Application of a Self-Organizing Map of Isotopic and Chemical Data for the Identiﬁcation of Groundwater Recharge Sources in Nasunogahara Alluvial Fan, Japan

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Article Application of a Self-Organizing Map of Isotopic and Chemical Data for the Identiﬁcation of Groundwater Recharge Sources in Nasunogahara Alluvial Fan, Japan

Takeo Tsuchihara *, Katsushi Shirahata, Satoshi Ishida and Shuhei Yoshimoto Institute for Rural Engineering, National Agriculture and Food Research Organization, Ibaraki 305-8609, Japan; shirahatak@affrc.go.jp (K.S.); ishidast@affrc.go.jp (S.I.); shuy@affrc.go.jp (S.Y.) * Correspondence: takeo428@affrc.go.jp

Received: 26 November 2019; Accepted: 16 January 2020; Published: 18 January 2020

Abstract: Paddy rice fields on an alluvial fan not only use groundwater for irrigation but also play an important role as groundwater recharge sources. In this study, we investigated the spatial distribution of isotopic and hydrochemical compositions of groundwater in the Nasunogahara alluvial fan in Japan and applied a self-organizing map (SOM) to characterize the groundwater. The SOM assisted with the hydrochemical and isotopic interpretation of the groundwater in the fan, and clearly classified the groundwater into four groups reflecting the different origins. Two groundwater groups with lower isotopic ratios of water than the mean precipitation values in the fan were influenced by the infiltration of river water flowing from higher areas in the catchments and were differentiated from each other by + their Na and Cl− concentrations. A groundwater group with higher isotopic ratios was influenced by the infiltration of paddy irrigation water that had experienced evaporative isotopic enrichment. Groundwater in the fourth group, which was distributed in the upstream area of the fan where dairy farms dominated, showed little influence of recharge waters from paddy rice fields. The findings of this study will contribute to proper management of the groundwater resources in the fan.

Keywords: groundwater; self-organizing map; stable isotope ratios; radon; major ions; alluvial fan; paddy rice ﬁeld

1. Introduction Alluvial fans are important lowland areas in Japan because approximately 70% of the total land area of Japan is mountainous or hilly. Vast paddy rice fields are situated on alluvial fans, which occupy more than half (54%) of Japan’s lowland area [1]. River water is usually diverted at the apexes of such alluvial fans and then delivered through a network of irrigation canals to each paddy rice field by gravity [2]. Alluvial fans are also vital aquifer systems that support local agricultural, industrial, and socio-economic development worldwide, wherever groundwater is utilized as an important water resource [3]. Aquifers of permeable sand and gravel formed in alluvial fans are vulnerable to contamination from the surface, and therefore interactions between surface water and groundwater can even affect hydrochemical processes (e.g., [4–7]). In addition, the infiltration of irrigation water contributes considerably to groundwater recharge [8] and also produces complex groundwater flows in paddy-dominant alluvial fans. For the sustainable use of groundwater resources in fans, it is important to understand these groundwater flows and recharge sources. Environmental isotopes and hydrochemical data are powerful tools for investigating groundwater flow conditions (e.g., [9,10]), including hydrogeological investigations of alluvial fans (e.g., [8,11,12]). A multifaceted analysis using a suite of tracers (multi-tracer approach) can be effectively used to

Water 2020, 12, 278; doi:10.3390/w12010278 www.mdpi.com/journal/water Water 2020, 12, 278 2 of 21 interpret complex groundwater flow conditions that cannot be observed directly. Multivariate analysis methods, such as principal component analysis and factor analysis, which allow for the dimensionality of multiple hydrochemical indicators to be reduced and features of groundwater flow conditions to be extracted, have been widely applied in hydrological system analysis (e.g., [13–15]). However, these multivariate analyses are generally based on linear principles and they cannot overcome difficulties arising from biases due to the complexity and nonlinearity of the datasets and from inherent correlations between variables [16]. Therefore, a self-organizing map (SOM) approach, a neural network–based pattern analysis with unsupervised learning, has recently been applied to map changes in groundwater levels and chemistry in space and time (e.g., [17–20]). An SOM, which can cluster a set of hydrochemical data into two or more independent groups, is superior to other statistical tools because it can: (1) deal with system nonlinearities, (2) be developed from data without requiring mechanistic knowledge of the system, (3) handle noisy or irregular data and be easily and quickly updated, and (4) be used to interpret and visualize information of multiple variables or parameters [16,21]. In this study, we performed an environmental isotopic and hydrochemical investigation of the Nasunogahara alluvial fan in central Japan, which was formed by several rivers and has a vast area (400 km2), about 40% of which is used for rice production. The paddy rice fields are irrigated by both river water and groundwater. The river water is transmitted through the Nasu canal system, one of the three major canals for paddy field irrigation in Japan. The yield of groundwater pumped out of the fan for irrigation is the second largest for agricultural use in Japan [22]. The groundwater in this fan also emerges as numerous springs in the central to lower part of the fan. These springs support a unique ecosystem inhabited by freshwater fish (three-spined stickleback, Gasterosteus aculeatus) that has been designated as a natural monument [23]. The groundwater in the alluvial fan is recharged not only by direct percolation of precipitation but also by the infiltration of water from the rivers that flow across the fan and water from irrigated paddy rice fields [24]. Thus, a part of the irrigation water used in the alluvial fan, whether derived from surface water or groundwater, returns from the paddy rice fields to the aquifer. Previous studies of groundwater in this area have focused on spatio-temporal variations in the groundwater quality [25,26], groundwater nitrate dynamics [27], and the groundwater budget, as simulated by a 2D horizontal model [28]. Stable isotope ratios of water have been effectively used to identify recharge sources of the groundwater in the central part of the fan [24], but the applicability of the method to the whole fan remains to be demonstrated, and how different recharge sources influence the flow of groundwater in the aquifer is still unclear. Therefore, further research is needed to provide information for farmers and other local residents to enable sustainable use of groundwater in the fan and conservation of the springs. Therefore, we measured multiple environmental isotopes and the hydrochemistry of groundwater in the Nasunogahara alluvial fan during two paddy cultivation periods, one with and one without irrigation. We then applied an SOM based on our results to identify the groundwater recharge sources and interpret groundwater flow through the aquifer system.

2. Study Area

2.1. Location, Land Use, and Water Use The Nasunogahara alluvial fan in central Japan was formed by the Naka, Sabi, Kuma, and Houki rivers, which flow out of the mountainous region to the northwest of the fan (Figure1). The alluvial fan extends over an area of approximately 400 km2 and is bounded by the Naka River along its eastern edge and the Houki River along its western edge. The flow in the upper reaches of the Sabi and Kuma rivers is usually underground, but these rivers emerge above ground in the central part of the fan and join the Houki River in the toe of the fan. Then, the Houki and Naka rivers converge in the southeasternmost part of the fan. As a result, the Nasunogahara fan is spindle-shaped, different from most alluvial fans. The topography of the fan is mainly flat; its elevation ranges from 60 to 560 m a.s.l., although scattered hills dissected by numerous streams occupy the lower part of the fan. The slope Water 2020, 12, 278 3 of 21 Water 2020, 12, x FOR PEER REVIEW 3 of 20

although scattered hills dissected by numerous streams occupy the lower part of the fan. The slope near the fan apex is 3/100, and the slope in the central and lower parts of the fan is 1/100; the average near the fan apex is 3/100, and the slope in the central and lower parts of the fan is 1/100; the average slope is 1.6/100. Paddy rice ﬁelds cover about 40% of the fan surface [29] and are densely distributed slope is 1.6/100. Paddy rice fields cover about 40% of the fan surface [29] and are densely distributed in the central and lower parts (Figure1). About one-quarter of the paddy rice ﬁelds are irrigated by in the central and lower parts (Figure 1). About one-quarter of the paddy rice fields are irrigated by surfacesurface water water diverted diverted from from the the rivers, rivers, and and thethe restrest are irrigated by by groundwater. groundwater. In In 2008, 2008, 240 240 million million 3 m mof3 of groundwater groundwater from from the the fan fan was was usedused forfor agricultureagriculture [22]. [22]. Livestock Livestock farms farms (mostly (mostly dairy dairy farms) farms) areare located located mainly mainly in in the the upper upper part part of of thethe fan,fan, wherewhere there there is is less less groundwater groundwater available available for for irrigation. irrigation. TheThe mean mean air air temperature temperature and and annual annual precipitationprecipitation from 1981 1981 to to 2010, 2010, measured measured at at Kuroiso Kuroiso weather weather stationstation (343 (343 m m a.s.l) a.s.l) (Figure (Figure1), 1), were were 11.7 11.7◦ °CC andand 15261526 mm, respectively.

FigureFigure 1. 1.Maps Maps showing showing the the ( a(a)) location location ofof thethe study site, site, ( (bb)) land land use, use, and and (c ()c water) water sampling sampling sites. sites. GroundwaterGroundwater was was not not sampled sampled at at the the sitessites denoteddenoted by white circles circles in in March March 2019 2019 because because the the wells wells werewere dry dry owing owing to to a dropa drop in in the the groundwater groundwater level.level. The land use use map map was was created created by by using using National National LandLand Numerical Numerical Information Information [29[29].]. TheThe gray topographic base base map map in in (c ()c )was was from from the the Geospatial Geospatial InformationInformation Authority Authority of of Japan Japan [ 30[30].].

2.2.2.2. Hydrogeological Hydrogeological Settings Settings TheThe Nasunogahara Nasunogahara alluvial alluvial fanfan consistsconsists mostlymostly of Quaternary deposits deposits overlying overlying a abasement basement composedcomposed of of pre-Quaternary pre-Quaternary green green tutuffff andand mudstonemudstone (Shioya and and Arakawa Arakawa groups). groups). In Inorder order of of decreasingdecreasing depth, depth, the the Sakaibayashi Sakaibayashi gravelgravel formation,formation, Tatenokawa Tatenokawa tuff tuff formation,formation, Kuroiso Kuroiso volcanic volcanic breccia,breccia, Nabekake Nabekake conglomerate, conglomerate, Kanemarubara Kanemarubara gravel gravel formation, formation, Nasuno Nasuno gravel gravel formation, formation, the Samuithe andSamui Okuzawa and Okuzawa gravel formations, gravel formations, and a flood and plain a flood gravel plain bed gravel overlie bed the overlie basement the basement rocks (Figure rocks2). (FigureAn unconfined 2). aquifer is in the flood plain gravel bed and the Samui and Okuzawa formations; in this aquifer,An unconfined the groundwater aquifer is table in the is higherflood plain during gravel summer bed and and the disappears Samui and during Okuzawa winter. formations; A second unconfinedin this aquifer, aquifer the ingroundwater the Nasuno table gravel is high formationer during supplies summer most and disappears of the groundwater during winter. used A for second unconfined aquifer in the Nasuno gravel formation supplies4 most3 of the groundwater used irrigation; it typically has a high hydraulic conductivity of 10− to 10− m/s [31,32]. The Tatenokawa for irrigation; it typically has a high hydraulic conductivity of 10−4 to 10−3 m/s [31,32]. The Tatenokawa tuff formation is impermeable and the gravels of overlying formations cover a paleotopography of hills tuff formation is impermeable and the gravels of overlying formations cover a paleotopography of and valleys on the upper surface of the Tatenokawa tuff. The Sakaibayashi gravel formation underlies hills and valleys on the upper surface of the Tatenokawa tuff. The Sakaibayashi gravel formation the Tatenokawa tuff formation and is a confined aquifer. In this study, groundwater samples were collected from depths down to 30.3 m in wells installed in the first and second unconfined aquifers, as Water 2020, 12, x FOR PEER REVIEW 4 of 20 Water 2020, 12, 278 4 of 21 underlies the Tatenokawa tuff formation and is a confined aquifer. In this study, groundwater samples were collected from depths down to 30.3 m in wells installed in the first and second describedunconfined in Section aquifers, 3.1 as. The described groundwater in Section level 3.1. of The the fangroundwater begins to riselevel in of April, the fan reaches begins a to maximum rise in inApril, October, reaches and thena maximum declines throughin October, March, and echoingthen declines changes through in precipitation March, echoing [24]. Groundwaterchanges in levelsprecipitation are thus high[24]. fromGroundwater April to September,levels are thus the paddyhigh from rice April irrigation to September, period, suggesting the paddy that rice the groundwaterirrigation period, may be suggesting recharged that by the paddy groundwate irrigationr may water. be recharged by paddy irrigation water.

N Lm Loam

Fd Flood plain gravel bed Samui and Okuzawa gravel

A’ Holocene AT formations Yokobayashi, Orito and Anazawa A AF fan gravel formations Elevation (m) Yumiya, Momomura and 400 YF Takabayashi fan gravel formations AA’ NF Nasuno gravel formation 05 km Houki R. Sabi R. Kuma R. KF Kanemarubara gravel formation Lm Lm Naka R. 300 Fd Kuroiso volcanic breccia and KF Lm Pleistocene KV Nabekake conglomerate KGT NF Lm KF KGT Tatenokawa tuff formation KV KGT KGS Sakaibayashi gravel formation KGS 200 SK Shioya group and Arakawa group SK Pre- Quaternary FigureFigure 2. 2.Geological Geological cross cross section section of of thethe Nasunogahara a alluviallluvial fan fan (modified (modiﬁed from from the the Kanto Kanto Regional Regional AgriculturalAgricultural Administration Administration O Officeﬃce [ 31[31]).]). 3. Methods 3. Methods 3.1. Sample Acquisition 3.1. Sample Acquisition Many hand-dug wells for paddy irrigation (as well as for upland crop irrigation and domestic Many hand-dug wells for paddy irrigation (as well as for upland crop irrigation and domestic use)use) are are distributed distributed across across the the Nasunogahara Nasunogahara alluvialalluvial fan, although there there are are a afew few wells wells inin the the upper upper partpart of of the the fan fan (Figure (Figure1). 1). We We collected collected samples samples for for thethe analysesanalyses of stable isotopic isotopic composition, composition, radon radon 222 radioisotoperadioisotope ( (Rn),222Rn), and and major major ion ion concentrations concentrations of waterof wa fromter from wells, wells, river river waters, waters, and paddyand paddy waters, bothwaters, during both the during irrigation the irrigation period (IP,period 15–18 (IP, May15–18 2018) May 2018) and duringand during the non-irrigationthe non-irrigation period period (NP, 4–7(NP, March 4–7 2019).March In 2019). the IP, In groundwaterthe IP, groundwater was collected was collected from 124 from wells, 124 butwells, in thebut NP,in the some NP, of some the wells of hadthe dried wells up had because dried of up a dropbecause in the of groundwatera drop in the levelgroundwater (white circles level in (white Figure circles1), so groundwaterin Figure 1), wasso collectedgroundwater from only was 98collected wells. Thefrom ground only 98 elevation wells. The of ground the wells elevation ranged of from the wells 134 to rang 417ed m from a.s.l., 134 and to the well417 depth m a.s.l., ranged and fromthe well 3.3 todepth 30.3 ranged m, with from an average 3.3 to 30.3 depth m, ofwith 9.6 an m belowaverage ground depth level.of 9.6 Towardm below the apexground of the level. fan, theToward wells the were apex deeper, of the fan, and the toward wells the were toe deeper, of the fan,and theytoward were the shallower. toe of the fan, The they mean depthswere ofshallower. groundwater The mean in the depths wells of in groundwater the IP and in NP the were wells 3.8 in andthe IP 2.5 and m, NP respectively. were 3.8 and Therefore, 2.5 m, isotopicrespectively. and chemical Therefore, compositions isotopic and of chemical groundwaters compositions were consideredof groundwaters to be were the same considered regardless to be of waterthe same depth. regardless An acrylic of wellwater bailer depth. was An usedacrylic to well collect bailer groundwater was used to samples collect groundwater from the wells. samples Paddy watersfrom werethe wells. collected Paddy from waters the were irrigation collected water from inlet the and irrigation outlet water of seven inlet paddy and outlet rice fieldsof seven only paddy in the IP.rice Spring fields waters only werein the collectedIP. Spring at waters six sites were (SP1–6) collected in the at IPsix and sites three (SP1–6) sites in (SP4–6) the IP and in the three NP. sites Some springs(SP4–6) dried in the up NP. in the Some NP springs because dried of the up drop in the in NP the because groundwater of the drop level. in A the polyethylene groundwater cup level. or EVAA polyethylene cup or EVA (ethylene-vinyl acetate copolymer) bucket were used to sample the paddy (ethylene-vinyl acetate copolymer) bucket were used to sample the paddy and spring waters. River and spring waters. River water samples were collected from each of the four rivers (Naka, Kuma, water samples were collected from each of the four rivers (Naka, Kuma, Sabi, and Houki rivers), either Sabi, and Houki rivers), either from the bank or from a bridge, using an EVA bucket. A relatively low from the bank or from a bridge, using an EVA bucket. A relatively low rainfall was observed and the rainfall was observed and the maximum daily rainfalls in and before the IP and NP were 8 and 14 maximum daily rainfalls in and before the IP and NP were 8 and 14 mm, respectively. Therefore, the mm, respectively. Therefore, the collected river waters were close to baseflow conditions. Rainwater collectedwas collected river waters at site were P1 (321 close m toa.s.l.; baseflow Figure conditions.1) from 14 March Rainwater 2018 wasto 21 collected August 2019 at site (527 P1 (days)321 m and a.s.l. ; Figuresite P21) from(179 m 14 a.s.l.) March from 2018 9 August to 21 August 2018 to 21 2019 August (527 days)2019 (377 and days) site P2at a (179 mean m interval a.s.l.) from of 33 9 days August in 2018rain to collectors 21 August designed, 2019 (377 following days) at a recommendations mean interval of 33described days in rainin International collectors designed, Atomic followingEnergy recommendationsAgency (IAEA)/Global described Network in International of Isotopes Atomic in Precipitation Energy Agency (GNIP) (IAEA) [33],/Global to prevent Network water of Isotopes re- in Precipitationevaporation. The (GNIP) electrical [33], toconductivity prevent water (EC) re-evaporation. of the sampled Thesurface electrical water conductivityand groundwater (EC) was of the sampledmeasured surface by using water a portable and groundwater EC sensor (TOA-DKK, was measured WM-32EP, by using Tokyo a portable, Japan) immediately EC sensor (TOA-DKK, after the WM-32EP,sampling. Tokyo, All water Japan) samples immediately for afterchemical the sampling.and isotopic All wateranalyses samples were for transferred chemical andto isotopicclean analyses were transferred to clean polyethylene vials and brought to the laboratory of the Institute for Rural Engineering, National Agriculture, and Food Research Organization (Ibaraki, Japan), where they Water 2020, 12, 278 5 of 21 were filtered through syringe filters with a polytetrafluoroethylenemembrane (0.45 µm) and stored in a refrigerator until analysis.

3.2. Analysis of Environmental Isotopic and Hydrochemical Compositions

+ + 2+ 2+ 2 Major ions (Na ,K , Mg , Ca , Cl−, NO3−, SO4 −) were quantified using ion chromatography (TOA-DKK, ICA-2000, Tokyo, Japan) using multicomponent standard solutions. Nitrate (NO3−) is reported as the nitrate-nitrogen (NO3-N) concentration. Alkalinity was determined using titration to pH 4.8 and is reported as HCO3−. For all water samples, errors in the cation–anion charge balance were less than 5%. 222Rn, which is a radioactive gas generated by the decay of 226Ra in geological strata, is soluble in water and has a half-life of 3.8 days. The 222Rn concentration in groundwater finally reaches radioactive equilibrium. It reaches 92% of the value at secular equilibrium after two weeks underground. Using these characteristics, 222Rn is often used as a target tracer to investigate the hydrological aspects of groundwater (e.g., estimation of infiltration from surface water to aquifers [34] and groundwater flow rate [35]), because far more 222Rn is contained in groundwater than in surface water. The radioactivity of 222Rn in samples was analyzed using a liquid scintillation counter (Packard, 2250 CA, CT, USA) after extraction with toluene within one week after collecting the water, and the concentrations of 222Rn were calculated based on the count rate [36]. The stable isotopic compositions of the water were measured using a cavity ringdown spectrometer-based water isotope analyzer (Picarro, L2140-i, Santa Clara, CA, USA). They were reported with respect to Vienna Standard Mean Ocean Water (VSMOW) and are described here in “delta” notation as δ18O and δ2H (in parts per thousand, %). Three working standards calibrated by international measurement standards were used for the analysis of δ18O and δ2H. The analytical precision in this study was 0.05% for δ18O and 0.50% for δ2H. ± ± The deuterium excess (d-excess, used to indicate the kinetic fractionation effect of evaporation [37]) of each water sample was calculated as d-excess = δ2H 8δ18O[38]. The Mann–Whitney U test, which is − applicable to non-normally distributed variables, was used to assess the independence of the measured isotopic and hydrochemical compositions between the IP and NP.

3.3. SOM An SOM is an unsupervised training algorithm of an artificial neural network model developed by Kohonen [39]. An SOM can project high-dimensional target data onto a low-dimensional format (typically a two-dimensional grid map called a “feature map”) [40]. Therefore, it is an effective and powerful analysis tool for the detection of data characteristics using pattern classification and visualization [16]. An SOM consists of an input layer and an output layer. The output layer often consists of a rectangular grid map with a hexagonal lattice (“nodes”). Each node in the output layer has a vector of coefficients associated with the input data, the so-called reference vector (also referred to as the weight vector and the codebook vector). The vectors can be obtained using iterative updates during a training phase consisting of three main procedures: competition between nodes, selection of a winning node, and updating of the reference vectors [40,41]. The determination of the SOM structure (calculation of the total number of nodes and the ratio of the number of map rows to the number of map columns) is an important step in the application of an SOM. If the number of nodes (i.e., map size) is too small, it might not explain some important differences that should be detected. Conversely, if the map size is too big, the differences between adjoining nodes are too small [42]. The optimal number of nodes in an SOM can be selected by using the heuristic rule m = 5 √n, where m denotes the total number of nodes and n is the number of samples in the data set [40,43]. The ratio of the number of rows to the number of columns in the feature map is determined by calculating the square root of the ratio of the largest eigenvalue of the correlation matrix of the input data to the second-largest eigenvalue [19]. In this study, the eigenvalues were obtained using principal component analysis of the input dataset. Normalization of the data was necessary prior to the application of the SOM to ensure that all values of the chemical parameters were given similar importance. The results of the SOM application are sensitive to the method used for Water 2020, 12, 278 6 of 21

Water 2020, 12, x FOR PEER REVIEW 6 of 20 datawere pre-processing given similar because importance. the SOM The results is trained of the to organizeSOM application itself according are sensitive to the to Euclidean the method distances used betweenfor data input pre-processing data [40]. In because this study, the allSOM variables is trained of the to inputorganize data itself were according transformed to the to haveEuclidean a range ofdistances 0 to 1 such between that they input would data have [40]. equalIn this importance study, all variables in the formation of the input of thedata SOM. were transformed to haveFor a clusteringrange of 0 to the 1 such trained that SOM,they would several have methods, equal importance each with in advantagesthe formation and of the disadvantages, SOM. are available.For clustering Agglomerative the trained clusteringSOM, several and methods, partition each clustering with advantages result in and clear disadvantages, groupings on are the trainedavailable. map, Agglomerative whereas application clustering of a and U-matrix partition (unified clustering distance result matrix), in clear which groupings is commonly on the trained used to createmap, a whereas rough visualization application of of a theU-matrix classification, (unified maydistance not matrix), define clear which boundaries is commonly (e.g., used [44 to]). create Ward’s agglomerativea rough visualization hierarchical of clusteringthe classification, (Ward’s may method) not isdefine an easy clear way boundaries to cluster (e.g., nodes [44]). on the Ward’s trained mapagglomerative because it is hierarchical not necessary clustering in this (Ward’s method method) to determine is an easy the way optimum to cluster number nodes of on groups the trained before performingmap because the it clustering is not necessary calculations in this [method42]. Ward’s to determine method the has optimum been commonly number usedof groups to subdivide before theperforming trained map the into clustering several calculations groups according [42]. Ward to the’s method similarity has ofbeen the commonly reference vectorsused to ofsubdivide the nodes (e.g.,the [ 43trained,45]). map Thus, into Ward’s several method groups was according applied to in the this similarity study to of identify the reference groups vectors on the of trained the nodes map. (e.g.,In this[43,45]). study, Thus, the Ward’s input layer method of thewas SOM applied consisted in this study of 11 chemicalto identify and groups isotopic on the parameters trained map. (δ2 H, 2 δ18O, 222InRn, this and study, the the major input ion layer concentrations) of the SOM consisted of groundwater of 11 chemical samples and collected isotopic atparameters 124 and 98 (δ wellsH, δ18O, 222Rn, and the major ion concentrations) of groundwater samples collected at 124 and 98 wells in the IP and NP, respectively. In accordance with the heuristic rules described above, a feature map in the IP and NP, respectively. In accordance with the heuristic rules described above, a feature map with 72 (9 8) nodes was selected. The “kohonen” package in the R statistical software environment with 72 (9× × 8) nodes was selected. The “kohonen” package in the R statistical software environment (version 3.5.1, Vienna, Austria) [46] was used to implement the SOM. Default values of the package (version 3.5.1, Vienna, Austria) [46] was used to implement the SOM. Default values of the package parameters (e.g., learning rate and size of the neighborhood) were used. The observed hydrochemical parameters (e.g., learning rate and size of the neighborhood) were used. The observed hydrochemical andand isotopic isotopic data data of of the the groundwater groundwater in in the the fan fan were were preprocessed preprocessed by by the the proposed proposed SOM, SOM, and and then then the groundwaterthe groundwater data weredata were spatially spatially clustered clustered into diintofferent different homogeneous homogeneous groups groups on theon the trained trained map. Tomap. identify To identify the groundwater the groundwater recharge recharge sources, sources, the hydrochemical the hydrochemical and and isotopic isotopic compositions compositions of of the 18 2 groupsthe groups were plottedwere plotted on various on variou typess types of diagrams of diagrams (i.e., (i.e., hexa-diagrams, hexa-diagrams, a trilinear a trilinear diagram, diagram, a δ a δO–18O–δ H scatterδ2H scatter diagram, diagram, and spatial and spatial distribution distribution maps) maps) and compared.and compared.

4. Results4. Results

4.1.4.1. Groundwater Groundwater Level Level WeWe mapped mapped groundwater groundwater levels, levels, depth depth to to the the water water table, table, and and cross-sections cross-sections ofof thethe waterwater tabletable in Figurein Figure3. The 3. groundwaterThe groundwater table table was was almost almost parallel paralle tol to the the ground ground surfacesurface of the the fan, fan, which which sloped sloped towardtoward the the southeast, southeast, and and the the groundwater groundwater ﬂowedflowed inin thethe same direction. direction.

ground surface ground surface (a) (20-m interval) (b) (20-m interval) (c) 400 400 380 380 Naka R. 360 300 Houki R. 360 340 340 320 320 300 300 250 280 A’ Groundwater surface 280 260

260 Elevation (m) Groundwater table (IP) 240 240 B’ AA’Groundwater table (NP) 220 200 220 360 360 340 200 Naka R. 340 200 320 270 Houki R. 320 300 C’ 300 180 180 280 280 220 160 160 260 260 A Elevation (m) BB’ 240 170 240 B 220 220 140 Houki R. 380−400 140 225 200 360−380 200 22−25 Naka R. 340−360 180 20−22 180 18−20 C 160 140 200 320−340 160 140 300−320 16−18 14−16 280−300 N 175 260−280 N 12−14 240−260 10−12 Elevation (m) CC’ 220−240 8−10 150 200−220 6−8 4−6 0 3 km 0 5 10 15 20 180−200 0 3 km Distance (km) 160−180 Groundwater level 2−4 Depth to water 140−160 in the IP (m, above sea level) 0−2 table in the IP (m) 120−140 FigureFigure 3. 3.(a ()a Spatial) Spatial distribution distribution of of thethe groundwatergroundwater level in in the the irrigation irrigation period period (IP), (IP), (b ()b depth) depth to tothe the waterwater table table in in the the IP, IP, and and (c ()c cross-sections) cross-sections ofof thethe groundwatergroundwater ta table.ble. The The contour contour line line of ofthe the ground ground surfacesurface was was modiﬁed modified from from the the Geospatial Geospatial InformationInformation Authority of of Japan Japan [30]. [30].

Water 2020, 12, 278 7 of 21

Water 2020, 12, x FOR PEER REVIEW 7 of 20 The depth to the groundwater table in the upper to middle part of the fan (from the apex to The depth to the groundwater table in the upper to middle part of the fan (from the apex to about 260 m above sea level) was relatively large. In this area, the Sabi and Kuma rivers were losing about 260 m above sea level) was relatively large. In this area, the Sabi and Kuma rivers were losing streams and hence recharged the aquifer. The terrace cliffs on the right bank of the Naka River along streams and hence recharged the aquifer. The terrace cliffs on the right bank of the Naka River along the eastern edge of the fan varied from several meters to several tens of meters in height (Figure3c), the eastern edge of the fan varied from several meters to several tens of meters in height (Figure 3c), and therefore, the river bed of the Naka River was lower than the groundwater table. This means and therefore, the river bed of the Naka River was lower than the groundwater table. This means that thatthere there was was no recharge no recharge from from the Naka the Naka river riverto the toaquifer. the aquifer. The riverbed The riverbed of the Houki of the River Houki was River about was about10 m 10higher m higher than the than groundwater the groundwater surface near surface the nearmiddle the part middle of the partfan. The of the Houki fan. River The Houkirecharged River rechargedthe aquifer the in aquifer this part in this and part the and infiltrated the infiltrated river water river waterflowed flowed in the indownstream the downstream direction direction in inaccordance accordance with with hydraulic hydraulic gradient, gradient, whereas whereas the gr theoundwater groundwater table became table became higher toward higher the toward toe of the toethe of fan. the fan.The Thelower lower part partof the of thefan, fan, where where the thewater water level level became became closer closer to the to the ground ground surface, surface, correspondedcorresponded to to the the groundwater groundwater dischargedischarge area.area.

4.2.4.2. Chemical Chemical Compositions Compositions AnalyticalAnalytical results results of of the the chemicalchemical and isotopic parameters parameters of of the the ground groundwater,water, along along with with those those ofof spring spring water, water, paddy paddy water, water, riverriver water,water, and rain rainwater,water, are are shown shown in in Table Table 1.1 .Four Four groups groups of ofthe the groundwatergroundwater were were classiﬁed classified using using the SOM,the SOM, as described as described later in Sectionlater in 4.4 Section. The chemical 4.4. The compositions chemical ofcompositions the groundwater, of the spring groundwater, water, and spring river water, water areand shown river water as a Piper are shown (trilinear) as a diagram Piper (trilinear) in Figure 4 anddiagram as hexa-diagrams in Figure 4 and in Figureas hexa-diagrams5. The Ca-HCO in Figure3 type 5. The typically Ca-HCO indicates3 type typically shallow indicates fresh groundwater, shallow whilefresh Na-HCO groundwater,3 type while typically Na-HCO indicates3 type weatheringtypically indicates and ion weatheri exchangeng and processes. ion exchange As shown processes. by the As shown by the key diagram and hexa-diagrams, some groundwater samples had a Ca-HCO3-to- key diagram and hexa-diagrams, some groundwater samples had a Ca-HCO3-to-Ca-SO4-Cl-type Ca-SO4-Cl-type composition, whereas the Ca-HCO3-type composition was dominant. Groundwater composition, whereas the Ca-HCO3-type composition was dominant. Groundwater samples were plottedsamples near were the plotted center ofnear the the cation center ternary of the diagram cation ternary (Figure diagram4), suggesting (Figure little 4), suggesting variability little in the relativevariability proportions in the relative of these proportions cations. The of these anion ca ternarytions. The diagram anion indicatedternary diagram larger indicated variability larger among variability among samples: Groundwater samples were mostly HCO3−-type water, whereas some samples: Groundwater samples were mostly HCO -type water, whereas some groundwater samples 3− − 2− groundwater samples were scattered between2 HCO3 and SO4 types. were scattered between HCO3− and SO4 − types.

100

Kuma R. Sabi R.

Groundwater Naka R. Spring water River water Paddy water 0 0 Houki R.

100 0 0 100

Kuma R. Sabi R.

Naka R.

Houki R. Sabi R. 100 0 Kuma R. Houki R. 0 Naka R. 100Ca2+ 0 0Cl− 100 FigureFigure 4. 4.Trilinear Trilinear diagramdiagram of groundwater, spring spring water, water, river river water, water, and and paddy paddy water. water.

Water 2020, 12, 278 8 of 21

Table 1. Analytical results for groundwater, spring water, paddy water, river water, and rainwater.

EC Na+ K+ Mg2+ Ca2+ HCO Cl SO 2 NO -N δ18O δ2H d-Excess 222Rn Type Group/Site n Period 3− − 4 − 3 mS/m mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L% % % Bq/L Mean 17.3 9.0 0.3 1.3 4.9 42.0 8.7 20.1 2.6 8.5 56 12.5 12.4 − − Median 17.4 8.6 0.3 1.2 4.6 40.3 8.1 20.7 2.5 8.6 56 12.6 12.9 All 222 IP, NP − − 25th 15.1 7.1 0.2 0.9 3.9 30.4 6.1 18.2 1.9 8.9 57 11.4 10.7 − − 75th 19.7 10.1 0.3 1.6 5.8 50.8 10.2 22.8 3.1 8.2 54 13.4 14.5 − − Mean 20.3 13.9 2.0 4.1 16.8 46.4 15.4 24.0 1.9 8.8 57 12.9 11.6 − − Median 20.0 13.8 2.1 3.8 16.4 46.9 15.4 23.5 1.6 8.8 57 13.0 12.4 Group 1 34 IP, NP − − 25th 19.5 13.3 1.9 3.6 15.8 44.4 13.8 22.8 1.2 9.1 59 12.4 10.1 − − 75th 20.6 15.1 2.3 4.1 17.5 50.3 16.5 24.4 2.0 8.6 56 13.3 13.5 − − Mean 17.2 8.6 1.4 5.1 13.5 39.0 8.6 20.2 3.0 8.0 53 11.0 12.5 Groundwater − − Median 17.0 8.5 1.4 5.2 13.0 38.4 8.1 20.6 3.0 8.1 54 11.1 12.9 Group 2 73 IP, NP − − 25th 16.2 7.6 1.1 4.6 12.1 33.6 7.1 18.6 2.6 8.3 55 10.2 10.6 − − 75th 18.0 9.1 1.5 5.7 14.6 45.2 9.8 23.5 3.4 7.7 52 11.8 14.6 − − Mean 14.4 6.8 1.1 3.9 10.7 29.6 5.5 19.2 2.1 8.8 57 13.5 12.2 − − Median 14.0 6.7 1.0 3.9 10.8 28.9 5.2 20.4 2.0 8.8 57 13.5 12.9 Group 3 72 IP, NP − − 25th 13.2 6.1 0.7 3.5 9.7 24.9 4.3 18.3 1.7 9.1 58 12.8 10.4 − − 75th 15.2 7.3 1.4 4.2 11.7 33.7 6.4 21.3 2.3 8.6 56 14.5 15.0 − − Mean 20.0 9.7 1.0 6.7 18.2 64.4 9.1 18.3 3.4 8.6 56 12.9 13.2 − − Median 20.0 9.8 1.0 6.6 17.8 62.8 8.9 18.5 3.2 8.7 56 12.9 13.0 Group 4 43 IP, NP − − 25th 19.3 9.2 0.8 6.0 16.4 56.3 8.3 15.8 2.8 8.8 57 12.3 11.2 − − 75th 21.2 10.3 1.2 7.1 19.8 69.7 9.8 20.7 4.0 8.4 55 13.4 14.8 − − SP1 1 IP 11.8 5.3 0.6 2.8 9.0 19.4 3.6 19.1 1.4 9.4 60 15.1 13.3 − − SP2 1 IP 11.3 5.3 0.7 2.8 9.5 24.4 3.8 20.9 1.5 9.3 60 14.3 8.4 − − SP3 1 IP 13.6 5.9 0.8 3.4 10.5 22.0 5.1 21.0 1.9 9.0 58 13.8 11.3 Spring water − − SP4 2 IP, NP Mean 14.1 7.7 1.2 4.0 11.5 36.9 4.9 18.9 1.9 8.9 57 13.7 11.7 − − SP5 2 IP, NP Mean 14.4 7.9 1.1 4.3 11.0 31.7 6.0 19.1 2.2 8.8 57 13.4 12.6 − − SP6 2 IP, NP Mean 18.4 12.2 2.0 3.7 16.4 42.6 13.1 23.8 1.9 8.9 58 13.1 13.3 − − Paddy water 14 IP Mean 11.7 6.2 3.3 2.9 7.1 19.1 6.9 20.5 0.5 5.4 45 2.2 – − − − Naka R. 2 IP, NP Mean 15.7 10.2 0.2 1.6 15.9 25.3 2.7 42.7 0.1 10.0 63 17.3 0.3 − − Kuma R. 2 IP, NP Mean 14.8 5.0 0.2 2.0 17.6 9.6 1.0 52.5 0.2 9.9 62 16.4 0.2 River water − − Sabi R. 2 IP, NP Mean 11.1 3.6 0.4 2.5 10.6 11.9 1.2 32.5 0.2 9.7 62 15.4 0.5 − − Houki R. 2 IP, NP Mean 23.2 23.3 2.8 2.8 14.9 47.5 23.9 27.5 0.3 10.0 64 15.8 0.3 − − P1 16 All year Mean – – – – – – – – – 8.5 56 12.1 – Rainwater − − P2 12 All year Mean – – – – – – – – – 8.0 52 12.1 – − − EC: electrical conductivity, IP: irrigation period, NP: non-irrigation period, –: not analyzed, 25th and 75th: 25th and 75th percentiles, mean values of rainwater were weighted using the precipitation amount. WaterWater 20202020,, 1212,, x 278 FOR PEER REVIEW 10 9of of 20 21

(a) Groundwater in the IP (b) Groundwater in the NP

(meq/L) (meq/L) 22101 22101

Na++K+ Cl− Na++K+ Cl− 2+ − 2+ − Ca HCO3 N Ca HCO3 N 2+ 2+ Mg 2− − 05km Mg 2− − 05km SO4 +NO3 SO4 +NO3

SP1 SP2 SP3 SP4SP5 SP6 Naka R. Houki R. Kuma R. Sabi R. Paddy water (meq/L) IP 101 Na++K+ Cl−

(mean) 2+ − Ca HCO3 NP 2+ 2− − Mg SO4 +NO3

FigureFigure 5. 5. Hexa-diagramsHexa-diagrams of of groundwater, groundwater, spring spring water, water, river river water, water, and and paddy paddy water. water.

TableTable 22 showsshows thethe matrixmatrix ofof thethe PearsonPearson correlationcorrelation coecoefficientsfficients for for EC EC and and major major ions ions of of all all groundwatergroundwater samples. samples. Ca Ca2+2, +which, which is ishighly highly correlated correlated with with EC, EC, is a is major a major controlling controlling factor factor of EC. of EC. A 2+ strongA strong positive positive correlation correlation between between Ca Ca2+ andand HCO HCO3− 3indicates− indicates that that a adominant dominant water water type type of of the the 2+ groundwatersgroundwaters in in this this aquifer aquifer was was Ca-HCO Ca-HCO3 3type.type. Concentrations Concentrations of of Ca Ca2+ andand HCO HCO3− in3− aquifersin aquifers of alluvialof alluvial fans fans increased increased due due to tomineralization mineralization induced induced by by a awater–rock water–rock interaction interaction during during the the 2+ groundwatergroundwater flow flow process (e.g.,(e.g., [[47]).47]). Figure 6 6 shows shows the the relationships relationships between between EC, EC, Ca Ca2+,, andand HCOHCO33− ofof the the groundwater groundwater with with groundwa groundwaterter level. level. These These values values did did not not increase increase with with the the downstream downstream directiondirection in thethe fan. fan. The The spatial spatial distribution distribution of theof hexa-diagramsthe hexa-diagrams in Figure in Figure5 indicates 5 indicates that the chemicalthat the chemicalcompositions compositions varied greatly varied depending greatly depending on the location, on the especially location, theespecially distances the from distances the rivers. from the rivers. Table 2. Pearson correlation coefficients for EC and major ions of all groundwater samples.

Table 2. Pearson correlation+ coefficients+ for2+ EC and major2+ ions of all groundwater samples.2 EC Na K Mg Ca HCO3− Cl− SO4 − NO3-N

EC 1 EC Na+ K+ Mg2+ Ca2+ HCO3− Cl− SO42− NO3-N Na+ EC 0.671 1 + K Na+0.24 0.67 0.45 1 1 Mg2+ 0.52 0.19 0.19 1 K+ 0.24 0.45 − 1 Ca2+ 0.82 0.60 0.19 0.56 1 Mg2+ 0.52 0.19 −0.19 1 HCO3− 0.67 0.50 0.02 0.70 0.84 1 2+ Cl− Ca 0.66 0.82 0.910.60 0.540.19 0.150.56 10.61 0.42 1 2 SO HCO0.313− 0.67 0.290.50 0.190.02 0.700.01 0.84 0.26 1 0.13 0.23 1 4 − − − NO -N− 0.32 0.01 0.07 0.69 0.27 0.28 0.08 0.18 1 3 Cl 0.66 0.91 − 0.54 0.15 0.61 0.42 1 −

SO42− 0.31 0.29 0.19 −0.01 0.26 −0.13 0.23 1 NO3-N 0.32 0.01 −0.07 0.69 0.27 0.28 0.08 −0.18 1

Water 2020,, 12,, x 278 FOR PEER REVIEW 1110 of of 20 21 Water 2020, 12, x FOR PEER REVIEW 11 of 20

500 500 500 500 IP 500 IP 500 IP IP IP IP NP NP NP 400 NP 400 NP 400 NP 400 400 400

300 300 300 300 300 300

200 200 200 200 200 200

100 100 100 100 Groundwater Groundwater level (m, above sea level) 5 1015202530 Groundwater level (m, above sea level) 100 0102030 Groundwater level (m, above sea level) 100 0 25 50 75 100 125 Groundwater Groundwater level (m, above sea level) Groundwater level (m, above sea level) Groundwater level (m, above sea level) 5 1015202530 01020302+ 0 25 50− 75 100 125 EC (mS/m) Ca (mg/L) HCO3 (mg/L) EC (mS/m) Ca2+ (mg/L) HCO − (mg/L) 3 FigureFigure 6.6. RelationshipsRelationships betweenbetween EC,EC, CaCa22++,, and and HCO HCO3− withwith groundwater groundwater level. level. Figure 6. Relationships between EC, Ca2+, and HCO33− with groundwater level. FigureFigure 7 7illustrates illustrates the the spatial spatial distributions distributions of EC andof EC the concentrationsand the concentrations of the three characteristicof the three Figure 7 illustrates the spatial distributions of EC and the concentrations of the three 2+ 2 2+ 2− − characteristicions Ca , SO 4ions−, andCa Cl, SO− of4 groundwater, and Cl of groundwater in the IP and in NP. the The IP spatialand NP. distribution The spatial of distribution EC, which was of characteristic ions Ca2+, SO42−, and Cl− of groundwater in the IP and NP. The spatial distribution of EC,low which along was the Sabi low andalong Kuma the Sabi rivers and in Kuma the central rivers part in the of central the fan, part was of very the consistentfan, was very with consistent previous EC, which was low along the Sabi and Kuma rivers in the central part of the fan, was very consistent withsurvey previous results survey [25,48]. results Along [2 these5,48]. two Along rivers, these Ca two2+, rivers, like EC, Ca was2+, like relatively EC, was low, relatively whereas low, SO whereas2 was with previous survey results [25,48]. Along these two rivers, Ca2+, like EC, was relatively low, whereas4 − 2− − SOrelatively4 was high.relatively In contrast, high. In Cl contrast,− concentrations Cl concentrations were higher were along higher the western along the edge western of the fanedge near of the the SO42− was relatively high. In contrast, Cl− concentrations were higher along the western edge of the fanHouki near River the Houki than elsewhere River than in elsewhere the fan. As in is the shown fan. byAs theis shown hexa-diagrams by the hexa-diagrams and ternary diagrams, and ternary the fan near the Houki River than elsewhere in the fan. As is shown by the hexa-diagrams and ternary 2 2− diagrams,Naka, Sabi, the and Naka, Kuma Sabi, River and waters Kuma were River characterized waters were by large characterized SO4 − contents, by large whereas SO4 thecontents, Houki diagrams, the Naka, Sabi, and Kuma River waters were characterized by large SO42− contents, whereasRiver water the Houki samples River had water higher samples Na+ and had Cl higherconcentrations Na+ and Cl than− concentrations the waters fromthan the the waters other rivers. from whereas the Houki River water samples had −higher Na+ and Cl− concentrations than the waters from 2 − 2− theSpring other waters rivers. were Spring plotted waters between were plotted the HCO between3− and the SO 4HCO− types3 and on SO the4 anion types ternary on the anion diagram; ternary SP1, the other rivers. Spring waters were plotted between the HCO3− and SO42− types on the anion ternary 2 2− diagram;SP2, and SP3SP1, were SP2, characterizedand SP3 were by characterized larger SO4 − bycontents, larger SO as4 indicated contents, by as their indicated hexa-diagram by their shapes. hexa- diagram; SP1, SP2, and SP3 were characterized by larger SO42− contents, as indicated by their hexa- diagramNo signiﬁcant shapes. seasonal No significant diﬀerence seasonal between differen the IP andce between NP was observedthe IP and in theNP chemical was observed compositions in the diagram shapes. No significant seasonal difference between the IP and NP was observed in the chemicalof the river compositions and spring waters.of the river and spring waters. chemical compositions of the river and spring waters.

2+ 2− − (a) EC (b) Ca (c) SO4 (d) Cl 2+ 2− − (a) EC (b) Ca (c) SO4 (d) Cl

IP IP IP IP IP IP IP IP 2+ 2- − EC Ca SO4 Cl 2+ 2- − (mS/m)EC (mg/L)Ca (mg/L)SO4 (mg/L)Cl (mS/m)10~ (mg/L)6~ (mg/L)2~ (mg/L)2.0~ 13~ 9~ 4.5~ 10~ 6~ 12~2~ 2.0~ 16~ 12~ 7.0~ 13~ 9~ 18~12~ 4.5~ 19~ 15~ 9.5~ 16~ 12~ 20~18~ 7.0~ 22~ 18~ 12.0~ 19~ 15~ 22~ 9.5~ 25~ 21~ 20~ 14.5~ 22~ 18~ 24~22~ 12.0~ 25~ 21~ 24~ 14.5~

NP NP NP NP NP NP NP NP 2+ 2- − EC Ca SO4 Cl 2+ 2- − (mS/m)EC (mg/L)Ca (mg/L)SO4 (mg/L)Cl (mS/m)10~ (mg/L)6~ (mg/L)2~ (mg/L)2.0~ 13~ 9~ 4.5~ 10~ 6~ 12~2~ 2.0~ 16~ 12~ 7.0~ 13~ 9~ 18~12~ 4.5~ 19~ 15~ 9.5~ 16~ 12~ 20~18~ 7.0~ 22~ 18~ 12.0~ 19~ 15~ 22~ 9.5~ 25~ 21~ 20~ 14.5~ 22~ 18~ 24~22~ 12.0~ 25~ 21~ 24~ 14.5~

2+2+ 2− 2 − FigureFigure 7. 7. DistributionsDistributions of of (a (a) )EC, EC, (b (b) Ca) Ca, (c,() SOc) SO4 ,4 and−, and (d) Cl (d) concentrations Cl− concentrations in the in groundwater. the groundwater. The Figure 7. Distributions of (a) EC, (b) Ca2+, (c) SO42−, and (d) Cl− concentrations in the groundwater. The upperThe upper and lower and lower maps maps show show the data the datain the in IP the and IP andNP, NP,respectively. respectively. upper and lower maps show the data in the IP and NP, respectively. 4.3.4.3. Isotopic Isotopic Compositions Compositions 4.3. Isotopic Compositions δ18 δ2 δ18 δ2 TheThe relationship relationship between δ18O and δ2HH( (δ18O–O–δ2HH scatter scatter diagram) diagram) in in all all water water samples samples is is shown shown The relationship between δ18O and δ2H (δ18O–δ2δH18 scatter δdiagram)2 in all water samples is shown inin Figure Figure 88.. In rainwater collectedcollected atat sitessites P1P1 andand P2,P2, δ18OO and and δ2HH varied varied over over relatively relatively wide wide ranges, ranges, in Figure 8. In rainwater collected at sites P1 and P2, δ18O and δ2H varied over relatively wide ranges, fromfrom −12.9‰12.9% toto −3.1‰3.1% andand from from −85‰85% toto −5‰,5% respectively., respectively. The The local local meteoric meteoric water water line line (LMWL) (LMWL) from− −12.9‰ to −3.1‰ and from −85‰ to −−5‰, respectively. The local meteoric water line (LMWL) in this study area was determined to be δ2H = 8.48δ18O + 20.09. The slope of the LMWL was slightly in this study area was determined to be δ2H = 8.48δ18O + 20.09. The slope of the LMWL was slightly

Water 2020, 12, x FOR PEER REVIEW 12 of 20 larger than the global meteoric water line (GMWL) slope of 8 [49] and the slope of the meteoric water line on the Pacific Ocean side in Japan (7.8) [50]. The weighted means of δ18O and δ2H (weighted using the precipitation amount during the sampling time interval) were −8.3‰ and −54‰ at P1 and P2, respectively; these values, which were plotted below the LMWL, reflect the low d-excess value of rainwater in summer in Japan [51] because the region of the Nasunogahara fan receives more precipitation in summer than in winter. The slope of the regression line between δ18O and δ2H of paddy waters, denoted as PWRL in Figure 8, was 3.6. The plots of the paddy water data deviated from the LMWL and the d-excess value of the paddy water (−2.2‰) was extremely low compared to those of the groundwater, rainwater, and river water (Table 1). Generally, kinetic fractionation of oxygen and hydrogen isotopes during evaporation causes the remaining water to become enriched in the heavier isotopes (18O and 2H); as a result, trends with slopes ranging from 4 to 7 are seen on the δ18O–δ2H scatter diagram, whereas the equilibrium isotopic fractionation line lies near the LMWL and has a slope close to 8 [52]. If there is no rain, irrigation water is supplied to the paddy fields almost every day and is affected by evaporative enrichment, even in just one day. Paddy waters affected by evaporative isotopic enrichment would have higher isotopic ratios and lower d-excess values [53] and effects the isotopic compositions in groundwater [54]. In this study, paddy waters collected at paddy field outlets had higher isotopic ratios than those collected at the inlets because evaporative isotopic enrichment occurred during the residence time of the water in the paddy rice fields. The slope of the regression line fitted to all groundwater data was 5.04, between those of the LMWL and the PWRL (Figure 8b). The isotopic ratios of the river water samples were smaller than those of groundwater samples and Waterplotted2020 near, 12, 278 the LMWL. 11 of 21 The spatial distributions of δ18O, d-excess, and the 222Rn concentration in groundwater in the IP inand this NP study are shown area was in Figure determined 9. The to groundwater be δ2H = 8.48 alδong18O the+ 20.09. Sabi and The Kuma slope of rivers the LMWL had comparatively was slightly largerlow δ18 thanO (less the than global −8.5‰) meteoric and water high line d-excess (GMWL) values slope (more of 8 [than49] and 12.0‰), the slope whereas of the groundwater meteoric water in linethe southeastern on the Paciﬁc part Ocean of the side fan in had Japan high (7.8) δ18O [50 (more]. The than weighted −8.0‰) meansand low of d-excessδ18O and valuesδ2H (weighted (less than using11.0‰). the Compared precipitation with amount the IP, during the domain the sampling with a timehigh interval) δ18O and were low 8.3%d-excess and values54% inat the P1 222 − − andsoutheastern P2, respectively; fan shrank these in the values, NP. Spatial which differences were plotted in the below Rn the concentration LMWL, reﬂect in groundwater the low d-excess were valueindistinct, of rainwater although in concentrations summer in Japan in the [51 NP] because were higher the region than ofthose the Nasunogaharain the IP. The 222 fanRn concentration receives more precipitationin waters of the in summerfour rivers than was in close winter. to zero (Table 1).

-40 (a)LMWL (b) 0 δ2H=8.43δ18O+19.35 (R2=0.90) -45 Groundwater δ2H=5.04δ18O−12.72 (R2=0.98) -20 -50 PWRL δ2H=3.57δ18O−26.10 -40 (R2=0.96) -55 H (‰) H 2 δ H (‰) H Illustrated area 2 δ -60 in (b) -60

Groundwater Rainwater (P1) Rainwater (P2) Spring water -80 -65 Precipitation weighted mean (P1) River water Precipitation weighted mean (P2) Precipitation weighted mean (P1+P2) Paddy water -100 -70 -15 -10 -5 0 5 10 -11 -10 -9 -8 -7 -6 -5 δ18O (‰) δ18O(‰) 18 2 Figure 8. Relationship between δ18OO and and δδ2HH of of ( (aa)) rainwater rainwater and and paddy paddy water water and and ( (bb)) groundwater. groundwater. LMWL is the local meteoric water line identifiedidentified by fittingfitting a regression line to observations at sites P1 and P2. PWRL is the regression line fittedfitted to the paddypaddy waterwater data.data.

The slope of the regression line between δ18O and δ2H of paddy waters, denoted as PWRL in Figure8, was 3.6. The plots of the paddy water data deviated from the LMWL and the d-excess value of the paddy water ( 2.2%) was extremely low compared to those of the groundwater, rainwater, − and river water (Table1). Generally, kinetic fractionation of oxygen and hydrogen isotopes during evaporation causes the remaining water to become enriched in the heavier isotopes (18O and 2H); as a result, trends with slopes ranging from 4 to 7 are seen on the δ18O–δ2H scatter diagram, whereas the equilibrium isotopic fractionation line lies near the LMWL and has a slope close to 8 [52]. If there is no rain, irrigation water is supplied to the paddy fields almost every day and is affected by evaporative enrichment, even in just one day. Paddy waters affected by evaporative isotopic enrichment would have higher isotopic ratios and lower d-excess values [53] and effects the isotopic compositions in groundwater [54]. In this study, paddy waters collected at paddy field outlets had higher isotopic ratios than those collected at the inlets because evaporative isotopic enrichment occurred during the residence time of the water in the paddy rice fields. The slope of the regression line fitted to all groundwater data was 5.04, between those of the LMWL and the PWRL (Figure8b). The isotopic ratios of the river water samples were smaller than those of groundwater samples and plotted near the LMWL. The spatial distributions of δ18O, d-excess, and the 222Rn concentration in groundwater in the IP and NP are shown in Figure9. The groundwater along the Sabi and Kuma rivers had comparatively low δ18O (less than 8.5%) and high d-excess values (more than 12.0%), whereas groundwater in − the southeastern part of the fan had high δ18O (more than 8.0%) and low d-excess values (less − than 11.0%). Compared with the IP, the domain with a high δ18O and low d-excess values in the southeastern fan shrank in the NP. Spatial diﬀerences in the 222Rn concentration in groundwater were indistinct, although concentrations in the NP were higher than those in the IP. The 222Rn concentration in waters of the four rivers was close to zero (Table1). WaterWater2020 2020, 12, 12, 278, x FOR PEER REVIEW 1213 ofof 2120 Water 2020, 12, x FOR PEER REVIEW 13 of 20

(a) δ18O (b) d-excess (c) 222Rn (a) δ18O (b) d-excess (c) 222Rn IP IP IP IP IP IP

δ18O d-excess 222Rn δ18(‰)O d-excess(‰) 222(Bq/L)Rn (‰)−10.0~ (‰) 8~ (Bq/L)1~ −10.0~−9.0~ 8~9~ 1~4~ −9.0~−8.5~ 9~10~ 4~7~ 11~ −8.5~−8.0~ 10~ 7~10~ 12~ −8.0~−7.5~ 11~ 10~13~ 12~13~ 16~ −7.5~−7.0~ 13~ 13~14~ 16~ −7.0~ 14~

NP NP NP NP NP NP

Figure 9. Distributions of (a) δ18O, (b) d-excess, and (c) the 222Rn concentration in groundwater in the FigureFigure 9. DistributionsDistributions of ( a)) δ18O,O, ( (bb)) d-excess, d-excess, and and ( (cc)) the the 222222RnRn concentration concentration in in groundwater groundwater in in the the IP (upper maps) and NP (lower maps). IPIP (upper (upper maps) maps) and and NP NP (lower maps).

4.4.4.4. SOM SOM and and Clustering Clustering Results Results 4.4. SOM and Clustering Results Figure 10 shows the component planes of the hydrochemical and isotopic parameters finally FigureFigure 10 shows the component planes of the hydrochemical and isotopic parameters finally finally obtained after iterative training of the SOM. Each component plane shows the value of one obtainedobtained afterafter iterativeiterative training training of theof SOM.the SOM. Each componentEach component plane showsplanethe shows value the of onevalue component of one component of the reference vector in each of the 72 nodes. Visualization of the data using component componentof the reference of the vector reference in each vector of thein each 72 nodes. of the Visualization72 nodes. Visualization of the data of using the data component using component planes is planes is helpful for finding interrelationships among the different parameters [21]. For example, the planeshelpful is for helpful finding for interrelationships finding interrelationships among the amon differentg the parameters different parameters [21]. For example, [21]. For the example, component the planecomponent patterns plane of Na patterns+ and Cl ofwere Na+ and visibly Cl− similar; were visibly both had similar; larger both values had in larger the lower-left values in and the smaller lower- component plane patterns of− Na+ and Cl− were visibly similar; both had larger values in the lower- left and smaller values in the upper-left part of the plane (Figure 10). The component planes of several leftvalues and in smaller the upper-left values in part the of upper-left the plane part (Figure of th 10e plane). The (Figure component 10). The planes component of several planes other parameterof several other parameter combinations18 2 (e.g., δ18O–δ2H, Ca–HCO2+ 3−, and Mg2+–NO3-N) also showed similar combinations (e.g., δ O–δ H, Ca–HCO183−, and2 Mg –NO3-N)− also showed2+ similar patterns (Figure 10), other parameter combinations (e.g., δ O–δ H, Ca–HCO3 , and Mg –NO3-N) also showed similar patterns (Figure 10), whereas the component2 + planes222 of SO42−, K+, and 222Rn did not exhibit any whereas the component planes of SO4 −,K , and Rn did not2− exhibit+ any222 similarity with those of patterns (Figure 10), whereas the component planes of SO4 , K , and Rn did not exhibit any similarity with those of other parameters. similarityother parameters. with those of other parameters.

Figure 10. Component planes of the isotopic and hydrochemical parameters: (a) δ18O, (b) δ2H, (c) Na+, Figure 10. Component planes of the isotopic and hydrochemical parameters: (a) δ18O, (b) δ2H, (c) Na+, + 2+ 2+ 2 222 18 2 + Figure(d)K ,( 10.e) Component Mg ,(f) Ca planes,(g) of HCO the3 isotopic−,(h) Cl −and,(i )hydrochemical NO3-N, (j) SO 4parameters:−, and (k) (a)Rn. δ O, Each (b) planeδ H, (c shows) Na , (d) K+, (e) Mg2+, (f) Ca2+, (g) HCO3−, (h) Cl−, (i) NO3-N, (j) SO42−, and (k) 222Rn. Each plane shows the (thed) K standardized+, (e) Mg2+, (f value) Ca2+, of (g each) HCO parameter3−, (h) Cl− (transformed, (i) NO3-N, ( toj) SO the42 range−, and from(k) 222 0Rn. to 1)Each in each plane node. shows the standardized value of each parameter (transformed to the range from 0 to 1) in each node. standardized value of each parameter (transformed to the range from 0 to 1) in each node.

WaterWater 20202020, ,1212, ,x 278 FOR PEER REVIEW 1413 of of 20 21 Water 2020, 12, x FOR PEER REVIEW 14 of 20 The SOM nodes were subdivided into four groups (groups 1 to 4, comprising 34, 73, 72, and 43 groundwaterTheThe SOMSOM samples, nodesnodes were wererespectively) subdividedsubdivided according intointo fourfour to groupsgroups the dendrogram (groups(groups 11 toto 4,produced4, comprisingcomprising by 34,the34, 73,73,hierarchical 72,72, andand 4343 clustergroundwatergroundwater analysis samples, samples, (Figure respectively) 11).respectively) Figure according12 according shows to the theto spatial dendrogramthe dendrogram distributions produced produced of by the the color-codedby hierarchical the hierarchical clusterhexa- diagramsanalysiscluster analysis (Figureof the four 11 (Figure). groups Figure 11). 12in showstheFigure IP and the12 spatialNP.shows Group distributions the 1spatial was mostly ofdistributions the distributed color-coded of nearthe hexa-diagrams color-codedthe Houki River, ofhexa- the whichfourdiagrams groups flows of along in the the four the IP and westerngroups NP. Groupin edge the ofIP 1 wastheand fan. mostlyNP. Grou Group distributedp 2 1was was distributed mostly near the distributed Houki in the River,central near which andthe Houkilower ﬂows parts alongRiver, ofthewhich the western fan. flows Group edge along 3 of was the the western fan.distribu Group tededge 2along wasof the distributedthe fan. Sabi Grou and inp 2Kuma the was central distributed rivers. and Group lower in the 4 parts was central distributed of the and fan. lower Group across parts 3 thewasof middlethe distributed fan. part Group of along the 3 was fan. the distribu Sabi andted Kuma along rivers.the Sabi Group and Kuma 4 was rive distributedrs. Group across 4 was the distributed middle part across of thethe fan.middle part of the fan.

FigureFigure 11. 11. (a()a )All All reference reference vectors vectors in in each each of of the the 72 72 nodes nodes and and (b (b) )classification classiﬁcation of of the the self-organizing self-organizing Figure 11. (a) All reference vectors in each of the 72 nodes and (b) classification of the self-organizing mapmap (SOM) (SOM) nodes nodes into into four four groups. groups. The The circles circles on on each each map map hexagon hexagon represent represent the the groundwater groundwater map (SOM) nodes into four groups. The circles on each map hexagon represent the groundwater samplessamples assigned assigned to to that that node. node. samples assigned to that node. (a) Groundwater in the IP (b) Groundwater in the NP (a) Groundwater in the IP (b) Groundwater in the NP

(meq/L) (meq/L) 22101 22101 (meq/L) (meq/L) 22101Group1 22101Group1 Group2Group1 Group2Group1 Group3Group2 Group3Group2 Group4Group3 Group4Group3 N N Na++K+ Group4Cl− 05kmNa++K+ Group4Cl− 05km − − Ca2+ HCO3 N Ca2+ HCO3 N Na++K+ Cl− Na++K+ Cl− 2+ 05km 2+ 05km Mg 2− − − Mg 2− − − Ca2+ SO4 HCO+NO3 3 Ca2+ SO4 HCO+NO3 3 2+ 2+ Mg 2− − Mg 2− − SO4 +NO3 SO4 +NO3 101101101101(meq/L) 101101101101(meq/L)

Median 25th and 75th percentiles Group 1 Group 2Group 3 Group 4 Median 25th and 75th percentiles Group 1 Group 2Group 3 Group 4 Figure 12. Spatial distribution of hexa-diagrams of groundwaters in four groups classiﬁed by the SOM Figure 12. Spatial distribution of hexa-diagrams of groundwaters in four groups classified by the SOM in the (a) IP and (b) NP. inFigure the (a) 12. IP Spatialand (b) distribution NP. of hexa-diagrams of groundwaters in four groups classified by the SOM in the (a) IP and (b) NP.

WaterWater 20202020,, 1212,, 278x FOR PEER REVIEW 1514 of 2120

The EC of group 3 groundwater was lower than that of the other groups (Table 1), indicating that itThe contained EC of group lower 3 amounts groundwater of dissolved was lower comp thanonents. that of Groundwater the other groups in groups (Table 1 ),and indicating 4 had higher that itEC contained than groundwater lower amounts in groups of dissolved 2 and 3. components. In particular, Groundwater concentrations in groups of Na+ 1and and Cl 4− had were higher notably EC + thanhigher groundwater in group 1, in whereas groups 2 those and 3. of In Ca particular,2+ and HCO concentrations3− were higher of Na in andgroup Cl −4.were Thenotably mean NO higher3-N 2+ inconcentration group 1, whereas was the those highest of Ca inand group HCO 4,3 −butwere all higher groundwater in group samples 4. The mean in this NO study3-N concentration had nitrate wasconcentrations the highest below in group the 4, standard but all groundwater level of 10 mg/L samples NO3 in-N this in Japan. study The had width nitrate of concentrations each hexa-diagram below theis an standard approximate level ofindication 10 mg/L NOof the3-N total in Japan. ionic Thecontent. width Thus, of each the hexa-diagramhexa-diagram iswidth an approximate of group 3, indicationwhich had ofa low the totalEC, was ionic small, content. whereas Thus, the the hexa-diagram hexa-diagram widths width of of groups group 3,1 and which 4, which had a lowhad EC,higher was ECs, small, were whereas large the(Figure hexa-diagram 12). Chemical widths compositions of groups 1of and groundwaters 4, which had in higherthe four ECs, groups, were largespring (Figure water, 12 and). Chemical river water compositions are shown as of a groundwaters trilinear diagram in the in fourFigure groups, 13. Groundwater spring water, samples and river of watergroup are4 were shown mostly as a trilinear HCO3−-type diagram water, in Figurewhereas 13 .those Groundwater of groups samples 1 to 3 oftended group to 4 werebe scattered mostly − 2− − 2 HCObetween3−-type HCO water,3 and whereas SO4 types. those In of addition, groups 1 the to 3 relative tended toabundance be scattered of Cl between in group HCO 1 3samples− and SO was4 − types.somewhat In addition, large. the relative abundance of Cl− in group 1 samples was somewhat large.

100

Groundwater Group 1 Kuma R. Group 2 Sabi R. Group 3 Group 4 Naka R. Spring water River water 0 0 Paddy water Houki R.

100 0 0 100

Kuma R. Sabi R.

Naka R.

Houki R. Sabi R. 100 0 Kuma R. Houki R. 0 Naka R.

100Ca2+ 0 0Cl− 100

Figure 13. Figure 13. Trilinear diagram of the four groundwater groups.groups. The δ18O–δ2H scatter diagram of groundwater in the four groups is shown in Figure 14. The δ18O–δ2H scatter diagram of groundwater in the four groups is shown in Figure 14. The The groundwater in each of the four groups had characteristic δ18O and δ2H values: group 2 waters groundwater in each of the four groups had characteristic δ18O and δ2H values: group 2 waters had had the highest values, followed by waters of groups 4 and 1, and group 3 waters had the lowest values the highest values, followed by waters of groups 4 and 1, and group 3 waters had the lowest values (Figure 14, Table1). Group 2 waters had the lowest d-excess value and group 3 waters had the highest (Figure 14, Table 1). Group 2 waters had the lowest d-excess value and group 3 waters had the highest d-excess value. d-excess value.

Water 2020, 12, 278 15 of 21 Water 2020, 12, x FOR PEER REVIEW 16 of 20

-40

-45 Groundwater δ2H=5.04δ18O−12.72 (R2=0.98)

-50

-55 H (‰) H 2

δ Groundwater -60 Group 1 Group 2 Group 3 -65 Group 4 Spring water River water -70 -11 -10 -9 -8 -7 -6 -5 δ18O(‰) 18 2 Figure 14.14. RelationshipRelationship betweenbetweenδ δ18OO and andδ δH2H of of the the four four groundwater groundwate groups.r groups. LMWL LMWL and and PWRL PWRL are theare localthe local meteoric meteoric water water line line and and the the paddy paddy water water regression regression line, line, respectively, respectively, as shown as shown in Figure in Figure8. 5. Discussion8.

5.1.5. Discussion Inﬂuence of Recharge Sources on Groundwater Hydrochemical and Isotopic Compositions

5.1. InfluenceIt has been of Recharge pointed outSources that shallowon Ground groundwaterwater Hydrochemical in the Nasunogahara and Isotopic Compositions alluvial fan is recharged by multiple sources, including the infiltration of rainwater, river water, and paddy irrigation water (e.g., [24]). The isotopicIt has been ratios pointed of groundwater out that shallow in this groundwater study, which in were the plottedNasunogahara as an elongated alluvial clusterfan is recharged between theby multiple LMWL and sources, the PWRL including in the theδ18 O–infiltrationδ2H diagram of rainwater, (Figure8), river also indicatewater, and a mixture paddy of irrigation groundwaters water recharged(e.g., [24]). from The three isotopic different ratios sources. of groundwater The fact that in thethis isotopic study, ratioswhich of were the groundwater plotted as an were elongated plotted 18 2 betweencluster between the LMWL the LMWL and PWRL and isthe strong PWRL evidence in the δ thatO–δ groundwaterH diagram (Figure in the fan8), also was indicate influenced a mixture by the infiltrationof groundwaters of not only recharged rainwater from but three also paddy different irrigation sources. water The (Figure fact 8that). In the addition, isotopic the ratios comparison of the betweengroundwater groundwater were plotted levels between and ground the LMWL surface and elevations PWRL indicated is strong thatevidence there wasthat infiltrationgroundwater from in thethe Houki,fan was Sabi,influenced and Kuma by the rivers infiltration to the aquiferof not only (Figure rainwater3). The but isotopic also paddy ratios irrigation of river waters water (Figure (mean values8). In addition, of four rivers the comparison of 9.9% for betweenδ18O and groundwater63% for δ 2levelsH) were and much ground less surface than the elevations weighted indicated means of − − rainwaterthat there (seewas Section infiltration 4.3) (Figure from 8the). The Houki, reason Sabi, for theand lower Kuma ratios rivers of theseto the river aquifer waters (Figure was that3). theThe − 18 − 2 riversisotopic gathered ratios of precipitation river waters that (mean falls values at high of altitude four rivers in the of mountains, 9.9‰ for which δ O and had lower63‰ isotopicfor δ H) ratioswere becausemuch less of thethan isotopic the weighted altitude means effect. of The rainwater isotopic ratios(see Section of some 4.3) groundwater (Figure 8). samplesThe reason were for lower the lower than theratios weighted of these mean river rainwater waters wasδ18 Othat and theδ2 riversH values, gathered suggesting precipitation that these that samples falls at represented high altitude a mixture in the ofmountains, waters with which low had isotopic lower ratios. isotopic We thereforeratios because inferred of the that isotopic the groundwaters altitude effect. in the The western isotopic part ratios and 18 2 upperof some part groundwater of the fan were samples affected were by lower the infiltration than the weighted of waters mean from therainwater Houki δ River,O and and δ theH values, Kuma andsuggesting Sabi rivers, that these respectively. samples The repr riveresented waters a mixture contained of waters baseflows with low supplied isotopic from ratios. the mountainousWe therefore watersheds,inferred that but the222 groundwatersRn concentrations in the of western river waters part and were upper low. This partindicates of the fan that were dissolved affected222 byRn the in riverinfiltration waters of was waters lostthrough from the dispersion Houki River, to the and atmosphere the Kuma and and radioactive Sabi rivers, decay respectively. before reaching The river the 222 fan.waters It had contained been expected baseflows that supplied the 222Rn fr concentrationom the mountainous of the groundwater watersheds, near but the Rn rivers concentrations decreased due of 222 toriver river waters water were infiltration, low. This but indicates the concentrations that dissolved were Rn not in lower river thanwaters those was far lost away through from dispersion the rivers to the atmosphere and radioactive decay before reaching the fan. It had been expected that the 222Rn (Figure9). This means that the residence time of infiltrated waters to reaching sampling wells was more concentration of the groundwater near the rivers decreased due to river water infiltration, but the than two weeks, which were required to reach radioactive equilibrium. concentrations were not lower than those far away from the rivers (Figure 9). This means that the The hydrochemical data also implied that infiltration of river water recharged some groundwaters. residence time of infiltrated waters to reaching sampling wells was more than two weeks, which were The groundwater around the Sabi and Kuma rivers, the EC, and some dissolved ion concentrations, required to reach radioactive2+ equilibrium. 2 such as that of Ca , were relatively low, but SO4 − was relatively high; thus, we can infer that the The hydrochemical data also implied that infiltration of river water recharged some groundwaters were influenced by the infiltration of waters from these rivers, which had similar groundwaters. The groundwater around the Sabi and Kuma rivers, the EC, and some dissolved ion concentrations, such as that of Ca2+, were relatively low, but SO42– was relatively high; thus, we can

Water 2020, 12, x FOR PEER REVIEW 17 of 20

Water 2020, 12, 278 16 of 21 infer that the groundwaters were influenced by the infiltration of waters from these rivers, which had similar compositions, and this inference was supported by the low isotopic ratios and high d-excess compositions,values of the groundwaters. and this inference The wasgroundwater supported in bythe the western low isotopic part of ratiosthe fan and had high relatively d-excess high values Na+ ofand the Cl groundwaters.− concentrations, The reflecting groundwater the infiltration in the western of Houki part River of thewater, fan which had relatively was also high characterized Na+ and + − Clby− relativelyconcentrations, high Na reflecting and Cl theconcentrations infiltrationof (Figures Houki 5 River and water,7). Similarly, which the was relatively also characterized low isotopic by + relativelyratios and highhigh NaEC ofand the Clgroundwa− concentrationsters suggested (Figures a large5 and contribution7). Similarly, of the infiltration relatively from low the isotopic Houki ratiosRiver (Figures and high 7 EC and of 9). the Thus, groundwaters groundwater suggested in the afan large reflected contribution three different of infiltration recharge from sources the Houki and Riverthe fraction (Figures of7 the and contribution9). Thus, groundwater from each insource the fan varied reflected by location. three di fferent recharge sources and the fraction of the contribution from each source varied by location. 5.2. Characterization of Groundwater Using SOM 5.2. Characterization of Groundwater Using SOM The SOM classified the groundwater in the fan into four groups with different hydrochemical and isotopicThe SOM compositions. classified the groundwaterThe groundwater in the of fan each into group four groupswas inferred with different to be affected hydrochemical by different and isotopicrecharge compositions. sources. Group The 1 groundwatergroundwaters, of distributed each group in was the inferred western to part be affected of the fan, by different had low rechargeisotopic sources.ratios and Group high Na 1 groundwaters,+ and Cl−, suggesting distributed the infiltration in the western of the part Houk of thei River. fan, The had relatively low isotopic high ratios isotopic and + highratios Na andand low Cl d-excess−, suggesting values the of infiltrationgroup 2 groundwa of the Houkiters indicated River. The a relatively large recharge high isotopic contribution ratios anddue lowto infiltration d-excess values from paddy of group rice 2 groundwatersfields. Group indicated3 groundwa a largeters around recharge the contribution Sabi and Kuma due to rivers infiltration were frominferred paddy to be rice influenced fields. Group by infiltration 3 groundwaters of these around rivers, the which Sabi had and similar Kuma riverschemical were and inferred isotopic to becompositions: influenced bylow infiltration EC, low dissolved of these rivers, ion concentrations which had similar excluding chemical SO4 and2–, and isotopic low stable compositions: isotopic 2 lowratios. EC, Group low dissolved 4 groundwaters, ion concentrations distributed at excluding higher elevations SO4 −, and than low group stable 2 groundwaters, isotopic ratios. had Group lower 4 groundwaters,isotopic ratios than distributed the weighted at higher means elevations of rainwate thanr, group suggesting 2 groundwaters, that these samples had lower were isotopic affected ratios by thana mixture the weighted of Sabi meansand Kuma of rainwater, river waters. suggesting Howeve thatr, thesethe relatively samples werelarge affectedEC and by ionic a mixture contents of Sabiindicated and Kuma a smaller river waters.contribution However, of infiltration the relatively from large these EC andriver ionic waters contents compared indicated to agroup smaller 3 contributiongroundwaters. of In infiltration the upper from part these of the river fan, watersthere are compared few paddy to grouprice fields 3 groundwaters. but many livestock In the upperfarms part(Figure of the1). Reflecting fan, there this are fewdifference paddy in rice land fields use, butNO3 many-N concentrations livestock farms in group (Figure 4 groundwaters1). Reflecting were this differencehigher than in those land in use, the NO other3-N groups concentrations (Table 1). in We group inferred 4 groundwaters that these higher were concentrations higher than those reflected in the otherthe high groups nitrogen (Table load1). Weassociated inferred with that theselivestock higher farm concentrationsing (e.g., [27]). reflected It can be the readily high nitrogen seen that load the associatedcontribution with of livestockinfiltration farming from paddy (e.g., [ 27rice]). field It can to be groundwater readily seen thatrecharge the contribution was smaller of for infiltration group 4 fromthan paddyfor group rice 2 field groundwaters. to groundwater In contrast, recharge wassmaller smaller ionic for contents, group 4 thanincluding for group NO3 2-N, groundwaters. in group 2 Ingroundwaters contrast, smaller indicate ionic a contents,dilution du includinge to infiltration NO3-N, of in paddy group waters. 2 groundwaters indicate a dilution due to infiltrationThe application of paddy and waters. results of the SOM assisted the interpretation of the difference in hydrochemicalThe application and andisotopic results compositions of the SOM assisteddepending the interpretationon the location of theand difference the influence in hydrochemical of multiple andrecharge isotopic sources compositions on the groundwater. depending on theData location that produce and the influencea scattered of multipledistribution recharge in the sources trilinear on thediagram, groundwater. hexa-diagrams, Data that and produce δ18O–δ a2H scattered diagram distribution make it difficult in the to trilinear resolve diagram, ambiguous hexa-diagrams, boundaries and δto18 O–produceδ2H diagram reasonable make classification it difficult to resolveof the ambiguousgroundwaters boundaries influenced and toby producemultiple reasonable recharge classificationsources, especially of the for groundwaters the shallow groundwater influenced by with multiple similar recharge chemical sources, and isotopic especially compositions. for the shallow The groundwaterSOM application with similarcould automatically chemical and isotopicestablish compositions. the four different The SOM groups, application independent could automatically of human- establishsubjective the criteria, four different and summarized groups, independent the spatial of human-subjectivedistribution of each criteria, group and with summarized the readily the spatialunderstandable distribution visualization of each group (Figure with 15). the readily understandable visualization (Figure 15).

222Rn 222Rn Group 4 1 18 1 18 Group 3 NO3-N δ O NO3-N δ O • Nitrate nitrogen • EC and stable isotopic 2− 2 2− 2 concentration was high. SO4 δ H SO4 δ H ratios were low. • Contribution of 0 0 • Groundwater was affected recharge from paddy Cl− Na+ Cl− Na+ by infiltration from the rice field was relatively Sabi and Kuma Rivers. small. − K+ − K+ • The area expanded during HCO3 HCO3 non-irrigation period. Ca2+ Mg2+ Ca2+ Mg2+ 222Rn 222Rn NO -N 1 δ18O 18 Group 1 3 NO3-N 1 δ O Group 2 • Na+ and Cl− SO 2− δ2H 2− 2 • Stable isotopic ratios 4 SO4 δ H concentrations were high. were higher. 0 0 − • Groundwater was Cl Na+ Cl− Na+ • Contribution of recharge affected by infiltration from paddy rice field was from the Houki Rivers. large. − K+ N + HCO3 05km HCO − K 3 • The area shrank during 2+ Mg2+ 2+ Ca Ca2+ Mg non-irrigation period. Figure 15. Characteristics of the Nasunogahara alluvial fan groundwaters in each of the four groups. groups. Medians of all variables plotted on thethe radarradar chartscharts werewere transformedtransformed toto rangerange fromfrom 00 toto 1.1.

Water 2020, 12, 278 17 of 21 Water 2020, 12, x FOR PEER REVIEW 18 of 20

Figure 12 illustrates the didifferencesfferences in the spatialspatial distribution of the four groundwater groups classifiedclassified by by the the SOM SOM between between the the IP IP and and NP. NP. We We fo focuscus here here on on the the central central and and eastern eastern parts parts of the of fanthe because fan because we could we could not collect not collect groundwater groundwater samples samples in the western in the western part of the part fan of in the the fan NP. in One the largeNP. One seasonal large seasonalchange was change that wasthe distribution that the distribution area of group area of 2 groupshrank 2 in shrank the NP, in thewhereas NP, whereas that of groupthat of 3 group expanded 3 expanded in the indo thewnstream downstream direction. direction. EC, δ18 EC,O, δδ218H,O, andδ2H, d-excess and d-excess values values and 222 andRn concentrations222Rn concentrations of groundwater of groundwater at the at observation the observation sites sites classified classified into into group group 2 2in in the the IP IP differed differed significantlysignificantly between the IP and NP ((p << 0.05) (Figure 16).16). Although Although the the EC apparently decreased in the NP in the southeastern part of the fan (where only group 2 samples were distributed in in the the IP) IP) (Figure7 7),), the the EC EC in in group group 2 groundwaters2 groundwaters did did not not di ff differer significantly significantly between between the IP the and IP NPand (p NP= 0.12 (p =). The0.12). major The major ion concentrations ion concentrations in group in group 2 groundwaters 2 groundwaters also did also not did diff noter significantly differ significantly between between the two periodsthe two (periodsp > 0.10). (p The> 0.10). median The medianδ18O and δ18δO2H and values δ2H ofvalues group of 2group groundwaters 2 groundwaters in the IP in were the IP8.0% were − −and8.0‰53% and ,− respectively,53‰, respectively, and the and median the median values values at the at same the same sites insites the in NP the decreased NP decreased to 8.4% to −8.4‰ and − − and55% −55‰,, respectively, respectively, whereas whereas the medianthe median d-excess d-excess value value increased increased from from 11.0% 11.0‰ in thein the IP toIP 12.3%to 12.3‰ in − inthe the NP. NP. These These results results indicated indicated that, that, with with respect respect to theto the relation relation between betweenδ18 δO18O and andδ2 δH,2H, the the group group 2 2groundwaters groundwaters deviated deviated more more from from the the LMWL LMWL in in the the IP IP than than in thein the NP. NP. The The high high isotopic isotopic ratios ratios of the of thegroup group 2 groundwaters 2 groundwaters indicated indicated that theythat werethey were likely likely more more greatly greatly affected affected by recharge by recharge from paddy from paddyrice fields rice than fields groundwaters than groundwaters of the other of the groups. other gr Thisoups. inference This inference is consistent is consistent with the with higher the isotopic higher isotopicratios that ratios were that observed were inobserved the IP than in the in the IP NP.than As in a the result NP. of As this a seasonalresult of di thisfference, seasonal some difference, sampling somesites shifted sampling from sites group shifted 2 in from the IP group to group 2 in 3the in theIP to NP, group reflecting 3 in the a reducedNP, reflecting influence a reduced of recharge influence from paddyof recharge rice fieldsfrom inpaddy the NP rice and fields a corresponding in the NP and relative a corresponding increase in relative the influence increase of infiltrationin the influence of river of watersinfiltration at these of river sites. waters In addition, at these222 sites.Rn In concentrations addition, 222Rn of concentrations groundwaters of were groundwaters lower in the were IP than lower in inthe the NP IP (Figure than in 16 the). NP Generally, (Figure222 16).Rn Generally, concentrations 222Rn concentrations in groundwater in decreasegroundwater during decrease the irrigation during periodthe irrigation because period of downward because of flow downward of soil water flow pushedof soil water out by pushed irrigation out waterby irrigation [55]. Accordingly, water [55]. Accordingly,the difference the in 222differenceRn concentrations in 222Rn concentrations between the between IP and NP the also IP and indicated NP also a changeindicated in a the change relative in thecontributions relative contributions of infiltration of frominfiltration paddy from rice fields paddy and rice rivers fields to and groundwater rivers to groundwater recharge. recharge.

18 2 222 (a) EC (b) δ O(c) δH (d) d-excess (e) Rn p-value: Mann–Whitney U test 24 -6.5 16 25 p=0.12 p<0.01 -45 p<0.01 p<0.01 p=0.02 Upper extreme value limit 22 (Q3+1.5IQR) -7.0 14 20 75th percentile (Q3) 20 -7.5 -50 12 15 Median (Q2) IQR

18 25th percentile (Q1) O (‰) O H (‰) 2 18 -8.0 10 10 δ Rn (Bq/L) Rn δ EC (mS/m) 16 -55 d-exces (‰) Lower extreme value limit (Q1−1.5IQR) -8.5 8 5 14 IP NP 12 -9.0 -60 6 0 IP NP IP NP IP NP IP NP IP NP Figure 16. Comparison of groundwatergroundwater ECEC valuesvalues and and isotopic isotopic compositions compositions between between the the IP IP and and NP NP at atobservation observation sites sites classified classified into into group group 2 in 2 thein the IP. IP. 6. Conclusions 6. Conclusions The groundwater in the Nasunogahara alluvial fan, which is mostly covered by paddy rice The groundwater in the Nasunogahara alluvial fan, which is mostly covered by paddy rice fields, fields, was investigated by using environmental isotopes and hydrochemical investigations and the was investigated by using environmental isotopes and hydrochemical investigations and the application of an SOM. The obtained isotopic and hydrochemical data indicated that the groundwater application of an SOM. The obtained isotopic and hydrochemical data indicated that the in the fan was affected by three different recharge sources: precipitation, river waters, and paddy rice groundwater in the fan was affected by three different recharge sources: precipitation, river waters, field irrigation water. Furthermore, the SOM clearly classified the groundwater in the fan into four and paddy rice field irrigation water. Furthermore, the SOM clearly classified the groundwater in the groups (groups 1 to 4), reflecting different recharge sources. The characteristics of the groundwater of fan into four groups (groups 1 to 4), reflecting different recharge sources. The characteristics of the each group can be summarized as follows: groundwater of each group can be summarized as follows: 1. GroupGroup 1 groundwater, distributeddistributed aroundaround the the Houki Houki River, River, which which flows flows along along the the western western edge edge of + ofthe the fan, fan, had relativelyhad relatively low isotopic low isotopic ratios, but ratios, high ECbut values high andEC highvalues Na andand high Cl− concentrations. Na+ and Cl− concentrations.Group 1 groundwater Group was1 groundwa thus inferredter was to havethus beeninferred greatly to ahaveffected been by infiltrationgreatly affected from theby infiltrationHouki River, from which the had Houki water River, of a di ffwhicherent chemicalhad water composition of a different compared chemical to the composition other rivers. compared to the other rivers.

Water 2020, 12, 278 18 of 21

2. Group 2 groundwater, distributed in the central and lower part of the fan, had high isotopic ratios and was inferred to be recharged mainly by infiltration of paddy waters that had been affected by evaporative isotopic enrichment. However, the area of group 2 shrank during the non-irrigation period when the infiltration of paddy water did not occur. 3. Group 3 groundwater, distributed around the Sabi and Kuma Rivers, which flows down the center of the fan, had a low EC and low isotopic ratios, indicating a greater influence of infiltration of water from the two rivers compared to other recharge sources. 4. Group 4 groundwater, distributed on the upstream side of group 2 groundwater, had lower isotopic ratios than group 2 groundwater. In the upper part of the fan, few paddy rice fields but many livestock farms are distributed throughout this region; thus, recharge from paddy rice fields was relatively small. Furthermore, NO3-N concentrations in group 4 groundwaters were higher than those in the other groups.

These results show that multiple tracers could be effectively used to evaluate the influence of different groundwater recharge sources, including paddy water in rice cultivation areas, and the application of an SOM can provide understandable and readily visualized results to assist in the interpretation of the characteristics of groundwater in the fan. The findings of this study can therefore contribute to proper planning for regional groundwater use and the preservation of spring waters in the fan.

Author Contributions: Design of the study framework, T.T. and S.I.; director of the field survey and analysis T.T.; water sample collection, all authors; writing, T.T.; reviewing and editing, K.S., S.I., and S.Y. All authors have read and agreed to the published version of the manuscript. Funding: This study was partly supported by JSPS KAKENHI (grant number JP19K06301). Acknowledgments: We express our gratitude to the Union Nasunogahara Land Improvement District (LID: farmers’ water management organization), other LIDs, and the individual well owners for permission to sample groundwater for this study. Finally, we would like to thank the academic editors and four anonymous reviewers for their helpful comments and suggestions. Conflicts of Interest: The authors declare no conflict of interest.

References

1. Seto, R. Land use by landform classification in Japan -Analysis with the data of grid square basis-. Map 1986, 24, 1–11, (In Japanese with English abstract). 2. Tabayashi, A. Irrigation systems in Japan. Geogr. Rev. Jpn. 1987, 60, 41–65. [CrossRef] 3. Li, F.; Pan, G.; Tang, C.; Zhang, Q.; Yu, J. Recharge source and hydrogeochemical evolution of shallow groundwater in a complex alluvial fan system, southwest of north China plain. Environ. Geol. 2008, 55, 1109–1122. [CrossRef] 4. Yu, H.L.; Chu, H.J. Understanding space-time patterns of groundwater system by empirical orthogonal functions: A case study in the Choshui River alluvial fan, Taiwan. J. Hydrol. 2010, 381, 239–247. [CrossRef] 5. Eastoe, C.J.; Hutchison, W.R.; Hibbs, B.J.; Hawley, J.; Hogan, J.F. Interaction of a river with an alluvial basin aquifer: Stable isotopes, salinity and water budgets. J. Hydrol. 2010, 395, 67–78. [CrossRef] 6. Bourke, S.A.; Cook, P.G.; Shanafield, M.; Dogramaci, S.; Clark, J.F. Characterisation of hyporheic exchange in a losing stream using radon-222. J. Hydrol. 2014, 519, 94–105. [CrossRef] 7. Karan, S.; Sebok, E.; Engesgaard, P. Air/water/sediment temperature contrasts in small streams to identify groundwater seepage locations. Hydrol. Process. 2017, 31, 1258–1270. [CrossRef] 8. Yoshioka, Y.; Nakamura, K.; Nakano, T.; Horino, H.; Shin, K.C.; Hashimoto, S.; Kawashima, S. Multiple- indicator study of groundwater flow and chemistry and the impacts of river and paddy water on groundwater in the alluvial fan of the Tedori River, Japan. Hydrol. Process. 2016, 30, 2804–2816. [CrossRef] 9. Doveri, M.; Mussi, M. Water isotopes as environmental tracers for conceptual understanding of groundwater flow: An application for fractured aquifer systems in the “Scansano-Magliano in Toscana” area (Southern Tuscany, Italy). Water 2014, 6, 2255–2277. [CrossRef] Water 2020, 12, 278 19 of 21

10. Yeh, H.F.; Lin, H.I.; Lee, C.H.; Hsu, K.C.; Wu, C.S. Identifying seasonal groundwater recharge using environmental stable isotopes. Water 2014, 6, 2849–2861. [CrossRef] 11. Zhong, C.H.; Yang, Q.C.; Ma, H.Y.; Bian, J.M.; Zhang, S.H.; Lu, X.G. Application of environmental isotopes to identify recharge source, age, and renewability of phreatic water in Yinchuan Basin. Hydrol. Process. 2019, 33, 2166–2173. [CrossRef] 12. Liu, Y.; Yamanaka, T. Tracing groundwater recharge sources in a mountain-plain transitional area using stable isotopes and hydrochemistry. J. Hydrol. 2012, 464, 116–126. [CrossRef] 13. Valder, J.F.; Long, A.J.; Davis, A.D.; Kenner, S.J. Multivariate statistical approach to estimate mixing proportions for unknown end members. J. Hydrol. 2012, 460–461, 65–76. [CrossRef] 14. Cortes, J.E.; Muñoz, L.F.; Gonzalez, C.A.; Niño, J.E.; Polo, A.; Suspes, A.; Siachoque, S.C.; Hernãndez, A.; Trujillo, H. Hydrogeochemistry of the formation waters in the San Francisco field, UMV basin, Colombia—A multivariate statistical approach. J. Hydrol. 2016, 539, 113–124. [CrossRef] 15. Islam, M.M.; Lenz, O.K.; Azad, A.K.; Ara, M.H.; Rahman, M.; Hassan, N. Assessment of spatio-temporal variations in water quality of Shailmari River, Khulna (Bangladesh) using multivariate statistical techniques. J. Geosci. Environ. Protect. 2017, 5, 1–26. [CrossRef] 16. Dieng, N.M.; Orban, P.; Stumpp, C.; Faye, S.; Dassargues, A. Temporal changes in groundwater quality of the Saloum coastal aquifer. J. Hydrol. Reg. Stud. 2017, 9, 163–182. [CrossRef] 17. Chen, I.T.; Chang, L.C.; Chang, F.J. Exploring the spatio-temporal interrelation between groundwater and surface water by using the self-organizing maps. J. Hydrol. 2018, 556, 131–142. [CrossRef] 18. Choi, B.Y.; Yun, S.T.; Kim, K.H.; Kim, J.W.; Kim, H.M.; Koh, Y.K. Hydrogeochemical interpretation of South Korean groundwater monitoring data using Self-Organizing Maps. J. Geochem. Explor. 2014, 137, 73–84. [CrossRef] 19. Nakagawa, K.; Amano, H.; Kawamura, A.; Berndtsson, R. Classification of groundwater chemistry in Shimabara, using self-organizing maps. Hydrol. Res. 2017, 48, 840–850. [CrossRef] 20. Agoubi, B. Assessing hydrothermal groundwater flow path using Kohonen’s SOM geochemical data and groundwater temperature cooling trend. Environ. Sci. Pollut. Res. 2018, 25, 13597–13610. [CrossRef] 21. An, Y.; Zou, Z.; Li, R. Descriptive Characteristics of Surface Water Quality in Hong Kong by a Self-Organising Map. Int. J. Environ. Res. Public Health 2016, 13, 115. [CrossRef][PubMed] 22. Rural Development Bureau, Ministry of Agriculture, Forestry and Fisheries (MAFF). Actual Use of Groundwater for Agriculture; MAFF: Tokyo, Japan, 2011; pp. 1–13. (In Japanese) 23. Otawara City Habitat of Three-Spined Stickleback (Taya River). Available online: https://www.city.ohtawara. tochigi.jp/docs/2013082781284/ (accessed on 28 October 2019). (In Japanese). 24. Wakui, H.; Yamanaka, T. Source of groundwater recharge and their local differences in the central part of Nasu fan as revealed by stable isotopes. J. Groundw. Hydrol. 2006, 48, 263–277, (In Japanese with English abstract). [CrossRef] 25. Hiyama, T.; Suzuki, Y. Groundwater in the Nasuno basin -Spatial and seasonal changes in water quality-. J. Jpn. Assoc. Hydrol. Sci. 1991, 21, 143–154, (In Japanese with English abstract). 26. Babiker, I.S.; Mohamed, M.A.A.; Hiyama, T. Assessing groundwater quality using GIS. Water Resour. Manag. 2007, 21, 699–715. [CrossRef] 27. Somura, H.; Goto, A.; Matsui, H.; Elhassan, A.M. Impacts of nutrient management and decrease in paddy field area on groundwater nitrate concentration: A case study at the Nasunogahara alluvial fan, Tochigi Prefecture, Japan. Hydrol. Process. 2008, 22, 4752–4766. [CrossRef] 28. Elhassan, A.M.; Goto, A.; Mizutani, M. Combining a tank model with a groundwater model for simulating regional groundwater flow in an alluvial fan. Trans. Jpn. Soc. Irrig. 2001, 215, 21–29. 29. National Land Information Division, National Spatial Planning and Regional Policy Bureau, MILT of Japan. National Land Numerical Information Download Service. Available online: http://nlftp.mlit.go.jp/ksj/ (accessed on 4 September 2019). 30. Geospatial Information Authority of Japan. GSI Map. Available online: https://maps.gsi.go.jp/ (accessed on 4 September 2019). 31. Hydrogeological Map of Nasuno-ga-hara Area. Kanto Regional Agricultural Administration Office; MAFF of Japan: Tokyo, Japan, 1993. (In Japanese) 32. The Survey Report of Subsurface Dam in Kanto Area. Kanto Regional Agricultural Administration Office; MAFF of Japan: Tokyo, Japan, 1999. (In Japanese) Water 2020, 12, 278 20 of 21

33. IAEA/GNIP Precipitation Sampling Guide (V2.02). September 2014. Available online: http://www-naweb. iaea.org/napc/ih/documents/other/gnip_manual_v2.02_en_hq.pdf (accessed on 31 October 2019). 34. Hoehn, E.; von Gunten, H.R. Radon in groundwater: A tool to assess infiltration from surface waters to aquifers. Water Resour. Res. 1989, 25, 1795–1803. [CrossRef] 35. Hamada, H. Estimation of groundwater flow rate using the decay of 222Rn in a well. J. Environ. Radioact. 2000, 47, 1–13. [CrossRef] 36. Hamada, H.; Komae, T. Investigation on shallow groundwater in a small basin using natural radioisotopes. Radioisotopes 1996, 45, 71–81. [CrossRef] 37. Gat, J.R. Oxygen and hydrogen isotopes in the hydrologic cycle. Annu. Rev. Earth Planet. Sci. 1996, 24, 225–262. [CrossRef] 38. Dansgaard, W. Stable isotopes in precipitation. Tellus 1964, 16, 436–468. [CrossRef] 39. Kohonen, T. Self-organized formation of topologically correct feature maps. Biol. Cybern. 1982, 43, 59–69. [CrossRef] 40. Jin, Y.H.; Kawamura, A.; Park, S.C.; Nakagawa, N.; Amaguchi, H.; Olsson, J. Spatiotemporal classification of environmental monitoring data in the Yeongsan River basin, Korea, using self-organizing maps. J. Environ. Monit. 2011, 13, 2886–2894. [CrossRef][PubMed] 41. Nguyen, T.T.; Kawamura, A.; Tong, T.N.; Nakagawa, N.; Amaguchi, H.; Gilbuena, R. Clustering spatio–seasonal hydrogeochemical data using self-organizing maps for groundwater quality assessment in the Red River Delta. J. Hydrol. 2015, 522, 661–673. [CrossRef] 42. Farsadnia, F.; Rostami Kamrood, M.; Moghaddam Nia, A.; Modarres, R.; Bray, M.T.; Han, D.; Sadatinejad, J. Identification of homogeneous regions for regionalization of watersheds by two-level self-organizing feature maps. J. Hydrol. 2014, 509, 387–397. [CrossRef] 43. Hentati, A.; Kawamura, A.; Amaguchi, H.; Iseri, Y. Evaluation of sedimentation vulnerability at small hillside reservoirs in the semi-arid region of Tunisia using the Self-Organizing Map. Geomorphology 2010, 122, 56–64. [CrossRef] 44. Vesanto, J.; Alhoniemi, R. Clustering of the self organizing map. IEEE Trans. Neural Netw. 2000, 11, 586–600. [CrossRef] 45. Faggiano, L.; Zwart, D.; García-Berthou, E.; Lek, S.; Gevrey, M. Patterning ecological risk of pesticide contamination at the river basin scale. Sci. Total Environ. 2010, 408, 2319–2326. [CrossRef] 46. Wehrens, R.; Buydens, L.M.C. Self-and super-organizing maps in r: The kohonen package. J. Stat. Softw. 2017, 21, 1–19. 47. Ogawa, R.; Yamanaka, M. Hydrogeochemical controlling on groundwater in the Hadano Basin, Kanagawa Prefecture: Processes of mineral weathering and dissolved inorganic carbon supply. In Proceedings of the Institute of Natural Sciences. Sect; Nihon University: Tokyo, Japan, 2018; Volume 53, pp. 125–134, (In Japanese with English abstract). 48. Yamanaka, T.; Tanaka, T.; Asanuma, J.; Hamada, Y. Interaction Between Groundwater and River Water in the Nasu Fan, Tochigi; Bull Terr Environment Research Ctr., University of Tsukuba: Tsukuba, Japan, 2003; Volume 4, pp. 51–59, (In Japanese with English abstract). 49. Craig, H. Isotopic variations in meteoric waters. Science 1961, 133, 1702–1703. [CrossRef] 50. Ichiyanagi, K.; Tanoue, M. Spatial analysis of annual mean stable isotopes in precipitation across Japan based on an intensive observation period throughout 2013. Istopes Environ. Health Stud. 2016, 52, 353–362. [CrossRef][PubMed] 51. Yoshimura, K.; Ichiyanagi, K. A reconsideration of seasonal variation in precipitation deuterium excess over East Asia. J. Jpn. Soc. Hydrol. Water Res. 2009, 22, 262–276, (In Japanese with English abstract). [CrossRef] 52. Gibson, J.J.; Prepas, E.E.; McEachern, P. Quantitative comparison of lake throughflow, residency, and catchment runoff using stable isotopes: Modelling and results from a regional survey of Boreal lakes. J. Hydrol. 2002, 262, 128–144. [CrossRef] 53. Tsuchihara, T.; Shirahata, K.; Yoshimoto, S.; Ishida, S. National-scale variations in the stable isotopic compositions of irrigation-pond and spring waters across Japan. Paddy Water Environ. 2019, 17, 429–438. [CrossRef] 54. Mahindawansha, A.; Breuer, L.; Chamorro, A.; Kraft, P. High-frequency water isotopic analysis using an automatic water sampling system in rice-based cropping systems. Water 2018, 10, 1327. [CrossRef] Water 2020, 12, 278 21 of 21

55. Hamada, H.; Komae, T. Analysis of recharge by paddy ﬁeld irrigation using 222Rn concentration in groundwater as an indicator. J. Hydrol. 1998, 205, 92–100. [CrossRef]

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