Sonali McDermid’s research while affiliated with New York University and other places

Agricultural irrigation withdraws water from multiple resources, possibly causing water scarcity issues. Previous studies predominantly relied on observations or land-only simulations, and therefore generally ignored land-atmosphere interactions, or failed to separate the effects of irrigation from other forcings. Here we analyse the effects of historical irrigation expansion on water fluxes and resources using seven Earth system models. Results show that irrigation expansion substantially increases local evapotranspiration and this extra water out-flux is not compensated by changes in local precipitation. Rapidly expanding irrigation therefore reduces the net water influx from the atmosphere to land (-0.61, -0.21, -0.06, -0.16 mmyr ⁻² in South Asia, the Mediterranean, Central North America, and West Central Asia, respectively). Moreover, other forcings, like climate change, also contribute to the decrease in some regions, further aggravating local water loss. Consequently, regional terrestrial water storage depletion is pronounced in most irrigation hot-spot regions (~500 mm from 1901 to 2014 over South Asia and Central North America, and 100-200 mm in the Mediterranean and Central North America). Our results attribute the land water loss to irrigation expansion and climate change, calling for immediate solutions to tackle the negative trends.

Irrigation rapidly expanded during the 20th century, affecting climate via water, energy, and biogeochemical changes. Previous assessments of these effects predominantly relied on a single Earth System Model, and therefore suffered from structural model uncertainties. Here we quantify the impacts of historical irrigation expansion on climate by analysing simulation results from six Earth system models participating in the Irrigation Model Intercomparison Project (IRRMIP). Results show that irrigation expansion causes a rapid increase in irrigation water withdrawal, which leads to less frequent 2-meter air temperature heat extremes across heavily irrigated areas (≥4 times less likely). However, due to the irrigation-induced increase in air humidity, the cooling effect of irrigation expansion on moist-heat stress is less pronounced or even reversed, depending on the heat stress metric. In summary, this study indicates that irrigation deployment is not an efficient adaptation measure to escalating human heat stress under climate change, calling for carefully dealing with the increased exposure of local people to moist-heat stress.

Background Rice is a major contributor to anthropogenic greenhouse gas (GHG) emissions, primarily methane, and at the same time will be negatively impacted by regional climate changes. Identifying rice management interventions to reduce methane emissions while improving productivity is, therefore, critical for climate change mitigation, adaptation, and food security. However, it can be challenging to conduct multivariate assessments of rice interventions in the field owing to the intensiveness of data collection and/or the challenges in testing long-term changes in meteorological and climate conditions. Process-based modeling, evaluated against site-based data, provides an entry point for evaluating the impacts of climate change on rice systems and assessing the impacts, co-benefits, and trade-offs of interventions under historical and future climate conditions. Methods We leverage existing site-based management data to model combined rice yields, methane emissions, and water productivity using a suite of process-based coupled crop-soil model experiments for 83 growing sites across the Red River Delta, Vietnam. We test three rice management interventions with our coupled crop-soil model, characterized by Alternate Wetting and Drying (AWD) water management and other principles representing the System of Rice Intensification (SRI). Our simulations are forced with historical as well as future climate conditions, represented by five Earth System Models for a high-emission climate scenario centered on the year 2050. We evaluate the efficacy of these interventions for combined climate change mitigation and adaptation under historical and future climate change. Results Two SRI interventions significantly increased yields (one by over 50%) under historical climate conditions while also reducing (or not increasing) methane emissions. These interventions also increase yields under future climate conditions relative to baseline management practices, although climate change decreases absolute yields across all management practices. Generally, where yield improved, so did crop water-use efficiency. However, impacts on methane emissions were mixed across the sites under future climate conditions. Two of the interventions resulted in increased methane emissions, depending on the baseline management point of comparison. Nevertheless, one intervention reduced (or did not significantly increase) methane under both historical and future climate conditions and relative to all baseline management systems, although there was considerable variation across five selected climate models. Conclusions SRI management principles combined with high-yielding varieties, implemented for site-specific conditions, can serve climate change adaptation and mitigation goals, although the magnitude of future climate changes, particularly warming, may reduce the efficacy of these interventions with respect to methane reductions. Future work should better bracket important sensitivities of coupled crop-soil models and disentangle which management and climate factors drive the responses shown. Furthermore, future analyses that integrate these findings into socio-economic assessment can better inform if and how SRI/AWD can potentially benefit farmer livelihoods now and in the future, which will be critical to the adoption and scaling of these management principles.

Terrestrial ecosystems store more than twice the carbon of the atmosphere, and are critical to climate change mitigation efforts. This has led to a proliferation of land‐based carbon sequestration efforts, such as re/afforestation associated with the Great Green Wall in the West African Sahel (WAS GGW). However, we currently lack comprehensive assessments of the long‐term viability of these ecosystems' carbon storage in the context of increasingly severe climate extremes. The WAS is particularly prone to recurrent and disruptive extremes, exemplified by the persistent and severe late‐20th century drought. We assessed the response and recovery of WAS GGW carbon stocks and fluxes to this late‐20th century drought, and the subsequent rainfall recovery, by leveraging a suite of terrestrial ecosystem models. While multi‐model mean carbon fluxes (e.g., gross primary production, respiration) partly recovered to pre‐drought levels, modeled total (above and below ground) ecosystem carbon stock falls to as much as two standard deviations below pre‐drought levels and does not recover even ∼20 years after the maximum drought anomaly. Furthermore, to the extent that the modeled regional carbon stock recovers, it is nearly entirely driven by atmospheric CO2 trends rather than the precipitation recovery. Uncertainties in regional ecosystem carbon simulation are high, as the models' carbon responses to drought displayed a nearly 10‐standard deviation spread. Nevertheless, the multi‐model average response highlights the strong and persistent impact of drought on terrestrial carbon storage, and the potential risks of relying on terrestrial ecosystems as a “natural climate solution” for climate change mitigation.

Natural climate phenomena like El Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) influence the Indian monsoon and thereby the region’s agricultural systems. Understanding their influence can provide seasonal predictability of agricultural production metrics to inform decision-making and mitigate potential food security challenges. Here, we analyze the effects of ENSO and IOD on four agricultural production metrics (production, harvested area, irrigated area, and yields) for rice, maize, sorghum, pearl millet, and finger millet across India from 1968 to 2015. El Niños and positive-IODs are associated with simultaneous reductions in the production and yields of multiple crops. Impacts vary considerably by crop and geography. Maize and pearl millet experience large declines in both production and yields when compared to other grains in districts located in the northwest and southern peninsular regions. Associated with warmer and drier conditions during El Niño, >70% of all crop districts experience lower production and yields. Impacts of positive-IODs exhibit relatively more spatial variability. La Niña and negative-IODs are associated with simultaneous increases in all production metrics across the crops, particularly benefiting traditional grains. Variations in impacts of ENSO and IOD on different cereals depend on where they are grown and differences in their sensitivity to climate conditions. We compare production metrics for each crop relative to rice in overlapping rainfed districts to isolate the influence of climate conditions. Maize production and yields experience larger reductions relative to rice, while pearl millet production and yields also experience reductions relative to rice during El Niños and positive-IODs. However, sorghum experiences enhanced production and harvested areas, and finger millet experiences enhanced production and yields. These findings suggest that transitioning from maize and rice to these traditional cereals could lower interannual production variability associated with natural climate variations.

Irrigation rapidly expanded during the 20th century, thereby affecting climate via changes in water, energy, and biogeochemical cycling. Previous assessments of these historical climate effects of irrigation expansion predominantly relied on a single Earth System Model, and therefore suffered from structural model uncertainties. Here we quantify the impacts of historical irrigation expansion on climate by analysing simulation results from six Earth system models participating in the Irrigation Model Intercomparison Project (IRRMIP). Despite the large range of simulated irrigation water withdrawal from those models (~900 to ~4000 km3 after the year 2000), our results show that irrigation expansion causes a rapid increase in irrigation water withdrawal, which leads to less frequent 2-meter air temperature heat extremes across heavily irrigated areas (>4 times less likely). However, due to the irrigation-induced increase in air humidity, the cooling effect of irrigation expansion on moist-heat stress is less pronounced or even reversed, depending on the heat stress metric. In summary, this study suggests the priorities in irrigation dataset collection and parameterisation development, and shows that irrigation deployment is not an efficient adaptation measure to escalating human heat stress under climate change, calling for carefully dealing with the increased exposure of local people to moist-heat stress.

The global population is projected to be around 8.5 billion in 2030 and 10.4 billion in 2100 (UN DESA, Department of Economic and Social Affairs (DESA) (2022). https://www.un.org/en/global-issues/population#:~:text=The%20world%20in%202100,surrounding%20these%20latest%20population%20projections). It is estimated that global agricultural production will need to increase by 60% in 2050 to feed the world’s growing population. India possesses 4% of the water resources of the world and covers 2.4% of the total geographical land area and 15% of the world’s livestock, which is sufficient to satisfy the livelihood needs of around 17% of the global population.