Integrating a novel irrigation approximation method with a process-based remote sensing model to estimate multi-years'winter wheat yield over the North China Plain

Accurate estimation of regional winter wheat yields is essential for understanding the food production status and ensuring national food security.However,using the existing remote sensing-based crop yield models to accurately reproduce the inter-annual and spatial variations in winter wheat yields remains challenging due to the limited ability to acquire irrigation information in water-limited regions.Thus,we proposed a new approach to approximating irrigations of winter wheat over the North China Plain(NCP),where irrigation occurs extensively during the winter wheat growing season.This approach used irrigation pattern parameters(IPPs)to define the irrigation frequency and timing.Then,they were incorporated into a newly-developed process-based and remote sensing-driven crop yield model for winter wheat(PRYM–Wheat),to improve the regional estimates of winter wheat over the NCP.The IPPs were determined using statistical yield data of reference years(2010–2015)over the NCP.Our findings showed that PRYM–Wheat with the optimal IPPs could improve the regional estimate of winter wheat yield,with an increase and decrease in the correlation coefficient(R)and root mean square error(RMSE)of 0.15(about 37%)and 0.90 t ha–1(about 41%),respectively.The data in validation years(2001–2009 and 2016–2019)were used to validate PRYM–Wheat.In addition,our findings also showed R(RMSE)of 0.80(0.62 t ha–1)on a site level,0.61(0.91 t ha–1)for Hebei Province on a county level,0.73(0.97 t ha–1)for Henan Province on a county level,and 0.55(0.75 t ha–1)for Shandong Province on a city level.Overall,PRYM–Wheat can offer a stable and robust approach to estimating regional winter wheat yield across multiple years,providing a scientific basis for ensuring regional food security.

Developing a process-based and remote sensing driven crop yield model for maize(PRYM–Maize) and its validation over the Northeast China Plain

Spatial dynamics of crop yield provide useful information for improving the production. High sensitivity of crop growth models to uncertainties in input factors and parameters and relatively coarse parameterizations in conventional remote sensing(RS) approaches limited their applications over broad regions. In this study, a process-based and remote sensing driven crop yield model for maize(PRYM–Maize) was developed to estimate regional maize yield, and it was implemented using eight data-model coupling strategies(DMCSs) over the Northeast China Plain(NECP). Simulations under eight DMCSs were validated against the prefecture-level statistics(2010–2012) reported by National Bureau of Statistics of China, and inter-compared. The 3-year averaged result could give more robust estimate than the yearly simulation for maize yield over space. A 3-year averaged validation showed that prefecture-level estimates by PRYM–Maize under DMCS8, which coupled with the development stage(DVS)-based grain-filling algorithm and RS phenology information and leaf area index(LAI), had higher correlation(R, 0.61) and smaller root mean standard error(RMSE, 1.33 t ha^(–1)) with the statistics than did PRYM–Maize under other DMCSs. The result also demonstrated that DVS-based grain-filling algorithm worked better for maize yield than did the harvest index(HI)-based method, and both RS phenology information and LAI worked for improving regional maize yield estimate. These results demonstrate that the developed PRYM–Maize under DMCS8 gives reasonable estimates for maize yield and provides scientific basis facilitating the understanding the spatial variations of maize yield over the NECP.

The Process-based Approach in English Writing Class

English writing is gaining high attention and emphasis in our college syllabus. It is a complicated process of written communication. In the process, the four basic skills—speaking, reading, listening and writing are organically integrated together. This paper is about the process-based approach in English writing class and its aim to emphasize the importance of process-based approach to writing. There are four parts in this paper. I start with a brief introduction to the importance of writing. Next, I will introduce my practice in English writing class. Then I will comment on the process approach and then make a little revision.

Evaluation of Parametric Limitations in Simulating Greenhouse Gas Fluxes from Irish Arable Soils Using Three Process-Based Models

Globally a large number of process-based models have been assessed for quantification of agricultural greenhouse gas (GHG) emissions. Modelling approaches minimize the presence of spatial variability of biogeochemical processes, leading to improved estimates of GHGs as well as identifying mitigation and policy options. The comparative performance of the three dynamic models (e.g., DNDC v9.4, DailyDayCent and ECOSSE v5+) with minimum numbers of common input parameters was evaluated against measured variables. Simulations were performed on conventionally-tilled spring barley crops receiving N fertilizer at 135 - 159 kg·N·ha-1·yr-1 and crop residues at 3 t·ha-1·yr-1. For surface soil nitrate (0 - 10 cm), the ECOSSE and DNDC simulated values showed significant correlations with measured values (R2 = 0.31 - 0.55, p 0.05). Only the ECOSSE-simulated N2O fluxes showed a significant relationship (R2 = 0.33, p 0.05) with values measured from fertilized fields, but not with unfertilized ones. The DNDC and DailyDayCent models significantly underestimated seasonal/annual N2O fluxes compared to ECOSSE, with emission factors (EFs), based on an 8-year average, were 0.09%, 0.31% and 0.52%, respectively. Predictions of ecosystem respiration by both DailyDayCent and DNDC showed reasonable agreement with Eddy Covariance data (R2 = 0.34 - 0.41, p 0.05). Compared to the measured value (3624 kg·C·ha-1·yr-1), the ECOSSE underestimated annual heterotrophic respiration by 7% but this was smaller than the DNDC (50%) and DailyDayCent (24%) estimates. All models simulated CH4 uptake we

Growth phenology adjusts to seasonal changes in water availability in coexisting evergreen and deciduous mediterranean oaks

Different leaf(evergreen vs.deciduous habit)and xylem(diffuse-vs.ring-porous wood)traits represent contrasting strategies to face seasonal changes in water availability and temperature.However,how contrasting leaf and xylem habits of coexisting tree species affect stem wood formation and tree-ring development remains poorly understood.Here,we investigated the spatio-temporal patterns of wood formation in two deciduous oaks(Quercus faginea and Quercus petraea)and two evergreen oaks(Quercus ilex and Quercus suber)coexisting in seasonally dry Mediterranean forests along an aridity gradient in Spain.We hypothesized that growth responses to drought and intra-and inter-annual growth patterns would differ between functional groups.We simulated intra-and interannual growth using a modified version of the Vaganov-Shashkin(VS)process-based,growth model.The VS model simulations were used to estimate growth changes under a high emission scenario(RCP 8.5)for the current distribution of the study oak species and to forecast their future performance under warm(4.8℃)conditions in the Iberian Peninsula.Our simulations indicate that climate warming would induce a shortening of the ringgrowth season and a reduction of radial growth in evergreen and deciduous Mediterranean oaks,particularly in dry sites from southern and eastern Iberia currently occupied by Q.ilex and Q.faginea.Evergreen oaks may better recover after dry periods than deciduous oaks by resuming growth after the summer drought.Low soil water availability in spring would be more detrimental to growth of deciduous oaks.Process-based growth models should be refined and validated to better forecast changes in tree growth as a function of climate.

A spatial-explicit dynamic vegetation model that couples carbon,water,and nitrogen processes for arid and semiarid ecosystems

Arid and semiarid ecosystems, or dryland, are important to global biogeochemical cycles. Dryland＇s community structure and vegetation dynamics as well as biogeochemical cycles are sensitive to changes in climate and atmospheric composition. Vegetation dynamic models has been applied in global change studies, but the com- plex interactions among the carbon （C）, water, and nitrogen （N） cycles have not been adequately addressed in the current models. In this study, a process-based vegetation dynamic model was developed to study the responses of dryland ecosystems to environmental changes, emphasizing on the interactions among the C, water, and N proc- esses. To address the interactions between the C and water processes, it not only considers the effects of annual precipitation on vegetation distribution and soil moisture on organic matter （SOM） decomposition, but also explicitly models root competition for water and the water compensation processes. To address the interactions between C and N processes, it models the soil inorganic mater processes, such as N mineralization/immobilization, denitrifica- tion/nitrification, and N leaching, as well as the root competition for soil N. The model was parameterized for major plant functional types and evaluated against field observations.

Global pattern and change of cropland soil organic carbon during 1901-2010: Roles of climate, atmospheric chemistry, land use and management

Soil organic carbon(SOC)in croplands is a key property of soil quality for ensuring food security and agricultural sustainability,and also plays a central role in the global carbon(C)budget.When managed sustainably,soils may play a critical role in mitigating climate change by sequestering C and decreasing greenhouse gas emissions into the atmosphere.However,the magnitude and spatio-temporal patterns of global cropland SOC are far from well constrained due to high land surface heterogeneity,complicated mechanisms,and multiple influencing factors.Here,we use a process-based agroecosystem model(DLEM-Ag)in combination with diverse spatially-explicit gridded environmental data to quantify the long-term trend of SOC storage in global cropland area during 1901-2010 and identify the relative impacts of climate change,elevated CO2,nitrogen deposition,land cover change,and land management practices such as nitrogen fertilizer use and irrigation.Model results show that the total SOC and SOC density in the 2000s increased by 125%and 48.8%,respectively,compared to the early 20th century.This SOC increase was primarily attributed to cropland expansion and nitrogen fertilizer use.Factorial analysis suggests that climate change reduced approximately 3.2%(or 2,166 Tg C)of the total SOC over the past 110 years.Our results indicate that croplands have a large potential to sequester C through implementing better land use management practices,which may partially offset SOC loss caused by climate change.

Numerical Modeling of the Undertow Structure and Sandbar Migration in the Surfzone

A process-based 3D numerical model for surfzone hydrodynamics and beach evolution was established. Comparisons between the experimental data and model results proved that the model could effectively describe the hydrodynamics, sediment transport feature and sandbar migration process in the surfzone with satisfactory precision. A series of numerical simulations on the wave breaking and shoaling up to a barred beach were carried out based on the model system. Analyzed from the model results, the wave-induced current system in the surfzone consists of two major processes, which are the phase-averaged undertow caused by wave breaking and the net drift caused by both of the nonlinear wave motion and surface roller effect. When storm waves come to the barred beach, the strong offshore undertow along the beach suppresses the onshore net drift, making the initial sandbar migrate to the seaside. Under the condition of calm wave environment, both the undertow and net drift flow to the shoreline at the offshore side of the sandbar, and then push the initial sandbar to the shoreline. The consideration of surface roller has significant impact on the modeling results of the sandbar migration. As the roller transfer rate increases, the sandbar moves onshore especially under the storm wave condition.

A review of stand basal area growth models

Growth and yield modeling has a long history in forestry. The methods of measuring the growth of stand basal area have evolved from those developed in the U.S.A. and Germany during the last century. Stand basal area modeling has progressed rapidly since the first widely used model was published by the U.S. Forest Service. Over the years, a variety of models have been developed for predicting the growth and yield of uneven/even-aged stands using stand-level approaches. The modeling methodology has not only moved from an empirical approach to a more ecological process-based approach but also accommodated a variety of techniques such as： 1） simultaneous equation methods, 2） difference models, 3） artificial neural network techniques, 4） linear/nonlinear regression models, and 5） matrix models. Empirical models using statistical methods were developed to reproduce accurately and precisely field observations. In contrast, process models have a shorter history, developed originally as research and education tools with the aim of increasing the understanding of cause and effect relationships. Empirical and process models can be married into hybrid models in which the shortcomings of both component approaches can, to some extent, be overcome. Algebraic difference forms of stand basal area models which consist of stand age, stand density and site quality can fully describe stand growth dynamics. This paper reviews the current literature regarding stand basal area models, discusses the basic types of models and their merits and outlines recent progress in modeling growth and dynamics of stand basal area. Future trends involving algebraic difference forms, good fitting variables and model types into stand basal area modeling strategies are discussed.

Method for process-based modeling of combat scenarios using interaction analysis weapon systems

With technological advancements,weapon system development has become increasingly complex and costly.Using modeling and simulation(M&S)technology in the conceptual design stage is effective in reducing the development time and cost of weapons.One way to reduce the complexity and trial-and-error associated with weapon development using M&S technology is to develop combat scenarios to review the functions assigned to new weapons.Although the M&S technology is applicable,it is difficult to analyze how effectively the weapons are functioning,because of the dynamic features inherent in combat scenario modeling,which considers interrelations among different weapon entities.To support review of weapon functions including these characteristics,this study develops a process-based modeling(PBM)method to model the interactions between weapons in the combat scenario.This method includes the following three steps:(1)construct virtual models by converting the weapons and the weapon functions into their corresponding components;(2)generate the combat process from the combat scenario,which is derived from the interrelations among weapons under consideration using reasoning rules;(3)develop a process-based model that describes weapon functions by combining the combat process with virtual models.Then,a PBM system based on this method is implemented.The case study executed on this system shows that it is useful in deriving process-based models from various combat scenarios,analyzing weapon functions using the derived models,and reducing weapon development issues in the conceptual design stage.

ADAPTABLE DESIGN IN PRODUCT DEVELOPMENT

A newly emerging design pattern, named as adaptable design （AD）, which aims at developing products that are adaptable from design to post-life cycle, is discussed. AD consists of four main phases： product modeling, design platform, specific design and product redesign. A new process-based design data model （PDDM） is presented which is organized according to the principles of convenient knowledge extraction, data representation, layout, sharing and reuse. Based on the PDDM, a universal design platform for product family development is established, which has characters of modularity, parameter-driven, variant design, etc. The framework of the platform is also proposed as a conceptual structure and overall logical organization for generating a family of products. AD methodology is successfully applied to develop a family of tunnel boring machine （TBM） for different engineering projects, with the efficiency of our developing team being greatly increased.

W eighted sum based method on the optimal weights for weapon evaluation

In order to solve instability problem of calculation precision resulting from the selection of each target weight in evaluating weapon systems, a weighted sum based method is proposed. Specif- ically, the subjective weights depending on experts＇ experience are substituted by the optimal weights. The optimal weights are acquired through constructing a mathematical programming model based on subjective weights and objective weights. The method of solving subjective weights is the same as before, and the objective weights were solved by means of grey theory. The case analysis shows that the method of improved weighted sum can improve the evaluation precision up to more than 5% , and minimize the instability of calculation precision resulting from only using subjective weights. The method that the optimal weights substituted the subjective weights is brought forward in improving evaluation precision for the first time. The ideas of the optimal weights and the pro- posed method are described and analyzed.

Predicting coastal morphological changes with empirical orthogonal function method

In order to improve the accuracy of prediction when using the empirical orthogonal function （EOF） method, this paper describes a novel approach for two-dimensional （2D） EOF analysis based on extrapolating both the spatial and temporal EOF components for long-term prediction of coastal morphological changes. The approach was investigated with data obtained from a process-based numerical model, COAST2D, which was applied to an idealized study site with a group of shore-parallel breakwaters. The progressive behavior of the spatial and temporal EOF components, related to bathymetric changes over a training period, was demonstrated, and EOF components were extrapolated with combined linear and exponential functions for long-term prediction. The extrapolated EOF components were then used to reconstruct bathymetric changes. The comparison of the reconstructed bathymetric changes with the modeled results from the COAST2D model illustrates that the presented approach can be effective for long-term prediction of coastal morphological changes, and extrapolating both the spatial and temporal EOF components yields better results than extrapolating only the temporal EOF component.

Public Transit Performance Evaluation Using Data Envelopment Analysis and Possibilities of Enhancement

This study evaluates the operational performance of all routes of Sajha Bus Yatayat operating inside Kathmandu valley using Data Envelopment Analysis (DEA) in terms of efficiency and effectiveness score. This approach allows us to access the relative performance of transit system in absence of historical data and research to compare with. To explore the possibility of enhancing the performance, scenarios were created for relatively underperforming routes and long route problem by changing the most important input variable and output variables accordingly with regression model where it was relevant. Partial Least Squares (PLS) regression was used to determine the most influential input variables to the output variables. DEA was conducted to access the performance of all routes under these scenarios. Underperforming routes except the longest route under the first set of scenarios, emerge to be better performing efficiently without considerable negative deviation in effectiveness. The result of second set of scenarios for long route problem suggests that the longest route’s performance can be enhanced significantly upon proper route alignment. Scenarios development and evaluation can help lead transit companies to explore the strategies to facilitate operational performance enhancement.

Modelling Manure Production in Beef Calves: Development, Evaluation, and Application of a Complete vs. Simplified Prediction Model

There has been increased interest in quantifying the manure production of livestock, primarily driven by public authorities, who aim to evaluate the environmental impact of livestock production, but also at the farm level, to manage manure storage and availability of fertilizer for crop production. Moreover, current manure production estimates from intensively reared beef calves are higher than actual production due to changes in farming systems, advances in animal genetics and feed efficiency. This study aims to redefine and update manure production estimates in intensively reared beef calves to predict manure production as a policy and planning tool, as there are no current models available. A trial was conducted to collect data on manure production during the growing-finishing period (243 d) of 54 Limousine calves (from 346.7 to 674.0 kg live weight, LW). Such data were used to develop two models to predict manure excretion: (1) a complex mechanistic model (CompM), and (2) a simplified empirical model (SimpM). Both models were evaluated against an independent dataset including a total of 4,692 animals on 31 farms and 5 breeds. Results from CompM require interpretation because the model does not output a single value but a range of manure production (minimum, medium and maximum), and would therefore be more suitable for professional use. The SimpM could be considered simple, reliable, and versatile for predicting manure excretion at farm level. SimpM could be refined and improved by including data from other studies on beef cattle with distinct characteristics and management.

Deep-reinforcement-learning-based water diversion strategy

Water diversion is a common strategy to enhance water quality in eutrophic lakes by increasing available water resources and accelerating nutrient circulation.Its effectiveness depends on changes in the source water and lake conditions.However,the challenge of optimizing water diversion remains because it is difficult to simultaneously improve lake water quality and minimize the amount of diverted water.Here,we propose a new approach called dynamic water diversion optimization(DWDO),which combines a comprehensive water quality model with a deep reinforcement learning algorithm.We applied DWDO to a region of Lake Dianchi,the largest eutrophic freshwater lake in China and validated it.Our results demonstrate that DWDO significantly reduced total nitrogen and total phosphorus concentrations in the lake by 7%and 6%,respectively,compared to previous operations.Additionally,annual water diversion decreased by an impressive 75%.Through interpretable machine learning,we identified the impact of meteorological indicators and the water quality of both the source water and the lake on optimal water diversion.We found that a single input variable could either increase or decrease water diversion,depending on its specific value,while multiple factors collectively influenced real-time adjustment of water diversion.Moreover,using well-designed hyperparameters,DWDO proved robust under different uncertainties in model parameters.The training time of the model is theoretically shorter than traditional simulation-optimization algorithms,highlighting its potential to support more effective decisionmaking in water quality management.

Assessing the effect of invasive organisms on forests under information uncertainty: The case of pine wood nematode in continental Europe

Forests worldwide are experiencing increasingly intense biotic disturbances;however,assessing impacts of these disturbances is challenging due to the diverse range of organisms involved and the complex interactions among them.This particularly applies to invasive species,which can greatly alter ecological processes in their invaded territories.Here we focus on the pine wood nematode(PWN,Bursaphelenchus xylophilus),an invasive pathogen that has caused extensive mortality of pines in East Asia and more recently has invaded southern Europe.It is expected to expand its range into continental Europe with heavy impacts possible.Given the unknown dynamics of PWN in continental Europe,we reviewed laboratory and field experiments conducted in Asia and southern Europe to parameterize the main components of PWN biology and host-pathogen interactions in the Biotic Disturbance Engine(BITE),a model designed to implement a variety of forest biotic agents,from fungi to large herbivores.To simulate dynamically changing host availability and conditions,BITE was coupled with the forest landscape model iLand.The potential impacts of introducing PWN were assessed in a Central European forest landscape(40,928​ha),likely within PWN’s reach in future decades.A parameter sensitivity analysis indicated a substantial influence of factors related to dispersal,colonization,and vegetation impact,whereas parameters related to population growth manifested a minor effect.Selection of different assumptions about biological processes resulted in differential timing and size of the main mortality wave,eliminating 40%–95%of pine trees within 100 years post-introduction,with a maximum annual carbon loss between 1.3%and 4.2%.PWN-induced tree mortality reduced the Gross Primary Productivity,increased heterotrophic respiration,and generated a distinct legacy sink effect in the recovery period.This assessment has corroborated the ecological plausibility of the simulated dynamics and highlighted the need for new strategies to navigate the substantial uncertainty in the agent’s biology and population dynamics.

Simulating Methane Emissions from the Littoral Zone of a Reservoir by Wetland DNDC Model

The littoral zone of freshwater ecosystems is believed to be a hotspot for methane （CH4） emissions, but in situ measurements are rare. This paper reports a study of CH4 fluxes from the Miyun Reservoir near Beijing in China based on an integrated observational and modelling approach. CH4 fluxes were measured at three sites with different water levels containing nine representative vegetation communities. A process-based model, Wetland-DNDC, was tested against observations for its applicability in simulating CH4 fluxes from the littoral zone of the reservoir. The R^2 values, which showed correlation between the modeled and observed results, were 0.89, 0.81 and 0.49 for the deep water, shallow water and seasonally flooded sites, respectively. The modeled data indicated that the observed CH4 fluxes were mainly driven by water level fluctuations, soil temperature and soil organic matter content. The modeled average annual flux from the littoral zone of Miyun Reservoir was 15.1 g CH4·m^-2, which was comparable with other studies in China. Our study suggests that Wetland-DNDC is a suitable choice as a model for CH4 flux simulation from littoral zone of reservoirs or lakes, although improvements in the vegetation module could enhance the model＇s accuracy and applicability.

Contributions of climate change and human activities to ET and GPP trends over North China Plain from 2000 to 2014

College of Resources and Environment/Sino-Danish Center, Univers Beijing 100049, China nstitute of Geographic Sciences and ty of Chinese Academy of SciencesAbstract： Quantifying the contributions of climate change and human activities to ecosystem evapotranspiration （ET） and gross primary productivity （GPP） changes is important for adaptation assessment and sustainable development. Spatiotemporal patterns of ET and GPP were estimated from 2000 to 2014 over North China Plain （NCP） with a physical and remote sensing-based model. The contributions of climate change and human activities to ET and GPP trends were separated and quantified by the first difference de-trending method and multivariate regression. Results showed that annual ET and GPP increased weakly, with climate change and human activities contributing 0.188 mm yr-2 and 0.466 mm yr-2 to ET trend of 0.654 mm yr-2, and -1.321 g C m-2 yr-2 and 7.542 g C m-2 yr-2 to GPP trend of 6.221 g C m-2 yr-2, respectively. In cropland, the increasing trends mainly occurred in wheat growing stage; the contributions of climate change to wheat and maize were both negative. Precipitation and sunshine duration were the major climatic factors regulating ET and GPP trends. It is concluded that human activities are the main drivers to the long term tendencies of water consumption and gross primary productivity in the NCP.

Long-term variability of the carbon balance in a large irrigated area along the lower Yellow River from 1984 to 2006

The irrigated areas in the northern region of China are important food production areas. Therefore, studies on the variability of the carbon balance in these agro-ecosystems are fundamental for the management of carbon sequestration. This paper simulated the long-term variability of the carbon balance in a typical irrigated area along the lower Yellow River from 1984 to 2006, using a process-based ecosystem model called the Simple Biosphere Model, version 2. The mean annual gross primary production (GPP), mean annual net assimilation rate (NAR), mean annual soil respiration (Rs ), and mean annual net ecosystem exchange (NEE) were 1733, 1642, 1304, and 338g C m-2 a-1 , respectively. A significant increasing trend in the seasonal total NAR during the wheat growing season, and a significant decreasing trend in the seasonal total NAR during the maize growing season were detected. However, no significant trend was found in the annual NAR, R s , and NEE. The average carbon sequestration was 1.93 Tg C a-1 when the grain harvest was not taken into account, and the carbon sequestration amount during the maize season was higher than that during the wheat season. However, the agro-ecosystem was a weak carbon source with a value of 0.23 Tg C a-1 , when the carbon in the grain was assumed emitted into the atmosphere.