Estimating catchment-scale groundwater dynamics from recession analysis – enhanced constraining of hydrological models

Skaugen, Thomas; Mengistu, Zelalem

doi:10.5194/hess-20-4963-2016

Cited by 15 publications

(18 citation statements)

References 40 publications

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“…The distance distribution dynamics (DDD) hydrological model was developed by Skaugen and Onof (2014) and currently runs operationally with daily and 3-hourly time steps at the Norwegian flood forecasting service. The model is a semi-distributed conceptual model, and it is applicable for catchments ranging from small (e.g., 1 km 2 ) to large (e.g., 5000 km 2 ) and temporal resolutions ranging from low (e.g., daily time step) to high (e.g., hourly time step).…”

Section: General Description Of the Modelmentioning

confidence: 99%

Hydrological impacts of climate change on small ungauged catchments – results from a global climate model–regional climate model–hydrologic model chain

Tsegaw

Pontoppidan

Kristvik

et al. 2020

Nat. Hazards Earth Syst. Sci.

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Abstract. Climate change is one of the greatest threats currently facing the world's environment. In Norway, a change in climate will strongly affect the pattern, frequency, and magnitudes of stream flows. However, it is challenging to quantify to what extent the change will affect the flow patterns and floods from small rural catchments due to the unavailability or inadequacy of hydro-meteorological data for the calibration of hydrological models and due to the tailoring of methods to a small-scale level. To provide meaningful climate impact studies at the level of small catchments, it is therefore beneficial to use high-spatial- and high-temporal-resolution climate projections as input to a high-resolution hydrological model. In this study, we used such a model chain to assess the impacts of climate change on the flow patterns and frequency of floods in small ungauged rural catchments in western Norway. We used a new high-resolution regional climate projection, with improved performance regarding the precipitation distribution, and a regionalized hydrological model (distance distribution dynamics) between a reference period (1981–2011) and a future period (2070–2100). The flow-duration curves for all study catchments show more wet periods in the future than during the reference period. The results also show that in the future period, the mean annual flow increases by 16 % to 33 %. The mean annual maximum floods increase by 29 % to 38 %, and floods of 2- to 200-year return periods increase by 16 % to 43 %. The results are based on the RCP8.5 scenario from a single climate model simulation tailored to the Bergen region in western Norway, and the results should be interpreted in this context. The results should therefore be seen in consideration of other scenarios for the region to address the uncertainty. Nevertheless, the study increases our knowledge and understanding of the hydrological impacts of climate change on small catchments in the Bergen area in the western part of Norway.

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Section: General Description Of the Modelmentioning

confidence: 99%

Hydrological impacts of climate change on small ungauged catchments – results from a global climate model–regional climate model–hydrologic model chain

Tsegaw

Pontoppidan

Kristvik

et al. 2020

Nat. Hazards Earth Syst. Sci.

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show abstract

“…The variables are defined as follows: TG is the 24 h average between 06:00 UTC of the day, reported as time stamp, and 06:00 UTC of the previous day; RR is the accumulated precipitation over the same time interval as TG, and RR data have been corrected for the wind-induced under-catch of the gauges; TX and TN are, respectively, the maximum and minimum observed temperatures between 18:00 UTC of the day reported as time stamp and 18:00 UTC of the previous day. TG and RR share the same day definition so as to serve hydrological applications (Saloranta, 2016;Skaugen and Mengistu, 2016). As a result of choices made in the past at MET Norway, TX and TN have a different day definition than RR and TG.…”

Section: Observationsmentioning

confidence: 99%

seNorge_2018, daily precipitation, and temperature datasets over Norway

Lussana

Tveito

Dobler

et al. 2019

Earth Syst. Sci. Data

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Abstract. seNorge_2018 is a collection of observational gridded datasets over Norway for daily total precipitation: daily mean, maximum, and minimum temperatures. The time period covers 1957 to 2017, and the data are presented over a high-resolution terrain-following grid with 1 km spacing in both meridional and zonal directions. The seNorge family of observational gridded datasets developed at the Norwegian Meteorological Institute (MET Norway) has a 20-year-long history and seNorge_2018 is its newest member, the first providing daily minimum and maximum temperatures. seNorge datasets are used for a wide range of applications in climatology, hydrology, and meteorology. The observational dataset is based on MET Norway's climate data, which have been integrated by the “European Climate Assessment and Dataset” database. Two distinct statistical interpolation methods have been developed, one for temperature and the other for precipitation. They are both based on a spatial scale-separation approach where, at first, the analysis (i.e., predictions) at larger spatial scales is estimated. Subsequently they are used to infer the small-scale details down to a spatial scale comparable to the local observation density. Mean, maximum, and minimum temperatures are interpolated separately; then physical consistency among them is enforced. For precipitation, in addition to observational data, the spatial interpolation makes use of information provided by a climate model. The analysis evaluation is based on cross-validation statistics and comparison with a previous seNorge version. The analysis quality is presented as a function of the local station density. We show that the occurrence of large errors in the analyses decays at an exponential rate with the increase in the station density. Temperature analyses over most of the domain are generally not affected by significant biases. However, during wintertime in data-sparse regions the analyzed minimum temperatures do have a bias between 2 ∘C and 3 ∘C. Minimum temperatures are more challenging to represent and large errors are more frequent than for maximum and mean temperatures. The precipitation analysis quality depends crucially on station density: the frequency of occurrence of large errors for intense precipitation is less than 5% in data-dense regions, while it is approximately 30 % in data-sparse regions. The open-access datasets are available for public download at daily total precipitation (https://doi.org/10.5281/zenodo.2082320, Lussana, 2018b); and daily mean (https://doi.org/10.5281/zenodo.2023997, Lussana, 2018c), maximum (https://doi.org/10.5281/zenodo.2559372, Lussana, 2018e), and minimum (https://doi.org/10.5281/zenodo.2559354, Lussana, 2018d) temperatures.

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“…The volume of the saturated zone and the unsaturated zone are inversely related i.e. the higher the unsaturated zone volume, the lower the saturated zone Mengistu, 2016, Skaugen andOnof, 2014).…”

Section: The Subsurface 170mentioning

confidence: 99%

“…The hillslope and river flow dynamics of DDD is hence described by unit hydrographs (UHs) derived from distance distributions from a GIS and celerity derived from recession analysis Mengistu, 2016, Skaugen andOnof, 2014). When the distance distributions are 195 associated with flow celerity of the hillslope and rivers, we obtain the distributions of travel times which constitutes the time area concentration curve (Maidment, 1993).…”

Section: Runoff Dynamicsmentioning

confidence: 99%

Hydrological impacts of climate change on small ungauged catchments-results from a GCM-RCM-hydrologic model chain

Tsegaw¹,

Pontoppidan²,

Kristvik³

et al. 2019

Preprint

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Abstract. Climate change is one of the greatest threats to the World's environment. In Norway, the change will strongly affect the pattern, frequency and magnitudes of stream flows. However, it is highly challenging to quantify to what extent it will affect flow patterns and floods from small ungauged rural catchments due to unavailability or inadequacy of hydro-meteorological data for the calibration of hydrological models and tailoring methods to a small-scale level. To provide meaningful climate impact studies at small catchments, it is therefore beneficial to use high spatial and temporal resolution climate projections as input to a high-resolution hydrological model. Here we use such a model chain to assess the impacts of climate change on flow patterns and frequency of floods in small ungauged rural catchments in western Norway using a new high-resolution regional climate projection, with improved performance with regards to the precipitation distribution, and the regionalized hydrological model (Distance Distribution Dynamics) between the reference period (1981–2011) and a future period (2071–2100). The FDCs of all study catchments show there will be more wetter periods in the future than the reference period. The results also show that in the future period, the mean annual flow increases by 16.5 % to 33.3 %, and there will be an increase in the mean autumn, mean winter and mean spring flows ranging from 4.3 % to 256.3 %. The mean summer flow decreases by 7.2 % to 35.2 %. The mean annual maximum floods increase by 28.9 % to 38.3 %, and floods of 2 to 200 years return periods increase by 16.1 % to 42.7 %. The findings of this study could be of practical use to regional decision-makers if considered alongside other previous and future findings.

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Estimating catchment-scale groundwater dynamics from recession analysis – enhanced constraining of hydrological models

Cited by 15 publications

References 40 publications

Hydrological impacts of climate change on small ungauged catchments – results from a global climate model–regional climate model–hydrologic model chain

Hydrological impacts of climate change on small ungauged catchments – results from a global climate model–regional climate model–hydrologic model chain

seNorge_2018, daily precipitation, and temperature datasets over Norway

Hydrological impacts of climate change on small ungauged catchments-results from a GCM-RCM-hydrologic model chain

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