scholarly journals Streamflow Forecasting Without Models

2019 ◽  
Author(s):  
Witold F. Krajewski ◽  
Ganesh Ghimire ◽  
Felipe Quintero
Keyword(s):  
Real Time ◽  
Spatial Scales ◽  
Strong Dependence ◽  
Area Ratio ◽  
Skill Assessment ◽  
River Networks ◽  
Lead Times ◽  
Streamflow Forecasting ◽  
Temporal Persistence ◽  
Basin Area

The authors explore simple concepts of persistence in streamflow forecasting based on the real-time streamflow observations from the years 2002 to 2018 at 140 U.S. Geological Survey (USGS) streamflow gauges in Iowa. The spatial scale of the basins ranges from about 7 km2 to 37,000 km2. Motivated by the need for evaluating the skill of real-time streamflow forecasting systems, the authors perform quantitative skill assessment of different persistence schemes across spatial scales and lead-times. They show that skill in temporal persistence forecasting has a strong dependence on basin size, and a weaker, but non-negligible, dependence on geometric properties of the river networks in the basins. Building on results from this temporal persistence, they extend the streamflow persistence forecasting to space through flow-connected river networks. The approach simply assumes that streamflow at a station in space will persist to another station which is flow-connected; these are referred to as pure spatial persistence forecasts (PSPF). The authors show that skill of PSPF of streamflow is strongly dependent on the monitored vs. predicted basin area-ratio and lead-times, and weakly related to the downstream flow distance between stations. River network topology shows some effect on the hydrograph timing and timing of the peaks, depending on the stream gauge configuration. The study shows that the skill depicted in terms of Kling-Gupta efficiency (KGE) > 0.5 can be achieved for basin area ratio > 0.6 and lead-time up to three days. The authors discuss the implications of their findings for assessment and improvements of rainfall-runoff models, data assimilation schemes, and stream gauging network design.

scholarly journals Streamflow Forecasting without Models

2020 ◽  
Vol 21 (8) ◽  
pp. 1689-1704
Author(s):  
Witold F. Krajewski ◽  
Ganesh R. Ghimire ◽  
Felipe Quintero
Keyword(s):  
Real Time ◽  
Spatial Scales ◽  
Strong Dependence ◽  
Area Ratio ◽  
Skill Assessment ◽  
River Networks ◽  
Lead Times ◽  
Streamflow Forecasting ◽  
Temporal Persistence ◽  
Basin Area

ABSTRACTThe authors explore persistence in streamflow forecasting based on the real-time streamflow observations. They use 15-min streamflow observations from the years 2002 to 2018 at 140 U.S. Geological Survey (USGS) streamflow gauges monitoring the streams and rivers throughout Iowa. The spatial scale of the basins ranges from about 7 to 37 000 km2. Motivated by the need for evaluating the skill of real-time streamflow forecasting systems, the authors perform quantitative skill assessment of persistence schemes across spatial scales and lead times. They show that skill in temporal persistence forecasting has a strong dependence on basin size, and a weaker dependence on geometric properties of the river networks. Building on results from this temporal persistence, they extend the streamflow persistence forecasting to space through flow-connected river networks. The approach simply assumes that streamflow at a station in space will persist to another station which is flow connected; these are referred to as pure spatial persistence forecasts (PSPF). The authors show that skill of PSPF of streamflow is strongly dependent on the monitored versus predicted basin area ratio and lead times, and weakly related to the downstream flow distance between stations. River network topology shows some effect on the hydrograph timing and timing of the peaks, depending on the stream gauge configuration. The study shows that the skill depicted in terms of Kling–Gupta efficiency (KGE) > 0.5 can be achieved for basin area ratio > 0.6 and lead time up to 3 days. The authors discuss the implications of their findings for assessment and improvements of rainfall–runoff models, data assimilation schemes, and stream gauging network design.

scholarly journals Scale Effect and Similarity Analysing of the Extracted River Networks Features Based on DEMs with Different Spatial Resolutions

2018 ◽  
Vol 53 ◽  
pp. 03035
Author(s):  
Yinjun Zhao ◽  
Qiongying Xie ◽  
Yuan Lu ◽  
Zhi Lee
Keyword(s):  
Scale Effect ◽  
Density Ratio ◽  
Area Ratio ◽  
River Networks ◽  
Threshold Values ◽  
Bifurcation Ratio ◽  
Proportional Relation ◽  
Stream Frequency ◽  
Basic Parameters ◽  
Basin Area

River networks are basic parameters in river studies and river management. Due to scale effect, most river networks are lack of certainty. We selected three leafy rivers from different climatic zone with an approximately proportional relation in basin area (1: 4.1: 19.9) to dig the scale effect and possible similarity. The result shows that the resolutions of DEM and drainage area threshold are two key parameters to extract river network features from DEM. The river lengths and densities of all extracted optimal river networks under the 6km2 threshold values decrease with the spatial resolution. Based on 15 optimal river networks, the length ratio is similar to the area ratio, the density ratio is same (1: 1: 1), and the stream frequency ratio is near to 1: 1: 1. Bifurcation ratio values of all basins are closer to the average (4.75). These characteristics may imply that there is the similarity among rivers.

scholarly journals Setting the Flow Accumulation Threshold Based on Environmental and Morphologic Features to Extract River Networks from Digital Elevation Models

2021 ◽  
Vol 10 (3) ◽  
pp. 186
Author(s):  
HuiHui Zhang ◽  
Hugo A. Loáiciga ◽  
LuWei Feng ◽  
Jing He ◽  
QingYun Du
Keyword(s):  
Spatial Scales ◽  
Digital Elevation Models ◽  
Regional Scale ◽  
Drainage Density ◽  
River Network ◽  
Landscape Patterns ◽  
Estimation Methods ◽  
River Networks ◽  
Flow Accumulation ◽  
Digital Elevation

Determining the flow accumulation threshold (FAT) is a key task in the extraction of river networks from digital elevation models (DEMs). Several methods have been developed to extract river networks from Digital Elevation Models. However, few studies have considered the geomorphologic complexity in the FAT estimation and river network extraction. Recent studies estimated influencing factors’ impacts on the river length or drainage density without considering anthropogenic impacts and landscape patterns. This study contributes two FAT estimation methods. The first method explores the statistical association between FAT and 47 tentative explanatory factors. Specifically, multi-source data, including meteorologic, vegetation, anthropogenic, landscape, lithology, and topologic characteristics are incorporated into a drainage density-FAT model in basins with complex topographic and environmental characteristics. Non-negative matrix factorization (NMF) was employed to evaluate the factors’ predictive performance. The second method exploits fractal geometry theory to estimate the FAT at the regional scale, that is, in basins whose large areal extent precludes the use of basin-wide representative regression predictors. This paper’s methodology is applied to data acquired for Hubei and Qinghai Provinces, China, from 2001 through 2018 and systematically tested with visual and statistical criteria. Our results reveal key local features useful for river network extraction within the context of complex geomorphologic characteristics at relatively small spatial scales and establish the importance of properly choosing explanatory geomorphologic characteristics in river network extraction. The multifractal method exhibits more accurate extracting results than the box-counting method at the regional scale.

scholarly journals Intercomparison of Spatial Forecast Verification Methods: Identifying Skillful Spatial Scales Using the Fractions Skill Score

2010 ◽  
Vol 25 (1) ◽  
pp. 343-354 ◽  
Author(s):  
Marion Mittermaier ◽  
Nigel Roberts
Keyword(s):  
Spatial Scales ◽  
Wrf Model ◽  
Skill Score ◽  
Area Ratio ◽  
Forecast Verification ◽  
Care Needs ◽  
Forecast Performance ◽  
Verification Methods ◽  
Upper Level ◽  
Formed Part

Abstract The fractions skill score (FSS) was one of the measures that formed part of the Intercomparison of Spatial Forecast Verification Methods project. The FSS was used to assess a common dataset that consisted of real and perturbed Weather Research and Forecasting (WRF) model precipitation forecasts, as well as geometric cases. These datasets are all based on the NCEP 240 grid, which translates to approximately 4-km resolution over the contiguous United States. The geometric cases showed that the FSS can provide a truthful assessment of displacement errors and forecast skill. In addition, the FSS can be used to determine the scale at which an acceptable level of skill is reached and this usage is perhaps more helpful than interpreting the actual FSS value. This spatial-scale approach is becoming more popular for monitoring operational forecast performance. The study also shows how the FSS responds to forecast bias. A more biased forecast always gives lower FSS values at large scales and usually at smaller scales. It is possible, however, for a more biased forecast to give a higher score at smaller scales, when additional rain overlaps the observed rain. However, given a sufficiently large sample of forecasts, a more biased forecast system will score lower. The use of percentile thresholds can remove the impacts of the bias. When the proportion of the domain that is “wet” (the wet-area ratio) is small, subtle differences introduced through near-threshold misses can lead to large changes in FSS magnitude in individual cases (primarily because the bias is changed). Reliable statistics for small wet-area ratios require a larger sample of forecasts. Care needs to be taken in the choice of verification domain. For high-resolution models, the domain should be large enough to encompass the length scale of the typical mesoscale forcing (e.g., upper-level troughs or squall lines). If the domain is too large, the wet-area ratios will always be small. If the domain is too small, fluctuations in the wet-area ratio can be large and larger spatial errors may be missed. The FSS is a good measure of the spatial accuracy of precipitation forecasts. Different methods are needed to determine other patterns of behavior.

A framework for the Integrated Assessment of SDG trade-offs in the Sundarbans Biosphere Reserve.

2021 ◽  
Author(s):  
Charlotte Marcinko ◽  
Robert Nicholls ◽  
Tim Daw ◽  
Sugata Hazra ◽  
Craig Hutton ◽  
...  
Keyword(s):  
Environmental Change ◽  
Integrated Assessment ◽  
Spatial Scales ◽  
Strong Dependence ◽  
Management Strategies ◽  
Rural Livelihoods ◽  
Biosphere Reserve ◽  
Integrated Analysis ◽  
Trade Offs ◽  
The Sundarbans

<p>The United Nations Sustainable Development Goals (SDGs) and their corresponding targets are significantly interconnected, with many interactions, synergies and trade-offs between individual goals across multiple temporal and spatial scales.  We propose a framework for the Integrated Assessment Modelling (IAM) of a complex deltaic socio-ecological system in order to analyse such SDG interactions. We focus on the Sundarbans Biosphere Reserve (SBR), India within the Ganges-Brahmaputra-Meghna Delta. It is densely populated with 4.4 million people (2011), high levels of poverty and a strong dependence on rural livelihoods. It is only 50 km from the growing megacity of Kolkata (about 15 million people in 2020). The area also includes the Indian portion of the world’s largest mangrove forest – the Sundarbans – hosting the iconic Bengal Tiger. Like all deltaic systems, this area is subject to multiple drivers of environmental change operating across different scales. The IAM framework is designed to investigate current and future trends in socio-environmental change and explore associated policy impacts, considering a broad range of sub-thematic SDG indicators. Integration is achieved through the soft coupling of multiple sub-models, knowledge and data of relevant environmental and socio-economic processes.  The following elements are explicitly considered: (1) agriculture; (2) aquaculture; (3) mangroves; (4) fisheries; and (5) multidimensional poverty. Key questions that can be addressed include the implications of changing monsoon patterns, trade-offs between agriculture and aquaculture, or the future of the Sundarbans mangroves under sea-level rise and different management strategies, including trade-offs with land use to the north.  The novel high-resolution analysis of SDG interactions allowed by the IAM will provide stakeholders and policy makers the opportunity to prioritize and explore the SDG targets that are most relevant to the SBR and provide a foundation for further integrated analysis.</p>

Skill assessment of post-processing methods for ECMWF SEAS5 seasonal forecasts over Europe

2021 ◽  
Author(s):  
Alice Crespi ◽  
Marcello Petitta ◽  
Lucas Grigis ◽  
Paola Marson ◽  
Jean-Michel Soubeyroux ◽  
...  
Keyword(s):  
Large Scale ◽  
Production Management ◽  
Spatial Scales ◽  
Research Field ◽  
Skill Assessment ◽  
Post Processing ◽  
Seasonal Forecasts ◽  
Climate Services ◽  
Local Climate ◽  
Climate Conditions

<p>Seasonal forecasts provide information on climate conditions several months ahead and therefore they could represent a valuable support for decision making, warning systems as well as for the optimization of industry and energy sectors. However, forecast systems can be affected by systematic biases and have horizontal resolutions which are typically coarser than the spatial scales of the practical applications. For this reason, the reliability of forecasts needs to be carefully assessed before applying and interpreting them for specific applications. In addition, the use of post-processing approaches is recommended in order to improve the representativeness of the large-scale predictions of regional and local climate conditions. The development and evaluation downscaling and bias-correction procedures aiming at improving the skills of the forecasts and the quality of derived climate services is currently an open research field. In this context, we evaluated the skills of ECMWF SEAS5 forecasts of monthly mean temperature, total precipitation and wind speed over Europe and we assessed the skill improvements of calibrated predictions.</p><p>For the calibration, we combined a bilinear interpolation and a quantile mapping approach to obtain corrected monthly forecasts on a 0.25°x0.25° grid from the original 1°x1° values. The forecasts were corrected against the reference ERA5 reanalysis over the hindcast period 1993–2016. The processed forecasts were compared over the same domain and period with another calibrated set of ECMWF SEAS5 forecasts obtained by the ADAMONT statistical method.</p><p>The skill assessment was performed by means of both deterministic and probabilistic verification metrics evaluated over seasonal forecasted aggregations for the first lead time. Greater skills of the forecast systems in Europe were generally observed in spring and summer, especially for temperature, with a spatial distribution varying with the seasons. The calibration was proved to effectively correct the model biases for all variables, however the metrics not accounting for bias did not show significant improvements in most cases, and in some areas and seasons even small degradations in skills were observed.</p><p>The presented study supported the activities of the H2020 European project SECLI-FIRM on the improvement of the seasonal forecast applicability for energy production, management and assessment.</p>

scholarly journals An Innovative Framework for Real Time Monitoring of Pollutant Point Sources in River Networks

2021 ◽  
Author(s):  
Mehdi Mazaheri ◽  
J. M. V. Samani ◽  
Fulvio Boano
Keyword(s):  
Real Time ◽  
Transport Model ◽  
River Network ◽  
Point Sources ◽  
Release Time ◽  
River Networks ◽  
Observation Data ◽  
Geostatistical Approach ◽  
Pollutant Sources ◽  
Simultaneous Identification

Abstract The simultaneous identification of location and source release history in complex river networks is a very complicated ill-posed problem, particularly in a case of multiple unknown pollutant sources with time-varying release pattern. This study presents an innovative method for simultaneous identification of the number, locations and release histories of multiple pollutant point sources in a river network using minimum observation data. Considering two different type of monitoring stations with an adaptive arrangement as well as real-time data collection at those stations and using a reliable numerical flow and transport model, at first the number and suspected reach of presence of pollutant sources are determined. Then the source location and its intensity function is calculated by solving inverse source problem using a geostatistical approach. A case study with three different scenarios in terms of the number, release time and location of pollutant sources are discussed, concerning a river network with unsteady and non-uniform flow. Results showed the capability of the proposed method in identifying of sought source characteristics even in complicated cases with simultaneous activity of multiple pollutant sources.

scholarly journals Potential of seasonal hydrological forecasting of monthly run-off volumes for the Rhone and Arve Rivers from April to July

2018 ◽  
Author(s):  
Oriane Etter ◽  
Frédéric Jordan ◽  
Anton J. Schleiss
Keyword(s):  
Confidence Interval ◽  
Seasonal Forecast ◽  
Seasonal Forecasting ◽  
Average Error ◽  
Lead Times ◽  
Streamflow Forecasting ◽  
Rhône River ◽  
Run Off ◽  
Rhone River ◽  
The Difference

Abstract. In a context where water management is becoming increasingly important, reliable seasonal forecasting of discharge in rivers is crucial for making decisions several months in advance. This paper explores the potential of seasonal forecasting of run-off volumes produced by ensemble streamflow forecasting using past climatology and comparing it to the more commonly used average of past discharge measurements. The seasonal forecast was obtained for the Arve and Rhone rivers by simulation using the Routing System model for lead times of 30, 90 and 120 days. The initialization was performed on a validated simulation of 12 and 16 years for the Arve and Rhone rivers, respectively, obtained through long-term calibration. The performance was assessed by indicators called accuracy and thinness. The normalized mean average error (NMAE) was used to compare the performance of the seasonal forecast with the average of the past measurements. After a bias correction of the seasonal forecast of the Rhone River with the observed run-off volumes during the different lead times, the correlation of the median forecast with the measurements (accuracy) was larger than 0.55 for all lead times from April to July. The Arve River's accuracy was improved by disregarding the year 2007 member, leading to the floods of the 3rd and 9th of July, for lead times of 90 and 120 days. This resulting in the period of April to July having correlation accuracies higher than 0.5. For both rivers, the 80 % confidence interval of the seasonal forecast was relatively thin compared to the measurements (thinness) for the months of April to July. The NMAE was used to validate the range of validity of the forecast. The correction of the forecast resulted in more months being favorable for seasonal forecasting for the Rhone River. The post-processing on the Arve River decreased the difference between the measurement and the forecast (NMAE). Further investigation should concentrate on dividing the meteorological datasets to produce a strong median forecast and confidence interval

scholarly journals Benchmarking Real-Time Streamflow Forecast Skill in the Himalayan Region

Forecasting ◽  
2020 ◽  
Vol 2 (3) ◽  
pp. 230-247
Author(s):  
Ganesh R. Ghimire ◽  
Sanjib Sharma ◽  
Jeeban Panthi ◽  
Rocky Talchabhadel ◽  
Binod Parajuli ◽  
...  
Keyword(s):  
Real Time ◽  
Water Policy ◽  
Low Complexity ◽  
Critical Issue ◽  
Forecast Skill ◽  
Streamflow Forecasting ◽  
Streamflow Forecast ◽  
Time Flow ◽  
Wide Range ◽  
Streamflow Forecasts

Improving decision-making in various areas of water policy and management (e.g., flood and drought preparedness, reservoir operation and hydropower generation) requires skillful streamflow forecasts. Despite the recent advances in hydrometeorological prediction, real-time streamflow forecasting over the Himalayas remains a critical issue and challenge, especially with complex basin physiography, shifting weather patterns and sparse and biased in-situ hydrometeorological monitoring data. In this study, we demonstrate the utility of low-complexity data-driven persistence-based approaches for skillful streamflow forecasting in the Himalayan country Nepal. The selected approaches are: (1) simple persistence, (2) streamflow climatology and (3) anomaly persistence. We generated the streamflow forecasts for 65 stream gauge stations across Nepal for short-to-medium range forecast lead times (1 to 12 days). The selected gauge stations were monitored by the Department of Hydrology and Meteorology (DHM) Nepal, and they represent a wide range of basin size, from ~17 to ~54,100 km2. We find that the performance of persistence-based forecasting approaches depends highly upon the lead time, flow threshold, basin size and flow regime. Overall, the persistence-based forecast results demonstrate higher forecast skill in snow-fed rivers over intermittent ones, moderate flows over extreme ones and larger basins over smaller ones. The streamflow forecast skill obtained in this study can serve as a benchmark (reference) for the evaluation of many operational forecasting systems over the Himalayas.

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