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-1901 Peer reviewed articles 2023 E.B. Karimov, B.K. Karimov, Martin Schletterer, Daniel S. Hayes Ichthyological research in the koksu river, Uzbekistan to identify key fish species in the context of small hydropower development Experimental Biology 10.26577/eb.2023.v96.i3.011 Simulation & Modeling Irrigation Systems No abstract available 101022905
publications-1902 Peer reviewed articles 2024 Erkin Karimov, Bernhard Zeiringer, Johan Coeck, Pieterjan Verhelst, Bakhtiyor Karimov, Otabek Omonov, Martin Schletterer, Daniel S. Hayes Length-Weight-Age Relationship of Schizothorax eurystomus Kessler, 1872 and Comparison to Other Snow Trout Species in Central Asia fishes 10.3390/fishes9030094 IoT & Sensors Uncategorized This study presents a comprehensive analysis of the length-weight relationship, condition factors, and age of Schizothorax eurystomus in the Shakhimardan River basin in Central Asia, along with a comparative perspective to other Schizothorax species in the region. The study found that S. eurystomus exhibits positive allometric growth, which is consistent with similar patterns observed in this species from the Syr Darya River basin. The two analyzed condition factors showed mean values within the normal range, indicating good feeding and environmental conditions. However, significant disparities between minimum and maximum values of these factors indicated varied growth conditions which may be influenced by anthropogenic factors. Age estimation using opercular bones showed variations in the total length among fish of the same age, and a clear age distribution pattern across different sites. Younger fish predominantly inhabited the shallower, warmer, and lower sections of the river, which is impacted by agricultural water diversion, while older specimens were found in areas with higher discharge and deeper pools. Overall, this research provides valuable insights into the life history traits of S. eurystomus, underlining the need for sustainable fishery management and conservation strategies in the Shakhimardan River basin. The findings also emphasize the importance of considering habitat quality and anthropogenic pressures regarding understanding both fish population dynamics and growth patterns. 101022905
publications-1903 Peer reviewed articles 2024 null null, Aidar Zhumabaev, Hannah Schwedhelm, null null, Beatrice Marti, null null, Silvan Ragettli, null null, Tobias Siegfried, null null Water Tales from Turkistan: Challenges and Opportunities for the Badam-Sayram Water System under a Changing Climate Central Asian Journal of Water Research 10.29258/cajwr/2024-r1.v10-2/1-25.eng Simulation & Modeling Water Distribution Networks The Badam River, a tributary to the Arys River located in the Syr Darya basin, is a crucial natural resource for ecological, social, and economic activities in the semi-arid region of southern Kazakhstan. The river basin is heavily influenced by manmade water infrastructure and faces water scarcity, particularly during summer, highlighting the importance of understanding its hydrological processes for effective water resource management. In this study, a semi-distributed conceptual hydrological model of the Badam River was implemented using the RS MINERVE hydrological software to evaluate the impacts of climate change on hydrology and to test the resilience of the water system. Connected HBV models were implemented for each of the hydrological response units that were defined as altitudinal zones. The hydrological model was calibrated using daily time steps between 1979 and 2011, and the resulting flow exceedance curves and hydrographs were used to assess the potential impacts of climate change on the basin, using CMIP6 precipitation and temperature scenarios. Future climate scenarios for the 2054 – 2064 period demonstrate that the peak discharge will be shifted to spring/late spring compared to the current early summer with no significant decrease in average discharge per day of the year. The insights gained from this hydrological-hydraulic model can be used to effectively manage the water system and inform future hydropower design decisions and serve as a blueprint for similar studies in the region and elsewhere. 101022905
publications-1904 Peer reviewed articles 2020 I. G. Pechlivanidis, L. Crochemore, J. Rosberg, T. Bosshard What Are the Key Drivers Controlling the Quality of Seasonal Streamflow Forecasts? Water Resources Research 10.1029/2019wr026987 IoT & Sensors Natural Water Bodies AbstractRecent technological advances in representation of processes in numerical climate models have led to skillful predictions, which can consequently increase the confidence of hydrological predictions and usability of hydroclimatic services. Given that many water‐related stakeholders are affected by seasonal hydrological variations, there is a need to manage such variations to their advantage through better understanding of the drivers that influence hydrological predictability. Here we analyze the seasonal forecasts of streamflow volumes across about 35,400 basins in Europe, which lie along a strong gradient in terms of climatology, scale, and hydrological regime. We then link the seasonal volumetric errors to various physiographic‐hydroclimatic descriptors and meteorological biases in order to identify the key drivers controlling predictability. Streamflow volumes over Europe are well predicted, yet with some geographic and seasonal variability; however, the predictability deteriorates with increasing lead time particularly in the winter months. Nevertheless, we show that the forecast quality is well correlated to a set of descriptors, which vary depending on the initialization month. The forecast quality of seasonal streamflow volumes is strongly dependent on the basin's hydrological regime, with limited predictability in relatively flashy basins. On the contrary, snow and/or baseflow dominated regions with long recessions show high streamflow predictability. Finally, climatology and precipitation forecast biases are also related to streamflow predictability, highlighting the importance of developing robust bias adjustment methods. Overall, this investigation shows that the seasonal streamflow predictability can be clustered, and hence regionalized, based on a priori knowledge of local hydroclimatic conditions. 870497
publications-1905 Peer reviewed articles 2020 Musuuza J., Gustafsson D., Pimentel R., Crochemore L., Pechlivanidis I. Impact of Satellite and In Situ Data Assimilation on Hydrological Predictions Remote Sensing 10.3390/rs12050811 Data Management & Analytics Groundwater The assimilation of different satellite and in situ products generally improves the hydrological model predictive skill. Most studies have focused on assimilating a single product at a time with the ensemble size subjectively chosen by the modeller. In this study, we used the European-scale Hydrological Predictions for the Environment hydrological model in the UmeÀlven catchment in northern Sweden with the stream discharge and local reservoir inflow as target variables to objectively choose an ensemble size that optimised model performance when the ensemble Kalman filter method is used. We further assessed the effect of assimilating different satellite products; namely, snow water equivalent, fractional snow cover, and actual and potential evapotranspiration, as well as in situ measurements of river discharge and local reservoir inflows. We finally investigated the combinations of those products that improved model predictions of the target variables and how the model performance varied through the year for those combinations. We found that an ensemble size of 50 was sufficient for all products except the reservoir inflow, which required 100 members and that in situ products outperform satellite products when assimilated. In particular, potential evapotranspiration alone or as combinations with other products did not generally improve predictions of our target variables. However, assimilating combinations of the snow products, discharge and local reservoir without evapotranspiration products improved the model performance. 870497
publications-1906 Peer reviewed articles 2021 O'Shea, R. E.; Pahlevan, N.; Smith, B.; Bresciani, M.; Egerton, T.; Giardino, C.; & VaičiĆ«tė, D. Advancing cyanobacteria biomass estimation from hyperspectral observations: Demonstrations with HICO and PRISMA imagery Remote Sensing of Environment 10.1016/j.rse.2021.112693 Simulation & Modeling Natural Water Bodies No abstract available 870497
publications-1907 Peer reviewed articles 2022 Federica Braga, Alice Fabbretto, Quinten Vanhellemont, Mariano Bresciani, Claudia Giardino, Gian Marco Scarpa, Giorgia ManfĂš, Javier Alonso Concha, Vittorio Ernesto Brando Assessment of PRISMA water reflectance using autonomous hyperspectral radiometry ISPRS Journal of Photogrammetry and Remote Sensing 10.1016/j.isprsjprs.2022.08.009 Simulation & Modeling Natural Water Bodies No abstract available 870497
publications-1908 Peer reviewed articles 2020 Claudia Giardino, Mariano Bresciani, Federica Braga, Alice Fabbretto, Nicola Ghirardi, Monica Pepe, Marco Gianinetto, Roberto Colombo, Sergio Cogliati, Semhar Ghebrehiwot, Marnix Laanen, Steef Peters, Thomas Schroeder, Javier A. Concha, Vittorio E. Brando First Evaluation of PRISMA Level 1 Data for Water Applications Sensors 10.3390/s20164553 IoT & Sensors Natural Water Bodies This study presents a first assessment of the Top-Of-Atmosphere (TOA) radiances measured in the visible and near-infrared (VNIR) wavelengths from PRISMA (PRecursore IperSpettrale della Missione Applicativa), the new hyperspectral satellite sensor of the Italian Space Agency in orbit since March 2019. In particular, the radiometrically calibrated PRISMA Level 1 TOA radiances were compared to the TOA radiances simulated with a radiative transfer code, starting from in situ measurements of water reflectance. In situ data were obtained from a set of fixed position autonomous radiometers covering a wide range of water types, encompassing coastal and inland waters. A total of nine match-ups between PRISMA and in situ measurements distributed from July 2019 to June 2020 were analysed. Recognising the role of Sentinel-2 for inland and coastal waters applications, the TOA radiances measured from concurrent Sentinel-2 observations were added to the comparison. The results overall demonstrated that PRISMA VNIR sensor is providing TOA radiances with the same magnitude and shape of those in situ simulated (spectral angle difference, SA, between 0.80 and 3.39; root mean square difference, RMSD, between 0.98 and 4.76 [mW m−2 sr−1 nm−1]), with slightly larger differences at shorter wavelengths. The PRISMA TOA radiances were also found very similar to Sentinel-2 data (RMSD < 3.78 [mW m−2 sr−1 nm−1]), and encourage a synergic use of both sensors for aquatic applications. Further analyses with a higher number of match-ups between PRISMA, in situ and Sentinel-2 data are however recommended to fully characterize the on-orbit calibration of PRISMA for its exploitation in aquatic ecosystem mapping. 870497
publications-1909 Peer reviewed articles 2021 El Serafy, G.Y.H.; Schaeffer, B.A.; Neely, M.-B.; Spinosa, A.; Odermatt, D.; Weathers, K.C.; Baracchini, T.; Bouffard, D.; Carvalho, L.; Conmy, R.N.; Keukelaere, L.D.; Hunter, P.D.; Jamet, C.; Joehnk, K.D.; Johnston, J.M.; Knudby, A.; Minaudo, C.; Pahlevan, N.; Reusen, I.; Rose, K.C.; Schalles, J.; Tzortziou, M. Integrating Inland and Coastal Water Quality Data for Actionable Knowledge Remote Sensing 10.3390/rs13152899 Data Management & Analytics Natural Water Bodies Water quality measures for inland and coastal waters are available as discrete samples from professional and volunteer water quality monitoring programs and higher-frequency, near-continuous data from automated in situ sensors. Water quality parameters also are estimated from model outputs and remote sensing. The integration of these data, via data assimilation, can result in a more holistic characterization of these highly dynamic ecosystems, and consequently improve water resource management. It is becoming common to see combinations of these data applied to answer relevant scientific questions. Yet, methods for scaling water quality data across regions and beyond, to provide actionable knowledge for stakeholders, have emerged only recently, particularly with the availability of satellite data now providing global coverage at high spatial resolution. In this paper, data sources and existing data integration frameworks are reviewed to give an overview of the present status and identify the gaps in existing frameworks. We propose an integration framework to provide information to user communities through the the Group on Earth Observations (GEO) AquaWatch Initiative. This aims to develop and build the global capacity and utility of water quality data, products, and information to support equitable and inclusive access for water resource management, policy and decision making. 870497
publications-1910 Peer reviewed articles 2023 Musuuza, Jude L, Crochemore, Louise, Pechlivanidis, Ilias G Evaluation of Earth Observations and In Situ Data Assimilation for Seasonal Hydrological Forecasting Water Resources Research 10.1029/2022wr033655 Simulation & Modeling Natural Water Bodies AbstractEarth observations (EOs) are a valuable complement to in situ measurements in hydrology because they provide information in locations where direct measurements are unavailable or prohibitively expensive to make. Recent advances have enabled the assimilation of data sets of different physical variables into hydrological models to better estimate states and fluxes. Along with the meteorological forcings, the assimilated data exert controls on forecasts; it is therefore important to apportion the contributions from the forcing and assimilated data. Quality assessment is sometimes conducted in the pseudo‐reality, which neglects observations and model limitations. Here we introduce a diagnostic framework that accounts for observations to assess the sources of skill and infer the seasonal importance of assimilated and forcing data. We test the framework with a forcing data set from a downscaled Global Circulation Model and assimilate four EO and two in situ data sets to initialize the forecasts. We investigate the hydrological response and seasonal predictions over a Swedish snowmelt‐dominated catchment using the HYPE model over 2001–2015. The framework allows assessing the improvement in seasonal skill due to the different assimilated data and meteorological forcing. For the studied catchment, all EO and in situ data sets add information to the final forecast. The lead times during which data assimilation influences forecast skill also differ between data sets and seasons for example, assimilating snow water equivalent impacts the forecast for more than 20 weeks during winter. Lastly, assimilated data sets are generally more important to streamflow forecasting skill than meteorological forcing in the studied snow‐dominated catchment. 870497