Groundwater Springs Research Articles

Groundwater spring potential assessment using new ensemble data mining techniques

The growing demand and exigency for groundwater resources warrant the demarcation of groundwater spring potential zones (GSPZ) for effective sustainable strategy in groundwater identification, conservation, and management. Here we utilized a novel data mining (DM) ensemble to generate groundwater spring potential maps (GSPMs) by combining RF-BRT (random forest-boosted regression tree), MARS-SVM (multivariate adaptive regression spline-support vector machine) and, FDA-GLM-MDA (functional data analysis-generalized linear model-mixture discriminant analysis). Initially, an aggregate of 1726 groundwater spring locations was collected from the regional water company of Tehran Province and field investigation, in which 1208 springs (70%) were taken for training purposes and the remaining 518 (30%) springs were applied for the validation process. Twelve conditioning factors including DEM (digital elevation model)/elevation, fault density, aspect, rainfall, distance from rivers, distance from faults, slope, MRVBF (multiresolution index of valley bottom flatness), TWI (topographic wetness index), lithology, land use/land cover, and permeability were utilized for mapping process and their importance in predicting the groundwater spring potential. The variable importance (VI) analysis using SVM (support vector machine) reveals that the most significant conditioning factors in the prediction process are rainfall, TWI, DEM-elevation, distance from rivers, slope, distance from faults, and MRVBF. The GSPMs generated from novel data-mining (DM) ensembles were validated using the cut-off reliant (recall, fallout, F-measure, accuracy, precision, specificity, TSS: true skill statistic, Cohen’s kappa, fourfold plot, CCI: corrected classified instances) and cut-off independent (ROC-AUC: receiver operating characteristic-area under the curve) measures. The outcome of the validation measures shows that RF-BRT has the superior values of recall, F-measure, overall accuracy, precision, specificity, TSS, Cohen’s kappa, fourfold plot, CCI followed by MARS-SVM, and FDA-GLM-MDA whereas the AUC value of RF-BRT (0.955), MARS-SVM (0.934), and FDA-GLM-MDA (0.914) also display similar result. The GSPMs generated using the novel DM ensemble models in our study can be utilized by policymakers in implementing the strategies for effective land use planning and sustainable groundwater management.

Fungal communities in groundwater springs along the volcanic zone of Iceland

ABSTRACT Aquatic fungi are a largely unexplored group of organisms with a still unknown diversity of ecological niches. Groundwater biomes comprise vast but poorly explored habitats. In this study, we sampled the Icelandic groundwater, a unique system that has been separated and isolated into distinct basins for millions of years due to volcanic activity. The aim was to explore the fungal diversity of the basins and its connection with the surface waters. We screened the DNA-based fungal diversity of 11 Icelandic groundwater springs and their emerging surface waters by ITS metabarcoding, revealing that the species turnover was indeed distinct for each groundwater basin. Furthermore, the groundwater taxa, which include many psychrophilic yeast-like and ascomycete fungi, seem to serve as a constant inoculum for the surface water. Nevertheless, the groundwater springs had a lower sequence proportion of early diverging, primary aquatic fungal lineages (10%) and completely unknown lineages (13%) than the surface waters (41% and 22%, respectively), likely explained by the lower diversity of suitable hosts for these presumed parasitic lineages. Our results highlight the importance of overlooked organism groups in the resolution of overarching research questions in ecology, nutrient circulation, and global change biology at large.

Spatial mapping of groundwater springs potentiality using grid search-based and genetic algorithm-based support vector regression

In this study, groundwater springs potentiality maps were prepared using a novel integrated model, support vector regression (SVR) with genetic algorithm (GA), for the Jerash and Ajloun region, Jordan.The conditioning factors such as altitude, aspect, slope angle, plan curvature, stream power index, topographic wetness index, length of slope, distance from drainage network, lithology, distance from faults, land use and normalised difference vegetation index were considered to map the groundwater spring potentiality. GA was used for two purposes. First, GA was used to optimize the hyper-parameters of the radial basis function (RBF) kernel of SVR model. Second, GA in combination with SVR was used as feature selection (FS).The results of these models were compared with common grid search (GS) method used in most of the studies. The GS method was employed to calculate the parameters related to the SVR model and also hyper-parameters of RBF kernel. The results show optimum values of the kernels in the SVR model and selecting the optimal features which have the most contribution in modeling were the major steps in modeling and also in achieving a desirable precision level.

Seasonal macrophyte growth constrains extent, but improves quality, of cold‐water habitat in a spring‐fed river

AbstractRooted aquatic macrophytes affect abiotic conditions in low‐gradient rivers by altering channel hydraulics, consuming biologically available nutrients, controlling sediment transport and deposition, and shading the water surface. Due to seasonal macrophyte growth and senescence, the magnitude of these effects may vary temporally. Seasonal changes in aquatic macrophyte biomass, channel roughness and flow velocity, were quantified and trends were related to spatiotemporal patterns in water temperature in a low‐gradient, spring‐fed river downstream from high‐volume, constant‐temperature groundwater springs. Between spring and summer, a nearly threefold increase in macrophyte biomass was positively correlated with channel roughness and inversely related to flow velocity. On average, flow velocity declined by 34% during the study period, and channel roughness increased 63% (from 0.064 to 0.104). During the spring and fall period, the location of a minimum water temperature variability “node” migrated upstream more than 4 km, whereas daily maximum water temperature cooled by 2–3°C. Water temperature modelling shows that the longitudinal extent of cold‐water habitat was shortened due to increased channel roughness independent of seasonal surface water diversions. These results suggest that macrophyte growth mediates spatiotemporal patterns of water temperature, constraining available cold‐water habitat while simultaneously improving its quality. Understanding complex spatial and temporal dynamics between macrophyte growth and water temperature is critical to developing regulatory standards reflective of naturally occurring variability and has important implications for the management and conservation of cold‐water biota.

Evaluating the usage of tree-based ensemble methods in groundwater spring potential mapping

The main goal of the study was to estimate the efficiency and predictive ability of a series of tree-based ensemble methods in groundwater spring potential mapping. The study introduces a novel hybrid integration approach based on J48 Decision Trees (J48), AdaBoost (AB), Bagging (Bag), RandomSubSpace (RS), Dagging (Dag) and Rotation Forest (RF) algorithms for constructing a groundwater spring potential map. The performance of the ensemble models were evaluated at the Wuqi County in Shaanxi Province, China. At first, a groundwater spring inventory map with 235 groundwater springs was constructed. The groundwater spring database was randomly divided into a training (70% of the total number of groundwater springs) and validating subset (remaining 30%). Secondly, sixteen groundwater spring related variables were selected and analyzed, such as elevation, slope, aspect, planform and profile curvature, curvature, topographic wetness index, stream transport and stream power indexes, distance from river network, lithology, soil and landuse cover, normalized difference vegetation index, mean annual rainfall and distance from road network. The contribution of each groundwater spring related variable so as to identify the most predictive was based on the correlation attribute evaluation method. The predictive performance of each model was evaluated by estimating the area under the receiver operating characteristic (ROC) curve (AUC) and several statistical indexes (accuracy, sensitivity, specificity). The outcomes revealed that all models had a good predictive performance, having AUC values greater than 0.74. Specifically, RF-J48 model appeared with the highest prediction capability (AUC = 0.797), followed by the AB-J48 model (0.793), the Bag-J48 (0.768), the RS-J48 (0.766) and the Dag-J48 (0.748), respectively. The study highlights that the proposed ensemble methodology is efficient and highly accurate appropriate for groundwater spring potential mapping.

Groundwater Spring Potential Mapping Using Artificial Intelligence Approach Based on Kernel Logistic Regression, Random Forest, and Alternating Decision Tree Models

This study presents a methodology for constructing groundwater spring potential maps by kernel logistic regression, (KLR), random forest (RF), and alternating decision tree (ADTree) models. The analysis was based on data concerning groundwater springs and fourteen explanatory factors (elevation, slope, aspect, plan curvature, profile curvature, stream power index, sediment transport index, topographic wetness index, distance to streams, distance to roads, normalized difference vegetation index (NDVI), lithology, soil, and land use), which were divided into training and validation datasets. Ningtiaota region in the northern territory of Shaanxi Province, China, was considered as a test site. Frequency Ratio method was applied to provide to each factor’s class a coefficient weight, whereas the linear support vector machine method was used as a feature selection method to determine the optimal set of factors. The Receiver Operating Characteristic curve and the area under the curve (AUC) were used to evaluate the performance of each model using the training dataset, with the RF model providing the highest AUC value (0.909) followed by the KLR (0.877) and ADTree (0.812) models. The same performance pattern was estimated based on the validation dataset, with the RF model providing the highest AUC value (0.811) followed by the KLR (0.797) and ADTree (0.773) models. This study highlights that the artificial intelligence approach could be considered as a valid and accurate approach for groundwater spring potential zoning.

Static and dynamic approach to estimation of spring water hardness cation content as a natural factor of groundwater formation

To assess the degree of influence of natural factors on the groundwater mineralization, it is proposed to simultaneously consider in a static and dynamic position the results of titrimetric determination of the total mineralization and concentration of calcium and magnesium ions in spring water. This is shown by the example of four springs located in the territory of Kaluga. The study made it possible to establish: the average annual background concentrations, as well as to justify the relative variable for groundwater in springs No. 1, No. 3 and No. 4 and the influence of the natural factor on the formation of the total hardness and concentration of calcium and magnesium ions in the study period, relatively stable for groundwater in spring No. 2 by the value of the molar ratio of the concentrations of calcium and magnesium ions.

Mapping of Water Resistivity Using Pool Water

The relationship between the resistivity (conductivity) of soil and that of pore water was stated in papers by G.E. Archie and V.N. Dakhnov. This means that water resistivity should be studied at each fieldwork area. In some areas, however, the access to groundwater (boreholes, wells, and springs) as well as to the surface reservoirs (rivers, brooks, ponds, and lakes) is not possible or is restricted. Can we measure water resistivity in pools? Immediately after a rain such water has no relationship with soil resistivity. The purpose of this work was to study the pattern of ion exchange between soil and rain water over time. In (Brunet et al., 2010) the changes in resistivity of water in contact with soil over time were measured. We have verified their results.

The thermal adaptation of the proteome

Genomics Climate change is likely to cause new and extreme environmental conditions, and we have little understanding of how species will cope. Caddisfly larvae live wrapped in protective cases of grit or fibers among the stones of fast-flowing freshwater streams. Ebner et al. examined the proteome of the northern European caddisfly Crunoecia irrorata when raised at different temperatures between 10° and 20°C. This species normally lives in the steady environment of groundwater springs, but this study shows that it appears to have compensatory mechanisms that respond to warming. The authors found that differential expression of proteins with functional annotation in candidate thermoregulatory systems increased as temperature increased. It seems that C. irrorata has a higher tolerance to temperature variation than previously thought. Molec. Ecol. 10.1111/mec.15225 (2019).

Bacterial diversity in Icelandic cold spring sources and in relation to the groundwater amphipod Crangonyx islandicus.

Crangonyx islandicus is a groundwater amphipod endemic to Iceland, considered to have survived the Ice Ages in subglacial refugia. Currently the species is found in spring sources in lava fields along the tectonic plate boundary of the country. The discovery of a groundwater species in this inaccessible habitat indicates a hidden ecosystem possibly based on chemoautotrophic microorganisms as primary producers. To explore this spring ecosystem, we assessed its microbial diversity and analysed whether and how the diversity varied between the amphipods and the spring water, and if was dependent on environmental factors and geological settings. Isolated DNA from spring water and from amphipods was analysed using metabarcoding methods, targeting the 16S rRNA gene. Two genera of bacteria, Halomonas and Shewanella were dominating in the amphipod samples in terms of relative abundance, but not in the groundwater samples where Flavobacterium, Pseudomonas and Alkanindiges among others were dominating. The richness of the bacteria taxa in the microbial community of the groundwater spring sources was shaped by pH level and the beta diversity was shaped by geographic locations.

A Hybrid Computational Intelligence Approach to Groundwater Spring Potential Mapping

This study proposes a hybrid computational intelligence model that is a combination of alternating decision tree (ADTree) classifier and AdaBoost (AB) ensemble, namely “AB–ADTree”, for groundwater spring potential mapping (GSPM) at the Chilgazi watershed in the Kurdistan province, Iran. Although ADTree and its ensembles have been widely used for environmental and ecological modeling, they have rarely been applied to GSPM. To that end, a groundwater spring inventory map and thirteen conditioning factors tested by the chi-square attribute evaluation (CSAE) technique were used to generate training and testing datasets for constructing and validating the proposed model. The performance of the proposed model was evaluated using statistical-index-based measures, such as positive predictive value (PPV), negative predictive value (NPV), sensitivity, specificity accuracy, root mean square error (RMSE), and the area under the receiver operating characteristic (ROC) curve (AUROC). The proposed hybrid model was also compared with five state-of-the-art benchmark soft computing models, including single ADTree, support vector machine (SVM), stochastic gradient descent (SGD), logistic model tree (LMT), logistic regression (LR), and random forest (RF). Results indicate that the proposed hybrid model significantly improved the predictive capability of the ADTree-based classifier (AUROC = 0.789). In addition, it was found that the hybrid model, AB–ADTree, (AUROC = 0.815), had the highest goodness-of-fit and prediction accuracy, followed by the LMT (AUROC = 0.803), RF (AUC = 0.803), SGD, and SVM (AUROC = 0.790) models. Indeed, this model is a powerful and robust technique for mapping of groundwater spring potential in the study area. Therefore, the proposed model is a promising tool to help planners, decision makers, managers, and governments in the management and planning of groundwater resources.

Stable isotope constraints on hydrostratigraphy and aquifer connectivity in the Table Mountain Group

Abstract The Table Mountain Group is a folded, faulted, quartzite-dominated sedimentary sequence, metamorphosed to lower greenschist facies, that forms steep mountains dominating the topography of the Western Cape and causing orographic rainfall in an otherwise semi-arid region. These quartzites are highly fractured to depths of kilometres and act as a complex aquifer system that supplies groundwater directly and indirectly, through baseflow, essential for sustaining the natural environment and human activity in the region. Hydrogen and oxygen isotope data for rain, rivers and groundwater (boreholes and springs) in the region give typical altitude effects of -1.8‰ δD/100 m and -0.33‰ δ18O/100 m, and a very strong continental effect of -30‰ δD/100 km and -4.7‰ δ18O/100 km. This allows for application of stable isotopes as natural hydrological tracers. Groundwater at several locations had stable isotope compositions different from ambient rainfall, but similar to rainfall at high altitudes in adjacent mountains, indicating recharge at high altitude. The groundwater flow is through the Skurweberg Aquifer, here defined as all three formations of the Nardouw Subgroup. Observations on the Peninsula Aquifer suggest a very well mixed aquifer, due to extensive fracturing. Potential exists to delineate groundwater protection zones, detect overabstraction and understand aquifer connectivity better by applying stable isotope hydrology to the Table Mountain Group.

Disentangling spatio-temporal drivers influencing benthic communities in temporary streams

Temporary streams on the island of Mallorca support unique fauna that differ from other temporary streams on the mainland. It comprises many poor-dispersing endemics as well as more ubiquitous species with better dispersal abilities. To determine the importance of spatial and temporal drivers, this study assesses on the composition and biological traits of macroinvertebrate assemblages, and their variation among 26 non impaired streams corresponding to 3 temporary stream types (mountain, canyon and lowland) during two hydrological phases (flow and disconnected pools) sampled seasonally between 2005 and 2008. Mountain and canyon streams showed longer water permanence, and higher biodiversity than lowland streams, which were characterized by higher drying impact. Significant differences (PERMANOVA and PERMDIST) in species and biological traits composition were found among hydrological phases and stream types, indicating the high spatial and temporal heterogeneity of these temporary systems. The lack of desiccation-resistant forms was dominant in the temporary streams community, suggesting that taxa may survive and persist over the summer in other refugia, such as the hyporheic zone and/or groundwater springs. The environmental variables (physico-chemical, hydromorphological and basin land uses) explained only 40% of the distribution of macroinvertebrate composition (dbRDA analysis), leaving spatial characteristics and temporal variables the highest percentages of variance. Moreover, a significant influence of distance on the macroinvertebrate dispersion (within the surrounding 5 km) was found. Therefore, both neutral (dispersal processes) and niche explanations (local environmental conditions) contribute to patterns of local diversity. Our study highlights the importance of determining the spatio-temporal drivers which influence the ecology and composition of aquatic communities of temporary streams. Such improvement on our knowledge is highly needed for the appropriate design of monitoring programs, being urgently required in the lowlands streams, as they are the most vulnerable to climate change (longer seasonal and supra-seasonal droughts) and human impacts.

Late Quaternary biotic and abiotic controls on long‐term sediment flux in a northern Australian tropical river system

AbstractThe modern distribution of monsoonal rainforest in the Australian tropics is patchy and is mainly associated with river corridors and groundwater springs, which indicates a strong dependence on hydrologic and geomorphic conditions. While their present distribution is well known, very little data exists on past spatial and temporal dynamics of these ecosystems, or their medium‐ to longer‐term controls. Factors such as (i) fire frequency and type, and/or (ii) hydroclimatic conditions (e.g. droughts) have been proposed to control riverine corridor rainforest extent. Recent observations, however, also suggest an additional (iii) geomorphic control induced by alluvial knickpoint migration. Sediment sequences provide valuable archives for the reconstruction of longer‐term (a) floodplain sedimentary dynamics, (b) local vegetation history, and (c) catchment‐wide fire histories. This study investigates such a sediment sequence at Wangi Creek, and shows that a phase of aggradation, lasting ~4000 years, was recently disrupted by channel incision and floodplain erosion. The aggradational phase is characterized by sand deposition with average vertical floodplain accretion rates of 0.8 cm/yr and includes phases of soil development. The recent incisional phase has changed hydro‐geomorphic conditions and caused widespread degradation of vegetation, erosion and lowering of the macro‐channel surface. While there is no evidence in our data for an erosional event of similar magnitude since the onset of late Holocene floodplain aggradation, Wangi Creek experienced significant erosion and incision immediately before ~4000 years, providing the first evidence for a tropical cut‐and‐fill river system. We hence argue that phases of aggradation mainly controlled by biotic processes alternate and depend on feedbacks with incision phases controlled mainly by abiotic processes. The results show that eco‐hydro‐geomorphic feedbacks may play a crucial role in the medium‐ to longer‐term history of tropical fluvial systems and need to be considered when interpreting fluvial archives with regards to climate, fire or human induced change. © 2019 John Wiley & Sons, Ltd.

Groundwater spring potential mapping using population-based evolutionary algorithms and data mining methods

Water scarcity in many regions of the world has become an unpleasant reality. Groundwater appears to be one of the main natural resources capable to reverse this situation. Uncovering the spatial patterns of groundwater occurrence is a crucial factor that could assist in carrying out successful water resources management projects. The main objective of the current study was to provide a novel methodology approach which utilized Genetic Algorithm (GA) in order to perform a feature selection procedure and data mining methods for generating a groundwater spring potential map. Three data mining methods, Naïve Bayes (NB), Support Vector Machine (SVM) and Random Forest (RF) were utilized to construct a groundwater spring potential map that had over 0.81 probability of occurrence for the Wuqi County, Shaanxi Province, China. Groundwater spring locations and sixteen related variables were analyzed, namely: lithology, soil cover, land use cover, normalized difference vegetation index (NDVI), elevation, slope angle, aspect, planform curvature, profile curvature, curvature, stream power index (SPI), stream transport index (STI), topographic wetness index (TWI), mean annual rainfall, distance from river network and distance from road network. The Frequency ratio method was used to weight the variables, whereas a multi-collinearity analysis was performed to identify the relation between the parameters and to decide about their usage. The optimal set of parameters, which was determined by the GA, reduced the number of parameters into twelve removing planform curvature, profile curvature, curvature and STI. The Receiver Operating Characteristic curve and the area under the curve (AUROC) were estimated so as to evaluate the predictive power of each model. The results indicated that the optimized models were superior in accuracy than the original models. The optimized RF model produced the best results (0.9572), followed by the optimized SVM (0.9529) and the optimized NB (0.8235). Overall, the current study highlights the necessity of applying feature selection techniques in groundwater spring assessments and also that data mining methods may be a highly powerful investigation approach for groundwater spring potential mapping.

Mineral Weathering and Podzolization Control Acid Neutralization and Streamwater Chemistry Gradients in Upland Glaciated Catchments, Northeastern United States

Headwater streams in the White Mountains, NH, USA have been shown to have downstream gradients of increasing pH and concentrations of base cations coupled with decreasing concentrations of aluminum. A two-stage acid neutralization model involving shallow soil exchange processes in headwaters coupled with deeper mineral weathering downstream had been proposed to explain these gradients. We conducted synoptic sampling of three headwater catchments in this region that showed variations in this longitudinal pattern, ranging from streams that remain acidic throughout their length to streams with circumneutral pH beginning at their source. To explain these differences, we mapped soils using a hydropedologic approach that emphasizes the influence of groundwater saturation frequency and water table regime on soil formation processes. Stream segments with lower pH and base cation concentrations, coupled with higher concentrations of dissolved organic carbon, aluminum, and in one case iron, were in subcatchments mapped with shallow to bedrock soils where eluvial soil forming processes dominated. In contrast, stream segments with higher pH and base cation concentrations coupled with low concentrations of dissolved organic carbon and aluminum were associated with subcatchments with deeper soils where illuvial processes were more dominant. Concentrations of sodium and silicon were relatively uniform across these gradients. Coupled with the higher concentrations of dissolved aluminum and small pools of exchangeable aluminum in the areas of bedrock outcrops and shallow soils, these data suggest that primary mineral dissolution is an important process influencing upper stream reaches. Some stream reaches with obvious groundwater springs show a more abrupt transition suggesting that neutralization along deeper flowpaths may play a role as well. These data suggest a new three stage model of stream chemistry evolution with introduction of organic acids by frequent flushing of organic soils on shallow bedrock along ridge areas, acidic reaches controlled by shallow mineral weathering processes coupled with eluvial soil development, giving way to downstream reaches influenced by illuviation as organometallic complexes precipitate in spodic soil horizons. This new model highlights differences in critical zone evolution along hillslopes in glaciated catchments with implications for understanding gradients in water quality, soil fertility, and response and recovery from disturbances.

Overwintering Habitat of American Dipper, <i>Cinclus mexicanus</i>, Observed in an Arctic Groundwater Spring Feeding on Dolly Varden, <i>Salvelinus malma</i>

Perennial groundwater springs along the Alaska and Yukon North Slope provide overwintering habitat for various organisms, including birds and fishes. We observed an American Dipper, Cinclus mexicanus, in the open water of a perennial spring situated in Fish Creek, Yukon, in Ivvavik National Park on 8 March 2018. The observation at Fish Creek was among the most northern documented sightings of an American Dipper during the winter in North America. Moreover, the observation was approximately 650 km farther north than where American Dippers have been documented overwintering in Yukon, making this the most northern Canadian observation documented for this species in any season. Additionally, the American Dipper was photographed feeding on a juvenile Dolly Varden, Salvelinus malma. Although American Dippers are known to feed on small fish, our observation was a novel documentation of a trophic interaction between both species during winter. The open-water habitat in Fish Creek, which is important for both species and has not been previously described, was short (~730 m long), shallow (mean = 20 cm deep), narrow (mean = 2.8 m wide), and cold (mean water temperature = 0.34ºC). While there is little information regarding the ecological interactions of American Dipper overwintering in the Arctic, we note that all observations in the North Slope area during winter occurred in river systems also used by Dolly Varden, which indicates that juvenile Dolly Varden could be an important source of food for American Dipper in winter.

Spatial prediction of groundwater potentiality using ANFIS ensembled with teaching-learning-based and biogeography-based optimization

Groundwater is one of the most beneficial natural resources worldwide. The main aim of the present study is the mapping of groundwater potential of the Zhangjiamao area in China using two novel hybrid data mining techniques that involve the adaptive neuro-fuzzy inference system (ANFIS) ensembled with teaching-learning-based optimization (TLBO) and biogeography-based optimization (BBO). First, 93 spring locations were identified in the study area, which were randomly divided in the ratio of 70:30 to be used for building models and validation, respectively. A total of 16 spring affecting factors viz., slope aspect, altitude, slope angle, plan curvature, profile curvature, curvature, sediment transport index, stream power index, topographic wetness index, distance to roads, distance to rivers, rainfall, lithology, soil, NDVI, and land use/land cover, were used as input variables. The probability certainty factor (PCF) method was applied for the correlation analysis between spring occurrences and the affecting factors. Subsequently, the ANFIS-TLBO and ANFIS-BBO models were generated to produce groundwater spring potential maps (GSPMs). Finally, the GSPMs were evaluated by using the area under the receiver operating characteristic (AUROC). The results showed that the AUROC values for the ANFIS-TLBO model based on the training and validation datasets were 0.866 and 0.905, respectively. In contrast, the AUROC values for the ANFIS-BBO model based on the training and validation datasets were 0.861 and 0.887, respectively. The results of ANFIS-TLBO showed that 25.66% of the study area could be classified into the high and very high groundwater spring potential classes as compared to the ANFIS-BBO model (30.52%). The results of the present study can be useful for groundwater management.

Recharge of River Water to Karst Aquifer Determined by Hydrogeochemistry and Stable Isotopes

The Jinan Karst Spring System in Shandong province, China, has suffered to maintain groundwater level and spring flowing for decades. Recharge of river water to karst aquifer in Jinan is important for the outflowing of four large karst springs in the city center. Field investigations were conducted for two times in May and October, 2015, respectively and water samples were collected for hydrogeochemical and isotopic measurements. Results showed that (a) the water type was predominantly Ca-HCO3-SO4 for karst groundwater, and Ca-Mg-SO4 for river water; (b) the concentration of HCO3− and NO3− in karst groundwater were higher than that in river water, in contrast, the concentration of SO42− and K+ in karst groundwater were lower than that in river water; (c) the δ2H and δ18O values with average of −51.2‰ and −6.6‰ for river water is more enriched than the values in groundwater samples (−59.1‰ and −8.3‰), in that river experienced evaporation in the upstream reservoir; (d) Based on the distribution pattern of δ18O, groundwater near river bank was found to be recharged from river water and found a preferential flow path in karst aquifer situated from Dongkema to Manzizhuang near the river bank. This study provides useful information for understanding of the hydraulic connection between river water and karst aquifer, and benefit the protection and management of water resources.

Freshwater spring indicator taxa of benthic invertebrates

Groundwater and subsurface water have important roles in river ecosystems: e.g., stabilizing flow regime and water temperature, supplying nutrients, and increasing biodiversity. However, detection of springs and quantitative estimation of groundwater contribution to the surface water ecosystems are not easy in the field. Although some indirect measurements combing simulations and models have been widely used to estimate the groundwater flow, they require certain assumptions, thus it is rather difficult to estimate the discharge of groundwater on a local scale. Direct field-scale measurement of groundwater flow is also difficult particularly for researches of ecology and environmental science.In this study, we present biological indicators of benthic invertebrates to evaluate the contribution of groundwater to surface water bodies. Because the benthic invertebrates show distribution patterns reflecting habitat scale environmental conditions in a sensitive manner, they are expected to serve as a biological indicator of groundwater spring sites. We collected data of benthic invertebrates from literature in a global scale and additional field research in Japan. By analyzing their taxonomic features and biological habits, we identified spring indicator taxa of benthic invertebrates based on the degree of dependency to groundwater environment.A total of 1,448 aquatic invertebrate species representing 58 orders were found from 249 research sites. The spring indicators were identified as spring dependent species including groundwater species (Stygobites and Stygophiles), cave species (Troglobites and Troglophile), and stenothermal species. Considering the geographical distribution patterns of the spring indicator taxa, stenothermal species were classified into "cold stenothermal species" which evolutionarily originate in more boreal regions and "warm stenothermal species" derived from more tropical regions. Ecological interpretation of these stenothermal species was discussed in relation to climatic zones and altitude of the basin concerned. Based on the variations of spring contribution into river ecosystems suggested by the spring indicator species, we proposed an application procedure of the spring indicators for environmental assessment and nature conservation works in river management.