scholarly journals Interannual Variation of Transpiration and Its Modeling of a Larch Plantation in Semiarid Northwest China

Forests ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1303
Author(s):  
Yanbing Wang ◽  
Yanhui Wang ◽  
Zhenhua Li ◽  
Pengtao Yu ◽  
Xinsheng Han
Keyword(s):  
Soil Water ◽  
Forest Canopy ◽  
Potential Evapotranspiration ◽  
Northwest China ◽  
Measured Data ◽  
Model Parameters ◽  
Individual Factor ◽  
Area Index ◽  
Continuous Multiplication ◽  
The Individual

Quantifying the variation of forest transpiration (T) is important not only for understanding the water and energy budget of forest ecosystems but also for the prediction, evaluation, and management of hydrological effects as well as many other ecosystem services of forests under the changes of climate, vegetation, and anthropological impacts. The accurate prediction of T, a key component of water used by forests, requires mechanism-based models describing the T response to environmental and canopy conditions. The daily T of a larch (Larix principis-rupprechtti) plantation was measured through monitoring the sap flow in the growing season (from May to September) of a dry year (2010), a normal year (2012), and a wet year (2014) at a shady slope in the semi-arid area of Liupan Mountains in northwest China. Meanwhile, the meteorological conditions, soil moisture, and forest canopy leaf area index (LAI) were monitored. To get a simple and easily applicable T model, the numerous influencing parameters were grouped into three factors: the atmospheric evapotranspiration demand indicated by the potential evapotranspiration (PET), the soil water supply ability indicated by the relative extractable soil water content (REW), and the vegetation transpiration capacity indicated by the forest canopy LAI. The T model was established as a continuous multiplication of the T response equations to individual factors, which were determined using the upper boundary lines of measured data. The effect of each factor on the T in a dry year (2010) or normal year (2012) was assessed by comparing the measured T in the baseline of the wet year (2014) and the model predicted T, which was calculated through inputting the actual data of the factor (i.e., PET) to be assessed in the dry or normal year and the measured data of other two factors (i.e., REW, LAI) in the baseline of the wet year. The results showed that the mean daily T was 0.92, 1.05, and 1.02 mm; and the maximum daily T was 1.78, 1.92, and 1.89 mm in 2010, 2012, and 2014, respectively. The T response follows a parabolic equation to PET, but a saturated exponential equation to REW and LAI. The T model parameters were calibrated using measured data in 2010 and 2012 (R2 = 0.89, Nash coefficient = 0.88) and validated using measured data in 2014 satisfactorily (R2 = 0.89, Nash coefficient = 0.79). It showed a T limitation in the dry year 2010 for all factors (18.5 mm by PET, 11.5 mm by REW, and 17.8 mm by LAI); while a promotion for PET (1.4 mm) and a limitation for REW (4.2 mm) and LAI (14.3 mm) in the normal year 2012. The daily T model established in this study can be helpful to assess the individual factor impact on T and improve the daily T prediction under changing environmental and canopy conditions.

scholarly journals Modeling Soil Water–Heat Dynamic Changes in Seed-Maize Fields under Film Mulching and Deficit Irrigation Conditions

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1330 ◽  
Author(s):  
Yin Zhao ◽  
Xiaomin Mao ◽  
Manoj K. Shukla ◽  
Sien Li
Keyword(s):  
Soil Temperature ◽  
Soil Water ◽  
Crop Growth ◽  
Future Climate ◽  
Soil Water Storage ◽  
Future Climate Change ◽  
Model Parameters ◽  
Area Index ◽  
Swap Model ◽  
Film Mulching

The Soil–Water–Atmosphere–Plant (SWAP) model does not have a mulching module to simulate the effect of film mulching on soil water, heat dynamics and crop growth. In this study, SWAP model parameters were selected to simulate the soil water–heat process and crop growth, taking into account the effect of film mulching on soil evaporation, temperature, and crop growth, in order to predict the influence of future climate change on crop growth and evapotranspiration (ET). A most suitable scheme for high yield and water use efficiency (WUE) was studied by an experiment conducted in the Shiyang River Basin of Northwest China during 2017 and 2018. The experiment included mulching (M1) and non-mulching (M0) under three drip irrigation treatments, including full (WF), medium (WM), low (WL) water irrigation. Results demonstrated that SWAP simulated soil water storage (SWS) well, soil temperature at various depths, leaf area index (LAI) and aboveground dry biomass (ADB) with the normalized root mean square error (NRMSE) of 16.2%, 7.5%, 16.1% and 16.4%, respectively; and yield, ET, and WUE with the mean relative error (MRE) of 10.5%, 12.4% and 14.8%, respectively, under different treatments on average. The measured and simulated results showed film mulching could increase soil temperature, promote LAI during the early growth period, and ultimately improve ADB, yield and WUE. Among the treatments, M1WM treatment with moderate water deficit and film mulching could achieve the target of more WUE, higher yield, less irrigation water. Changes in atmospheric temperature, precipitation, and CO2 concentration are of worldwide concern. Three Representative Concentration Pathway (RCP) scenarios (RCP2.6, RCP4.5, RCP8.5) showed a negative effect on LAI, ADB and yield of seed-maize. The yield of seed-maize on an average decreased by 33.2%, 13.9% under the three RCPs scenarios for film mulching and non-mulching, respectively. Predicted yields under film mulching were lower than that under non-mulching for the next 30 years demonstrating that current film mulching management might not be suitable for this area to improve crop production under the future climate scenarios.

Modeling of a forested study site with the Community Land Model version 5 using climate projections for the 21st century.

2020 ◽  
Author(s):  
Lukas Strebel ◽  
Klaus Goergen ◽  
Bibi S. Naz ◽  
Heye Bogena ◽  
Harry Vereecken ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Land Surface ◽  
Model Parameters ◽  
Climate Projections ◽  
Modeling Framework ◽  
Area Index ◽  
Community Land Model

<p>Modeling forest ecosystems is important to facilitate adaptations in forest management approaches necessary to address the challenges of climate change, particularly of interest are ecohydrological states and fluxes such as soil water content, biomass, leaf area index, and evapotranspiration.</p><p>The community land model in its current version 5 (CLM5) simulates a broad collection of important land-surface processes; from moisture and energy partitioning, through biogeophysical processes, to surface and subsurface runoff. Additionally, CLM5 contains a biogeochemistry model (CLM5-BGC) which includes prognostic computation of vegetation states and carbon and nitrogen pools. However, CLM5 predictions are affected by uncertainty related to uncertain model forcings and parameters. Here, we use data assimilation methods to improve model performance by assimilating soil water content observations into CLM5 using the parallel data assimilation framework (PDAF).</p><p> </p><p>The coupled modeling framework was applied to the small (38.5 ha) forested catchment Wüstebach located in the Eifel National Park near the German-Belgian border. As part of the terrestrial environmental observatories (TERENO) network, the SoilNet sensors at the study site provide soil water content and soil temperature measurements since 2009.</p><p>CLM5 simulations for the period 2009-2100 were made, using local atmospheric observations for the period of 2009-2018 and an ensemble of regional climate model projections for 2019-2100. Simulations illustrate that data assimilation of soil water content improves the characterization of past model states, and that estimated model parameters and default model parameters result in different trajectories of ecohydrological states for 2019-2100. The simulations also illustrate that this site is hardly affected by increased water stress in the future.</p><p>The developed framework will be extended and applied for both ecosystem reanalysis as well as further simulations using climate projections across forested sites over Europe.</p>

Modeling Evapotranspiration and Crop Growth of Irrigated and Non-Irrigated Corn in the Texas High Plains Using RZWQM

2018 ◽  
Vol 61 (5) ◽  
pp. 1653-1666 ◽  
Author(s):  
Huihui Zhang ◽  
Robert Wayne Malone ◽  
Liwang Ma ◽  
Lajpat R. Ahuja ◽  
Saseendran S. Anapalli ◽  
...  
Keyword(s):  
Water Stress ◽  
Soil Water ◽  
Crop Production ◽  
Root Zone ◽  
Potential Evapotranspiration ◽  
Water Productivity ◽  
Stress Factors ◽  
Irrigated Agriculture ◽  
High Plains ◽  
Area Index

Abstract. Accurate quantification and management of crop evapotranspiration (ET) are critical to optimizing crop water productivity for both dryland and irrigated agriculture, especially in the semiarid regions of the world. In this study, four weighing lysimeters in Bushland, Texas, were planted to maize in 1994 with two fully irrigated and two non-irrigated for measuring crop ET. The Root Zone Water Quality Model (RZWQM2) was used to evaluate soil water balance and crop production with potential evapotranspiration (PET) estimated from either the Shuttleworth-Wallace method (PTSW) or the ASCE standardized alfalfa reference ET multiplied by crop coefficients (PTASCE). As a result, two water stress factors were defined from actual transpiration (AT) and were tested in the model against the lysimeter data, i.e., AT/PTSW and AT/PTASCE. For both water stress factors, the simulated daily ET values were reasonably close to the measured values, with underestimated ET during mid-growing stage in both non-irrigated lysimeters. Root mean squared deviations (RMSDs) and relative RMSDs (RMSD/observed mean) values for leaf area index, biomass, soil water content, and daily ET were within simulation errors reported earlier in the literature. For example, the RMSDs of simulated daily ET were less than 1.52 mm for all irrigated and non-irrigated lysimeters. Overall, ET was simulated within 3% of the measured data for both fully irrigated lysimeters and undersimulated by less than 11% using both stress factors for the non-irrigated lysimeters. Our results suggest that both methods are promising for simulating crop production and ET under irrigated conditions, but the methods need to be improved for dryland and non-irrigated conditions. Keywords: ET, RZWQM modeling, Stress factor, Weighing lysimeter.

An exploratory evaluation of APSIM to simulate growth and yield processes for winter cereals in rotation systems in South Australia

2004 ◽  
Vol 44 (8) ◽  
pp. 787 ◽  
Author(s):  
I. A. M. Yunusa ◽  
W. D. Bellotti ◽  
A. D. Moore ◽  
M. E. Probert ◽  
J. A. Baldock ◽  
...  
Keyword(s):  
Grain Yield ◽  
Soil Water ◽  
Dry Matter ◽  
Wheat Yield ◽  
South Australia ◽  
Measured Data ◽  
Growth And Yield ◽  
Soil Nitrate ◽  
Area Index ◽  
Nitrogen Fertiliser

The Agricultural Production Systems Simulator (APSIM) suite of models was used to predict dynamics in water and nitrogen in soil, as well as the growth and yield of sequential crops of wheat and barley in pasture–wheat–barley rotations, between 1995 and 1997 at Roseworthy, South Australia. The NWHEAT model satisfactorily predicted above-ground dry matter, leaf area index and grain yields for both crops in rotations with either grassy (Grass) or medic (Medic) pastures, including the lack of significant response of yield to nitrogen fertiliser applied to wheat at sowing. Simulation data for soil water, from SOILWAT2, was consistent with measured data. Simulation with SOILN2, however, largely underestimated soil nitrogen, due to excessive uptake by the simulated wheat during the season when nitrogen was abundant and water supply readily available. Thus, the soil nitrate had to be reset at sowing for the following barley crop; simulated soil nitrate agreed with the measured data in this season when this nutrient was low. For most variables of crop growth and soil water, the simulated data were mostly within 2 standard errors of the measured means. Prediction of grain protein was underestimated in all cases, including where nitrogen in the shoot was overestimated. This was possibly due to inadequate remobilisation of nitrogen from the straw and roots to the grain by the simulated crop. A satisfactory prediction of dry matter, grain yield and grain weight was obtained for wheat when the models were extended to other trials at Roseworthy (Lower North), Minnipa (Upper Eyre Peninsula) and Wunkar (Murray Mallee), based on limited soil data. Long-term simulations of wheat yields showed that, with early sowing in the Lower North, median wheat yield increased by 50 kg/ha for every kilogram of nitrogen applied at sowing, up to a maximum nitrogen rate of 50 kg/ha. In the drier districts of the Upper Eyre Peninsula and the Murray Mallee, nitrogen fertiliser of no more than 25 kg/ha, applied at sowing, was enough to achieve yield benefits in any given season. At these drier sites, crop failures occurred in 5% (Upper Eyre Peninsula) and 10% (Murray Mallee) of the seasons simulated. Median sowing dates from these simulations were 15 May for the Lower North, 30 May for the Upper Eyre Peninsula and 24 May for Murray Mallee. This suggested that sowing could be conducted at least a week earlier than currently practised in the 3 environments. This study demonstrated the capability of APSIM to predict growth and grain yield of wheat and barley, as well as the associated dynamics of soil water in the main cereal belts of South Australia.

scholarly journals UAV Based Estimation of Forest Leaf Area Index (LAI) through Oblique Photogrammetry

2021 ◽  
Vol 13 (4) ◽  
pp. 803
Author(s):  
Lingchen Lin ◽  
Kunyong Yu ◽  
Xiong Yao ◽  
Yangbo Deng ◽  
Zhenbang Hao ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Point Cloud ◽  
Forest Canopy ◽  
Estimation Method ◽  
Vertical Direction ◽  
Estimation Accuracy ◽  
3D Point Cloud ◽  
Area Index ◽  
Better Than

As a key canopy structure parameter, the estimation method of the Leaf Area Index (LAI) has always attracted attention. To explore a potential method to estimate forest LAI from 3D point cloud at low cost, we took photos from different angles of the drone and set five schemes (O (0°), T15 (15°), T30 (30°), OT15 (0° and 15°) and OT30 (0° and 30°)), which were used to reconstruct 3D point cloud of forest canopy based on photogrammetry. Subsequently, the LAI values and the leaf area distribution in the vertical direction derived from five schemes were calculated based on the voxelized model. Our results show that the serious lack of leaf area in the middle and lower layers determines that the LAI estimate of O is inaccurate. For oblique photogrammetry, schemes with 30° photos always provided better LAI estimates than schemes with 15° photos (T30 better than T15, OT30 better than OT15), mainly reflected in the lower part of the canopy, which is particularly obvious in low-LAI areas. The overall structure of the single-tilt angle scheme (T15, T30) was relatively complete, but the rough point cloud details could not reflect the actual situation of LAI well. Multi-angle schemes (OT15, OT30) provided excellent leaf area estimation (OT15: R2 = 0.8225, RMSE = 0.3334 m2/m2; OT30: R2 = 0.9119, RMSE = 0.1790 m2/m2). OT30 provided the best LAI estimation accuracy at a sub-voxel size of 0.09 m and the best checkpoint accuracy (OT30: RMSE [H] = 0.2917 m, RMSE [V] = 0.1797 m). The results highlight that coupling oblique photography and nadiral photography can be an effective solution to estimate forest LAI.

scholarly journals Testing the Role of Emotion Dysregulation as a Predictor of Juvenile Recidivism

2021 ◽  
Vol 11 (1) ◽  
pp. 83-95
Author(s):  
Kalin Z. Salinas ◽  
Amanda Venta
Keyword(s):  
Emotion Regulation ◽  
Juvenile Offenders ◽  
Emotion Dysregulation ◽  
Longitudinal Research ◽  
Future Research ◽  
Individual Factor ◽  
Treatment And Prevention ◽  
One Year ◽  
The Individual

The current study proposed to determine whether adolescent emotion regulation is predictive of the amount and type of crime committed by adolescent juvenile offenders. Despite evidence in the literature linking emotion regulation to behaviour problems and aggression across the lifespan, there is no prior longitudinal research examining the predictive role of emotion regulation on adolescent recidivism, nor data regarding how emotion regulation relates to the occurrence of specific types of crimes. Our primary hypothesis was that poor emotion regulation would positively and significantly predict re-offending among adolescents. We tested our hypothesis within a binary logistic framework utilizing the Pathways to Desistance longitudinal data. Exploratory bivariate analyses were conducted regarding emotion regulation and type of crime in the service of future hypothesis generation. Though the findings did not indicate a statistically significant relation between emotion regulation and reoffending, exploratory findings suggest that some types of crime may be more linked to emotion regulation than others. In sum, the present study aimed to examine a hypothesized relation between emotion regulation and juvenile delinquency by identifying how the individual factor of dysregulated emotion regulation may have played a role. This study’s findings did not provide evidence that emotion regulation was a significant predictor of recidivism over time but did suggest that emotion regulation is related to participation in certain types of crime one year later. Directions for future research that build upon the current study were described. Indeed, identifying emotion regulation as a predictor of adolescent crime has the potential to enhance current crime prevention efforts and clinical treatments for juvenile offenders; this is based on the large amount of treatment literature, which documents that emotion regulation is malleable through treatment and prevention programming.

Multi-day flow forecasting using the Kalman filter

1991 ◽  
Vol 18 (2) ◽  
pp. 320-327 ◽  
Author(s):  
Murray A. Fitch ◽  
Edward A. McBean
Keyword(s):  
Kalman Filter ◽  
Prediction Model ◽  
Linear Prediction ◽  
Measured Data ◽  
System Model ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Forecasting Performance ◽  
Optimal Forecasting ◽  
Linear Prediction Model

A model is developed for the prediction of river flows resulting from combined snowmelt and precipitation. The model employs a Kalman filter to reflect uncertainty both in the measured data and in the system model parameters. The forecasting algorithm is used to develop multi-day forecasts for the Sturgeon River, Ontario. The algorithm is shown to develop good 1-day and 2-day ahead forecasts, but the linear prediction model is found inadequate for longer-term forecasts. Good initial parameter estimates are shown to be essential for optimal forecasting performance. Key words: Kalman filter, streamflow forecast, multi-day, streamflow, Sturgeon River, MISP algorithm.

scholarly journals Temporal stability of electrical conductivity in a sandy soil

2016 ◽  
Vol 30 (3) ◽  
pp. 349-357 ◽  
Author(s):  
Aura Pedrera-Parrilla ◽  
Eric C. Brevik ◽  
Juan V. Giráldez ◽  
Karl Vanderlinden
Keyword(s):  
Electrical Conductivity ◽  
Spatial Variability ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Precision Agriculture ◽  
Temporal Stability ◽  
Principal Component ◽  
Model Parameters ◽  
Soil Spatial Variability

Abstract Understanding of soil spatial variability is needed to delimit areas for precision agriculture. Electromagnetic induction sensors which measure the soil apparent electrical conductivity reflect soil spatial variability. The objectives of this work were to see if a temporally stable component could be found in electrical conductivity, and to see if temporal stability information acquired from several electrical conductivity surveys could be used to better interpret the results of concurrent surveys of electrical conductivity and soil water content. The experimental work was performed in a commercial rainfed olive grove of 6.7 ha in the ‘La Manga’ catchment in SW Spain. Several soil surveys provided gravimetric soil water content and electrical conductivity data. Soil electrical conductivity values were used to spatially delimit three areas in the grove, based on the first principal component, which represented the time-stable dominant spatial electrical conductivity pattern and explained 86% of the total electrical conductivity variance. Significant differences in clay, stone and soil water contents were detected between the three areas. Relationships between electrical conductivity and soil water content were modelled with an exponential model. Parameters from the model showed a strong effect of the first principal component on the relationship between soil water content and electrical conductivity. Overall temporal stability of electrical conductivity reflects soil properties and manifests itself in spatial patterns of soil water content.

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