| 1. |
- Blösch, Günter, et al.
(författare)
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Twenty-three unsolved problems in hydrology (UPH) - a community perspective
- 2019
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Ingår i: Hydrological Sciences Journal. - : Informa UK Limited. - 0262-6667 .- 2150-3435. ; 64:10, s. 1141-1158
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Tidskriftsartikel (refereegranskat)abstract
- This paper is the outcome of a community initiative to identify major unsolved scientific problems in hydrology motivated by a need for stronger harmonisation of research efforts. The procedure involved a public consultation through online media, followed by two workshops through which a large number of potential science questions were collated, prioritised, and synthesised. In spite of the diversity of the participants (230 scientists in total), the process revealed much about community priorities and the state of our science: a preference for continuity in research questions rather than radical departures or redirections from past and current work. Questions remain focused on the process-based understanding of hydrological variability and causality at all space and time scales. Increased attention to environmental change drives a new emphasis on understanding how change propagates across interfaces within the hydrological system and across disciplinary boundaries. In particular, the expansion of the human footprint raises a new set of questions related to human interactions with nature and water cycle feedbacks in the context of complex water management problems. We hope that this reflection and synthesis of the 23 unsolved problems in hydrology will help guide research efforts for some years to come.
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| 2. |
- Feiccabrino, James
(författare)
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Air mass boundary identification : improvement of precipitation phase determination in surface based modeling
- 2012
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Snowpack properties derived from hydrological models play an important role for many ecological, water resource, and climate applications; such as winter survival of plants, reindeer, small mammals and birds, avalanche hazards, glaciers and polar ice accumulation, growth of sea and lake ices, climate change, snow melt flooding etc. These hydrological models need accurate precipitation phase discrimination schemes to closely portray e. g. energy balance for melt and refreeze cycles, water lost to sublimation, and snow water equivalent within a watershed for the above applications. Precipitation phase is seldom reported from automated surface meteorological stations, so most hydrological models apply an empirical formula based on surface air temperature. There are many different empirical formulas used for precipitation type determination in hydrological models. The most commonly used formulas have one or two fixed air temperatures to separate rain from snow, however, some use more elaborate algorithms. The first part of this study consists of a comparison of common precipitation phase determination schemes to a database of 45 years of three-hour man-made weather observations for nineteen Swedish meteorological stations. These observations consist of surface air and dew point temperatures, precipitation mass and phase (classified as snow, rain, or mixed precipitation). Model schemes using two air temperature thresholds, one threshold all snow (TS) and one all rain (TR) having a linear snow fraction decrease between the thresholds (TS = 0.0˚C; TR = 2.0˚C, or TS = -1.0; TR = 3.0˚C) performed better than using a single rain/snow temperature threshold at all but two of 19 stations. A fitted air temperature dependent snow probability polynomial scheme resulted in similar, but slightly improved classification than a linear decreasing snow fraction approach at 13 of 19 locations. However, using the same empirical formula for all surface weather observations is a flawed technique since surface precipitation phase results from energy exchanges between falling precipitation and air in the lower atmosphere. Different lower atmospheric conditions cause dissimilar precipitation phase probabilities for near-freezing temperatures. Directly measured lower atmospheric conditions are seldom available for remote areas. However, meteorological observations occurring before/after similar air mass boundaries can be assumed to have alike atmospheric conditions which vary from most other observations. Therefore, hydrological models can indirectly account for lower atmospheric conditions. The second part of this study used twenty years of manual observations from eight U.S. weather stations to compare misclassified precipitation proportions when analyzing (a) all precipitation observations together and (b) identified cold air mass boundary observations (CAB) and non-CAB observations separately. The CAB observations were identified by a rapid surface air temperature decrease. Applying a linear decrease in snow fraction method, CAB had a TS (0˚C), and TR (4˚C) 1˚C warmer than non-CAB (-1˚C, 3˚C). Analyzing CAB and non-CAB separately reduced misclassified precipitation by 23%, from 7.0 to 5.4%.
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| 3. |
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| 4. |
- Feiccabrino, James, et al.
(författare)
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Improving surface based precipitation phase determination through air mass boundary identification
- 2012
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Ingår i: Nordic Hydrology. - : IWA Publishing. - 0029-1277 .- 1996-9694 .- 1998-9563 .- 2224-7955. ; 43:3, s. 179-191
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Tidskriftsartikel (refereegranskat)abstract
- Most hydrological models apply one empirical formula based on surface air temperature for precipitation phase determination. This approach is flawed as surface precipitation phase results from energy exchanges between falling precipitation and air in the lower atmosphere. Different lower atmospheric conditions cause different precipitation phase probabilities for near-freezing temperatures. Often directly measured lower atmospheric conditions are not available for remote areas. However, meteorological observations occurring before/after similar air mass boundaries have similar atmospheric conditions that vary from most other observations. Therefore, hydrological models can indirectly account for lower atmospheric conditions. Twenty years of manual observations from eight United States weather stations were used to compare misclassified precipitation proportions when analyzing (a) all precipitation observations together and (b) identified cold air mass boundary observations (CAB) and non-CAB observations separately. The CAB observations were identified by a rapid surface air temperature decrease. A two-surface air temperature threshold method with one threshold all snow (T-S degrees C) and one all rain (T-R degrees C) having a linear snow fraction decrease between the thresholds was used. The T-S (0 degrees C), and T-R (4 degrees C) values for CAB were 1 degrees C warmer than for non-CAB (-1 degrees C, 3 degrees C). Analyzing CAB and non-CAB separately reduced misclassified precipitation 23%, from 7.0 to 5.4%.
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| 5. |
- Feiccabrino, James M., et al.
(författare)
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A new GIS landscape classification method for rain/snow temperature thresholds in surface based models
- 2017
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Ingår i: Hydrology Research. - : IWA Publishing. - 1998-9563 .- 0029-1277 .- 2224-7955. ; 48:4, s. 902-914
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Tidskriftsartikel (refereegranskat)abstract
- Landscape air temperature thresholds (TA) and percent misclassified precipitation (error) for 12 years of meteorological observations from 40 stations across the Scandinavian Peninsula were derived and compared using both manual and geographic information system (GIS) landscape classification methods. Dew-point, wet-bulb, and wet bulb 0.5 were also tested. Both classification methods used the following west to east landscape categories: windward (WW) ocean, coast, fjord, hill, and mountain in Norway; and leeward (LW) mountain, hill, rolling terrain, and coast in Sweden. GIS landscape classification has the advantages of automating the classification process and increasing objectivity. The GIS classification was based on station location (LW or WW) relative to the Scandinavian mountain range, and the % water or range of elevation change within 15 km. The GIS and manual method had the same TA for 20 stations, and similar total reduction in error (2.29 to 2.17% respectively) when compared to country TA. Therefore, automated GIS landscape classification can be used to decrease error from common country or global scale TA. Wet-bulb temperature thresholds for GIS landscapes resulted in a greater reduction in error (8.26%) compared to air (2.29%), and dew-point (-16.67%) thresholds. However, finding stations reporting relative humidity or wetbulb temperature may limit its widespread use.
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| 6. |
- Feiccabrino, James M.
(författare)
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Precipitation phase uncertainty in cold region conceptual models resulting from meteorological forcing time-step intervals
- 2020
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Ingår i: Hydrology Research. - : IWA Publishing. - 1998-9563 .- 0029-1277 .- 2224-7955. ; 51:2, s. 180-187
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Tidskriftsartikel (refereegranskat)abstract
- Precipitation phase determination is a known source of uncertainty in surface-based hydrological, ecological, safety, and climate models. This is primarily due to the surface precipitation phase being a result of cloud and atmospheric properties not measured at surface meteorological or hydrological stations. Adding to the uncertainty, many conceptual hydrological models use a 24-h average air temperature to determine the precipitation phase. However, meteorological changes to atmospheric properties that control the precipitation phase often substantially change at sub-daily timescales. Model uncertainty (precipitation phase error) using air temperature (AT), dew-point temperature (DP), and wet-bulb temperature (WB) thresholds were compared using averaged and time of observation readings at 1-, 3-, 6-, 12-, and 24-h periods. Precipitation phase uncertainty grew 35–65% from the use of 1–24 h data. Within a sub-dataset of observations occurring between AT -6 and 6 °C representing 57% of annual precipitation, misclassified precipitation was 7.9% 1 h and 11.8% 24 h. Of note, there was also little difference between 1 and 3 h uncertainty, typical time steps for surface meteorological observations.
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| 7. |
- Feiccabrino, James M.
(författare)
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Reducing misclassified precipitation phase in conceptual models using cloud base heights and relative humidity to adjust air temperature thresholds
- 2021
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Ingår i: Hydrology Research. - : IWA Publishing. - 1998-9563 .- 0029-1277 .- 2224-7955. ; 52:3, s. 749-759
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Tidskriftsartikel (refereegranskat)abstract
- In cold region, conceptual models assigned precipitation phase, liquid (rain) or solid (snow), cause vastly different atmospheric, hydrological, and ecological responses, along with significant differences in evaporation, runoff, and infiltration fates for measured precipitation mass. A set air temperature threshold (ATT) applied to the over 30% annual precipitation events occurring with surface air temperatures between -3 and 5 °C resulted in 11.0 and 9.8% misclassified precipitation in Norway and Sweden, respectively. Surface air temperatures do not account for atmospheric properties causing precipitation phase changes as snow falls toward the ground. However, cloud base height and relative humidity (RH) measured from the surface can adjust ATT for expected hydrometeor-atmosphere interactions. Applying calibrated cloud base height ATTs or a linear RH function for Norway (Sweden) reduced misclassified precipitation by 4.3% (2.8%) and 14.6% (8.9%) misclassified precipitation, respectively. Cloud base height ATTs had lower miss-rates with low cloud bases, 100 m in Norway and 300 m in Sweden. Combining the RH method with cloud base ATT in low cloud conditions resulted in 16.1 and 10.8% reduction in misclassified precipitation in Norway and Sweden, respectively. Therefore, the conceptual model output should improve through the addition of available surface data without coupling to an atmospheric model.
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| 8. |
- Feiccabrino, James, et al.
(författare)
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Meteorological Knowledge Useful for the Improvement of Snow Rain Separation in Surface Based Models
- 2015
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Ingår i: Hydrology. - : MDPI AG. - 2306-5338. ; 2:4, s. 266-288
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 9. |
- Feiccabrino, James
(författare)
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Precipitation phase determination in cold region conceptual models - analysis and method development
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Precipitation phase uncertainty is a known source of error in conceptual models used for many hydrological, climatological, and environmental applications. These conceptual models often use the simple approach of calibrating an air temperature threshold (TRS) over a large area irrespective of physiographic characteristics such as distance to the ocean and topographic relief. Conceptual modeling requires empirical formulas to simplify physical processes. However, there is a plethora of literature against this TRS approach. The magnitude of uncertainty caused by the use of a set TRS is greatest in areas such as Scandinavia, where an average of 39% annual station precipitation occurs in the air temperature (TA) range of -3 to 5°C. One argument for the use of set threshold temperatures in conceptual models was the reduction of computational load, but this came at the cost of accuracy. To compound this error, surface conditions only have a minor contribution to the surface precipitation phase. Instead, microphysics (air-hydrometeor energy exchanges) and properties of the air in the lower atmosphere are the major influences on the observed precipitation phase. However, without adding atmospheric data, improvements to cold region conceptual model, precipitation phase can be achieved through the use of other reported surface data. Meteorological data from 169 observing stations were used to determine percent misclassified precipitation when air temperature (TA), Wet-bulb temperature 0.5°C (TW 0.5°C) thresholds, and an air temperature adjusted by relative humidity (TRH) = 0.75+0.085*(100-RH) formula were applied. The main dataset had roughly 400,000 precipitation events between TA -3 and 5°C. When analysed by country, Norwegian stations had average misclassified precipitation of 10.8% (1.2°C) for TA, 8.3% for TW 0.5°C, and 8.7% for TRH. In comparison, Swedish stations had misclassified precipitation totals of 9.3% (0.9°C) for TA, 8.2% for TW 0.5°C, and 8.7% for TRH. TW 0.5°C resulted in the least misclassified precipitation for both countries. However, saturation vapor pressure, relative humidity (RH), and other parameters required to calculate TW are often not reported by hydrological or meteorological stations. Therefore, improvement in TA methods is preferential over TW or RH methods. Cloud base height TA thresholds were found to increase with height and could be used as a proxy for RH. Cloud base height thresholds had 10.3%, and 9.1% misclassified precipitation in Norway and Sweden respectively. This method had greater error than RH, but performed better in low cloud conditions (100m in Norway and 300m in Sweden), so combining the methods is an option. However, cloud base height is not reported by all stations. If restricted to TA methods, sub-grouping stations by physiographic characteristics within a 15km radius decreased TA misclassified precipitation by 0.5% in Norway with little change in Sweden. This is a result of the Norwegian landscape varying to a greater extent than in Sweden. TA thresholds in Norway ranged from 2.4°C for ocean platforms to 0.9°C in the hills. Particularly high misclassified precipitation rates in mountains and hills can be reduced by nearly 10% when assigning TA for different station sub-groups using 1km maximum elevation or relief. For oceans/coast stations, TA assigned for water temperature sub-groups (reported by 16 stations) reduced misclassified precipitation by 17%. Models applying a daily TA threshold, had precipitation phase uncertainty reduced 10% with RH methods. Changing to an hourly timestep reduced error by more than 29%. Therefore, decreasing temporal resolution to 1-hour was more beneficial than adding parameters to the 24-hour model.
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| 10. |
- Feiccabrino, James, et al.
(författare)
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Precipitation phase discrimination by dew point and air temperature
- 2008
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Ingår i: Proceedings of the Western Snow Conference, Kailua-Kona, Hawaii, April, 16 - 19, 2007. - Soda Springs, Calif : Western Snow Conference. ; , s. 141-145
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Konferensbidrag (refereegranskat)abstract
- Correctly reported precipitation phases are crucial for estimation of snow storage in hydrological, regional and global climate models. Precipitation phase is especially critical for models simulating processes in tree canopies, since the canopy storage capacity is about one order of magnitude larger for snow than rain. The number of manned meteorological stations is decreasing, making determination of precipitation phase more difficult. Most hydrological models use an air temperature threshold to separate rain from snow, but there are indications that a dew-point temperature threshold might work better. This study utilized forty-five years of three-hour man-made observations for nineteen Swedish station ranging from 55˚N to 68˚N consisting of precipitation mass and phase, air and dew point temperatures. Precipitation events classed as snow or rain, excluding mixed precipitation, were used for the initial analysis. Air temperature was found to be a better indicator of precipitation phase then dew point temperature. On occasion 0˚C is used as an air temperature threshold, but if the air temperature rain/snow threshold 0˚C is replaced by 1.0˚C the misclassified precipitation would be reduced by almost half in Sweden. Further analysis to identify mixed precipitation, totaling 16% of the precipitation is also included.
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| 11. |
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| 12. |
- Feiccabrino, James, et al.
(författare)
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Surface-based precipitation phase determination methods in hydrological models
- 2013
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Ingår i: Hydrology Research. - : IWA Publishing. - 1998-9563 .- 0029-1277 .- 2224-7955. ; 44:1, s. 44-57
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Tidskriftsartikel (refereegranskat)abstract
- We compared solid and liquid precipitation mass output from three categories of common model precipitation phase determination schemes (PPDS) to the recorded precipitation phase in a set of 45 years of 3-hour manual meteorological observations from 19 Swedish meteorological stations. In the first category of rain/snow thresholds, it was found that rain/snow air temperature threshold (ATT) is a better precipitation phase indicator than a rain/snow dew point temperature threshold. When a rain/snow ATT of 0.0 degrees C (a default value used in some recent models) was replaced by 1.0 degrees C, misclassified precipitation was reduced by almost one half. A second category of PPDS use two ATTs, one snow and one rain, with a linear decrease in snow fraction between. This category identified precipitation phase better than a rain/snow ATT at 17 stations. Using all observations from all the meteorological stations, a final category using an air-temperature-dependent snow probability curve resulted in slightly lower misclassified precipitation mass at 13 of the 19 stations. However, schemes from the linear decrease in snow fraction category had the lowest misclassified precipitation mass at four meteorological stations.
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| 13. |
- Granlund, Nils, et al.
(författare)
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Laboratory test of snow wetness influence on electrical conductivity measured with ground penetrating radar
- 2009
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Ingår i: Nordic Hydrology. - : IWA Publishing. - 0029-1277 .- 1996-9694 .- 1998-9563 .- 2224-7955. ; 40:1, s. 33-44
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Tidskriftsartikel (refereegranskat)abstract
- Ground penetrating radar operated from helicopters or snowmobiles is used to determine snow water equivalent (SWE) for annual snowpacks from radar wave two-way travel time. However, presence of liquid water in a snowpack is known to decrease the radar wave velocity, which for a typical snowpack with 5% (by volume) liquid water can lead to an overestimation of SWE by about 20%. It would therefore be beneficial if radar measurements could also be used to determine snow wetness. Our approach is to use radar wave attenuation in the snowpack, which depends on electrical properties of snow (permittivity and conductivity) which in turn depend on snow wetness. The relationship between radar wave attenuation and these electrical properties can be derived theoretically, while the relationship between electrical permittivity and snow wetness follows a known empirical formula, which also includes snow density. Snow wetness can therefore be determined from radar wave attenuation if the relationship between electrical conductivity and snow wetness is also known. In a laboratory test, three sets of measurements were made on initially dry 1m thick snowpacks. Snow wetness was controlled by stepwise addition of water between radar measurements, and a linear relationship between electrical conductivity and snow wetness was established.
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| 14. |
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| 15. |
- Grigg, Laurie D., et al.
(författare)
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Testing the applicability of physiographic classification methods toward improving precipitation phase determination in conceptual models
- 2020
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Ingår i: Hydrology Research. - : IWA Publishing. - 1998-9563 .- 0029-1277 .- 2224-7955. ; 51:2, s. 169-179
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Tidskriftsartikel (refereegranskat)abstract
- Regions with a large percentage of precipitation occurring near freezing experience high percentages (>10%) of misclassified precipitation events (rain versus snow) and necessitate efforts to improve precipitation phase determination schemes through the use of more accurate surface air temperature thresholds (Trs). Meteorological data from 169 sites in Scandinavia were used to test the applicability of using physiographic categories to determine Trs. Three classification methods involving varying degrees of automation were evaluated. The two automated methods tested did not perform as well as when tested on a smaller region, showing only 0.16% and 0.20% reduction in error. A semi-manual method produced the largest average reduction in misclassified precipitation (0.53%) across all sites. Further refinement of classification criteria for mountain and hill stations showed that at mesoscales (>5 km), maximum elevation is a better predictor of Trs (0.89% average reduction in error) than terrain relief (0.22%), but that relief becomes increasingly important at microscales (0.90%). A new method for categorizing mountainous stations based on upslope or downslope air movement increased the average reduction in error up to 0.53%. These results provide a framework for future landscape classification methods and confirm the importance of microscale topography for determining Trs in alpine regions.
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| 16. |
- Gustafsson, David, et al.
(författare)
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Distribuerade system för förbättrade snö- och avrinningsprognoser : Integration i hydrologiska modeller. Slutrapport
- 2012
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Det övergripande målet med projektet har varit att minska totala volymfelet i prognoser för vårflödesavrinningen samt att förbättra tids- och volym-bestämningen av flödespikarna för dessa. Projektet har fokuserat på att kombinera utveckling av modell- och mätteknik för att studera hur modellstrukturer och metoder för att integrera mätinformation (data-assimilering) kan optimeras i förhållande till tillgänglig snöinformation. Ett syfte har också varit att de utvecklade metoderna skall vara operationellt användbara och baserade på kostnads- och tidseffektiva mättekniker och modelleringsverktyg, samtidigt som de skall ge en betydande förbättring av prognoserna. I projektet har en rad mättekniker testats och vidareutvecklas (tex snökuddar, automatiska sensorer för snödjup- och densitet, samt markradartekniker). Störst fokus har varit på vidareutveckling av radarteknik för linjemätning av snötäckets djup, densitet och fuktighet. För torr snö har djup och densitet uppskattats med hjälp av radarvågornas snöutbredningshastighet direkt från radardata med ett flerkanalradarsystem, [så kallad ”common-mid point” (CMP) metod)]. För blöt snö krävs förutom utbrednings-hastigheten också information om snöns fuktighet för korrigering av uppskattningen av snöns densitet. Inom projektet har därför en ny metod utvecklats för bestämning av snös fuktighet baserad på det faktum att utsläckningen av radarsignalens amplitud beror på snöns fuktighet. Två olika hydrologiska modeller har använts inom projektet: SMHI:s nya vattenbalans- och vattenkvalitetsmodell HYPE samt en egenutvecklad modell. Den senare modellen har utvecklats för att kunna jämföra tillrinnings-prognosernas känslighet för val av snömodellstruktur (representation av processer och distribution i tid och rum). Modellen består av en rumsligt distribuerad snömodell kopplad till en odistribuerad avrinningsmodell (en förenklad variant av HBV-modellen). Modellen utvecklades inom det hydrologiska modelleringssystemet HYSS utvecklat på SMHI, men kan i princip kopplas till vilken modellplattform som helst. Snö-smältningen kan beräknas antingen med temperatur- och strålnings-indexmetod eller med energibalansmetod. Den rumsliga distribueringen kan göras antingen med ett 2-dimensionellt nät eller genom uppdelning av avrinningsområdet i representativa enheter baserad på klassificering av topografi (höjd, lutning väderstreck) och vegetation. HYPE-modellen har för närvarande en enklare snömodell än den egenutvecklade modellen, men erbjuder istället hög rumslig uppdelning, öppen källkod (HYPE Open Source Community) och en enkel hantering av drivdata och modelluppsättningar för nya områden genom den operationella sverigeapplikationen (S-HYPE). HYPE-modellen har därför använts för att göra projektets modellutveckling lättare tillgänglig för andra. Den har också använts för att jämföra värdet av assimilering av snödata med värdet av val av prognosdata för nederbörd och temperatur. På sikt kan den egenutvecklade snömodellen göras tillgänglig som en valbar modul i HYPE. En dataassimileringsrutin baserad på Ensemble Kalmanfilter (EnKF) har utvecklats för integrering av snöinformation i simuleringarna och har implementerats som en modul i HYPE. Med EnKF metoden uppdateras modelltillstånd som funktion av kovariansen mellan modelltillstånd och modellfel. Uppdateringen sker sekventiellt, det vill säga under simuleringens gång vartefter nya observationer tillkommer. Kovariansen mellan modelltillstånd och modellfel uppskattas genom att skapa en ensemble av modeller med en viss spridning i modelltillstånden. Spridningen genereras genom att köra flera parallella modeller med slumpmässiga avvikelser i drivvariabler och parametervärden. En styrka med metoden är att osäkerheter i observationer, modellparametrar och indata kan uppskattas var för sig och användas för en automatisk uppdatering av modelltillstånden. Resterande spridning i den uppdaterade prognosen nyttjas för skattning av osäkerheten i resultaten. Beräkningsbördan ökar jämfört med en enskild simulering (ca 100 ensemblemedlemmar behövs), men jämfört med andra dataassimileringsmetoder är EnKF metoden mycket effektiv. De flesta hydrologiska modeller använder samma tröskeltemperatur för att skilja på regn och snö för alla nederbördstillfällen Förhållanden högre upp i atmosfären påverkar emellertid också hur stor andel av nederbörden som faller som snö respektive regn vid en viss markytetemperatur. Situationen i atmosfären beror i sin tur till stor del på vilken typ av front (gräns mellan luftmassor med olika temperatur) som producerar nederbörden. Vi har visat att man kan minska andelen felklassad nederbörd genom att identifiera vilken typ av front (varm- eller kall) som orsakar nederbörden vid ett specifikt tillfälle och anpassa tröskeltemperaturen efter fronttypen. Simuleringar med det nyutvecklade modellsystemet för testområdet Kultsjön i Västerbotten visar att assimilering med EnKF av distribuerade snödata förbättrade vårflodsprognoserna samtliga 4 år i delområdet Kultsjön och 3 av 4 år i delområdet Ransarn. Den relativa förbättringen var i medel 10-15 % beroende på vilka drivdata som användes. Störst förbättring av vårflodsprognosen, jämfört med den traditionella metoden med ensembler av historiska år, erhölls emellertid genom att använda säsongsprognoser från ECMWF (European Centre for Medium Range Weather Forecasts) som drivdata. Det var överraskande att dessa simuleringar gav bättre resultat än simuleringar med stationsmätningar. En möjlig förklaring kan vara att den interpolation av stationsdata som ligger till grund för SMHIs operationella drivdata (nederbörd och temperatur, PTHBV) kan ge både över- och underskattning av nederbörd i fjällområden beroende på om vädersystemen kommer från väster eller öster. Medelvolymfelet för Kultsjön förbättrades från 17 % till 8 % för de undersökta åren när en kombination av säsongsprognoser från ECMWF och assimilering av snöradardata användes istället för en deterministisk PTHBV-simulering. Den utvecklade dataassimileringstekniken har således visats sig vara ett effektivt sätt att automatiskt uppdatera modellerna inför vårflodsprognosen, och bör enkelt kunna anpassas för operationell användning. Det är också tydligt att assimilering av väderprognosdata från ECWMF gav en bättre prognos för Kultsjöns avrinningsområde än nuvarande PTHBV data. Mer arbete med att förstå hur osäkerheter och korrelationer i såväl snödata som modelldata krävs dock för att med säkerhet slå fast att målsättningarna i projektet har uppnåtts. Användningen av väderprognosdata som input i kombination med assimilering av snödata var mycket lovande och bör vidareutvecklas.
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| 17. |
- Lundberg, Angela, et al.
(författare)
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Sea ice growth : modeling of precipitation phase
- 2009
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Ingår i: Proceedings of the 20th International Conference on Port and Ocean Engineering under Arctic Conditions.
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Konferensbidrag (refereegranskat)abstract
- Snow insulates and changes ice albedo, therefore the precipitation phase identification scheme is important when modeling lake and sea ice growth. Precipitation phase separation schemes in coupled atmospheric-ice models are usually based on air temperatures but, snow fractions as a function of air temperature vary between models. Two examples of models which use 2-temperature thresholds, one for all rain and one for all snow with a linear decrease in snow fraction in-between, are the CAM-3 model used by National Centre for Atmospheric Research NCAR and the coupled Ocean Sea-Ice Model for Earth Simulators (OIFES). CAM-3 simulates 50% snow at 0°C while OIFES simulates 0% snow at the same temperature. Forty-five years of three-hour man-made precipitation phase observations for nineteen Swedish meteorological stations were used to compare different phase separation schemes. Observations of mixed precipitation were included (assumed to be half rain and half snow). A larger fraction (about 70%) of the precipitation was found to be snow at zero degrees as compared to the fractions simulated with the models mentioned above. This indicates that too large a fraction of the precipitation is classified as rain in these models. Consequently they underestimate the insulation of the snow as well as the albedo. For example, the reduction in (conduction driven) ice growth for a 0.5-m ice with 0.1-m low density (100 kg m-3) snow cover is about 90% compared to pure ice. Solar radiation absorption on the other hand is overestimated and this counterbalance might explain why the models perform fairly well with regard to ice growth even if the snow fraction is underestimated.
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| 18. |
- Lundberg, Angela, et al.
(författare)
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Spatiotemporal Variations in Snow and Soil Frost : A Review of Measurement Techniques
- 2016
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Ingår i: Hydrology. - : MDPI AG. - 2306-5338. ; 3:3
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Tidskriftsartikel (refereegranskat)abstract
- 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. View Full-Text
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| 19. |
- Lundberg, Angela, et al.
(författare)
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Urban snow deposits versus snow cooling plants in northern Sweden : A quantitative analysis of snow melt pollutant releases
- 2014
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Ingår i: Water quality research journal of Canada. - : IWA Publishing. - 1201-3080 .- 2408-9443. ; 49:1, s. 32-42
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Tidskriftsartikel (refereegranskat)abstract
- High-velocity runoff from snow deposit transports suspended grain-attached contaminants while underground snow storages trapped these contaminants within the storage. The aim here is to quantify pollutant masses from an urban snow deposit and to investigate the conditions when pollutant control was increased by turning a snow deposit into a snow cooling plant with permeable underground snow storage. Pollutant masses in an urban snow deposit in northern Sweden were: Cu = 67, Pb = 17, Zn = 160, P = 170, SS = 620, 000, Cl = 1, 200, N = 380 kg. A theoretical analysis showed that the fraction of surface runoff from a surface deposit largely depends on the hydraulic conductivity (K, m s-1) of the soil. For a melt rate of 30 mm, day-1, surface runoff would be about 97% for a soil with K = 10-8, while nonexistent for K>10-6. Similar soil conductivities are needed to ensure that all snow melt could be transported as groundwater from an underground storage. The largest pollution-control advantage with underground snow storage compared to a surface deposit would thus be that piping and filters for operation of the plant could be used to filter surface snow melt runoff before rejection
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| 20. |
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