scholarly journals A method for simulating risk profiles of wheat yield in data-sparse conditions

2021 ◽  
pp. 1-12
Author(s):  
G. Bracho-Mujica ◽  
P.T. Hayman ◽  
V.O. Sadras ◽  
B. Ostendorf
Keyword(s):  
Reference Site ◽  
Climate Impacts ◽  
Weather Data ◽  
Climate Data ◽  
High Quality ◽  
Risk Profiles ◽  
Record Length ◽  
Daily Weather Data ◽  
Study Sites

Abstract Process-based crop models are a robust approach to assess climate impacts on crop productivity and long-term viability of cropping systems. However, these models require high-quality climate data that cannot always be met. To overcome this issue, the current research tested a simple method for scaling daily data and extrapolating long-term risk profiles of modelled crop yields. An extreme situation was tested, in which high-quality weather data was only available at one single location (reference site: Snowtown, South Australia, 33.78°S, 138.21°E), and limited weather data was available for 49 study sites within the Australian grain belt (spanning from 26.67 to 38.02°S of latitude, and 115.44 to 151.85°E of longitude). Daily weather data were perturbed with a delta factor calculated as the difference between averaged climate data from the reference site and the study sites. Risk profiles were built using a step-wise combination of adjustments from the most simple (adjusted series of precipitation only) to the most detailed (adjusted series of precipitation, temperatures and solar radiation), and a variable record length (from 10 to 100 years). The simplest adjustment and shortest record length produced bias of modelled yield grain risk profiles between −10 and 10% in 41% of the sites, which increased to 86% of the study sites with the most detailed adjustment and longest record (100 years). Results indicate that the quality of the extrapolation of risk profiles was more sensitive to the number of adjustments applied rather than the record length per se.

scholarly journals The Passive Microwave Neural Network Precipitation Retrieval Algorithm for Climate Applications (PNPR-CLIM): Design and Verification

2021 ◽  
Vol 13 (9) ◽  
pp. 1701
Author(s):  
Leonardo Bagaglini ◽  
Paolo Sanò ◽  
Daniele Casella ◽  
Elsa Cattani ◽  
Giulia Panegrossi
Keyword(s):  
Neural Network ◽  
Passive Microwave ◽  
Retrieval Algorithm ◽  
Climate Data ◽  
High Quality ◽  
Data Record ◽  
Global Precipitation ◽  
Climate Data Record

This paper describes the Passive microwave Neural network Precipitation Retrieval algorithm for climate applications (PNPR-CLIM), developed with funding from the Copernicus Climate Change Service (C3S), implemented by ECMWF on behalf of the European Union. The algorithm has been designed and developed to exploit the two cross-track scanning microwave radiometers, AMSU-B and MHS, towards the creation of a long-term (2000–2017) global precipitation climate data record (CDR) for the ECMWF Climate Data Store (CDS). The algorithm has been trained on an observational dataset built from one year of MHS and GPM-CO Dual-frequency Precipitation Radar (DPR) coincident observations. The dataset includes the Fundamental Climate Data Record (FCDR) of AMSU-B and MHS brightness temperatures, provided by the Fidelity and Uncertainty in Climate data records from Earth Observation (FIDUCEO) project, and the DPR-based surface precipitation rate estimates used as reference. The combined use of high quality, calibrated and harmonized long-term input data (provided by the FIDUCEO microwave brightness temperature Fundamental Climate Data Record) with the exploitation of the potential of neural networks (ability to learn and generalize) has made it possible to limit the use of ancillary model-derived environmental variables, thus reducing the model uncertainties’ influence on the PNPR-CLIM, which could compromise the accuracy of the estimates. The PNPR-CLIM estimated precipitation distribution is in good agreement with independent DPR-based estimates. A multiscale assessment of the algorithm’s performance is presented against high quality regional ground-based radar products and global precipitation datasets. The regional and global three-year (2015–2017) verification analysis shows that, despite the simplicity of the algorithm in terms of input variables and processing performance, the quality of PNPR-CLIM outperforms NASA GPROF in terms of rainfall detection, while in terms of rainfall quantification they are comparable. The global analysis evidences weaknesses at higher latitudes and in the winter at mid latitudes, mainly linked to the poorer quality of the precipitation retrieval in cold/dry conditions.

scholarly journals Long-Term (2001–2020) Nutrient Transport from a Small Boreal Agricultural Watershed: Hydrological Control and Potential of Retention Ponds

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2731
Author(s):  
Sari Uusheimo ◽  
Tiina Tulonen ◽  
Jussi Huotari ◽  
Lauri Arvola
Keyword(s):  
Climate Change ◽  
Total Nitrogen ◽  
Total Phosphorus ◽  
Nutrient Loading ◽  
Nitrogen Loading ◽  
Agricultural Watershed ◽  
Weather Data ◽  
Pond System ◽  
Daily Weather Data

Agriculture contributes significantly to phosphorus and nitrogen loading in southern Finland. Climate change with higher winter air temperatures and precipitation may also promote loading increase further. We analyzed long-term nutrient trends (2001–2020) based on year-round weekly water sampling and daily weather data from a boreal small agricultural watershed. In addition, nutrient retention was studied in a constructed sedimentation pond system for two years. We did not find any statistically significant trends in weather conditions (temperature, precipitation, discharge, snow depth) except for an increase in discharge in March. Increasing trends in annual concentrations were found for nitrate, phosphate, and total phosphorus and total nitrogen. In fact, phosphate concentration increased in every season and nitrate concentration in other seasons except in autumn. Total phosphorus and total nitrogen concentrations increased in winter as well and total phosphorus also in summer. Increasing annual loading trend was found for total phosphorus, phosphate, and nitrate. Increasing winter loading was found for nitrate and total nitrogen, but phosphate loading increased in winter, spring, and summer. In the pond system, annual retention of total nitrogen was 1.9–4.8% and that of phosphorus 4.3–6.9%. In addition, 25–40% of suspended solids was sedimented in the ponds. Our results suggest that even small ponds can be utilized to decrease nutrient and material transport, but their retention efficiency varies between years. We conclude that nutrient loading from small boreal agricultural catchments, especially in wintertime, has already increased and is likely to increase even further in the future due to climate change. Thus, the need for new management tools to reduce loading from boreal agricultural lands becomes even more acute.

SGS Water Theme: influence of soil, pasture type and management on water use in grazing systems across the high rainfall zone of southern Australia

2003 ◽  
Vol 43 (8) ◽  
pp. 907 ◽  
Author(s):  
R. E. White ◽  
B. P. Christy ◽  
A. M. Ridley ◽  
A. E. Okom ◽  
S. R. Murphy ◽  
...  
Keyword(s):  
Water Use ◽  
Western Australia ◽  
Weather Data ◽  
Rooting Depth ◽  
Marginal Effect ◽  
Deep Drainage ◽  
High Rainfall ◽  
Daily Weather Data ◽  
Grazing Systems

Eleven experimental sites in the Sustainable Grazing Systems (SGS) national experiment were established in the high rainfall zone (HRZ, >600 mm/year) of Western Australia, Victoria and New South Wales to measure components of the water balance, and pathways of water movement, for a range of pastures from 1997 to 2001. The effect of widely spaced river red gums (Eucalyptus camaldulensis) in pasture, and of belts of plantation blue gums (E. globulus), was studied at 2 of the sites. The soil types tested ranged from Kurosols, Chromosols and Sodosols, with different subsoil permeabilities, to Hydrosols and Tenosols. The pasture types tested were kikuyu (Pennisetum clandestinum), phalaris (Phalaris aquatica), redgrass (Bothriochloa macra) and annual ryegrass (Lolium rigidum), with subterranean clover (Trifolium subterraneum) included. Management variables were set stocking v. rotational grazing, adjustable stocking rates, and level of fertiliser input. Soil, pasture and animal measurements were used to set parameters for the biophysical SGS pasture model, which simulated the long-term effects of soil, pasture type, grazing method and management on water use and movement, using as inputs daily weather data for 31 years from selected sites representing a range of climates. Measurements of mean maximum soil water deficit Sm were used to estimate the probability of surplus water occurring in winter, and the average amount of this surplus, which was highest (97–201 mm/year) for pastures in the cooler, winter-rainfall dominant regions of north-east and western Victoria and lowest (3–11 mm/year) in the warmer, lower rainfall regions of the eastern Riverina and Esperance, Western Australia. Kikuyu in Western Australia achieved the largest increase in Sm compared with annual pasture (55–71 mm), while increases due to phalaris were 18–45 mm, and those of native perennials were small and variable. Long-term model simulations suggested rooting depth was crucial in decreasing deep drainage, to about 50 mm/year for kikuyu rooting to 2.5 m, compared with 70–200 mm/year for annuals rooting to only 0.8 m. Plantation blue gums dried the soil profile to 5.25 m by an average of 400 mm more than kikuyu pasture, reducing the probability of winter surplus water to zero, and eliminating drainage below the root zone. Widely spaced river red gums had a much smaller effect on water use, and would need to number at least 14 trees per hectare to achieve extra soil drying of about 50 mm over a catchment. Soil type affected water use primarily through controlling the rooting depth of the vegetation, but it also changed the partitioning of surplus water between runoff and deep drainage. Strongly duplex soils such as Sodosols shed 50% or more surplus water as runoff, which is important for flushing streams, provided the water is of good quality. Grazing method and pasture management had only a marginal effect in increasing water use, but could have a positive effect on farm profitability through increased livestock production per hectare and improved persistence of perennial species.

scholarly journals Defining guidelines for ditch depth in drained Scots pine dominated peatland forests

Silva Fennica ◽  
2021 ◽  
Vol 55 (3) ◽  
Author(s):  
Hannu Hökkä ◽  
Ari Laurén ◽  
Leena Stenberg ◽  
Samuli Launiainen ◽  
Kersti Leppä ◽  
...  
Keyword(s):  
Scots Pine ◽  
Forest Growth ◽  
Weather Data ◽  
Daily Weather Data ◽  
Network Maintenance ◽  
Daily Water ◽  
Environmentally Sound ◽  
Water Outflow ◽  
Peat Properties

We used a process-based hydrological model SUSI to improve guidelines for ditch network maintenance (DNM) operations on drained peatland forests. SUSI takes daily weather data, ditch depth, strip width, peat properties, and forest stand characteristics as input and calculates daily water table depth (WTD) at different distances from ditch. The study focuses on Scots pine ( L.) dominated stands which are the most common subjects of DNM. Based on a literature survey, and consideration of the tradeoffs between forest growth and detrimental environmental impacts, long term median July–August WTD of 0.35 m was chosen as a target WTD. The results showed that ditch depths required to reach such WTD depends strongly on climatic locations, stand volume, ditch spacing, and peat thickness and type. In typical ditch cleaning areas in Finland with parallel ditches placed about 40 m apart and tree stand volumes exceeding 45 m ha, 0.3–0.8 m deep ditches were generally sufficient to lower WTD to the targeted depth of 0.35 m. These are significantly shallower ditch depths than generally recommended in operational forestry. The main collector ditch should be naturally somewhat deeper to permit water outflow. Our study brings a firmer basis on environmentally sound forestry on drained peatlands.Pinus sylvestris3–1

scholarly journals Community climate simulations to assess avoided impacts in 1.5 and 2  °C futures

2017 ◽  
Vol 8 (3) ◽  
pp. 827-847 ◽  
Author(s):  
Benjamin M. Sanderson ◽  
Yangyang Xu ◽  
Claudia Tebaldi ◽  
Michael Wehner ◽  
Brian O'Neill ◽  
...  
Keyword(s):  
Coupled Model ◽  
Historical Record ◽  
Climate Impacts ◽  
The Arctic ◽  
Climate Data ◽  
Climate Simulations ◽  
Community Earth System Model ◽  
Temperature Levels ◽  
Model Variability

Abstract. The Paris Agreement of December 2015 stated a goal to pursue efforts to keep global temperatures below 1.5 °C above preindustrial levels and well below 2 °C. The IPCC was charged with assessing climate impacts at these temperature levels, but fully coupled equilibrium climate simulations do not currently exist to inform such assessments. In this study, we produce a set of scenarios using a simple model designed to achieve long-term 1.5 and 2 °C temperatures in a stable climate. These scenarios are then used to produce century-scale ensemble simulations using the Community Earth System Model, providing impact-relevant long-term climate data for stabilization pathways at 1.5 and 2 °C levels and an overshoot 1.5 °C case, which are realized (for the 21st century) in the coupled model and are freely available to the community. Here we describe the design of the simulations and a brief overview of their impact-relevant climate response. Exceedance of historical record temperature occurs with 60 % greater frequency in the 2 °C climate than in a 1.5 °C climate aggregated globally, and with twice the frequency in equatorial and arid regions. Extreme precipitation intensity is statistically significantly higher in a 2.0 °C climate than a 1.5 °C climate in some specific regions (but not all). The model exhibits large differences in the Arctic, which is ice-free with a frequency of 1 in 3 years in the 2.0 °C scenario, and 1 in 40 years in the 1.5 °C scenario. Significance of impact differences with respect to multi-model variability is not assessed.

scholarly journals Community Climate Simulations to assess avoided impacts in 1.5 °C and 2 °C futures

2017 ◽  
Author(s):  
Benjamin M. Sanderson ◽  
Yangyang Xu ◽  
Claudia Tebaldi ◽  
Michael Wehner ◽  
Brian O'Neill ◽  
...  
Keyword(s):  
Historical Record ◽  
Climate Impacts ◽  
The Arctic ◽  
Climate Data ◽  
Climate Simulations ◽  
Community Earth System Model ◽  
Temperature Levels ◽  
Key Aspects ◽  
Fully Coupled

Abstract. The Paris Agreement of December 2015 stated a goal to pursue efforts to keep global temperatures below 1.5 °C above pre-industrial levels and well below 2 °C. The IPCC was charged with assessing climate impacts at these temperature levels, but fully coupled equilibrium climate simulations do not currently exist to inform such assessments. In this study, we produce a set of scenarios using a simple model designed to achieve long term 1.5 °C and 2 °C temperatures in a stable climate. These scenarios are then used to produce century scale ensemble simulations using the Community Earth System Model, providing impact-relevant long term climate data for stabilization pathways at 1.5 °C and 2 °C levels and an overshoot 1.5 °C case, which are freely available to the community. Here we describe the design of the simulations and key aspects of their impact-relevant climate response. Exceedance of historical record temperature occurs with 60 percent greater frequency in the 2 °C climate than in a 1.5 °C climate aggregated globally, and with twice the frequency in equatorial and arid regions. Extreme precipitation intensity is statistically significantly higher in a 2.0 °C climate than a 1.5 °C climate in several regions. The model exhibits large differences in the Arctic which is ice-free with a frequency of 1 in 3 years in the 2.0 °C scenario, and only 1 in 40 years in the 1.5 °C scenario.

scholarly journals ATTRICI v1.1 – counterfactual climate for impact attribution

2021 ◽  
Vol 14 (8) ◽  
pp. 5269-5284
Author(s):  
Matthias Mengel ◽  
Simon Treu ◽  
Stefan Lange ◽  
Katja Frieler
Keyword(s):  
Climate Change ◽  
Climate Impact ◽  
Climate Impacts ◽  
General Definition ◽  
Impact Model ◽  
Climate Data ◽  
Climate Trends ◽  
Long Term Changes ◽  
The Impact

Abstract. Attribution in its general definition aims to quantify drivers of change in a system. According to IPCC Working Group II (WGII) a change in a natural, human or managed system is attributed to climate change by quantifying the difference between the observed state of the system and a counterfactual baseline that characterizes the system's behavior in the absence of climate change, where “climate change refers to any long-term trend in climate, irrespective of its cause” (IPCC, 2014). Impact attribution following this definition remains a challenge because the counterfactual baseline, which characterizes the system behavior in the hypothetical absence of climate change, cannot be observed. Process-based and empirical impact models can fill this gap as they allow us to simulate the counterfactual climate impact baseline. In those simulations, the models are forced by observed direct (human) drivers such as land use changes, changes in water or agricultural management but a counterfactual climate without long-term changes. We here present ATTRICI (ATTRIbuting Climate Impacts), an approach to construct the required counterfactual stationary climate data from observational (factual) climate data. Our method identifies the long-term shifts in the considered daily climate variables that are correlated to global mean temperature change assuming a smooth annual cycle of the associated scaling coefficients for each day of the year. The produced counterfactual climate datasets are used as forcing data within the impact attribution setup of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a). Our method preserves the internal variability of the observed data in the sense that factual and counterfactual data for a given day have the same rank in their respective statistical distributions. The associated impact model simulations allow for quantifying the contribution of climate change to observed long-term changes in impact indicators and for quantifying the contribution of the observed trend in climate to the magnitude of individual impact events. Attribution of climate impacts to anthropogenic forcing would need an additional step separating anthropogenic climate forcing from other sources of climate trends, which is not covered by our method.

scholarly journals Climate Change Effects on Temperate Grassland and Its Implication for Forage Production: A Case Study from Northern Germany

Agriculture ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 232
Author(s):  
Iraj Emadodin ◽  
Daniel Ernesto Flores Corral ◽  
Thorsten Reinsch ◽  
Christof Kluß ◽  
Friedhelm Taube
Keyword(s):  
Climate Change ◽  
Agricultural Land ◽  
Standardized Precipitation Index ◽  
Forage Yield ◽  
Weather Data ◽  
Organic N ◽  
Positive Trend ◽  
Forage Production ◽  
Daily Weather Data

The effects of climate change on agricultural ecosystems are increasing, and droughts affect many regions. Drought has substantial ecological, social, and economic consequences for the sustainability of agricultural land. Many regions of the northern hemisphere have not experienced a high frequency of meteorological droughts in the past. For understanding the implications of climate change on grassland, analysis of the long-term climate data provides key information relevant for improved grassland management strategies. Using weather data and grassland production data from a long-term permanent grassland site, our aims were (i) to detect the most important drought periods that affected the region and (ii) to assess whether climate changes and variability significantly affected forage production in the last decade. For this purpose, long-term daily weather data (1961–2019) and the standardized precipitation index (SPI), De Martonne index (IDM), water deficit (WD), dryness index (DI), yield anomaly index (YAI), and annual yield loss index (YL) were used to provide a scientific estimation. The results show that, despite a positive trend in DI and a negative trend in WD and precipitation, the time-series trends of precipitation, WD, and DI indices for 1961–2019 were not significant. Extreme dry conditions were also identified with SPI values less than −2. The measured annual forage yield (2007–2018) harvested in a four-cut silage system (with and without organic N-fertilization) showed a strong correlation with WD (R = 0.64; p ˂ 0. 05). The main yield losses were indicated for the years 2008 and 2018. The results of this study could provide a perspective for drought monitoring, as well as drought warning, in grassland in northwest Europe.

An empirical approach to the analysis of local and global climate and weather data and to the determination of CO2 sensitivities

2021 ◽  
Author(s):  
Wolf Timm
Keyword(s):  
Global Climate ◽  
Global Temperature ◽  
Weather Data ◽  
Data Sets ◽  
Climate Data ◽  
Local Data ◽  
Simple Method ◽  
Co2 Sensitivity ◽  
Data Points

Abstract Some freely available global temperature data sets which document the weather for a period of over 100 years, e.g. from NASA, from NOAA, additionally also local data e.g. for Germany (DWD) were analyzed in order to derive meaningful empirical long-term trends with suitable multi-annual averages. This is first demonstrated using global climate data with different approaches, whereby the results are to a high degree consistent. Analyzes of the German temperature and weather data and of climate data from other continents are carried out in a similar manner. For reliable forecasts it is important to determine the CO2 sensitivity as precisely as possible. A very simple method is to smooth out temperatures over 20 years at a time. If these values are plotted at intervals of 10 years over the associated (also averaged) CO2 content, the temperature database (since 1961) is condensed to 5 data points and a statement can be made about the quality of the linearity for the respective database. Both the NASA data and the NOAA data show an unusually good linearity with almost identical CO2 sensitivity (approx. 0.0105 K/ppm CO2). This indicates that the long-term trend in global temperature since around 1960 has been largely determined solely by greenhouse gases. If the regional weather data is used as a basis, there is also in many cases strict linearity with increasing CO2 content. The analysis of the regional data allows the conclusion that there is approximately a specific CO2 sensitivity for every region on earth with specific statistical uncertainties: For mean global land, it is 0.017 K, for Germany it is 0.022 K, and for Alaska even 0.028 K per ppm CO2 .

scholarly journals Generation of Synthetic Daily Weather for Climate Change Scenarios and Extreme Storm Intensification

2019 ◽  
Vol 9 (2) ◽  
pp. 1
Author(s):  
Jurgen Garbrecht ◽  
X. C. Zhang ◽  
David Brown ◽  
Phillip Busteed
Keyword(s):  
Climate Change ◽  
Weather Data ◽  
Climate Projections ◽  
Climate Change Scenarios ◽  
Generation Model ◽  
Traditional Role ◽  
Climate Conditions ◽  
Daily Weather ◽  
Daily Weather Data

Long-term simulations in watershed hydrology, soil and nutrient transport, and sustainability of agricultural production systems require long-term weather records that are often not available at the location of interest. Generation of synthetic daily weather data is a common approach to augment limited weather observations. Here a synthetic daily weather generation model (called SYNTOR) is described. SYNTOR fulfills the traditional role of generating alternative weather realizations that have statistical properties similar to those of the parent historical weather it is intended to simulate. In addition, it has the capability to simulate daily weather records for climate change scenarios and storm intensification due to climate change. The various model components are briefly summarized and an application is presented for semi-arid climate conditions in west-central Oklahoma. SYNTOR generated daily weather compared well with observed weather values. Climate change is simulated by adjusting weather generation parameters to reflect the changed mean monthly weather values of climate projections. Storm intensification is approximated by increasing the top 10 percentile of storm distribution by a predefined amount based on previous studies of trends in United States precipitation. Further evaluation of published storm intensification values and associated uncertainties and spatial variability is recommended.

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