scholarly journals GRACE Satellites Enable Long-Lead Forecasts of Mountain Contributions to Streamflow in the Low-Flow Season

2021 ◽  
Vol 13 (10) ◽  
pp. 1993
Author(s):  
Xingcai Liu ◽  
Qiuhong Tang ◽  
Seyed-Mohammad Hosseini-Moghari ◽  
Xiaogang Shi ◽  
Min-Hui Lo ◽  
...  
Keyword(s):  
Linear Models ◽  
Yellow River ◽  
Low Flow ◽  
Lead Times ◽  
High Mountain ◽  
Streamflow Forecasting ◽  
Mountain Areas ◽  
Model Based ◽  
Monthly Streamflow ◽  
Terrestrial Water

Terrestrial water storage (TWS) in high mountain areas contributes large runoff volumes to nearby lowlands during the low-flow season when streamflow is critical to downstream water supplies. The potential for TWS from GRACE (Gravity Recovery and Climate Experiment) satellites to provide long-lead streamflow forecasting in adjacent lowlands during the low-flow season was assessed using the upper Yellow River as a case study. Two linear models were trained for forecasting monthly streamflow with and without TWS anomaly (TWSA) from 2002 to 2016. Results show that the model based on streamflow and TWSA is superior to the model based on streamflow alone at up to a five-month lead-time. The inclusion of TWSA reduced errors in streamflow forecasts by 25% to 50%, with 3–5-month lead-times, which represents the role of terrestrial hydrologic memory in streamflow changes during the low-flow season. This study underscores the high potential of streamflow forecasting using GRACE data with long lead-times that should improve water management in mountainous water towers and downstream areas.

scholarly journals Open-Surface River Extraction Based on Sentinel-2 MSI Imagery and DEM Data: Case Study of the Upper Yellow River

2020 ◽  
Vol 12 (17) ◽  
pp. 2737
Author(s):  
Dan Li ◽  
Baosheng Wu ◽  
Bowei Chen ◽  
Chao Qin ◽  
Yanjun Wang ◽  
...  
Keyword(s):  
Yellow River ◽  
River Network ◽  
High Mountain ◽  
Open Surface ◽  
Mountain Regions ◽  
Mountain Areas ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Upper Yellow River ◽  
Sentinel 2

Water is essential for the survival of plants, animals, and human beings. It is imperative to effectively manage and protect aquatic resources to sustain life on Earth. Small tributaries are an important water resource originating in mountain areas, they play an important role in river network evolution and water transmission and distribution. Snow and cloud cover cast shadows leading to misclassification in optical remote sensing images, especially in high-mountain regions. In this study, we effectively extract small and open-surface river information in the Upper Yellow River by fusing Sentinel-2 with 10 m resolution optical imagery corresponding to average discharge of the summer flood season and the 90 m digital elevation model (DEM) data. To effectively minimize the impact of the underlying surface, the study area was divided into five sub-regions according to underlying surface, terrain, and altitude features. We minimize the effects of cloud, snow, and shadow cover on the extracted river surface via a modified normalized difference water index (MNDWI), revised normalized difference water index (RNDWI), automated water extraction index (AWEI), and Otsu threshold method. Water index calculations and water element extractions are operated on the Google Earth Engine (GEE) platform. The river network vectors derived from the DEM data are used as constraints to minimize background noise in the extraction results. The accuracy of extracted river widths is assessed using different statistical indicators such as the R-square (R2) value, root mean square error (RMSE), mean bias error (MBE). The results show the integrity of the extracted small river surface by the RNDWI index is optimal. Overall, the statistical evaluation indicates the accuracy of the extracted river widths is satisfactory. The effective river width that can be accurately extracted based on satellite images is three times the image resolution. Sentinel-2 MSI images with a spatial resolution of 10 m are used to find that the rivers over 30 m wide can be connectedly, accurately extracted with the proposed method. Results of this work can enrich the river width database in the northeast Tibetan Plateau and its boundary region. The river width information may provide a foundation for studying the spatiotemporal changes in channel geometry of river systems in high-mountain regions. They can also supplement the necessary characteristic river widths information for the river network in unmanned mountain areas, which is of great significance for the accurate simulation of the runoff process in the hydrological model.

scholarly journals Multiscale nonlinear model for monthly streamflow forecasting: a wavelet-based approach

2011 ◽  
Vol 14 (2) ◽  
pp. 424-442 ◽  
Author(s):  
Maheswaran Rathinasamy ◽  
Rakesh Khosa
Keyword(s):  
Nonlinear Models ◽  
Model Performance ◽  
Low Flow ◽  
Data Series ◽  
Streamflow Forecasting ◽  
Volterra Models ◽  
Proposed Model ◽  
Monthly Streamflow ◽  
Streamflow Data ◽  
Flow Medium

The dynamics of the streamflow in rivers involve nonlinear and multiscale phenomena. An attempt is made to develop nonlinear models combining wavelet decomposition with Volterra models. This paper describes a methodology to develop one-month-ahead forecasts of streamflow using multiscale nonlinear models. The method uses the concept of multiresolution decomposition using wavelets in order to represent the underlying integrated streamflow dynamics and this information, across scales, is then linked together using the first- and second-order Volterra kernels. The model is applied to 30 river data series from the western USA. The mean monthly data series of 30 rivers are grouped under the categories low, medium and high. The study indicated the presence of multiscale phenomena and discernable nonlinear characteristics in the streamflow data. Detailed analyses and results are presented only for three stations, selected to represent the low-flow, medium-flow and high-flow categories, respectively. The proposed model performance is good for all the flow regimes when compared with both the ARMA-type models as well as nonlinear models based on chaos theory.

scholarly journals Application of Entropy Spectral Method for Streamflow Forecasting in Northwest China

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 132 ◽  
Author(s):  
Gengxi Zhang ◽  
Zhenghong Zhou ◽  
Xiaoling Su ◽  
Olusola Ayantobo
Keyword(s):  
Flood Control ◽  
Performance Metrics ◽  
Configurational Entropy ◽  
Northwest China ◽  
Low Flow ◽  
Future Research ◽  
Streamflow Forecasting ◽  
Periodic Patterns ◽  
Forecasting Accuracy ◽  
Monthly Streamflow

Streamflow forecasting is vital for reservoir operation, flood control, power generation, river ecological restoration, irrigation and navigation. Although monthly streamflow time series are statistic, they also exhibit seasonal and periodic patterns. Using maximum Burg entropy, maximum configurational entropy and minimum relative entropy, the forecasting models for monthly streamflow series were constructed for five hydrological stations in northwest China. The evaluation criteria of average relative error (RE), root mean square error (RMSE), correlation coefficient (R) and determination coefficient (DC) were selected as performance metrics. Results indicated that the RESA model had the highest forecasting accuracy, followed by the CESA model. However, the BESA model had the highest forecasting accuracy in a low-flow period, and the prediction accuracies of RESA and CESA models in the flood season were relatively higher. In future research, these entropy spectral analysis methods can further be applied to other rivers to verify the applicability in the forecasting of monthly streamflow in China.

scholarly journals Parsimonious statistical learning models for low flow estimation

2021 ◽  
Author(s):  
Johannes Laimighofer ◽  
Michael Melcher ◽  
Gregor Laaha
Keyword(s):  
Support Vector Machine ◽  
Linear Model ◽  
Statistical Learning ◽  
Prediction Accuracy ◽  
Linear Models ◽  
Low Flow ◽  
Support Vector ◽  
Learning Models ◽  
Model Based ◽  
Non Linear

Abstract. Statistical learning methods offer a promising approach for low flow regionalization. We examine seven statistical learning models (lasso, linear and non-linear model based boosting, sparse partial least squares, principal component regression, random forest, and support vector machine regression) for the prediction of winter and summer low flow based on a hydrological diverse dataset of 260 catchments in Austria. In order to produce sparse models we adapt the recursive feature elimination for variable preselection and propose to use three different variable ranking methods (conditional forest, lasso and linear model based boosting) for each of the prediction models. Results are evaluated for the low flow characteristic Q95 (Pr(Q>Q95) = 0.95) standardized by catchment area using a repeated nested cross validation scheme. We found a generally high prediction accuracy for winter (R2CV of 0.66 to 0.7) and summer (R2CV of 0.83 to 0.86). The models perform similar or slightly better than a Top-kriging model that constitutes the current benchmark for the study area. The best performing models are support vector machine regression (winter) and non-linear model based boosting (summer), but linear models exhibit similar prediction accuracy. The use of variable preselection can significantly reduce the complexity of all models with only a small loss of performance. The so obtained learning models are more parsimonious, thus easier to interpret and more robust when predicting at ungauged sites. A direct comparison of linear and non-linear models reveals that non-linear relationships can be sufficiently captured by linear learning models, so there is no need to use more complex models or to add non-liner effects. When performing low flow regionalization in a seasonal climate, the temporal stratification into summer and winter low flows was shown to increase the predictive performance of all learning models, offering an alternative to catchment grouping that is recommended otherwise.

scholarly journals Seasonal streamflow forecasting by conditioning climatology with precipitation indices

2017 ◽  
Vol 21 (3) ◽  
pp. 1573-1591 ◽  
Author(s):  
Louise Crochemore ◽  
Maria-Helena Ramos ◽  
Florian Pappenberger ◽  
Charles Perrin
Keyword(s):  
Long Range ◽  
Precipitation Forecast ◽  
Low Flow ◽  
Drought Risk ◽  
Streamflow Forecasting ◽  
Streamflow Forecasts ◽  
Precipitation Indices ◽  
Seasonal Streamflow ◽  
Prediction Approach ◽  
The Impact

Abstract. Many fields, such as drought-risk assessment or reservoir management, can benefit from long-range streamflow forecasts. Climatology has long been used in long-range streamflow forecasting. Conditioning methods have been proposed to select or weight relevant historical time series from climatology. They are often based on general circulation model (GCM) outputs that are specific to the forecast date due to the initialisation of GCMs on current conditions. This study investigates the impact of conditioning methods on the performance of seasonal streamflow forecasts. Four conditioning statistics based on seasonal forecasts of cumulative precipitation and the standardised precipitation index were used to select relevant traces within historical streamflows and precipitation respectively. This resulted in eight conditioned streamflow forecast scenarios. These scenarios were compared to the climatology of historical streamflows, the ensemble streamflow prediction approach and the streamflow forecasts obtained from ECMWF System 4 precipitation forecasts. The impact of conditioning was assessed in terms of forecast sharpness (spread), reliability, overall performance and low-flow event detection. Results showed that conditioning past observations on seasonal precipitation indices generally improves forecast sharpness, but may reduce reliability, with respect to climatology. Conversely, conditioned ensembles were more reliable but less sharp than streamflow forecasts derived from System 4 precipitation. Forecast attributes from conditioned and unconditioned ensembles are illustrated for a case of drought-risk forecasting: the 2003 drought in France. In the case of low-flow forecasting, conditioning results in ensembles that can better assess weekly deficit volumes and durations over a wider range of lead times.

scholarly journals Formation Patterns of Mediterranean High-Mountain Water-Bodies in Sierra-Nevada, SE Spain

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 438
Author(s):  
Jose Luis Diaz-Hernandez ◽  
Antonio Jose Herrera-Martinez
Keyword(s):  
Sierra Nevada ◽  
Unknown Origin ◽  
Slope Instability ◽  
Water Bodies ◽  
High Mountain ◽  
Mountain Areas ◽  
Glacial Processes ◽  
The Mediterranean ◽  
Water Volumes

At present, there is a lack of detailed understanding on how the factors converging on water variables from mountain areas modify the quantity and quality of their watercourses, which are features determining these areas’ hydrological contribution to downstream regions. In order to remedy this situation to some extent, we studied the water-bodies of the western sector of the Sierra Nevada massif (Spain). Since thaw is a necessary but not sufficient contributor to the formation of these fragile water-bodies, we carried out field visits to identify their number, size and spatial distribution as well as their different modelling processes. The best-defined water-bodies were the result of glacial processes, such as overdeepening and moraine dams. These water-bodies are the highest in the massif (2918 m mean altitude), the largest and the deepest, making up 72% of the total. Another group is formed by hillside instability phenomena, which are very dynamic and are related to a variety of processes. The resulting water-bodies are irregular and located at lower altitudes (2842 m mean altitude), representing 25% of the total. The third group is the smallest (3%), with one subgroup formed by anthropic causes and another formed from unknown origin. It has recently been found that the Mediterranean and Atlantic watersheds of this massif are somewhat paradoxical in behaviour, since, despite its higher xericity, the Mediterranean watershed generally has higher water contents than the Atlantic. The overall cause of these discrepancies between watersheds is not connected to their formation processes. However, we found that the classification of water volumes by the manners of formation of their water-bodies is not coherent with the associated green fringes because of the anomalous behaviour of the water-bodies formed by moraine dams. This discrepancy is largely due to the passive role of the water retained in this type of water-body as it depends on the characteristics of its hollows. The water-bodies of Sierra Nevada close to the peak line (2918 m mean altitude) are therefore highly dependent on the glacial processes that created the hollows in which they are located. Slope instability created water-bodies mainly located at lower altitudes (2842 m mean altitude), representing tectonic weak zones or accumulation of debris, which are influenced by intense slope dynamics. These water-bodies are therefore more fragile, and their existence is probably more short-lived than that of bodies created under glacial conditions.

scholarly journals Streamflow forecasting by combining neural networks and fuzzy models using advanced methods of input variable selection

2017 ◽  
Vol 20 (2) ◽  
pp. 520-532 ◽  
Author(s):  
A. B. Dariane ◽  
Sh. Azimi
Keyword(s):  
Wavelet Transform ◽  
Multistep Method ◽  
Test Period ◽  
Fast Method ◽  
Streamflow Forecasting ◽  
Input Selection ◽  
Fuzzy Models ◽  
Monthly Streamflow ◽  
Input Variables ◽  
Learning Machine

Abstract In this paper the performance of extreme learning machine (ELM) training method of radial basis function artificial neural network (RBF-ANN) is evaluated using monthly hydrological data from Ajichai Basin. ELM is a newly introduced fast method and here we show a novel application of this method in monthly streamflow forecasting. ELM may not work well for a large number of input variables. Therefore, an input selection is applied to overcome this problem. The Nash–Sutcliffe efficiency (NSE) of ANN trained by backpropagation (BP) and ELM algorithm using initial input selection was found to be 0.66 and 0.72, respectively, for the test period. However, when wavelet transform, and then genetic algorithm (GA)-based input selection are applied, the test NSE increase to 0.76 and 0.86, respectively, for ANN-BP and ANN-ELM. Similarly, using singular spectral analysis (SSA) instead, the coefficients are found to be 0.88 and 0.90, respectively, for the test period. These results show the importance of input selection and superiority of ELM and SSA over BP and wavelet transform. Finally, a proposed multistep method shows an outstanding NSE value of 0.97, which is near perfect and well above the performance of the previous methods.

scholarly journals Trapping of Organochlorine Compounds in High Mountain Lakes

2001 ◽  
Vol 1 ◽  
pp. 609-611 ◽  
Author(s):  
Joan O. Grimalt ◽  
Pilar Fernandez ◽  
Rosa M. Vilanova
Keyword(s):  
Atmospheric Transport ◽  
Mountain Lakes ◽  
Organochlorine Compounds ◽  
High Mountain ◽  
High Latitudes ◽  
High Mountain Lakes ◽  
Mountain Areas ◽  
Persistent Pollutants

High mountain areas have recently been observed to be polluted by organochlorine compounds (OC) despite their isolation. These persistent pollutants arrive at these remote regions through atmospheric transport. However, the mechanisms involving the accumulation of these compounds from the atmospheric pool to the lacustrine systems still need to be elucidated. These mechanisms must be related to the processes involving the transfer of these pollutant from low to high latitudes[1] as described in the global distillation effect[2].

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