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In the last decades since the dramatic increase in flood frequency and magnitude, floods have become a crucial problem in West Africa. National and international authorities concentrate efforts on developing early warning systems (EWS) to deliver flood alerts and prevent loss of lives and damages. Usually, regional EWS are based on hydrological modeling, while local EWS adopt field observations. This study aims to integrate outputs from two regional hydrological models—Niger HYPE (NH) and World-Wide HYPE (WWH)—in a local EWS developed for the Sirba River. Sirba is the major tributary of Middle Niger River Basin and is supported by a local EWS since June 2019. Model evaluation indices were computed with 5-day forecasts demonstrating a better performance of NH (Nash–Sutcliffe efficiency NSE = 0.58) than WWH (NSE = 0.10) and the need of output optimization. The optimization conducted with a linear regression post-processing technique improves performance significantly to “very good” for NH (Heidke skill score HSS = 0.53) and “good” for WWH (HSS = 0.28). HYPE outputs allow to extend local EWS warning lead-time up to 10 days. Since the transfer informatic environment is not yet a mature operational system 10–20% of forecasts were unfortunately not produced in 2019, impacting operational availability.
Giovanni Massazza; Vieri Tarchiani; Jafet C. M. Andersson; Abdou Ali; Mohamed Housseini Ibrahim; Alessandro Pezzoli; Tiziana De Filippis; Leandro Rocchi; Bernard Minoungou; David Gustafsson; Maurizio Rosso. Downscaling Regional Hydrological Forecast for Operational Use in Local Early Warning: HYPE Models in the Sirba River. Water 2020, 12, 3504 .
AMA StyleGiovanni Massazza, Vieri Tarchiani, Jafet C. M. Andersson, Abdou Ali, Mohamed Housseini Ibrahim, Alessandro Pezzoli, Tiziana De Filippis, Leandro Rocchi, Bernard Minoungou, David Gustafsson, Maurizio Rosso. Downscaling Regional Hydrological Forecast for Operational Use in Local Early Warning: HYPE Models in the Sirba River. Water. 2020; 12 (12):3504.
Chicago/Turabian StyleGiovanni Massazza; Vieri Tarchiani; Jafet C. M. Andersson; Abdou Ali; Mohamed Housseini Ibrahim; Alessandro Pezzoli; Tiziana De Filippis; Leandro Rocchi; Bernard Minoungou; David Gustafsson; Maurizio Rosso. 2020. "Downscaling Regional Hydrological Forecast for Operational Use in Local Early Warning: HYPE Models in the Sirba River." Water 12, no. 12: 3504.
The benefits of fractional snow cover area, as an additional dataset for calibration, were evaluated for an Icelandic catchment with a low degree of glaciation and limited data. For this purpose, a Hydrological Projections for the Environment (HYPE) model was calibrated for the Geithellnaá catchment in south-east Iceland using daily discharge (Q) data and satellite-retrieved MODIS snow cover (SC) images, in a multi-dataset calibration (MDC) approach. By comparing model results using only daily discharge data with results obtained using both datasets, the value of SC data for model calibration was identified. Including SC data improved the performance of daily discharge simulations by 7% and fractional snow cover area simulations by 11%, compared with using only the daily discharge dataset (SDC). These results indicate that MDC improves the overall performance of the HYPE model, confirming previous findings. Therefore, MDC could improve discharge simulations in areas with extra sources of uncertainty, such as glaciers and snow cover. Since the change in fractional snow cover area was more accurate when MDC was applied, it can be concluded that MDC would also provide more realistic projections when calibrated parameter sets are extrapolated to different situations.
Julia De Niet; David Christian Finger; Arvid Bring; David Egilson; David Gustafsson; Zahra Kalantari. Benefits of Combining Satellite-Derived Snow Cover Data and Discharge Data to Calibrate a Glaciated Catchment in Sub-Arctic Iceland. Water 2020, 12, 975 .
AMA StyleJulia De Niet, David Christian Finger, Arvid Bring, David Egilson, David Gustafsson, Zahra Kalantari. Benefits of Combining Satellite-Derived Snow Cover Data and Discharge Data to Calibrate a Glaciated Catchment in Sub-Arctic Iceland. Water. 2020; 12 (4):975.
Chicago/Turabian StyleJulia De Niet; David Christian Finger; Arvid Bring; David Egilson; David Gustafsson; Zahra Kalantari. 2020. "Benefits of Combining Satellite-Derived Snow Cover Data and Discharge Data to Calibrate a Glaciated Catchment in Sub-Arctic Iceland." Water 12, no. 4: 975.
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 Umeälven 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.
Jude Lubega Musuuza; David Gustafsson; Rafael Pimentel; Louise Crochemore; Ilias Pechlivanidis. Impact of Satellite and In Situ Data Assimilation on Hydrological Predictions. Remote Sensing 2020, 12, 811 .
AMA StyleJude Lubega Musuuza, David Gustafsson, Rafael Pimentel, Louise Crochemore, Ilias Pechlivanidis. Impact of Satellite and In Situ Data Assimilation on Hydrological Predictions. Remote Sensing. 2020; 12 (5):811.
Chicago/Turabian StyleJude Lubega Musuuza; David Gustafsson; Rafael Pimentel; Louise Crochemore; Ilias Pechlivanidis. 2020. "Impact of Satellite and In Situ Data Assimilation on Hydrological Predictions." Remote Sensing 12, no. 5: 811.
This study details the enhancement and calibration of the Arctic implementation of the HYdrological Predictions for the Environment (HYPE) hydrological model established for the BaySys group of projects to produce freshwater discharge scenarios for the Hudson Bay Drainage Basin (HBDB). The challenge in producing estimates of freshwater discharge for the HBDB is that it spans over a third of Canada’s continental landmass and is 40% ungauged. Scenarios for BaySys require the separation between human and climate interactions, specifically the separation of regulated river discharge from a natural, climate-driven response. We present three key improvements to the modelling system required to support the identification of natural from anthropogenic impacts: representation of prairie disconnected landscapes (i.e., non-contributing areas), a method to generalize lake storage-discharge parameters across large regions, and frozen soil modifications. Additionally, a unique approach to account for irregular hydrometric gauge density across the basins during model calibration is presented that avoids overfitting parameters to the densely gauged southern regions. We summarize our methodologies used to facilitate improved separation of human and climate driven impacts to streamflow within the basin and outline the baseline discharge simulations used for the BaySys group of projects. Challenges remain for modeling the most northern reaches of the basin, and in the lake-dominated watersheds. The techniques presented in this work, particularly the lake and flow signature clusters, may be applied to other high latitude, ungauged Arctic basins. Discharge simulations are subsequently used as input data for oceanographic, biogeochemical, and ecosystem studies across the HBDB.
Tricia A. Stadnyk; Matthew K. Macdonald; Andrew Tefs; Stephen J. Déry; Kristina Koenig; David Gustafsson; Kristina Isberg; Berit Arheimer. Hydrological modeling of freshwater discharge into Hudson Bay using HYPE. Elementa: Science of the Anthropocene 2020, 8, 1 .
AMA StyleTricia A. Stadnyk, Matthew K. Macdonald, Andrew Tefs, Stephen J. Déry, Kristina Koenig, David Gustafsson, Kristina Isberg, Berit Arheimer. Hydrological modeling of freshwater discharge into Hudson Bay using HYPE. Elementa: Science of the Anthropocene. 2020; 8 (1):1.
Chicago/Turabian StyleTricia A. Stadnyk; Matthew K. Macdonald; Andrew Tefs; Stephen J. Déry; Kristina Koenig; David Gustafsson; Kristina Isberg; Berit Arheimer. 2020. "Hydrological modeling of freshwater discharge into Hudson Bay using HYPE." Elementa: Science of the Anthropocene 8, no. 1: 1.
Large parts of the northern hemisphere are covered by snow and seasonal frost. Climate warming is affecting spatiotemporal variations of snow and frost, hence influencing snowmelt infiltration, aquifer recharge and river runoff patterns. Measurement difficulties have hampered progress in properly assessing how variations in snow and frost impact snowmelt infiltration. This has led to contradicting findings. Some studies indicate that groundwater recharge response is scale dependent. It is thus important to measure snow and soil frost properties with temporal and spatial scales appropriate to improve infiltration process knowledge. The main aim with this paper is therefore to review ground based methods to measure snow properties (depth, density, water equivalent, wetness, and layering) and soil frost properties (depth, water and ice content, permeability, and distance to groundwater) and to make recommendations for process studies aiming to improve knowledge regarding infiltration in regions with seasonal frost. Ground-based radar (GBR) comes in many different combinations and can, depending on design, be used to assess both spatial and temporal variations in snow and frost so combinations of GBR and tracer techniques can be recommended and new promising methods (auocostics and self potential) are evolving, but the study design must be adapted to the scales, the aims and the resources of the study.
Angela Lundberg; David Gustafsson; Christine Stumpp; Bjørn Kløve; James Feiccabrino. Spatiotemporal Variations in Snow and Soil Frost—A Review of Measurement Techniques. Hydrology 2016, 3, 28 .
AMA StyleAngela Lundberg, David Gustafsson, Christine Stumpp, Bjørn Kløve, James Feiccabrino. Spatiotemporal Variations in Snow and Soil Frost—A Review of Measurement Techniques. Hydrology. 2016; 3 (3):28.
Chicago/Turabian StyleAngela Lundberg; David Gustafsson; Christine Stumpp; Bjørn Kløve; James Feiccabrino. 2016. "Spatiotemporal Variations in Snow and Soil Frost—A Review of Measurement Techniques." Hydrology 3, no. 3: 28.
An accurate precipitation phase determination—i.e., solid versus liquid—is of paramount importance in a number of hydrological, ecological, safety and climatic applications. Precipitation phase can be determined by hydrological, meteorological or combined approaches. Meteorological approaches require atmospheric data that is not often utilized in the primarily surface based hydrological or ecological models. Many surface based models assign precipitation phase from surface temperature dependent snow fractions, which assume that atmospheric conditions acting on hydrometeors falling through the lower atmosphere are invariant. This ignores differences in phase change probability caused by air mass boundaries which can introduce a warm air layer over cold air leading to more atmospheric melt energy than expected for a given surface temperature, differences in snow grain-size or precipitation rate which increases the magnitude of latent heat exchange between the hydrometers and atmosphere required to melt the snow resulting in snow at warmer temperatures, or earth surface properties near a surface observation point heating or cooling a shallow layer of air allowing rain at cooler temperatures or snow at warmer temperatures. These and other conditions can be observed or inferred from surface observations, and should therefore be used to improve precipitation phase determination in surface models.
James Feiccabrino; William Graff; Angela Lundberg; Nils Sandström; David Gustafsson. Meteorological Knowledge Useful for the Improvement of Snow Rain Separation in Surface Based Models. Hydrology 2015, 2, 266 -288.
AMA StyleJames Feiccabrino, William Graff, Angela Lundberg, Nils Sandström, David Gustafsson. Meteorological Knowledge Useful for the Improvement of Snow Rain Separation in Surface Based Models. Hydrology. 2015; 2 (4):266-288.
Chicago/Turabian StyleJames Feiccabrino; William Graff; Angela Lundberg; Nils Sandström; David Gustafsson. 2015. "Meteorological Knowledge Useful for the Improvement of Snow Rain Separation in Surface Based Models." Hydrology 2, no. 4: 266-288.