Watershed Forest Management Information System (WFMIS)

Stephen F. Austin State University SFA ScholarWorks

Faculty Publications Spatial Science

2008

Yanli Zhang Arthur Temple College of Forestry and Agriculture, Stephen F. Austin State University, [email protected]

P. K. Barten Department of Natural Resources Conservation, University of Massachusetts Amherst

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Repository Citation Zhang, Yanli and Barten, P. K., "Watershed Forest Management Information System (WFMIS)" (2008). Faculty Publications. 24. https://scholarworks.sfasu.edu/spatialsci/24

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Environmental Modelling & Software xxx (2008) 1–7

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Environmental Modelling & Software

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Short communication 55 1 56 2 Watershed Forest Management Information System (WFMIS) 57 3 58 4 59 a,* b 5 Yanli Zhang , Paul K. Barten 60 6 Q1 a Department of Geography, University of Northern Iowa, 205 ITTC, Cedar Falls, IA 50614, USA 61 7 b Department of Natural Resources Conservation, University of Massachusetts Amherst, Amherst, MA 01003, USA 62 8 63 9 64 10 article info abstract 65 11 66 12 Article history: Maintenance of a sustainable clean water supply is critical for our future. However, watershed degra- 67 Received 13 February 2008 13 dation is a common phenomenon around the world that leads to poor water quality. In order to protect 68 Received in revised form water resources, the Watershed Forest Management Information System (WFMIS), was developed as an 69 14 9 October 2008 extension of ArcGISÒ and is described in this paper. There are three submodels to address nonpoint 15 Accepted 10 October 2008 70 source pollution mitigation, road system management, and silvicultural operations, respectively. The 16 Available online xxx 71 Watershed Management Priority Indices (WMPI) is a zoning approach to prioritize critical areas for 17 72 conservation and restoration management.PROOF It meets the critical need to spatially differentiate land cover 18 Keywords: 73 Decision support system and site characteristics within a watershed to quantify their relative influence on overall water quality. 19 74 GIS The Forest Road Evaluation System (FRES) is a module to evaluate road networks in order to develop 75 20 Watershed management preventive management strategies. The Harvest Schedule Review System (HSRS) is a module to analyze 21 Water resources and evaluate multi-year and multi-unit forest harvesting to assist in the reduction of impact on water 76 22 Zoning yield and associated changes in water quality. The WFMIS utilizes commonly available spatial data and 77 23 Road has user friendly interfaces to assist foresters and planners to manage watersheds in an environmentally 78 Forest harvest 24 healthy way. Application examples of each submodel are presented. 79 25 Published by Elsevier Ltd. 80 26 81 27 82 28 83 29 Software availability 1. Introduction 84 30 85 31 Name of software: Watershed Forest Management Information Life on earth depends on sustainable clean water supplies and 86 32 System (WFMIS) systematic watershed management is critical to water resources 87 33 Developed by: Yanli Zhang protection. Watersheds are characterized by meteorological, surface 88 34 Contact information: The Watershed Exchange and Technology water and groundwater, and physical and biological factors func- 89 35 Partnership, Department of Natural Resources Conserva- tioning within the context of natural and human disturbance 90 36 tion, University of Massachusetts, Amherst, MA 01003, regimes. The quantity, quality, and timing of streamflow within 91 37 USA a watershed are influenced by these factors (McCammon et al.,1998; 92 38 Tel.: þ1 413 545 4358 de la Cre´taz and Barten, 2007). In order to improve the efficiency of 93 39 Fax: þ1 413 545 4853 limited conservation resources, the identification of critical areas 94 40 Email: [email protected] and human activities that influence water resources is the primary 95 41 Availability: free at http://www.wetpartnership.org/software_ objective of watershed analysis. Biophysical factors (soil, slope, land 96 42 downloads1.html cover/use, etc.) and human impacts (road and timber harvest) 97 43 Available since: June 2006 should be considered systematically in forested watershed 98 44 Learning materials at: http://www.wetpartnership.org/ management. However, watershed models such as WAMView 99 45 softwareapps.html (Bottcher and Hiscock, 2001), WARMF (Weintraub et al., 2001), 100 46 Software required: ArcGIS 9.0 and up with Spatial Analyst RESTORE (Lamy et al., 2002), EMDS (Girvetz and Shilling, 2003), 101 47 extension. UNCORRECTEDWAWER (Girvetz and Shilling, 2003), and Mas et al. (2004) defor- 102 48 estation prediction model only deal with one aspect of watershed 103 49 management. An integrated Management Information System 104 50 (MIS), the Watershed Forest MIS (WFMIS), was therefore developed 105 51 to facilitate watershed management to protect water resources. 106 52 Development of the WMFIS began during the Source Water Stew- 107 53 * Corresponding author. Tel.: þ1 319 273 3877; fax: þ1 319 273 7103. ardship Project (Barten and Ernst, 2004) and in consultation with 108 54 E-mail address: [email protected] (Y. Zhang). the foresters at Quabbin reservoir (MA). It was designed as a general 109

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Please cite this article in press as: Zhang, Y., Paul K. Barten, Watershed Forest Management Information System (WFMIS), Environ. Model. Softw. (2008), doi:10.1016/j.envsoft.2008.10.006 ARTICLE IN PRESS ENSO2146_proof 10 November 2008 2/7

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110 tool with three submodels to cover crucial aspects of forest water- according to local requirements. The WMPI tool was designed to be 176 111 shed management for foresters, watershed management coordina- generic to allow wide use. 177 112 tors, or other water resources related managers. The Watershed The results of a WMPI analysis highlight critical areas of the 178 113 Management Priority Indices (WMPI) submodel addresses the watershed for conservation and restoration. Each Priority Index (PI) 179 114 critical need to spatially differentiate land cover and site charac- is displayed using a different color (CPI with green, RPI with orange, 180 115 teristics within a watershed to quantify their relative influence on and SMPI with red). The results are presented as a graduated legend 181 116 overall water quality. The Forest Road Evaluation System (FRES) is with darker colors indicating a higher value or higher priority for 182 117 used to evaluate existing road networks for maintenance planning. conservation, restoration, or stormwater management. 183 118 The Harvest Schedule Review System (HSRS) focuses on the analysis Dry Run Creek watershed (Cedar Falls, IA) was used to demon- 184 119 of cumulative timber harvesting with the goal of minimizing the strate an application of WMPI. This watershed has an area of 185 120 adverse effects of forest harvesting on water resources such as water 61.5 km2 (61.3% agricultural land, 21.6% developed area, and 17.0% 186 121 yield and quality. natural area). All of the original spatial data, such as the USGS 30-m 187 122 The software was developed with Visual Basic as an extension resolution DEM, 2002 land cover, road network, the Soil Survey 188 123 for ArcGIS 9.0 and higher versions. Required input data include Geographic (SSURGO) data, rivers, wetlands, and lakes, were 189 124 a digital elevation model (DEM), land cover, soils, stream networks, collected from Iowa Department of Natural Resources (DNR). The 190 125 wetlands, roads, and other spatial data which are generally acces- results (Fig. 3) indicate that management priorities could be given 191 126 sible through Geographic Information System (GIS) data clearing- to those areas with the highest scores after field verification and 192 127 houses at federal, state, city, and private levels. The following assessment, for example, a conservation easement for an area with 193 128 sections document the theories, functions, and application of the high CPI value and a stormwater retention pond for an area with 194 129 three submodels. high SMPI value. The Dry Run Creek watershed coordinator has 195 130 used WMPI to identify hot spots to build stormwater retention 196 131 ponds and to restore stream banks. It also has been used to 197 132 2. Watershed Management Priority Indices (WMPI) demonstrate watershed analysis in local watershed management 198 133 meetings involving a diversity of stakeholders. 199 134 Nonpoint source pollution from agriculture and urban and 200 135 suburban development accounts for more than 60% of the impair- 3. ForestPROOF Road Evaluation System (FRES) 201 136 ment in U.S. waterways (US EPA, 1996). Land conservation and 202 137 pollution prevention have proven to be cost effective strategies Forest roads provide basic accessibility for people to enjoy and 203 138 (NRC, 2000). However, with limited resources, where and how to manage natural resources. However, they are a primary source of 204 139 start are critical questions in watershed management (Pullar and sediment (Wemple et al., 2001). As noted by the USDA Forest 205 140 Springer, 2000). It is essential to evaluate and justify selection of Service (2000), not all roads have the same effects on watersheds. 206 141 crucial areas for environmental benefits (Rao et al., 2007). A GIS Variation is great and differentiation between high impact and low 207 142 analysis approach, the WMPI, is designed to identify and rank impact roads is an important analytical challenge. This challenge 208 143 place-based conservation, restoration, and stormwater manage- led to the development of the Forest Road Evaluation System 209 144 ment priorities to mitigate nonpoint source pollution (Barten and (FRES). The FRES assists foresters in finding potential problems 210 145 Ernst, 2004). The WMPI system can combine, analyze, and interpret within existing road networks to develop an effective maintenance 211 146 multiple spatial factors efficiently in consideration of water quality plan to protect water resources. 212 147 protection and improvement. It is a multi-source GIS data modeling In the FRES, factors such as road slope, cutslope, fillslope, stream 213 148 which can substantially improve the classification accuracy over crossing location, and distance to water body are analyzed when 214 149 techniques that use single source data by providing stronger considering road related erosion and sediment loading. Because the 215 150 correlation between geospatial data and features of interest (Rao accuracy of slope calculation is determined by spatial resolution 216 151 et al., 2007). Within WMPI, for every physical factor (slope, soil (Longley et al., 2001) and in consideration of common forest road 217 152 permeability, etc.), each cell (spatial analysis unit of the watershed) width and the availability of DEM data, 5-m resolution DEM data 218 153 is assigned a score to relatively estimate its influence on water were used in the design and testing of the FRES. The 3 3 window 219 154 resources. Then all the scores are weighted and added together algorithm (8 neighboring cells’ elevation are used in calculating the 220 155 using raster overlay. At the same time, cells are classified into three central cell’s slope) is the most common method of calculating 221 156 indices broadly representing the principal conditions of land: (1) slope. However, when calculating road slope, the cells neighboring 222 157 forests and wetland, (2) agriculture and open space, and (3) resi- the road will incorrectly influence the calculation of road slope, 223 158 dential, commercial, and industrial areas. These indices were especially when the road is parallel to contour lines. In order to 224 159 named as conservation, restoration, and stormwater management avoid this problem, an intermediate road elevation dataset (only 225 160 priorities (CPI, RPI, and SMPI), respectively. The WMPI analysis flow cells reflecting road segments have elevation values) was created to 226 161 chart is shown in Fig. 1. Additionally, optional layers such as public use in the general slope calculation algorithm. This approach was 227 162 water supply restriction areas, aquifers, or other spatial factors that validated by comparing calculation results with field measure- 228 163 are important for local water resources can be included. The final ments at typical road segments in the Quabbin Forest. The elevation 229 164 result is identification of the crucialUNCORRECTED areas (those with the highest difference between road surface and its neighboring cells is used to 230 165 scores) within land falling in the three index types: conservation, reflect cutslope or fillslope and Fig. 4 shows the calculation flow 231 166 restoration, and stormwater management. chart. Stream crossing locations are calculated through the inter- 232 167 Four consecutive user interfaces were designed (Fig. 2)to ception of roads and streams. Buffers and intersections are used to 233 168 facilitate the usage of the model. The interfaces’ functions are input find roads near water bodies. 234 169 data selection, priority index setting, parameter setting, and output Fig. 5 shows the test results (road slope, cutslope, and fillslope) 235 170 format selection. As different watersheds may have different land for a section of roads in the Quabbin Forest. Red is used to 236 171 use/cover categories, the system will dynamically track the input symbolize road slope, the darker the color, the steeper the slope is. 237 172 land use/cover categories and set up the second interface. Input Cutslope and fillslope are shown with green and purple, respec- 238 173 spatial data include but are not limited to DEM, land use/cover, tively. Again, the darker the color, the greater the cut height or fill 239 174 soils, and water bodies. Users can change inputs and their weights, depth is. Table 1 shows the statistical summary from the FRES 240 175 adjust priority indices, and use different parameters for the analysis analysis. This information and output maps form a useful database 241

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242 308 243 wetlands 309 streams 244 DEM lakes 310 245 311 246 312 247 313 Flow riparian Optional slope soils 248 accumulation area data 314 249 315 250 316 251 317 252 318 CPI overlay 253 land use CPI 319 254 320 255 321 RPI intermediate 256 land use RPI 322 land use data 257 323 258 324 259 A SMPI BCSMPI 325 260 land use 326 261 327

262 Fig. 1. Flow chart of Watershed Management Priority Indices (WMPI). 328 263 329 264 for watershed managers and foresters to manage the existing road 4. Harvest Schedule Review System (HSRS) 330 265 system in a way that can minimize sediment loading, water treat- 331 266 ment costs, and adverse environmental effects on aquatic Timber harvesting changes headwater stream characteristics 332 267 ecosystems. such asPROOF the quantity and timing of base ﬂow and storm ﬂow, 333 268 334 269 335 270 336 271 337 272 338 273 339 274 340 275 341 276 342 277 343 278 344 279 345 280 346 281 347 282 348 283 349 284 350 285 351 286 352 287 353 288 354 289 355 290 356 291 357 292 358 293 359 294 360 295 361 296 UNCORRECTED 362 297 363 298 364 299 365 300 366 301 367 302 368 303 369 304 370 305 371 306 372 307 Fig. 2. Interfaces of Watershed Management Priority Indices (WMPI). 373

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374 440 375 441 376 442 377 443 378 444 379 445 380 446 381 447 382 448 383 449 384 450 385 451 386 452 387 453 388 454 389 455 390 456 391 457 392 458 393 459 394 460 395 461 396 462 397 463 398 464 399 PROOF 465 400 466 401 467 402 468 403 469 404 470 405 471 406 472 407 473 408 474 409 475 Fig. 3. Test of Watershed Management Priority Indices (WMPI) at Dry Run Creek Watershed (Cedar Falls, IA). 410 476 411 477 concentration of sediment and dissolved nutrients, water temper- precipitation variance does not affect water yield increase caused by 412 478 ature, and the stability of the stream channels (Zhang, 2006, 2008). forest harvesting. Along with increased water yield, wetter soil, 413 479 For a given watershed, suppose that precipitation, water storage, nutrient mobilization, decreased water quality, and increased 414 480 and water leakage will not change much from year to year under channel erosion will occur. The relationship between timber har- 415 481 normal conditions. Timber harvesting generally means less tran- vesting and water yield increase, and the long-term change of this 416 482 spiration and canopy interception (Hornbeck et al., 1997). Evapo- increase, have been studied extensively. Previous studies (Kovner, 417 483 transpiration will be reduced and, in consequence, water yield will 1956; Lull and Reinhart, 1967; Douglas and Swank, 1972; Bosch and 418 484 increase. Kovner (1956) analyzed a case in Coweeta (North Carolina) Hewlett, 1982; Douglas, 1983; Verry, 1986; Hornbeck et al., 1997; 419 485 and demonstrated that streamflow increases were independent of Swank et al., 2001; Hornbeck and Kochenderfer, 2004) demon- 420 486 the annual precipitation after harvesting. Lull and Reinhart (1967) strated a mathematical relationship between forest harvesting and 421 487 also concluded that below normal or above normal annual precip- corresponding water yield increase. Generally the water yield 422 488 itation after forest removal did not have a pronounced effect on increase will disappear after 5–20 years if the forest is fully recov- 423 489 water yield increases. These historical studies confirmed that ered. Based on a careful literature review (Lull and Reinhart, 1967; 424 490 Douglas and Swank, 1972; Hornbeck et al., 1997; Swank et al., 2001), 425 491 a ‘‘disturbance threshold’’ theory was proposed to study the influ- 426 492 DEM ence of forest harvesting on water yield. This threshold is applied as 427 cell value real 493 roads either the proportion of treated area or the proportion of biomass 428 setting elev. 494 road-elev-UNCORRECTEDremoval in the watershed. Below this threshold, water resources are 429 expand 495 2sides considered as not being significantly influenced by forest harvest. 430 convert to 496 raster In order to mathematically evaluate accumulated forest har- 431 road 497 vesting effect, a disturbance index (R) is used to consider multi-year 432 elev. 498 road raster harvesting, multi-harvesting units, and regrowth after harvesting. 433 minus 499 434 500 cell value PN 435 setting ðXiYiAiÞ 501 436 i 502 DEM fill/cut slope R ¼ (1) 437 Total Watershed Area 503 438 where N is the number of management units, for each management 504 439 Fig. 4. Cutslope and fillslope calculation flow chart. unit (i), Xi is recovery time index (0 X 1) which accounts for tree 505 Please cite this article in press as: Zhang, Y., Paul K. Barten, Watershed Forest Management Information System (WFMIS), Environ. Model. Softw. (2008), doi:10.1016/j.envsoft.2008.10.006 ARTICLE IN PRESS ENSO2146_proof 10 November 2008 5/7

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506 572 507 573 508 574 509 575 510 576 511 577 512 578 513 579 514 580 515 581 516 582 517 583 518 584 519 585 520 586 521 587 522 588 523 589 524 590 525 591 526 592 527 593 528 594 529 595 530 596 531 PROOF 597 532 598 533 599 534 600 535 601 536 602 537 603 538 604 539 605 540 606 541 607 542 608 Fig. 5. Quabbin road analysis with Forest Road Evaluation System (FRES). 543 609 544 610 545 611

546 growth after harvest, Yi is treatment index (0 Y 1) which is the management unit’s harvest year and full recovery period. The 612 547 percentage of the area cut or percentage of biomass removal treatment index (Yi) is the value set by the user in the harvest 613 548 (Zhang, 2006, 2008). Yi represents harvest type, whether it is clear layer’s attribute table according to actual harvest method. The 614 549 cutting, strip cutting, or thinning. Ai is the area of the management retrospective analysis uses historical harvest data to calculate R for 615 550 unit within the subwatershed. each watershed (block) of interest for past and current years. This 616 551 Based on Eq. (1), the HSRS interface (Fig. 6) was designed to can help foresters to accurately quantify the effects of earlier 617 552 facilitate development of retrospective and future harvest plan cutting. Foresters also could combine this result with past water 618 553 analyses. The recovery time index (Xi) is derived from the quality/quantity records to establish a local disturbance threshold. 619 554 Future harvest plan analysis is based on historical harvest data (to 620 555 establish initial conditions) and future harvest plan data to calcu- 621 556 622 Table 1 late the potential R for each watershed. This can help foresters to 557 Roads management information generated with FRES (Quabbin Forest roads near predict the potential impact of a given harvest plan on water 623 558 Pelham, MA). quality/quantity, and then make necessary harvest plan changes as 624 559 625 Total length 64,332 m needed to protect water resources. 560 Steam crossingsUNCORRECTED 32 The HSRS was applied with forest harvest data from the Quabbin 626 561 Roads within 30 m of water 7243 m Forest and Fig. 7 shows the analysis result. Users of the HSRS need 627 562 Cutslope 1-m cut: 21,782 m to set the disturbance threshold and recovery time based on the 628 563 2-m cut: 3357 m local situation (climate, tree species, topography, soil, etc.) as forest 629 564 3-m cut: 135 m recovery is influenced by these factors. The watersheds with an R- 630 565 Fillslope 1-m fill: 23,442 m value above the user specified threshold are in white, alerting 631 566 2-m fill: 2409 m planners and foresters to watersheds where changes may be 632 567 3-m fill: 118 m necessary. For example, delaying a proposed harvest by 2 or 3 years 633 568 Road slope 0 < slope 5%: 46,760 m (73%) could allow adequate time for regeneration on earlier harvest units 634 569 5% < slope 10%: 12,391 m (19%) to ensure the watershed’s R-value stays below the threshold. 635 570 10% < slope 15%: 1766 m (3%) Similarly, shifting the harvest unit to an adjacent subwatershed or 636 15% < slope: 182 m (0.3%) 571 altering harvest area can help too. 637

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638 704 639 705 640 706 641 707 642 708 643 709 644 710 645 711 646 712 647 713 648 714 649 715 650 716 651 717 652 718 653 719 654 720 655 721 656 722 657 723 658 724 659 725 660 726 661 727 662 728 Fig. 6. Interface of Harvest Schedule Review System (HSRS). 663 PROOF 729 664 730 665 5. Summary management strategies, and the HSRS analyzes the spatial 731 666 distribution and silvicultural method of timber harvesting in 732 667 The theories, main functions, and example applications of the consideration of their impacts on water resources. As water 733 668 newly developed WFMIS are reported to introduce this tool to resources protection is a complex issue and includes many 734 669 the research community and foresters for protection of water aspects, the main effort for the future version of this software 735 670 resources through watershed management. Within the system, would be covering more of those aspects, such as soil erosion 736 671 the WMPI focuses on prioritizing land for conservation and prediction, road network planning, and wildlife habitat 737 672 restoration, the FRES evaluates road networks to optimize inﬂuence. 738 673 739 674 740 675 741 676 742 677 743 678 744 679 745 680 746 681 747 682 748 683 749 684 750 685 751 686 752 687 753 688 754 689 755 690 756 691 757 692 UNCORRECTED 758 693 759 694 760 695 761 696 762 697 763 698 764 699 765 700 766 701 767 702 768 703 Fig. 7. Test of Harvest Schedule Review System (HSRS) with Hardwick Block (Quabbin Forest, MA) harvest data. 769

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Please cite this article in press as: Zhang, Y., Paul K. Barten, Watershed Forest Management Information System (WFMIS), Environ. Model. Softw. (2008), doi:10.1016/j.envsoft.2008.10.006