Beyond peak reservoir storage? A global estimate of declining water storage capacity in large reservoirs

Water storage is an important way to cope with temporal variation in water supply and demand. The storage capacity and the lifetime of water storage reservoirs can be significantly reduced by the inflow of sediments. A global, spatially explicit assessment of reservoir storage loss in conjunction with vulnerability to storage loss has not been done. We estimated the loss in reservoir capacity for a global data set of large reservoirs from 1901 to 2010, using modeled sediment flux data. We use spatially explicit population data sets as a proxy for storage demand and calculate storage capacity for all river basins globally. Simulations suggest that the net reservoir capacity is declining as a result of sedimentation (5% compared to the installed capacity). Combined with increasing need for storage, these losses challenge the sustainable management of reservoir operation and water resources management in many regions. River basins that are most vulnerable include those with a strong seasonal flow pattern and high population growth rates such as the major river basins in India and China. Decreasing storage capacity globally suggests that the role of reservoir water storage in offsetting sea-level rise is likely weakening and may be changing sign

The use and re-use of unsustainable groundwater for irrigation: A global budget

Depletion of groundwater aquifers across the globe has become a significant concern, as groundwater is an important and often unsustainable source of irrigation water. Simultaneously, the field of water resource management has seen a lively debate over the concepts and metrics used to assess the downstream re-use of agricultural runoff, with most studies focusing on surface water balances. Here, we bring these two lines of research together, recognizing that depletion of aquifers leads to large amounts of groundwater entering surface water storages and flows by way of agricultural runoff. While it is clear that groundwater users will be impacted by reductions in groundwater availability, there is a major gap in our understanding of potential impacts downstream of groundwater pumping locations. We find that the volume of unsustainable groundwater that is re-used for irrigation following runoff from agricultural systems is nearly as large as the volume initially extracted from reservoirs for irrigation. Basins in which the volume of irrigation water re-used is equal to or greater than the volume of water initially used (which is possible due to multiple re-use of the same water) contain 33 million hectares of irrigated land and are home to 1.3 billion people. Some studies have called for increasing irrigation efficiency as a solution to water shortages. We find that with 100% irrigation efficiency, global demand for unsustainable groundwater is reduced by 52%, but not eliminated. In many basins, increased irrigation efficiency leads to significantly decreased river low flows; increasing irrigation efficiency to 70% globally decreases total surface water supplies by backsim600 km3 yr−1. These findings illustrate that estimates of aquifer depletion alone underestimate the importance of unsustainable groundwater to sustaining surface water systems and irrigated agriculture

Maize Genomes to Fields: 2014 and 2015 field season genotype, phenotype, environment, and inbred ear image datasets

Objectives: Crop improvement relies on analysis of phenotypic, genotypic, and environmental data. Given large, well-integrated, multi-year datasets, diverse queries can be made: Which lines perform best in hot, dry environments? Which alleles of specific genes are required for optimal performance in each environment? Such datasets also can be leveraged to predict cultivar performance, even in uncharacterized environments. The maize Genomes to Fields (G2F) Initiative is a multi-institutional organization of scientists working to generate and analyze such datasets from existing, publicly available inbred lines and hybrids. G2F’s genotype by environment project has released 2014 and 2015 datasets to the public, with 2016 and 2017 collected and soon to be made available. Data description: Datasets include DNA sequences; traditional phenotype descriptions, as well as detailed ear, cob, and kernel phenotypes quantified by image analysis; weather station measurements; and soil characterizations by site. Data are released as comma separated value spreadsheets accompanied by extensive README text descriptions. For genotypic and phenotypic data, both raw data and a version with outliers removed are reported. For weather data, two versions are reported: a full dataset calibrated against nearby National Weather Service sites and a second calibrated set with outliers and apparent artifacts removed

Serotonin Reduction in Post-acute Sequelae of Viral Infection

Post-acute sequelae of COVID-19 (PASC, Long COVID ) pose a significant global health challenge. The pathophysiology is unknown, and no effective treatments have been found to date. Several hypotheses have been formulated to explain the etiology of PASC, including viral persistence, chronic inflammation, hypercoagulability, and autonomic dysfunction. Here, we propose a mechanism that links all four hypotheses in a single pathway and provides actionable insights for therapeutic interventions. We find that PASC are associated with serotonin reduction. Viral infection and type I interferon-driven inflammation reduce serotonin through three mechanisms: diminished intestinal absorption of the serotonin precursor tryptophan; platelet hyperactivation and thrombocytopenia, which impacts serotonin storage; and enhanced MAO-mediated serotonin turnover. Peripheral serotonin reduction, in turn, impedes the activity of the vagus nerve and thereby impairs hippocampal responses and memory. These findings provide a possible explanation for neurocognitive symptoms associated with viral persistence in Long COVID, which may extend to other post-viral syndromes

Beyond peak reservoir storage? A global estimate of declining water storage capacity in large reservoirs

Water storage is an important way to cope with temporal variation in water supply anddemand. The storage capacity and the lifetime of water storage reservoirs can besignificantly reduced by the inflow of sediments. A global, spatially explicit assessment ofreservoir storage loss in conjunction with vulnerability to storage loss has not been done.We estimated the loss in reservoir capacity for a global data set of large reservoirs from1901 to 2010, using modeled sediment flux data. We use spatially explicit population datasets as a proxy for storage demand and calculate storage capacity for all river basinsglobally. Simulations suggest that the net reservoir capacity is declining as a result ofsedimentation ( $5% compared to the installed capacity). Combined with increasing needfor storage, these losses challenge the sustainable management of reservoir operation andwater resources management in many regions. River basins that are most vulnerable includethose with a strong seasonal flow pattern and high population growth rates such as the majorriver basins in India and China. Decreasing storage capacity globally suggests that the roleof reservoir water storage in offsetting sea-level rise is likely weakening and may bechanging sign

Beyond peak reservoir storage? A global estimate of declining water storage capacity in large reservoirs

Water storage is an important way to cope with temporal variation in water supply anddemand. The storage capacity and the lifetime of water storage reservoirs can besignificantly reduced by the inflow of sediments. A global, spatially explicit assessment ofreservoir storage loss in conjunction with vulnerability to storage loss has not been done.We estimated the loss in reservoir capacity for a global data set of large reservoirs from1901 to 2010, using modeled sediment flux data. We use spatially explicit population datasets as a proxy for storage demand and calculate storage capacity for all river basinsglobally. Simulations suggest that the net reservoir capacity is declining as a result ofsedimentation ( $5% compared to the installed capacity). Combined with increasing needfor storage, these losses challenge the sustainable management of reservoir operation andwater resources management in many regions. River basins that are most vulnerable includethose with a strong seasonal flow pattern and high population growth rates such as the majorriver basins in India and China. Decreasing storage capacity globally suggests that the roleof reservoir water storage in offsetting sea-level rise is likely weakening and may bechanging sign

Identification and Characterization of Regions of the Rice Genome Associated With Broad-Spectrum, Quantitative Disease Resistance

Much research has been devoted to understanding the biology of plant-pathogen interactions. The extensive genetic analysis of disease resistance in rice, coupled with the sequenced genome and genomic resources, provides the opportunity to seek convergent evidence implicating specific chromosomal segments and genes in the control of resistance. Published data on quantitative and qualitative disease resistance in rice were synthesized to evaluate the distributions of and associations among resistance loci. Quantitative trait loci (QTL) for resistance to multiple diseases and qualitative resistance loci (R genes) were clustered in the rice genome. R genes and their analogs of the nucleotide binding site–leucine-rich repeat class and genes identified on the basis of differential representation in disease-related EST libraries were significantly associated with QTL. Chromosomal segments associated with broad-spectrum quantitative disease resistance (BS-QDR) were identified. These segments contained numerous positional candidate genes identified on the basis of a range of criteria, and groups of genes belonging to two defense-associated biochemical pathways were found to underlie one BS-QDR region. Genetic dissection of disease QTL confidence intervals is needed to reduce the number of positional candidate genes for further functional analysis. This study provides a framework for future investigations of disease resistance in rice and related crop species

Utility of Climatic Information via Combining Ability Models to Improve Genomic Prediction for Yield Within the Genomes to Fields Maize Project

Genomic prediction provides an efficient alternative to conventional phenotypic selection for developing improved cultivars with desirable characteristics. New and improved methods to genomic prediction are continually being developed that attempt to deal with the integration of data types beyond genomic information. Modern automated weather systems offer the opportunity to capture continuous data on a range of environmental parameters at specific field locations. In principle, this information could characterize training and target environments and enhance predictive ability by incorporating weather characteristics as part of the genotype-by-environment (G×E) interaction component in prediction models. We assessed the usefulness of including weather data variables in genomic prediction models using a naïve environmental kinship model across 30 environments comprising the Genomes to Fields (G2F) initiative in 2014 and 2015. Specifically four different prediction scenarios were evaluated (i) tested genotypes in observed environments; (ii) untested genotypes in observed environments; (iii) tested genotypes in unobserved environments; and (iv) untested genotypes in unobserved environments. A set of 1,481 unique hybrids were evaluated for grain yield. Evaluations were conducted using five different models including main effect of environments; general combining ability (GCA) effects of the maternal and paternal parents modeled using the genomic relationship matrix; specific combining ability (SCA) effects between maternal and paternal parents; interactions between genetic (GCA and SCA) effects and environmental effects; and finally interactions between the genetics effects and environmental covariates. Incorporation of the genotype-by-environment interaction term improved predictive ability across all scenarios. However, predictive ability was not improved through inclusion of naive environmental covariates in G×E models. More research should be conducted to link the observed weather conditions with important physiological aspects in plant development to improve predictive ability through the inclusion of weather data

Maize Genomes to Fields: 2014 and 2015 field season genotype, phenotype, environment, and inbred ear image datasets

Objectives: Crop improvement relies on analysis of phenotypic, genotypic, and environmental data. Given large, well-integrated, multi-year datasets, diverse queries can be made: Which lines perform best in hot, dry environments? Which alleles of specific genes are required for optimal performance in each environment? Such datasets also can be leveraged to predict cultivar performance, even in uncharacterized environments. The maize Genomes to Fields (G2F) Initiative is a multi-institutional organization of scientists working to generate and analyze such datasets from existing, publicly available inbred lines and hybrids. G2F’s genotype by environment project has released 2014 and 2015 datasets to the public, with 2016 and 2017 collected and soon to be made available. Data description: Datasets include DNA sequences; traditional phenotype descriptions, as well as detailed ear, cob, and kernel phenotypes quantified by image analysis; weather station measurements; and soil characterizations by site. Data are released as comma separated value spreadsheets accompanied by extensive README text descriptions. For genotypic and phenotypic data, both raw data and a version with outliers removed are reported. For weather data, two versions are reported: a full dataset calibrated against nearby National Weather Service sites and a second calibrated set with outliers and apparent artifacts removed