Variation Characteristics of the Main Hydrochemical Indexes in Typical Subterranean Rivers in the South China Karst Region Based on Curve Fitting

<table class="figure-group"><tr class="fig-image" id="a"><td><object data="https://static-preview.hindawi.com/articles/tswj/volume-2019/2787349/figures/2787349.fig.008a.svgz" name="2787349.fig.008a" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(a) </b>The fitting curves of the means of K<sup>+</sup> and Na<sup>+</sup>, and the data points of the December 2015 sampling results</td></tr><tr class="fig-image" id="b"><td><object data="https://static-preview.hindawi.com/articles/tswj/volume-2019/2787349/figures/2787349.fig.008b.svgz" name="2787349.fig.008b" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(b) </b>The fitting curves of the means of Cl<sup>-</sup>, and the data points of the December 2015 sampling results</td></tr><tr class="fig-image" id="c"><td><object data="https://static-preview.hindawi.com/articles/tswj/volume-2019/2787349/figures/2787349.fig.008c.svgz" name="2787349.fig.008c" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(c) </b>The fitting curves of the means of <span class="nowrap"><svg height="14.7729pt" id="M96" style="vertical-align:-3.181499pt" version="1.1" viewbox="-0.0498162 -11.5914 31.5059 14.7729" width="31.5059pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M409 504C401 567 396 607 392 642C354 654 312 665 266 665C137 665 60 583 60 487C60 374 161 325 225 290C300 250 355 215 355 141C355 68 311 21 235 21C131 21 86 122 71 183L41 176C48 128 61 42 68 21C78 16 93 8 118 0C142 -7 175 -15 216 -15C349 -15 438 69 438 174C438 287 344 333 265 374C186 414 138 449 138 522C138 576 172 631 249 631C336 631 363 562 380 499L409 504Z"></path></g><g transform="matrix(.013,0,0,-0.013,6.162,0)"><path d="M381 665C170 665 44 498 44 318C44 125 188 -15 369 -15C552 -15 703 117 703 333C703 534 550 665 381 665ZM359 629C491 629 601 517 601 306C601 114 502 21 390 21C249 21 146 158 146 346S248 629 359 629Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,15.932,3.132)"><path d="M462 177V227H365V632H320C217 496 116 350 21 208V177H284V109C284 43 280 38 190 31V0H451V31C369 38 365 43 365 108V177H462ZM284 227H88C155 336 218 430 282 519H284V227Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,20.879,-5.741)"><path d="M414 144C384 79 371 75 317 75H135L276 221C367 316 408 376 408 465C408 570 327 635 237 635C179 635 131 609 100 575L42 494L67 471C94 510 138 565 205 565C277 565 321 517 321 435C321 348 258 270 195 195C146 137 88 81 33 26V0H411C423 44 433 88 446 135L414 144Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,25.311,-5.741)"><path d="M556 236V289H56V236H556Z"></path></g></svg>,</span> and the data points of the December 2015 sampling results</td></tr><tr class="fig-image" id="d"><td><object data="https://static-preview.hindawi.com/articles/tswj/volume-2019/2787349/figures/2787349.fig.008d.svgz" name="2787349.fig.008d" type="image/svg+xml"></object></td></tr><tr class="fig-caption"><td><b>(d) </b>The fitting curves of the means of <span class="nowrap"><svg height="14.0878pt" id="M97" style="vertical-align:-3.291101pt" version="1.1" viewbox="-0.0498162 -10.7967 30.4764 14.0878" width="30.4764pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M719 650H483V622C553 619 576 606 581 560C585 532 588 487 588 394V148H585L167 650H20V622C67 618 88 610 109 585C127 562 129 557 129 484V264C129 173 126 124 123 93C118 44 92 31 35 28V0H272V28C204 32 181 44 176 95C173 124 170 172 170 264V515H172L598 -9H629V394C629 487 631 532 635 563C639 606 663 619 719 622V650Z"></path></g><g transform="matrix(.013,0,0,-0.013,9.568,0)"><path d="M381 665C170 665 44 498 44 318C44 125 188 -15 369 -15C552 -15 703 117 703 333C703 534 550 665 381 665ZM359 629C491 629 601 517 601 306C601 114 502 21 390 21C249 21 146 158 146 346S248 629 359 629Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,19.351,3.132)"><path d="M290 377C321 398 342 415 358 430C378 450 389 473 389 502C389 578 329 635 238 635H237C184 635 137 610 109 578L64 515L88 493C112 529 154 573 208 573S303 542 303 482C303 409 233 370 141 341L149 308C165 313 190 319 215 319C272 319 341 283 341 193C342 98 292 43 222 43C163 43 122 72 96 94C88 101 79 100 70 94C61 87 47 73 46 60C44 47 48 37 62 23C76 10 118 -12 165 -12C238 -12 430 62 430 223C430 297 379 359 290 375V377Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,24.298,-5.741)"><path d="M556 236V289H56V236H556Z"></path></g></svg>,</span> and the data points of the December 2015 sampling results</td></tr></table>

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Figure 8

Figure 8: Variation Characteristics of the Main Hydrochemical Indexes in Typical Subterranean Rivers in the South China Karst Region Based on Curve Fitting 

Figure 8 | Variation Characteristics of the Main Hydrochemical Indexes in Typical Subterranean Rivers in the South China Karst Region Based on Curve Fitting 