scholarly journals Impact of Geology on Seasonal Hydrological Predictability in Alpine Regions by a Sensitivity Analysis Framework

Water ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2255 ◽  
Author(s):  
Maria Stergiadi ◽  
Nicola Di Marco ◽  
Diego Avesani ◽  
Maurizio Righetti ◽  
Marco Borga
Keyword(s):  
Initial Conditions ◽  
Forecast Skill ◽  
Added Value ◽  
Climate Forcing ◽  
Major Influence ◽  
Analysis Framework ◽  
Alpine Regions ◽  
End Members ◽  
Seasonal Streamflow ◽  
Hydrological Predictability

Catchment geology has a major influence on the relative impact of the main seasonal hydrological predictability sources (initial conditions (IC), climate forcing (CF)) on the forecast skill as it defines the system’s persistence. A quantification of its effect, though, on the contribution of the predictability sources to the forecast skill has not been previously investigated. In this work we apply the End Point Blending (EPB) framework to assess the contribution of IC and CF to the seasonal streamflow forecast skill over two catchments that represent the end members of a set of catchments of contrasting geology, hence contrasting hydrological response: a highly-permeable, hence slow-responding catchment and a fast-responding catchment of low permeability. Our results show that the contribution of IC in the slow-responding catchment is higher by up to 44% for forecasts initialized in winter and spring and by up to 21% for forecasts initialized in summer. IC are important for up to 4 months of lead in the slow-responding catchment and 2 months of lead in the flashier catchment. Our analysis highlights the added value of the EPB in comparison to the traditional ESP/revESP approach for identifying the sources of seasonal hydrological predictability, on the basis of catchment geology.

Sensitivity of seasonal hydrological predictability sources to catchment properties

2020 ◽  
Author(s):  
Maria Stergiadi ◽  
Nicola Di Marco ◽  
Diego Avesani ◽  
Marco Borga ◽  
Maurizio Righetti
Keyword(s):  
Forecast Skill ◽  
Added Value ◽  
Climate Forcing ◽  
Hydrological Response ◽  
Elevation Range ◽  
Start Date ◽  
Production Water ◽  
Hydropower Production ◽  
The Impact ◽  
Hydrological Predictability

<p>Seasonal hydrological forecasts are a powerful tool for water-related decision making associated to hydropower production, water supply and irrigation. The skill of these forecasts depends mainly on knowledge of the initial hydrologic conditions (ICs) on the start date of the forecast and knowledge of climate forcing (CF) during the forecast period. Identification of the sensitivity of the forecast skill to these two main predictability sources is crucial to funnel the efforts into improving the appropriate predictive tools, by either improving the ICs estimates or by enhancing the quality of the CF. This work aims at investigating the impact of catchment properties in terms of soil permeability on the contribution of the dominant predictability sources (ICs, CF) to the seasonal forecast skill. To this end, we apply the End Point Blending (EPB) framework to create forecasts with intermediate levels of uncertainty concerning ICs and CF. The methodology is applied in two catchments in the upper Adige River Basin that are representative of the two extremes of hydrological response: the Gadera catchment closed at Mantana (area: 390 km<sup>2</sup>, elevation range: 810–3050 m a.s.l.) that is highly permeable, hence slow-responding and the Passirio catchment closed at Merano (area: 402 km<sup>2</sup>, elevation range: 360–3500 m a.s.l.) that is characterized by low permeability, hence by a fast-responding regime. Our analysis highlights the contribution of each predictability source to the forecast skill over catchments of contradicting hydrological response, as well as the added value of the elasticity framework introduced by the EPB in comparison to the traditional ESP/revESP approach for identifying the sources of seasonal hydrological predictability in alpine areas.</p>

Evoluzione della copertura vegetale in aree alpestri del Canton Ticino nel periodo 1971–2001 | Evolution of Vegetation Cover in the Alpine Regions of Canton Tessin: 1971–2001

2004 ◽  
Vol 155 (7) ◽  
pp. 284-289 ◽  
Author(s):  
Pietro Stanga ◽  
Niklaus Zbinden
Keyword(s):  
Initial Conditions ◽  
Ground Cover ◽  
Climatic Conditions ◽  
Human Pressure ◽  
Aerial Photos ◽  
Alpine Regions ◽  
Subsequent Decrease ◽  
Site Fertility ◽  
Strong Expansion ◽  
Evolution Of Vegetation

The retrospective study based on aerial photos (1971–2001) of the Canton Tessin made it possible to measure and analyze the evolution of the vegetation of eleven Alpine zones. The analysis shows a strong expansion of the arborescent vegetation and, at the same time, a decrease in other forms of ground cover (bush, shrub, meadow and unproductive spaces). Analysis of the data gives rise to the conjecture that the strong evolutionary dynamism evidenced by the areas under investigation is a result of the vast clearings carried out in past centuries to create pastures. Following the subsequent decrease in human pressure, nature today is attempting to rebalance the level of the biomass. These processes manifest themselves in different ways and with various intensity, depending on the interaction of numerous factors (e.g. climatic conditions, site fertility, initial conditions, evolution of anthropological pressure, etc.).

Dependence of Indian summer monsoon rainfall forecast skill of CFSv2 on initial conditions and the role of bias in SST boundary forcing

2021 ◽  
Author(s):  
Stella Jes Varghese ◽  
Kavirajan Rajendran ◽  
Sajani Surendran ◽  
Arindam Chakraborty
Keyword(s):  
Summer Monsoon ◽  
Indian Summer Monsoon ◽  
Lead Time ◽  
Monsoon Rainfall ◽  
Indian Summer Monsoon Rainfall ◽  
Initial Conditions ◽  
Summer Monsoon Rainfall ◽  
Forecast Skill ◽  
Boreal Summer ◽  
Central Pacific

<p>Indian summer monsoon seasonal reforecasts by CFSv2, initiated from January (4-month lead time, L4) through May (0-month lead time, L0) initial conditions (ICs), are analysed to investigate causes for the highest Indian summer monsoon rainfall (ISMR) forecast skill of CFSv2 with February (3-month lead time, L3) ICs. Although theory suggests forecast skill should degrade with increase in lead-time, CFSv2 shows highest skill with L3, due to its forecasting of ISMR excess of 1983 which other ICs failed to forecast. In contrast to observation, in CFSv2, ISMR extremes are largely decided by sea surface temperature (SST) variation over central Pacific (NINO3.4) associated with El Niño-Southern Oscillation (ENSO), where ISMR excess (deficit) is associated with La Niña (El Niño) or cooling (warming) over NINO3.4. In 1983, CFSv2 with L3 ICs forecasted strong La Niña during summer, which resulted in 1983 ISMR excess. In contrast, in observation, near normal SSTs prevailed over NINO3.4 and ISMR excess was due to variation of convection over equatorial Indian Ocean, which CFSv2 fails to capture with all ICs. CFSv2 reforecasts with late-April/early-May ICs are found to have highest deterministic ISMR forecast skill, if 1983 is excluded and Indian monsoon seasonal biases are also reduced. During the transitional ENSO in Boreal summer of 1983, faster and intense cooling of NINO3.4 SSTs in L3, could be due to larger dynamical drift with longer lead time of forecasting, compared to L0. Boreal summer ENSO forecast skill is also found to be lowest for L3 which gradually decreases from June to September. Rainfall occurrence with strong cold bias over NINO3.4, is because of the existence of stronger ocean-atmosphere coupling in CFSv2, but with a shift of the SST-rainfall relationship pattern to slightly colder SSTs than the observed. Our analysis suggests the need for a systematic approach to minimize bias in SST boundary forcing in CFSv2, to achieve improved ISMR forecasts.</p>

scholarly journals Complex relationship between seasonal streamflow forecast skill and value in reservoir operations

2017 ◽  
Vol 21 (9) ◽  
pp. 4841-4859 ◽  
Author(s):  
Sean W. D. Turner ◽  
James C. Bennett ◽  
David E. Robertson ◽  
Stefano Galelli
Keyword(s):  
System Performance ◽  
Research Effort ◽  
Forecast Accuracy ◽  
Strong Relationship ◽  
Forecast Skill ◽  
Severe Drought ◽  
Forecast Performance ◽  
Performance Improvements ◽  
Sensitivity Assessment ◽  
Seasonal Streamflow

Abstract. Considerable research effort has recently been directed at improving and operationalising ensemble seasonal streamflow forecasts. Whilst this creates new opportunities for improving the performance of water resources systems, there may also be associated risks. Here, we explore these potential risks by examining the sensitivity of forecast value (improvement in system performance brought about by adopting forecasts) to changes in the forecast skill for a range of hypothetical reservoir designs with contrasting operating objectives. Forecast-informed operations are simulated using rolling horizon, adaptive control and then benchmarked against optimised control rules to assess performance improvements. Results show that there exists a strong relationship between forecast skill and value for systems operated to maintain a target water level. But this relationship breaks down when the reservoir is operated to satisfy a target demand for water; good forecast accuracy does not necessarily translate into performance improvement. We show that the primary cause of this behaviour is the buffering role played by storage in water supply reservoirs, which renders the forecast superfluous for long periods of the operation. System performance depends primarily on forecast accuracy when critical decisions are made – namely during severe drought. As it is not possible to know in advance if a forecast will perform well at such moments, we advocate measuring the consistency of forecast performance, through bootstrap resampling, to indicate potential usefulness in storage operations. Our results highlight the need for sensitivity assessment in value-of-forecast studies involving reservoirs with supply objectives.

scholarly journals Application of Multivariate Sensitivity Analysis Techniques to AGCM-Simulated Tropical Cyclones

2018 ◽  
Vol 146 (7) ◽  
pp. 2065-2088 ◽  
Author(s):  
Fei He ◽  
Derek J. Posselt ◽  
Naveen N. Narisetty ◽  
Colin M. Zarzycki ◽  
Vijayan N. Nair
Keyword(s):  
Sensitivity Analysis ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Tropical Cyclones ◽  
Radiative Forcing ◽  
Initial Conditions ◽  
Sea Surface ◽  
Precipitation Rate ◽  
Analysis Framework ◽  
Control Factors

Abstract This work demonstrates the use of Sobol’s sensitivity analysis framework to examine multivariate input–output relationships in dynamical systems. The methodology allows simultaneous exploration of the effect of changes in multiple inputs, and accommodates nonlinear interaction effects among parameters in a computationally affordable way. The concept is illustrated via computation of the sensitivities of atmospheric general circulation model (AGCM)-simulated tropical cyclones to changes in model initial conditions. Specifically, Sobol’s variance-based sensitivity analysis is used to examine the response of cyclone intensity, cloud radiative forcing, cloud content, and precipitation rate to changes in initial conditions in an idealized AGCM-simulated tropical cyclone (TC). Control factors of interest include the following: initial vortex size and intensity, environmental sea surface temperature, vertical lapse rate, and midlevel relative humidity. The sensitivity analysis demonstrates systematic increases in TC intensity with increasing sea surface temperature and atmospheric temperature lapse rates, consistent with many previous studies. However, there are nonlinear interactions among control factors that affect the response of the precipitation rate, cloud content, and radiative forcing. In addition, sensitivities to control factors differ significantly when the model is run at different resolution, and coarse-resolution simulations are unable to produce a realistic TC. The results demonstrate the effectiveness of a quantitative sensitivity analysis framework for the exploration of dynamic system responses to perturbations, and have implications for the generation of ensembles.

Estimation of the added value of using rainfall–runoff transformation and statistical models for seasonal streamflow forecasting

2018 ◽  
Vol 63 (4) ◽  
pp. 630-645 ◽  
Author(s):  
Ketvara Sittichok ◽  
Ousmane Seidou ◽  
Abdouramane Gado Djibo ◽  
Neeranat Kaewprasert Rakangthong
Keyword(s):  
Statistical Models ◽  
Added Value ◽  
Rainfall Runoff ◽  
Streamflow Forecasting ◽  
Seasonal Streamflow

Why is large sample hydrology important in hydrological forecasting? 

2021 ◽  
Author(s):  
Ilias Pechlivanidis ◽  
Louise Crochemore ◽  
Marc Girons Lopez
Keyword(s):  
River Flow ◽  
Forecast Skill ◽  
Machine Learning Techniques ◽  
Precipitation Forecast ◽  
River Systems ◽  
Local Conditions ◽  
Large Sample ◽  
Streamflow Forecasts ◽  
Seasonal Streamflow

<p>The scientific community has made significant progress towards improving the skill of hydrological forecasts; however, most investigations have normally been conducted at single or in a limited number of catchments. Such an approach is indeed valuable for detailed process investigation and therefore to understand the local conditions that affect forecast skill, but it is limited when it comes to scaling up the underlying hydrometeorological hypotheses. To advance knowledge on the drivers that control the quality and skill of hydrological forecasts, much can be gained by comparative analyses and from the availability of statistically significant samples. Large-scale modelling (at national, continental or global scales) can complement the in-depth knowledge from single catchment modelling by encompassing many river systems that represent a breadth of physiographic and climatic conditions. In addition to large sample sizes which cover a gradient in terms of climatology, scale and hydrological regime, the use of machine learning techniques can contribute to the identification of emerging spatiotemporal patterns leading to forecast skill attribution to different regional physiographic characteristics.</p><p>Here, we draw on two seasonal hydrological forecast skill investigations that were conducted at the national and continental scales, providing results for more than 36,000 basins in Sweden and Europe. Due to the large generated samples, we are capable of demonstrating that the quality of seasonal streamflow forecasts can be clustered and regionalized, based on a priori knowledge of the local hydroclimatic conditions. We show that the quality of seasonal streamflow forecasts is linked to physiographic and hydroclimatic descriptors, and that the relative importance of these descriptors varies with initialization month and lead time. In our samples, hydrological similarity, temperature, precipitation, evaporative index, and precipitation forecast biases are strongly linked to the quality of streamflow forecasts. This way, while seasonal river flow can generally be well predicted in river systems with slow hydrological responses, predictability tends to be poor in cold and semiarid climates in which river systems respond immediately to precipitation signals.</p>

Digitalization to Reduce Tasks Without Added Value or Human Contact in a Pandemic

2021 ◽  
pp. 25-45
Author(s):  
Md. Masudul Hassan ◽  
Samira Islam Resmi
Keyword(s):  
Supply Chain ◽  
Labor Markets ◽  
Business Models ◽  
Business Processes ◽  
Added Value ◽  
Major Influence ◽  
Business Context ◽  
Sales Channels ◽  
Human Contact ◽  
Operational Costs

Digitalization is the use of technological innovations within the business context with a major influence on products, services, business processes, sales channels, and supply channels. The associated potential advantages include, among others, increased sales or productivity, innovations in price creation, and new sorts of client interaction. Global enterprises are facing supply chain issues, and consequently, potentially higher operational costs, lower inventory, and the prospects of lower demand will make them reluctant to disburse resources and time to connect in M&A and financing activities, predominantly if valuations of targets remain high. Digitalization of the supply chain (DSC) could be a way that companies can start to strategize and accomplish trade strength against supply chain disturbance. The main focus of this chapter is that digitalization enhances prosperity without human contact in a pandemic, will alter labor markets, and impacts business models.

scholarly journals Impacts of Assimilating ATMS Radiances on Heavy Rainfall Forecast in RMAPS-ST

2020 ◽  
Vol 12 (7) ◽  
pp. 1147
Author(s):  
Yanhui Xie ◽  
Min Chen ◽  
Jiancheng Shi ◽  
Shuiyong Fan ◽  
Jing He ◽  
...  
Keyword(s):  
Heavy Rainfall ◽  
Initial Conditions ◽  
Positive Impact ◽  
Weather Prediction ◽  
Advanced Technology ◽  
Added Value ◽  
Rainfall Forecast ◽  
Hourly Rainfall ◽  
Radiance Data ◽  
The Impact

The Advanced Technology Microwave Sounder (ATMS) mounted on the Suomi National Polar-Orbiting Partnership (NPP) satellite can provide both temperature and humidity information for a weather prediction model. Based on the rapid-refresh multi-scale analysis and prediction system—short-term (RMAPS-ST), we investigated the impact of ATMS radiance data assimilation on strong rainfall forecasts. Two groups of experiments were conducted to forecast heavy precipitation over North China between 18 July and 20 July 2016. The initial conditions and forecast results from the two groups of experiments have been compared and evaluated against observations. In comparison with the first group of experiments that only assimilated conventional observations, some added value can be obtained for the initial conditions of temperature, humidity, and wind fields after assimilating ATMS radiance observations in the system. For the forecast results with the assimilation of ATMS radiances, the score skills of quantitative forecast rainfall have been improved when verified against the observed rainfall. The Heidke skill score (HSS) skills of 6-h accumulated precipitation in the 24-h forecasts were overall increased, more prominently so for the heavy rainfall above 25 mm in the 0–6 h of forecasts. Assimilating ATMS radiance data reduced the false alarm ratio of quantitative precipitation forecasting in the 0–12 h of the forecast range and thus improved the threat scores for the heavy rainfall storm. Furthermore, the assimilation of ATMS radiances improved the spatial distribution of hourly rainfall forecast with observations compared with that of the first group of experiments, and the mean absolute error was reduced in the 10-h lead time of forecasts. The inclusion of ATMS radiances provided more information for the vertical structure of features in the temperature and moisture profiles, which had an indirect positive impact on the forecasts of the heavy rainfall in the RMAPS-ST system. However, the deviation in the location of the heavy rainfall center requires future work.

scholarly journals Resilience–Vulnerability Analysis: A Decision-Making Framework for Systems Assessment

2020 ◽  
Vol 12 (22) ◽  
pp. 9306
Author(s):  
Nikolaos A. Skondras ◽  
Demetrios E. Tsesmelis ◽  
Constantina G. Vasilakou ◽  
Christos A. Karavitis
Keyword(s):  
Decision Making ◽  
Complex Systems ◽  
Forest Fire ◽  
Decision Makers ◽  
Added Value ◽  
Initial Assessment ◽  
Analysis Framework ◽  
Current Effort ◽  
Policy Makers ◽  
Combined Use

The terms ‘resilience’ and ‘vulnerability’ have been widely used, with multiple interpretations in a plethora of disciplines. Such a variety may easily become confusing, and could create misconceptions among the different users. Policy makers who are bound to make decisions in key spatial and temporal points may especially suffer from these misconceptions. The need for decisions may become even more pressing in times of crisis, where the weaknesses of a system are exposed, and immediate actions to enhance the systemic strengths should be made. The analysis framework proposed in the current effort, and demonstrated in hypothetical forest fire cases, tries to focus on the combined use of simplified versions of the resilience and vulnerability concepts. Their relations and outcomes are also explored, in an effort to provide decision makers with an initial assessment of the information required to deal with complex systems. It is believed that the framework may offer some service towards the development of a more integrated and applicable tool, in order to further expand the concepts of resilience and vulnerability. Additionally, the results of the framework can be used as inputs in other decision making techniques and approaches. This increases the added value of the framework as a tool.

Sign in / Sign up

Export Citation Format

Share Document