Scientific Results

This catalogue is obtained by conducting a systematic literature review of scientific studies and reviews related to monitoring, forecasting, and simulating the inland water cycle. The analysis maps scientific expertise across research groups and classifies findings by the type of inland water studied, application focus, and geographical scope. A gap analysis will identify missing research areas and assess their relevance to policymaking.

ID â–Č Type Year Authors Title Venue/Journal DOI Research type Water System Technical Focus Abstract Link with Projects Link with Tools Related policies ID
publications-1871 Peer reviewed articles 2022 Syed Md Touhidul Mustafa; Kedar Ghag; Anandharuban Panchanathan; Bishal Dahal; Amirhossein Ahrari; Toni Liedes; Hannu Marttila; Tamara Avellån; Mourad Oussalah; Björn Klöve; Ali Torabi Haghighi Smart drainage management to limit summer drought damage in Nordic agriculture under the circular economy concept Hydrological Processes 10.1002/hyp.14560 Uncategorized River Basins No abstract available 858375
publications-1872 Peer reviewed articles 2022 Devakunjari Vadibeler, Michael P.Stockinger, Leonard I.Wassenaar, ChristineStumppa Influence of equilibration time, soil texture, and saturation on the accuracy of porewater water isotope assays using the direct H2O(liquid)–H2O(vapor) equilibration method Journal of Hydrology 10.1016/j.jhydrol.2022.127560 Uncategorized River Basins No abstract available 858375
publications-1873 Peer reviewed articles 2023 Tamås Magyar, Zsolt Fehér, Erika Buday-Bódi, Jånos Tamås, Attila Nagy Modeling of soil moisture and water fluxes in a maize field for the optimization of irrigation Computers and Electronics in Agriculture 10.1016/j.compag.2023.108159 Simulation & Modeling River Basins No abstract available 858375
publications-1874 Peer reviewed articles 2022 Laura García-Herrero; Stevo Lavrnić; Valentina Guerrieri; Attilio Toscano; Mirco Milani; Giuseppe Luigi Cirelli; Matteo Vittuari Cost-benefit of green infrastructures for water management: A sustainability assessment of full-scale constructed wetlands in Northern and Southern Italy Ecological Engineering 10.1016/j.ecoleng.2022.106797 Predictive Analytics River Basins No abstract available 858375
publications-1875 Peer reviewed articles 2021 Attila Nagy; Andrea Szabó; Odunayo David Adeniyi; János Tamás Wheat Yield Forecasting for the Tisza River Catchment Using Landsat 8 NDVI and SAVI Time Series and Reported Crop Statistics Agronomy, Vol 11, Iss 652, p 652 (2021) 10.3390/agronomy11040652 Data Management & Analytics River Basins Due to the increasing global demand of food grain, early and reliable information on crop production is important in decision making in agricultural production. Remote sensing (RS)-based forecast models developed from vegetation indices have the potential to give quantitative and timely information on crops for larger regions or even at farm scale. Different vegetation indices are being used for this purpose, however, their efficiency in estimating crop yield certainly needs to be tested. In this study, wheat yield was derived by linear regressing reported yield values against a time series of six different peak-seasons (2013–2018) using the Landsat 8-derived Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI). NDVI- and SAVI-based forecasting models were validated based on 2018–2019 datasets and compared to evaluate the most appropriate index that performs better in forecasting wheat production in the Tisza river basin. Nash-Sutcliffe efficiency index was positive with E1 = 0.716 for the model from NDVI and for SAVI E1 = 0.909, which means that the forecasting method developed and performed good forecast efficiency. The best time for wheat yield prediction with Landsat 8-SAVI and NDVI was found to be the beginning of full biomass period from the 138th to 167th day of the year (18 May to 16 June; BBCH scale: 41–71) with high regression coefficients between the vegetation indices and the wheat yield. The RMSE of the NDVI-based prediction model was 0.357 t/ha (NRMSE: 7.33%). The RMSE of the SAVI-based prediction model was 0.191 t/ha (NRMSE 3.86%). The validation of the results revealed that the SAVI-based model provided more accurate forecasts compared to NDVI. Overall, probable yield amount is possible to predict far before harvest (six weeks earlier) based on Landsat 8 NDVI and SAVI and generating simple thresholds for yield forecasting, and a potential loss of wheat yield can be mapped. 858375
publications-1876 Peer reviewed articles 2023 Hekmat Ibrahim; Zaher Mundher Yaseen; Miklas Scholz; Mumtaz Ali; Mohamed Gad; Salah Elsayed; Mosaad Khadr; Hend Hussein; Hazem H. Ibrahim; Mohamed Hamdy Eid; Attila KovĂĄcs; SzƱcs PĂ©ter; Moataz M. Khalifa Evaluation and Prediction of Groundwater Quality for Irrigation Using an Integrated Water Quality Indices, Machine Learning Models and GIS Approaches: A Representative Case Study Volume 15 10.3390/w15040694 Data Management & Analytics Natural Water Bodies Agriculture has significantly aided in meeting the food needs of growing population. In addition, it has boosted economic development in irrigated regions. In this study, an assessment of the groundwater (GW) quality for agricultural land was carried out in El Kharga Oasis, Western Desert of Egypt. Several irrigation water quality indices (IWQIs) and geographic information systems (GIS) were used for the modeling development. Two machine learning (ML) models (i.e., adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM)) were developed for the prediction of eight IWQIs, including the irrigation water quality index (IWQI), sodium adsorption ratio (SAR), soluble sodium percentage (SSP), potential salinity (PS), residual sodium carbonate index (RSC), and Kelley index (KI). The physicochemical parameters included T°, pH, EC, TDS, K+, Na+, Mg2+, Ca2+, Cl−, SO42−, HCO3−, CO32−, and NO3−, and they were measured in 140 GW wells. The hydrochemical facies of the GW resources were of Ca-Mg-SO4, mixed Ca-Mg-Cl-SO4, Na-Cl, Ca-Mg-HCO3, and mixed Na-Ca-HCO3 types, which revealed silicate weathering, dissolution of gypsum/calcite/dolomite/ halite, rock–water interactions, and reverse ion exchange processes. The IWQI, SAR, KI, and PS showed that the majority of the GW samples were categorized for irrigation purposes into no restriction (67.85%), excellent (100%), good (57.85%), and excellent to good (65.71%), respectively. Moreover, the majority of the selected samples were categorized as excellent to good and safe for irrigation according to the SSP and RSC. The performance of the simulation models was evaluated based on several prediction skills criteria, which revealed that the ANFIS model and SVM model were capable of simulating the IWQIs with reasonable accuracy for both training “determination coefficient (R2)” (R2 = 0.99 and 0.97) and testing (R2 = 0.97 and 0.76). The presented models’ promising accuracy illustrates their potential for use in IWQI prediction. The findings indicate the potential for ML methods of geographically dispersed hydrogeochemical data, such as ANFIS and SVM, to be used for assessing the GW quality for irrigation. The proposed methodological approach offers a useful tool for identifying the crucial hydrogeochemical components for GW evolution assessment and mitigation measures related to GW management in arid and semi-arid environments. 858375
publications-1877 Peer reviewed articles 2021 Shokoufeh Salimi, Martin Berggren, Miklas Scholz Response of the peatland carbon dioxide sink function to future climate change scenarios and water level management Global Change Biology 10.1111/gcb.15753 Data Management & Analytics Natural Water Bodies AbstractStress factors such as climate change and drought may switch the role of temperate peatlands from carbon dioxide (CO2) sinks to sources, leading to positive feedback to global climate change. Water level management has been regarded as an important climate change mitigation strategy as it can sustain the natural net CO2 sink function of a peatland. Little is known about how resilient peatlands are in the face of future climate change scenarios, as well as how effectively water level management can sustain the CO2 sink function to mitigate global warming. The authors assess the effect of climate change on CO2 exchange of south Swedish temperate peatlands, which were either unmanaged or subject to water level regulation. Climate chamber simulations were conducted using experimental peatland mesocosms exposed to current and future representative concentration pathway (RCP) climate scenarios (RCP 2.6, 4.5 and 8.5). The results showed that all managed and unmanaged systems under future climate scenarios could serve as CO2 sinks throughout the experimental period. However, the 2018 extreme drought caused the unmanaged mesocosms under the RCP 4.5 and RCP 8.5 switch from a net CO2 sink to a source during summer. Surprisingly, the unmanaged mesocosms under RCP 2.6 benefited from the warmer climate, and served as the best sink among the other unmanaged systems. Water level management had the greatest effect on the CO2 sink function under RCP 8.5 and RCP 4.5, which improved their CO2 sink capability up to six and two times, respectively. Under the current climate scenario, water level management had a negative effect on the CO2 sink function, and it had almost no effect under RCP 2.6. Therefore, the researchers conclude that water level management is necessary for RCP 8.5, beneficial for RCP 4.5 and unimportant for RCP 2.6 and the current climate. 858375
publications-1878 Peer reviewed articles 2022 Dewi Fitria; Miklas Scholz; Gareth M Swift; Furat Al-Faraj Impact of Temperature and Coagulants on Sludge Dewaterability International Journal of Technology, Vol 13, Iss 3, Pp 596-605 (2022) 10.14716/ijtech.v13i3.4886 Data Management & Analytics Natural Water Bodies No abstract available 858375
publications-1879 Peer reviewed articles 2023 Alba Canet-MartĂ­, Angela Morales-Santos, Reinhard Nolz, GĂŒnter Langergraber, Christine Stumpp Quantification of water fluxes and soil water balance in agricultural fields under different tillage and irrigation systems using water stable isotopes Soil and Tillage Research 10.1016/j.still.2023.105732 Data Management & Analytics Natural Water Bodies No abstract available 858375
publications-1880 Peer reviewed articles 2022 Aashna Mittal, Lisa Scholten, Zoran Kapelan A review of serious games for urban water management decisions: current gaps and future research directions Water Research 10.1016/j.watres.2022.118217 Predictive Analytics River Basins No abstract available 858375