1615-1645 (30mins) Dynamic, process, and spatial (modelling) approachesMark Mulligan, King’s College London mark.mulligan@kcl.ac.ukwww.policysupport.org @policysupport

What are you talking about?● Integrated, process, spatio-temporal models (for policy support)● Research problems where these approaches are best used:

○ complex, exploratory, data poor, poorly specified, at policy relevant scales, physics well known, scale and location dependent.

● Common challenges in applying such approaches:○ scale sensitivity of outcomes, data quality/uncertainty, managing model

and outcome complexity, computing requirements, interfacing with practitioners, representing human response and behaviour

● Contributions possible in terms of evidence, policy and practice:○ site-specific solutions (not one size fits all), prototyping spatial

intervention scenarios, spatio-temporal trade-offs, trialling in silico

Modelling: sometimes measurement is not enoughFor example when we need to:● Go beyond the point or plot scale towards the

policy-relevant scales: site, region or continent.

● Work at policy-relevant time scales.● Understand the impacts of scenarios for

change (that have not happened yet).● Experiment to understand the impacts of

policy interventions (in new settings).● Integrate across many processes or

disciplines to understand the entire system, feedbacks and emergence.

Everyone does it:● Even data are - in fact - models (that is simplified representations of

process, time and space compared with reality), all sensors form a model of reality. Your whole perception of reality is a model based on your sensors.

● All scientists employ some form of mental or conceptual model of the system they work with.

● Thus there is little difference between mathematical modelling and many other scientific endeavours.

● Those who distrust models either should distrust the science behind them or what people (sometimes) choose to do with them (e.g. purporting to ‘predict’ the future), not the method itself.

Ubiquitous Modelling

But...in complex socio-environmental systems :● our understanding is fragmented and over-simplified,● our datasets are poor over the long term and at the landscape-scale● in many cases our understanding of the links and interactions is elementary.

How can our models be any good?They are not great but are one of the best research tools to understand environmental management holistically, and they have value in:● formalising our understanding of processes (mathematically) ● applying these formalised concepts to measured data,● combining knowledge from many (researchers, disciplines, institutions) ● exploring the results of process interactions …over time…over space● sharing and communicating the understanding in a dynamic and transferable

way.

Models are not crystal balls for predicting the future but are tools to assimilate knowledge and explore the potential consequences of system perturbations…All models (science) is wrong but some are/is useful (sometimes)....and if you can convince someone to use it.

The human brain● Is not well adapted to understanding or tracking the outcomes

of multiple processes and feedbacks (even at only one point in space and time).

● Miller (1956) A person can store 7 ± 2 items (numbers, faces, words, processes) in their short term memory under optimal conditions.

● In the presence of distractions even 3 items can become difficult and may disappear in 2-18 seconds (Peterson and Peterson, 1959; Marsh et al., 1997).

Let’s try itSimplified causality diagram

How many can you remember?

● So, let humans define the letters (processes) and connections but build computer systems capable of integrating the outcomes of their connections over time, space and variability, ad infinitum

● Move from reductionism (good for understanding slices of science) to integration (better for understanding the whole socio-environment, its interactions and emergent properties)

An example the MEDACTION model:

Integrated :-)Spatial :-)Dynamic :-)Scenario-based :-)Complex :-(

Integrated Modelling

The biggest problem with such integrated models iskeeping a handle on the complexity……

After Briggs and Smithson (1985)

Models (and model results) are complex (like the reality is).

An ecosystem services example: the hydrological ecosystem services of tropical montane cloud forests (in relation to PES)

Zadroga (1981):● Observed more runoff than rainfall on Atlantic slopes of

Costa Rica- therefore fog contributions to cloud forests must be hydrologically significant?

● If we lose the forests, we lose that contribution to water resources?

Understanding the processes of cloud water interception to forests and the changes on conversion to pasture. Hardware models (instruments) capture data to understand processes to build software models (DfID FRP project, VUA, KCL, others)

Building an integrated process-based, spatial model - FIESTA

● Mountain hydrology● Wind-driven rain● Fog inputs to different

vegetation structures● Contribution of fog

inputs to runoff● Implications of forest

loss for water quantity downstream

Realising that fog contributions are small, highly localised and decrease quickly downstream... ● Zadroga’s gauges significantly underestimated

rainfall on the windy Atlantic slopes leading to the appearance of more runoff than rainfall

● Cloud forests are important, but for water quality, not quantity benefits

● The model changes the question (and the instrument)

Spatial, process models can help ESPA:

“ESPA’s research considers the links between multiple ecosystem services and multiple dimensions of poverty, their interactions, synergies, trade-offs and tipping points, whilst also recognising that these relationships vary in space and time.”Non-linear, feedbacks, phase transitions, discontinuities, scale variance, upstream-downstream...how else is it possible to understand this at scale without some spatial modelling?

...but some challenges remain to migrate such models into usable knowledge (policy support systems):

● Need for models to be usable in data poor environments (self-parameterisation, global/national data)

● Need to ensure relevance and uptake (co-design with stakeholders). Models must be used to be useful.

● Need to avoid wheel re-invention (open-source, modular. APIs)● Need to enable easy model inter-comparison (inter-operability,

APIs)● Need for relevance beyond the project/site (process basis,

continuous two-way interaction with a user community)● Need for research tools to become operational systems (web-

based, long term support, self funding)

The science-policy model continuum

Models for research-support and models for policy-support are fundamentally different, see Mulligan (2004)

An example policy support system: WaterWorld

● Detailed, process based, since 1998● Spatial (1ha or 1km spatial resolution)● All required data supplied for anywhere globally (self-parameterising)● Fast (full analysis in 30 minutes)● Data uncertainty and validation tools, 3 model structures (v1,v2,v3)● Sophisticated scenarios and intervention tools● Simple to use (web-based), inter-operates through API● Results downloadable in GIS formats● Scientist user level free for non-commercial use, significant community● Free training programme, more than 1000 users globally● Published e.g.: Mulligan and Burke (2005); Mulligan (2013); Mulligan et al. (2010); Bruijnzeel, Mulligan and

Scatena (2011); van Soesbergen and Mulligan (2013)

How to use WaterWorld

What to use WaterWorld forFocus: targeting watershed conservation to maximize hydrological ecosystem services (HES):● Where are HES produced? (quantity,quality,sedimentation, some

regulation services.) ● Who benefits? (spatially, demographically, socioecon.)● What might (continued) land use change do?● What might specific policy/mgt interventions do?● What might climate change do?● What might all of these do combined? Who wins/loses?● What are the data and model sensitivities and uncertainties and

how reduce?

Users of WaterWorldWho? Where? National scale applications

Where? Local scale applicationsUsed by 823 organisations from 114 countries In the last year Most frequent non-commercial users: Conservation International, UNEP-WCMC, WWF, FFI, TNC, World Bank Group, Resources for the Future, ZSL, Amazon Conservation, RSPB, Birdlife International, Earthwatch, EPA, USAID, CAFOD

Insights gained (and lost) I: restoring ecosystem services is expen$$$ive

● Conservation interventions lead to negative as well as positive impacts!

● Even millions of $$$ investment lead to relatively small changes compared with the much larger ongoing “business as usual” changes (agriculturalisation) (that represent much larger on-going investments).

● Conservation investments must thus be situated carefully relative to specific downstream beneficiaries to help

Advising the Rio Daule Water Fund with WaterWorld and RIOS

Insights gained (and lost) II: ‘where’ matters as much as ‘how big’

● The hydrological footprint of a site (eg protected area) decays quickly downstream and does so in relation to the distribution of rainfall and other land uses.

● The footprint also varies seasonally

Insights gained (and lost) III: don’t adapt, be adaptable

The impacts of climate change are so uncertain that it is better to change the question from: What climate change do I need to adapt to?to:How can I be more adaptable to whatever comes?

SRES a2a 2050s direction of change: 17 GCMs generally agree on wetting in the Andes, no consensus in the Amazon

The ensemble mean leads to no change over 86% of area and a mean decrease in water stress of -0.24% overall:

The ensemble mean-1 SD (dry-end) leads to no change over 79% of the area and a mean increase in water stress of +1.5% overall:

So, who knows, really?

Insights gained (and lost) IV: the model is only ever a small part of the process

Models can help as negotiation support systems by developing local hydro-literacy empowering stakeholders with the information to engage in processes to share the benefits of water....but only if the political and legal mechanisms exist and the stakeholders are properly supported.

Insights gained (and lost) V: how do we get to the people?

● Whilst protected areas provide many potential water services (left), few of these are realised compared with those from more populous/agricultural areas (right).

● Focus BSM on agricultural improvement as the impact on realised services will be greater.

● But co-benefits for carbon, biodiversity may be less

Realised:

Potential:

Where are we now?

Not in our ‘neck of the woods’

__________________________

Kim/ BEST

Absolutely in our ‘neck of the woods’But how do we make them usable, useful and used?More on that tomorrow

Seth/ WDNACE

Elisa/ ALTER

Mark/ WISER

Thankyou

WaterWorld (and Co$ting Nature) are freely available at www.policysupport.org