BNPP/ASB Functional Value of Biodiversity Project – Phase II 

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

iv Undertake synoptic modeling of hydrological impacts of land use change

           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

        Task 2  Synoptic modeling and analysis

        Task 3  A Global "Local Hazard model"  

        Task 4  Pantropical mapping and overlays

1iv.Task 2: Synoptic modeling and analysis

WBM (Water Balance Model) back to roadmap page

(complete metadata: sources, definitions, dates, resolution, etc)

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 [updated from UNH/IFPRI meeting 09/17/2003]

·        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).[1] 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 pan-tropical 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.

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.

(including paramaterization, validation, sensitivity tests)

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.

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

v. 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

Note: Socio-Economic Impact/Threat[1]

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”)

Notes, 

Questions, 

Comments 

Data requirements and data availability for WBM [from MvN Impl. Protocol Act. 2]

• 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. 
• 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

[1] Shared between Act. 1 A iv Tasks 2-4.

Design and update: Sandra Velarde

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