scholarly journals Land-use influence on the soil hydrology: An approach in upper Grande River basin, Southeast Brazil

2019 ◽  
Vol 43 ◽  
Author(s):  
Leandro Campos Pinto ◽  
Carlos Rogério de Mello ◽  
Lloyd Darrell Norton ◽  
Nilton Curi
Keyword(s):  
Land Use ◽  
River Basin ◽  
Water Availability ◽  
Machine Learning Algorithms ◽  
Southeastern Brazil ◽  
Support Vector ◽  
Native Forest ◽  
Soil Hydrology ◽  
Large Country ◽  
Study Objective

ABSTRACT Brazil is a large country that depends on the hydroelectricity generation hydropower dams. The Upper Grande River Basin (UGRB) is one of the most important Brazilian hydrological regions in terms of water availability and electric energy production. Therefore, studies of water availability are indispensable for a better and more successful decision making in water resources management in the region. This study objective to approach the land-use influence on the soil hydrology in the Upper Grande River Basin, a strategic headwater basin of southeastern Brazil. This study uses hydrological indicators (baseflow/runoff (BF/R) and overland flow/runoff (OF/R)) extracted from eight watersheds, varying the size and localization in the region, to support the results found. Soil saturated hydraulic conductivity (Ko) was determined in situ using a constant flow permeameter, totaling 224 sampled points. Five machine learning algorithms were compared in their performance to predict Ko (Random Forest, Support Vector Machine, Gradient Boosting, Linear Regression, Regularization) using terrain attributes as covariates. The tested methods for predicting Ko resulted in a relatively low coefficient of determination (R2) due to the high spatial variability of this soil hydrologic attribute. The hydrological indicator BF/R was sensitive to land-use changes in the watersheds. The greatest Ko values were associated with native forest and the least values area associated with pasture and rupestrian field.

scholarly journals ANALISIS NERACA AIR LAHAN DI SUB DAS KRUENG JREUE KABUPATEN ACEH BESAR

2018 ◽  
Vol 15 (1) ◽  
pp. 1-6
Author(s):  
MAWARDIANA MAWARDIANA ◽  
HELMI HASAN BASRI ◽  
TARMIZI TARMIZI
Keyword(s):  
Land Use ◽  
Water Balance ◽  
Ground Water ◽  
River Basin ◽  
Water Availability ◽  
Field Survey ◽  
Primary Forest ◽  
Average Water ◽  
Basin Area ◽  
Descriptive Method

Krueng Aceh River Basin area (Krueng Jreue Sub-watershed) is a critical watershed, including a priority basin I of 23,218.06 ha. The research was conducted in Krueng Aceh River Basin (DAS), Krueng Jreue Sub-district of Aceh Besar. The study was conducted from December 2015 to February 2016. The research used Descriptive method with field survey and analysis in labotarium. This study aims to analyze the water balance of land in Krueng Jreue Sub-watershed. The methods used in the calculation of the water balance are Thornthwaite & Mather (1957). The results showed that the average water availability in Krueng Jreue Sub-watersheds on the highest land use varieties was found in November and the lowest in July. The rainfall surplus occurred from October to April (7 months) and the deficit occurred from May to September (5 months). In various types of land use, ground water averages are quite available from October to May (8 months), while less available in June until September (4 months). The highest percentage of groundwater was found in the primary forest (47.20%), while the lowest was in the bush (36.36%)

scholarly journals Modelling hydrological responses under climate change using machine learning algorithms – semi-arid river basin of peninsular India

2020 ◽  
Vol 3 (1) ◽  
pp. 481-498
Author(s):  
G. Sireesha Naidu ◽  
M. Pratik ◽  
S. Rehana
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Random Forest ◽  
River Basin ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Peninsular India ◽  
Hydrological Responses ◽  
Semi Arid

Abstract Catchment scale conceptual hydrological models apply calibration parameters entirely based on observed historical data in the climate change impact assessment. The study used the most advanced machine learning algorithms based on Ensemble Regression and Random Forest models to develop dynamically calibrated factors which can form as a basis for the analysis of hydrological responses under climate change. The Random Forest algorithm was identified as a robust method to model the calibration factors with limited data for training and testing with precipitation, evapotranspiration and uncalibrated runoff based on various performance measures. The developed model was further used to study the runoff response under climate change variability of precipitation and temperatures. A statistical downscaling model based on K-means clustering, Classification and Regression Trees and Support Vector Regression was used to develop the precipitation and temperature projections based on MIROC GCM outputs with the RCP 4.5 scenario. The proposed modelling framework has been demonstrated on a semi-arid river basin of peninsular India, Krishna River Basin (KRB). The basin outlet runoff was predicted to decrease (13.26%) for future scenarios under climate change due to an increase in temperature (0.6 °C), compared to a precipitation increase (13.12%), resulting in an overall reduction in water availability over KRB.

Evaluation of effectiveness of the water management strategies with the focus on agricultural water use: A case study on Upper-Sakarya River Basin in Turkey

2020 ◽  
Author(s):  
Beyza Özel ◽  
Yasemin Demir ◽  
Oğuz Başkan ◽  
Emre Alp
Keyword(s):  
Land Use ◽  
Water Resources ◽  
River Basin ◽  
Water Availability ◽  
Irrigation Water ◽  
Cropping Pattern ◽  
Management Scenarios ◽  
Efficient Management ◽  
Sakarya River ◽  
Technical Measures

<p>Water, energy and food nexus is an integrated framework suggests that the security of one resource is inevitably linked to another’s. Water availability assures healthy food production whereas agriculture is the dominant user of global freshwater. Water stress due to population growth, climate change or malpractices threatens food security. Within the scope of water for food governance, the water efficiency of agricultural irrigation has to be improved to aid sustainable water and agricultural management. The study investigates water availability and withdrawals, evaluates water resources management scenarios in the agricultural sector in the Sakarya River Basin, Turkey’s third-largest river basin. Demand-oriented management scenarios propose a variety of technical measures which include improvements in irrigation technology, shifts in the cropping pattern and water-saving irrigation strategies. The effectiveness of scenarios was evaluated using the Water Evaluation and Planning (WEAP) system developed for the upper sub-basin where significant agricultural activities are held with approximately 1 million ha of total effective arable land. WEAP is an integrated water resources system modeling that operates based on the principle of water balance accounting. A climate data set of precipitation, temperature, relative humidity, and wind speed were applied across each sub-basin, partitioned into land-use classes. A one dimensional, two-bucket model for each land-use class transmits water as surface runoff, interflow, percolation, baseflow and evapotranspiration components. The model was calibrated and validated for observed streamflow, reservoir volume, and irrigation water amount. The mean annual precipitation and evapotranspiration in the upper sub-basin are 387 mm/a and 245 mm/a respectively. Agriculture is the dominant user of both surface water and groundwater resources and accounts for the %88 of total water withdrawals in the upper sub-basin. Impacts of agricultural management on irrigation water supply and flow dynamics of streamflow gauges were evaluated upon each measure. When compared to a historic baseline scenario, efficient management measures can save irrigation water up to %10 by shifting crop patterns from sunflower to safflower, %6 by establishing drip irrigation instead of sprinkler, %4 by applying deficient irrigation on cereal cultivated areas. Furthermore, mean streamflow increases by %8 in June where deficient irrigation strategy is practiced on cereals, by %9 in October where cropping pattern is shifted from sunflower to safflower. After a review of various technical measures related to the efficient management of water resources, the study concluded that sustainable agricultural development is possible by adapting conservative agricultural practices that assure water and food security.</p>

Impacts of Land-use Changes on the Hydrology of the Grande River Basin Headwaters, Southeastern Brazil

2014 ◽  
Vol 28 (13) ◽  
pp. 4537-4550 ◽  
Author(s):  
M. R. Viola ◽  
C. R. Mello ◽  
S. Beskow ◽  
L. D. Norton
Keyword(s):  
Land Use ◽  
River Basin ◽  
Land Use Changes ◽  
Southeastern Brazil

New species of Glossoscolex and Fimoscolex earthworms (Oligochaeta: Glossoscolecidae) from Embrapa Forestry, Paraná, Brazil

Zootaxa ◽  
2018 ◽  
Vol 4496 (1) ◽  
pp. 492 ◽  
Author(s):  
ALEXANDER FEIJOO M. ◽  
GEORGE G. BROWN
Keyword(s):  
Land Use ◽  
New Species ◽  
Land Use Change ◽  
Soil Disturbance ◽  
Species Group ◽  
Southeastern Brazil ◽  
Native Forest ◽  
Fallow Land ◽  
Araucaria Forest ◽  
Native Grassland

Three new earthworm species in two genera of the Glossoscolecidae family, common in Southern and Southeastern Brazil, collected in the highland plateaus of Paraná, are described: Glossoscolex (Glossoscolex) maschio sp. nov. and Glossoscolex (Glossoscolex) embrapaensis sp. nov., and Fimoscolex nivae sp. nov. G. embrapaensis is a small, unpigmented endogeic belonging to the truncatus species group, having male pore on segment 17, while G. maschio is a large, pigmented epiendogeic species belonging to the giganteus species group, having male pore on segment 19. F. nivae is a very slender, small unpigmented endogeic species. All specimens were collected in native Araucaria forest on the grounds of Embrapa Forestry, but G. maschio was found exclusively associated with native forest, while G. embrapaensis and F. nivae were also collected in Eucalyptus, Pinus or Araucaria plantations, native grassland or fallow land, indicating their resistance to soil disturbance and land use change.

scholarly journals Physical characterization of a watershed through GIS: a study in the Schmidt stream, Brazil

2015 ◽  
Vol 75 (4 suppl 2) ◽  
pp. 16-29 ◽  
Author(s):  
D. R. Reis ◽  
R. Plangg ◽  
J. G. Tundisi ◽  
D. M. Quevedo
Keyword(s):  
Environmental Management ◽  
Land Use ◽  
River Basin ◽  
Physical Characterization ◽  
Annual Rainfall ◽  
Native Forest ◽  
Southern Brazil ◽  
Small Streams ◽  
Integrated Environmental Management

Remote sensing and geoprocessing are essential tools for obtaining and maintaining records of human actions on space over the course of time; these tools offer the basis for diagnoses of land use, environmental interference and local development. The Schmidt stream watershed, located in the Sinos River basin, in southern Brazil, has an environmental situation similar to that of the majority of small streams draining rural and urban areas in southern Brazil: agricultural and urbanization practices do not recognize the riparian area and there is removal of original vegetation, disregarding the suitability of land use; removal of wetlands; intensive water use for various activities; and lack of control and monitoring in the discharge of wastewater, among other factors, deteriorate the quality of this important environment.This article aims to achieve a physical characterization of the Schmidt stream watershed (Sinos river basin) identifying elements such as land use and occupation, soil science, geology, climatology, extent and location of watershed, among others, so as to serve as the basis for a tool that helps in the integrated environmental management of watersheds. By applying geographic information system - GIS to the process of obtaining maps of land use and occupation, pedologicaland geological, and using climatological data from the Campo Bom meteorological station, field visit, review of literature and journals, and publicly available data, the physical characterization of the Schmidt stream watershed was performed, with a view to the integrated environmental management of this watershed. Out of the total area of the Schmidt stream watershed (23.92 km2), in terms of geology, it was observed that 23.7% consist of colluvial deposits, 22.6% consist of grass facies, and 53.7% consist of Botucatu formation. Major soil types of the watershed: 97.4% Argisols and only 2.6% Planosols. Land use and occupation is characterized by wetland (0.5%), Native Forest (12.83%), Native Forest + Rural Anthropic + Secondary Vegetation + Forestry (43.81%), Urban Anthropic/Urban Area (39.85%), and also Urban Anthropic/Expansion areas (3.01%). Mean annual rainfall is 1337 mm, maximum temperatures range from 10.5°C to 41.6°C and minimum temperatures range from –1.80°C and 26°C, weak winds, occasionally over 5 m/s. Conducting an environmental assessment in this watershed is essential for environmental and land management. However, these assessments are not conducted in all watersheds and, when they are, their frequency is not sufficiency to allow for continuous monitoring, in order to model and predict scenarios, with a view to adopt medium and long-term measures for environmental protection.

scholarly journals Impact of changes in land use in the flow of the Pará River Basin, MG

2015 ◽  
Vol 19 (1) ◽  
pp. 70-76 ◽  
Author(s):  
Evandro L. Rodrigues ◽  
Marcos A. T. Elmiro ◽  
Francisco de A. Braga ◽  
Claudia M. Jacobi ◽  
Rafael D. Rossi
Keyword(s):  
Land Use ◽  
River Basin ◽  
Water Availability ◽  
Assessment Tool ◽  
Hydrological Cycle ◽  
Water Cycle ◽  
Plant Cover ◽  
Semideciduous Forest ◽  
Cerrado Vegetation ◽  
The Impact

Plant cover plays an essential role in the maintenance and balance of the hydrological cycle, performing functions in the control of water availability, which guarantee flow permanence. The use of mathematical models is an alternative to represent the hydrological system and help in the understanding of phenomena involving the variables of the water cycle, in order to anticipate and predict impacts from potential changes in land use. In the present study, the hydrological model SWAT (Soil and Water Assessment Tool) was used to analyse the dynamics of flow and water flow in the Pará River Basin, Minas Gerais, Brazil, aiming to evaluate the impact caused by changes in land use in water availability. The adjusted model was assessed by the coefficient of efficiency of Nash-Sutcliffe (between -0.057 to -0.059), indicating high correlation and coefficient of residual mass (0.757 to 0.793) and therefore a satisfactory fit. An increase of about 10% in the basin flow was estimated, as a function of changes in land use, when simulating the removal of the original 'Cerrado' vegetation and of the seasonal semideciduous forest for pasture implementation in 38% of the basin.

scholarly journals Mapping and Monitoring of Land Cover/Land Use (LCLU) Changes in the Crozon Peninsula (Brittany, France) from 2007 to 2018 by Machine Learning Algorithms (Support Vector Machine, Random Forest, and Convolutional Neural Network) and by Post-classification Comparison (PCC)

2021 ◽  
Vol 13 (19) ◽  
pp. 3899
Author(s):  
Guanyao Xie ◽  
Simona Niculescu
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Support Vector Machine ◽  
Random Forest ◽  
Land Cover ◽  
Convolutional Neural Network ◽  
Coastal Area ◽  
Machine Learning Algorithms ◽  
High Rate ◽  
Support Vector

Land cover/land use (LCLU) is currently a very important topic, especially for coastal areas that connect the land and the coast and tend to change frequently. LCLU plays a crucial role in land and territory planning and management tasks. This study aims to complement information on the types and rates of LCLU multiannual changes with the distributions, rates, and consequences of these changes in the Crozon Peninsula, a highly fragmented coastal area. To evaluate the multiannual change detection (CD) capabilities using high-resolution (HR) satellite imagery, we implemented three remote sensing algorithms: a support vector machine (SVM), a random forest (RF) combined with geographic object-based image analysis techniques (GEOBIA), and a convolutional neural network (CNN), with SPOT 5 and Sentinel 2 data from 2007 and 2018. Accurate and timely CD is the most important aspect of this process. Although all algorithms were indicated as efficient in our study, with accuracy indices between 70% and 90%, the CNN had significantly higher accuracy than the SVM and RF, up to 90%. The inclusion of the CNN significantly improved the classification performance (5–10% increase in the overall accuracy) compared with the SVM and RF classifiers applied in our study. The CNN eliminated some of the confusion that characterizes a coastal area. Through the study of CD results by post-classification comparison (PCC), multiple changes in LCLU could be observed between 2007 and 2018: both the cultivated and non-vegetated areas increased, accompanied by high deforestation, which could be explained by the high rate of urbanization in the peninsula.

scholarly journals Projection of land use to 2030 and its impacts on water availability in a brazilian sub-basin: A LCM and SWAT approach

2022 ◽  
Vol 61 (1) ◽  
pp. 66-87
Author(s):  
Adriana Paulo de Sousa Oliveira ◽  
Rafaela Ribeiro Gracelli ◽  
Arthur Amaral e Silva ◽  
Vitor Juste dos Santos ◽  
Jackeline De Siqueira Castro ◽  
...  
Keyword(s):  
Land Use ◽  
Water Resources ◽  
Water Resources Management ◽  
Water Availability ◽  
Assessment Tool ◽  
Swat Model ◽  
Water Security ◽  
Climatic Conditions ◽  
Southeastern Brazil ◽  
Resources Management

Changes in land use and land cover (LULC) can result in significant changes in a hydrographic ba- sin flow regime. Future projections about LULC and its interference with water availability help to identify extreme events in advance and help propose appropriate management measures. Thus, this study aimed to make the LULC projection for the year 2030 for the Alto Rio Grande (ARG) sub- basin, located in Southeastern Brazil. This region was chosen because of its intense water resources use and for having recently faced water scarcity as result of prolonged droughts and inadequate water resources management. To identify the LULC trend for the year 2030, the Land Change Modeler (LCM) was used, the map obtained was inserted in the Soil and Water Assessment Tool (SWAT) model previously calibrated and validated for the region’ environmental and climatic conditions. The ARG sub-basin was affected by heavy rains in 2011, which resulted in changes in the landscape due to landslides. This particularity of the region contributed to the projection of LULC for the year 2030 to present an increase in forest and pastures to the agricultural areas detriment. When evaluating the impacts of these changes in water availability, it was observed that the SWAT model presented, for the same rainfall conditions, a reduction in peak streamflows of up to 59% and a reduction in the average monthly flow of up to 63% in 2030 in relation to the LULC observed in 2017. Thus, this study provides an important contribution by identifying a considerable reduction in water availability. These results will help to formulate strategies for water resources management and the adoption of measures to promote water security in the region.

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