Pub Date : 2023-04-26DOI: 10.1080/02626667.2023.2192873
Marco Lompi, L. Mediero, E. Soriano, E. Caporali
ABSTRACT Climate change will likely increase the frequency and magnitude of extreme precipitation events and floods, increasing design peak flows that could lead to underestimates in current spillway capacity. Therefore, new methodologies for hydrological dam safety assessment considering climate change are required. This study presents a methodology that considers the impact of climate change on both inflow hydrographs and initial reservoir water levels. Moreover, the uncertainty in the procedure is assessed. The methodology is applied to the Eugui Dam in the River Arga catchment (Spain). An ensemble of 12 climate models is used. The results show an increase in the maximum reservoir water level during flood events and in the overtopping probability in the Representative Concentration Pathway 8.5 (RCP 8.5 scenario), especially in the 2071–2100 time window. The proposed methodology can be useful to assess future hydrological dam safety, fulfilling the requirements of recent regulations to consider the impact of climate change on dams.
{"title":"Climate change and hydrological dam safety: a stochastic methodology based on climate projections","authors":"Marco Lompi, L. Mediero, E. Soriano, E. Caporali","doi":"10.1080/02626667.2023.2192873","DOIUrl":"https://doi.org/10.1080/02626667.2023.2192873","url":null,"abstract":"ABSTRACT Climate change will likely increase the frequency and magnitude of extreme precipitation events and floods, increasing design peak flows that could lead to underestimates in current spillway capacity. Therefore, new methodologies for hydrological dam safety assessment considering climate change are required. This study presents a methodology that considers the impact of climate change on both inflow hydrographs and initial reservoir water levels. Moreover, the uncertainty in the procedure is assessed. The methodology is applied to the Eugui Dam in the River Arga catchment (Spain). An ensemble of 12 climate models is used. The results show an increase in the maximum reservoir water level during flood events and in the overtopping probability in the Representative Concentration Pathway 8.5 (RCP 8.5 scenario), especially in the 2071–2100 time window. The proposed methodology can be useful to assess future hydrological dam safety, fulfilling the requirements of recent regulations to consider the impact of climate change on dams.","PeriodicalId":55042,"journal":{"name":"Hydrological Sciences Journal-Journal Des Sciences Hydrologiques","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48259040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-24DOI: 10.1080/02626667.2023.2206032
R. Araki, Y. Mu, H. McMillan
ABSTRACT We evaluated the Global Land Data Assimilation System surface soil moisture product (GLDAS v. 2.1) against in situ soil moisture networks in arid climates in Australia and the United States, using common statistical metrics and seasonality metrics. Our results showed that GLDAS performed well (root mean square error (RMSE) = 0.100 m3/m3; unbiased RMSE (ubRMSE) = 0.060 m3/m3; correlation coefficient (R) = 0.555 on average) but systematically overestimated the soil moisture values (Bias = 0.067 m3/m3). The performance was better in Australian Oznet and the U.S. Climate Reference Network (USCRN), compared to the US Soil Climate Analysis Network (SCAN) network. In terms of seasonality, GLDAS soil moisture seasons were biased to start earlier; on average, drying and wetting transitions started 28 and 16 days earlier than in situ data, respectively. The end dates of GLDAS seasonal transitions showed good agreement with in situ data; the errors in transition timings were limited to within a week. This tendency is stronger in the US networks compared to the Australian network.
{"title":"Evaluation of GLDAS soil moisture seasonality in arid climates","authors":"R. Araki, Y. Mu, H. McMillan","doi":"10.1080/02626667.2023.2206032","DOIUrl":"https://doi.org/10.1080/02626667.2023.2206032","url":null,"abstract":"ABSTRACT We evaluated the Global Land Data Assimilation System surface soil moisture product (GLDAS v. 2.1) against in situ soil moisture networks in arid climates in Australia and the United States, using common statistical metrics and seasonality metrics. Our results showed that GLDAS performed well (root mean square error (RMSE) = 0.100 m3/m3; unbiased RMSE (ubRMSE) = 0.060 m3/m3; correlation coefficient (R) = 0.555 on average) but systematically overestimated the soil moisture values (Bias = 0.067 m3/m3). The performance was better in Australian Oznet and the U.S. Climate Reference Network (USCRN), compared to the US Soil Climate Analysis Network (SCAN) network. In terms of seasonality, GLDAS soil moisture seasons were biased to start earlier; on average, drying and wetting transitions started 28 and 16 days earlier than in situ data, respectively. The end dates of GLDAS seasonal transitions showed good agreement with in situ data; the errors in transition timings were limited to within a week. This tendency is stronger in the US networks compared to the Australian network.","PeriodicalId":55042,"journal":{"name":"Hydrological Sciences Journal-Journal Des Sciences Hydrologiques","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41811807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-24DOI: 10.1080/02626667.2023.2193700
Shaini Naha, M. Rico‐Ramirez, R. Rosolem
ABSTRACT Global environmental changes are likely to have a large impact on water resources across the developing countries. However, most of these countries suffer from acute shortage of local data. New high-resolution global products are available, which can be integrated with large-scale hydrological models. Most of these global products, however, are rarely evaluated in developing regions. We propose a thorough evaluation of five new global products in Mahanadi River basin in India. We employ the variable infiltration capacity (VIC) model over the basin and perform model experiments to directly evaluate the impacts of the specific combinations of local and global datasets. Results suggest that the reference experiment, which uses all local datasets, most closely represents the observed discharge. However, some of the global datasets could be used as a viable alternative to local observations in this river basin and potentially in nearby basins where there is a lack of observations.
{"title":"Impact of local versus global datasets on hydrological responses in Mahanadi River basin in India","authors":"Shaini Naha, M. Rico‐Ramirez, R. Rosolem","doi":"10.1080/02626667.2023.2193700","DOIUrl":"https://doi.org/10.1080/02626667.2023.2193700","url":null,"abstract":"ABSTRACT Global environmental changes are likely to have a large impact on water resources across the developing countries. However, most of these countries suffer from acute shortage of local data. New high-resolution global products are available, which can be integrated with large-scale hydrological models. Most of these global products, however, are rarely evaluated in developing regions. We propose a thorough evaluation of five new global products in Mahanadi River basin in India. We employ the variable infiltration capacity (VIC) model over the basin and perform model experiments to directly evaluate the impacts of the specific combinations of local and global datasets. Results suggest that the reference experiment, which uses all local datasets, most closely represents the observed discharge. However, some of the global datasets could be used as a viable alternative to local observations in this river basin and potentially in nearby basins where there is a lack of observations.","PeriodicalId":55042,"journal":{"name":"Hydrological Sciences Journal-Journal Des Sciences Hydrologiques","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44178410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-20DOI: 10.1080/02626667.2023.2171797
M. Tabarmayeh, M. Zarei, F. Jaramillo, O. Batelaan
ABSTRACT Groundwater resources are the most reliable freshwater supply in arid regions where many aquifers face dramatic depletion due to natural and anthropogenic causes. The annual average rate of decline of groundwater level is about 1.65 m. This research focuses on an aquifer that suffers from severe groundwater stress, and it aims to identify the main causes of the stress and to understand the effects of climate change and human activity. Monthly data on groundwater level, precipitation, temperature, river discharge, evapotranspiration, soil moisture, and vegetation cover were collected from 2000 to 2020. The results indicate that declining groundwater levels mainly resulted from the expansion of vegetation cover rather than changes in hydro-climatic variables. Finally, this work highlights how significant financial investment in improving irrigation efficiency in the absence of socio-economic plans, education, awareness, and monitoring programmes unproductively resulted in the expansion of agricultural activities rather than preserving groundwater storage.
{"title":"Identifying the main factors driving groundwater stress in a semi-arid region, southern Iran","authors":"M. Tabarmayeh, M. Zarei, F. Jaramillo, O. Batelaan","doi":"10.1080/02626667.2023.2171797","DOIUrl":"https://doi.org/10.1080/02626667.2023.2171797","url":null,"abstract":"ABSTRACT Groundwater resources are the most reliable freshwater supply in arid regions where many aquifers face dramatic depletion due to natural and anthropogenic causes. The annual average rate of decline of groundwater level is about 1.65 m. This research focuses on an aquifer that suffers from severe groundwater stress, and it aims to identify the main causes of the stress and to understand the effects of climate change and human activity. Monthly data on groundwater level, precipitation, temperature, river discharge, evapotranspiration, soil moisture, and vegetation cover were collected from 2000 to 2020. The results indicate that declining groundwater levels mainly resulted from the expansion of vegetation cover rather than changes in hydro-climatic variables. Finally, this work highlights how significant financial investment in improving irrigation efficiency in the absence of socio-economic plans, education, awareness, and monitoring programmes unproductively resulted in the expansion of agricultural activities rather than preserving groundwater storage.","PeriodicalId":55042,"journal":{"name":"Hydrological Sciences Journal-Journal Des Sciences Hydrologiques","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48824527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-20DOI: 10.1080/02626667.2023.2203824
Dilek Sabancı, K. Yurekli, Mehmet Murat Comert, Serhat Kılıçarslan, Müberra Erdoğan
ABSTRACT This paper aimed to estimate the reference evapotranspiration (ET0) due to some limitations of the Food and Agriculture Organization-56 Penman-Monteith (FAO 56-PM) approach by using five alternative machine learning models. The study makes an important contribution to the ET0 estimation success for of the ET0 of 12 stations with variable climate characteristics in the Central Anatolian Region (CAR). The performances of the models were compared with the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) metrics that are frequently cited in the literature, and also with the performance index (PI). Long short-term memory (LSTM), artificial neural networks (ANN), and multivariate adaptive regression splines (MARS) models provided the best performance in eight, three, and one stations, respectively. The R2, MAE, RMSE, and PI values of the selected models from each station vary in the range of 0.987-0.999, 1.948-4.567, 2.671-6.659, and 1.544-4.018, respectively.
{"title":"Predicting reference evapotranspiration based on hydro-climatic variables: comparison of different machine learning models","authors":"Dilek Sabancı, K. Yurekli, Mehmet Murat Comert, Serhat Kılıçarslan, Müberra Erdoğan","doi":"10.1080/02626667.2023.2203824","DOIUrl":"https://doi.org/10.1080/02626667.2023.2203824","url":null,"abstract":"ABSTRACT This paper aimed to estimate the reference evapotranspiration (ET0) due to some limitations of the Food and Agriculture Organization-56 Penman-Monteith (FAO 56-PM) approach by using five alternative machine learning models. The study makes an important contribution to the ET0 estimation success for of the ET0 of 12 stations with variable climate characteristics in the Central Anatolian Region (CAR). The performances of the models were compared with the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) metrics that are frequently cited in the literature, and also with the performance index (PI). Long short-term memory (LSTM), artificial neural networks (ANN), and multivariate adaptive regression splines (MARS) models provided the best performance in eight, three, and one stations, respectively. The R2, MAE, RMSE, and PI values of the selected models from each station vary in the range of 0.987-0.999, 1.948-4.567, 2.671-6.659, and 1.544-4.018, respectively.","PeriodicalId":55042,"journal":{"name":"Hydrological Sciences Journal-Journal Des Sciences Hydrologiques","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43643524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-19DOI: 10.1080/02626667.2023.2201450
C. Chadwick, J. Gironás, Fernando González-Leiva, Sebastián Aedo
ABSTRACT Standard quantile mapping (QM) performs well, as a bias adjustment method, in removing historical climate biases, but it can significantly alter a global climate model (GCM) signal. Methods that do incorporate GCM changes commonly consider mean changes only. Quantile delta mapping (QDM) is an exception, as it explicitly preserves relative changes in the quantiles, but it might present biases in preserving GCMs changes in standard deviation. In this work we propose the unbiased quantile mapping (UQM) method, which by construction preserves GCM changes of the mean and the standard deviation. Synthetic experiments and four Chilean locations are used to compare the performance of UQM against QDM, QM, detrended quantile mapping, and scale distribution mapping. All the methods outperform QM, but a tradeoff exists between preserving the GCM relative changes in the quantiles (QDM is recommended in this case), or changes in the GCM moments (UQM is recommended in this case).
{"title":"Bias adjustment to preserve changes in variability: the unbiased mapping of GCM changes","authors":"C. Chadwick, J. Gironás, Fernando González-Leiva, Sebastián Aedo","doi":"10.1080/02626667.2023.2201450","DOIUrl":"https://doi.org/10.1080/02626667.2023.2201450","url":null,"abstract":"ABSTRACT Standard quantile mapping (QM) performs well, as a bias adjustment method, in removing historical climate biases, but it can significantly alter a global climate model (GCM) signal. Methods that do incorporate GCM changes commonly consider mean changes only. Quantile delta mapping (QDM) is an exception, as it explicitly preserves relative changes in the quantiles, but it might present biases in preserving GCMs changes in standard deviation. In this work we propose the unbiased quantile mapping (UQM) method, which by construction preserves GCM changes of the mean and the standard deviation. Synthetic experiments and four Chilean locations are used to compare the performance of UQM against QDM, QM, detrended quantile mapping, and scale distribution mapping. All the methods outperform QM, but a tradeoff exists between preserving the GCM relative changes in the quantiles (QDM is recommended in this case), or changes in the GCM moments (UQM is recommended in this case).","PeriodicalId":55042,"journal":{"name":"Hydrological Sciences Journal-Journal Des Sciences Hydrologiques","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44358641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-18DOI: 10.1080/02626667.2023.2200143
Santiago Zazo, José-Luis Molina, H. Macian-Sorribes, M. Pulido‐Velazquez
ABSTRACT This research assesses the predictive capacity of Bayesian causality through causal reasoning (CR), which has been successfully applied to the study of reservoir inflows. We combined autoregressive development with a causal modelling approach through a “proof of concept” that assesses the predictive capacity of the approach. The analytical power of CR revealed the logical temporal structure that defines the general behaviour of inflows, which was latent in the historical records. This allowed identifying/quantifying, through a dependence matrix, two temporal runoff fractions, one due to time and the other not. Finally, a predictive model for each temporal fraction was implemented, evaluating its forecasting skills through mean absolute error and root mean square error. This was applied to the reservoirs that supply water to the city of Ávila (Spain), whose watersheds present strong independent temporal behaviour. These results open new possibilities for developing predictive hydrological models within a CR modelling framework.
{"title":"Assessment of the predictability of inflow to reservoirs through Bayesian causality","authors":"Santiago Zazo, José-Luis Molina, H. Macian-Sorribes, M. Pulido‐Velazquez","doi":"10.1080/02626667.2023.2200143","DOIUrl":"https://doi.org/10.1080/02626667.2023.2200143","url":null,"abstract":"ABSTRACT This research assesses the predictive capacity of Bayesian causality through causal reasoning (CR), which has been successfully applied to the study of reservoir inflows. We combined autoregressive development with a causal modelling approach through a “proof of concept” that assesses the predictive capacity of the approach. The analytical power of CR revealed the logical temporal structure that defines the general behaviour of inflows, which was latent in the historical records. This allowed identifying/quantifying, through a dependence matrix, two temporal runoff fractions, one due to time and the other not. Finally, a predictive model for each temporal fraction was implemented, evaluating its forecasting skills through mean absolute error and root mean square error. This was applied to the reservoirs that supply water to the city of Ávila (Spain), whose watersheds present strong independent temporal behaviour. These results open new possibilities for developing predictive hydrological models within a CR modelling framework.","PeriodicalId":55042,"journal":{"name":"Hydrological Sciences Journal-Journal Des Sciences Hydrologiques","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46480499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-18DOI: 10.1080/02626667.2023.2201449
T. Skaugen, Anne Ellekjær Stavang, D. Lawrence, K. Møen
ABSTRACT In this study we explore how varying the river network (RN) density affects the distribution of hillslope to RN distances, the subsurface water celerities and hence the response times. Eleven Norwegian catchments (with areas of 0.007 to 500 km2) were used for the analysis, and the Distance Distribution Dynamics (DDD) hydrological model was calibrated for each catchment and RN. Equally good Kling-Gupta efficiency scores suggest a degree of equifinality in that many constellations of RNs and subsurface celerities have equally good model performance. All catchments display a linear relationship between the calibrated mean subsurface water celerity and mean hillslope to RN distance, consistent with a constant mean response time (MRT). The MRTs vary from 1 to 49 days for the different catchments and agree with MRT estimated from recession analysis and regionalized through regression and catchment characteristics. The latter aids in the estimation of model parameters for ungauged basins.
{"title":"Catchment response times – understanding runoff dynamics from catchment distances and celerities","authors":"T. Skaugen, Anne Ellekjær Stavang, D. Lawrence, K. Møen","doi":"10.1080/02626667.2023.2201449","DOIUrl":"https://doi.org/10.1080/02626667.2023.2201449","url":null,"abstract":"ABSTRACT In this study we explore how varying the river network (RN) density affects the distribution of hillslope to RN distances, the subsurface water celerities and hence the response times. Eleven Norwegian catchments (with areas of 0.007 to 500 km2) were used for the analysis, and the Distance Distribution Dynamics (DDD) hydrological model was calibrated for each catchment and RN. Equally good Kling-Gupta efficiency scores suggest a degree of equifinality in that many constellations of RNs and subsurface celerities have equally good model performance. All catchments display a linear relationship between the calibrated mean subsurface water celerity and mean hillslope to RN distance, consistent with a constant mean response time (MRT). The MRTs vary from 1 to 49 days for the different catchments and agree with MRT estimated from recession analysis and regionalized through regression and catchment characteristics. The latter aids in the estimation of model parameters for ungauged basins.","PeriodicalId":55042,"journal":{"name":"Hydrological Sciences Journal-Journal Des Sciences Hydrologiques","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59280443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-18DOI: 10.1080/02626667.2023.2203825
M. Bordbar, M. Nikoo, Ahmad Sana, Banafsheh Nematollahi, G. Al-Rawas, A. Gandomi
ABSTRACT This study introduced an innovative hybrid framework using statistical-based, multi-attribute decision-making (MADM), and multi-objective optimization methods to assess the vulnerability of the Oman's Al-Khoud coastal aquifer without temporal variations.. Firstly, an extra parameter, bedrock topography (BT), was added to a commonly used index model, GALDIT and the parameter of aquifer type was removed from the model. Also, the random forest (RF) method was used to define the relative importance of parameters. Then, both frequency ratio (FR) and stepwise weight assessment ratio analysis (SWARA) methods were applied to modify the GALDIT rates. The GALDIT weights were optimized using the non-dominated sorting genetic algorithm-II (NSGA-II). Finally, the coastal aquifer vulnerability index (CAVI) model was obtained based on the hybrid FR-SWARA and NSGA-II models. The CAVI vulnerability map indicated high vulnerability in the Northern aquifer areas. Furthermore, the Spearman correlation coefficient between the CAVI and total dissolved solids (TDS) obtained 0.78.
本研究引入了一个创新的混合框架，利用基于统计的多属性决策(MADM)和多目标优化方法来评估阿曼Al-Khoud沿海含水层的脆弱性，而不考虑时间变化。首先，在常用的指数模型GALDIT中增加基岩地形(BT)参数，去掉含水层类型参数;同时，采用随机森林(RF)方法定义参数的相对重要性。然后，采用频率比(FR)和逐步权重评估比分析(SWARA)方法对GALDIT率进行修正。采用非支配排序遗传算法- ii (NSGA-II)对GALDIT权重进行优化。最后，基于FR-SWARA和NSGA-II混合模型建立了沿海含水层脆弱性指数(CAVI)模型。CAVI脆弱性图显示北部含水层的脆弱性较高。CAVI与总溶解固形物(TDS)的Spearman相关系数为0.78。
{"title":"Assessment of the vulnerability of hybrid coastal aquifers: application of multi-attribute decision-making and optimization models","authors":"M. Bordbar, M. Nikoo, Ahmad Sana, Banafsheh Nematollahi, G. Al-Rawas, A. Gandomi","doi":"10.1080/02626667.2023.2203825","DOIUrl":"https://doi.org/10.1080/02626667.2023.2203825","url":null,"abstract":"ABSTRACT This study introduced an innovative hybrid framework using statistical-based, multi-attribute decision-making (MADM), and multi-objective optimization methods to assess the vulnerability of the Oman's Al-Khoud coastal aquifer without temporal variations.. Firstly, an extra parameter, bedrock topography (BT), was added to a commonly used index model, GALDIT and the parameter of aquifer type was removed from the model. Also, the random forest (RF) method was used to define the relative importance of parameters. Then, both frequency ratio (FR) and stepwise weight assessment ratio analysis (SWARA) methods were applied to modify the GALDIT rates. The GALDIT weights were optimized using the non-dominated sorting genetic algorithm-II (NSGA-II). Finally, the coastal aquifer vulnerability index (CAVI) model was obtained based on the hybrid FR-SWARA and NSGA-II models. The CAVI vulnerability map indicated high vulnerability in the Northern aquifer areas. Furthermore, the Spearman correlation coefficient between the CAVI and total dissolved solids (TDS) obtained 0.78.","PeriodicalId":55042,"journal":{"name":"Hydrological Sciences Journal-Journal Des Sciences Hydrologiques","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47421403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-18DOI: 10.1080/02626667.2023.2203322
Alexis Jeantet, G. Thirel, Thibault Lemaitre-Basset, J. Tournebize
ABSTRACT Analysis of the uncertainty propagation along a hydroclimatic modelling chain has been performed by few studies to date on subsurface drainage hydrology. We performed such an analysis in a representative French drainage site. A set of 30 climate projections provided future climatic conditions for three representative concentration pathways (RCPs): RCP2.6, RCP4.5, and RCP8.5. Three hydrological models for drainage systems, MACRO, DRAINMOD for DRAINage MODel, and SIDRA-RU for “SImulation du DRAinage - Réserve Utile” in French, on the three different parameter sets were used to quantify uncertainties from hydrological components. Results showed that the RCP contribution to total uncertainty reaches almost 40% for air temperature, does not exceed 15% for precipitation, and is almost negligible for hydrological indicators (HIs). The main source of uncertainty comes from the climate models, representing 50–90% of the total uncertainty. The contribution of the hydrological components (models and parameter sets) to the HI uncertainty is almost negligible too, not exceeding 5%.
{"title":"Uncertainty propagation in a modelling chain of climate change impact for a representative French drainage site","authors":"Alexis Jeantet, G. Thirel, Thibault Lemaitre-Basset, J. Tournebize","doi":"10.1080/02626667.2023.2203322","DOIUrl":"https://doi.org/10.1080/02626667.2023.2203322","url":null,"abstract":"ABSTRACT Analysis of the uncertainty propagation along a hydroclimatic modelling chain has been performed by few studies to date on subsurface drainage hydrology. We performed such an analysis in a representative French drainage site. A set of 30 climate projections provided future climatic conditions for three representative concentration pathways (RCPs): RCP2.6, RCP4.5, and RCP8.5. Three hydrological models for drainage systems, MACRO, DRAINMOD for DRAINage MODel, and SIDRA-RU for “SImulation du DRAinage - Réserve Utile” in French, on the three different parameter sets were used to quantify uncertainties from hydrological components. Results showed that the RCP contribution to total uncertainty reaches almost 40% for air temperature, does not exceed 15% for precipitation, and is almost negligible for hydrological indicators (HIs). The main source of uncertainty comes from the climate models, representing 50–90% of the total uncertainty. The contribution of the hydrological components (models and parameter sets) to the HI uncertainty is almost negligible too, not exceeding 5%.","PeriodicalId":55042,"journal":{"name":"Hydrological Sciences Journal-Journal Des Sciences Hydrologiques","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46537486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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