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The Marsh Ecology Research Program (MERP) was a long-term interdisciplinary study on the ecology of prairie wetlands. A scientific team from a variety of disciplines (hydrology, plant ecology, invertebrate ecology, vertebrate ecology, nutrient dynamics, marsh management) was assembled to design and oversee a long-term experiment on the effects of water-level manipulation on northern prairie wetlands. Ten years of fieldwork (1980 -1989), combines a routine long-term monitoring program and a series of short-term studies, generated a wealth of new and diverse information on the ecology and function of prairie wetlands (Murkin, Batt, Caldwell, Kadlec and van der Valk, 2000). This data set includes belowground macrophyte production data, collected as part of the vegetation section of MERP. Determination of aquatic macrophyte annual net primary production is vital to the understanding of the dynamics of freshwater marshes. Macrophyte biomass, both live and dead, is a major storage compartment for carbon, nitrogen and phosphorus in a marsh and a major potential energy and nutrient source for the faunal component of the marsh ecosystem. Macrophyte communities are also essential structural components of the habitat of both invertebrates and vertebrates. The major objective of the long-term monitoring of aquatic macrophytes was to determine the impact of the wet-dry cycle on macrophyte above and belowground net annual production. Standard harvest techniques were used because they were the most direct, simple and reliable techniques available for estimating net annual primary production of macrophytes per unit area (van der Valk, 1989). In order to estimate net annual belowground macrophyte production, core samples of the belowground biomass were harvested in the late spring and in the fall. Shoot initiation early in the growing season depletes most of the belowground standing crop, and therefore spring sampling was done quickly (within 2 weeks) to capture this state. Underground biomass then reaches its seasonal maxima in the fall and was captured with the fall sampling. The resulting differences between the fall and spring standing crop biomass provided an estimate of net belowground macrophyte production (van der Valk, 1989). References: Murkin, H.R., B.D.J. Batt, P.J. Caldwell, J.A. Kadlec and A.G. van der Valk. 2000a. Introduction to the Marsh Ecology Research Program. In Prairie Wetland Ecology: The Contribution of the Marsh Ecology Research Program. (Eds) H.R. Murkin, A.G. van der Valk and W.R. Clark. pp. 3-15. Ames: Iowa State University Press. van der Valk, A. 1989. Macrophyte production. In Marsh Ecology Research Program: Long-term Monitoring Procedures Manual. (Eds.) E.J. Murkin and H.R. Murkin, pp. 23-29. Manitoba, Canada: Delta Waterfowl Research Station.

This is a two-hundred-year long dataset of the annual average, minimum, and maximum discharges at five stations draining the Mississippi River watershed: at Clinton, IA, Herman, MO, St. Louis, MO, Louisville, KY, and Vicksburg, MS. The data are useful to test for increases in the three discharge metrics, and correlations with air pressure differentials represented in the North Atlantic Oscillation (NAO) Index. These data may be useful for climate change assessments through modeling or synthetic assessments using other data sets. Search of archival records published by the Mississippi River Commission (Corps. of Engineers) and the U.S. Geological Survey

We estimated numbers of individuals for each species, using a backpack electroshocker with standard electrofishing procedures. We used block nets to provide closure at the ends of the segment during years when the stream reach was flowing. We used a two-pass removal depletion method to estimate abundances within segments. After placing captured fish in aerated coolers filled with stream water, we identified fish to species and categorized them by life stage (juvenile or adult) based on standard length, and then returned the fish to the same section of stream. In 2011 (the first year), size data for R. balteatus were only available for the first 30 fish caught (sampling in 2011 was focused mainly on R. osculus, and L. copei, for a mark-recapture study that is reported elsewhere). However, we recorded number captured of R. balteatus for each segment and pass of the stream reach. We calculated the ratio of adult to juvenile life stages of the first 30 fish, and used that ratio to estimate the R. balteatus life stage distribution (adult or juvenile) for additional segments for 2011 only. To estimate abundances, we used a maximum-likelihood population estimator (Microfish, Van Deventer 1998). The data has been given both as the estimate generated by the maximum-likelihood population estimate, as well as a log transformed version of the original estimate.  Climate change projections in the western United States suggest that snowpack levels and winter precipitation will decline, but mean annual precipitation levels will remain unchanged. Mountain streams that once saw a constant source of water from snowpack will begin to see large seasonal variation in flow. Increased stream intermittency will create significant conservation risks for fish species; however, few studies have examined the abundance responses of fish in high elevation streams to the shift from perennial to intermittent flow. To determine the effects of stream intermittency on fish abundance in a montane stream, we quantified changes in abundance for five species over a five-year period that exhibited extreme variation in streamflow. Responses varied by species and life stage, suggesting that the shift from perennial to intermittent flow will cause significant declines in abundance for some species. Northern leatherside chub, may experience large decreases in their range as the availability of perennial streams decreases. The study of drought effects on fish abundance will be crucial to the conservation of biodiversity in montane regions of the world. Data is provided in a .xlsx file. It can be opened on Excel, Google Sheets, or Apple Numbers.

As climate change continues to increase air temperature in high-altitude ecosystems, it has become critical to understand the controls and scales of aquatic habitat vulnerability to warming. Here we used a nested array of high-frequency sensors, and advances in time-series models, to examine spatiotemporal variation in thermal vulnerability in a model Sierra Nevada watershed. Stream thermal sensitivity to atmospheric warming fluctuated strongly over the year and peaked in spring and summer—when hot days threaten invertebrate communities most. The reach scale (~50 m) best captured variation in summer thermal regimes. Elevation, discharge, and conductivity were important correlates of summer water temperature across reaches, but upstream water temperature was the paramount driver—supporting that cascading warming occurs downstream in the network. Finally, we used our estimated summer thermal sensitivity and downscaled projections of summer air temperature to forecast end-of-the-century stream warming, when extreme drought years like 2020-2021 become the norm. We found that 25.5% of cold-water habitat may be lost under business-as-usual RCP 8.5 (or 7.9% under mitigated RCP 4.5). This estimated reduction suggests that 27.2% of stream macroinvertebrate biodiversity (11.9% under the mitigated scenario) will be stressed or threatened in what was previously cold‑water habitat. Our quantitative approach is transferrable to other watersheds with spatially‑replicated time series and illustrates the importance of considering variation in the vulnerability of mountain streams to warming over both space and time. This approach may inform watershed conservation efforts by helping identify, and potentially mitigate, sites and time windows of peak vulnerability. Please see the README.md document. Please see the README.md document.

Natural potentials for future cropland expansion The potential for the expansion of cropland is restricted by the availability of land resources and given local natural conditions. As a result, area that is highly suitable for agriculture according to the prevailing local biophysical conditions but is not under cultivation today has a high natural potential for expansion. Policy regulations can further restrict the availability of land for expansion by designating protected areas, although they may be suitable for agriculture. Conversely, by applying e.g. irrigation practices, land can be brought under cultivation, although it may naturally not be suitable. Here, we investigate the potentials for agricultural expansion for near future climate scenario conditions to identify the suitability of non-cropland areas for expansion according to their local natural conditions. We determine the available energy, water and nutrient supply for agricultural suitability from climate, soil and topography data, by using a fuzzy logic approach according to Zabel et al. (2014). It considers the 16 globally most important staple and energy crops. These are: barley, cassava, groundnut, maize, millet, oil palm, potato, rapeseed, rice, rye, sorghum, soy, sugarcane, sunflower, summer wheat, winter wheat. The parameterization of the membership functions that describe each of the crops’ specific natural requirements is taken from Sys et al. (1993). The considered natural conditions are: climate (temperature, precipitation, solar radiation), soil properties (texture, proportion of coarse fragments and gypsum, base saturation, pH content, organic carbon content, salinity, sodicity), and topography (elevation, slope). As a result of the fuzzy logic approach, values in a range between 0 and 1 describe the suitability of a crop for each of the prevailing natural conditions at a certain location. The smallest suitability value over all parameters finally determines the suitability of a crop. The daily climate data is provided by simulation results from the global climate model ECHAM5 (Jungclaus et al. 2006) for near future (2011-2040) SRES A1B climate scenario conditions. Soil data is taken from the Harmonized World Soil Database (HWSD) (FAO et al. 2012), and topography data is applied from the Shuttle Radar Topography Mission (SRTM) (Farr et al. 2007). In order to gather a general crop suitability, which does not refer to one specific crop, the most suitable crop with the highest suitability value is chosen at each pixel. In addition the natural biophysical conditions, we consider today’s irrigated areas according to (Siebert et al. 2013). We assume that irrigated areas globally remain constant until 2040, since adequate data on the development of irrigated areas do not exist, although it is likely that freshwater availability for irrigation could be limited in some regions, while in other regions surplus water supply could be used to expand irrigation practices (Elliott et al. 2014). However, it is difficult to project where irrigation practices will evolve, since it is driven by economic investment costs that are required to establish irrigation infrastructure. In principle, all agriculturally suitable land that is not used as cropland today has the natural potential to be converted into cropland. We assume that only urban and built-up areas are not available for conversion, although more than 80% of global urban areas are agriculturally suitable (Avellan et al. 2012). However, it seems unlikely that urban areas will be cleared at the large scale due to high investment costs, growing cities and growing demand for settlements. Concepts of urban and vertical farming usually are discussed under the aspects of cultivating fresh vegetables and salads for urban population. They are not designed to extensively grow staple crops such as wheat or maize for feeding the world in the near future. Urban farming would require one third of the total global urban area to meet only the global vegetable consumption of urban dwellers (Martellozzo et al. 2015). Thus, urban agriculture cannot substantially contribute to global agricultural production of staple crops. Protected areas or dense forested areas are not excluded from the calculation, in order not to lose any information in the further combination with the biodiversity patterns (see chapter 2.3). We use data on current cropland distribution by Ramankutty et al. (2008) and urban and built-up area according to the ESA-CCI land use/cover dataset (ESA 2014). From this data, we calculate the ‘natural expansion potential index’ (Iexp) that expresses the natural potential for an area to be converted into cropland as follows: Iexp = S * Aav The index is determined by the quality of agricultural suitability (S) (values between 0 and 1) multiplied with the amount of available area (Aav) for conversion (in percentage of pixel area). The available area includes all suitable area that is not cultivated today, and not classified as urban or artificial area. The index ranges between 0 and 100 and indicates where the conditions for cropland expansion are more or less favorable, when taking only natural conditions into account, disregarding socio-economic factors, policies and regulations that drive or inhibit cropland expansion. The index is a helpful indicator for identifying areas where cropland expansion could take place in the near future. Further information Detailled information are available in the following publication: Delzeit, R., F. Zabel, C. Meyer and T. Václavík (2017). Addressing future trade-offs between biodiversity and cropland expansion to improve food security. Regional Environmental Change 17(5): 1429-1441. DOI: 10.1007/s10113-016-0927-1 Contact Please contact: Dr. Florian Zabel, f.zabel@lmu.de, Department für Geographie, LMU München (www.geografie.uni-muenchen.de) This research was carried out within the framework of the GLUES (Global Assessment of Land Use Dynamics, Greenhouse Gas Emissions and Ecosystem Services) Project, which has been supported by the German Ministry of Education and Research (BMBF) program on sustainable land management (grant number: 01LL0901E).

The yield gap for maize across the Ethiopia has been estimated using crop model LINTUL5 embedded into the modeling framework SIMPLACE (Scientific Impact Assessment and Modelling Platform for Advanced Crop and Ecosystem Management. The yield gap of a crop grown in a certain location and cropping system is defined as the difference between the yield and biomass under optimum management and the average yield achieved by farmers. Yield under optimum management is labeled as potential yield (Yp) under irrigated conditions or water-limited potential yield (Yw) under rain-fed conditions.Yp is location specific because of the climate, and not dependent on soil properties assuming that the required water and nutrients are non-limiting and can be added through management. Thus, in areas without major soil constraints, Yp is the most relevant benchmark for irrigated systems. Whereas, for rain-fed crops, Yw, equivalent to water-limited potential yield, is the most relevant benchmark. Both Yp and Yw are calculated for optimum planting dates, planting density and region-specific crop variety which is critical in determining the feasible growth duration, particularly in tropical climatic conditions where two or even three crops are produced each year on the same field. Purpose: To increase food production, identifying the regions with untapped production capacity is of prime importance and can be achieved by quantitative and spatially explicit estimates of Yield gaps, thus considering the spatial variation in environment and the production system. This dataset was first published on the institutional Repository "Zentrum für Entwicklungsforschung: ZEF Data Portal" with ID={c2bbd5ed-fd4c-4a3f-b0b1-113a5d4f3ddf}. The yield gaps plotted in the map were calculated as the average values of 7 years (the year 2004 -2010). The unit is Megagram per hectare (Mg ha-1) which is equivalent to tons ha-1. The climate data at the national scale was made available from the National Aeronautics and Space Administration (NASA), Goddard Institute of Space Studies(https://data.giss.nasa.gov/impacts/agmipcf/agmerra/), AgMERRA.The dataset is stored at 0.25°×0.25° horizontal resolution (~25km). Soil parameter values were extracted from the soil property maps of Africa at 1 km x 1 km resolution (http://www.isric.org/data/soil-property-maps-africa-1-km). Maize yields (Mg ha-1) and fertilizer application (Nitrogen and Phosphorus) rates over seven years (2004 - 2010) at administrative zone level have been collected from the Central Statistical Agency, Ethiopia.

1. Aphids are abundant in natural and managed vegetation, supporting a diverse community of organisms and causing damage to agricultural crops. Due to a changing climate, periods of drought are anticipated to increase, and the potential consequences of this for aphid-plant interactions are unclear. 2. Using a meta-analysis and synthesis approach, we aimed to advance understanding of how increased drought incidence will affect this ecologically and economically important insect group, and to characterise any potential underlying mechanisms. We used qualitative and quantitative synthesis techniques to determine whether drought stress has a negative, positive, or null effect on aphid fitness and examined these effects in relation to 1) aphid biology, 2) geographical region, 3) host plant biology. 3. Across all studies, aphid fitness is typically reduced under drought. Subgroup analysis detected no difference in relation to aphid biology, geographical region, or the aphid-plant combination, indicating the negative effect of drought on aphids is potentially universal. Furthermore, drought stress had a negative impact on plant vigour and increased plant concentrations of defensive chemicals, suggesting the observed response of aphids is associated with reduced plant vigour and increased chemical defence in drought-stressed plants. 4. We propose a conceptual model to predict drought effects on aphid fitness in relation to plant vigour and defence to stimulate further research. Please check the ReadMe for an explanation of the values included in the dataset. Please note that n/a values are included in the Global_Dataset tab for plant meta-analysis data (_Plant_Vigour, _Plant_Defence, and _Plant_Nutrition), these indicate studies that did not report these parameters. Data was collected and curated using standard systematic literature synthesis approaches. The effect size (Hedges' g) reported in the dataset was calculated from extracted means and standard deviations.

Livestock grazing is among the most intensive land-use activities in grasslands and can affect plant communities directly or indirectly via grazing-induced soil legacies. Under climate change, grasslands are threatened globally by recurrent drought. However, the extent to which drought influences grazing-induced soil legacy effects on plant biomass production and community composition remains largely unexplored. We grew five naturally co-occurring plant species (three dominants and two subordinates) in mixed communities in a glasshouse experiment in live and sterilized soil that had or had not been subjected to 19 years of grazing; these plant communities were then exposed to a subsequent drought. We tested the treatment effects on plant community biomass, proportional aboveground biomass of individual species, arbuscular mycorrhizal (AM) fungal root colonization, and soil nutrient availability. Under drought-free conditions, soils from grazed plots produced significantly higher plant aboveground and total community biomass compared to soils from ungrazed plots. In contrast, plant aboveground and total community biomass were similar between grazed and ungrazed soils under drought conditions. Similarly, soils from grazed plots increased the proportional biomass of dominant species but decreased the proportion of subordinate species; however, the proportional biomass of dominant and subordinate species was similar between grazed and ungrazed soils under drought conditions. Soil NO3--N in grazed soil was significantly higher compared to ungrazed soil. Drought dramatically increased soil NO3--N in sterilized soil and had a more pronounced increase in grazed soil than in ungrazed soil. Arbuscular mycorrhizal fungal root colonization from grazed soil was lower compared to ungrazed soil. Drought significantly increased the soil available phosphorus concentration, as well as plant community AM fungal root colonization. Synthesis. Our study suggests that drought can neutralize positive grazing effects on plant community biomass production via altered plant-soil interactions. Also, we found that drought can alleviate the negative effects of grazing legacies on subordinate species by reducing the competitiveness of dominant species. Our study provides new insights for understanding the underlying mechanisms of grazing effects on grassland productivity under climate change. Please see the README document and the accompanying published article: Duan, DD., Tian, Z., Wu, NN., Feng, XX., Hou, FJ., Nan, ZB., Kardol, P., and Chen, T. 2023. Drought neutralizes positive effects of long-term grazing on grassland productivity through altering plant-soil interactions. Functional Ecology. 

Optimisation results for the lowest lifetime cost system consisting of solar photovoltaic (PV), hybrid photovoltaic-thermal (PV-T) and solar-thermal collectors alongside battery and hot-water storage systems for meeting the electrical and thermal (hot-water) needs of three multi-effect distillation (MED) plants. The updated results are from optimisations runs carried out in response to peer-review comments.

Climate data used in this study was drawn from the California Basin Characterization Model v8, and consists of monthly estimates of cumulative water deficit (CWD) and actual evapotranspiration (AET) from 1951 – 2016. This dataset represents a 270-m grid-based model of water balance calculations that incorporates climate inputs through PRISM data in addition to solar radiation, topographic shading, cloudiness, and soil properties to estimate evapotranspiration. Using these monthly values, we calculated the 1980 – 2009 mean CWD and AET normals, as well as mean deviations from those normals over a three-year period preceding each year of interest. Cultivated and agricultural areas were identified using the 2016 National Land Cover Database data, which estimated dominant land cover throughout North America at 30-m resolution. The proportion of cultivated area and of water features that covered each 1-km pixel were then calculated by resampling to 1-km scale. Mean housing density data was drawn from the Integrated Climate and Land-Use Scenarios (ICLUS) dataset, which provides decadal estimates of housing density throughout the United states from 1970 - 2020. As precise continuous estimates of housing density were not available, housing density within each pixel was set to the mean of its class. Annual values were estimated from decadal data using linear interpolation. Ecoregions within California (hereafter referred to as “regions”) were delineated using CalVeg ecosystem provinces data. Road data were drawn from 2018 TIGER layer data, and consisted of all primary and secondary roads across California. Electrical infrastructure data was drawn from 2020 transmission lines data. In both cases, the distance of nearest roads or transmission lines to each pixel were then calculated. Pixels which contained roads or electrical infrastructure were assigned distances of 0 km. Fire history data was drawn from FRAP fire perimeter data, which incorporates perimeters of all known timber fires >10 acres (>0.04 km2), brush fires >30 acres (>0.12 km2), and grass fires >300 acres (>1.21 km2) from 1878 – 2017. Using this data, the presence of fire in each 1-km pixel was classified in a binary fashion (e.g. 1 for burned, 0 for unburned) for each year of interest. Due to computational limits and the quantity of data involved in this study, we did not calculate the burned area within each pixel, or distinguish pixels in which a single fire occurred in a given year from those in which multiple fires occurred. This data was also used to calculate the number of years since the most recent fire within any pixel, prior to each year in which fire probability was projected. Thus, locations in which no fire was observed throughout the fire record were treated as having gone a maximum of 100 years without a fire event for the purposes of model construction. These pixels comprised 29% - 33% of data annually (depending on year), and included both locations in which fire would not be expected (such as highly xeric regions) as well as locations in fire-prone areas in which no fire had been documented within the FRAP fire perimeter data used in this study.  In the face of recent wildfires across the Western United States, it is essential that we understand both the dynamics that drive the spatial distribution of wildfire, and the major obstacles to modeling the probability of wildfire over space and time. However, it is well documented that the precise relationships of local vegetation, climate, and ignitions, and how they influence fire dynamics, may vary over space and among local climate, vegetation, and land use regimes. This raises questions not only as to the nature of the potentially nonlinear relationships between local conditions and the fire, but also the possibility that the scale at which such models are developed may be critical to their predictive power and to the apparent relationship of local conditions to wildfire. In this study we demonstrate that both local climate – through limitations posed by fuel dryness (CWD) and availability (AET) – and human activity – through housing density, roads, electrical infrastructure, and agriculture, play important roles in determining the annual probabilities of fire throughout California. We also document the importance of previous burn events as potential barriers to fire in some environments, until enough time has passed for vegetation to regenerate sufficiently to sustain subsequent wildfires. We also demonstrate that long-term and short-term climate variations exhibit different effects on annual fire probability, with short-term climate variations primarily impacting fire probability during periods of extreme climate anomaly. Further, we show that, when using nonlinear modeling techniques, broad-scale fire probability models can outperform localized models at predicting annual fire probability. Finally, this study represents a powerful tool for mapping local fire probability across the state of California under a variety of historical climate regimes, which is essential to avoided emissions modelling, carbon accounting, and hazard severity mapping for the application of fire-resistant building codes across the state of California. Please refer to Readme.txt file.