603 research outputs found

    Using hydrological models and digital soil mapping for the assessment and management of catchments: A case study of the Nyangores and Ruiru catchments in Kenya (East Africa)

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    Human activities on land have a direct and cumulative impact on water and other natural resources within a catchment. This land-use change can have hydrological consequences on the local and regional scales. Sound catchment assessment is not only critical to understanding processes and functions but also important in identifying priority management areas. The overarching goal of this doctoral thesis was to design a methodological framework for catchment assessment (dependent upon data availability) and propose practical catchment management strategies for sustainable water resources management. The Nyangores and Ruiru reservoir catchments located in Kenya, East Africa were used as case studies. A properly calibrated Soil and Water Assessment Tool (SWAT) hydrologic model coupled with a generic land-use optimization tool (Constrained Multi-Objective Optimization of Land-use Allocation-CoMOLA) was applied to identify and quantify functional trade-offs between environmental sustainability and food production in the ‘data-available’ Nyangores catchment. This was determined using a four-dimension objective function defined as (i) minimizing sediment load, (ii) maximizing stream low flow and (iii and iv) maximizing the crop yields of maize and soybeans, respectively. Additionally, three different optimization scenarios, represented as i.) agroforestry (Scenario 1), ii.) agroforestry + conservation agriculture (Scenario 2) and iii.) conservation agriculture (Scenario 3), were compared. For the data-scarce Ruiru reservoir catchment, alternative methods using digital soil mapping of soil erosion proxies (aggregate stability using Mean Weight Diameter) and spatial-temporal soil loss analysis using empirical models (the Revised Universal Soil Loss Equation-RUSLE) were used. The lack of adequate data necessitated a data-collection phase which implemented the conditional Latin Hypercube Sampling. This sampling technique reduced the need for intensive soil sampling while still capturing spatial variability. The results revealed that for the Nyangores catchment, adoption of both agroforestry and conservation agriculture (Scenario 2) led to the smallest trade-off amongst the different objectives i.e. a 3.6% change in forests combined with 35% change in conservation agriculture resulted in the largest reduction in sediment loads (78%), increased low flow (+14%) and only slightly decreased crop yields (3.8% for both maize and soybeans). Therefore, the advanced use of hydrologic models with optimization tools allows for the simultaneous assessment of different outputs/objectives and is ideal for areas with adequate data to properly calibrate the model. For the Ruiru reservoir catchment, digital soil mapping (DSM) of aggregate stability revealed that susceptibility to erosion exists for cropland (food crops), tea and roadsides, which are mainly located in the eastern part of the catchment, as well as deforested areas on the western side. This validated that with limited soil samples and the use of computing power, machine learning and freely available covariates, DSM can effectively be applied in data-scarce areas. Moreover, uncertainty in the predictions can be incorporated using prediction intervals. The spatial-temporal analysis exhibited that bare land (which has the lowest areal proportion) was the largest contributor to erosion. Two peak soil loss periods corresponding to the two rainy periods of March–May and October–December were identified. Thus, yearly soil erosion risk maps misrepresent the true dimensions of soil loss with averages disguising areas of low and high potential. Also, a small portion of the catchment can be responsible for a large proportion of the total erosion. For both catchments, agroforestry (combining both the use of trees and conservation farming) is the most feasible catchment management strategy (CMS) for solving the major water quantity and quality problems. Finally, the key to thriving catchments aiming at both sustainability and resilience requires urgent collaborative action by all stakeholders. The necessary stakeholders in both Nyangores and Ruiru reservoir catchments must be involved in catchment assessment in order to identify the catchment problems, mitigation strategies/roles and responsibilities while keeping in mind that some risks need to be shared and negotiated, but so will the benefits.:TABLE OF CONTENTS DECLARATION OF CONFORMITY........................................................................ i DECLARATION OF INDEPENDENT WORK AND CONSENT ............................. ii LIST OF PAPERS ................................................................................................. iii ACKNOWLEDGEMENTS ..................................................................................... iv THESIS AT A GLANCE ......................................................................................... v SUMMARY ............................................................................................................ vi List of Figures......................................................................................................... x List of Tables........................................................................................................... x ABBREVIATION..................................................................................................... xi PART A: SYNTHESIS 1. INTRODUCTION ............................................................................................... 1 1.1 Catchment management ...................................................................................1 1.2 Tools to support catchment assessment and management ..............................4 1.3 Catchment management strategies (CMSs)......................................................9 1.4 Concept and research objectives.......................................................................11 2. MATERIAL AND METHODS................................................................................15 2.1. STUDY AREA ..................................................................................................15 2.1.1. Nyangores catchment ...................................................................................15 2.1.2. Ruiru reservoir catchment .............................................................................17 2.2. Using SWAT conceptual model and land-use optimization ..............................19 2.3. Using soil erosion proxies and empirical models ..............................................21 3. RESULTS AND DISCUSSION..............................................................................24 3.1. Assessing multi-metric calibration performance using the SWAT model...........25 3.2. Land-use optimization using SWAT-CoMOLA for the Nyangores catchment. ..26 3.3. Digital soil mapping of soil aggregate stability ..................................................28 3.4. Spatio-temporal analysis using the revised universal soil loss equation (RUSLE) 29 4. CRITICAL ASSESSMENT OF THE METHODS USED ......................................31 4.1. Assessing suitability of data for modelling and overcoming data challenges...31 4.2. Selecting catchment management strategies based on catchment assessment . 35 5. CONCLUSION AND RECOMMENDATIONS ....................................................36 6. REFERENCES ............................ .....................................................................38 PART B: PAPERS PAPER I .................................................................................................................47 PAPER II ................................................................................................................59 PAPER III ...............................................................................................................74 PAPER IV ...............................................................................................................8

    Multi-annual grassland mowing dynamics in Germany: spatio-temporal patterns and the influence of climate, topographic and socio-political conditions

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    Introduction: Grasslands cover one third of the agricultural area in Germany and are mainly used for fodder production. However, grasslands fulfill many other ecosystem functions, like carbon storage, water filtration and the provision of habitats. In Germany, grasslands are mown and/or grazed multiple times during the year. The type and timing of management activities and the use intensity vary strongly, however co-determine grassland functions. Large-scale spatial information on grassland activities and use intensity in Germany is limited and not openly provided. In addition, the cause for patterns of varying mowing intensity are usually not known on a spatial scale as data on the incentives of farmers behind grassland management decisions is not available.Methods: We applied an algorithm based on a thresholding approach utilizing Sentinel-2 time series to detect grassland mowing events to investigate mowing dynamics in Germany in 2018–2021. The detected mowing events were validated with an independent dataset based on the examination of public webcam images. We analyzed spatial and temporal patterns of the mowing dynamics and relationships to climatic, topographic, soil or socio-political conditions.Results: We found that most intensively used grasslands can be found in southern/south-eastern Germany, followed by areas in northern Germany. This pattern stays the same among the investigated years, but we found variations on smaller scales. The mowing event detection shows higher accuracies in 2019 and 2020 (F1 = 0.64 and 0.63) compared to 2018 and 2021 (F1 = 0.52 and 0.50). We found a significant but weak (R2 of 0–0.13) relationship for a spatial correlation of mowing frequency and climate as well as topographic variables for the grassland areas in Germany. Further results indicate a clear value range of topographic and climatic conditions, characteristic for intensive grassland use. Extensive grassland use takes place everywhere in Germany and on the entire spectrum of topographic and climatic conditions in Germany. Natura 2000 grasslands are used less intensive but this pattern is not consistent among all sites.Discussion: Our findings on mowing dynamics and relationships to abiotic and socio-political conditions in Germany reveal important aspects of grassland management, including incentives of farmers

    The impact of traditional gold mining on land use changes and vegetation index in Mandor Subwatershed, West Kalimantan

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    Traditional gold mining activities altered the environmental structure of the Mandor Subwatershed significantly. The expansion of critical land in the Mandor Subwatershed causes flooding due to the lack of water catchment areas. The purpose of this study was to identify the impact of traditional gold mining on land use change in the Mandor Subwatershed. The research was conducted with a spatial analysis approach using Landsat multitemporal images from 2002, 2013, and 2022, followed by a field survey. A comparison of the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) methods was used to determine the changing process of vegetation density. The accuracy of vegetation index analysis indicated that the EVI method was more accurate for identifying vegetation density than the NDVI method. Land use change from 2002 to 2022 was dominated by an increase in the land area devoted to mining and oil palm plantations. The impact of this traditional gold mining has led to significant deforestation and land degradation over the past 20 years in the Mandor Subwatershed. This affects the condition of the surrounding environment as well as human health

    Refining terrestrial biosphere feedbacks to climate change through precise characterization of terrestrial vegetation

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    Climate change is primarily driven by the human activities of fossil fuel combustion and land use change, which together result in the emissions of greenhouse gases such as carbon dioxide (COâ‚‚). The terrestrial biosphere currently absorbs about a third of total anthropogenic COâ‚‚ emissions, mostly through primary production by vegetation. The continued function of vegetation as a COâ‚‚ sink is uncertain, as climate change has the potential to enhance or restrict the carbon uptake capacity of vegetation. Uncertainty in terrestrial vegetation function in the context of climate change, due in part to a lack of precise observations of leaf biochemistry and function with which to develop models, therefore limits the confidence of climate change projections. In its entirety, this thesis examines the potential for more precise observations of leaf function and their integration across a variety of models and observational scales. The first chapter provides an introductory overview of the subsequent four chapters and how each compliments the other. The second chapter demonstrates the role of the terrestrial biosphere in influencing the relationship between temperature change and cumulative COâ‚‚ emissions. The third chapter provides adaptations to current radiative transfer modelling approaches to improve estimations of leaf biochemical constituents. The fourth chapter applies high spatiotemporal resolution observations of leaf phenology, the timing of leaf emergence and senescence, across North America to predict species-specific leaf phenology patterns under various emissions scenarios throughout the 21st century. The fifth chapter provides an approach to detect declines in ecosystem processes such as carbon uptake using observational leaf phenology networks. These chapter results indicate that 1) uncertainty in the land-borne fraction of carbon emissions contributes largely to uncertainty in the relationship between temperature change and emissions, 2) spectral subdomains and prior estimation of leaf structure improves leaf biochemistry estimations, 3) leaf senescence timing may diverge between boreal and temperate species under a high emissions scenario, and 4) declines in vegetational carbon uptake can be accurately detected using quantitative phenocam-based indicators. The fundamental and technical insights provided through this thesis will facilitate more reliable and functionally resolved projections of terrestrial biosphere feedbacks to climate change

    Enabling Precision Fertilisers Application Using Digital Soil Mapping in Australian Sugarcane Areas

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    Sugar is Australia's second largest export crop after wheat, generating a total annual revenue of almost $2 billion. It is produced from sugarcane, with approximately 95% grown in Queensland. While highly productive and contributing to the area’s economic sustainability, the soils in these areas have low fertility. The soils typically contain sand content > 60%, low organic carbon (SOC 6%). Hence, sugarcane farmers need to apply fertilisers and ameliorants to maintain soil quality and productivity. Unfortunately, the high intensity rainfall in the region results in sediments, nutrients, and ameliorants run-off from these farms, resulting in environmental degradation and threats to marine ecology in the adjacent World Heritage Listed Great Barrier Reef. To mitigate these issues, the Australian sugarcane industry introduced the Six-Easy-Step Nutrient Management Guidelines. To apply these guidelines, a labour-intensive high-density soil sampling is typically required at the field level, followed by expensive laboratory analysis, spanning the myriad of biological, physical, and chemical properties of soils that need to be determined. To assist in sampling site selection, remote (e.g., Landsat-8, Sentinel-2, and DEM-based terrain attributes) and/or proximal sensing (e.g., electromagnetic [EM] induction and gamma-ray [γ-ray] spectrometry) digital data are increasingly being used. Moreover, the soil and digital data can be modelled using geostatistical (e.g., ordinary kriging [OK]), linear (e.g., linear mixed model [LMM]), machine learning (e.g., random forest [RF], quantile regression forest [QRF], support vector machine [SVM], and Cubist) and hybrid (e.g., RFRK, SVMRK, and CubistRK) approaches to enable prediction of soil properties from the rich source of digital data. However, there are many questions that need to be answered to determine appropriate recommendations including but not limited to i) which modelling approach is optimal, ii) which source of digital data is optimal and does fusion of various sources of digital data improve prediction accuracy, iii) which methods can be used to combine these digital data, iv) what is a minimum number of samples to establish a suitable calibration, v) which soil sampling designs could be used, and vi) what approaches are available to enable prediction of soil properties at various depths simultaneously? In this thesis, Chapter 1 introduces the research questions and defines the problems facing the Australian Sugarcane Industry in terms of the applications of the Six-Easy-Steps Nutrient Management Guidelines, research aims and thesis structure. Chapter 2 is a systematic literature review on various facets of DSM, which includes digital and soil data, models and outputs, and their application across various spatial scales and properties. In Chapter 3, prediction of topsoil (0-0.3 m) SOC is examined in the context of comparing predictive models (i.e., geostatistical, linear, machine learning [ML], and hybrid) using various digital data (i.e., remote [Landsat-8] and proximal sensors [EM and γ-ray]) either individually or in combination and determining minimum number of calibration samples. Chapter 4 shows to predict top- (0-0.3 m) and subsoil (0.6-0.9 m) Ca and Mg, various sampling designs (simple random [SRS], spatial coverage [SCS], feature space coverage [FSCS], and conditioned Latin hypercube sampling [cLHS]) were assessed, with different modelling approaches (i.e., OK, LMM, QRF, SVM, and CubistRK) and calibration sample size effect evaluated, using a combination of proximal data (EM and γ-ray) and terrain (e.g., elevation, slope, and aspect, etc.) attributes. Chapter 5 shows to enable the three-dimensional mapping of CEC and pH at topsoil (0-0.3 m), subsurface (0.3-0.6 m), shallow- (0.6-0.9 m) and deep-subsoil (0.9-1.2 m), an equal-area spline depth function can be used, with remote (Sentinel-2) and proximal data (EM and γ-ray) used alone or fused together, and various fusion methods (i.e., concatenation, simple averaging [SA], Bates-Granger averaging [BGA], Granger-Ramanathan averaging [GRA], and bias-corrected eigenvector averaging [BC-EA]) investigated. Chapter 6 explored the synergistic use of proximal (EM and γ-ray), and time-series of remote data (Landsat-8 and Sentinel-2) to map top- (0-0.15 m) and subsoil (0.30-0.45 m) ESP. The results show that, across these case studies, hybrid and ML models generally achieved higher prediction accuracy. The fusion of remote and proximal data produced better predictions, compared to single source of sensors. Granger-Ramanathan averaging (GRA) and concatenation were the most effective methods to combine digital data. A minimum of less than 1 sample ha-1 would be required to calibrate a good predictive model. There were differences in prediction accuracy amongst the sampling designs. The application of depth function splines enables the simultaneous mapping of soil properties from various depths. The produced DSM of soil properties can be used to inform farmers of spatial variability of soils and enable them to precisely apply fertilisers and/or ameliorants based on the Six-Easy-Step Nutrient Management Guidelines

    Comprehensive evaluation system for vegetation ecological quality: a case study of Sichuan ecological protection redline areas

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    Dynamic monitoring and evaluation of vegetation ecological quality (VEQ) is indispensable for ecological environment management and sustainable development. Single-indicator methods that have been widely used may cause biased results due to neglect of the variety of vegetation ecological elements. We developed the vegetation ecological quality index (VEQI) by coupling vegetation structure (vegetation cover) and function (carbon sequestration, water conservation, soil retention, and biodiversity maintenance) indicators. The changing characteristics of VEQ and the relative contribution of driving factors in the ecological protection redline areas in Sichuan Province (EPRA), China, from 2000 to 2021 were explored using VEQI, Sen’s slope, Mann-Kendall test, Hurst index, and residual analysis based on the XGBoost (Extreme gradient boosting regressor). The results showed that the VEQ in the EPRA has improved over the 22-year study period, but this trend may be unsustainable in the future. Temperature was the most influential climate factor. And human activities were the dominant factor with a relative contribution of 78.57% to VEQ changes. This study provides ideas for assessing ecological restoration in other regions, and can provide guidance for ecosystem management and conservation

    Identification and delineation of groundwater-dependentecosystems (GDEs) in the Khakea–Bray transboundaryaquifer region using geospatial techniques

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    Identifying and delineating groundwater-dependent ecosystems(GDEs) is critical in understanding their location, distribution andgroundwater allocation. However, this information is inadequatelyunderstood due to limited available data for most areas where theyoccur. Thus, this study aims to address this gap using remotelysensed, analytical hierarchy process (AHP) andin situdata to identifyand delineate GDEs in the Khakea–Bray transboundary aquifer region.The study tested various spatial-explicit GDE indices that integratesenvironmental factors that predict occurrence of GDEs. These includethe normalized difference vegetation index as a proxy for vegetationproductivity and modified normalized difference water index asproxy for moisture availability, land-use and landcover, topographicalfactors such as slope, topographic wetness index,flow accumulationand curvature

    Nitrogenous and Phosphorus Soil Contents in Tierra del Fuego Forests: Relationships with Soil Organic Carbon, Climate, Vegetation and Landscape Metrics

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    Soil nitrogen (SN) and soil phosphorus (SP) contents support several ecosystem services and define the forest type distribution at local scale in Southern Patagonia. The quantification of nutrients during forest surveys requires soil samplings and estimations that are costly and difficult to measure. For this, predictive models of soil nutrients are needed. The objective of this study was to quantify SN and SP contents (30 cm depth) using different modelling approaches based on climatic, topographic and vegetation variables. We used data from 728 stands of different forest types for linear regression models to map SN and SP. The fitted models captured the variability of forest types well (R²-adj. 92–98% for SN and 70–87% for SP). The means were 9.3 ton ha−1 for SN and 124.3 kg ha−1 for SP. Overall, SN values were higher in the deciduous forests than those in the mixed evergreen, while SP was the highest in the Nothofagus pumilio forests. SN and SP are relevant metrics for many applications, connecting major issues, such as forest management and conservation. With these models, the quantification of SN and SP stocks across forests of different protection status (National Law 26,331/07) and national/provincial reserve networks is possible, contributing to the determination of nutrient contents at landscape level.Fil: Martínez Pastur, Guillermo José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas; ArgentinaFil: Aravena Acuña, Marie Claire Alejandra. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas; ArgentinaFil: Chaves, Jimena Elizabeth. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas; ArgentinaFil: Cellini, Juan Manuel. Universidad Nacional de La Plata. Facultad de Ciencias Agrarias y Forestales. Laboratorio de Investigaciones en Maderas; ArgentinaFil: Silveira, Eduarda M. O.. University of Florida. Department of Wildlife Ecology and Conservation; Estados UnidosFil: Rodriguez Souilla, Julian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas; ArgentinaFil: Von Müller, Axel Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Patagonia Sur. Estación Experimental Agropecuaria Esquel; ArgentinaFil: la Manna, Ludmila Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de la Patagonia "San Juan Bosco". Facultad de Ingeniería - Sede Esquel. Centro de Estudios Ambientales Integrados; ArgentinaFil: Lencinas, María Vanessa. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas; ArgentinaFil: Peri, Pablo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria; Argentina. Universidad Nacional de la Patagonia Austral; Argentin

    Impacto de los cambios en el uso del suelo sobre el balance hídrico en zonas de llanura : Caso de estudio, cuenca superior del arroyo del Azul (provincia de Buenos Aires, Argentina)

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    Los constantes cambios en el uso del suelo, provocados principalmente por el fenómeno de agriculturización, ha facilitado el predominio de cultivos de alta rentabilidad como la soja conduciendo a una menor diversidad de coberturas vegetales. Este hecho ha generado múltiples impactos principalmente en llanuras con alto potencial agrícola, ya que el aumento en la escala de explotación del suelo y las prácticas repetitivas alteran la estructura y el funcionamiento de los ecosistemas y su interacción con los sistemas hídricos, terrestres y atmosféricos. En cuanto al componente hídrico, algunos de los procesos que intervienen en el balance hídrico de las llanuras resultan ser más sensibles a los cambios de vegetación. Por lo tanto, se planteó como objetivo principal cuantificar el balance hídrico bajo tres escenarios de usos del suelo. Esto con el fin de realizar un análisis más preciso del impacto que estos cambios generan al balance hídrico de una zona de llanura. La llanura bajo estudio es la cuenca superior del arroyo Del Azul, región que ha seguido la misma tendencia de la llanura pampeana en su actividad agrícola y que, además, presenta de forma periódica eventos de inundaciones y sequías. Para cumplir con el objetivo planteado, se simuló el balance hídrico de la cuenca empleando el modelo Soil and Water Assessment Tool (SWAT). El balance hídrico se calibró y validó a escala diaria para un periodo de 13 años (2003-2015) y se contrastó con tres escenarios de usos del suelo para los periodos 2006-2007 (P1), 2010-2011 (P2) y 2015-2016 (P3). Los usos del suelo presentes en cada periodo fueron caracterizados por una metodología basada en la fusión de imágenes satelitales de media resolución. Los resultados obtenidos representaron con un nivel de certidumbre bastante aceptable el sistema agrícola de la cuenca, evidenciando en qué medida cada cobertura fue reemplazada por los diferentes usos del suelo y en qué sectores de la cuenca estos cambios tuvieron lugar. En términos generales, al comparar los cambios que se llevaron a cabo en el transcurso de 10 años (2006-2015), se encontró que el uso del suelo que representó la mayor cantidad de reemplazos fue el cultivo de soja aumentando alrededor de 280%. El sistema de doble cultivo trigo-soja mantuvo un porcentaje de ocupación de aproximadamente 35%, mientras que los cultivos de invierno, maíz y las pasturas y pastizales naturales disminuyeron cerca de un 5%, 67% y 52%, respectivamente. En cuanto al proceso de simulación con SWAT, se logró cuantificar con un grado de acierto bastante satisfactorio el balance hídrico de la cuenca superior del arroyo Del Azul. El ajuste del modelo se realizó con la información de caudales registrados en la estación hidrométrica de Seminario. Se obtuvo valores de Nash Sutcliffe (NS) y coeficientes de determinación (R2) para el periodo de calibración (2006-2011) de 0,5 y 0,6 respectivamente y, para el periodo de validación (2012-2015) valores aproximados de 0,5, tanto para NS como para R2. Los resultados de la simulación con SWAT permitieron analizar la dinámica espacial de los procesos hidrológicos a través del tiempo, de los cuales, la evapotranspiración y la recarga representaron aproximadamente el 94% de la precipitación anual. A grandes rasgos, la respuesta de la evapotranspiración no varió de forma representativa con cada escenario de usos del suelo. Por el contrario, variables como la escorrentía superficial y la recarga fueron los procesos que presentaron mayores alteraciones espacio-temporales. Con el escenario P2, la recarga aumentó un 5%, pero la escorrentía superficial decreció 14%. En cuanto al escenario P3, la escorrentía superficial se incrementó 5% y la recarga disminuyó 7%. De acuerdo a los resultados, se espera que con los usos del suelo caracterizados en el escenario P2 sea menor el impacto de las transformaciones de coberturas. Al presentar mayor diversidad de usos del suelo, con este escenario la resiliencia de la cuenca ante los extremos hídricos sería mayor que con usos del suelo más homogéneos A escala mensual, se evidenció cómo el estado vegetativo de las coberturas influyó en la dinámica hídrica de la cuenca. Por eso a finales de primavera el déficit hídrico fue más severo con el escenario P3, ya que los cultivos que predominaron en este periodo entran en su etapa de maduración para esta época del año, aumentando la evapotranspiración y disminuyendo la humedad del suelo. Por el contrario, a finales de otoño, cuando gran parte de la superficie queda descubierta por la temporada de cosecha de cultivos de secano, la evapotranspiración fue más baja y la escorrentía superficial se incrementó y con ello, el impacto de las inundaciones. Adicionalmente, con SWAT fue posible identificar las regiones más vulnerables ante los excesos hídricos. Estas resultaron ser las zonas donde la pendiente es menor al 3%, es decir, hacia el norte de la cuenca, donde las pasturas y pastizales naturales fueron reemplazados en mayor medida. Finalmente, con la cuantificación del balance hídrico a escala mensual y anual, se concluye que efectivamente los cambios en el uso del suelo han impactado en la dinámica hídrica de la cuenca superior del arroyo Del Azul, zona con características propias de las llanuras. Al comparar los balances hídricos para cada escenario de usos del suelo, se constató que cuando un territorio asume un régimen de monocultivo, como es el caso del escenario P3, aumenta la escorrentía superficial y disminuye la tasa de recarga y humedad del suelo, lo cual podría aumentar la magnitud del impacto cuando se presentan periodos de excesos hídricos. Por el contrario, cuando el paisaje agrícola es más heterogéneo, como el del escenario P2, la escorrentía superficial se reduce y la recarga incrementa haciendo que posiblemente la resiliencia de las llanuras ante las inundaciones sea mayor. Al analizar un año con bajo índice pluviométrico como el 2008, el escenario de uso del suelo P1 fue el que más conservo la humedad del suelo y produjo una menor evapotranspiración. Teniendo en cuenta que este escenario presentó el mayor porcentaje de área cubierta por pasturas y pastizales naturales, se podría afirmar que esta cobertura vegetal influye en reducir el impacto de las sequías. A escala mensual, en los meses de primavera la cuenca presentó mayor déficit hídrico con el escenario P3, y en otoño inundaciones más severas. Por lo tanto, homogenizar el paisaje agrícola disminuye la resiliencia de la cuenca ante inundaciones y sequías. Igualmente, hay que resaltar que las prácticas agrícolas llevadas a cabo en las últimas décadas, no han tenido muy en cuenta factores, servicios y procesos naturales indispensables para un desarrollo sustentable de los territorios. Asimismo, se espera que los resultados obtenidos proporcionen pautas para que las entidades competentes formulen políticas y estrategias de gestión que protejan la economía y los ecosistemas de la región.Facultad de Ciencias Naturales y MuseoFacultad de Ingenierí
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