Using publicly-available average monthly groundwater level data in 478 sub-basins and 30 basins in Iran, we quantify country-wide groundwater depletion in Iran. Natural and anthropogenic elements affecting the dynamics of groundwater storage are taken into account and quantified during the period of 2002-2015. We estimate that the total groundwater depletion in Iran to be ~ 74 km3 during this period with highly localized and variable rates of change at basin and sub-basin scales. The impact of depletion in Irans groundwater reserves is already manifested by extreme overdrafts in ~ 77% of Irans land area, a growing soil salinity across the entire country, and increasing frequency and extent of land subsidence in Irans planes. While meteorological/hydrological droughts act as triggers and intensify the rate of depletion in country-wide groundwater storage, basin-scale groundwater depletions in Iran are mainly caused by extensive human water withdrawals. We warn that continuation of unsustainable groundwater management in Iran can lead to potentially irreversible impacts on land and environment, threatening countrys water, food, socio-economic security.

In the wake of climate change, extreme events such as heatwaves are considered to be key players in the terrestrial biosphere. In the past decades, the frequency and severity of heatwaves have risen substantially, and they are projected to continue to intensify in the future. One key question is therefore: how do changes in extreme heatwaves affect the carbon cycle? Although soil respiration (Rs) is the second largest contributor to the carbon cycle, the impacts of heatwaves on Rs have not been fully understood. Using a unique set of continuous high frequency in-situ measurements from our field site, we characterize the relationship between Rs and heatwaves. We further compare the Rs response to heatwaves across ten additional sites spanning the contiguous United States (CONUS). Applying a probabilistic framework, we conclude that during heatwaves Rs rates increase significantly, on average, by ~ 26% relative to that of non-heatwave conditions over the CONUS. Since previous in-situ observations have not measured the Rs response to heatwaves (e.g., rate, amount) at the high frequency that we present here, the terrestrial feedback to the carbon cycle may be underestimated without capturing these high frequency extreme heatwave events.

Globally, soils store two to three times as much carbon as currently resides in the atmosphere, and it is critical to understand how soil greenhouse gas (GHG) emissions and uptake will respond to ongoing climate change. In particular, the soil-to-atmosphere CO₂ flux, commonly though imprecisely termed soil respiration (R_S ), is one of the largest carbon fluxes in the Earth system. An increasing number of high-frequency R_S measurements (typically, from an automated system with hourly sampling) have been made over the last two decades; an increasing number of methane measurements are being made with such systems as well. Such high frequency data are an invaluable resource for understanding GHG fluxes, but lack a central database or repository. Here we describe the lightweight, open-source COSORE (COntinuous SOil REspiration) database and software, that focuses on automated, continuous and long-term GHG flux datasets, and is intended to serve as a community resource for earth sciences, climate change syntheses and model evaluation. Contributed datasets are mapped to a single, consistent standard, with metadata on contributors, geographic location, measurement conditions and ancillary data. The design emphasizes the importance of reproducibility, scientific transparency and open access to data. While being oriented towards continuously measured R_S , the database design accommodates other soil-atmosphere measurements (e.g. ecosystem respiration, chamber-measured net ecosystem exchange, methane fluxes) as well as experimental treatments (heterotrophic only, etc.). We give brief examples of the types of analyses possible using this new community resource and describe its accompanying R software package.