Ayman Suleiman

Simple model to estimate field-measured soil water limits

Engineering and modeling applications often require reasonable estimates of the upper and lower limits of plant extractable water. Laboratory measurements of these limits do not always coincide with field observations. Statistical models to estimate soil water retention can be complicated, and usually are not based on field measurements. The objective of this work was to develop simple, generic equations to estimate the field-measured limits of the soil water reservoir based on soil survey data such as texture and bulk density. We used linear regression to estimate the gravimetric upper limit from sand and clay content; the volumetric upper limit can be estimated from the gravimetric upper limit and bulk density. We used non-linear regression to estimate the volumetric plant extractable water from sand content. The lower limit can be estimated as the difference between the upper limit and the plant extractable water. We adjusted the predictions for coarse fragments and organic C and we placed reasonable upper and lower boundaries to our estimates. The equation for the drained upper limit has a RMSE of 0.030 kg kg–1 and uses two coefficients. The equation for the plant extractable water has a RMSE of 0.030 m3 m–3 and uses three coefficients. We tested the simple model with an independent data set. The RMSE of our model was similar or better than that of a more complex statistical model.

Estimating Saturated Hydraulic Conductivity from Soil Porosity

Measuring the tempospatial variability of saturated hydraulic conductivity (Ks ) is time consuming, expensive, and encounters many uncertainties. This work aimed to develop a new model (REPM, Relative Effective Porosity Model) that estimates Ks from relative effective porosity (er ) and then compare it with a model (EPM, Effective Porosity Model) that estimates it from effective porosity (e ). The effective porosity (e ) is defined as the total porosity minus field capacity (FC), and the relative effective porosity (er ) is defined as effective porosity (e ) divided by FC. Both er and e can be estimated from FC and bulk density (Bd ). Data from 11 homogeneous textural–class mean soils and several international and American soils were used to evaluate REPM and EPM. For the 11 textural–class mean soils, log (Ks ) was highly correlated to log (er ) as well as to log (e ). For the international soils, log (Ks ) was highly correlated to log (er ) (r 2 = 0.77), but the correlation was less pronounced between log (Ks ) and log ( e ) (r 2 = 0.58). The saturated hydraulic conductivity of soils from an international database was more accurately predicted by REPM (RMSE of 539 cm d –1 ) than by EPM (RMSE of 733 cmd –1 ), while both of them performed as well for American soils. The slope and the intercept of REPM and the slope of EPM were independent of soil. These results suggest that our new model gives reasonable estimates of Ks for different soils.

Analytical Land–Atmosphere Radiometer Model

Abstract Conversion of radiometric land surface temperature (θr) to an equivalent isothermal (aerodynamic) surface temperature (θi) is important in balancing the land surface energy budget with satellite-based θr measurements. An analytical land–atmosphere radiometer model (ALARM) has been developed to convert θr taken at any zenith view angle to θi at a specified scalar roughness length z0h,i. Field data from 1987 and 1989 at the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) were used to evaluate the performance of ALARM. It was possible to find an optimal foliage temperature profile such that ALARM is consistent with these radiometric and atmospheric field data. The errors were significantly less when radiometer zenith angles were less than 40°. The foliage temperature at the canopy top θfh and the foliage temperature profile curvature parameter b were parameterized as functions of air temperature and leaf area index, respectively. Using these parameteri...

MODIFICATIONS TO THE DSSAT VERTICAL DRAINAGE MODEL FOR MORE ACCURATE SOIL WATER DYNAMICS ESTIMATION

Accurate modeling of soil water dynamics during vertical drainage is needed for reasonable prediction of crop yield and agrochemical leaching. This study was carried out to improve the soil water drainage model in the DSSAT (Decision Support System for Agrotechnology Transfer) family of crop models. In the existing model, the daily change in soil water content (&thetas;) from a soil layer is calculated by multiplying the drainable soil water (initial &thetas; - drained upper limit &thetas; (&thetas;dul)) by a parameter, C, that represents the soil hydraulic properties. In this model, one value for C is assumed for the whole soil profile, and C is lacking theoretical basis. Two changes have been introduced to this model: a relationship between C and &thetas;dul was developed and a new coefficient, F, which accounts for the incoming soil water flow into a particular layer of the profile (Qi), was added. By stepping through the texture triangle in increments of 5% clay and 5% sand, theoretical values of C for all possible soil texture combinations were calculated using numeric solutions. A quadratic relationship between these C values and the corresponding &thetas;dul was developed. A published international data set of drainage cycles of more than 50 soil profiles was used to examine this relationship. The root mean square difference (RMSD) between estimated and numerical C for the international soils was 0.136 days−1. The performance of the DSSAT and of the modified DSSAT models as a whole was evaluated for two soils in the summer of 1997. The modified DSSAT estimated the daily &thetas; reasonably well at the different depths throughout the drainage cycle (maximum RMSD < 0.013 m3 m−3), outperforming the original DSSAT by reducing the RMSD 2–4 fold at most of the soil depths

Determining FAO-56 crop coefficients for peanut under different water stress levels

Accurate estimates of peanut (Arachis hypogaea L.) water requirements are needed for water conservation. The objective of this study was to evaluate the FAO-56 crop coefficients for peanut grown under various levels of water stress in a humid climate. Two experiments were conducted in three automated rainout shelters located at the University of Georgia Griffin Campus in Griffin, Georgia, USA in 2006 and 2007. Irrigation was applied when the modeled soil water content in the effective root zone dropped below a specific threshold of the available water content (AWC). The irrigation treatments corresponded to irrigation thresholds (IT) of 40, 60 and 90% of AWC. The soil water balance was used to compute observed evapotranspiration (ETcm) from measured soil water content at six different soil depths. The length of the four developmental stages was different than the values listed in FAO-56. The 2-year average absolute relative error of Kcini was 8, 19 and 6% for 40, 60 and 90% IT, respectively. For the 90% IT, the FAO-56 Kcmid and Kcend were almost identical to the 2-year averages of the observed Kcmid and Kcend, respectively. The findings of this study confirmed that the FAO-56 procedure was reasonably accurate for estimating peanut ET under water stress in a humid climate.

Intercomparison of Evapotranspiration Estimates at the Different Ecological Zones in Jordan

An estimate of evapotranspiration (ET) is needed for many applications in diverse disciplines such as agriculture, hydrology, and meteorology. The objective of this study was to compare two methods for estimating daily actual ET (ETa) from six sites located in four different ecological zones within Jordan. The first method used the analytical land–atmosphere radiometer model (ALARM) and the dimensionless temperature procedure, whereas the second method used ETa calculated from the FAO-56 reference evapotranspiration. ALARM converts general remotely sensed surface temperatures to aerodynamic temperature. Standard meteorological data from weather stations were used with both methods, and the Moderate Resolution Imaging Spectroradiometer (MODIS)–based leaf area index, surface temperature, and albedo were obtained to estimate ETa, using the former method. A validation study was conducted on an alfalfa field in Jordan Valley using ALARM and the American Society of Civil Engineers’ (ASCE) method, which is very similar to FAO-56 except it uses alfalfa as a reference crop. Because this alfalfa field was irrigated and because of warm air advection, ET rates based on measurements of soil moisture change ranged from about 6 to 10 mm day 1 . For this range, the root-mean-square error (RMSE) for ALARM was 0.87 mm day 1 and the coefficient of determination r 2 was 0.36, whereas the RMSE for ASCE was 1.25 mm day 1 and r 2 0.06. There was good agreement between minimum, maximum, and average ETa for the two methods at all sites except for Irbid, for which the minimum and, consequently, the average were different. Much of the site-to-site and temporal variability was found to be statistically significant. Reasons for this variability include soil types, vegetation cover, irrigation, and warm advection.

Simple model to estimate daily lateral drainage

Lateral movement of soil solution could be a major cause for the spatial variability of crop yield within sloping landscapes. This study was an effort to develop a functional lateral downslope drainage model (LDDM) by expanding the Suleiman and Ritchie (2004) vertical drainage model. Within LDDM, the daily change of soil water content from a soil layer, due to lateral downslope drainage, was calculated by multiplying the drainable soil water [initial &thetas; − drained upper limit &thetas; (&thetas;dul)] by hydraulic gradient and two coefficients. The two coefficients are C, which represents the soil hydraulic properties, and F, which accounts for the incoming soil water flow into a particular layer of the profile (Qi). To evaluate LDDM, soil water content profile and water table height were measured periodically at 15 locations along a transect along a sloping landscape during a lateral drainage cycle. The LDDM estimated lateral downslope soil water flow rate and the water table height reasonably well. The root mean square difference between estimated and measured daily lateral downslope soil water flow rate was 1.8 mm d−1, and between estimated and measured cumulative weekly lateral downslope, soil water flow was 4.4 (first week), 16.3 (second week), and 8.7 (third week) mm.

Estimating Actual Evapotranspiration using ALARM and the Dimensionless Temperature

where Rn (W m-2) is the net incoming radiation, G is the heat flux into the ground (W m-2), and H (W m-2) and E (W m-2) are the sensible and latent (evaporative) heat fluxes into the atmosphere. For the energy balance to close, any part of (Rn G) that does not contribute to E must be converted into H. In order for that to happen, the surface has to have the temperature (Ts) that forces the energy balance to close. Estimation of H (or ET as a residual) over vegetated terrain is based on an aerodynamic temperature (Ti), which is the temperature that gives the correct value of H at a specified value (denoted z0h,i) of the scalar roughness length, zoh, based on Monin-Obukhov Similarity (MOS) theory in the surface sublayer (Brutsaert, 1982; Stull, 1988). Specification of the value of z0h to give the correct value of H for use with a radiometric surface temperature Tr is a difficult problem (e.g., Mahrt and Vickers, 2004); Crago and Suleiman (2005) outlined a method (discussed here in section 2.a) to specify z0h,i and to convert Tr to Ti. In the MOS theory, the flux is proportional to the difference between Ti and air temperature (Ta), with the ratio H / (Ti-Ta) depending on variables characterizing the atmospheric turbulence and the land surface. This relationship can be expressed as (e.g., Brutsaert, 1982): ( ) *

Determination of the FAO-56 Crop Coefficients for Peanut under Deficit Irrigation in a Humid Climate

There is a lack of information about crop coefficients to be used with the FAO-56 reference evapotranspiration (ETo) approach for peanut grown in humid climates in general and under deficit irrigation in particular. The objective of this study was to determine the crop coefficients for peanut under different deficit drip irrigation treatments for a humid climate. Peanut was grown in 2006 in three automated rainout shelters located at the University of Georgia Campus in Griffin, Georgia, USA. The irrigation treatments were 40%, 60% and 90% irrigation thresholds (IT). The least irrigated treatment corresponded to the 40% IT and the most irrigated was the 90% IT. When the modeled soil water content in the effective root zone dropped below a specific threshold of the available water content (AWC), irrigation was applied until the soil water reached 100% of AWC. The length of the initial stage was similar for the different treatments, while 40% IT had longer development and mid-season stages and shorter late stage than the other two treatments. The length of the development, mid-season, and late stages were close for the 60 and 90% IT. Deficit irrigation had a pronounced impact on the crop coefficient values, especially for the mid-season stage. The crop coefficient values for the 90% IT may be used in humid climates to calculate the peanut water requirements under full irrigation. The use of the appropriate crop coefficient values along with the appropriate length of the development stages will result in more efficient irrigation scheduling and water use planning in humid climates.

Remote sensing indices for monitoring land degradation in a semiarid to arid basin in Jordan

Spectral reflectance for soils and vegetation of the Yarmouk basin were correlated with surficial soil properties and vegetation biomass and cover. The overall aim of the study was to identify bands suitable for assessing soil and vegetation as indices for land degradation and desertification. Results showed that vegetation was well separated from soils in the shortwave infrared wavelength at 1480 nm. For most sites, the differences in the bandwidths (in the range of 8.5 nm to 90 nm) did not improve the differentiation of vegetation types. For all wavelengths, stronger correlation values (maximum R2 = 0.85) were obtained for vegetation cover when compared with biomass (maximum R2 = 0.54). Soil spectral reflectance tended to increase with salinity, with maximum correlations obtained in the blue wavelengths (470±10 nm, 485±90 nm), followed by green and the NIR bands, where R2 values were around 0.60. Comparing results from radiometer measurements with results obtained from ASTER image bands showed that correlations tended to decrease with decreased spatial resolution for the investigated soil properties. For all wavelengths, spectral reflectance of degraded soils was higher than that for natural vegetation and irrigated crops with partial surface cover. Results of the study showed that the use of remote sensing indices related to vegetation cover and soil salinity would be recommended to map the extent of land degradation in the study area and similar environments. However, spectral unmixing should be applied to improve the correlations between satellite remote sensing data and surficial soil properties.