ALBERT

Description: Recent years have seen an increase in the use of non-contact methods of river discharge estimation, which calls for the measurement of surface velocity in the top portion of the cross-section utilizing non-contact doppler radar sensors like Handheld or Mounted radars. Given the constrained and probabilistic character of the flow, these radars can be utilized to predict discharge based on the entropy theory. Additionally, a river section can be utilized to measure surface velocity at several predetermined verticals using the traditional area-velocity approach. One of the major prerequisites for the estimation of discharge through non-contact techniques is the characterization of the nature of the surface velocity profile at the section. The problem arises when the flow experiences large turbulence due to sidewall effects and other characteristics of the channel, especially in narrow mountainous channels. This study compares different velocity profiles for the measured surface velocity at several predefined verticals at two cross-sections of Bhagirathi and Ganga, two significant Himalayan rivers having widths of 30m and 60m respectively. . Three velocity profiles i.e. Elliptic, Parabolic, and Polynomial profiles obtained through the curve-fitting technique were compared based on error distribution and uncertainty analysis using Forecast Range Error Estimate method. Further, to determine which profile would be optimal for the selected segments, the measured mean velocity of current-meter was compared to the estimated mean velocity for each profile. The study offers a straightforward method for choosing the ideal surface velocity profile for a river by just measuring the surface velocity at predetermined verticals.

Description: Rainfall erosivity (R-factor) represents the potential erosive power of rainfall to erode soil surfaces. The R-factor is generally estimated using the method adopted in the Universal Soil Loss Equation (USLE) or Revised-USLE. The R-factor estimation method requires high-temporal resolution data from a dense network of gauged rainfall stations for reliable mapping of the R-factor. However, the unavailability of a dense network of high temporal resolution gauge rainfall datasets hinders the R-factor mapping over a large spatial scale. In recent decades the availability of satellite rainfall datasets at a higher spatiotemporal resolution provided an opportunity to estimate rainfall erosivity at a large spatial scale. However, many studies have shown that satellite datasets underestimate erosivity values, and improvement is required to make them more representative before further use. This study aimed to improve the R-factor estimated from GPM-IMERG and CMORPH satellite precipitation datasets over India using gauge-based rainfall erosivity data. The high-resolution hourly gauge rainfall records from 1969 to 2021 of more than 250 stations across the country were used to improve the satellite-based rainfall erosivity estimates. The gauge-based rainfall erosivity data was merged with the satellite-based rainfall erosivity products. The result demonstrates that the merging satellite and gauge rainfall erosivity can accurately estimate rainfall erosivity over a broad spatial scale. The merged products are spatially effective for many regions where a dense network of high-resolution gauge rainfall datasets is limitedly available. The approach can be applied on the continental and global scale lacking ground observations and/or satellite records.

Description: Irrigation accounts for ~70% of global freshwater withdrawals and ~90% of consumptive water use, driving myriad Earth system impacts. In this Review, we summarize how irrigation currently impacts key components of the Earth system. Estimates suggest that more than 3.6 million km2 of currently irrigated land, with hot spots in the intensively cultivated US High Plains, California Central Valley, Indo-Gangetic Basin and northern China. Process-based models estimate that ~2,700 ± 540 km3 irrigation water is withdrawn globally each year, broadly consistent with country-reported values despite these estimates embedding substantial uncertainties. Expansive irrigation has modified surface energy balance and biogeochemical cycling. A shift from sensible to latent heat fluxes, and resulting land–atmosphere feedbacks, generally reduce regional growing season surface temperatures by ~1–3 °C. Irrigation can ameliorate temperature extremes in some regions, but conversely exacerbates moist heat stress. Modelled precipitation responses are more varied, with some intensive cropping regions exhibiting suppressed local precipitation but enhanced precipitation downstream owing to atmospheric circulation interactions. Additionally, irrigation could enhance cropland carbon uptake; however, it can also contribute to elevated methane fluxes in rice systems and mobilize nitrogen loading to groundwater. Cross-disciplinary, integrative research efforts can help advance understanding of these irrigation–Earth system interactions, and identify and reduce uncertainties, biases and limitations.

Description: Extreme events, such as those caused by climate change, economic or geopolitical shocks, and pest or disease epidemics, threaten global food security. The complexity of causation, as well as the myriad ways that an event, or a sequence of events, creates cascading and systemic impacts, poses significant challenges to food systems research and policy alike. To identify priority food security risks and research opportunities, we asked experts from a range of fields and geographies to describe key threats to global food security over the next two decades and to suggest key research questions and gaps on this topic. Here, we present a prioritization of threats to global food security from extreme events, as well as emerging research questions that highlight the conceptual and practical challenges that exist in designing, adopting, and governing resilient food systems. We hope that these findings help in directing research funding and resources toward food system transformations needed to help society tackle major food system risks and food insecurity under extreme events.