Prediction of Soil Depth from Digital Terrain Data by Integrating Statistical and Visual Approaches

Information about the spatial distribution of soil attributes is indispensable for many land resource management applications; however, the ability of soil maps to supply such information for modern modeling tools is questionable. The objectives of this study were to investigate the possibility of predicting soil depth using some terrain attributes derived from digital elevation models （DEMs） with geographic information systems （GIS） and to suggest an approach to predict other soil attributes. Soil depth was determined at 652 field observations over the A1-Muwaqqar Watershed （70 km2） in Jordan. Terrain attributes derived from 30-m resolution DEMs were utilized to predict soil depth. The results indicated that the use of multiple linear regression models within small watershed subdivisions enabled the prediction of soil depth with a difference of 50 cm for 77% of the field observations. The spatial distribution of the predicted soil depth was visually coincided and had good correlations with the spatial distribution of the classes amalgamating three terrain attributes, slope steepness, slope shape, and compound topographic index. These suggested that the modeling of soil-landscape relationships within small watershed subdivisions using the three terrain attributes was a promising approach to predict other soil attributes.

Quantifying Multi-hazards and Impacts Over Different Growth Periods of Maize:A Study Based on Index Construction

Owing to the complexity and variability of global climate,the study of extreme events to ensure food security is particularly critical.The standardized precipitation requirement index(SPRI)and chilling injury index(I_(Ci))were introduced using data from agrometeorological stations on the Songliao Plain between 1981 and 2020 to identify the spatial and temporal variability of drought,waterlogging,and low-temperature cold damage during various maize growth periods.Compound drought and low-temperature cold damage events(CDLEs)and compound waterlogging and low-temperature cold damage events(CWLEs)were then identified.To measure the intensity of compound events,the compound drought and low-temperature cold damage magnitude index(CDLMI),and compound waterlogging and low-temperature cold damage magnitude index(CWLMI)were constructed by fitting marginal distributions.Finally,the effects of extreme events of various intensities on maize output were examined.The findings demonstrate that:(1)There were significant differences in the temporal trends of the SPRI and ICiduring different maize growth periods.Drought predominated in the middle growth period(MP),waterlogging predominated in the early growth period(EP)and late growth period(LP),and both drought and waterlogging tended to increase in intensity and frequency.The frequency of low-temperature cold damage showed a decreasing trend in all periods.(2)The CDLMI and CWLMI can effectively determine the intensity of CDLEs and CWLEs in the study area;these CDLEs and CWLEs had higher intensity and frequency in the late growth period.(3)Compared to single events,maize relative meteorological yield had a more significant negative correlation with the CDLMI and CWLMI.