Climate Data to Predict Geometry of Cracks in Expansive Soils in a Tropical Semiarid Region

Jacques Carvalho Ribeiro Filho,Helba Araujo De Queiroz Palácio,Maria Simas Guerreiro,José Bandeira Brasil,Eunice Maia De Andrade

doi:10.3390/su14020675

Abstract

The nonlinear dynamics of the determining factors of the morphometric characteristics of cracks in expansive soils make their typification a challenge, especially under field conditions. To overcome this difficulty, we used artificial neural networks to estimate crack characteristics in a Vertisol under field conditions. From July 2019 to June 2020, the morphometric characteristics of soil cracks (area, depth and volume), and environmental factors (soil moisture, rainfall, potential evapotranspiration and water balance) were monitored and evaluated in six experimental plots in a tropical semiarid region. Sixty-six events were measured in each plot to calibrate and validate two sets of inputs in the multilayer neural network model. One set was comprised of environmental factors with significant correlations with the morphometric characteristics of cracks in the soil. The other included only those with a significant high and very high correlation, reducing the number of variables by 35%. The set with the significant high and very high correlations showed greater accuracy in predicting crack characteristics, implying that it is preferable to have fewer variables with a higher correlation than to have more variables of lower correlation in the model. Both sets of data showed a good performance in predicting area and depth of cracks in the soils with a clay content above 30%. The highest dispersion of modeled over predicted values for all morphometric characteristics was in soils with a sand content above 40%. The model was successful in evaluating crack characteristics from environmental factors within its limitations and may support decisions on watershed management in view of climate-change scenarios.

Highlights

Introduction published maps and institutional affilSoil cracking is a natural phenomenon observed in soils with expansive clay minerals upon desiccation, and occurs mostly in drylands that cover approximately 40% of the world’s land area [1] in South Africa, Australia, America, India, and China
The swelling and shrinking nature of expansive vertic soils may damage civil engineering structures [4,5], promote environmental pollution through preferential flow paths and compromise carbon storage [6], compromise tilling in agricultural fields [7,8], and affect slope stability [3,9]
To predict the morphometric characteristics of cracks in the soil based on climate and environmental data, we developed artificial neural network (ANN) models with the acquired data

Summary

Introduction

Soil cracking is a natural phenomenon observed in soils with expansive clay minerals upon desiccation, and occurs mostly in drylands that cover approximately 40% of the world’s land area [1] in South Africa, Australia, America, India, and China. Vertisols cover approximately 335 Mha [2], and are more common in the semiarid tropics [2,3] with an annual rainfall between 500 mm and 1000 mm, expanding when wet and contracting when dry, due to the high content of the expansive 2:1 clay mineral. Even though there is relevant information available [3,10], understanding the expansion and contraction processes in expansive soils at the field scale still remains a challenge iations. The swelling and shrinking nature of expansive vertic soils may damage civil engineering structures [4,5], promote environmental pollution through preferential flow paths and compromise carbon storage [6], compromise tilling in agricultural fields [7,8], and affect slope stability [3,9].

Methods

Results

Conclusion