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Patricia Jimeno-Sáez
Department of Civil Engineering, Universidad Católica San Antonio de Murcia, Campus de Los Jerónimos s/n, 30107 Murcia, Spain

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Journal article
Published: 20 August 2021 in Remote Sensing
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Hydrological modelling requires accurate climate data with high spatial-temporal resolution, which is often unavailable in certain parts of the world—such as Central America. Numerous studies have previously demonstrated that in hydrological modelling, global weather reanalysis data provides a viable alternative to observed data. However, calibrating and validating models requires the use of observed discharge data, which is also frequently unavailable. Recent, global-scale applications have been developed based on weather data from reanalysis; these applications allow streamflows with satisfactory resolution to be obtained. An example is the Global Flood Awareness System (GloFAS), which uses the fifth generation of reanalysis data produced by the European Centre for Medium-Range Weather Forecasts (ERA5) as input. It provides discharge data from 1979 to the present with a resolution of 0.1°. This study assesses the potential of GloFAS for calibrating hydrological models in ungauged basins. For this purpose, the quality of data from ERA5 and from the Climate Hazards Group InfraRed Precipitation and Temperature with Station as well as the Climate Forecast System Reanalysis (CFSR) was analysed. The focus was on flow simulation using the Soil and Water Assessment Tool (SWAT) model. The models were calibrated using GloFAS discharge data. Our results indicate that all the reanalysis datasets displayed an acceptable fit with the observed precipitation and temperature data. The correlation coefficient (CC) between the reanalysis data and the observed data indicates a strong relationship at the monthly level all of the analysed stations (CC > 0.80). The Kling–Gupta Efficiency (KGE) also showed the acceptable performance of the calibrated SWAT models (KGE > 0.74). We concluded that GloFAS data has substantial potential for calibrating hydrological models that estimate the monthly streamflow in ungauged watersheds. This approach can aid water resource management.

ACS Style

Javier Senent-Aparicio; Pablo Blanco-Gómez; Adrián López-Ballesteros; Patricia Jimeno-Sáez; Julio Pérez-Sánchez. Evaluating the Potential of GloFAS-ERA5 River Discharge Reanalysis Data for Calibrating the SWAT Model in the Grande San Miguel River Basin (El Salvador). Remote Sensing 2021, 13, 3299 .

AMA Style

Javier Senent-Aparicio, Pablo Blanco-Gómez, Adrián López-Ballesteros, Patricia Jimeno-Sáez, Julio Pérez-Sánchez. Evaluating the Potential of GloFAS-ERA5 River Discharge Reanalysis Data for Calibrating the SWAT Model in the Grande San Miguel River Basin (El Salvador). Remote Sensing. 2021; 13 (16):3299.

Chicago/Turabian Style

Javier Senent-Aparicio; Pablo Blanco-Gómez; Adrián López-Ballesteros; Patricia Jimeno-Sáez; Julio Pérez-Sánchez. 2021. "Evaluating the Potential of GloFAS-ERA5 River Discharge Reanalysis Data for Calibrating the SWAT Model in the Grande San Miguel River Basin (El Salvador)." Remote Sensing 13, no. 16: 3299.

Journal article
Published: 20 May 2021 in Remote Sensing
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Assessing how climate change will affect hydrological ecosystem services (HES) provision is necessary for long-term planning and requires local comprehensive climate information. In this study, we used SWAT to evaluate the impacts on four HES, natural hazard protection, erosion control regulation and water supply and flow regulation for the Laguna del Sauce catchment in Uruguay. We used downscaled CMIP-5 global climate models for Representative Concentration Pathways (RCP) 2.6, 4.5 and 8.5 projections. We calibrated and validated our SWAT model for the periods 2005–2009 and 2010–2013 based on remote sensed ET data. Monthly NSE and R2 values for calibration and validation were 0.74, 0.64 and 0.79, 0.84, respectively. Our results suggest that climate change will likely negatively affect the water resources of the Laguna del Sauce catchment, especially in the RCP 8.5 scenario. In all RCP scenarios, the catchment is likely to experience a wetting trend, higher temperatures, seasonality shifts and an increase in extreme precipitation events, particularly in frequency and magnitude. This will likely affect water quality provision through runoff and sediment yield inputs, reducing the erosion control HES and likely aggravating eutrophication. Although the amount of water will increase, changes to the hydrological cycle might jeopardize the stability of freshwater supplies and HES on which many people in the south-eastern region of Uruguay depend. Despite streamflow monitoring capacities need to be enhanced to reduce the uncertainty of model results, our findings provide valuable insights for water resources planning in the study area. Hence, water management and monitoring capacities need to be enhanced to reduce the potential negative climate change impacts on HES. The methodological approach presented here, based on satellite ET data can be replicated and adapted to any other place in the world since we employed open-access software and remote sensing data for all the phases of hydrological modelling and HES provision assessment.

ACS Style

Celina Aznarez; Patricia Jimeno-Sáez; Adrián López-Ballesteros; Juan Pacheco; Javier Senent-Aparicio. Analysing the Impact of Climate Change on Hydrological Ecosystem Services in Laguna del Sauce (Uruguay) Using the SWAT Model and Remote Sensing Data. Remote Sensing 2021, 13, 2014 .

AMA Style

Celina Aznarez, Patricia Jimeno-Sáez, Adrián López-Ballesteros, Juan Pacheco, Javier Senent-Aparicio. Analysing the Impact of Climate Change on Hydrological Ecosystem Services in Laguna del Sauce (Uruguay) Using the SWAT Model and Remote Sensing Data. Remote Sensing. 2021; 13 (10):2014.

Chicago/Turabian Style

Celina Aznarez; Patricia Jimeno-Sáez; Adrián López-Ballesteros; Juan Pacheco; Javier Senent-Aparicio. 2021. "Analysing the Impact of Climate Change on Hydrological Ecosystem Services in Laguna del Sauce (Uruguay) Using the SWAT Model and Remote Sensing Data." Remote Sensing 13, no. 10: 2014.

Journal article
Published: 05 May 2021 in Journal of Hydrology: Regional Studies
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Peninsular Spain. Weather data are the key drivers of hydrological modelling. However, available weather data can present gaps in data sequences and are often limited in their spatial coverage for use in such hydrological models as the Soil and Water Assessment Tool (SWAT). To overcome this limitation, SWAT includes a weather generator algorithm that can complete this data based on long-term weather statistics. This work presents a newly developed weather statistics dataset for Peninsular Spain (PSWG), calculated from national gridded datasets according to the SWAT model format. PSWG provides a higher resolution that stands as a compelling alternative to the statistics calculated from the Climate Forecast System Reanalysis (CFSR) that are available on the SWAT website. The dataset has been evaluated using PSWG and CFSR datasets for different data availability scenarios to reconstruct weather series in three watersheds with contrasting weather climates. Results underscore the superiority of the PSWG dataset in reconstructing missing data for hydrological simulations. This approach provides a strong alternative for SWAT applications in Peninsular Spain and the applied methodology can be replicated in other countries that dispose of high-resolution gridded rainfall and temperature datasets.

ACS Style

Javier Senent-Aparicio; Patricia Jimeno-Sáez; Adrián López-Ballesteros; José Ginés Giménez; Julio Pérez-Sánchez; José M. Cecilia; Raghavan Srinivasan. Impacts of swat weather generator statistics from high-resolution datasets on monthly streamflow simulation over Peninsular Spain. Journal of Hydrology: Regional Studies 2021, 35, 100826 .

AMA Style

Javier Senent-Aparicio, Patricia Jimeno-Sáez, Adrián López-Ballesteros, José Ginés Giménez, Julio Pérez-Sánchez, José M. Cecilia, Raghavan Srinivasan. Impacts of swat weather generator statistics from high-resolution datasets on monthly streamflow simulation over Peninsular Spain. Journal of Hydrology: Regional Studies. 2021; 35 ():100826.

Chicago/Turabian Style

Javier Senent-Aparicio; Patricia Jimeno-Sáez; Adrián López-Ballesteros; José Ginés Giménez; Julio Pérez-Sánchez; José M. Cecilia; Raghavan Srinivasan. 2021. "Impacts of swat weather generator statistics from high-resolution datasets on monthly streamflow simulation over Peninsular Spain." Journal of Hydrology: Regional Studies 35, no. : 100826.

Journal article
Published: 08 April 2020 in Water
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Gauges modify wind fields, producing important systematic errors (undercatching) in the measurement of solid precipitation (Ps), especially under windy conditions. A methodology that combines geostatistical techniques and hydrological models to perform a preliminary assessment of global undercatch and precipitation patterns in alpine regions is proposed. An assessment of temperature and precipitation fields is performed by applying geostatistical approaches assuming different hypothesis about the relationship between climatic fields and altitude. Several experiments using different approximations of climatic fields in different approaches to a hydrological model are evaluated. A new hydrological model, the Snow-Témez Model (STM), is developed including two parameters to correct the solid (Cs) and liquid precipitation (Cr). The procedure allows identifying the best combination of geostatistical approach and hydrological model for estimating streamflow in the Canales Basin, an alpine catchment of the Sierra Nevada (Spain). The sensitivity of the results to the correction of the precipitation fields is analyzed, revealing that the results of the streamflow simulation are improved when the precipitation is corrected considerably. High values of solid Cs are obtained, while Cr values, although smaller than the solid one, are also significant.

ACS Style

Patricia Jimeno-Sáez; David Pulido-Velazquez; Antonio-Juan Collados-Lara; Eulogio Pardo-Igúzquiza; Javier Senent-Aparicio; Leticia Baena-Ruiz. A Preliminary Assessment of the “Undercatching” and the Precipitation Pattern in an Alpine Basin. Water 2020, 12, 1061 .

AMA Style

Patricia Jimeno-Sáez, David Pulido-Velazquez, Antonio-Juan Collados-Lara, Eulogio Pardo-Igúzquiza, Javier Senent-Aparicio, Leticia Baena-Ruiz. A Preliminary Assessment of the “Undercatching” and the Precipitation Pattern in an Alpine Basin. Water. 2020; 12 (4):1061.

Chicago/Turabian Style

Patricia Jimeno-Sáez; David Pulido-Velazquez; Antonio-Juan Collados-Lara; Eulogio Pardo-Igúzquiza; Javier Senent-Aparicio; Leticia Baena-Ruiz. 2020. "A Preliminary Assessment of the “Undercatching” and the Precipitation Pattern in an Alpine Basin." Water 12, no. 4: 1061.

Journal article
Published: 29 February 2020 in Water
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This paper couples the Soil and Water Assessment Tool (SWAT) model and the chloride mass balance (CMB) method to improve the modeling of streamflow in high-permeability bedrock basins receiving interbasin groundwater flow (IGF). IGF refers to the naturally occurring groundwater flow beneath a topographic divide, which indicates that baseflow simulated by standard hydrological models may be substantially less than its actual magnitude. Identification and quantification of IGF is so difficult that most hydrological models use convenient simplifications to ignore it, leaving us with minimal knowledge of strategies to quantify it. The Castril River basin (CRB) was chosen to show this problematic and to propose the CMB method to assess the magnitude of the IGF contribution to baseflow. In this headwater area, which has null groundwater exploitation, the CMB method shows that yearly IGF hardly varies and represents about 51% of mean yearly baseflow. Based on this external IGF appraisal, simulated streamflow was corrected to obtain a reduction in the percent bias of the SWAT model, from 52.29 to 22.40. Corrected simulated streamflow was used during the SWAT model calibration and validation phases. The Nash–Sutcliffe Efficiency (NSE) coefficient and the logarithmic values of NSE (lnNSE) were used for overall SWAT model performance. For calibration and validation, monthly NSE was 0.77 and 0.80, respectively, whereas daily lnNSE was 0.81 and 0.64, respectively. This methodological framework, which includes initial system conceptualization and a new formulation, provides a reproducible way to deal with similar basins, the baseflow component of which is strongly determined by IGF.

ACS Style

Javier Senent-Aparicio; Francisco J. Alcalá; Sitian Liu; Patricia Jimeno-Sáez. Coupling SWAT Model and CMB Method for Modeling of High-Permeability Bedrock Basins Receiving Interbasin Groundwater Flow. Water 2020, 12, 657 .

AMA Style

Javier Senent-Aparicio, Francisco J. Alcalá, Sitian Liu, Patricia Jimeno-Sáez. Coupling SWAT Model and CMB Method for Modeling of High-Permeability Bedrock Basins Receiving Interbasin Groundwater Flow. Water. 2020; 12 (3):657.

Chicago/Turabian Style

Javier Senent-Aparicio; Francisco J. Alcalá; Sitian Liu; Patricia Jimeno-Sáez. 2020. "Coupling SWAT Model and CMB Method for Modeling of High-Permeability Bedrock Basins Receiving Interbasin Groundwater Flow." Water 12, no. 3: 657.

Journal article
Published: 13 February 2020 in International Journal of Environmental Research and Public Health
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The Mar Menor is a hypersaline coastal lagoon with high environmental value and a characteristic example of a highly anthropized hydro-ecosystem located in the southeast of Spain. An unprecedented eutrophication crisis in 2016 and 2019 with abrupt changes in the quality of its waters caused a great social alarm. Understanding and modeling the level of a eutrophication indicator, such as chlorophyll-a (Chl-a), benefits the management of this complex system. In this study, we investigate the potential machine learning (ML) methods to predict the level of Chl-a. Particularly, Multilayer Neural Networks (MLNNs) and Support Vector Regressions (SVRs) are evaluated using as a target dataset information of up to nine different water quality parameters. The most relevant input combinations were extracted using wrapper feature selection methods which simplified the structure of the model, resulting in a more accurate and efficient procedure. Although the performance in the validation phase showed that SVR models obtained better results than MLNNs, experimental results indicated that both ML algorithms provide satisfactory results in the prediction of Chl-a concentration, reaching up to 0.7 R2CV (cross-validated coefficient of determination) for the best-fit models.

ACS Style

Patricia Jimeno-Sáez; Javier Senent-Aparicio; José M. Cecilia; Julio Pérez-Sánchez. Using Machine-Learning Algorithms for Eutrophication Modeling: Case Study of Mar Menor Lagoon (Spain). International Journal of Environmental Research and Public Health 2020, 17, 1189 .

AMA Style

Patricia Jimeno-Sáez, Javier Senent-Aparicio, José M. Cecilia, Julio Pérez-Sánchez. Using Machine-Learning Algorithms for Eutrophication Modeling: Case Study of Mar Menor Lagoon (Spain). International Journal of Environmental Research and Public Health. 2020; 17 (4):1189.

Chicago/Turabian Style

Patricia Jimeno-Sáez; Javier Senent-Aparicio; José M. Cecilia; Julio Pérez-Sánchez. 2020. "Using Machine-Learning Algorithms for Eutrophication Modeling: Case Study of Mar Menor Lagoon (Spain)." International Journal of Environmental Research and Public Health 17, no. 4: 1189.

Journal article
Published: 11 November 2019 in Water
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This study assessed how changes in terms of temperature and precipitation might translate into changes in water availability and droughts in an area in a developing country with environmental interest. The hydrological model Soil and Water Assessment Tool (SWAT) was applied to analyze the impacts of climate change on water resources of the Guajoyo River Basin in El Salvador. El Salvador is in one of the most vulnerable regions in Latin America to the effects of climate change. The predicted future climate change by two climate change scenarios (RCP 4.5 and RCP 8.5) and five general circulation models (GCMs) were considered. A statistical analysis was performed to identify which GCM was better in terms of goodness of fit to variation in means and standard deviations of the historical series. A significant decreasing trend in precipitation and a significant increase in annual average temperatures were projected by the middle and the end of the twenty–first century. The results indicated a decreasing trend of the amount of water available and more severe droughts for future climate scenarios with respect to the base period (1975–2004). These findings will provide local water management authorities useful information in the face of climate change to help decision making.

ACS Style

Pablo Blanco-Gómez; Patricia Jimeno-Sáez; Javier Senent-Aparicio; Julio Pérez-Sánchez. Impact of Climate Change on Water Balance Components and Droughts in the Guajoyo River Basin (El Salvador). Water 2019, 11, 2360 .

AMA Style

Pablo Blanco-Gómez, Patricia Jimeno-Sáez, Javier Senent-Aparicio, Julio Pérez-Sánchez. Impact of Climate Change on Water Balance Components and Droughts in the Guajoyo River Basin (El Salvador). Water. 2019; 11 (11):2360.

Chicago/Turabian Style

Pablo Blanco-Gómez; Patricia Jimeno-Sáez; Javier Senent-Aparicio; Julio Pérez-Sánchez. 2019. "Impact of Climate Change on Water Balance Components and Droughts in the Guajoyo River Basin (El Salvador)." Water 11, no. 11: 2360.

Journal article
Published: 03 October 2019 in Applied Sciences
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Wildfires in Mediterranean regions have become a serious problem, and it is currently the main cause of forest loss. Numerous prediction methods have been applied worldwide to estimate future fire activity and area burned in order to provide a stable basis for future allocation of fire-fighting resources. The present study investigated the performance of an artificial neural network (ANN) in burned area size prediction and to assess the evolution of future wildfires and the area concerned under climate change in southern Spain. The study area comprised 39.41 km2 of land burned from 2000 to 2014. ANNs were used in two subsequential phases: classifying the size of the wildfires and predicting the burned surface for fires larger than 30,000 m2. Matrix of confusion and 10-fold cross-validations were used to evaluate ANN classification and mean absolute deviation, root mean square error, mean absolute percent error and bias, which were the metrics used for burned area prediction. The success rate achieved was above 60–70% depending on the zone. An average temperature increase of 3 °C and a 20% increase in wind speed during 2071–2100 results in a significant increase of the number of fires, up to triple the current figure, resulting in seven times the average yearly burned surface depending on the zone and the climate change scenario.

ACS Style

Julio Pérez-Sánchez; Patricia Jimeno-Sáez; Javier Senent-Aparicio; José María Díaz-Palmero; Juan De Dios Cabezas-Cerezo; Pérez- Sánchez; Jimeno- Sáez; Senent- Aparicio; Díaz- Palmero; De Dios Cabezas-Cerezo. Evolution of Burned Area in Forest Fires under Climate Change Conditions in Southern Spain Using ANN. Applied Sciences 2019, 9, 4155 .

AMA Style

Julio Pérez-Sánchez, Patricia Jimeno-Sáez, Javier Senent-Aparicio, José María Díaz-Palmero, Juan De Dios Cabezas-Cerezo, Pérez- Sánchez, Jimeno- Sáez, Senent- Aparicio, Díaz- Palmero, De Dios Cabezas-Cerezo. Evolution of Burned Area in Forest Fires under Climate Change Conditions in Southern Spain Using ANN. Applied Sciences. 2019; 9 (19):4155.

Chicago/Turabian Style

Julio Pérez-Sánchez; Patricia Jimeno-Sáez; Javier Senent-Aparicio; José María Díaz-Palmero; Juan De Dios Cabezas-Cerezo; Pérez- Sánchez; Jimeno- Sáez; Senent- Aparicio; Díaz- Palmero; De Dios Cabezas-Cerezo. 2019. "Evolution of Burned Area in Forest Fires under Climate Change Conditions in Southern Spain Using ANN." Applied Sciences 9, no. 19: 4155.

Journal article
Published: 14 September 2018 in Sustainability
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Climate change and the land-use and land-cover changes (LULC) resulting from anthropic activity are important factors in the degradation of an ecosystem and in the availability of a basin’s water resources. To know how these activities affect the quantity of the water resources of basins, such as the Segura River Basin, is of vital importance. In this work, the Soil and Water Assessment Tool (SWAT) was used for the study of the abovementioned impacts. The model was validated by obtaining a Nash–Sutcliffe efficiency (NSE) of 0.88 and a percent bias (PBIAS) of 17.23%, indicating that SWAT accurately replicated monthly streamflow. Next, land-use maps for the years of 1956 and 2007 were used to establish a series of scenarios that allowed us to evaluate the effects of these activities on both joint and individual water resources. A reforestation plan applied in the basin during the 1970s caused that the forest area had almost doubled, whereas the agricultural areas and shrubland had been reduced by one-third. These modifications, together with the effect of climate change, have led to a decrease of 26.3% in the quantity of generated water resources, not only due to climate change but also due to the increase in forest area.

ACS Style

Javier Senent-Aparicio; Sitian Liu; Julio Pérez-Sánchez; Adrián López-Ballesteros; Patricia Jimeno-Sáez. Assessing Impacts of Climate Variability and Reforestation Activities on Water Resources in the Headwaters of the Segura River Basin (SE Spain). Sustainability 2018, 10, 3277 .

AMA Style

Javier Senent-Aparicio, Sitian Liu, Julio Pérez-Sánchez, Adrián López-Ballesteros, Patricia Jimeno-Sáez. Assessing Impacts of Climate Variability and Reforestation Activities on Water Resources in the Headwaters of the Segura River Basin (SE Spain). Sustainability. 2018; 10 (9):3277.

Chicago/Turabian Style

Javier Senent-Aparicio; Sitian Liu; Julio Pérez-Sánchez; Adrián López-Ballesteros; Patricia Jimeno-Sáez. 2018. "Assessing Impacts of Climate Variability and Reforestation Activities on Water Resources in the Headwaters of the Segura River Basin (SE Spain)." Sustainability 10, no. 9: 3277.

Journal article
Published: 25 July 2018 in Biosystems Engineering
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A correct estimation of the instantaneous peak flow (IPF) is crucial to reducing the consequences of flash floods. An approach to estimate the IPF, obtained by combining Soil and Water Assessment Tool (SWAT) simulation and machine-learning models, was proposed and then verified by comparison with observation-based results in the Ladra river basin, northwest Spain. The SWAT model has been used to estimate the maximum mean daily flow (MMDF), and machine-learning models have been used to estimate the IPF based on MMDF. Four nonlinear time-series intelligence models, artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and extreme learning machine (ELM) were applied, and their results were compared. The Modified Nash-Sutcliffe efficiency coefficient (MNSE) and the index of agreement (d) were used to evaluate SWAT performance while simulating MMDF, and the coefficient of determination (R2) and the root mean square error (RMSE) were employed to evaluate the performance of these intelligent systems. According to the results, the SWAT hydrological model is a useful tool to simulate MMDF. Validation analyses resulted in values of statistical indexes (MNSE = 0.64 and d = 0.95). Regarding intelligent systems, the results show that they can be successfully used in predicting IPF, but ELM has demonstrated a superior ability to estimate IPF from the MMDF (R2 = 0.86 and RMSE = 48.59). The results of this study can contribute to predicting IPF in areas where sub-daily observational data are scarce, thereby reducing uncertainties associated with IPF estimations.

ACS Style

Javier Senent-Aparicio; Patricia Jimeno Sáez; Andrés Bueno-Crespo; Julio Pérez-Sánchez; David Pulido-Velázquez. Coupling machine-learning techniques with SWAT model for instantaneous peak flow prediction. Biosystems Engineering 2018, 177, 67 -77.

AMA Style

Javier Senent-Aparicio, Patricia Jimeno Sáez, Andrés Bueno-Crespo, Julio Pérez-Sánchez, David Pulido-Velázquez. Coupling machine-learning techniques with SWAT model for instantaneous peak flow prediction. Biosystems Engineering. 2018; 177 ():67-77.

Chicago/Turabian Style

Javier Senent-Aparicio; Patricia Jimeno Sáez; Andrés Bueno-Crespo; Julio Pérez-Sánchez; David Pulido-Velázquez. 2018. "Coupling machine-learning techniques with SWAT model for instantaneous peak flow prediction." Biosystems Engineering 177, no. : 67-77.

Journal article
Published: 11 February 2018 in Water
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Streamflow data are of prime importance to water-resources planning and management, and the accuracy of their estimation is very important for decision making. The Soil and Water Assessment Tool (SWAT) and Artificial Neural Network (ANN) models have been evaluated and compared to find a method to improve streamflow estimation. For a more complete evaluation, the accuracy and ability of these streamflow estimation models was also established separately based on their performance during different periods of flows using regional flow duration curves (FDCs). Specifically, the FDCs were divided into five sectors: very low, low, medium, high and very high flow. This segmentation of flow allows analysis of the model performance for every important discharge event precisely. In this study, the models were applied in two catchments in Peninsular Spain with contrasting climatic conditions: Atlantic and Mediterranean climates. The results indicate that SWAT and ANNs were generally good tools in daily streamflow modelling. However, SWAT was found to be more successful in relation to better simulation of lower flows, while ANNs were superior at estimating higher flows in all cases.

ACS Style

Patricia Jimeno-Sáez; Javier Senent-Aparicio; Julio Pérez-Sánchez; David Pulido-Velazquez. A Comparison of SWAT and ANN Models for Daily Runoff Simulation in Different Climatic Zones of Peninsular Spain. Water 2018, 10, 192 .

AMA Style

Patricia Jimeno-Sáez, Javier Senent-Aparicio, Julio Pérez-Sánchez, David Pulido-Velazquez. A Comparison of SWAT and ANN Models for Daily Runoff Simulation in Different Climatic Zones of Peninsular Spain. Water. 2018; 10 (2):192.

Chicago/Turabian Style

Patricia Jimeno-Sáez; Javier Senent-Aparicio; Julio Pérez-Sánchez; David Pulido-Velazquez. 2018. "A Comparison of SWAT and ANN Models for Daily Runoff Simulation in Different Climatic Zones of Peninsular Spain." Water 10, no. 2: 192.

Conference paper
Published: 02 June 2017 in Transactions on Petri Nets and Other Models of Concurrency XV
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ACS Style

Ramón Rueda-Delgado; Luis Gonzaga Baca Ruiz; Patricia Jimeno Sáez; Manuel Pegalajar Cuellar; David Pulido-Velazquez; Mª Carmen Pegalajar. Experimental Evaluation of Straight Line Programs for Hydrological Modelling with Exogenous Variables. Transactions on Petri Nets and Other Models of Concurrency XV 2017, 447 -458.

AMA Style

Ramón Rueda-Delgado, Luis Gonzaga Baca Ruiz, Patricia Jimeno Sáez, Manuel Pegalajar Cuellar, David Pulido-Velazquez, Mª Carmen Pegalajar. Experimental Evaluation of Straight Line Programs for Hydrological Modelling with Exogenous Variables. Transactions on Petri Nets and Other Models of Concurrency XV. 2017; ():447-458.

Chicago/Turabian Style

Ramón Rueda-Delgado; Luis Gonzaga Baca Ruiz; Patricia Jimeno Sáez; Manuel Pegalajar Cuellar; David Pulido-Velazquez; Mª Carmen Pegalajar. 2017. "Experimental Evaluation of Straight Line Programs for Hydrological Modelling with Exogenous Variables." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 447-458.

Journal article
Published: 15 May 2017 in Water
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The design of hydraulic structures and flood risk management is often based on instantaneous peak flow (IPF). However, available flow time series with high temporal resolution are scarce and of limited length. A correct estimation of the IPF is crucial to reducing the consequences derived from flash floods, especially in Mediterranean countries. In this study, empirical methods to estimate the IPF based on maximum mean daily flow (MMDF), artificial neural networks (ANN), and adaptive neuro-fuzzy inference system (ANFIS) have been compared. These methods have been applied in 14 different streamflow gauge stations covering the diversity of flashiness conditions found in Peninsular Spain. Root-mean-square error (RMSE), and coefficient of determination (R2) have been used as evaluation criteria. The results show that: (1) the Fuller equation and its regionalization is more accurate and has lower error compared with other empirical methods; and (2) ANFIS has demonstrated a superior ability to estimate IPF compared to any empirical formula.

ACS Style

Patricia Jimeno-Sáez; Javier Senent-Aparicio; Julio Pérez-Sánchez; David Pulido-Velazquez; José María Cecilia. Estimation of Instantaneous Peak Flow Using Machine-Learning Models and Empirical Formula in Peninsular Spain. Water 2017, 9, 347 .

AMA Style

Patricia Jimeno-Sáez, Javier Senent-Aparicio, Julio Pérez-Sánchez, David Pulido-Velazquez, José María Cecilia. Estimation of Instantaneous Peak Flow Using Machine-Learning Models and Empirical Formula in Peninsular Spain. Water. 2017; 9 (5):347.

Chicago/Turabian Style

Patricia Jimeno-Sáez; Javier Senent-Aparicio; Julio Pérez-Sánchez; David Pulido-Velazquez; José María Cecilia. 2017. "Estimation of Instantaneous Peak Flow Using Machine-Learning Models and Empirical Formula in Peninsular Spain." Water 9, no. 5: 347.