Extremes

Brazil is widely recognized as a global hotspot for flash drought, which sets in over weeks rather than a season, yet events are often reported under other names, among them wet-season dry spells, leaving the evidence unconsolidated. We reviewed 92 articles from 2012 to September 2024. From these, 23 of them were Brazil-focused events. Only five of those explicitly examined flash drought, and at least 35 definitions were in use across the reviewed set, hampering cross-study comparison and early warning; the North, Center-West and South remain underrepresented.

While potential maize yields in Zambia are 9 t per hectare (t/ha), the average farmer produces only 1–2. We evaluate the contribution of responses to weather risk to that gap by decomposing the yield gap in maize in Zambia. Risk aversion explained 24% of the average gap, varying widely by location, implying 6.75 tons per hectare attainable without added risk against 8.84 when maximizing profit.

Although there have been significant advances in river routing and sub-grid heterogeneity (i.e., tiling) schemes in Earth system models over the past decades, there has yet to be a concerted effort to couple these two concepts. This paper aims to bridge this gap through the development of a two-way coupling between tiling schemes and river networks in the HydroBlocks land surface model. The scheme is implemented and tested over a 1 arc degree domain in Oklahoma, United States.

Drought monitoring and yield prediction often rely on coarse-scale hydroclimate data or (infrequent) vegetation indexes that do not always indicate the conditions farmers face in the field. Consequently, decision-making based on these indices can often be disconnected from the farmer reality. Our study focuses on smallholder farming systems in data-sparse developing countries, and it shows how field-scale soil moisture can leverage and improve crop yield prediction and drought impact assessment.

Precipitation frequency and the intensity of the heaviest events underpin infrastructure design and climate model evaluation, yet earlier near-global climatologies drew on just one of satellites, model reconstructions or gauges, with only 16% of global land placed within 25 kilometers of a gauge. We developed a machine learning method to combined all three, mapping both quantities from gauge records for 1979-2018 onto a 0.1-degree global grid. Against withheld gauges, the resulting PPDIST dataset explained 78% of the variance in daily occurrence and 72% in peak intensity, outperforming reconstruction- and satellite-based estimates.