monthly runoff
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2022 ◽  
Vol 14 (2) ◽  
pp. 270
Author(s):  
Seyyed Hasan Hosseini ◽  
Hossein Hashemi ◽  
Ahmad Fakheri Fard ◽  
Ronny Berndtsson

Satellite remote sensing provides useful gridded data for the conceptual modelling of hydrological processes such as precipitation–runoff relationship. Structurally flexible and computationally advanced AI-assisted data-driven (DD) models foster these applications. However, without linking concepts between variables from many grids, the DD models can be too large to be calibrated efficiently. Therefore, effectively formulized, collective input variables and robust verification of the calibrated models are desired to leverage satellite data for the strategic DD modelling of catchment runoff. This study formulates new satellite-based input variables, namely, catchment- and event-specific areal precipitation coverage ratios (CCOVs and ECOVs, respectively) from the Global Precipitation Mission (GPM) and evaluates their usefulness for monthly runoff modelling from five mountainous Karkheh sub-catchments of 5000–43,000 km2 size in west Iran. Accordingly, 12 different input combinations from GPM and MODIS products were introduced to a generalized deep learning scheme using artificial neural networks (ANNs). Using an adjusted five-fold cross-validation process, 420 different ANN configurations per fold choice and 10 different random initial parameterizations per configuration were tested. Runoff estimates from five hybrid models, each an average of six top-ranked ANNs based on six statistical criteria in calibration, indicated obvious improvements for all sub-catchments using the new variables. Particularly, ECOVs were most efficient for the most challenging sub-catchment, Kashkan, having the highest spacetime precipitation variability. However, better performance criteria were found for sub-catchments with lower precipitation variability. The modelling performance for Kashkan indicated a higher dependency on data partitioning, suggesting that long-term data representativity is important for modelling reliability.


Water ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 3390
Author(s):  
Zhanxing Xu ◽  
Jianzhong Zhou ◽  
Li Mo ◽  
Benjun Jia ◽  
Yuqi Yang ◽  
...  

Runoff forecasting is of great importance for flood mitigation and power generation plan preparation. To explore the better application of time-frequency decomposition technology in runoff forecasting and improve the prediction accuracy, this research has developed a framework of runoff forecasting named Decomposition-Integration-Prediction (DIP) using parallel-input neural network, and proposed a novel runoff forecasting model with Variational Mode Decomposition (VMD), Gated Recurrent Unit (GRU), and Stochastic Fractal Search (SFS) algorithm under this framework. In this model, the observed runoff series is first decomposed into several sub-series via the VMD method to extract different frequency information. Secondly, the parallel layers in the parallel-input neural network based on GRU are trained to receive the input samples of each subcomponent and integrate their output adaptively through the concatenation layers. Finally, the output of concatenation layers is treated as the final runoff forecasting result. In this process, the SFS algorithm was adopted to optimize the structure of the neural network. The prediction performance of the proposed model was evaluated using the historical monthly runoff data at Pingshan and Yichang hydrological stations in the Upper Yangtze River Basin of China, and seven various single and decomposition-based hybrid models were developed for comparison. The results show that the proposed model has obvious advantages in overall prediction performance, model training time, and multi-step-ahead prediction compared to several comparative methods, which is a reasonable and more efficient monthly runoff forecasting method based on time series decomposition and neural networks.


Water ◽  
2021 ◽  
Vol 13 (21) ◽  
pp. 3121
Author(s):  
Devendra M. Amatya ◽  
Ssegane Herbert ◽  
Carl C. Trettin ◽  
Mohammad Daud Hamidi

The objective of this study was to test pre-treatment hydrologic calibration relationships between paired headwater watersheds (WS77 (treatment) and WS80 (control)) and explain the difference in flow, compared to earlier published data, using daily rainfall, runoff, and a water table measured during 2011–2019 in the Santee Experimental Forest in coastal South Carolina, USA. Mean monthly runoff difference between WS80 and WS77 of −6.80 mm for 2011–2019, excluding October 2015 with an extreme flow event, did not differ significantly from −8.57 mm (p = 0.27) for the 1969–1978 period or from −3.89 mm for 2004–2011, the post-Hurricane Hugo (1989) recovery period. Both the mean annual runoff coefficient and monthly runoff were non-significantly higher for WS77 than for WS80. The insignificant higher runoff by chance was attributed to WS77’s three times smaller surface storage and higher hypsometrical integral than those of WS80, but not to rainfall. The 2011–2019 geometric mean regression-based monthly runoff calibration relationship, excluding the October 2015 runoff, did not differ from the relationship for the post-Hugo recovery period, indicating complete recovery of the forest stand by 2011. The 2011–2019 pre-treatment regression relationship, which was not affected by periodic prescribed burning on WS77, was significant and predictable, providing a basis for quantifying longleaf pine restoration effects on runoff later in the future. However, the relationship will have to be used cautiously when extrapolating for extremely large flow events that exceed its flow bounds.


2021 ◽  
Author(s):  
Wenchuan Wang ◽  
Yu-jin Du ◽  
Kwok-wing Chau ◽  
Chun-Tian Cheng ◽  
Dong-mei Xu ◽  
...  

Abstract The optimal planning and management of modern water resources depends highly on reliable and accurate runoff forecasting. Data preprocessing technology can provide new possibilities for improving the accuracy of runoff forecasting, when basic physical relationships cannot be captured using a single prediction model. Yet, few researches evaluated the performances of various data preprocessing technology in predicting monthly runoff time series so far. In order to fill this research gap, this paper investigates the potential of five data preprocessing techniques based on gated recurrent unit network (GRU) model in monthly runoff prediction, namely variational mode decomposition (VMD), wavelet packet decomposition (WPD), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), extreme-point symmetric mode decomposition (ESMD), and singular spectrum analysis (SSA). In this study, the original monthly runoff data is first decomposed into a set of subcomponents using five data preprocessing methods; second, each component is predicted by developing an appropriate GRU model; finally, the forecasting results of different two-stage hybrid models are obtained by aggregating of forecast results of the corresponding subcomponents. Four performance metrics are employed as the evaluation benchmarks. The experimental results from two hydropower stations in China show that five data preprocessing techniques can attain satisfying prediction results, while VMD and WPD methods can yield better performance than CEEMDAN, ESMD and SSA in both training and testing periods in terms of four indexes. Indeed, it is significantly important to carefully determine an appropriate data preprocessing method according to the actual characteristics of the study area.


2021 ◽  
pp. 143-179
Author(s):  
Halima Belarbi ◽  
Bénina Touaibia ◽  
Nadir Boumechra ◽  
Chérifa Abdelbaki ◽  
Sakina Amiar

AbstractThe aim of this work is to study the temporal evolution of the rainfall-runoff relations of four basins in northwestern Algeria: the Tafna Maritime, Isser Sikkak, downstream Mouilah and Upper Tafna basins. The adopted approach consists of analyzing hydroclimatic variables using statistical methods and testing the nonstationarity of the rainfall-runoff relation by the cross-simulation method using the GR2M model. The results of the different statistical methods applied to the series of rainfall and hydrometric variables show a decrease due to a break in stationarity detected since the mid-1970s and the beginning of the 1980s. The annual rainfall deficits reached average values of 34.6% during the period of 1941–2006 and 29.1% during the period of 1970–2010. The average annual wadi flows showed average deficits of 61.1% between 1912 and 2000 and 53.1% between 1973 and 2009. The GR2M conceptual model simulated the observed hydrographs in an acceptable manner by providing calculated runoff values in the calibration and validation periods greater or less than the observed runoff values. The application of the cross-simulation method highlighted the nonstationarity of the rainfall-runoff relations in three of the four studied basins, indicating downward trends of monthly runoff.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pakorn Ditthakit ◽  
Sirimon Pinthong ◽  
Nureehan Salaeh ◽  
Fadilah Binnui ◽  
Laksanara Khwanchum ◽  
...  

AbstractEstimating monthly runoff variation, especially in ungauged basins, is inevitable for water resource planning and management. The present study aimed to evaluate the regionalization methods for determining regional parameters of the rainfall-runoff model (i.e., GR2M model). Two regionalization methods (i.e., regression-based methods and distance-based methods) were investigated in this study. Three regression-based methods were selected including Multiple Linear Regression (MLR), Random Forest (RF), and M5 Model Tree (M5), and two distance-based methods included Spatial Proximity Approach and Physical Similarity Approach (PSA). Hydrological data and the basin's physical attributes were analyzed from 37 runoff stations in Thailand's southern basin. The results showed that using hydrological data for estimating the GR2M model parameters is better than using the basin's physical attributes. RF had the most accuracy in estimating regional GR2M model’s parameters by giving the lowest error, followed by M5, MLR, SPA, and PSA. Such regional parameters were then applied in estimating monthly runoff using the GR2M model. Then, their performance was evaluated using three performance criteria, i.e., Nash–Sutcliffe Efficiency (NSE), Correlation Coefficient (r), and Overall Index (OI). The regionalized monthly runoff with RF performed the best, followed by SPA, M5, MLR, and PSA. The Taylor diagram was also used to graphically evaluate the obtained results, which indicated that RF provided the products closest to GR2M's results, followed by SPA, M5, PSA, and MLR. Our finding revealed the applicability of machine learning for estimating monthly runoff in the ungauged basins. However, the SPA would be recommended in areas where lacking the basin's physical attributes and hydrological information.


Author(s):  
Tien Giang Nguyen ◽  
Huu Duy Nguyen ◽  
Thu Thao Hoang ◽  
Duy Huy Binh Pham ◽  
Ngoc Anh Tran ◽  
...  

The Langcang-Mekong River Basin is the most important transboundary river basin in Asia. However, over the recent decades, dam construction has been increasingly recognized as having profound effects on hydrological processes, aquatic and riparian ecosystems. Understanding these impacts is critical for the foundation of sustainable runoff surface management. In this study, different methods based on both graphical and numerical techniques were applied to assess the effects of the dams on annual, seasonal, and monthly runoff and to detect hydroclimatic trends in the Upper Mekong Basin during the period 1960–2020. The results reveal two change points with respect to seasonal and annual flow regimes; that is 2003 for the flood season and annual flows, and 2013 for the dry season flow. The duration of the flood season and the volume of annual discharges have both significantly decreased since 2003 and the dry season discharge has significantly increased since 2013 (with both p-values < 0.05). The quantitative assessment suggests that, due to the effect of dams, the monthly discharges increased around 10–450 m3/s during the dry season (December to May), while the flood season’s monthly flows decreased significantly, approximately 1028–2150 m3/s from July to October at Chiang Saen station. The study of hydrological changes in the Mekong watershed is expected to be a significant contribution towards a better understanding of large watersheds in which the hydrological responses are influenced not only by climate change at large spatial and temporal scales but also by changes in the physical environment due to the construction of dams.


2021 ◽  
Vol 25 (6) ◽  
pp. 3429-3453
Author(s):  
Sarah Hanus ◽  
Markus Hrachowitz ◽  
Harry Zekollari ◽  
Gerrit Schoups ◽  
Miren Vizcaino ◽  
...  

Abstract. Hydrological regimes of alpine catchments are expected to be strongly affected by climate change, mostly due to their dependence on snow and ice dynamics. While seasonal changes have been studied extensively, studies on changes in the timing and magnitude of annual extremes remain rare. This study investigates the effects of climate change on runoff patterns in six contrasting Alpine catchments in Austria using a process-based, semi-distributed hydrological model and projections from 14 regional and global climate model combinations for two representative concentration pathways, namely RCP4.5 and RCP8.5. The study catchments represent a spectrum of different hydrological regimes, from pluvial–nival to nivo-glacial, as well as distinct topographies and land forms, characterizing different elevation zones across the eastern Alps to provide a comprehensive picture of future runoff changes. The climate projections are used to model river runoff in 2071–2100, which are then compared to the 1981–2010 reference period for all study catchments. Changes in the timing and magnitude of annual maximum and minimum flows, as well as in monthly runoff and snowmelt, are quantified and analyzed. Our results indicate a substantial shift to earlier occurrences in annual maximum flows by 9 to 31 d and an extension of the potential flood season by 1 to 3 months for high-elevation catchments. For low-elevation catchments, changes in the timing of annual maximum flows are less pronounced. Magnitudes of annual maximum flows are likely to increase by 2 %–18 % under RCP4.5, while no clear changes are projected for four catchments under RCP8.5. The latter is caused by a pronounced increase in evaporation and decrease in snowmelt contributions, which offset increases in precipitation. In the future, minimum annual runoff will occur 13–31 d earlier in the winter months for high-elevation catchments, whereas for low-elevation catchments a shift from winter to autumn by about 15–100 d is projected, with generally larger changes for RCP8.5. While all catchments show an increase in mean magnitude of minimum flows by 7–30% under RCP4.5, this is only the case for four catchments under RCP8.5. Our results suggest a relationship between the elevation of catchments and changes in the timing of annual maximum and minimum flows. For the magnitude of the extreme flows, a relationship is found between catchment elevation and annual minimum flows, whereas this relationship is lacking between elevation and annual maximum flow.


Author(s):  
Kebi Yang ◽  
Ting Chen ◽  
Tianqi Ao ◽  
Xu Zhang ◽  
Li Zhou ◽  
...  

Abstract Climate change affects water cycle in different regions. The response of annual runoff and seasonal distribution to climate change in the upper reaches of the Minjiang River during 2021–2050 was studied by coupling the Statistical Downscaling Model (SDSM) and the Soil and Water Assessment Tool (SWAT). This model was driven by the second-generation Canadian Earth System Model (CanESM2) under RCP2.6, RCP4.5, and RCP8.5 scenarios. The results show that the runoff in the upper reaches of the Minjiang River has a unique response to climate change. The maximum and minimum temperatures will increase with the increase in emissions, especially in December–January. The daily precipitation shows an upward trend, especially in July–August in the RCP4.5 scenario. The annual runoff shows an upward trend with the increase in emissions. Compared with the current increase of 13–26%, the most prominent period is November–April. Because the study area covers high mountains and gorge landforms, the altitude difference is great, and the influence of evapotranspiration and snow melting processes is more prominent, causing the monthly runoff to decrease in June–July with an increase in precipitation. From April to May, precipitation decreased while runoff increased.


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