Soil fragmentation study applying different tillage systems

Runoff generation depends on rainfall, infiltration, interception, and surface depressional storage. Surface depressional storage depends on surface microtopography, usually quantified trough soil surface roughness (SSR). SSR is subject to spatial and temporal changes that create a high variability. In an agricultural environment, tillage operations produce abrupt changes in roughness. Subsequent rainfall gradually decreases roughness. Beside it, local variation in soil properties and hydrology cause its SSR to vary spatially at different scales. The methods commonly used to measure it involve collecting point elevations in regular grids using laser profilers or scanners, digital close range stereo-photogrammetry and terrestrial laser scanning or LIDAR systems. In this case, a laser-scanning instrument was used to obtain representative digital elevation models (DEMs) at a grid resolution of 7.2x7.2mm that cover an area of 0.9x0.9m. The DEMs were obtained from two study sites with different soils. The first study site was an experimental field on which five conventional tillage methods were applied. The second study site was a large olive orchard with trees planted at 7.5x5.0m and bare soils between rows. Here, three tillage treatments were applied. In this work we have evaluated the spatial variability of SSR at several scales studying differences in height calculated from points separated by incremental distances h were raised to power values q (from 0 to 4 in steps of 0.1). The q = 2 data were studied as a semivariogram model. The logarithm of average differences plotted vs. log h were characterized by their slope, ?(q). Structure functions [?(q) vs. q] were fitted showing that data had nonlinear structure functions typical of multiscale phenomena. Comparisson of the two types of soil in their respective structure functions are shown.

Thresholding Soil Surface Images

Soil erosion is a complex phenomenon involving the detachment and transport of soil particles, storage and runoff of rainwater, and infiltration. The relative magnitude and importance of these processes depends on several factors being one of them surface micro-topography, usually quanti[U+FB01]ed trough soil surface roughness (SSR). SSR greatly affects surface sealing and runoff generation, yet little information is available about the effect of roughness on the spatial distribution of runoff and on flow concentration. The methods commonly used to measure SSR involve measuring point elevation using a pin roughness meter or laser, both of which are labor intensive and expensive. Lately a simple and inexpensive technique based on percentage of shadow in soil surface image has been developed to determine SSR in the field in order to obtain measurement for wide spread application. One of the first steps in this technique is image de-noising and thresholding to estimate the percentage of black pixels in the studied area. In this work, a series of soil surface images have been analyzed applying several de-noising wavelet analysis and thresholding algorithms to study the variation in percentage of shadows and the shadows size distribution

Land cover dynamics and climate change implications on water resources in South Pacific Costa Rica

Water is fundamental to human life and the availability of freshwater is often a constraint on human welfare and economic development. Consequently, the potential effects of global changes on hydrology and water resources are considered among the most severe and vital ones. Water scarcity is one of the main problems in the rural communities of Central America, as a result of an important degradation of catchment areas and the over-exploitation of aquifers. The present Thesis is focused on two critical aspects of global changes over water resources: (1) the potential effects of climate change on water quantity and (2) the impacts of land cover and land use changes on the hydrological processes and water cycle. Costa Rica is among the few developing countries that have recently achieved a land use transition with a net increase in forest cover. Osa Region in South Pacific Costa Rica is an appealing study site to assess water supply management plans and to measure the effects of deforestation, forest transitions and climate change projections reported in the region. Rural Community Water Supply systems (ASADAS) in Osa are dealing with an increasing demand of freshwater due to the growing population and the change in the way of life in the rural livelihoods. Land cover mosaics which have resulted from the above mentioned processes are characterized by the abandonment of marginal farmland with the spread over these former grasslands of high return crops and the expansion of secondary forests due to reforestation initiatives. These land use changes have a significant impact on runoff generation in priority water-supply catchments in the humid tropics, as evidenced by the analysis of the Tinoco Experimental Catchment in the Southern Pacific area of Costa Rica. The monitoring system assesses the effects of the different land uses on the runoff responses and on the general water cycle of the basin. Runoff responses at plot scale are analyzed for secondary forests, oil palm plantations, forest plantations and grasslands. The Oil palm plantation plot presented the highest runoff coefficient (mean RC=32.6%), twice that measured under grasslands (mean RC=15.3%) and 20-fold greater than in secondary forest (mean RC=1.7%). A Thornthwaite-type water balance is proposed to assess the impact of land cover and climate change scenarios over water availability for rural communities in Osa Region. Climate change projections were obtained by the downscaling of BCM2, CNCM3 and ECHAM5 models. Precipitation and temperature were averaged and conveyed by the A1B, A2 and B1 IPCC climate scenario for 2030, 2060 and 2080. Precipitation simulations exhibit a positive increase during the dry season for the three scenarios and a decrease during the rainy season, with the highest magnitude (up to 25%) by the end of the 21st century under scenario B1. Monthly mean temperature simulations increase for the three scenarios throughout the year with a maximum increase during the dry season of 5% under A1B and A2 scenarios and 4% under B1 scenario. The Thornthwaite-type Water Balance model indicates important decreases of water surplus for the three climate scenarios during the rainy season, with a maximum decrease on May, which under A1B scenario drop up to 20%, under A2 up to 40% and under B1 scenario drop up to almost 60%. Land cover scenarios were created taking into account current land cover dynamics of the region. Land cover scenario 1 projects a deforestation situation, with forests decreasing up to 15% due to urbanization of the upper catchment areas; land cover scenario 2 projects a forest recovery situation where forested areas increase due to grassland abandonment on areas with more than 30% of slope. Deforestation scenario projects an annual water surplus decrease of 15% while the reforestation scenario projects a water surplus increase of almost 25%. This water balance analysis indicates that climate scenarios are equal contributors as land cover scenarios to future water resource estimations.