scholarly journals Spatio-temporal precipitation error propagation in runoff modelling: a case study in central Sweden

2006 ◽  
Vol 6 (4) ◽  
pp. 597-609 ◽  
Author(s):  
J. Olsson
Keyword(s):  
Winter Precipitation ◽  
Early Spring ◽  
Time Variability ◽  
Spring Flood ◽  
Study Region ◽  
Flood Peak ◽  
Spring Runoff ◽  
Precipitation Estimates ◽  
Spatio Temporal ◽  
Hbv Model

Abstract. The propagation of spatio-temporal errors in precipitation estimates to runoff errors in the output from the conceptual hydrological HBV model was investigated. The study region was the Gimån catchment in central Sweden, and the period year 2002. Five precipitation sources were considered: NWP model (H22), weather radar (RAD), precipitation gauges (PTH), and two versions of a mesoscale analysis system (M11, M22). To define the baseline estimates of precipitation and runoff, used to define seasonal precipitation and runoff biases, the mesoscale climate analysis M11 was used. The main precipitation biases were a systematic overestimation of precipitation by H22, in particular during winter and early spring, and a pronounced local overestimation by RAD during autumn, in the western part of the catchment. These overestimations in some cases exceeded 50% in terms of seasonal subcatchment relative accumulated volume bias, but generally the bias was within ±20%. The precipitation data from the different sources were used to drive the HBV model, set up and calibrated for two stations in Gimån, both for continuous simulation during 2002 and for forecasting of the spring flood peak. In summer, autumn and winter all sources agreed well. In spring H22 overestimated the accumulated runoff volume by ~50% and peak discharge by almost 100%, owing to both overestimated snow depth and precipitation during the spring flood. PTH overestimated spring runoff volumes by ~15% owing to overestimated winter precipitation. The results demonstrate how biases in precipitation estimates may exhibit a substantial space-time variability, and may further become either magnified or reduced when applied for hydrological purposes, depending on both temporal and spatial variations in the catchment. Thus, the uncertainty in precipitation estimates should preferably be specified as a function of both time and space.

scholarly journals Geo-climatic hazards in the eastern subtropical Andes: distribution, climate drivers and trends

2020 ◽  
Vol 20 (5) ◽  
pp. 1353-1367 ◽  
Author(s):  
Iván Vergara ◽  
Stella M. Moreiras ◽  
Diego Araneo ◽  
René Garreaud
Keyword(s):  
20Th Century ◽  
Meteorological Data ◽  
Winter Precipitation ◽  
Positive Trend ◽  
Study Region ◽  
Climatic Hazards ◽  
Eastern Zone ◽  
Western Zone ◽  
Spatio Temporal

Abstract. Detecting and understanding historical changes in the frequency of geo-climatic hazards (G-CHs) is crucial for the quantification of current hazards and project them into the future. Here we focus in the eastern subtropical Andes (32–33∘ S), using meteorological data and a century-long inventory of 553 G-CHs triggered by rainfall or snowfall. We first analyse their spatio-temporal distributions and the role of climate variability in the year-to-year changes in the number of days per season with G-CHs. Precipitation is positively correlated with the number of G-CHs across the region and year-round; mean temperature is negatively correlated with snowfall-driven hazards in the western (higher) half of the study region during winter and with rainfall-driven hazards in the eastern zone during summer. The trends of the G-CH frequency since the mid-20th century were calculated, paying attention to their non-systematic monitoring. The G-CH series for the different triggers, zones and seasons were generally stationary. Nonetheless, there is a small positive trend in rainfall-driven G-CHs in the eastern zone during summer, congruent with a rainfall increase there. We also found a decrease in snowfall-driven G-CHs in the western zone from the late 1990s onwards, most likely due to a reduction in winter precipitation rather than to an increase in temperature.

scholarly journals Geo-climatic hazards in the eastern subtropical Andes: Distribution, Climate Drivers and Trends

2020 ◽  
Author(s):  
Iván Vergara ◽  
Stella M. Moreiras ◽  
Diego Araneo ◽  
René Garreaud
Keyword(s):  
Meteorological Data ◽  
Winter Precipitation ◽  
Positive Trend ◽  
Future Projection ◽  
Study Region ◽  
Climatic Hazards ◽  
Eastern Zone ◽  
Western Zone ◽  
Spatio Temporal

Abstract. Detection and understanding of historical changes in the frequency of geo-climatic hazards (G-CHs) is crucial for the quantification of current hazard and their future projection. Here we focus in the eastern subtropical Andes (32–33° S), using meteorological data and a century-long inventory on 553 G-CHs triggered by rainfall or snowfall. First we analysed their spatio-temporal distributions and the role of climate variability on the year-to-year changes in the number of days with G-CHs. Precipitation is positively correlated with the number of G-CHs across the region and year-round; mean temperature is negatively correlated with snowfall-driven hazards in the western (higher) half of the study region during winter, and with rainfall-driven hazards in the eastern zone during summer. The trends of the G-CHs frequency since the mid-20th century were calculated taking cautions for their non-systematic monitoring. The G-CHs series for the different triggers, zones and seasons were generally stationary. Nonetheless, there is a small positive trend in rainfall-driven G-CHs in the eastern zone during summer congruent with a rainfall increase there. We also found a decrease in snowfall-driven G-CHs in the western zone since the late 1990's onwards, most likely due to a reduction in winter precipitation rather than an increase in temperature.

scholarly journals Validation of CHIRPS Precipitation Estimates over Taiwan at Multiple Timescales

2021 ◽  
Vol 13 (2) ◽  
pp. 254 ◽  
Author(s):  
Jie Hsu ◽  
Wan-Ru Huang ◽  
Pin-Yi Liu ◽  
Xiuzhen Li
Keyword(s):  
Annual Cycle ◽  
Interannual Variation ◽  
Winter Precipitation ◽  
Monthly Precipitation ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Precipitation Variation ◽  
Global Precipitation ◽  
Multiple Timescale ◽  
Better Than

The Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), which incorporates satellite imagery and in situ station information, is a new high-resolution long-term precipitation dataset available since 1981. This study aims to understand the performance of the latest version of CHIRPS in depicting the multiple timescale precipitation variation over Taiwan. The analysis is focused on examining whether CHIRPS is better than another satellite precipitation product—the Integrated Multi-satellitE Retrievals for Global Precipitation Mission (GPM) final run (hereafter IMERG)—which is known to effectively capture the precipitation variation over Taiwan. We carried out the evaluations made for annual cycle, seasonal cycle, interannual variation, and daily variation during 2001–2019. Our results show that IMERG is slightly better than CHIRPS considering most of the features examined; however, CHIRPS performs better than that of IMERG in representing the (1) magnitude of the annual cycle of monthly precipitation climatology, (2) spatial distribution of the seasonal mean precipitation for all four seasons, (3) quantitative precipitation estimation of the interannual variation of area-averaged winter precipitation in Taiwan, and (4) occurrence frequency of the non-rainy grids in winter. Notably, despite the fact that CHIRPS is not better than IMERG for many examined features, CHIRPS can depict the temporal variation in precipitation over Taiwan on annual, seasonal, and interannual timescales with 95% significance. This highlights the potential use of CHIRPS in studying the multiple timescale variation in precipitation over Taiwan during the years 1981–2000, for which there are no data available in the IMERG database.

scholarly journals L’hydrologie printanière de la rivière Saint-François

2005 ◽  
Vol 9 (18) ◽  
pp. 197-221 ◽  
Author(s):  
Pierre Cazalis
Keyword(s):  
Electric Power ◽  
Winter Precipitation ◽  
Flood Damage ◽  
Critical Conditions ◽  
Spring Flood ◽  
Run Off ◽  
Specific Discharge ◽  
Steep Slopes ◽  
Annual Discharge

The abundant spring run-off in Southern Québec is a result of the heavy winter precipitation and the length of the retaining period. One half of the annual discharge occurs in March, April and May, yet the maximum monthly coefficient (April) on the Saint-François is little more than 300. This low figure is due to the length of the thawing season, which extends the flood over at least four weeks, and to the retaining action of the numerous lakes. Occasionally a heavy spring rainfall may alter the character of the run-off, but even then there is never any question of spring flood damage to land or property — the river s are swollen rather than in flood. Critical conditions can arise however on the Saint-François following storm rains and rapid run-off (impermeability and steep slopes). The water rises rapidly, but the fall extends over a week. These floods are more severe than in spring, but damage is still minimal, the lakes in fact store 50% of the surface run-off and in the case of certain tributaries, 75%. Furthermore, the maximum specific discharge is not more than 20 cu. ft/sec/sq. m. for the regulated tributaries (Magog, Massawippï) compared with 80 or more for those that are not. Through the regulating influence of the main tributaries and that of the hydro-electric power dams on the Saint-François itself], the regime of the river is one of the most serene in Southern Québec.

scholarly journals Spatio-temporal modelling for non-stationary point referenced data

2021 ◽  
Author(s):  
Lindsay Morris
Keyword(s):  
Random Forest ◽  
Goodness Of Fit ◽  
Clustering Algorithm ◽  
Dependence Structure ◽  
Geostatistical Model ◽  
Study Region ◽  
Global Estimate ◽  
Covariance Functions ◽  
Power Of The Test ◽  
Spatio Temporal

<p><b>Spatial and spatio-temporal phenomena are commonly modelled as Gaussian processes via the geostatistical model (Gelfand & Banerjee, 2017). In the geostatistical model the spatial dependence structure is modelled using covariance functions. Most commonly, the covariance functions impose an assumption of spatial stationarity on the process. That means the covariance between observations at particular locations depends only on the distance between the locations (Banerjee et al., 2014). It has been widely recognized that most, if not all, processes manifest spatially nonstationary covariance structure Sampson (2014). If the study domain is small in area or there is not enough data to justify more complicated nonstationary approaches, then stationarity may be assumed for the sake of mathematical convenience (Fouedjio, 2017). However, relationships between variables can vary significantly over space, and a ‘global’ estimate of the relationships may obscure interesting geographical phenomena (Brunsdon et al., 1996; Fouedjio, 2017; Sampson & Guttorp, 1992). </b></p> <p>In this thesis, we considered three non-parametric approaches to flexibly account for non-stationarity in both spatial and spatio-temporal processes. First, we proposed partitioning the spatial domain into sub-regions using the K-means clustering algorithm based on a set of appropriate geographic features. This allowed for fitting separate stationary covariance functions to the smaller sub-regions to account for local differences in covariance across the study region. Secondly, we extended the concept of covariance network regression to model the covariance matrix of both spatial and spatio-temporal processes. The resulting covariance estimates were found to be more flexible in accounting for spatial autocorrelation than standard stationary approaches. The third approach involved geographic random forest methodology using a neighbourhood structure for each location constructed through clustering. We found that clustering based on geographic measures such as longitude and latitude ensured that observations that were too far away to have any influence on the observations near the locations where a local random forest was fitted were not selected to form the neighbourhood. </p> <p>In addition to developing flexible methods to account for non-stationarity, we developed a pivotal discrepancy measure approach for goodness-of-fit testing of spatio-temporal geostatistical models. We found that partitioning the pivotal discrepancy measures increased the power of the test.</p>

Commercial microwave links instead of rain gauges: fiction or reality?

2014 ◽  
Vol 71 (1) ◽  
pp. 31-37 ◽  
Author(s):  
Martin Fencl ◽  
Jörg Rieckermann ◽  
Petr Sýkora ◽  
David Stránský ◽  
Vojtěch Bareš
Keyword(s):  
Rainfall Variability ◽  
A Priori ◽  
Rain Gauges ◽  
Precipitation Estimates ◽  
Spatio Temporal ◽  
Areal Rainfall ◽  
Quantitative Precipitation Estimates ◽  
Commercial Microwave Links ◽  
Unique Dataset

Commercial microwave links (MWLs) were suggested about a decade ago as a new source for quantitative precipitation estimates (QPEs). Meanwhile, the theory is well understood and rainfall monitoring with MWLs is on its way to being a mature technology, with several well-documented case studies, which investigate QPEs from multiple MWLs on the mesoscale. However, the potential of MWLs to observe microscale rainfall variability, which is important for urban hydrology, has not been investigated yet. In this paper, we assess the potential of MWLs to capture the spatio-temporal rainfall dynamics over small catchments of a few square kilometres. Specifically, we investigate the influence of different MWL topologies on areal rainfall estimation, which is important for experimental design or to a priori check the feasibility of using MWLs. In a dedicated case study in Prague, Czech Republic, we collected a unique dataset of 14 MWL signals with a temporal resolution of a few seconds and compared the QPEs from the MWLs to reference rainfall from multiple rain gauges. Our results show that, although QPEs from most MWLs are probably positively biased, they capture spatio-temporal rainfall variability on the microscale very well. Thus, they have great potential to improve runoff predictions. This is especially beneficial for heavy rainfall, which is usually decisive for urban drainage design.

ASSESSMENT OF CHANGES IN THE CHARACTERISTICS OF THE WATER, SNOW AND ICE REGIMES IN THE BASINS OF THE SMALL NORTHERN DVINA RIVER UNDER MODERN CONDITIONS AND THEIR POSSIBLE CHANGES IN THE FUTURE

2021 ◽  
pp. 117-127
Author(s):  
M. V. GEORGIEVSKY ◽  
◽  
N. I. GOROSHKOVA ◽  
V. A. KHOMYAKOVA ◽  
A. V. STRIZHENOK
Keyword(s):  
Snow Cover ◽  
Climate Warming ◽  
Snow Water Equivalent ◽  
Winter Precipitation ◽  
Spring Runoff ◽  
The Future ◽  
Snow Water ◽  
Project Data ◽  
The Impact ◽  
Slight Downward Trend

The article presents an analysis of the impact of climate change on the main characteristics of ice phenomena, snow cover and the water regime in the Small Northern Dvina River basin occurring in recent decades. Recently, a significant climate warming has been observed in the basin. As a result, winters are getting warmer and shorter. There is also an increase in winter precipitation and the number of thaws. Climate warming directly affects the duration of snow cover, which decreases both due to the later formation and to the earlier destruction of snow. There is also a slight downward trend in the annual values of the maximum snow water equivalent, which may be the result of an increase in the number of thaws in winter, when a part of the snow cover melts contributing to the winter river runoff. The analysis of the main characteristics of the ice cover on the rivers of the studied basin shows that their changes are similarly to changes in the snow cover: there is a reduction in the freeze-up period due to its later formation and earlier complete destruction. The maximum ice thickness on the rivers of the basin also tends to decrease. There is an increase in winter and a decrease in spring runoff. Predictive estimates of changes in the observed trends in the future are presented in the fi nal part of the article based on the CMIP5 project data.

scholarly journals Factors delaying spring runoff in the upper Ürümqi River basin, China

1992 ◽  
Vol 16 ◽  
pp. 225-230 ◽  
Author(s):  
Richard Kattelmann ◽  
Yang Daqing
Keyword(s):  
Snow Cover ◽  
River Basin ◽  
Early Spring ◽  
Ice Formation ◽  
Stream Channels ◽  
Spring Runoff ◽  
Basal Ice ◽  
Interface Growth ◽  
Streamflow Generation ◽  
Soil Interface

Although less than 100 mm of precipitation generally falls during the winter months in the upper Ürümqi River basin, an uneven snowpack of 20 to 100 cm depth is present in early spring. When the surface of this snow cover begins to melt in April, the meltwater is not immediately transformed into streamflow. Several processes are responsible for the five to 15 days of delay in streamflow generation: refreezing in the snow cover itself, creation of a basal ice layer at the snow-soil interface, growth of superimposed ice on the glaciers of the basin, ice formation in stream channels and restoration of high albedo by snowstorms that occur about once a week in spring.

scholarly journals Guest or pest? Spatio-temporal occurrence and effects on soil and vegetation of the wild boar on Elba island

2020 ◽  
Author(s):  
Ilaria Greco ◽  
Ettore Fedele ◽  
Marco Salvatori ◽  
Margherita Giampaoli Rustichelli ◽  
Flavia Mercuri ◽  
...  
Keyword(s):  
Natural Habitat ◽  
Early Spring ◽  
Wild Boars ◽  
Occupancy Probability ◽  
Lower Elevation ◽  
Site Use ◽  
Temporal Occurrence ◽  
Elba Island ◽  
Spatio Temporal ◽  
One Year

AbstractWhere allochthonous large mammals, such as the wild boars, occur in high density, human-wildlife conflicts may arise. Thus, assessing their spatio-temporal patterns is paramount to their management. We studied the wild boars on Elba island, Italy, where they have been introduced and are perceived as pests to address their occurrence and impact of foraging on natural habitat. We surveyed the western island with three camera trapping surveys within one year. We found that the species' estimated occupancy probability was higher in summer-autumn (0.75 ± 0.14) and winter-early spring (0.70 ± 0.10) than in spring–summer (0.53 ± 0.15), whereas detection probability did not vary. Occupancy was significantly associated with lower elevation and woodland cover. Lower site use of wild boars during spring–summer might reflect lower food availability in this season and/or boars’ movements towards landfarms outside the sampled area. Detectability increased with proximity to roads during spring–summer and decreased with humans’ relative abundance in other periods. Boars were mainly nocturnal, with decreasing overlap with human activity when human presence was higher in the park. Soil degradation caused by wild boars was higher in pine plantations, which is the cover with a lower conservation interest. The spatio-temporal activity of wild boars on the island appears driven by seasonal preferences for food-rich cover and avoidance of human disturbance. The lowered site use in months with lower resources could partially reflect increased proximity to settled and farmed areas, which may trigger crop-raiding and the negative perception by residents.

scholarly journals Estimating Spatial and Temporal Trends in Environmental Indices Based on Satellite Data: A Two-Step Approach

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 361 ◽  
Author(s):  
Brigitte Colin ◽  
Kerrie Mengersen
Keyword(s):  
Satellite Data ◽  
Temporal Trends ◽  
Regression Tree ◽  
Spatial And Temporal Patterns ◽  
Boosted Regression Tree ◽  
Environmental Indices ◽  
Study Region ◽  
Explanatory Variables ◽  
Green Vegetation ◽  
Spatio Temporal

This paper presents a method for employing satellite data to evaluate spatial and temporal patterns in environmental indices of interest. In the first step, linear regression coefficients are extracted for each area in the image. These coefficients are then employed as a response variable in a boosted regression tree with geographic coordinates as explanatory variables. Here, a two-step approach is described in the context of a substantive case study comprising 30 years of satellite derived fractional green vegetation cover for a large region in Queensland, Australia. In addition to analysis of the entire image and timeframe, separate analyses are undertaken over decades and over sub-regions of the study region. The results demonstrate both the utility of the approach and insights into spatio-temporal trends in green vegetation for this site. These findings support the feasibility of using the proposed two-step approach and geographic coordinates in the analysis of satellite derived indices over space and time.

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