Activity 1 - ASB Partnership for the Tropical Forest Margins

Alternatives to Slash-and Burn-Programme

FUNCTIONAL VALUE OF BIODIVERSITY - PHASE II

(December 2002-December 2003)

Implementation Protocol for Activity 1:

Pantropic/meso-scale analysis and synthesis

Contributors: Kate Sebastian and Ellen Douglas

Edited by: Sandra Velarde and Thomas Tomich

Kenneth Chomitz: World Bank Project Manager

Thomas Tomich: ICRAF Project Manager

Activity 1 Leader: Stanley Wood

Contract No. 7114805, Phase II, Modification B, signed between the World Bank and

International Centre for Research in AgroForestry (ICRAF), 28 November 2002

Funded by: BNPP (Bank Netherlands Partnership Programme)

This project is a component of ASB's crosscutting assessment entitled "Forest and

Agroecosystem Tradeoffs in the Humid Tropics", which is a Sub-global component of the

Millennium Ecosystem Assessment (MA).

October, 2003

List of Acronyms

ASB Alternatives to Slash-and-Burn Programme

B Biodiversity

BDI

BNPP

CGIAR

ET

FVOB

ICRAF

Biodiversity Index

Bank Netherlands Partnership Programme

Consultative Group on International Agricultural Research

Evapotranspiration

Functional Value of Biodiversity

International Center for Research in Agroforestry, The World

Agroforestry Centre

International Food Policy Research Institute

Montane Mainland South East Asia

IFPRI

MMSEA

Q

UNH

UofW

W

Hydrograph

University of New Hampshire

University of Washington

Watershed functions

2

Table of Contents

Summary ................................................................................................................................... 5

1 Introduction ...................................................................................................................... 6

1.1

IFPRI’s role & project activities ............................................................................... 6

1.2

Deliverables ............................................................................................................... 6

2 Implementation Plan for Activity 1 (Pantropic/meso scale analysis and synthesis) .. 7

2.1

Activity 1 A. Improved spatial characterization of the focus area at the pantropic scale. …………………………………………………………………………………………………7

2.1.1 Activity 1.A.i - Assemble more detailed information on biodiversity-rich tropical habitats (IFPRI lead initiative).............................................................................. 8

2.1.2 Activity 1.A.ii Integrate improved data on human population distribution ... 11

2.1.3 Activity 1.A.iii Measure historic change in land cover and develop scenarios for areas of rapid change in land cover ............................................................................ 13

2.1.4 Activity 1.A.iv Undertake synoptic modeling of hydrological impacts of land use change ........................................................................................................................ 24

2.2

Activity 1B Pantropic assessment of the potential threat posed by hydrological disturbance and impact ........................................................................................................ 33

2.2.1 Activity 1B.i: Characterize areas vulnerable to changes in hydrological function and identify hydrological “hotspots” ................................................................. 33

3 Scientific papers based on the above ............................................................................ 38

3.1

Paper 1. Sensitivity of river systems to forest cover change: a pan-tropical perspective. .......................................................................................................................... 38

3.2

Paper 2. A typology of hydrologic sensitivity to land cover change: application to the pan-tropics ..................................................................................................................... 39

3.3

Paper 3. Searching for Synergy in Tropical Forest Ecosystem Services: Historic and Projected Land Cover Scenarios for Exploring Biodiversity and Watershed Function

Linkages ............................................................................................................................... 40

Figures.

Appendix 1. General background document

Appendix 2. Links between activity 1 and 2 and policybriefs

3

Implementation Protocol for Activity 1: Pantropic meso-scale analysis and synthesis

List of Figures

Figure 1 . Scope of the study: Pantropic definitions.

Figure 2 . TEMveg classes in pantropic domain.

Figure 3 . Sensitivity of basin discharge to change in 20% increase in leaf resistance and to change in leaf width.

Figure 4 . Targeting Conservation to Areas of High Biodiversity and Hydrological Service

(changes in level of service brought about by land cover change).

Figure 5 . Land Cover Change Scenarios for the Pan-Tropic Assessment

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Summary

This Implementation Protocol is for ACTIVITY 1 of two interrelated sets of activities comprising the second phase of a collaborative research project entitled “Functional Value of Biodiversity (FVOB)”.

The general background to the overall project is described in Appendix 1. This project will assist the

World Bank and its clients in project development and policy analysis by:

 providing systematic assessments, for significant areas of the humid tropics, of the hydrological value of forests with biodiversity significance in promoting local livelihoods and resilience to economic and environmental shocks; and

 assessing the nature, magnitude, geographical scope, and relation to poverty of these hydrological values and processes.

The present ACTIVITY 1, entitled “Pantropic/meso Scale Analysis and Synthesis”, builds on preliminary work on biophysical and human geography undertaken in Phase I of this project. Phase I results demonstrated the feasibility of this approach for specific ecologically-based categories (viz., the humid and subhumid moist broadleaf forest zone – the tropical rainforest biome -- of the WWF

Global 200 Ecoregions) and of combining these with complementary data on human population distribution. The project has benefited from publication of new datasets, especially those compiled for the Millennium Ecosystem Assessment, in which several key project partners are active participants.

New spatial tools produced by a University of New Hampshire (UNH) team were identified that could provide the basis for a coarse pantropic analysis of human vulnerability to degradation of certain hydrological functions.

In Phase II, the International Food Policy Research Institute (IFPRI), which led Phase I work on this activity, has been joined by the UNH team. IFPRI, UNH, and other FVOB team members are applying these new tools and state-of-the-art datasets to “pantropic” mapping of three distinct problem domains: (1) biodiversity loss, as represented by globally-significant ecosystems, (2) relationship between land cover change and specific hydrological functions, with particular attention to flooding risk, represented using coarse scale synoptic models, and (3) human population densities. The overall goal of these activities is to identify locations within the humid tropics where the hydrology/biodiversity/poverty nexus is likely to be important; and to assess the size of the human population of these areas. The specific research activities, which include major efforts in data assembly, development of pantropic land cover change scenarios, synoptic modeling and simulation, and overlay of the problem domains are described in detail in Part 2 of this protocol.

It is anticipated that a number of multi-authored research articles will be produced as a result of these activities, including at least one manuscript based on results from Activity 1 before the end of Phase II in December 2003 and others subsequently. The contents of those research papers depend, of course, on the research results. However, some indicative outlines of these papers are presented in Part 3 of this protocol.

Appendix 2 of this implementation protocol describes the links and flow of key results between

ACTIVITIES 1 and 2. Tables 3 and 4 of Appendix 2 list a number of possible issues in the ASB

Policybriefs series and show how these activities contribute to those issues. Among the candidate

ASB Policybriefs listed, the text for at least two will be completed by the end of Phase II and additional potential issues will be covered as technical notes to be refined further as policybriefs in

2004.

5


1 Introduction

1.1

IFPRI’s role & project activities 1

IFPRI is the partner working on Activity 1, the pantropic/meso scale analysis and synthesis.

We are tasked with providing an improved spatial characterization of the focus area at the pantropic scale (Activity 1A). This involves the following sub-activities:



Assemble more detailed information on biodiversity-rich tropical habitats



Integrate improved data on human population distribution



Develop scenarios for areas of rapid land cover change



Undertake synoptic modeling of hydrological impacts of land cover change (lead by

UNH).

In close collaboration with UNH, ICRAF and the World Bank, we will also perform a pantropic assessment of areas of hydrological disturbance and impact (Activity 1B). This involves the following sub-activities:



Characterize areas vulnerable to changes in hydrological function

 Identify ‘hydrological hotspots’ areas of disturbance



Characterize the hydrological hotspot areas in terms of biodiversity and population

1.2

Deliverables



Implementation protocols for all sub-activities under Activity 1



Technical report covering all activities, detailing data sources, methods and models applied, substantive outputs, and policy or methodologically relevant conclusions.



Spatial datasets and analyses, with appropriate metadata, in archival form (e.g. CD-ROM) available by ftp from a public website covering the humid pantropics and impact areas, including an integrated global gridded dataset incorporating key variables from Activity

1a (population, biodiversity, land use change scenarios, hydrological impact areas & hydrological hotspot areas)



Collaborative work on 1-2 manuscripts/policybriefs corresponding to activity 1.

1 Per IFPRI’s Terms of Reference (TOR)

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2 Implementation Plan for Activity 1 (Pantropic/meso scale analysis and synthesis)

2.1

Activity 1 A. Improved spatial characterization of the focus area at the pantropic scale.

Lead : IFPRI Collaborator : UNH

IFPRI is responsible for coordinating the tasks related to Activity 1. We will work in close collaboration with the team from the University of New Hampshire (UNH). The specific steps for Activity 1 are listed below with details for each regarding the scope of the study, the data resolution and specific input datasets, the methodologies employed, the status of the work, the expected dates of completion, the linkages to other activities within the FVOB study, and the expected outputs from each activity/task.

One key component to successfully completing the tasks for Activity 1 is the process of collaboration between the team members. IFPRI and UNH have established a strong and reliable process for working together which includes the review of methodologies, trouble shooting processes, and the transfer of data. This specifically involves:

1.

Regular communication in regards to the status of work, the tasks outstanding, roadblocks, deadlines, data exchange, etc. (via email or phone on a weekly or biweekly basis, depending on travel schedules).

2.

Technical communication of the parameters required for the modeling work, for scenario development and for deriving the historic land cover surfaces.

3.

Regular discussion of methodologies and establishment of a trouble-shooting practice (ie. to provide a sounding board for ideas and processes).

4.

Collaboration or, at minimum, opportunity to comment on submitted documents (e.g. implementation protocol; paper abstracts; etc).

5.

Open exchange of existing data that are necessary components of the specified task or tasks (e.g. SAGE’s pasture surface). The provision of data should include all available supporting documentation.

6.

Timely provision of data created specifically for this project. Along with the data, the creator of each dataset should provide sufficient meta-data which describes the data format and the methodological steps employed in the creation of the dataset.

7.

IFPRI and UNH have also agreed to copy all team members on correspondence related to

Activity 1 or the FVOB project overall. This is at the member’s discretion but it is stressed that all team members should be kept informed of developments related to this project.

Amendment to Activity 1 Implementation Protocols based on discussions during BNPP team meeting in Prague, Czech Republic, 11-12 October 2003:

The BNPP/FVOB Activity 1 team (comprising IFPRI, UNH, ICRAF and World Bank staff) met in Prague 11-12 October. This meeting occurred immediately prior to a major

7


Millennium Ecosystem Assessment (MA) meeting that involved several BNPP/FVOB researchers, thereby reducing meeting costs significantly for this project. The focus of much of the discussion in the Prague meeting was refinement of mappable and graphable indicators that could be developed from available data and simulation results regarding overall objectives of the project: (1) human population ‘exposed’ to hydrological effects of tropical deforestation; (2) indicators of hydrological ‘leverage’: points that have a great impact on human populations if deforested; (3) biodiversity distinctiveness; and (4) conversion threat.

2.1.1

Activity 1.A.i - Assemble more detailed information on biodiversity-rich tropical habitats (IFPRI lead initiative)

2.1.1.1

Task 1: Determine the extent of the pantropics based on ecosystem/biome boundaries

An ecosystem is a spatial unit made up of complex plant, animal, and microorganism communities and the nonliving environment within which these communities function.

Ecosystems thus provide units of analysis based on natural characteristics and not politically defined boundaries. Most ecosystem variables are geographically continuous so distinct boundaries are hard to define but there are patterns that arise in biological communities that do allow for delineation and often relate to patterns in underlying abiotic conditions.

An initial review of globally consistent sources of ecosystem and biodiversity information revealed the approach and databases developed by the WWF were most consistent with the needs of the FVOB project. WWF defines ecoregions as ‘relatively large units of land containing a distinct assemblage of natural communities and species, with boundaries that approximate the original extent of natural communities prior to major land-use change (Olson et al. 2001 p933).’ WWF have created a global map of ecoregions containing more that 800 ecosystems which are classified into biogeographical realms and biomes. The extent of the pantropic study area is thus based on the boundaries of WWF’s tropical forest biomes. The initial review of biodiversity and ecosystem data sources is being extended and documented

as a separate task (see Task 2 below).

a. Status: Completed. An extensive period of review (via emails, meetings and conference calls) lead to decisions about how broadly we should define the extent of the area within which we would examine biodiversity richness and watershed (forest cover) conservation.

2

It was decided that we would include all of the forest biome areas that drain into or out of the pantropics. This has implications for both the scope of the hydrological modeling (UNH’s

Water Balance Model (WBM)) and the land use scenario development (see Activity 1.A.iii

).

The extent of the study area is thus defined by the following WWF tropical forest biomes:

Biome 1: Tropical and subtropical moist broadleaf forests

Biome 2: Tropical and subtropical dry broadleaf forests

Biome 3: Tropical and subtropical coniferous forests

b. Scope: pantropic

2 The overall study areas includes downstream locations where human populations might be affected by changes in hydrological regime as a consequence of the changes taking place in the tropical forest biomes delineated by this task.

8

Implementation Protocol for Activity 1: Pantropic meso-scale analysis and synthesis c. Scale: 0.5 dd (30 arc minutes)

d. Methods:



Discussion of implications of extending the area beyond the tropics (e.g. including dry and moist broadleaf forests versus just including moist)



Visual interpretation of mapped area and extent within biomes 1-3

e. Data Inputs: WWF biomes

f. Output-analyses: Pantropic boundaries shapefile (ptrop123.shp). g. Linkages: defines full extent of the area within which biodiversity and, hence, watershed conservation interventions might be targeted. h. Milestones: Completion of final boundary file defining the study area. i. Date expected: July 2003. j. General notes/comments: N/A.

k. References: Olson et al. (2001) 'Terrestrial Ecoregions of the World: A New Map of Life on Earth'; region specific assessments.

2.1.1.2

Task 2: Operationalize a definition of biodiversity for Activity 1

This project focuses on the nexus between hydrology, biodiversity and populations. In order to better understand these relationships it is imperative that we clearly define what we mean by biodiversity. There are a number of different biodiversity schemes and defining paradigms (e.g. WWF, CI, TNC etc.). Based on analyses undertaken in phase I, IFPRI proposes the use of the WWF delineation of the worlds 800+ ecosystems and the accompanying ratings of the ecosystems’ biological distinctiveness index (BDI) and conservation status to define biodiversity. To more comprehensively justify this choice we are undertaking a ‘due diligence’ review of this and competing schemes. We anticipate that this review will validate our prior assessment that the WWF schema is the most appropriate for this study, and are thus continuing our biodiversity analysis in parallel using this data source. a. Status: Literature review and comparative assessment are still in progress based on a series of documents obtained from a variety of sources (e.g, WWF, CI and TNC). We are in continued dialogue with Taylor Ricketts, WWF-US, Director of Research, and others at

WWF and are visiting with WWF, CI, and TNC to discuss our interpretation of their various approaches to defining biodiversity and developing ecosystem boundaries. We have also reviewed the MA chapter/outlines on biodiversity. b. Scope: Global.

c. Scale: Various.

d. Methods: Literature review, expert consultation, and write up of different approaches to defining biodiversity to be reviewed by the BNPP/ASB team members for comment/approval. e. Inputs: sources described above. f. Output-analyses:

9




Review of available ecosystem classification and related biodiversity assessment data sources



Specific assessment of the strengths and weaknesses of the WWF ecoregions and biodiversity measures used in the FVOB project



Working definitions of biodiversity & biodiversity threats.



Tabular databases of the status of biodiversity within each ecoregion, regional summaries of biome and ecoregion extents.


Decision on mapping indicators of conversion threat: the team will seek to generate our own threat index, rather than relying on other sources. g. Linkages:

Outputs will help describe both the geographic extent and spatial variability of various types of tropical forest ecosystems and the nature of the biodiversity that each contains. This lays the basis for linking biodiversity “value” to the loss of hydrological function avoided if the remaining intact areas of such ecosystems are conserved. Thus, these results directly feed into the analysis that underpins the discussion papers/policy briefs. h. Milestones:



White paper on biodiversity concept outlining different approaches and describing the specific pros and cons of using WWF ecoregions as a basis for the type of policy analyses being undertaken by the FVOB project. This paper will be distributed for comment/discussion via email to the ASB-BNPP distribution list.



Closure on biodiversity concepts and characterization measures for use in FVOB



Written biodiversity component of manuscripts and policy briefs i. Date expected: 8 october 2003.

j. General comments/notes: Current understanding of WWF versus other (e.g. CI) maps of biodiversity rich areas is that the WWF is the best dataset for use in the FVOB project since it is broad in scope (global), offers continuous coverage, & does not consider the influence of humans in defining the ecoregions. The two indicators available for each ecoregion that are of potential value to the FVOB project are the biodiversity distinctiveness index (BDI) and the conservation status. The BDI is a scale-dependent attribute of biological richness that was determined based on 5 criteria: species richness; endemism; complexity of species distributions; uniqueness and rarity; geographic uniqueness. It is important to note that the

BDI was derived independent of threat and is thus a ‘pure’ (ie. no human element) indicator of biodiversity. The conservation status is determined at the landscape level and is based on: loss of original habitat; number and size of habitat blocks; fragmentation/ degradation; conversion rate and degree of protection with habitat loss carrying the most weight. There is a 'snapshot' conservation status (c.s.) and a 'final' c.s. with the 'final' adjusting the snapshot c.s. to account for threat. The value if the 'final' c.s. to this project depends on what WWF see as threats versus what we see as threats.

k. References:

10




Olson et al. (2001) 'Terrestrial Ecoregions of the World: A New Map of Life on Earth'; region specific assessments (e.g. Dinerstein et al. 'A Conservation Assessment of the

Terrestrial Ecoregions of Latin America and the Caribbean')



Bailey, RG. (1998) Ecoregions: The Ecosystem Geography of Oceans and Continents'

Dassman, R.F. (1973) - IUCN.



Holdridge (1967) Life Zone Ecology.



Schultz J. (1995). The Ecozones of the World: The Ecological Divisions of the Geosphere.

Berlin: Springer Verlag, 449 pp.



Udvardy, M. D. F. (1975). A classification of the biogeographical provinces of the world.

IUCN Occassional Paper No 18.



Conservation International (1998). Biodiversity Hotspots & Major Tropical Wilderness

Areas’. Conservation Biology, 12(3):516:520.



TNC 'Conservation by Design' program (limited to Asia-Pacific & LAC).



Ricketts & Wood (2002). White Paper on Geographic Units of Analysis. Millennium

Ecosystem Assessment.

2.1.2

Activity 1.A.ii Integrate improved data on human population distribution

2.1.2.1

Task 1: Perform overlay analysis of ecoregions within the pantropics and population datasets.

a. Status: Delayed due to lack of updated population datasets (CIESIN GPW v3.0 and

Rural/Urban population datasets are currently available for Africa – the global product is due for completion in August/ Sept). In the event of further delays the analysis can be done using

GPW v2.0 and/or LandScan 2001.

b. Scope: Pan-tropic c. Scale: 5x5 km d. Methods: Data overlay to determine number of people in focus area; average density, urban/rural composition and the location of human settlements within ecoregions e. Data Inputs:



GPW v2.0 (CIESIN, 2000)






Human settlements (CIESIN, 2003)



LandScan Population (ORNL 2001)



Citylights (ORNL) f. Output-Analyses:



Comparative analysis for Africa of the 4 currently available population datasets



Statistical analysis of population characteristics for each ecoregion



Location and size of urban populations for each ecoregion

11




Location and density of rural populations for each ecoregion



Outputs will directly feed into the discussion papers/policy briefs g. Linkages:

 input to hydrological vulnerability analysis (e.g. assessing the threat of local and farfield effects on human populations)

 input to identification of biodiversity hotspots (e.g. potential population pressure on forest

(biodiversity) resources)

 provides underlying analysis for Activity 1 manuscript.

h. Milestones: comparative analysis for Africa to determine value of waiting until the new/revised datasets are released to complete analysis. i. Date expected: Preliminary analysis can be done with GPW v2.0 and/or LandScan 2001 by

July; final analysis cannot be done until release of human settlements database and GPW v3.0

(see notes for expected release dates) j. General notes/comments: GPW v3.0 - Africa currently available; global expected in

August; Human Settlements database - Africa currently available; global expected in Sept k. References: see data inputs above.

2.1.2.2

Task 2: Identify biodiversity ‘hot-spots’

a. Status: to begin late July b. Scope: Pan-tropic c. Scale: 5x5 km d. Methods: Perform data overlays of the ecoregions with population count, population density, land cover and protected area maps to determine 1) the remaining biodiversity richness of the pan-tropic rainforest biomes, 2) what is the biodiversity richness of the areas where humans reside, 3) what is the dominant land cover and what are the land use patterns in the ecoregions that fall within the pan-tropics, 4) what share of the intact, biodiversity rich areas are currently designated as protected areas.

e. Data Inputs:



WWF Ecoregions (analysis extent, BDI and CS indicators)



GLCCD v2.0



‘current’ land cover surface developed in

Activity 1.A.iii

.






LandScan 2001 (ORNL)



GPW v3.0 (CIESIN, 2003) ?



Human settlements (CIESIN, 2003) ?



Protected Areas (WCMC) f. Output-Analyses:

12




Extent of conversion of pantropic forest ecosystems



Level of protection (area share) of intact forest ecosystems



Population pressure on intact forest ecosystems



Cross tabulation of level of protection and population pressure on intact ecosystems g. Linkages:



Provides underlying biodiversity analysis for manuscripts and policy briefs.



Input to hydrological vulnerability analysis (e.g. provides target areas for conservation based on biodiversity criteria).



Key component of biodiversity sections of policy briefs and manuscripts. h. Milestones: draft papers (0, 1st, 2nd) and final paper.

i. Date expected: 0 draft in July, 1st draft 8th September, 2nd draft 8th October. Final paper on 1st December 2003.

j. General notes/comments: 0 order manuscripts here may be combined with others under

Activity 1. k. References: see data inputs above.


Suggested alternative graphical representations for biodiversity data for Scenario 4 (‘business as usual’): x-axis: BDI, richness, distinctiveness y-axis: natural logarithm of proportion remaining under forest cover of ‘original’ or ‘initial’ forest extent.

2.1.3

Activity 1.A.iii Measure historic change in land cover and develop scenarios for areas of rapid change in land cover

2.1.3.1

Task 1: Identify basins that generate runoff in the pan-tropic focus area

Team responsible : UNH Collaborators : IFPRI a. Status: Completed (version 1 based only on biome 1 completed 2/03; final version based on biomes 1-3 completed 4/03) b. Scope: Pan-tropic c. Scale: 0.5dd (30 arc minutes) d. Methodology:

13




Overlay basins derived from the 30-min Simulated Topologic Network (STN-30) with the pantropic extent as defined in Activity 1.A.i Task 1



Isolate all basins that overlap with any portion of the pantropic biomes 1,2&3 e. Data Inputs: WWF Ecoregions/Biomes; Basin boundaries f. Output:



Basin extent boundaries (i.e. mask of basin area)



Pantropic study area basin surface with individual basin boundaries g. Linkages: Provides window of analysis for all components of the pan-tropic study. h. Milestones: Basin boundaries agreed, Analysis extent defined. i.Dates expected: April 2003.

j. General notes/comments : The hydrological analysis will be performed and results summarized separately for each biome. k. References: N/A .

2.1.3.2

Task 2: Derive land cover surfaces for different points in time as a basis for assessing the historic and potential impacts of land use change in tropical forest biomes

This task generates assessments of land cover and land cover change over two distinct time periods: original (“potential”/pre-industrial) to “current”, and current to a range of stylized future conditions – primarily as a consequence of the conversion of forest to cultivated (and urban) systems. Sub-tasks involve generating separate land cover maps for each of the specificied “points” in time, as well as reconciling the land cover classes in pantropic land cover datasets with the land cover classes represented in the pantropic hydrological model,

WBM. Reconciliation involved generating some new land cover options within WBM to accomodate agricultural (post conversion) land uses, as well as simplifying land cover classes used in available global datasets to match the range of “natural” land cover classes embedded in WBM.

2.1.3.2.1

Task 2a: Enhance the capability of the WBM to represent (post-deforestation) agricultural land cover classes

The WBM determines runoff by cell based on a number of parameters including land cover.

The extent to which various land cover types can be integrated into the model is the key to success in determining the effects of land cover change on hydrological function. For this study, the primary focus in looking at historic and potential changes in land cover/use is to gain a better understanding of the relationship between loss of biodiversity (as a result of land cover change) and changes in hydrological function. The types of change in which we are most interested are human induced changes. These changes are encompassed primarily in conversion of land for agricultural and urban use. Changes in agricultural land use are customarily grouped into 2 sub-sets: extensification (expanding the extent of agricultural lands) and intensification (farming the existing agricultural lands in a more intensified manner, e.g. irrigation, mechanization).

14


The current list of land cover classes used in the WBM is available in legends.xls. This file also lists the classes for which UNH currently has information on root depth types (which is one of the parameters that must be available if a class is distinguished in the WBM).

TEMVeg is a land cover dataset based on a distillation of the NASA / IGBP 1- km Local

Area Coverage (LAC) AVHRR data set for land cover. For application to global-scale modeling, the TEMVeg classes are typically grouped into eight classes used by the WBM

(for example, TEMVeg classes 4, 10, 16 and 18 would be reclassified as WBM class 2, broadleaf forest). Each WBM land cover class has the associated model parameters (i.e., rooting depth, leaf conductance, leaf width, roughness height, etc.) assigned based on literature values and modeling exercises. The TEMVeg and WBM classification schemes currently have only one class for land under agricultural use (Cultivation – TEMVeg class 32,

WBM class 6). Since this limits the modeling of hydrological responses to different farming methods, an attempt has been made to provide some greater flexibility in the definition of agricultural land use types. For application to the pan-tropics, WBM classes have been expanded to distinguish deciduous from evergreen forests and to incorporate the following agricultural classes:



Cultivated – irrigated



Cultivated – rainfed



Pasture

At the coarse resolution of the pan-tropic study these classes might be sufficient to represent the broad nature of landscape level changes in land cover following conversion of forest to agricultural land uses. However, we could test this assumption by making some sensitivity tests of hydrological response across a range of polar cases of agricultural land uses.

15


Team responsible : UNH Collaborators : IFPRI a. Status: Completed b. Scope: Pan-tropic c. Scale: 0.5dd (30 arc minutes) d. Methodology:

IFPRI presented to UNH a ‘wish list’ of classes for inclusion in the WBM. Initially we requested that the model be capable of differentiating between irrigated areas, cereal crops, root crops and pasture.

UNH – Ellen discussed with the model programmer whether they have the parameter information (e.g. root depth) to include these classes.

UNH consulted with IFPRI to determine the final list of TEMVeg classes for input into the

WBM. From these discussions it was determined that the model could not accommodate these classes but it could differentiate between rainfed, irrigated and pasture. e. Data Inputs: N/A. f. Output: Enhanced set of WBM agricultural land cover classes. g. Linkages: Directly links to the WBM; determines available classes for use in deriving the hydrological signature of land cover classes; determines available classes for use in deriving the land cover scenarios. h. Milestones: TEMVeg and WBM classes established. i.Date Expected: August 2003.

j. Notes,Questions,Comments: N/A.

k. References: N/A.

2.1.3.2.2

Task 2b: Create the baseline or ‘potential’ land cover surface

Team responsible : IFPRI Collaborators : UNH a. Status: Completed b. Scope: Pan-tropic c. Scale: 0.5dd (30 arc minutes) based on interpretation of 0.05dd ecoregions surface d. Methodology:



Based on an overlay of the pantropic basin area extent defined by UNH and the WWF ecoregions, generate and provide to UNH a list of ecoregions that fall within the study area basins (IFPRI).

 Determine the ‘relationship’ of each ecoregion to the TEMVeg and WBM land cover classes (this task was accomplished primarily by Ellen Douglas (UNH) in consultation with Kate Sebastian (IFPRI) through a steady dialogue and exchange of ideas and classification assumptions).

16




Review and finalize the WWF ecoregions to WBM landcover class relationship

(UNH/IFPRI).



Create 'potential' land cover surface at 0.05dd resolution based on a reclassification of the ecoregions into the TEMVeg classes (IFPRI).

 Intersect the TEMVeg ‘potential’ land cover surface (0.05dd) with the unique id surface

(0.5dd) (IFPRI).



Calculate the shares of each TEMVeg class for each 0.5dd cell and assign the majority value to the cell (IFPRI).



Resulting surface provides 1 TEMVeg class value per cell and the share of each TEMVeg class within that cell (at the finer resolution).



Provide UNH with the resulting surfaces. e. Data Inputs:



Ecoregions/Biomes (WWF 2000)



Pantropic Basin extent (UNH 2003)



List of relationship between ecoregions and TEMVeg & WBM land cover classes f. Output: Original/Potential/Pre-industrial land cover surface expressed in terms of

TEMVeg land cover classes g. Linkages: Input into WBM as part of analysis of the effects of historical changes in land cover on hydrological function. h. Milestones: completion of ‘potential’ surface i. Date Expected : September 2003.

j. Notes,Questions,Comments: N/A. k. References: N/A.

2.1.3.2.3

Task 2c: Create the “current” land cover surface

Team responsible : IFPRI Collaborator s: UNH a. Status: Version 1.0 completed; final version in progress – expected date of completion: late July 2003 b. Scope: Pan-tropic c. Scale: 0.5dd (30 arc minutes) d. Methodology: The ‘current’ land cover surface is a compilation of 2 different interpretations of the GLCCD v2.0 dataset, the University of Kassel’s irrigated area dataset

(GMIAv2.1), Nightlights of the World (ORNL), and SAGE’s pasture land surface. Detailed documentation on the steps taken to create the current surface are included in current_lc.doc.

The basic steps are as follows:



Create a revised classification of GLCCD based on a combination of classes from IGBP and IFPRI’s agricultural extent surface

17




Identify urban areas based on the CityLights database using a 50% threshold



Calculate the shares of each land cover class within each unique 0.5dd cell of the pantropic unique id surface



Disaggregate the area under mosaic classes into individual classes for each cell (e.g. break Agriculture / Forest mosaic into 50% agriculture and 50% forest)



Calculate the area within each cell that is equipped for irrigation



Adjust the existing land cover classes to incorporate the irrigated area by first taking the amount of irrigated area from the agricultural area; if irrigated area exceeds agricultural area then adjust the remaining land cover classes equally to result in the same total area for that cell



Calculate the share of each cell occupied by each land cover class and determine the aggregate land cover value for each cell



Review methodology with UNH / make adjustments if necessary / provide UNH with final surface and documentation e. Data Inputs:



Pantropic Basin extent (UNH 2003)



GLCCDv2.0 (land cover data) – IGBP classification (USGS/EDC)



Agricultural extent based on a reinterpretation of GLCCDv2.0 (IFPRI)



CityLights (ORNL)



GMIA v2.1 – University of Kassel Percent Irrigated Database



Pastureland surface – University of Wisconsin, Center for Sustainability and the Global

Environment (SAGE) f. Output: ‘current’ land cover surface (approx. 1992-93) g. Linkages: Input into WBM as part of analysis of the effects of historical changes in land cover on hydrological function. h. Milestones: Completion of ‘current’ land cover surface.

i. Date Expected: August 2003.

j. Notes,Questions,Comments: The rules by which contemporary data on the extent of converted land cover classes (e.g. cropland, pasture and urban) are assumed to have been converted from “pre-industrial” land cover classes. Complexities arise since all pixel scales used – including the 1 km resolution source data – contain mosaics within which constituent land classes can be identified, at best, by area shares rather than by their sub-pixel location.

An important goal was to find some way to retain Pan-Tropic “Scenario 1” – assessing the scale of habitat/biodiversity loss and changes in hydrological regime associated with conversion of our target tropical forest biomes from pre-industrial to contemporary times.

Given the problems being experienced in reconciling various sources of land cover information to develop this Scenario as well as in obtaining intuitive/defensible hydrological impacts, a view was expressed that we could postpone analysis of this historic perspective and focus only on the forward looking scenarios using contemporary land cover as a basis.

18


However, we are very reluctant to shortcut this scenario since it provides (or not) a significant proportion of our credibility base regarding the data and modeling approaches we are employing. Getting the wrinkles ironed out of this scenario will, we believe, both facilitate and significantly improve the reliability of results obtained from the forward-looking scenarios (as well provide a yardstick for the scale of impacts they are likely to generate).

Recapping Scenario 1 (‘contemporary’).

To minimize problems of reconciling natural ecosystem classes between the pre-industrial (WWF ecoregion) and contemporary (IBGP) map legends, Scenario 1 has been defined as examining the difference between

The pre-industrial (WWF ecoregion) land cover, and

The pre-industrial land cover superimposed with converted (agriculture and urban) areas within the 3 focus tropical forest biomes

In the case of Scenario 1, the way in which information on agricultural land cover shares are netted out of (converted from) pre-industrial land cover class shares was reviewed. It was felt that the rules currently being applied were biasing conversion away from forested areas (and hence, reducing the average hydrological signal associated with conversion). It was agreed that IFPRI would sample the highest resolution (1km) land cover data by region in order to derive regional “rules of thumb” for the relative contribution of natural vegetation classes to agricultural land at the scale required by the hydrological model (50km). This analysis also feeds into item 2 above. Kate will supply the new “current” landcover surface along with the landcover conversion rules to Ellen so that it can be converted to WBM classes

3

. k. References: N/A.

2.1.3.3

Task 3: Develop scenarios for projecting from current to a range of stylized future land use/cover conditions

Assessing the likely nature and rate of land cover change across the entire pan tropics is a major exercise in speculation. Even if the processes were well understood in each location, we have very incomplete information on the status – let alone the future evolution – of the drivers of land cover change. The objective, therefore, is to design a set of land cover experiments that explore the potential future domains of land cover change. In a subsequent activity (1.A.iv) the hydrological responses associated with each potential future will be assessed using WBM. The responses will be assessed for each of the three forest biomes and will be reported both separately and as a whole. The significance of the difference between the hydrological responses of the “current” land cover and each of the hypothetical futures can then be evaluated from a number of perspectives. Two basic clusters of hydrological experiments will be undertaken, one to assess the likely severity of the “local” hazards of land cover change, and the other to assess the likely severity of hydrograph shifts on downstream populations (the “far field” effects).

Team responsible: IFPRI Collaborators: UNH

3 From FVOB-Pantropic Componet – Meeting notes 17 th September 2003, submitted by S.Wood, K.Sebastian and E. Douglas.

19


a. Status: In progress. A range of 4-6 future land cover datasets to be provided to UNH between August 1 and August 15. b. Scope: Significant debate has taken place over the geographical scope within which land cover change should be modeled for this study. The ceteris paribus perspective holds that the study should focus on examining changes in hydrological function that arise within river basins solely as a consequence of projected land use change within the tropical forest biome portions of those basins, e.g., holding constant current land use in all remaining portions of the basin. The inclusionist perspective holds that given the existence of non-linearities and thresholds in the potential threats from changes in hydrological function, it is important to project land cover change across each basin in its entirety in order to assess the true net effects of change, and hence potential threat, at key points along the river network. While, conceptually, the inclusionist perspective is preferable it does present some implementation problems. First, the data and analytical work involved in formulating “wall to wall” land cover change scenarios may have significant resource implications – especially since many of the study’s river basins contain only a minor share of (intact) tropical forests (e.g., most work needs to be done in assessing land cover change in areas that are (likely) not as biodiversityrich as the tropical forest biomes). Secondly, the datasets and models being used are likely inadequate to capture or predict the non-linear and threshold processes that this perspective seeks to include. Our initial preference was for the ceteris paribus approach. However, as we have started to develop the land cover scenarios we have found it necessary to make disaggregated spatial projections based on national level data, in which case it becomes easier to automate the analysis of land cover change over entire countries (and their river basins).

Thus, as a practical matter, we are leaning toward the inclusionist perspective but need to get final agreement on that with UNH since there are some ramifications from the hydrological modeling perspective (The ceteris paribus approach requires only one run of the WBM model for each scenario, whereas the inclusionist perspective requires two runs for each scenario, one with all land cover change and one that suppresses land cover change in the tropical forest biomes).

c. Scale: 0.5dd (30 arc minutes) d. Methodology:

Taking as a base the “current” pan-tropic land cover assessment (Task 2c above) a minimum of four future land cover scenarios will be developed. For the moment these are the “Vaporize”, “Ag. Expansion”, “Ag. Intensification” and “Mission

Accomplished” scenarios. None are expected to come true, but their results should bound the space of real-world outcomes. Briefly the scenarios will entail:



Vaporize: In this scenario the remaining intact tropical forest areas will be notionally cleared (complete deforestation). This is a polar case of land use change, and is designed to shed light on the “bottom line” impact of land cover change on hydrological function

(the bottom line for biodiversity is pretty clear in this scenario!). Our a priori belief is that the “far field” effect of this radical scenario may be rather modest in large basins and for large flood events. Local hazard impacts, however, are likely to be much more significant.

Some of the subtleties of this draconian scenario involve assigning appropriate postconversion land cover classes, and determining how the scenario would be applied differently to each of the 3 tropical forest biomes.



Agricultural expansion: In this scenario we assume that future increases in production to meet growing food demand would be met by expanding the agricultural frontier. An implicit assumption is that there will be no net progress in improved crop and livestock

20

Implementation Protocol for Activity 1: Pantropic meso-scale analysis and synthesis productivity (a reality in some parts of the world). Projections about increased output by region will come from examination of IFPRI’s 2020 (Rosegrant et al 2002) and FAO’s

2015/30 (Bruinsma 2003) global food perspective studies. Additional production will be allocated amongst three post-conversion land cover types: irrigated cultivation, rainfed cultivation and pasture.



Agricultural intensification: Here we assume that increased production is met primarily through increased productivity – this reflects the baseline assumptions of both the IFPRI and FAO studies, where productivity typically increases between 0.5-2 percent per year.

This leads to less land cover conversion. This scenario would be achieved through slight increases in irrigated and rainfed cultivated areas as well as converting some of the current rainfed cultivated lands to irrigated and regionally increasing production in areas where productivity is expected to increase.



Mission Accomplished: This would use scenario 3 but would disallow landuse/cover change in areas of highest biological diversity within the tropical forest biomes (as determined by the biodiversity assessment analysis described above). This scenario would approximate the desired outcome of limiting forest conversion in areas of high biodiversity and would allow the hydrological benefits of that to be compared with other scenarios.

For each of these scenarios we are examining two additional sources of land cover change: (1)

Increased “urban” land cover to allow for increased population demands on built space in both urban and rural areas, (2) Assumptions about tropical forest deforestation related to logging/timber supply – additional to the driving force of agricultural land demands. The method for this latter driver is not fully resolved. We are benchmarking the definition of

“future” to be 2020.

e. Data Inputs:



Pantropic Basin extent

 ‘Current’ land cover surface



GLCCDv2.0 (land cover data) – IGBP classification (EDC)



Agricultural extent based on a reinterpretation of GLCCDv2.0 (IFPRI)



CityLights (ORNL)



GMIA v2.1 – University of Kassel Percent Irrigated Database



Pastureland surface – University of Wisconsin, Center for Sustainability and the Global

Environment (SAGE)



Protected Areas (WCMC)



Road & river networks



CIESIN population datasets and UN population growth projections



Food perspective studies from IFPRI (2020) and FAO (2015/30) that assess the likely levels of rainfed and irrigated crop production area expansion and (implicitly) projected pasture areas.

21

Implementation Protocol for Activity 1: Pantropic meso-scale analysis and synthesis f. Output: A range of 4-6 future land cover datasets together with corresponding documentation related to the scenario design and analytical procedures that generated each of them.

g. Linkages: Primary linkages are to the UNH pan-tropic modeling exercise. Each scenario will be applied to the assessment of both local hazard and far field effects. The documentation of the design and implementation of land cover change scenarios will feed into two papers (#1 Land Cover Change Scenarios, #2 Synthesis of Activity 1) h. Milestones: (1) Delivery of the land cover datasets to UNH (Aug 1 –Aug 15) with limited documentation. (2) Completion of full documentation of datasets (Sept 19 th

). (3) Scenarios manuscript . (4) Synthesis manuscript. i. Date Expected: 0 order drafts expected in July; 1 st

drafts 8 Sept 2003, 2 nd

drafts 8 Oct.

2003, final paper 1 st

Dec. 2003. j. Notes,Questions,Comments: The converted areas (only) were obtained from the contemporary land cover map of the pan tropics developed for this project. Note: that the measure of change forall other scenarios is based on the contemporary land cover map in its entirety. Thus, there is an acknowledged disjoint between the backward looking and forward looking scenarios.

The “converted lands” surface provided by IFPRI to UNH for their initial runs was judged as underestimating the full extent of pasture conversion since there were significant regional differences in the way pastures were treated in the 1km global dataset used (GLCCD v2.0

2001). The agricultural surface is now being re-estimated to integrate the best available global pasture/rangeland dataset (Ramankutty 2003) attempting to avoid double counting of pastures between the ag. extent and pasture datasets. Thus IFPRI is in the process of generating an integrated and coherent “’Current’ Agricultural Land Cover’ (that incorporates cropland, improved and natural pasture, irrigated areas and human settlements). This, in and of itself will be a significant product of the study, and will be needed as a basis for ALL scenarios. The new surface should be available to UNH by Sept 29. k. References: Rosegrant et al 2002, Bruinsma 2003 and data sources listed above.


Scenario 1: What damage have we already done?



Comparison of change between the historic land cover surface and the synthetic contemporary land cover surface.



The synthetic contemporary is the agricultural (cropland, irrigated & pasture) and urban portions of the derived contemporary land cover surface.



Change will be measured based only on the forested areas in the historic land cover surface.

Scenario 2-4 – general rules:

22

Implementation Protocol for Activity 1: Pantropic meso-scale analysis and synthesis change will be measured using the derived contemporary land cover surface as a base protected areas will not be altered

Scenario 2 – what might we lose (from a biodiversity perspective)?



The focus of this scenario is the loss of areas with high levels of biodiversity



The areas of potential conversion will be those indicated by a high conversion threat

(based on WWF’s conversion threat index) (Note: if this index is not available then we will use the areas with critical and endangered Future Conservation Status)



All of the forest within the threatened areas will be converted to agriculture

Scenario 3 – what might we lose (from a biodiversity and hydrology perspective) or inversely, where might we win-win?



This scenario builds on scenario 2 but incorporates the hydrological leveraging index



The hydrological leveraging indicator will be the Upstream Hazard Index (UHI)



Note: Ellen Douglas will circulate the explanation of the UHI for approval/comment from the group / if approved Ellen will determine the threshold for the UHI (see section 2.2.1.).

Scenario 4 – What is the near-term Business as Usual?

This scenario represents the most likely near-term scenario



We will use FAO & IFPRI projection data to determine the expected area growth in agriculture by country



The current mosaic classes will be the first slotted for expansion followed by the cells that are adjacent to agricultural or ag-mosaic cells in the contemporary land cover



Each cell will be given an index based on proximity to agricultural and ag-mosaic cells – these cells will be converted in order until the projected agricultural area has been satisfied



We will incorporate a slope cut-off (Kate Sebastian to talk to Ken Chomitz about the value for this by region)



The order by which to convert each land cover type within a cell will be determined by region based on the rules of Lambin & Geist and the FRA analysis that Ken Chomitz is doing



How pasture will be handled is still an possible issue – Kate Sebastian to review the projections and see if they include pasture.

(update by K. Sebastian 10/17/2003).

23


2.1.4

Activity 1.A.iv Undertake synoptic modeling of hydrological impacts of land use change

2.1.4.1

Task 1: Prepare daily precipitation data and hydrological model representation as a basis for assessing the incidence and severity of flooding at the pantropic scale in the WBM

Team responsible: UNH

2.1.4.1.1

Task 1a: Check availability and accessibility of all model input data (e.g. precipitation, humidity, etc…) in common spatial and temporal resolutions

a. Status: In progress; MarkSIM model on order; Evaluating applicability of NCEP R2 data

(available at 2.5 x 2.5 deg resolution) b. Scope: Regional to Pan-tropic (depending on data source)

c. Scale: 0.5dd (30 arc minutes) d. Methodology: Obtain data in available format and resolution and interpolate to 30-min scale. e. Output: Model inputs for daily WBM runs f. Linkages: Synoptic modeling; hydrologic vulnerability analysis g. Milestones:



Evaluation of daily data sources and accessibility.



Interpolation of daily rainfall gridded surface for the pantropic region. i. Date Expected: 8 October 2003.


k. References: N/A.

2.1.4.1.2

Task 1b: Revise WBM for application at daily time step

a. Status: In progress

b. Scope: Regional to Pan-tropic (depending on data source)

c. Scale: 0.5dd (30 arc minutes)

d. Methodology: Daily WBM analysis within model domain appropriate for local vulnerability analysis. This may entail monthly analysis for entire pan-tropics and then daily analysis for selected areas.

e. Data Inputs: Results of sub-task 2a and zero-order local hazards analysis.

24

Implementation Protocol for Activity 1: Pantropic meso-scale analysis and synthesis f. Output:



Daily runoff generation resulting from "potential" and "current" land cover.



Statistical analysis to compare shift in hydrologic behavior due to change in land cover



Evaluate changes in local hazards due to results of statistical analysis

g. Linkages: Synoptic modeling; hydrologic vulnerability analysis

h. Milestones:



Evaluation of daily data accessibility.



Interpolation of daily rainfall gridded surface. i. Date expected: Assessment of feasibility expected 8 October 2003.

j. General notes/comments: Engineering effects will only be studied where info is available at the specific point, no engineering effects will be studied on watershed functions. Effects of engineering/human alterations will be inferred from composite runoff field developed by

UNH, which combine modeled and observed RO. However, composite runoff available at monthly time step. k. References: N/A.

Note: The following section (Task 2-4) is presented in the implementation protocol ‘road map’ format. This section was submitted by Ellen Douglas and inserted as a part of this document by Kate Sebastian.

25


2.1.4.2

Task 2: Synoptic modeling and analysis

The Water Balance Model

2.1.4.2.1

Scope, dataframe, spatial resolution (complete metadata: sources, definitions, dates, resolution, etc) study area (domain) pan-tropics, defined as river basins that have any amount of tropical forest biome. Tropical forest biomes based on World

Wildlife Fund (WWF) delineation of Biomes 1, 2 and 3. See

Pan_trop definitions.gif

( Figure 1 ).

land cover (including classes distinguished) i.

Potential landcover: Classes based on the reclassification of WWF ecoregions (reference needed) to TEMVeg classes (derived from AVHRR,

Melillo et al., 1993). See WWF2Tvg.xls and

Potential_lc.gif ( Figure 2 ).

ii.

Current landcover: Classes based on combination of EDC’s Global Land Cover

Characterization Database (GLCCD v2.0) for 1992/3 and Seasonal Agricultural Extent See current_lc.doc x,y,vegcode grid 10 & 50 km WWF, IGBP

DEM stream network soils streamflow data aggregation of Hydro1K DEM to 30-min resolution. x,y,z grid 10 & 50 km

Simulated Topological Network (STN-30, Fekete et al,

2001). x,y,flow direction grid 10 & 50 km

FAO Global Soils Database 2000. x,y, texture grid 10 & 50 km yes (FAO/IGPB) simulated streamflow is accumulation of Water Balance

Model (WBM, Vorosmarty et al., 1998) runoff. Observed streamflow based from Global Runoff Data Centre (GRDC,

Koblenz, Germany). dams NA

2.1.4.2.2

Climatology variables sources (real or simulated?) spatiotemporal resolution, original and interpolated time series

New et al., 1998. See clim_var.xls

Gridded fields interpolated from station observations. monthly observations interpolated to 30-min (0.5dd).

Monthly means 1950 to 1995

26


2.1.4.2.3

Machinery

The Water Balance Model

Boilerplate: See Description of Water Balance Model.doc

Modifications: the following modifications to WBM codes will be made in order to appropriately model the pan-tropical system:

1.

Revise deciduosicty (leaf on/leaf off) module to operate based on soil moisture threshold rather than temperature threshold.

2.

Incorporate simple interception function.

3.

Adjust internal parameters (i.e., rooting depth, leaf resistance) appropriately for tropical biomes

4.

Define new classification and specify internal parameters for cultivated and urban classes.

Notes: Hydrological issues



There are potentially three climatological regimes that can be applied to the pan-tropic land cover: long-term average monthly, monthly time series, dekad/daily (these values are simulated from monthly data using # rain days and other ancilliary information).

4

So far analyses have been performed using the long-term average conditions. It seems unlikely that the dekad/daily analysis can be performed in the current phase of work (apart from for illustrative purposes).



It was agreed that the following hydrological indicators could be generated at the pantropical scale:

- (Changes in) Mean annual runoff

- (Changes in) mean highest monthly runoff

- (Changes in) mean lowest monthly runoff



Probably not sensible to report on seasonal flows since this would require definition of

“season” in all locations.



Assessments based on changes related to different return periods would require a set of runs to be performed using a climatological time series.



A possible shortcut method to relate changes in the above long term average indicators to impacts at other levels of probability and/or other indicators, such as instantaneous peak flow, is to perform a set of regressions between the long-term and such measures using

GRDC/UNH gauging station records (noting that these records embody conversion, and so might need stratification by both catchment size and extent of conversion - if sufficient degrees of freedom exist).



To be checked: That WBM is cycled through long-term climatology to steady state, i.e. that runoffs are not an artifact of assumed initial storage conditions.

4 Even using monthly data, WBM works by internally generating a “pseudo-daily” set of rainfall events so as to compute vegetative water use and (soil-)water balance processes on a daily basis. Model outputs, however, are aggregated to a monthly time step.

27


Structural issues influencing outcomes of hydrological responses to (changes in) land cover



Vegetative and soil-water characteristics of land cover types. The WBM family of land cover types has been extended to include: pasture and irrigated and urban. Each new land cover is characterized by vegetative characteristics as well as soil moisture use characteristics. Urban and agricultural land cover types are assigned lower rooting depth parameters which, ceteris paribus , would increase the volume of runoff following conversion. (Any systematic effects of other parameters e.g., LAI, albedo, roughness?)



Interception storage. Original WBM does not model interception storage. Interception storage in tropical forest vegetation is likely significantly higher than in most crops and pasture. To the extent that water could be evaporated directly from interception storage

(e.g. leaf, trunk) in addition to evapotranspiration and evaporation from soil, “effective” rainfall would be less over forests than over agriculture and urban areas. Thus, ignoring interception storage would introduce a systematic bias in underestimating the hydrological impacts of forest conversion. Ellen has reviewed the literature to assess the potential impact of this bias and considers it significant over the most humid regions of the pantropics. The UNH team met on Monday, 9/22/03 to discuss and develop an interception algorithm and are now in the process of incorporating this into WBM.



Potential evapotranspiration (PE). UNH have been using the Shuttleworth-Wallace method of assessing PE (a modified Penman-Monteith method). Applying this approach in the humid tropics, however, some anomalous results have been generated. This was also discussed in the meeting on Monday, 9/22/03.



Routing: WBM does not have any lag structures associated with channel flow. Channel flow is generated using a flow direction grid that allows for accumulation of runoff generated by all (on-flow-path) upstream grid cells in each time step. For large catchments this could cause some significant temporal distortion of high flows, even at a monthly scale. The distortion is likely irrelevant in an annual time scale.

2.1.4.2.4

Land use scenarios preindustrial contemporary loss of high biodiverse areas extensification intensification

Potential, based on WWF ecoregions (see 1.A.iii)

Current, based on GLCCD+Seasonal Ag Extent (see 1.A.iii)

Scenario 1 = eliminate 100% of high biodiversity regions

(‘vaporization’ scenario)

Scenario 2 = conversion of forest/agricultural mosaics to agricultural land use.

Scenario 3 = specified percentage of rainfed agricultural land

(to be determined) converted to irrigated agriculture.

28


2.1.4.2.5

Process parameterization for flow, infiltration, evapotranspiration, etc.

WBM parameters will be assigned based on literature review, discussion with experts and inputs from higher resolution models where appropriate. validation validation is performed by comparing WBM results with results of other studies of similar scope and scale. Comparison will also be made with finer-scale results in order to evaluate the impacts of scale. details on inputs and processing

NA sensitivity analyses sensitivity of model outputs (ET, Q) evaluated by changing model parameters within the range of reasonable values. See

Sensitivity.gif ( Figure 3 ) for example.

2.1.4.2.6

Reporting and analysis

Reporting of direct hydrological flows grid cell or stream flow grid cell statistics of runoff and streamflow (runoff accumulated along STN-30) excedance probability graphs – for what, at what locations? empirical probability distributions of runoff for potential and current landcover simulations. matryushka diagrams? – of what, for what subbasins? total basin yields, high monthly and seasonal flows, low monthly and seasonal flows plotted against: a. basin area (potential, current landcovers and scenarios), b. percent forest (potential, current landcovers and scenarios), c. percent agriculture (current landcovers and scenarios), d. percent area converted from forest to agricultural, e. percent area converted from natural to urban.

2.1.4.2.7

Overlays a.

overlay what: i.

population: CIESIN, LandScan2000 ii.

Relief roughness: data layer developed by UNH iii.

simulated floodplains: data layer developed by UNH iv.

biodiversity a.

WWF regions? b.

protected areas?

B.

economic characteristics? Poverty data? check with Uwe on small area GDP? b.

overlay where? i.

population and biodiversity in areas causing hydrological changes ii.

population and biodiversity in areas subject to hydrological changes

29


Note: Socio-Economic Impact/Threat 5

We discussed the measures of potential impact/threat from a policy/socio-economic perspective. Several measures were agreed:

1. Change in annual, highest monthly, lowest monthly average flows:



On each cell (basic results) (map)

 Within the “flood plain” (Ellen’s global assessment of flood plain based on higher resolution – 6 minutes/10km, using criteria of slope, physiography/ roughness, and distance from river) (map)



At the specific locations of major cities in focus basins (e.g the 95 used in the recently released WB/WWF “Running Pure” report on forest conservation and drinking water)



For each major catchment/basin.

2. Number of people potentially impacted

 Population density in the flood plains (same geography as “flood plain” above)



Potential downstream population having access to the water generated from each pixel

(Ellen has developed the routine to calculate this surface)



In selected major cities in focus basins (e.g corresponding to WB/WWF “Running Pure”)

2.1.4.3

Task 3. A Global “Local Hazard” model a. Status: Cannot be completed in Phase II; deferred to 2004 pending additional funding. b. Scope: Pantropic.

c. Scale: to be determined.

d. Methods

A. Deriving the rules for identifying local hazards. i. Specify which hazards are to be considered: flooding potential ii. UNH team will classify the range of values that define tropical watershed typology, such as landform, soils, geology, landcover, deciduosity and climate. These ranges will then be used to constrain an experiment on hypothetical tropical watersheds (virtual watersheds) performed by the ICRAF-SEAsia team using GenRiver. These experiments will define a set of “rules” which will then be mapped at the pan-tropical scale to determine the watershed sensitive to land-use change and an increase in local hazards.

See Virtual_watershed.doc for details.

B. Pantropic mapping of local hazards

See Relief and population.ppt [777Kb] (sent on 6/10/03) for example. e. Inputs: Deferred to 2004.

5 From FVOB-Pantropic Componet – Meeting notes 17 th September 2003, submitted by S.Wood, K.Sebastian and E. Douglas.

30

Implementation Protocol for Activity 1: Pantropic meso-scale analysis and synthesis f. Output-analyses: Deferred to 2004 g. Linkages to PB, and other deliverables: Deferred to 2004.

h. Milestones: Deferred to 2004. Cannot be completed in BNPP Phase II.

i. Date Expected: Deferred to 2004.


k. References:

See references related to Tasks 2-4.

2.1.4.4

Task 4. Pantropical mapping and overlays:

Description: See Linking Act 1&2.doc

a. Status: ongoing.

b. Scope: Pantropic.

c. Scale: 0.5 (30 arc minutes).

d. Methodology: This combines reporting and analysis of the pantropic 'far field' hydrological modeling and the pantropical local hazard model. It involves:

 defining, measuring, mapping biodiversity · integrating population (and any ancillary location based data)

 developing and mapping land use change scenarios

 assessing the characteristics of sources of hydrological disturbance

 assessing the characteristics of areas subject to hydrological disturbance

 assessing the characteristics of areas of high biodiversity interest.

e. Data Inputs: coverages from preceding activities.

f. Output: overlays as described aboved in ‘methodology’.

g. Linkages to policybriefs: -

h. Milestones: Scientific papers based on 1Aiv. Tasks 2-4. See Papers titles 1 and 2 . Note:

These manuscripts may be combined products with other tasks in Activity 1. i. Date Expected : 1st draft 8 Oct, final document 1st Dec. 2003.


k. References :



Fekete, B. M., C. J. Vorosmarty and R. B. Lammers. 2001. Scaling gridded river networks for macroscale hydrology: Development, analysis and control of error, Water

Resources Research, 37 (7): 1955-1967.



Melillo, J. M., McGuire, A.D, Kickligher, D.W., Moore, B., Vorosmarty, C. J., Schloss,

A.L. 1993. Global climate change and terrestrial net primary production, Nature, 363:

234-240.



New, M., M. Hulme, and P. Jones. 1998. Representing twentieth century space-time climate variability, Part II: Development of 1901-1996 monthly grids, Journal of Climate,

13: 2217-2238.

31




Vorosmarty, C. J., C. A. Federer, A. L. Schloss. 1998. Potential evaporation functions compared on US watersheds: Possible implications for global-scale water balance and terrestrial ecosystem modeling, Journal of Hydrology, 207: 147-169.

32


2.2

Activity 1B Pantropic assessment of the potential threat posed by hydrological disturbance and impact

The preceding tasks will generate assessments of the hydrological impacts of each of the land cover change scenarios from both a local and a far-field perspective. This task evaluates the potential consequence of such hydrological impacts on human well-being. The basic metric used to scale these effects will be either the local population in the case of local hazards, or downstream populations living adjacent to rivers in key (predetermined) locations, particularly those large urban areas situated adjacent to rivers.

2.2.1

Activity 1B.i: Characterize areas vulnerable to changes in hydrological function and identify hydrological “hotspots”

Team responsible : IFPRI Collaborators : UNH a. Status : Not started b. Methodology : The WBM hydrological assessment aims to evaluate differences in the annual distributions of total water yields, dry weather flows and peak flows associated with each scenario. For the local hazard assessment these differences only need be calculated within the tropical forest biomes. For the far field assessments the differences will be calculated at key river control sections throughout each river basin. By default (and subject to confirmation by UNH) such control sections will be defined as occurring at major confluences, and also at the location of larger human settlement along the river (or known to be located in a river floodplain). The potential hazards may be summarized as:



Changes in average annual water yield: reservoir inflows for water supply, hydro-electric power



Changes in dry weather flows: reduced effluent dilution capacity, reduced navigational and ecosystem services, insufficient flushing of saline water in rivers or aquifers



Changes in peak flows: flooding hazard, increased soil erosion, river bed/bank scouring, landslides.

Since it is challenging to summarize the differences in the frequency distribution of each of these hydrological metrics a more practical and policy relevant format may be simply to tabulate percentage differences for a number of specific likelihoods of occurrence. For example, percentage change in the average, 1 in 5, 1 in 10, 1 in 20, 1 in 50 and 1 in 100 year extreme dry and peak flows could be reported for each river control section. The location of larger human settlements and population densities and totals will be taken from Activity

1.A.ii above. Population estimates will be overlaid with estimates of change by hydrologic function and return period and a typology of potential type and level of hazard developed.

There is scope for this stage of the methodology to be extended and refined as the WBM simulations are being run and the preliminary outputs obtained. c. Scope: Pan-tropic . Forest biomes for local hazards, all study basins for far field effects.

33

Implementation Protocol for Activity 1: Pantropic meso-scale analysis and synthesis d. Scale : 0.5dd (30 arc minutes). e. Data Inputs:



Outputs from WBM runs for local and far field effects of individual scenarios on the 3 hydrological attributes of total yield, low flows, and peak flows at specified return periods



Maps of population density



Maps of (riparian) human settlements and associated populations f. Output : Assessments of the level of threat by type, location and severity (% change from current situation) of change in hydrological function, and size of populations potentially impacted g. Linkages : Represents the culmination of assessment of potential hydrological threat, but then needs to be integrated with the associated levels of loss (vaporized scenario) or conservation (mission accomplished scenario) of biodiversity. h. Milestones :



Availability of outputs from WBM



Development of a typology of hazard i. Date Expected : Some elements of this task may not be feasible during Phase II. Feasibility will be assessed at the team meeting in Prague 11-12 October 2003.


k. References: N/A.


Decision, based on characteristics of the UNH Water Balance Model (WBM): minimum basin size for application of WBM is 30,000 km2 (12 pixels). 108 basins in the study domain are that size or larger. Note that, by this construction, ‘coastal’ basins (i.e., small basins near the coast) fall out of the WBM analysis.

Suggested overlays regarding the basins in the WBM domain: human population in each.

Suggested hydrological ‘leverage’ indicator for total yield:

Percent of annual average of precipitation for the entire basin / pixel

Suggested alternative graphical representations of results for total yield at the basin level: x-axis: percent basin area converted from forest – or – percent of basin area forested y-axis: absolute and relative changes in total yield; absolute change in total yield divided by total water demand for the basin.

34


Suggested representations for water demand at the basin level are:

Change in total yield / human population of the basin

Change in total yield / (total yield – total demand), with demand referring to use or offtake by humans for domestic needs, agriculture, industry, etc

Suggested alternative spatial representations for changes in total yield at the basin level, delineated by biome: absolute and relative change in yield for each pixel

Suggested hydrological ‘leverage’ indicator for flooding risk: an upland hazard index was proposed.

Development of a potentially useful index for identifying vulnerability to upstream hazards.

E.M.Douglas, 10/18/03

Description of the Upstream Hazard Index: This index was developed based on my assumption that narrow and steep basins would be associated with a potentially higher hazard vulnerability of downstream populations living on floodplains, relative to wider, shallower basins. Presumably as the basin width narrows and the slope increases, the possibility of upstream hazards increases (i.e., due to minimal attenuation of peak flow). I wanted an index that would identify basins or sub-basins that were both narrow and steep. For any grid cell, the UHI was computed as

35


UHI = cumulative (upstream) area / cumulative (upstream) distance * mean slope for that grid cell.

In the figure above I have plotted the UHI (see legend) and also the occurrence of population living on floodplains (pink/purple color) for southern and Southeast Asia. The Salween and the upstream portions of the Mekong and Yangtze have a high (>5) UHI. In the case of the

Mekong and the Yangtze, the occurrence of populations on floodplains can be seen directly downstream of where the UHI changes from >5 to <1, indicating that these populations may be vulnerable to upstream hazards (i.e. extreme floods or landslides). However, the Chao

Phraya has a very low UHI, which may be more indicative of the scale of this analysis (30min) than the usefulness of the index. The Chao Phraya contains only 10 grid cells at this resolution.

Cumulative distance / cumulative area is essentially an “effective upstream basin width”.

Because I wanted the index to increase with narrower basins, I used the reciprocal of the effective width. I chose to use the mean slope rather than a moving average slope because I wanted a dramatic change in slope to cause an identifiable change in the index. Because of this, the index is dominated by, but not completely prescribed by, the grid cell slope. The mean grid cell slope (in m/km) was computed as the mean slope derived from the GTOPO30

1-km DEM within each 30-min grid cell (see Meybeck et al., 2001). Cumulative distance (km) and area (km

2

) were computed from STN-30 (Fekete et al., 2001). The index units are m/km 2 which are not very physically meaningful. I tried to create a unitless version of this index or one at least with physically meaningful units (i.e., m/km), but other versions had extremely small or large values ranging over many orders of magnitude and none seemed to be as good at identifying the narrow steep basins or sub-basins as this one.

I have not yet tested the index with respect scalability or a threshold value for indicating hazard/no hazard, both which will need to be done to really make this index useful. However, we are considering using the UHI as a means of identifying forested areas that should be protected in Activity 1 landcover scenario 4. We would like the thoughts of the Activity 2 team with respect to the correctness and usefulness of this index.

References



Fekete, B. M., C. J. Vorosmarty, and R. B. Lammers. 2001. Scaling gridded river networks for macroscale hydrology: development, analysis and control of error, Water

Resources Research, 37 (7): 1955-1967.



Meybeck, M., P. Green and C. Vorosmarty. 2001. A new typology for mountains and other relief classes: an application to global continental water resources and population,

Mountain Research and Development, 21 (1): 34-45.

36


Comments and questions on UHI from Ken Chomitz:

1. Remind me on the definition of upstream length -- it is the longest upstream path? Also, I think that the correct formula is length/area as in the figure, not area/length in the text.

2. Why slope at just the point of calculation? At 50 km resolution that's a pretty crude slope measure anyway. And maybe the hazards are greatest where the local slope is lowest? It's probably worth being a little more precise about the intuition here. Are we talking specifically about flooding? You seem to want to identify flooding risks as "the place where the index changes from >5 to <1". But that complicated phrase suggests that the index isn't doing its job -- somehow you want it to peak where it hits the floodplains.

3. Somehow rainfall should enter into the index, it seems to me. Why not multiply by total upstream rainfall. Holding basin width constant, don't you expect impacts to scale up with rainfall?

4. Why not somehow incorporate a variant of the Chomitz-Nelson forest-nonforest interface?

Where the forest is all gone, there's no leverage. Where the forest is intact, there's probably no threat. So a quick proxy for potential land use change is:

[sum over upstream cells of (proportion of forest cover)(proportion of nonforest cover)]/(number of upstream cells) You could multiply by 0.25 to get something that scales from 0 to 1. Probably works best, the smaller the cells.

5. How to test or calibrate?

37


3 Scientific papers based on the above

Paper 1 . Sensitivity of river systems to forest cover change: a pan-tropical perspective.

Paper 2 . A typology of hydrologic sensitivity to land cover change: application to

the pan-tropics.

Paper 3 . Searching for Synergy in Tropical Forest Ecosystem Services: Historic and

Projected Land Cover Scenarios for Exploring Biodiversity and Watershed Function

Linkages.

3.1

Paper 1. Sensitivity of river systems to forest cover change: a pan-tropical perspective.

Title

Sensitivity of river systems to forest cover change: a pantropical perspective.

Base

Authors

Activity 1.A.iv Undertake synoptic modeling of hydrological impacts of land use change

Lead author(s): Douglas/ Vörösmarty

Co-authors: Sebastian/Wood/others?

Target journal(s) ?

Abstract/Outline a. Questions to be addressed:

1. What is the extent of human impacts on pan-tropical land cover?

2. How do hydrologic impacts of land cover/land use change

(LUCC) vary with basin area?

3. How do hydrologic impacts affect human vulnerability? b. Proposed figures and tables:

1. map of potential vs current land cover

2. “matryushka” plot (DQ vs Area)

3. example(s) of change in hydrograph

4. example(s) of change in frequency/return periods.

5. map of population exposed to DQ

6. table of datasets

7. table of DQ at selected return periods

8. table of populations at risk

38


3.2

Paper 2. A typology of hydrologic sensitivity to land cover change: application to the pan-tropics

Title

A typology of hydrologic sensitivity to land cover change: application to the pan-tropics

Base

Authors

Activity 1.A.iv

Undertake synoptic modeling of hydrological

impacts of land use change?

Lead author: van Noordwijk

Co-authors: Douglas/ Vörösmarty

Target journal(s) ?

Abstract/Outline a. Premises:

1. LUCC results in DQ

2. Impacts are spatially complex but policy makers require practical decision tools

3. Typologies have been used successfully to generalize geophysical attributes globally a. Meybeck et al: mountain typology b. Green et al: nitrogen transport b. Approach:

1. Meine develops “rules of thumb” based on virtual watershed simulations.

2. UNH applies “rules” to the pan-tropics to develop pantropical typology.

3. Summarize by typology the sensitivity to LUCC w.r.t. changes in floods, low flows, total yield.

39


3.3

Paper 3. Searching for Synergy in Tropical Forest Ecosystem Services: Historic and

Projected Land Cover Scenarios for Exploring Biodiversity and Watershed Function

Linkages

Title

Searching for Synergy in Tropical Forest Ecosystem

Services: Historic and Projected Land Cover Scenarios for

Exploring Biodiversity and Watershed Function Linkages

Base

Authors

Target journal(s)

Activity 1.A.iii Measure historic change in land cover and

develop scenarios for areas of rapid change in land cover

Kate Sebastian, Stanley Wood, Ellen Douglas and Charles

Vorosmarty

Abstract/Outline

The pan-tropical component of the FVOB study is predicated on the existence of significant tracts of tropical forest that simultaneously provide havens of biodiversity richness as well as socially beneficial watershed services. The study seeks to identify the location and extent of such tracts and generate evidence of the nature and scale of the watershed services they provide. This new knowledge will help assess the scope for targeting and prioritizing watershed conservation efforts that would, by design, provide significant spillover benefits in terms of biodiversity conservation. Put another way, the strategy recognizes and capitalizes upon the relative political efficacy of justifying investments in watershed protection rather than in biodiversity protection per se.

The FVOB study builds on, and seeks to test, a number of key hypotheses and assumptions . Micro and meso level data and models are being used to characterize relationships between climatology and streamflow response. By juxtaposing various spatial and temporal scales of climatology with various spatial scales and patterns of land cover change, this work is establishing “rules of thumb” for predicting local hydrological responses to land cover change that can be applied across the tropics. Another thrust is in assessing the potential “far field” threats to downstream human settlements posed by land cover changes occurring in upstream forest areas. A key element in assessing the likely dynamics of either local or far field effects is the definition of relevant land use change scenarios. This paper describes a set of land use change scenarios designed to generate information of relevance to addressing the central questions of the pan-tropic study: Where are remaining tracts of tropical forest? To what extent have biodiversity and hydrological function already been impacted by deforestation? Where are the most important and most threatened areas of biodiversity in remaining forest tracts?

40


Figures

What would be the benefit, and to whom, of maintaining the existing hydrological function in such areas?

Four scenarios are described. The first scenario attempts to reconstruct the change in land cover that has taken place since pre-industrial times. The second and third lay the basis for assessing the potential magnitude of the hydrological consequences of conserving (a) currently threatened forest areas, and (b) the most biodiversity-rich forested areas. The fourth scenario attempts to calibrate the most likely rate and location of actual land cover change on the basis of current projections of agricultural expansion to 2015.

Several issues, some unresolved, have surfaced during the course of this (on-going) effort. First, many incompatibilities exist between the vegetation classification schema of global datasets on ecoregions (potential or climax vegetation) and land cover (actual vegetation) that limit analytical options in defining the first (pre-industrial to current) scenario. Second, the qualitative and often subjective steps necessary to generate globally applicable measures of biodiversity and conservation often make it difficult to “unbundle” such measures into component parts that might provide greater flexibility in defining scenarios. Third, use of any specific hydrological model brings its own set of opportunities and constraints. It is non-trivial to triangulate and arbitrate amongst the limits imposed by the spatial and temporal resolution of available data, the limits imposed by the spatial and temporal intervals over which the hydrological and hydraulic analytical routines are valid, and the need for impact-relevant information that resonates with policymakers. Finally, projections about likely expansion of the agricultural frontier are speculative at best, and will be influenced by factors ranging from the impact of WTO negotiations on regional patterns of comparative advantage, the role of technology and improved practices in raising land productivity, and the emergence of innovative production opportunities such as biofuels and other bioengineered industrial crops that bring new deforestation pressures.

See Figure 4

and Figure 5 .

41


Figure 1 . Scope of the study: Pantropic definitions.

42


Figure 2 . TEMveg classes in pantropic domain.

43


Figure 3 . Sensitivity of basin discharge to change in 20% increase in leaf resistance and to change in leaf width.

44


- ve

Change in biodiversity service

HIGH

Target

Conservation

Areas

LOW o

+ ve

LOW

HIGH

- ve

Change in hydrological service

Figure 4 . Targeting Conservation to Areas of High Biodiversity and Hydrological Service

(changes in level of service brought about by land cover change).

45


Pre-Industrial

Land Cover

(WWF 2001 Biomes and

Ecoregions)

1. What have we lost?

Synthetic

Contemporary

(WWF + Contemp. Ag)

Contemporary

Land Cover

(1993 GLCCD/IGBP,

2002 IFPRI [Cropland +],

2002 Kassel [Irrigated] ,

2002 Ramankutty [Pasture])

2. What might we lose?

Vaporization of

“Threatened”

Forest Tracts

(WWF 2001-2003)

3. Where can we winwin?

4. What’s the near-term

BAU?

2030: Projected Expansion of

Agricultural Frontier

2015: Projected

Agricultural Frontier

(FAO 2003, IFPRI 2001)

2015/30 1600s?

1990s

BAU - “Business as usual”

Figure 5 . Land Cover Change Scenarios for the Pan-Tropic Assessment

Protection of Areas of “Outstanding”

Biodiversity

Unspecified

Future

46

Activity 1 - ASB Partnership for the Tropical Forest Margins

Alternatives to Slash-and Burn-Programme

FUNCTIONAL VALUE OF BIODIVERSITY - PHASE II

(December 2002-December 2003)

Implementation Protocol for Activity 1:

Pantropic/meso-scale analysis and synthesis

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Activity 1 - ASB Partnership for the Tropical Forest Margins

Alternatives to Slash-and Burn-Programme

FUNCTIONAL VALUE OF BIODIVERSITY - PHASE II

(December 2002-December 2003)

Implementation Protocol for Activity 1:

Pantropic/meso-scale analysis and synthesis

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