Financing agricultural drought risk through ex-ante cash transfers.

Despite advances in drought early warning systems, forecast information is rarely used for triggering and financing early actions, such as cash transfer. Scaling up cash transfer pay-outs, and overcoming the barriers to actions based on forecasts, requires an understanding of costs resulting from False Alarms, and the potential benefits associated with appropriate early interventions. On this study, we evaluate the potential cost-effectiveness of cash transfer responses, comparing the relative costs of ex-ante cash transfers during the maize growing season to ex-post cash transfers after harvesting in Kenya. For that, we developed a forecast model using Fast-and Frugal Trees that unravels early warning relationships between climate variability, vegetation coverage, and maize yields at multiple lead times. Results indicate that our models correctly forecast low maize yield events 85% of the time across the districts studied, some already six months before harvesting. The models' performance improves towards the end of the growing season driven by a decrease of 29% in the probability of False Alarms. Overall, we show that timely cash transfers ex-ante to a disaster can often be more cost-effective than investing in ex-post expenditures. Our findings suggest that early response can yield significant cost savings, and can potentially increase the effectiveness of existing cash transfer systems.

Biodiversity Areas under Threat: Overlap of Climate Change and Population Pressures on the World's Biodiversity Priorities.

Humans and the ecosystem services they depend on are threatened by climate change. Places with high or growing human population as well as increasing climate variability, have a reduced ability to provide ecosystem services just as the need for these services is most critical. A spiral of vulnerability and ecosystem degradation often ensues in such places. We apply different global conservation schemes as proxies to examine the spatial relation between wet season precipitation, population change over three decades, and natural resource conservation. We pose two research questions: 1) Where are biodiversity and ecosystem services vulnerable to the combined effects of climate change and population growth? 2) Where are human populations vulnerable to degraded ecosystem services? Results suggest that globally only about 20% of the area between 50 degrees latitude North and South has experienced significant change-largely wetting-in wet season precipitation. Approximately 40% of rangelands and 30% of rainfed agriculture lands have experienced significant precipitation changes, with important implications for food security. Over recent decades a number of critical conservation areas experienced high population growth concurrent with significant wetting or drying (e.g. the Horn of Africa, Himalaya, Western Ghats, and Sri Lanka), posing challenges not only for human adaptation but also to the protection and sustenance of biodiversity and ecosystem services. Identifying areas of climate and population risk and their overlap with conservation priorities can help to target activities and resources that promote biodiversity and ecosystem services while improving human well-being.

The climate hazards infrared precipitation with stations--a new environmental record for monitoring extremes.

The Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset builds on previous approaches to 'smart' interpolation techniques and high resolution, long period of record precipitation estimates based on infrared Cold Cloud Duration (CCD) observations. The algorithm i) is built around a 0.05° climatology that incorporates satellite information to represent sparsely gauged locations, ii) incorporates daily, pentadal, and monthly 1981-present 0.05° CCD-based precipitation estimates, iii) blends station data to produce a preliminary information product with a latency of about 2 days and a final product with an average latency of about 3 weeks, and iv) uses a novel blending procedure incorporating the spatial correlation structure of CCD-estimates to assign interpolation weights. We present the CHIRPS algorithm, global and regional validation results, and show how CHIRPS can be used to quantify the hydrologic impacts of decreasing precipitation and rising air temperatures in the Greater Horn of Africa. Using the Variable Infiltration Capacity model, we show that CHIRPS can support effective hydrologic forecasts and trend analyses in southeastern Ethiopia.