HS8.1.8

Human modernizations and achievements are at peak but in contrary we ignore facts about the tremendous amount of anthropogenic waste that are exposed directly to atmosphere which later reach groundwater aquifer and contaminate through natural water cycle. So, to limelight man-made sources, Wanaparthy watershed is chosen as it is densely agriculture urban area covering about 1600 km² were peoples solely get their daily wages from farming. Samples were collected systematically for pre (march-may) and post monsoon (september-december) through gird preparation (5*6 sq.km) to determine the hydrogeochemical characteristics of physicochemical parameters and major analytes concentration to preside the types, quality, permissibility, facies and nitrate health risk of different age groups in groundwater. In short findings are as follows: Major analytes concentration for pre-monsoon, Cl^- (8.02%) >HCO₃^- (3.27%) >SO₄^2-(2.75%) >NO₃^-(1.09%) >F^-(0.02%); Na⁺(4.08%) >Ca⁺2(1.71%) >Mg⁺² (0.45%) >K⁺ (0.26%)while post-monsoon, HCO₃^-(8.94%) >Cl^- (6.9%) >SO₄^2- (2.46%) >NO₃^- (2.42%) >F^-(0.02%); Na⁺(3.28%) >Mg⁺²(2.14%) >Ca⁺²(0.75%) >K⁺(0.28%) respectively. Piper diagram explains the major water types for pre- and post-monsoon as Na-Cl type and Ca-Mg-Cl type. Gibb’s plot shows that in both seasons the dominant environmental facies are influence by rock and evaporation conditions. Water quality index shows deterioration increase at most twice from “poor to unfit class” (36.21% - 60.34%) during pre to post season. Health Quotient evaluation for nitrate indicates “children group” as most effected where HQ value range from 5.40E-03 to 1.23E+01 (72.4 pc) followed by infant group, 5.80E-03 to 1.31E+01 (68.97 pc) and adult 2.10E-03 to 4.68E+00 (32.7pc). And, from spatial distribution maps it is observed that slope and structure have direct response to recharge/discharge as well as aquifer properties.

How to cite: Vaiphei, S. P. and Mohan, K. R.: Characteristics of groundwater quality and its non-carcinogenic health risk assessment through monsoon inputs in Wanaparthy watershed, Telangana, India, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-173, https://doi.org/10.5194/egusphere-egu22-173, 2022.

The outermost regions of Europe are nine: Guadeloupe, French Guiana, Martinique, Mayotte, Reunion and Saint Martin (France), the Canary Islands (Spain), the Azores and Madeira (Portugal). These regions enrich the EU economically, culturally and geographically, hosting 80% of its biodiversity. However, due to their remoteness and other unique features, they pose challenges for their development. The particular case of the Canary Islands will be developed in the framework of the European H2020 project Arsinoe, where the hydrological cycle and agriculture in the Canary archipelago will be studied in El Hierro and La Palma. These two islands have been selected for the following reasons: i) El Hierro is a pioneer in presenting a self-sufficient energy model (La Gorona del Viento project) and is rich in groundwater, this being the most used water resource on the island; ii) La Palma has been selected because it is an island rich in groundwater (in fact, it is the only island in the Canary archipelago that does not have desalination plants) and where agriculture is very important (especially tropical crops such as banana, mango, avocado, etc.) and, due to the volcanic eruption that began in September 2021, the situation of the aquifer is uncertain, something that is worrying since La Palma depends on groundwater resources to guarantee the water demand of agriculture, local population and tourism. The effect of the eruption on the vulnerability of the aquifer of La Palma is still unknown, so it is desired to study in depth the effects on the aquifer in quantitative and qualitative terms therefore, ARSINOE will focus on the ecological transition and vulnerability of aquifers in volcanic islands and will put further efforts to the primary production including agriculture, forestry, fisheries and aquaculture, water management and clean energy infrastructure. ARSINOE will take into account the interdependence between water and agriculture. The agricultural sector is the largest water user in the Canary Islands, where wine, potatoes and tomatoes are the main exports. Therefore, greater sustainability within the water sector (through the water footprint and the carbon footprint) will positively affect the agricultural sector and, therefore, the water and energy situation of the archipelago. But aquifers of both islands are also at risk due to other circumstances, specifically those derived from climate change: greater saline intrusion (due to rising sea levels), losses in freshwater inputs (due to decreased rainfall), changes in physic-chemical conditions of all water bodies… All these effects will be studied on both islands, through the ARSINOE Project, and from a local point of view. 

How to cite: Santamarta, J. C., Cruz-Pérez, N., Rodríguez-Martín, J., Marazuela, M. Á., Álvarez-Vázquez, R., and García-Gil, A.: Vulnerability of aquifers on volcanic islands: the case of La Palma and El Hierro (Canary Islands, Spain), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4617, https://doi.org/10.5194/egusphere-egu22-4617, 2022.

Climate change, urbanization, and growing population have led to the rapid increase in the use of groundwater. Therefore, monitoring the groundwater (GW) changes is essential for water management and decision-makers. Due to frequent lack of reliable and sufficient in-situ information, remote sensing and hydrological models can be counted as the alternative sources for assessing GW storage changes on a regional and global scale. Here, we test such an approach for Qazvin Plain in Iran, one of the regions that recently have been facing severe drought conditions. The main purpose of this study is to downscale GW storage anomaly (GWSA) of the WaterGAP Global Hydrology Model (WGHM) from a coarse (0.5-degree) to a finer spatial resolution (0.1-degree) using fine spatial resolution auxiliary datasets (0.1-degree) such as the evaporation, surface and subsurface runoff, snow depth, volumetric soil water, and soil temperature from the ERA5-Land model and precipitation from integrated multi-satellite retrievals for global precipitation measurement (IMERG). Different regression models were tested for the GWSA downscaling. Moreover, since different water budget components such as precipitation or storage are known to have temporal lead or lag relative to each other, the approach also includes a time shift factor among the components. The most suitable regression model with the highest skill score during the validation test was selected and applied to predict the 0.1-degree GWSA. The downscaled results showed a high agreement with the in-situ groundwater levels for Qazvin Plain in both interannual and monthly scales, with a correlation coefficient of 0.99 and 0.65, respectively. Moreover, the downscaled product clearly proves that the developed downscaling technique is able to learn from high-resolution auxiliary data to capture GWSA features at higher spatial resolution. The major benefit of this method is in utilizing only the auxiliary data that are available with global coverage and free of cost, and that this method does not need in-situ GW records for training. Therefore, the proposed downscaling technique can potentially be applied to a global scale, other geographical regions, or aquifers.

This study has received funding from the European Union’s Horizon 2020 research and innovation programme for G3P (Global Gravity-based Groundwater Product) under grant agreement nº 870353.

How to cite: Zarghami Dastjerdi, S., Sharifi, E., Saghafian, B., and Güntner, A.: Downscaling WGHM-Based Groundwater Storage Data Using Regression Method: A Regional Study over Qazvin Plain, Iran, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5856, https://doi.org/10.5194/egusphere-egu22-5856, 2022.

Arid and semi-arid areas characterised by low precipitation and high evaporation rates are highly vulnerable to alterations in precipitation regimes, leading to water deficiency and an increase in dependence on groundwater resources. Flash floods have become more frequent in several semi-arid regions due to changing climatic conditions. Thus, an efficient water management system is needed for these regions to manage flash floods and support groundwater recharge. A coupled surface water-groundwater model is an advanced tool for simulating large-scale hydrologic processes and quantifying factors influencing floods and drought. To accommodate the high variability and heterogeneous spatial distribution of surface and groundwater resources, distributed modelling tools are essential. However, scarce monitoring networks may lead to the unavailability of spatio-temporal input data and limit the applicability of these models. Advances in remote sensing (RS) techniques for monitoring hydrological parameters like precipitation, soil moisture, evapotranspiration, and groundwater depth can mitigate this problem.

This study analyses the remote sensing product MOD11A1.006 of Moderate Resolution Imaging Spectroradiometer (MODIS), which provides daily day and night land surface temperature (LST) at a spatial resolution of 1000 m, facilitating the analysis of surface water-groundwater interactions through distributed hydrological modelling in the semi-arid Banas River basin (~6800 km²). Remotely sensed LST data allowed air surface temperature (T_a), which is crucial for estimating reference evapotranspiration, to be retrieved. While T_a at weather stations 2 m above the ground are more accurate, those data have limited spatial coverage. The Banas River basin contains five weather stations located primarily in the central region. To improve the spatial distribution of reference evapotranspiration, which is a significant input of the hydrological models, a linear regression model using T_a observed at the weather stations of Banas basin, along with LST, elevation, Normalized Difference Vegetation Index (NDVI), latitude, and longitude of the pixels coinciding with the location of weather stations was developed to estimate the air surface temperature for the whole basin.

A multiple linear regression model was built by stepwise linear regression (SLR) method using the OLSRR package of R. Calibration using day LST, latitude and longitude provided the best estimate of maximum T_a, with an adjusted R² value of 0.60, Pearson correlation coefficient (r) of 0.72, and Root Mean Square Error (RMSE) of 3.2^o C. While calibration using night LST and elevation data provided the best estimate of minimum T_a with an adjusted R² value of 0.81, r of 0.84 and RMSE of 3.02^o C. The daily LST data and daily T_a data have shown a good agreement. This research improves the understanding of the spatial distribution of daily day and night air temperature in the Banas River basin. It opens a new methodological perspective for groundwater and surface water management through hydrological modelling with a spatial resolution greater than that of the existing monitoring networks.

How to cite: Singh, N., Cartwright, I., and Chinnasamy, P.: Estimation of air surface temperature using MODIS land surface temperature over data-scarce Banas River basin, Western India, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6881, https://doi.org/10.5194/egusphere-egu22-6881, 2022.

Climate simulations indicate that the Mediterranean region will be severely affected by climate change and is often referred to as the most prominent climate change hotspot (Gao and Giorgi, 2008). This study addresses the combined effects of climate change and three groundwater consumption scenarios on the water availability of the Western Mountain Aquifer (WMA) in Israel and the West Bank. Generally applied methods to quantify recharge and water resources rely on linear regressions or simplified models, such as data-driven approaches (i.e., lumped-parameter and black-box approaches). However, they are unfit to assess climate change impacts because the predictive power of data-driven approaches is low, should the variability of, e.g., precipitation expand beyond historically observed fluctuations, such as expected from climate change effects. Furthermore,  they do not honor the physics of flow. Therefore, assessing the impact of climate change requires the application of distributed process-based numerical models that incorporate as many relevant physical flow processes as feasible. For example, when karstified vadose zones measure several hundreds of meters, such as in the case of the WMA, variably saturated flow is a highly relevant flow process controlling vadose storage at large timescales and altering recharge flux at the “control plane” groundwater table.
We simulate the complex dynamics of the dual-domain infiltration and partitioning of the precipitation input signal by employing HydroGeoSphere (HGS) for transient variably saturated water flow. Flow in the limestone rock matrix and secondary high porosity system (i.e., conduits and fractures) is modeled by overlapping individual continua based on the bulk effective Richards’ equation with van Genuchten (VG) parameterization. The model input of this study stems from two coherent dynamically downscaled high-resolution regional climate projections (daily, 3km, and 8km resolution) until the year 2070, assuming the IPCC RCP4.5 climate change scenario. The results indicate that long-term average recharge quantities will decrease by circa 10 % compared to the reduction in average precipitation by 30 %. The mitigated impact on recharge is an effect of the pronounced heterogeneity of karstic flow (i.e., preferential recharge along with karst dissolution features) and increased intensity of individual rainfall events, emphasizing the need to apply spatiotemporally resolved climate models with daily precipitation values as input to the recharge assessment. However, despite the comparatively moderate decrease in recharge, the length and severity of consecutive drought years with low recharge values are likely to increase while freshwater demand is believed to increase during these periods, emphasizing the need to adjust the current management practices to climate change. Finally, the model is used to simulate managed aquifer recharge applications to mitigate the effects of more extended drought periods by strategic freshwater reserves. 

How to cite: Bresinsky, L., Kordilla, J., Engelhardt, I., Livshitz, Y., and Sauter, M.: Impact of climate change and groundwater consumption scenarios on a major transboundary karst water resource - The Western Mountain Aquifer in Israel and the West Bank, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8221, https://doi.org/10.5194/egusphere-egu22-8221, 2022.

The source of the Grande river and its tributaries on the left bank are located in a tropical region adjacent to the humid valley of the Tocantins river, with rainfall that favors the continuity of the rivers. In the middle and east part of the basin, the predominance is of the semi-arid climate, as well as typical of the middle São Francisco basin, with irregular rainfall that does not contribute to the supply of the rivers. It is noteworthy that most of the tributaries of the hydrographic basin are intermittent. The Grande river basin is part of two important hydrogeological systems, the Group Bambuí system and the Urucuia system (SAU), which is the most important system in the western region of Bahia, as well as one of the most important in the São Francisco river basin, as well as the entire brazilian Northeast region, in addition to being one of the most relevant in the country, since it is a strategic source of water. Such hydrogeological systems are directly responsible for supplying the hydrographic basin in its dry periods. The Brazilian semi-arid region, with its limited water resources, is currently classified as a critical dry and water-poor area. This study aims to identify potential areas of groundwater in the aforementioned hydrographic basin, located in the middle eastern portion of the São Francisco river basin, in the State of Bahia. Integrating geological and hydrogeological analyses, remote sensing, geographic information systems (GIS) and multicriteria statistical evaluation (AHP) techniques. It is intended to create thematic layers in a GIS where values will be assigned using appropriate weights and classifications in relation to their relative contribution to the occurrence of groundwater through multicriteria assessment techniques. These layers include lithology, geomorphology, lineament density, drainage density, soil texture, precipitation, and slope. The final groundwater potential map is composed of five classes of groundwater potential: very high, high, moderate, low and very low. The validity of the results of this GIS-based model was performed by superimposing existing wells, analyzing the statistical probability of the existence of groundwater. The single parameter sensitivity test was performed to evaluate the influence of the signaled weights on the groundwater potential model, and new effective weights will be derived after the analysis, as a way to calibrate the model, and the ROC analysis was applied to validate the model

Keywords :Groundwater, Grande River Basin, AHP, Prospecting, Statistical probability, GIS

How to cite: Soares Lima, T. A. and Amorim Castro, P. D. T.: Prospecting Potential Groundwater Zones through Geotechnology and Statistics Techniques in the Grande River Basin, Bahia,Brazil, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8732, https://doi.org/10.5194/egusphere-egu22-8732, 2022.

Under salinity stress, plant physiology and yield characteristics deteriorate, showing, among others, symptoms similar to those of water stress. Tomato (Solanum lycopersicum) is moderately sensitive to salinity stress and suffers yield losses of over 15% at irrigation water electrical conductivity (EC_w) of  3 dSm^-1 (Malash et al., 2008) and over 25% at EC_w of 3.5 dSm^-1 (Daliakopoulos et al., 2019). As salinity can often buildup in soils and substrates, it can have a creeping effect not readily measurable in irrigation water EC_w, therefore it is essential that plant physiology symptoms are spotted early to take corrective action. Here we investigate the potential of multispectral imaging, to detect early symptoms of salinity stress on S.lycopersicum plants (var. Elpida) due to NaCl accumulation in the nutrient solution of a soilless cultivation system. In this context, we established a control (0.5 mM) and five salinity treatments of 5.0, 10.0, 15.0, 20.0 mM NaCl, with three tomato plants (replications) per treatment, resulting in a total number of 18 S.lycopersicum plants. During the experiment, multispectral images (bands 460, 540, 630, 850, and 980 nm) were obtained at three stages of plant development (30, 60, and 90 days after transplant) using a MUSES9-MS sensor. For each multispectral image, four spectral indices (NDVI, OSAVI, LWSI and GOSAVI) were calculated. Although, the statistical analysis of the results reveal low sensitivity to the increasing salinity at early sampling stage (60 DAT), during the third sampling stage (120 DAT) all spectral indicators demonstrate significant sensitivity for treatments over 10.0 mM NaCl.

References

Daliakopoulos, I.N., Apostolakis, A., Wagner, K., Deligianni, A., Koutskoudis, D., Stamatakis, A., Tsanis, I.K., 2019. Effectiveness of Trichoderma harzianum in soil and yield conservation of tomato crops under saline irrigation. Catena 175. https://doi.org/10.1016/j.catena.2018.12.009

Malash, N.M., Ali, F.A., Fatahalla, M.A., A. khatab, E., Hatem, M.K., Tawfic, S., 2008. Response of tomato to irrigation with saline water applied by different irrigation methods and water management stratigies. Int. J. Plant Prod. 2, 101–116.

Acknowledgements
This research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship, and Innovation, under the call RESEARCH-CREATE-INNOVATE (project codes: T1EDK-04171)

How to cite: Papadimitriou, D., Daliakopoulos, I., Constantinopoulos, C., Manios, T., and Kosmopoulos, D.: Monitoring Solanum lycopersicum var. Elpida salinity stress using multispectral imaging, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12997, https://doi.org/10.5194/egusphere-egu22-12997, 2022.

In sub-humid areas, supplementary irrigation is often needed to meet crop water requirements and avoid yield reduction. The effect of water scarcity on agriculture is worsened in locations where summer rainfall is decreasing as a consequence of climate change. In order to stabilize crop production through sustainable water management, improvements of irrigation scheduling methods are required. For instance, traditional irrigation scheduling criteria that provided adequate yields in the past, may no longer be appropriate under drier conditions. Models that combine crop physiological and soil hydrological processes can help improving irrigation scheduling to optimize water productivity. The purpose of this study was to evaluate irrigation management approaches traditionally used by farmers in Austria, specifically at a study site located in the largest crop production area of the country (ca. 35 km east from Vienna), by means of a modelling approach. This study also aimed at proposing an irrigation schedule that increases water productivity, thereby aiding water conservation.

AquaCrop is a crop growth model developed by the Food and Agriculture Organization of the United Nations (FAO) that uses an empirical and mechanistic approach to simulate yield response to water for a variety of crops. In this study, AquaCrop was used to simulate crop water requirements of soybeans in a sub-humid environment under different water management practices. The model was validated after adjustments on the non-conservative crop parameters based on field measurements. The experimental field was divided into four plots. One plot was rainfed and the others were irrigated – each of them by means of a different irrigation system. The systems used were sprinklers on a pipe network, drip lines and a hose reel boom. Irrigation was managed by the farmers based on their experience. The collected data included leaf area index to obtain green canopy cover development and soil samples at different depths to characterize the soil. After the validation process, an irrigation schedule that covered the full crop water requirements was automatically generated by the model. Additionally, irrigation schedules for each irrigated plot were generated based on percentage of readily available soil water (RAW) thresholds.

The simulated yields were in good agreement with the observed data, with a model efficiency coefficient (EF) > 0.80 for the four plots. The irrigated plots revealed a certain level of stress during the critical crop growth stage of flowering, even though they were expected to represent well-watered conditions. After simulating net crop water requirements, the resulting potential yield was larger than the observed yields. Furthermore, the irrigation events generated using RAW thresholds also produced larger yields than the observed ones. The results showed that to schedule the irrigation events earlier in the season and distribute the same total irrigation water amount in four events rather than three – as scheduled by the farmers – increased the yield and thus, water productivity. Therefore, AquaCrop model predictions can help improving farmers’ irrigation scheduling strategies for soybeans under this study conditions. This might be helpful for local farmers in situations of increasing pressure on water resources.

How to cite: Morales Santos, A. G., Nolz, R., and García-Vila, M.: Impact of irrigation scheduling on yield and water productivity of soybeans in a sub-humid environment: A modelling approach., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12385, https://doi.org/10.5194/egusphere-egu22-12385, 2022.