soil losses, and this is important to enable sustainable environmental planning and risk prevention. We investigated the temporal and spatial variability of the relationships of rainfall, runoff, and sediment yield with atmospheric patterns (weather types, WTs) in the western Mediterranean basin. For this purpose, we analyzed a large database of rainfall events collected between 1985 and 2015 in 46 experimental plots and catchments with the aim to: (i) evaluate seasonal differences in the contribution of rainfall, runoff, and sediment yield produced by the WTs; and (ii) to analyze the seasonal efficiency of the different WTs (relation frequency and magnitude) related to rainfall, runoff, and sediment yield. The results indicate two different temporal patterns: the first weather type exhibits (during the cold period: autumn and winter) westerly flows that produce the highest rainfall, runoff, and sediment yield values throughout the territory; the second weather type exhibits easterly flows that predominate during the warm period (spring and summer) and it is located on the Mediterranean coast of the Iberian Peninsula. However, the cyclonic situations present high frequency throughout the whole year with a large influence extended around the western Mediterranean basin. Contrary, the anticyclonic situations, despite of its high frequency, do not contribute significantly to the total rainfall, runoff, and sediment (showing the lowest efficiency) because of atmospheric stability that currently characterize this atmospheric pattern. Our approach helps to better understand the relationship of WTs on the seasonal and spatial variability of rainfall, runoffand sediment yield with a regional scale based on the large dataset and number of soil erosion experimental stations. Keywords: weathertypes; rainfall; runoff; erosion; sediment yield; seasonalanalyses; Mediterraneanbasin Atmosphere 2020,11, 609 4 of 20 1. Introduction It is well-known that there is a close relationship between atmospheric circulation and climatic variables (i.e., precipitation, snow accumulation, temperature) [ 1 – 3 ]. In addition, precipitation variability is a recognized characteristic of Mediterranean environments [ 4 ] and the relationship between rainfall and atmospheric circulation, and its spatial and temporal variability have been widely studied [ 5 , 6 ]. Also, recent research has achieved promising results in the relationship among atmospheric pattern, runoff, and sediment yield [7–9]. The studies of rainfall variability and its causes are of particular interest for hydrology and soil erosion research, because rainfall patterns directly affect runoffand soil erosion and its temporal distribution [ 10 ]. There are several studies that analyzed the relationships between rainfall and flood generation, runoff, erosion processes, and sediment yield [ 11 – 13 ], but few studies focused on their relationship with the atmospheric circulation patterns, being one of the leading controlling causes. Caspary [ 14 ] analyzed the relationships between the occurrence of floods in southwest Germany and westerly circulation patterns. Another example is the study by Quinn and Wilby [ 15 ] that analyzed the relationships between weather types and variations in multi-decadal floods in England, Scotland, and Wales since the 1870s. Recently, Mountreuil et al. [ 16 ] and Tylkowski [ 17 ] examined the storm surge events associated with erosion and its relationships with weather types. In the Mediterranean region, different studies have analyzed WTs and environmental variables. Kostopoulou and Jones [ 18 ] investigated the relationships between atmospheric circulation patterns and surface climatic elements (temperature and precipitation) in the eastern Mediterranean basin. Fernandez-Raga et al. [ 19 ] studied the relation between the kinetic energy and other rainfall characteristic with the WTs in forest plantation in northern central Portugal. Furthermore, Grimalt et al. [ 20 ] determined a temporal analysis of the weather types for the western Mediterranean basin over the 1948–2009 period. On the other hand, Roy é et al. [ 21 ] focused on the spatial and temporal patterns to cloud-to-ground lightning related to the circulation weather types over the northwest Iberian Peninsula. More specific research analyzing the synoptic situations associated with flood episodes were presented by Llasat et al. [ 22 ] in Catalonia (Spain) between 1840–1870. Also, Rodrigo-Comino et al. [ 23 ] focused on the identification of which WTs were associated to rainfall events were able to generate specific surface flows and soil loss rates in Málaga (Spain). However, despite the fact that the marked seasonal variability of rainfall regime in Mediterranean areas clearly determines the hydrological and erosion response, few studies have been carried out to define these temporal patterns and the relationships between atmospheric conditions and the hydrological response. Gilabert and Llasat [ 24 ] analyzed the circulation weather types associated with extreme floods in Catalonia (North-eastern Spain) and established their temporal patterns. The previous study shows that most synoptic situations were pure cyclonic structures, in both extraordinary and catastrophic events in Catalonia. Lastly, there are scarce contributions about the efficiency of the WTs related to rainfall events, hydrological responses, and sediment transport particularly at seasonal scale. For the Iberian Peninsula, Nadal-Romero et al. [ 7 ] calculated the efficiency of the WTs in sediment transport by means of magnitude-frequency analyses, i.e., ‘work done’ in physical concept (see the classical contribution of Wolman and Miller [ 25 ], Thornes and Brunsden [ 26 ], Wolman and Gerson [ 27 ], and Thorn [ 28 ]), and found that the most efficient WTs in sediment production were westerly flows, although spatial differences were identified. Following the previous paragraphs, the main objective of this study was to analyze the seasonal variability of the relationships between rainfall, runoff, and sediment yield (SY, used to refer to erosion depths at a plot scale and sediment yield at a catchment scale) with weather types (WTs) in the western Mediterranean basin, specifically Portugal, Spain, and south France areas. The specific objectives were: (i) to detect the seasonal contribution of the different WTs in the magnitude of rainfall, runoff, and SY in the study sites; and (ii) to analyze the seasonal efficiency of the different WTs to result in rainfall event, hydrological responses and sediment production based on the relation frequency and magnitude of Atmosphere 2020,11, 609 5 of 20 rainfall, runoffand SY, respectively. We hypothesized that: (i) there is a temporal (i.e., seasonal) and spatial pattern in the relationships between rainfall, runoffand SY with WTs in western Mediterranean basin; and (ii) these relationships vary seasonally and differ among rainfall, runoff, and SY, as well as the seasonal efficiency in terms of rainfall, runoffgeneration, and sediment fluxes of the different WTs. More specifically, we expect that some WTs, despite their high frequency, will contribute little to the generation of rainfall, runoff, and SY (i.e., they are not very effective), while other (low-frequency) WTs will show a very effective contribution to rainfall runoffand SY. We also expect that the efficiency of the WTs will be affected by season. To a certain extent, this study provides seasonal analyses of previous Mediterranean analyses regarding the relationships of hydrological and sediment response to different WTs in the Mediterranean region by Nadal-Romero et al. [7] and Peña-Angulo et al. [8]. 2. Experiments 2.1. Data A database of rainfall events with hydrological and SY information was compiled from a network of experimental plots and catchments (<50 km 2 ) in the Mediterranean basin. From the database compiled by Peña-Angulo et al. [ 8 ], only those study sites with at least 3 years of data were included in this study (Table 1). In that sense, this investigation is focused on 8245 rainfall events obtained from 46 sites (29 catchments and 17 plots) from Portugal, Spain, and France during 1985–2015 period (Table 1). This dataset has involved the collaboration of many researchers, which have allowed us to compile the most extensive collection of real rainfall, surface hydrology and SY in Mediterranean basin at plot and catchment scales (for more details see Peña-Angulo et al. [8]). The WTs classification relies on the daily sea level pressure dataset from NCEP/NCAR 40-year Reanalysis Project [ 29 ]. We used the WTs classification proposed by Jenkinson and Collinson [ 30 ], based on the original work of Lamb [ 31 ], following the approach suggested by Jones et al. [ 32 ], and Trigo and DaCamara [ 33 ] for the Iberian Peninsula. Furthermore, the 26 WTs of the original classification defines directional: north (N), northeast (NE), east (E), southeast (SE), south (S), southwest (SW), west (W), and northwest (NW); two WTs dominates by the strength of vorticity: anticyclonic (A) and cyclonic (C); and hybrid types (eight for each C or A). However, we decided to follow the methodology explained by Cortesi et al. [ 5 ] and Nadal et al. [ 6 ], aggregated in 10 classes. WTs were aggregated into 10 types, the original (A and C), and the combination of pure directional types with hybrid types accordingly to wind direction (NE, E, SE, S, SW, W, NW, and N). Table 1. Location of study sites (catchments and plots), data collection period (start and end), number of records, and reference of the study sites included in the database ID Name Lat. Long. Scale Start End Records Reference 1 Aisa 42.6744 −0.6119 Plots 1995 2010 637 Nadal-Romero et al. [34] 2 Aixola 43.1529 −2.5014 Catch. 2003 2008 222 Zabaleta et al. [35] 3 Albaladejito 40.0762 −2.1957 Plots 1994 1997 28 Bienes et al. [36,37] 4 Araguás 42.5958 −0.6208 Catch. 2005 2015 360 Nadal-Romero and Regüés [38] 5 Aranjuez 40.0798 −3.5250 Plots 1994 1997 38 Bienes et al. [36,37] 6 Abanilla 38.1994 −1.0917 Plots 1988 1992 40 Díaz et al. [39] 7 Ardal 38.0741 −1.5383 Plots 1989 2000 146 Romero-Díaz et al. [40] 8 Arnas 42.6430 −0.5847 Catch. 1999 2009 96 Lana-Renault et al. [41] 9 Bardenas Norte 42.1677 −1.4547 Plots 1993 2004 118 Desir and Marín [42] 10 Bardenas Sur 42.1550 −1.4191 Plots 1993 2004 89 Desir and Marín [42] 11 Burete 38.0500 −1.7667 Plots 2006 2011 142 Martínez-Mena et al. [43] 12 Can Revull 39.5500 3.1011 Catch. 2004 2007 19 Estrany et al. [44] 13 Corbeira 43.2181 −8.2285 Catch. 2005 2014 651 Rodríguez-Blanco et al. [45] Atmosphere 2020,11, 609 6 of 20 Table 1. Cont. ID Name Lat. Long. Scale Start End Records Reference 14 El Cautivo 37.0027 −2.4404 Catch. 1992 2014 134 Cantón et al. [46] 15 Idanha 39.8467 −7.1667 Catch. 2010 2015 27 Canatario-Duarte [47] 16 La Conchuela 37.8178 −4.8958 Catch. 2006 2011 185 Gómez et al. [48] 17 La Concordia 39.7500 −0.7167 Plots 1995 2012 203 Gimeno-García et al. [49] 18 La Parrilla 37.7333 −5.1500 Catch. 2010 2013 74 Cid et al. [50] 19 La Puebla 41.6645 −0.7239 Plots 1991 2003 187 Desir et al. [51] 20 La Tejeria 42.7363 −1.9492 Catch. 2000 2014 177 Casali et al. [52] 21 Lanaja 41.7797 −0.2889 Plots 1991 2004 163 Sirvent et al. [53] 22 Latxaga 42.7854 −1.4364 Catch. 2003 2014 189 Casali et al. [52] 23 Laval 44.1406 5.6392 Catch. 1985 2014 465 Cambon et al. [54] 24 Marchamalo 40.6822 −3.2147 Plots 1994 1997 48 Bienes et al. [36,37] 25 Mediana 41.4534 −0.7158 Plots 1991 2004 137 Desir et al. [51] 26 Morille 40.8315 −5.7053 Catch. 2002 2010 88 Hernández-Santana and Martínez [55] 27 Moulin 44.1406 5.6392 Catch. 1988 2003 149 Cambon et al. [54] 28 Munilla 42.1912 −2.2908 Catch. 2012 2015 17 Lana-Renault et al. [56] 29 Oskotz 42.9584 −1.7792 Catch. 2003 2014 416 Casali et al. [57] 30 Porta Coeli 39.6590 −0.4890 Plots 1988 2012 240 Andreu et al. [58] 31 Puente Genil 37.4128 −4.8383 Catch. 2005 2011 93 Taguas et al. [59] 32 Rinconada 40.6003 −6.0367 Catch. 2000 2010 331 Hernández-Santana and Martínez [55] 33 Roujan 43.4917 3.3213 Catch. 1992 2015 410 Molénat et al. [60] 34 Santomera 38.2700 −1.1167 Plots 1989 2002 283 Martínez-Mena et al. [61] 35 Sa Vall 39.6386 3.1766 Catch. 2004 2006 77 Estrany et al. [62] 36 Setenil 36.8736 −5.1269 Catch. 2005 2011 121 Taguas et al. [63] 37 Venta Olivo 38.3544 −1.5194 Catch. 1997 2011 108 Castillo et al. [64] 38 Venta Olivo plot 38.3833 −1.1667 Plots 2001 2008 161 Boix-Fayos et al. [65] 39 Vernega Bosc 41.8772 2.9325 Catch. 1993 2011 44 Outeiro et al. [66] 40 Vernega Campas 41.8738 2.9213 Catch. 1993 2011 44 Outeiro et al. [66] 41 Villamor 41.2457 −5.5839 Catch. 2002 2010 87 Martínez Fernádez et al. [67] 42 Navalón 38.9166 −0.8333 Plots 2004 2014 470 Cerdàet al. [68] 43 Ca L’Isard 42.1934 1.8232 Catch. 2005 2012 55 Latron et al. [69] 44 Can Vila 42.1981 1.8234 Catch. 2005 2012 93 Latron et al. [70] 45 Parapuños 39.6105 −6.1333 Catch. 2001 2015 161 Schnabel and Gómez Gutiérrez [71] 46 Montnegre 41.7000 2.5666 Catch. 1998 2002 77 Bernal and Sabater [72] 2.2. Method The analyses of the temporal variability of the three study variables (rainfall, runoff, and SY) due to the WTs, was done seasonally considering classical monthly aggregation (winter: December, January, and February; spring: March, April, May; summer: June, July, August; and autumn: September, October, November). Each of the 8245 daily events was associated with WTs for individual sites. For each site and daily events, the percentage of rainfall, runoffand SY produced was estimated over the whole period, and we obtained the total percentage in each season for each study site (see Annex Table 1for more details). First, we checked seasonal differences in the percentage of rainfall, runoff and SY using boxplot and we applied Wilcoxon signed-rank test [ 73 ] between seasons for each study variable. After, we analyzed the spatial distribution of rainfall, runoffand SY in each season with maps and we obtained around 25% of the sites in each study variable. The value 25% represents a uniform distribution over the four seasons. Secondly, to identify temporal patterns of the relationships between rainfall, runoff, and SY with WTs, we calculated in each site the seasonal percentage of rainfall, runoffand SY produced by each WTs. The statistical distribution of the percentage of rainfall, runoff, and SY was represented in the boxplots. Then, the analysis of the spatial variability was carried out by Atmosphere 2020,11, 609 7 of 20 mapping the percentage of rainfall, runoffand SY below and above 2.5% for the different WTs per each season and study site. The 2.5% value represents the uniform distribution of 10 WTs and four seasons. We applied three specific analyses to detect the most efficient WTs in terms of generating the largest contribution (% of magnitude) of rainfall, runoffand SY in each season. First, we checked whether the frequency of rainfall events during the study period at each site responds to the frequency of the WTs with the aim to verify if the contribution of the WTs to rainfall were due to the frequency of the WTs or due to another cause (i.e., direction of the wind). This analysis was done by comparing the frequency of the WTs in the reference climate period (1981–2010, 30 years) with the frequency of the rainfall events in the study period (1985–2015, 30 years). Secondly, we obtained the frequency of the WTs in each season with the major contribution, more than 5% of magnitude of rainfall, runoff, and SY. Finally, we calculated an efficiency index in each study sites, following the methodology used in Nadal-Romero et al. [ 7 ]. The index is defined as the product of the contribution to rainfall, runoff, and sediment yield (% of magnitude) and frequency of rainfall events, hydrological responses and sediment production in the study period. High values for the index represent high efficiency of WTs, and low values mean WTs less efficiency. We obtained the mean of the efficient index in each season, WTs, and study variable. All analyses and figures were carried out using R software (R, version 3.2.3) [74]. 3. Results 3.1. Temporal Relationships between Rainfall, Runoff, and Sediment Yield with Weather Types The seasonal percentage of rainfall, runoffand SY is shown in the boxplots presented in Figure 1. Rainfall peaks in winter and autumn and shows the smallest contribution in summer. Rainfall shows significant differences between seasons, except between winter and autumn and for winter and spring. Runoffoccurred mostly during winter and autumn, which, on average, accounted for more than 50% of the total runoff. Yet, the seasonal percentage of runoffwas highly variable across sites, especially in winter and autumn, as indicated by the long whiskers of the box plots that could range from 0 to 80%. Runoffshows significant differences between seasons, except between winter and autumn. SY variability is very large for all the seasons and no significant differences were observed between the seasons, only occur between summer and autumn. The median SY percentage values of the seasons reach the highest contribution in autumn, followed by spring, winter, and summer. Atmosphere 2020, 11, x FOR PEER REVIEW 7 of 20 3. Results 3.1. Temporal Relationships between Rainfall, Runoff, and Sediment Yield with Weather Types The seasonal percentage of rainfall, runoff and SY is shown in the boxplots presented in Figure 1. Rainfall peaks in winter and autumn and shows the smallest contribution in summer. Rainfall shows significant differences between seasons, except between winter and autumn and for winter and spring. Runoff occurred mostly during winter and autumn, which, on average, accounted for more than 50% of the total runoff. Yet, the seasonal percentage of runoff was highly variable across sites, especially in winter and autumn, as indicated by the long whiskers of the box plots that could range from 0 to 80%. Runoff shows significant differences between seasons, except between winter and autumn. SY variability is very large for all the seasons and no significant differences were observed between the seasons, only occur between summer and autumn. The median SY percentage values of the seasons reach the highest contribution in autumn, followed by spring, winter, and summer. Figure 1. Seasonal distribution of the percentage of rainfall, runoff, and sediment yield. The ends of the box are the upper and lower quartiles, so the box spans the interquartile range, the median is marked by a vertical line inside the box, and the whiskers are the two lines outside the box that extend to the highest and lowest observations. The significant value at p < 0.05 (***) and not significance (-) of Wilcoxon signed-rank test are paired remarked between winter and spring, winter and summer, winter and autumn, spring and summer, spring and autumn, and summer and autumn. Figure 2 shows the spatial distribution of the seasonal percentages of rainfall, runoff, and SY, below and above 25% of annual value. Sites with >25% rainfall in winter, spring and autumn show a fairly homogeneous spatial distribution, while on the contrary in summer, high rainfall contribution values are only reached in the northeastern inland region. Runoff follows a similar pattern in sites with >25% contribution in winter, spring and autumn, while in summer high values are only reached in the eastern area. Last, SY distribution shows less spatial coherence and high seasonal spatial variability: winter and spring show a fairly homogeneous spatial distribution, while for summer and autumn sites with >25% are mostly located in the eastern area. Figure 1. Seasonal distribution of the percentage of rainfall, runoff, and sediment yield. The ends of the box are the upper and lower quartiles, so the box spans the interquartile range, the median is marked by a vertical line inside the box, and the whiskers are the two lines outside the box that extend to the highest and lowest observations. The significant value at p<0.05 (***) and not significance (-) of Wilcoxon signed-rank test are paired remarked between winter and spring, winter and summer, winter and autumn, spring and summer, spring and autumn, and summer and autumn. Atmosphere 2020,11, 609 8 of 20 Figure 2shows the spatial distribution of the seasonal percentages of rainfall, runoff, and SY, below and above 25% of annual value. Sites with >25% rainfall in winter, spring and autumn show a fairly homogeneous spatial distribution, while on the contrary in summer, high rainfall contribution values are only reached in the northeastern inland region. Runofffollows a similar pattern in sites with >25% contribution in winter, spring and autumn, while in summer high values are only reached in the eastern area. Last, SY distribution shows less spatial coherence and high seasonal spatial variability: winter and spring show a fairly homogeneous spatial distribution, while for summer and autumn sites with >25% are mostly located in the eastern area. Atmosphere 2020, 11, x FOR PEER REVIEW 8 of 20 Figure 2. Spatial distribution of the percentage of rainfall, runoff, and sediment yield for each season in the study sites, and the number of sites with values higher or lower than 25%. The seasonal percentage contribution of rainfall, runoff and SY by different WTs are shown in Figure 3. The WTs with more generalized high percentage contribution are C (cyclonic) and W (west) types. The C type contributes to the three variables in all the seasons, reaching the lowest contributions in summer SY. The westerly types (W, NW, and SW) present a high contribution in rainfall and runoff for winter, spring, and autumn, and to a lesser extent in summer SY. The easterly types (E, NE, and SE) have a high contribution in summer rainfall and runoff, and also, in relative terms, in summer SY. North WT shows a high contribution for rainfall in spring and autumn, and for runoff and SY in spring. South WT contribution is mainly concentrated in spring in rainfall, runoff, and SY. Figure 3. Seasonal distribution of the percentage of rainfall, runoff and sediment yield for the different WTs by each season. The ends of the box are the upper and lower quartiles, so the box spans the Figure 2. Spatial distribution of the percentage of rainfall, runoff, and sediment yield for each season in the study sites, and the number of sites with values higher or lower than 25%. The seasonal percentage contribution of rainfall, runoffand SY by different WTs are shown in Figure 3. The WTs with more generalized high percentage contribution are C (cyclonic) and W (west) types. The C type contributes to the three variables in all the seasons, reaching the lowest contributions in summer SY. The westerly types (W, NW, and SW) present a high contribution in rainfall and runoff for winter, spring, and autumn, and to a lesser extent in summer SY. The easterly types (E, NE, and SE) have a high contribution in summer rainfall and runoff, and also, in relative terms, in summer SY. North WT shows a high contribution for rainfall in spring and autumn, and for runoffand SY in spring. South WT contribution is mainly concentrated in spring in rainfall, runoff, and SY. The seasonal percentage of rainfall, runoff, and SY produced under WTs shows a high spatial variability (Figure S1: rainfall, Figure S2: runoff, Figure S3: SY). Figure 4is an example and shows the spatial distribution of rainfall, runoff, and SY below and above 2.5% under W and E types. In winter, and also in spring and autumn, westerly WTs produce preferably the highest rainfall and runoffvalues in the central-western areas, being no so clear for the SY response. Contrarily, easterly WTs predominate in summer, especially in the northeast and eastern part of the Iberian Peninsula. In general, the seasonal percentage of SY distribution accordingly to WTs is more heterogeneous than the observed in rainfall and runoff, suggesting a more complex relationship between SY and WTs than rainfall or runoff. Atmosphere 2020,11, 609 9 of 20 Atmosphere 2020, 11, x FOR PEER REVIEW 8 of 20 Figure 2. Spatial distribution of the percentage of rainfall, runoff, and sediment yield for each season in the study sites, and the number of sites with values higher or lower than 25%. The seasonal percentage contribution of rainfall, runoff and SY by different WTs are shown in Figure 3. The WTs with more generalized high percentage contribution are C (cyclonic) and W (west) types. The C type contributes to the three variables in all the seasons, reaching the lowest contributions in summer SY. The westerly types (W, NW, and SW) present a high contribution in rainfall and runoff for winter, spring, and autumn, and to a lesser extent in summer SY. The easterly types (E, NE, and SE) have a high contribution in summer rainfall and runoff, and also, in relative terms, in summer SY. North WT shows a high contribution for rainfall in spring and autumn, and for runoff and SY in spring. South WT contribution is mainly concentrated in spring in rainfall, runoff, and SY. Figure 3. Seasonal distribution of the percentage of rainfall, runoff and sediment yield for the different WTs by each season. The ends of the box are the upper and lower quartiles, so the box spans the Figure 3. Seasonal distribution of the percentage of rainfall, runoffand sediment yield for the different WTs by each season. The ends of the box are the upper and lower quartiles, so the box spans the interquartile range, the median is marked by a vertical line inside the box, and the whiskers are the two lines outside the box that extend to the highest and lowest observations. Atmosphere 2020, 11, x FOR PEER REVIEW 9 of 20 interquartile range, the median is marked by a vertical line inside the box, and the whiskers are the two lines outside the box that extend to the highest and lowest observations. The seasonal percentage of rainfall, runoff, and SY produced under WTs shows a high spatial variability (Figure S1: rainfall, Figure S2: runoff, Figure S3: SY). Figure 4 is an example and shows the spatial distribution of rainfall, runoff, and SY below and above 2.5% under W and E types. In winter, and also in spring and autumn, westerly WTs produce preferably the highest rainfall and runoff values in the central-western areas, being no so clear for the SY response. Contrarily, easterly WTs predominate in summer, especially in the northeast and eastern part of the Iberian Peninsula. In general, the seasonal percentage of SY distribution accordingly to WTs is more heterogeneous than the observed in rainfall and runoff, suggesting a more complex relationship between SY and WTs than rainfall or runoff. Figure 4. Spatial distribution of the percentage of rainfall, runoff and sediment yield in the west (W) and east (E) weather types for each season. The 2.5% value represents the uniform distribution of 10 WTs and 4 seasons. 3.2. Seasonal Efficiency of Weather Types to Produce Rainfall, Runoff, and Sediment Yield The analysis of the WT efficiency in rainfall, runoff, and SY shows great differences among them. Figure 5 shows the seasonal frequency distribution of each WTs during the reference climate period Figure 4. Spatial distribution of the percentage of rainfall, runoffand sediment yield in the west (W) and east (E) weather types for each season. 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