LOWER ZAMBEZI RIVER BASIN BASELINE DATA ON LANDUSE ...

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EXECUTIVE SUMMARY1. Landuse/Landcover Change from Landsat. Remote sensing produced a multi-scaleanalysis of vegetation distributions:(1) For the study region, several images are available in data archives. Of the availableNASA ASTER, MODIS and Landsat 5 and 7 imagery spanning the period 1986-2005only three Landsat images met our criteria for science-quality data.(2) An initial analysis of March 1991 and December 2000 Landsat imagery showed thatperceived differences are far more sensitive to plant life cycle state than to land-coverchange. The floodplain region of the western portion of the Landsat images showedradical changes in both amount of flooded lands and the actual shape of the riverthroughout the region.(3) Examinations of Landsat imagery using composite analytical techniques, indicatedmapped areas of probable woodland/dense savanna, open savanna, herbaceousdominated,and degraded ecosystems.(4) The study has resulted in new and significant baseline knowledge about land cover in theproject area. It provides a live, spatially-referenced database for the Government ofMozambique (GOM).(5) Further analyses of land-cover, elevation and soil (topo-edaphic) conditions,climatological stress and plant life cycles (phenology) are needed and would require acombination of satellite data. The Zambezi Valley and surrounding region are dominatedby ecosystems displaying variation in vegetation structure and high seasonality.Therefore, it is important to discriminate between actual changes in land-cover (e.g.woodland clearing events) and plant phenological state. Further work should combinethe occasional high-resolution Landsat imagery with high-temporal resolution MODISimages to attempt a separation of phenology from land-cover changes.2. Biodiversity., Field survey and institutional information were used to assess biodiversitycoupled with capacity building:(1) Institutional information about biodiversity in the project area is poor to almost nonexistent.(2) An intensive training course on methods of rapid, aboveground biodiversity assessment(‘Training the trainers’) was completed for 13 participants (GOM, UEM) four of whomwere subsequently engaged in a biophysical baseline survey of the project area.(3) A survey of the project area was conducted using gradient-based (gradsect) methodologycovering key environmental gradients within the lower Zambezi valley. A standardprotocol was used to collect data that facilitates comparison with similar data collected inother countries. Additional data included soils and soil infiltration (for hydrology model)and landuse history.(4) Even allowing for a dry season, the baseline study reveals low levels of biodiversity whencompared to 27 other, mostly tropical countries. The low counts are offset by theoccurrence of numerous species of high horticultural significance as well as a number ofrare, endangered or vulnerable species.(5) Spatial data analysis using high resolution satellite imaging coupled with potentialmapping procedures indicates transects from the gradient-based survey represent morethan 90% of land cover variability in the lower Zambezi basin.(6) Detailed soil analyses and herbarium identifications are expected to reveal a closerelationship between plant-based biodiversity, aboveground carbon and potentialagricultural productivity. Preliminary analyses suggest simple, readily observable, plantbasedvariables can be used to indicate biodiversity that is relevant to land management,land use zoning and economic planning.6
(7) Baseline data (vegetation structure, plant species and plant functional types, includingadaptive photosynthetic attributes) can be quantitatively compared with remotely-sensed,biophysical variables. Initial findings reveal statistically significant relationships betweencombined sets of remotely sensed, photosynthetic, non-photosynthetic plant variables andsoil, and ground-based vegetation structure. As land cover varies with surface drainagepatterns, a close coupling is expected between the recorded plant and soil-based data,remotely sensed imagery and hydrological parameters.(8) Costing and logistic guidelines are indicated for future surveys.(9) The study highlights the global benefits of uniform survey technology to facilitatecomparison with other countries (e.g. biodiversity values, aboveground carbon) and tohelp evaluate tradeoffs between biodiversity management and income generation. Dataacquired along land use intensity gradients provide a framework for forecastingenvironmental change impacts on biodiversity and thus adaptive management options forSPA and related NAPA planning.(10) The study has resulted in new and significant baseline knowledge about biodiversity inthe project area. It provides a live, spatially-referenced database for the GOM and animproved operational framework and enhanced institutional capacity for the regionalassessment of biological diversity.3. Water Resources and Physical Data. A state-of-the art hydrology model and accompanyingdataset were used to derive:(1) A multi-scaled, high resolution, digital elevation model (DEM) capable of representingthe basic structure of the Zambezi basin, has established the central organizing constructfor the multiple information layers of the Baseline study.(2) Using the base DEM, elevation bands and a routing network file for river and flowaccumulation were derived for the entire Zambezi basin.(3) An evaluation of climate data was limited due to a data gap arising from the prolongedconflicts in Mozambique. Hence the convergence of two independent data streams wereevaluated: data records from the World Meteorological Organization (WMO), and thedaily re-analysis product (ERA40), from the European Center for Medium Range WeatherForecast (ECMWF). Despite some discrepancies between the ERA40 and the WMOstations datasets, it was possible to compile a useful database comprising daily andmonthly time series of the climate variables at specific grid cells and annual and seasonalclimatology.(4) The river flow regime of the lower Zambezi valley is dominated by the inputs to anddischarge from the Cahora Bassa dam. The overall flow regime reflects the pronouncedwet/dry season cycle while flow at Tete reflects the input of tributaries, including theRevubo river.(5) Detailed biophysical attributes of landcover for the Zambezi basin have been derived from1-km MODIS/TERRA products for the hydrology modeling and detailed Landsat analyseswere used to define the Smallholders target region. Soils (type, depth, texture) arerepresented at several scales, and for several sets of attributes with a three-layered depthprofile already partly calibrated.(6) The hydrology of the Zambezi basin is being simulated by the Variable InfiltrationCapacity Hydrology Model (VIC). Two VIC runs have been performed, one forced withthe 1994-2005 WMO stations datasets, and one forced with 1994-August 2002 ERA40datasets. A preliminary study shows that a minimum of 3 years of model initiation(spinup) is needed over the Zambezi Basin for the soil moisture to reach equilibrium.(7) Activities addressing data acquisition and modelling approaches for regional staff havebeen initiated, primarily with Eduardo Mondlane University (UEM) and Regional WaterAgency for the Zambezi (AraZambezi).7
Page 1 and 2: LOWER ZAMBEZI RIVER BASINBASELINE D
Page 3 and 4: TABLE OF CONTENTSLIST OF FIGURES AN
Page 5: ACKNOWLEDGEMENTSThis report was pre
Page 9 and 10: 1. INTRODUCTION1.1. PROJECT PURPOSE
Page 11 and 12: 2. LAND COVER AND LAND USE CHANGES
Page 13 and 14: Asner et al. (2003, 2004) collected
Page 15 and 16: 3. BIODIVERSITY ASSESSMENT3.1. BACK
Page 17 and 18: CBD.29 The country of Mozambique (8
Page 19 and 20: 3.3 A RAPID APPRAISAL METHOD AND A
Page 21 and 22: 39. Georeferenced, digitalphotograp
Page 23 and 24: VegClass software and training manu
Page 25 and 26: classifications within the region a
Page 27 and 28: in addition, provide an important r
Page 29 and 30: 4. THE DYNAMIC HYDROLOGY ANALYSIS F
Page 31 and 32: using mostly ArcInfo spatial analys
Page 33 and 34: MODIS’ automated classification p
Page 35 and 36: ERAERAWMO StationsFig. 4.6 ERA40 an
Page 37 and 38: 4.8. RIVER DISCHARGE DATA AND RESER
Page 39 and 40: PrecipitationRunoffEvapotranspirati
Page 41 and 42: Southern Africa MODISZambezi BasinV
Page 43 and 44: (1) Data and information contained
Page 45 and 46: Gillison, A.N. (2002). Bioregional
Page 47 and 48: ANNEXTable 1. List of data variable
Page 49 and 50: Table 3. Site physical featuresTran
Page 51 and 52: Table 5. Description of vegetation
Page 53 and 54: Table 7. Example page of tabulated
Page 55 and 56: ABCDEFFig 1. Examples of ornamental
Page 57 and 58:
No. Country Location Georeference P
Page 59 and 60:
250250Species diversity200150100500
Page 61 and 62:
ABCDEFFig. 4 Transport and water us
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