Drought Avoidance Assessment for Summer Annual Crops Using Long-Term Weather Data

2003 ◽  
Vol 95 (6) ◽  
pp. 1566-1576 ◽  
Author(s):  
Larry C. Purcell ◽  
Thomas R. Sinclair ◽  
Ronald W. McNew
Keyword(s):  
Weather Data ◽  
Annual Crops ◽  
Drought Avoidance

Evaluation of the CERES-Rice Model for Precision Nitrogen Management for Rice in Northeast China

2017 ◽  
Vol 8 (2) ◽  
pp. 328-332
Author(s):  
J. Zhang ◽  
Y. Miao ◽  
W.D. Batchelor
Keyword(s):  
Northeast China ◽  
Weather Data ◽  
Cool Season ◽  
Use Efficiency ◽  
Optimum N Rate ◽  
Management Recommendations ◽  
N Management ◽  
N Rates ◽  
N Use

Over-application of nitrogen (N) in rice (Oryza sativaL.) production in China is common, leading to low N use efficiency (NUE) and high environmental risks. The objective of this work was to evaluate the ability of the CERES-Rice crop growth model to simulate N response in the cool climate of Northeast China, with the long term goal of using the model to develop optimum N management recommendations. Nitrogen experiments were conducted from 2011–2015 in Jiansanjiang, Heilongjiang Province in Northeast China. The CERES-Rice model was calibrated for 2014 and 2015 and evaluated for 2011 and 2013 experiments. Overall, the model gave good estimations of yield across N rates for the calibration years (R2=0.89) and evaluation years (R2=0.73). The calibrated model was then run using weather data from 2001–2015 for 20 different N rates to determine the N rate that maximized the long term marginal net return (MNR) for different N prices. The model results indicated that the optimum mean N rate was 120–130 kg N ha–1, but that the simulated optimum N rate varied each year, ranging from 100 to 200 kg N ha–1. Results of this study indicated that the CERES-Rice model was able to simulate cool season rice growth and provide estimates of optimum regional N rates that were consistent with field observations for the area.

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 A Typical Meteorological Year Generation Based on NASA Satellite Imagery (GEOS-I) for Sokoto, Nigeria

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Olayinka S. Ohunakin ◽  
Muyiwa S. Adaramola ◽  
Olanrewaju M. Oyewola ◽  
Richard L. Fagbenle ◽  
Fidelis I. Abam
Keyword(s):  
Energy Systems ◽  
Building Energy ◽  
Global Solar Radiation ◽  
Dew Point ◽  
Weather Data ◽  
Maximum Temperature ◽  
Building Energy Simulation ◽  
Dew Point Temperature ◽  
Northwest Region

Computer simulation of buildings and solar energy systems are being used increasingly in energy assessments and design. This paper evaluates the typical meteorological year (TMY) for Sokoto, northwest region, Nigeria, using 23-year hourly weather data including global solar radiation, dew point temperature, mean temperature, maximum temperature, minimum temperature, relative humidity, and wind speed. Filkenstein-Schafer statistical method was utilized for the creation of a TMY for the site. The persistence of mean dry bulb temperature and daily global horizontal radiation on the five candidate months were evaluated. TMY predictions were compared with the 23-year long-term average values and are found to have close agreement and can be used in building energy simulation for comparative energy efficiency study.

scholarly journals Detecting and correcting sensor drifts in long-term weather data

2012 ◽  
Vol 185 (6) ◽  
pp. 4483-4489 ◽  
Author(s):  
Georg von Arx ◽  
Matthias Dobbertin ◽  
Martine Rebetez
Keyword(s):  
Weather Data

Monitoring and Calibration of Long Term Mooring System Design Assumptions Through Use of AIS and Weather Data

2021 ◽  
Author(s):  
Dag Børre Lillestøl ◽  
Odd Torbjørn Kårvand ◽  
Are Torstensen
Keyword(s):  
Low Cost ◽  
Environmental Data ◽  
Weather Data ◽  
Mooring System ◽  
System Behavior ◽  
Original Design ◽  
Actual System ◽  
Early Failures ◽  
And Performance

Abstract This paper outlines an approach on how to improve the mooring integrity of existing long term mooring systems by using existing and commercially available data. It will be demonstrated how the use of AIS and hindcast weather data can be used to increase understanding of mooring systems and to monitor and quantify gaps between "as-designed", "as-installed" and "as-is" of a long term mooring system. Long term moored units have traditionally suffered from many early failures, caused by damages and errors introduced in the installation phase, and costly and unnecessary "late in life" failures. A fact rated high on the agenda of the underwriters. Numerous papers have been written on this topic, but it is only in recent years the industry have started to ensure that systems are inspected to a sufficient degree with respect to the physical condition, taking these learnings into account. However, the second important element, the calibration of the mooring analysis vs. actual vessel and mooring system behavior/performance, have not yet gotten the attention required. Deviations from the intended design are introduced in the installation phase of a mooring system. In addition, the design assumptions will never be fully accurate. The gap between the design assumptions and the actual system will increase over time, and the industry today do not focus on mapping and quantifying the effect of this gap sufficiently. The described method explains how one can introduce a pro-active approach, without installing onboard equipment, but rather utilizing algorithms on existing data and design documentation. This paper focuses on the use of AIS data in combination with historic weather/environmental data and seek to demonstrate how this low-cost method can provide useful information with respect to the mooring system. To emphasize the mapped importance of such calibrations, the July 2021 Edition of the in-service DNV Class Rules, DNVGL-OS-0300, formally introduces requirements to calibration of design assumptions of long term mooring units through use of survey data, service history and actual mooring system behavior in order to ensure a unit's mooring system condition and performance is known in light of the original design assumptions.

scholarly journals Updated Typical Weather Years for the Energy Simulation of Buildings in Mediterranean Climate. A Case Study for Sicily

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4115 ◽  
Author(s):  
Vincenzo Costanzo ◽  
Gianpiero Evola ◽  
Marco Infantone ◽  
Luigi Marletta
Keyword(s):  
Residential Building ◽  
Building Energy ◽  
Energy Simulation ◽  
Weather Data ◽  
Weather Station ◽  
Simulation Tools ◽  
The Difference ◽  
Energy Simulations

Building energy simulations are normally run through Typical Weather Years (TWYs) that reflect the average trend of local long-term weather data. This paper presents a research aimed at generating updated typical weather files for the city of Catania (Italy), based on 18 years of records (2002–2019) from a local weather station. The paper reports on the statistical analysis of the main recorded variables, and discusses the difference with the data included in a weather file currently available for the same location based on measurements taken before the 1970s but still used in dynamic energy simulation tools. The discussion also includes a further weather file, made available by the Italian Thermotechnical Committee (CTI) in 2015 and built upon the data registered by the same weather station but covering a much shorter period. Three new TWYs are then developed starting from the recent data, according to well-established procedures reported by ASHRAE and ISO standards. The paper discusses the influence of the updated TWYs on the results of building energy simulations for a typical residential building, showing that the cooling and heating demand can differ by 50% or even 65% from the simulations based on the outdated weather file.

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.

scholarly journals Realistic Choice of Annual Matrices Contracts the Range of λS Estimates

Mathematics ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 2252
Author(s):  
Dmitrii O. Logofet ◽  
Leonid L. Golubyatnikov ◽  
Nina G. Ulanova
Keyword(s):  
Markov Chain ◽  
Local Population ◽  
Quantitative Measure ◽  
Weather Data ◽  
Variable Environment ◽  
Annual Population ◽  
Novel Method ◽  
Stochastic Growth Rate ◽  
Realistic Choice

In matrix population modeling the multi-year monitoring of a population structure results in a set of annual population projection matrices (PPMs), which gives rise to the stochastic growth rate λS, a quantitative measure of long-term population viability. This measure is usually found in the paradigm of population growth in a variable environment. The environment is represented by the set of PPMs, and λS ensues from a long sequence of PPMs chosen at random from the given set. because the known rules of random choice, such as the iid (independent and identically distributed) matrices, are generally artificial, the challenge is to find a more realistic rule. We achieve this with the a following a Markov chain that models, in a certain sense, the real variations in the environment. We develop a novel method to construct the ruling Markov chain from long-term weather data and to simulate, in a Monte Carlo mode, the long sequences of PPMs resulting in the estimates of λS. The stochastic nature of sequences causes the estimates to vary within some range, and we compare the range obtained by the “realistic choice” from 10 PPMs for a local population of a Red-Book species to those using the iid choice. As noted in the title of this paper, this realistic choice contracts the range of λS estimates, thus improving the estimation and confirming the Red-Book status of the species.

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