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Famine in Somalia

I recently read “Famine in Somalia: Competing imperatives collective failures, 2011-2012”, which (as you may have guessed) picks apart the lead-up and response to the 2011-2012 famine in East Africa. It was fascinating, and I wanted to jot down three quick thoughts / takeaways from the book:

1. The famine was predicted. In fact, even the drought that set up the conditions for a potential famine was predicted. But the famine happened anyway. There were a number of reasons for this, chief among them the rise of Al-Shabaab, a fear of food-aid diversion, and a lack of coordination between remotely-managed disaster responses. But this points to the fact that it’s not only predicting disasters that we need to improve on. We need to consider how we can improve our responses to disasters in ways other than prediction.

2. The climate and disaster response community is already using some information about ENSO life-cycles in disaster management, but they could be doing much more. As the authors note, FEWSNET raised the alarm about a potential famine nearly a year in advance using information that La Niñas, which lead to drought in the Horn of Africa, often follow El Niños. We need to be incorporating this information into our “medium-term” preparedness, not just our disaster responses. For example, we could use this knowledge to know when we may need to pre-position food aid, or develop in-country networks for disaster response. The time it took to develop the in-country networks necessary was a major contributing factor to the famine. We could be using climate information to inform not only the delivery of food stock, but also for allocating time/money towards developing anticipatory in-country connections with key players ahead of an expected emergency.

3. One of the major themes of the book was a focus on bringing accountability to famines. I believe that as long as famines are treated as unforeseeable disasters, this will remain impossible. If, however, we move out of a disaster-response framework, and into a risk-reduction framework we may be able to make headway. If major droughts are viewed as an expected, recurring phenomena then a general preparedness may be reasonably expected. In this framework we need to begin focusing on how long we should expect between droughts. How severe will they be? By posing these questions publicly we can normalize the expectation of drought (and therefore of a planned response). This may provide political accountability for investments in institutions and infrastructure during non-crisis periods. Without a shift in the way we talk about food-security crises, we’re unlikely to see any change.

On ENSO and the timing of food shortages

Much has been written about how the El Niño Southern Oscillation (ENSO) affects temperature and precipitation globally (these impacts are often referred to as ENSO teleconnections). In two recent studies (one on climate teleconnections, one on their impacts on crop production), my coauthors and I try to bring attention to the predictable interannual evolution of ENSO, which is often overlooked in discussions of food security although not in the climate science or climate forecasting communities. In particular we look to highlight the fact that ENSO events follow a predictable pattern in which La Niñas (cold ENSO events) only occur following El Niños, and often persist for two years. While not all El Niños develop into La Niñas, the pattern is still noteworthy, particularly when one considers the implication of La Niñas for food security in North and South America.

Here we need to be precise about a few terms. I’m going to talk about three main concepts: 1.ENSO creates global teleconnections, which means that the risks posed by ENSO are correlated in space, 2. ENSO has a characteristic multi-year evolution, which means risks posed by ENSO are correlated in time, and 3.The sign and timing of the anomalies (i.e. do good years follow bad or vice versa) are critical for food security.

So to save you some time (you are after all reading this blog post rather than the paper) I’ll skip to our conclusions. 1.ENSO poses a correlated risk across much of North and South America. This means that plentiful harvests in North America tend to coincide with good harvests in major producing regions of South America (some notable exceptions mentioned in the paper). 2.The characteristic multi-year evolution of ENSO is apparent not only in patterns of temperature and precipitation, but also in yield anomalies. While not all states are influenced by ENSO in the Americas, many of the major production areas for wheat, maize and soybeans are. And in significant portions of these major production areas there is a multi-year progression of yield anomalies attributable to ENSO. 3. El Niños bring favorable growing conditions while La Niñas increase the probability of crop failures. This point is crucial when we reconnect it to the life-cycle of ENSO (i.e. La Niñas only occur following El Niños). In other words, despite the spatial correlation in the risk (remember the global teleconnections), the temporal correlation brings a silver lining: Poor Pan-American production years are likely to occur following years of above expected production. I don’t think I need to hammer home why this is positive from the perspective of food stocks and food security.

So that’s the new analysis. Some reason to worry (largely ignored correlations in the risks posed by ENSO) and some information that may be used to improve food security. There’s obviously much more in the paper, including the importance of soil moisture for each crop, so I encourage you to give them a read!

Cartography of Global Cropping Systems

In this era of big data, sophisticated computer models and near ubiquitous satellite remote sensing estimates, it’s amazing how limited we are in our ability to map cropping systems at a global scale. My colleagues at IFPRI and I investigated this issue in a recent study released in Global Ecology and Biogeography. Also see the IFPRI blog post for a short take on the issue and some additional info on IFPRI’s SPAM model.

The problem is that while developed countries have begun to map their crops, most developing countries simply don’t have the resources to do so. Data is often only available through national or sub-national statistics, which provide limited spatial information (none beyond the administrative boundaries associated with the statistic). This data constraint makes mapping crops in a spatially explicit manner difficult to say the least.

Our analysis looks at differences between the four major models used to map global cropping systems (M3, MIRCA, SPAM and GAEZ) as well as the implications of those differences for subsequent food security analyses. The differences between the models is significant for not only the crop-specific information, but even the extent of cropland delineated by each model. Below is a figure from the analysis showing the differences in the cropland extent:CroplandExtentComparison2

While it may seem unsurprising that these differences have implications for food security analyses, such as calculating the global yield gap, this has yet to be widely recognized. For example, having calculated the global yield gap using each of the four data sets, we found that the differences in the results were even larger than the estimated yield gap itself (i.e. the average from the four models).

Improving spatial understanding of crop production systems is a vital part of providing accurate analyses of global food security. Recognizing that the differences between models is significant is only the first step. Moving forward, we need to begin to reconcile these differences by evaluating external information against each of the four models to provide an indication of confidence in particular regions for each model, or more generally for each methodology.

How (and whether) to Disseminate Climate Forecasts

One topic that I’ve been interested in for a while now, but haven’t yet had the chance to explore in any depth is the way in which we disseminate drought forecasts. In this blog post I’d like to look a little further into how we disseminate, what we disseminate, and whether it makes a difference. The short version is this: we have thought quite a bit about what we provide, but surprisingly little about whether it is effective (cost or otherwise).

Numerous studies have explored the way in which we provide information (see here and edited collections here and here). They have made some real advances in shedding some light on how farmers’ make use of climate forecasts, as well as the estimated impact (i.e. whether farmers changed their responses in the context of a workshop participation).

As it turns out, farmers’ are quite capable of understanding and acting on probabilistic information. For forecasters, this is good news. One question that I would be interested in exploring further is whether the workshops required to train farmers to use forecasts is cost effective. This question relates both to the initial cost, and to the question of information retention. When testing principles in the context of daylong participatory workshops, we are unable to address issues such as usage retention (particularly following forecasts that do not match the eventual seasonal totals).

A related question, raised by a colleague of mine here at IFPRI, is whether we should really be providing the information to individual farmers or if it is more effective to provide the information to regional met agencies. Again, the question is not whether farmers are capable of using the forecasts, but rather whether providing them directly is cost effective in the long run.

The most pressing question, however, is in many ways the most obvious: do climate forecasts improve yields? A rigorous study (read randomized control) of the real-world implications of climate yields is badly needed as a means of addressing whether climate forecasts are effective. Although I understand the desire to provide a high quality product (accurate forecast) in a reliable manner, it is past time that we begin discussing the hard evidence of cost effectiveness.

Much time has been dedicated to studying climate forecasts, but surprisingly little has been invested in understanding what role climate forecasts are likely to play in improving livelihoods. We can’t afford to silo these questions any longer.

Workshop in Bhutan, building relationships

Workshop participants

Workshop participants in Thimphu, Bhutan

I’m recently returning from a workshop in Bhutan that colleagues here at IFPRI and I organized to address sustainable land management. The workshop was by most measures a tremendous success. It was well attended- including the Honorable Minister of Agriculture and Forests – the attendees were engaged in the discussion, and there was even local media coverage. It was refreshing to see real interest in scientific research coming from high-level members of parliament (something we don’t get much of in the US).

Yet the workshop also demonstrated the limitations of any single piece of research. The discussion among participants highlighted just how many different interests are at play in a project such as this. It’s impossible to provide a piece of research that addresses all of these needs simultaneously. In our study that manifested itself in our inability to include roads in our hydrologic model, which was a major discussion during the workshop. Although we do address the issue in the report, we could not incorporate it in a dynamic or quantified sense. This led to representatives from the Druk Green Power Corporation being honest in telling us that they would not be able to use portions of our research. This feedback is invaluable. It is only through this honest feedback that we are able to refine the methods and focus of our research.

These limitations made it immediately clear that ongoing collaborations are crucial for a project such as this. To build on existing relationships and extend past research is clearly a necessity when addressing complex issues, but it is not always the norm in international organizations. As we built on our relationships within Bhutan, we began to get a better understanding of the problem as well as some of the possible (and practical) solutions. Not only did we gain access to additional data of better quality over time, we also began to better understand the politics of the situation.

In research, exploring a problem in-depth can be a difficult sell to funding partners, which makes strategic project management important. Funding sources often (understandably) want to pursue novel ideas. This puts the onus on researchers to find ways of folding existing relationships and projects into new proposals as a means of continuing collaborations. Ephraim, here at IFPRI, for example, is using case-study countries as a means of maintaining (and funding) existing collaborations while simultaneously expanding his research into a new global scale. This is an example that I (and many researchers) would do well to follow.

Operational Drought Monitoring

To reduce the risk to food security posed by drought, it’s crucial that we develop systems able to disseminate accurate information in a timely manner. Although doing this may seem straightforward in an era of near-ubiquitous satellite measurements and increasingly high-powered computational models, there are challenges relating both to the analysis and dissemination of information in an operational context. I’ll explore the challenges to producing operational drought forecasts and monitors in this post and write about the challenges of disseminating those forecasts in my next post.

Broadly, most operational drought systems may be subdivided into those that monitor current conditions (and perhaps make historical readings available) and those that provide projections for future conditions. Coincidentally, while doing some research for this post I started to compile a list of available hydrological monitors and data portals, which you can find here. I focused on drought, but included a few others as well.

Drought monitoring, despite the availability of near real-time satellite data, faces great challenges of data availability. In fact, methods using satellite measurements currently perform comparably to those using only a handful of gauge stations. Satellite measurements are additionally challenged with a short climatology, and inconsistencies between products. Never-the-less, these products provide unparalleled coverage for regions with sparse in-situ measurements (as is often the case in developing countries), where drought monitoring has a crucial role to play in maintaining adequate levels of food security.

Although it is often described as a “slow onset” phenomenon, the development and evolution of drought can be remarkably difficult to predict. Part of this stems from an incomplete understanding of the oceanic forcings of drought (ENSO), and part of it stems from the inherently chaotic nature of the atmosphere. A chaotic systems sensitive to initial conditions creates an environment in which errors propagate through models and forecasts quickly diverge from one another. Shukla et al., 2013 analyzed the skill of a forecast as it relates to either (1) the initial conditions of the model or (2) the forecast skill, and found that the skill was dependent on both region and time of the year.

One means of improving both the monitoring and the forecasting of drought is to further explore the limitations to current model skill under different climate regimes. While Smith et al., explored times of the year and regions, the underlying factors of substance are the moisture-temperature-atmosphere regimes. It shouldn’t be ignored that during a drought, the region of interest (which may normally be energy-limited) will be abnormally arid (and therefore potentially moisture-limited). This shift may in fact mean that where the initial condition of soil moisture was once not a limiting factor for forecast skill, it may become one during the forecast of drought recovery. In that sense, forecasting the onset as opposed to the recovery of a drought may be two problems with distinct characteristics.

A second aspect of the drought system that warrants further exploration is the dynamics of vegetation during the evolution of multi-seasonal droughts. Previous studies have pointed towards dynamic vegetation as one source of increased interannual variability in precipitation, however, the impact of this dynamic vegetation on evapotranspiration and therefore on the atmospheric boundary layer may also play a significant role in determining how a drought develops. This is particularly true during multi-year droughts when drying of the soil occurs more deeply than during one-season droughts.

Himalayan Countries Launch Study on Climate Change

For the Himalayas, hydrologic variability poses problems that range from migrating plant species to drought to monsoon flooding. Although not all of these are necessarily driven solely by climate change, they will almost certainly be impacted by a changing climate. And while these changes will impact nearly 1/5 of the world’s population, the way in which precipitation and temperature are likely to change in the future is not well known. The complex terrain and steep climatic gradients of the Himalayas make pinpointing probable changes difficult.

The International Center for Integrated Mountain Development (ICIMOD) – a regional intergovernmental organization consisting of Afghanistan, Bangladesh, Bhutan, China, Myanmar, Nepal and Pakistan – recently announced that they are launching a three-year study on the state of the Himalayas. The proposed study is reminiscent of a regional IPCC report in that it aims to address the state of scientific knowledge on the impacts of climate change for the Himalayas, and how the mountain range may be preserved as a resource for future generations.

The announcement comes as a welcome indication of how intergovernmental bodies can provide regionally relevant scientific consultation to policy makers, even as the countries involved continue to focus on developing their resources. As I discussed in my previous post, many of the countries involved (including Bhutan) are already experiencing the impact of climate change on their water resources. The dependence of Himalayan countries on the mountain water storage and runofff puts them in a particularly vulnerable position when it comes to climate change. Any change in glacial water storage or patterns of rainfall impacts not only their water – and therefore agriculture and food security – but also their power generation.

I strongly believe that the development of national and intergovernmental organizations aimed at better understanding regional changes in climate will benefit not only the countries involved, but the wider climate science community. The most immediate impact of such organizations will be the focused scientific attention devoted to a regional issue, but I believe the impact of such science will pale in comparison to the indirect effects. Such efforts are likely to encourage an interest in the sciences from the general population and policy makers, but perhaps more importantly they will provide a national sense of pride in scientific achievement. If making decisions based on climate science becomes a priority, then these nations are likely to invest in developing technical infrastructure and training future scientists, both of which will benefit the community at large in the long run.