Groundwater Recharge in the Cerrado Biome, Brazil—A Multi-Method Study at Experimental Watershed Scale

Groundwater recharge is a key hydrological process for integrated water resource management, as it recharges aquifers and maintains the baseflow of perennial rivers. In Brazil, the Cerrado biome is an important continental recharge zone, but information on rates and spatial distribution is still lacking for this country. The objective of this work was to characterize the groundwater recharge process in phreatic aquifers of the Cerrado biome. For this, an experimental watershed representative of the referred biome was established and intensively monitored. The methodology consisted of an inverse numerical modeling approach of the saturated zone and three classic methods of recharge evaluation—hydrological modeling, baseflow separation, and water table elevation. The results indicated average potential recharge around 35% of the annual precipitation, average effective recharge around 21%, and higher rates occurring in flat areas of Ferralsols covered with natural vegetation of the Cerrado biome. As the level of uncertainty inferred from the methods was high, these results were considered a first attempt and will be better evaluated by comparison with other methods not applied in this work, such as the lysimeter and chemical tracer methods.

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The Recharge Process and Influencing Meteorological Parameters Indicated by Cave Pool Hydrology in the Bare Karst Mountainous Area

Sustainability ◽

10.3390/su13041766 ◽

2021 ◽

Vol 13 (4) ◽

pp. 1766

Author(s):

Fan Liu ◽

Guanghui Jiang ◽

Jia Wang ◽

Fang Guo

Keyword(s):

Resource Management ◽

Water Resource ◽

Vadose Zone ◽

Water Resource Management ◽

Hydrological Modeling ◽

Meteorological Data ◽

Mountainous Area ◽

Hydrological Process ◽

Mountain Areas ◽

Mountain Area

Understanding the recharge and runoff processes of the vadose zone is significant for water resource management and utilization in karst mountain areas. Hydrological modeling of the vadose zone in karst caves has provided new methods of evaluating water resources in vadose zones. This paper provides modeling of vadose zone hydrology in a subtropical karst cave. The monitoring was conducted in Yuanyang Cave, Fengshan County, Guangxi Province, Southwest China. By monitoring the water level of a pool recharged by drop water in a cave, a model was established to calculate the natural leakage from the bottom and the infiltrated recharge from the vadose zone above. Combined with meteorological data records, the occurrence of recharge events in the vadose zone was analyzed. The correlation between them was established by multiple linear regression. The results showed that the infiltration ratio of precipitation was 20.88%. Recent rainfall of 4–7 days had shown a greater impact on recharge events than that of 3 days. The effect of evaporation was significant. The regression model in the cave pool was used to understand the hydrological process of the vadose zone, which provided a useful method for water resource management and evaluation in the remote karst mountain area.

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Hydrological modeling in a basin of the Brazilian Cerrado biome

Ambiente e Agua - An Interdisciplinary Journal of Applied Science ◽

10.4136/ambi-agua.2639 ◽

2021 ◽

Vol 16 (1) ◽

pp. 1

Author(s):

Jéssica Assaid Martins Rodrigues ◽

Alberto Carlos de Oliveira Andrade ◽

Marcelo Ribeiro Viola ◽

Danton Diego Ferreira ◽

Carlos Rogério de Mello ◽

...

Keyword(s):

Water Resource ◽

Water Resource Management ◽

Hydrological Modeling ◽

Assessment Tool ◽

Swat Model ◽

Coefficient Of Determination ◽

Brazilian Cerrado ◽

Ann Model ◽

Cerrado Biome ◽

Daily Streamflow

The Brazilian Cerrado biome (BCB) is among 25 biodiversity hotspots identified worldwide, and covers the recharge area of important aquifers and rivers in South America. The increase in deforestation has been threatening water availability in this region. In order to assist in the water-resource management of the BCB, this study models the daily streamflow in a basin of the Cerrado, using two approaches: a process-based model (Soil and Water Assessment Tool - SWAT) and the data-driven model (Artificial Neural Network - ANN). The performance of the models was evaluated by the Nash-Sutcliffe coefficient (NSE), coefficient of determination (R2) and flow-duration-curves (FDC). The results indicate that SWAT (NSE > 0.61; R2 > 0.68) and ANN (NSE > 0.91; R2 > 0.79) models are suitable tools in daily streamflow modeling of the studied basin, with the ANN model being the most accurate. Based on FDC, the ANN model was also better than the SWAT model for all frequencies evaluated. Thus, the ANN model is a promising new approach for daily streamflow modelling in this region. Moreover, the results of this study can help water-resource managers in planning and implementing appropriate water allocation and conservation measures in the Brazilian Cerrado biome.

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India’s Commitments to Increase Tree and Forest Cover: Consequences for Water Supply and Agriculture Production within the Central Indian Highlands

Water ◽

10.3390/w13070959 ◽

2021 ◽

Vol 13 (7) ◽

pp. 959

Author(s):

Benjamin Clark ◽

Ruth DeFries ◽

Jagdish Krishnaswamy

Keyword(s):

Groundwater Recharge ◽

Forest Cover ◽

Hydrological Modeling ◽

Monsoon Season ◽

Forest Policy ◽

Central India ◽

Field Measurements ◽

Tree Cover ◽

Policy Goals ◽

The Impact

As part of its nationally determined contributions as well as national forest policy goals, India plans to boost tree cover to 33% of its land area. Land currently under other uses will require tree-plantations or reforestation to achieve this goal. This paper examines the effects of converting cropland to tree or forest cover in the Central India Highlands (CIH). The paper examines the impact of increased forest cover on groundwater infiltration and recharge, which are essential for sustainable Rabi (winter, non-monsoon) season irrigation and agricultural production. Field measurements of saturated hydraulic conductivity (Kfs) linked to hydrological modeling estimate increased forest cover impact on the CIH hydrology. Kfs tests in 118 sites demonstrate a significant land cover effect, with forest cover having a higher Kfs of 20.2 mm hr−1 than croplands (6.7mm hr−1). The spatial processes in hydrology (SPHY) model simulated forest cover from 2% to 75% and showed that each basin reacts differently, depending on the amount of agriculture under paddy. Paddy agriculture can compensate for low infiltration through increased depression storage, allowing for continuous infiltration and groundwater recharge. Expanding forest cover to 33% in the CIH would reduce groundwater recharge by 7.94 mm (−1%) when converting the average cropland and increase it by 15.38 mm (3%) if reforestation is conducted on non-paddy agriculture. Intermediate forest cover shows however shows potential for increase in net benefits.

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Spatial evapotranspiration, rainfall and land use data in water accounting – Part 1: Review of the accuracy of the remote sensing data

Hydrology and Earth System Sciences ◽

10.5194/hess-19-507-2015 ◽

2015 ◽

Vol 19 (1) ◽

pp. 507-532 ◽

Cited By ~ 48

Author(s):

P. Karimi ◽

W. G. M. Bastiaanssen

Keyword(s):

Remote Sensing ◽

Land Use ◽

Water Resource Management ◽

Hydrological Modeling ◽

Remote Sensing Data ◽

Actual Evapotranspiration ◽

Primary Concern ◽

Suitable Alternative ◽

Sensing Data ◽

Water Accounting

Abstract. The scarcity of water encourages scientists to develop new analytical tools to enhance water resource management. Water accounting and distributed hydrological models are examples of such tools. Water accounting needs accurate input data for adequate descriptions of water distribution and water depletion in river basins. Ground-based observatories are decreasing, and not generally accessible. Remote sensing data is a suitable alternative to measure the required input variables. This paper reviews the reliability of remote sensing algorithms to accurately determine the spatial distribution of actual evapotranspiration, rainfall and land use. For our validation we used only those papers that covered study periods of seasonal to annual cycles because the accumulated water balance is the primary concern. Review papers covering shorter periods only (days, weeks) were not included in our review. Our review shows that by using remote sensing, the absolute values of evapotranspiration can be estimated with an overall accuracy of 95% (SD 5%) and rainfall with an overall absolute accuracy of 82% (SD 15%). Land use can be identified with an overall accuracy of 85% (SD 7%). Hence, more scientific work is needed to improve the spatial mapping of rainfall and land use using multiple space-borne sensors. While not always perfect at all spatial and temporal scales, seasonally accumulated actual evapotranspiration maps can be used with confidence in water accounting and hydrological modeling.

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Groundwater Recharges Technology for Water Resource Management: A Case Study

10.5772/intechopen.93946 ◽

2020 ◽

Author(s):

Jatoth Veeranna ◽

Pawan Jeet

Keyword(s):

Land Use ◽

Groundwater Recharge ◽

Water Availability ◽

Water Resource Management ◽

Land Use Land Cover ◽

Indirect Methods ◽

Groundwater Availability ◽

Severe Water Deficit ◽

Direct And Indirect Methods

The irregularity in monsoon has severely affected the water availability at surface and sub-surface systems. Diminishing surface and sub-surface availability has not only decreased the water availability, but it additionally affected the ecosystem and increased disastrous situations like floods and droughts, resulting problems of stress on groundwater recharge. Groundwater recharge is a technique by which infiltrated water passes through the unsaturated region of groundwater and joins the water table. It is based upon soil type, land use land cover, geomorphology, geophysical and climate (viz. rainfall, temperature, humidity etc.) characteristics of a region. Over the years, due to variations in weather pattern and overexploitation of aquifers groundwater recharge has decreased and groundwater level has reduced in the most parts of the country. This has led to severe water deficit problems in several parts of the country. This can be solved by different direct and indirect methods of groundwater recharge technology. This technology can reduce the wastage of water and enhance groundwater availability for uses in different sector like irrigation, domestic and industrial uses.

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Modeling of Stage-Discharge Using Back Propagation ANN, ANFIS, and WANN-based Computing Technique

10.21203/rs.3.rs-696059/v1 ◽

2021 ◽

Author(s):

Ravi Shukla ◽

Pravendra Kumar ◽

Dinesh Kumar Vishwakarma ◽

Rawshan Ali ◽

Rohitashw Kumar ◽

...

Keyword(s):

Water Resource Management ◽

Hydrological Modeling ◽

Fuzzy Inference ◽

Back Propagation ◽

Activation Function ◽

Neural System ◽

Back Propagation Algorithm ◽

Inference System ◽

Computing Technique ◽

Artificial Neural

Abstract The development of the stage-discharge relationship is a fundamental issue in hydrological modeling. Due to the complexity of the stage-discharge relationship, discharge prediction plays an essential role in planning and water resource management. The present study was conducted for modeling of discharge at the Gaula barrage site in Uttarakhand state of India. The study evaluated, Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN) and Wavelet-Based Artificial Neural System (WANN) based models to estimate the discharge. The daily data of 12 years (2007-2018) were used to train and test the models. The Gamma test was used to identify the best model for discharge prediction. The input data having a stage with one-day lag and discharge with one and two-days lag and current-day discharge as output was used for discharge modeling. In the case of ANN models, the back-propagation algorithm and hyperbolic tangent sigmoid activation function was used. WANN used Haar, a trous based wavelet function. In ANFIS models, triangular, psig, generalized bell, and Gaussian membership functions were used to train and test the models. The models were evaluated qualitatively and quantitatively using correlation coefficient, root means square error, Willmott index, and coefficient of efficiency. It was found that ANFIS model performed better than ANN and WANN-based models for discharge prediction at the Gaula barrage.

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Runoff Predicting and Variation Analysis in Upper Ganjiang Basin under Projected Climate Changes

Sustainability ◽

10.3390/su11215885 ◽

2019 ◽

Vol 11 (21) ◽

pp. 5885 ◽

Cited By ~ 3

Author(s):

Chao Deng ◽

Weiguang Wang

Keyword(s):

Resource Management ◽

Water Resource ◽

Water Resource Management ◽

Hydrological Modeling ◽

Hydrologic Model ◽

Global Climate Models ◽

Balance Model ◽

Model Parameters ◽

The Mean ◽

Parameter Approach

Catchment runoff is significantly affected by climate condition changes. Predicting the runoff and analyzing its variations under future climates play a vital role in water security, water resource management, and the sustainable development of the catchment. In traditional hydrological modeling, fixed model parameters are usually used to transfer the global climate models (GCMs) to runoff, while the hydrologic model parameters may be time-varying. It is more appropriate to use the time-variant parameter for runoff modeling. This is achieved by incorporating the time-variant parameter approach into a two-parameter water balance model (TWBM) through the construction of time-variant parameter functions based on the identified catchment climate indicators. Using the Ganjiang Basin with an outlet of the Dongbei Hydrological Station as the study area, we developed time-variant parameter scenarios of the TWBM model and selected the best-performed parameter functions to predict future runoff and analyze its variations under the climate model projection of the BCC-CSM1.1(m). To synthetically assess the model performance improvements using the time-variant parameter approach, an index Δ was developed by combining the Nash–Sutcliffe efficiency, the volume error, the Box–Cox transformed root-mean-square error, and the Kling–Gupta efficiency with equivalent weight. The results show that the TWBM model with time-variant C (evapotranspiration parameter) and SC (water storage capacity of catchment), where growing and non-growing seasons are considered for C, outperformed the model with constant parameters with a Δ value of approximately 5% and 10% for the calibration and validation periods, respectively. The mean annual values of runoff predictions under the four representative concentration pathways (RCPs) exhibited a decreasing trend over the future three decades (2021–2050) when compared to the runoff simulations in the baseline period (1982–2011), where the values were about −9.9%, −19.5%, −16.6%, and −11.4% for the RCP2.6, RCP4.5, RCP6.0, and RCP8.5, respectively. The decreasing trend of future precipitation exerts impacts on runoff decline. Generally, the mean monthly changes of runoff predictions showed a decreasing trend from January to August for almost all of the RCPs, while an increasing trend existed from September to November, along with fluctuations among different RCPs. This study can provide beneficial references to comprehensively understand the impacts of climate change on runoff prediction and thus improve the regional strategy for future water resource management.

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Determining Regional-Scale Groundwater Recharge with GRACE and GLDAS

Remote Sensing ◽

10.3390/rs11020154 ◽

2019 ◽

Vol 11 (2) ◽

pp. 154 ◽

Cited By ~ 11

Author(s):

Qifan Wu ◽

Bingcheng Si ◽

Hailong He ◽

Pute Wu

Keyword(s):

Groundwater Recharge ◽

Water Resource Management ◽

Root Zone ◽

Groundwater Resources ◽

Ordos Basin ◽

Regional Scale ◽

Chinese Loess Plateau ◽

Soil Water Storage ◽

Chinese Loess

Groundwater recharge (GR) is a key component of regional and global water cycles and is a critical flux for water resource management. However, recharge estimates are difficult to obtain at regional scales due to the lack of an accurate measurement method. Here, we estimate GR using Gravity Recovery and Climate Experiment (GRACE) and Global Land Data Assimilation System (GLDAS) data. The regional-scale GR rate is calculated based on the groundwater storage fluctuation, which is, in turn, calculated from the difference between GRACE and root zone soil water storage from GLDAS data. We estimated GR in the Ordos Basin of the Chinese Loess Plateau from 2002 to 2012. There was no obvious long-term trend in GR, but the annual recharge varies greatly from 30.8 to 66.5 mm year−1, 42% of which can be explained by the variability in the annual precipitation. The average GR rate over the 11-year period from GRACE data was 48.3 mm year−1, which did not differ significantly from the long-term average recharge estimate of 39.9 mm year−1 from the environmental tracer methods and one-dimensional models. Moreover, the standard deviation of the 11-year average GR is 16.0 mm year−1, with a coefficient of variation (CV) of 33.1%, which is, in most cases, comparable to or smaller than estimates from other GR methods. The improved method could provide critically needed, regional-scale GR estimates for groundwater management and may eventually lead to a sustainable use of groundwater resources.

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Simulating Land Cover Change Impacts on Groundwater Recharge under Selected Climate Projections, Maui, Hawaiʻi

Remote Sensing ◽

10.3390/rs11243048 ◽

2019 ◽

Vol 11 (24) ◽

pp. 3048

Author(s):

Laura Brewington ◽

Victoria Keener ◽

Alan Mair

Keyword(s):

Land Cover ◽

Groundwater Recharge ◽

Land Cover Change ◽

Hydrological Modeling ◽

Planning Process ◽

Urban Expansion ◽

Scenario Planning ◽

Future Climate ◽

Climate Projections ◽

Modeling Framework

This project developed an integrated land cover/hydrological modeling framework using remote sensing and geographic information systems (GIS) data, stakeholder input, climate information and projections, and empirical data to estimate future groundwater recharge on the Island of Maui, Hawaiʻi, USA. End-of-century mean annual groundwater recharge was estimated under four future land cover scenarios: Future 1 (conservation-focused), Future 2 (status-quo), Future 3 (development-focused), and Future 4 (balanced conservation and development), and two downscaled climate projections: a coupled model intercomparison project (CMIP) phase 5 (CMIP5) representative concentration pathway (RCP) 8.5 “dry climate” future and a CMIP3 A1B “wet climate” future. Results were compared to recharge estimated using the 2017 baseline land cover to understand how changing land management and climate could influence groundwater recharge. Estimated recharge increased island-wide under all future land cover and climate combinations and was dominated by specific land cover transitions. For the dry future climate, recharge for land cover Futures 1 to 4 increased by 12%, 0.7%, 0.01%, and 11% relative to 2017 land cover conditions, respectively. Corresponding increases under the wet future climate were 10%, 0.9%, 0.6%, and 9.3%. Conversion from fallow/grassland to diversified agriculture increased irrigation, and therefore recharge. Above the cloud zone (610 m), conversion from grassland to native or alien forest led to increased fog interception, which increased recharge. The greatest changes to recharge occurred in Futures 1 and 4 in areas where irrigation increased, and where forest expanded within the cloud zone. Furthermore, new future urban expansion is currently slated for coastal areas that are already water-stressed and had low recharge projections. This study demonstrated that a spatially-explicit scenario planning process and modeling framework can communicate the possible consequences and tradeoffs of land cover change under a changing climate, and the outputs from this study serve as relevant tools for landscape-level management and interventions.

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