7,484 research outputs found
Climate Services for Resilient Development (CSRD) Partnership’s work in Latin America
The Climate Services for Resilient Development (CSRD)
Partnership is a private-public collaboration led by USAID,
which aims to increase resilience to climate change in
developing countries through the development and
dissemination of climate services. The partnership
began with initial projects in three countries: Colombia,
Ethiopia, and Bangladesh. The International Center for
Tropical Agriculture (CIAT) was the lead organization for
the Colombian CSRD efforts – which then expanded to
encompass work in the whole Latin American region
Forecasting tillage and soil warming effects on earthworm populations
1. Healthy soils are crucial for sustainable food production, but tillage limits the biological regulation of essential ecosystem services. Better understanding of the mechanisms driving management effects on soil ecosystem engineers is needed to support sustainable management under environmental change.
2. This paper presents the EEEworm (Energy–Environment–Earthworm) model, a mechanistic individual-based model (IBM) of Lumbricus terrestris populations. L. terrestris is a dominant earthworm species in undisturbed habitats and is closely associated with numerous ecosystem services such as water flow regulation, soil structure and crop production. In reduced tillage agriculture a decline in mechanical disturbance allows for L. terrestris proliferation, whilst the activities of L. terrestris can replace many of the soil functions provided by tillage.
3. Extensive EEEworm validation with eight published studies (average R2 = 0.84) demonstrates a mechanistic approach which can extrapolate between diverse soil,
management and weather conditions. EEEworm simulation experiments elucidate that a combination of direct and indirect tillage effects lead to population declines in
tilled fields, with litter removal from the soil surface being the main driver.
4. We investigate the effects of different tillage intensities under historical and projected
soil warming conditions, and find that future warmer and drier soils in our simulation exacerbate the effects of deep ploughing on L. terrestris population declines. These effects result from warmer and drier soil conditions increasing individual metabolic rates and tillage reducing food availability to meet energy demands.
5. Synthesis and applications.
Pre-emptive strategies to mitigate climate change impacts on soil health in agroecosystems should focus on decreasing tillage intensity and retention of crop residues following tillage. EEEworm has the potential to benefit land managers, policy makers, risk assessors and regulators by providing a tool to forecast how soil systems respond to combinations of land management and
environmental change. To allow better cost-benefit analysis of contrasting land management systems a future aim of mechanistic models like EEEworm is to incorporate the links between earthworm populations, soil functions and ecosystem services
A Review of Harmful Algal Bloom Prediction Models for Lakes and Reservoirs
Anthropogenic activity has led to eutrophication in water bodies across the world. This eutrophication promotes blooms, cyanobacteria being among the most notorious bloom organisms. Cyanobacterial blooms (more commonly referred to as harmful algal blooms (HABs)) can devastate an ecosystem. Cyanobacteria are resilient microorganisms that have adapted to survive under a variety of conditions, often outcompeting other phytoplankton. Some species of cyanobacteria produce toxins that ward off predators. These toxins can negatively affect the health of the aquatic life, but also can impact animals and humans that drink or come in contact with these noxious waters. Although cyanotoxin’s effects on humans are not as well researched as the growth, behavior, and ecological niche of cyanobacteria, their health impacts are of large concern. It is important that research to mitigate and understand cyanobacterial blooms and cyanotoxin production continues. This project supports continued research by addressing an approach to collect and summarize published articles that focus on techniques and models to predict cyanobacterial blooms with the goal of understanding what research has been done to promote future work. The following report summarizes 34 articles from 2003 to 2020 that each describe a mechanistic or data driven model developed to predict the occurrence of cyanobacterial blooms or the presence of cyanotoxins in lakes or reservoirs with similar climates to Utah. These articles showed a shift from more mechanistic approaches to more data driven approaches with time. This resulted in a more individualistic approach to modeling, meaning that models are often produced for a single lake or reservoir and are not easily comparable to other models for different systems
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Multimodel combination techniques for analysis of hydrological simulations: Application to distributed model intercomparison project results
This paper examines several multimodel combination techniques that are used for streamflow forecasting: the simple model average (SMA), the multimodel superensemble (MMSE), modified multimodel superensemble (M3SE), and the weighted average method (WAM). These model combination techniques were evaluated using the results from the Distributed Model Intercomparison Project (DMIP), an international project sponsored by the National Weather Service (NWS) Office of Hydrologic Development (OHD). All of the multimodel combination results were obtained using uncalibrated DMIP model simulations and were compared against the best-uncalibrated as well as the best-calibrated individual model results. The purpose of this study is to understand how different combination techniques affect the accuracy levels of the multimodel simulations. This study revealed that the multimodel simulations obtained from uncalibrated single-model simulations are generally better than any single-member model simulations, even the best-calibrated single-model simulations. Furthermore, more sophisticated multimodel combination techniques that incorporated bias correction step work better than simple multimodel average simulations or multimodel simulations without bias correction. © 2006 American Meteorological Society
The Parameter Houlihan: a solution to high-throughput identifiability indeterminacy for brutally ill-posed problems
One way to interject knowledge into clinically impactful forecasting is to
use data assimilation, a nonlinear regression that projects data onto a
mechanistic physiologic model, instead of a set of functions, such as neural
networks. Such regressions have an advantage of being useful with particularly
sparse, non-stationary clinical data. However, physiological models are often
nonlinear and can have many parameters, leading to potential problems with
parameter identifiability, or the ability to find a unique set of parameters
that minimize forecasting error. The identifiability problems can be minimized
or eliminated by reducing the number of parameters estimated, but reducing the
number of estimated parameters also reduces the flexibility of the model and
hence increases forecasting error. We propose a method, the parameter Houlihan,
that combines traditional machine learning techniques with data assimilation,
to select the right set of model parameters to minimize forecasting error while
reducing identifiability problems. The method worked well: the data
assimilation-based glucose forecasts and estimates for our cohort using the
Houlihan-selected parameter sets generally also minimize forecasting errors
compared to other parameter selection methods such as by-hand parameter
selection. Nevertheless, the forecast with the lowest forecast error does not
always accurately represent physiology, but further advancements of the
algorithm provide a path for improving physiologic fidelity as well. Our hope
is that this methodology represents a first step toward combining machine
learning with data assimilation and provides a lower-threshold entry point for
using data assimilation with clinical data by helping select the right
parameters to estimate
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