Monthly River Flow Research Articles

Simulation of monthly river flow using SVR neural network improved with population-based optimization algorithms

Simulation of monthly river flow using SVR neural network improved with population-based optimization algorithms

Modeling surface water potential using the SWAT model combined with principal component analysis in the ungauged Gelana watershed, Ethiopia

Abstract This study aims to model and assess surface water potential in an ungauged watershed using the Soil and Water Assessment Tool (SWAT), principal component analysis (PCA), and regression-based regionalization techniques in the Gelana River, Ethiopia. The SWAT model was calibrated (and validated) for the 1989–2007 (2008–2015) period, and it showed a very good performance to model the river flow. The 18 physical catchment characteristics that affect the production of streamflow were selected for correlation, and to make an equation using 19 optimized SWAT model parameters. These characteristics were categorized as two climate descriptors, three soil descriptors, seven land use land cover descriptors, and six topographical descriptors. The regression equations for each SWAT parameter with a function of the physical characteristics and principal components were developed. Then, SWAT model-sensitive parameters were transferred and validated. Results reveal that the watershed has a greatest surface runoff volume of 162.14 MCM in May and a least runoff of 9.15 MCM in January. The contribution of the water balance during the spring season is highest with a total surface runoff of 103.72 mm. From the whole Gelana watershed area of 336,460 ha, 778.4 MCM of yearly surface runoff was produced. The maximum mean monthly river flow is 15.7 m3/s in May.

Modern changes in climatic characteristics and minimum flow of the rivers in the basin of lake Sevan

Formulation of the problem. Studies and data on modern changes in the winter minimum average monthly flow of the rivers of the Lake Sevan basin and their climatic characteristics have acquired great practical importance and considerable interest due to the increased use of water resources for the purposes of various types of economic activities, in particular, in connection with the development of the economy, hydropower, irrigation, population growth and rising living standards. Especially relevant are the calculations of the minimum flow in relation to solving the problems of water supply for settlements, industrial enterprises. The aim of the work. Assessment of modern changes in the winter minimum average monthly runoff of rivers in the Lake Sevan basin and their climatic characteristics. Methods. The following methods were used in the work: mathematical-statistical, extrapolation, interpolation, spatial analysis, analogy, correlation and cartography. Results. A physical map has been compiled with a network of meteorological stations and hydrological posts in the Lake Sevan basin. Close correlations were obtained between the values of the winter minimum average monthly runoff and the average runoff of the winter period for a hydrological year. These dependences can be used for preliminary estimates of the minimum runoff of the winter low-water period of unstudied rivers in the territory under consideration. In the course of the research, the winter minimum average monthly discharges were calculated by months and for the entire observation period, the average value of the minimum runoff for the winter low water, and the linear trend of the climatic characteristic. Based on the obtained results, a map of the distribution of the modul of the average minimum runoff of the winter low-water period and the coefficients of the linear trend of the winter air temperature were constructed. Examples of curves of long-term changes in precipitation amounts for the autumn and winter periods, the sum of positive temperatures and the number of days with a positive average daily air temperature in the winter period for a series of meteorological stations in the study area are presented. The rivers of the Lake Sevan basin are characterized by an uneven spatio-temporal distribution of winter minimum runoff and climatic characteristics, changes in the winter average monthly minimum runoff, precipitation amounts for the autumn and winter periods are multidirectional, and air temperature changes are only an upward trend. In the area under consideration, there is mainly (by 8 out of 12 stations under study) a tendency to decrease in the values of the minimum average monthly river flow. The conducted studies lead us to the conclusion that the main factor causing changes in the low-water runoff of the rivers in the Lake Sevan basin in winter is the air temperature, or rather, its increase. It follows from this that when calculating and forecasting winter runoff, it is necessary to relate the values of runoff and air temperature.

Examining the impacts of climate variabilities and land use change on hydrological responses of Awash River basin, Ethiopia

Environment and climate changes have been posing pressure on the quality and quantity of water resources. The research examined the hydrological response of upper Awash River basin under the influences of environmental and climate changes. The climate change results up to 77.5% and 100.5% increament in annual and wet season monthly river flow, respectively when using projected climate data of RCP4.5. Whereas, the land use change alterss river flow in a one digit percentile increment/ decrement. It was observed that effects of both climate and land use changes will likely to increase the river flow and possibly cause flooding in the upper Awash River basin. The land use change impact can be alleviated by implementing appropriate land use management practices. The climate change mitigation needs long term process; as a result climate change adaptation mechanisms are recommended for the short term while employing climate mitigation options in the long run.

Forecasting Monthly River Flows in Ukraine under Different Climatic Conditions

River-flow forecasts are important for the management and planning of water resources and their rational use. The present study, based on direct multistep-ahead forecasting with multiple time series specific to the XGBoost algorithm, estimates the long-term changes and forecast monthly flows of selected rivers in Ukraine. In a new, applied approach, a single multioutput model was proposed that forecasts over both short- and long-term horizons using grouped or hierarchical data series. Three forecast stages were considered: using train and test subsets, using a model with train-test data, and training with all data. The historical period included the measurements of the monthly flows, precipitation, and air temperature in the period 1961–2020. The forecast horizons of 12, 60, and 120 months into the future were selected for this dataset, i.e., December 2021, December 2025, and December 2030. The research was conducted for diverse hydrological systems: the Prut, a mountain river; the Styr, an upland river; and the Sula, a lowland river in relation to the variability and forecasts of precipitation and air temperature. The results of the analyses showed a varying degree of sensitivity among rivers to changes in precipitation and air temperature and different projections for future time horizons of 12, 60, and 120 months. For all studied rivers, variable dynamics of flow was observed in the years 1961–2020, yet with a clearly marked decrease in monthly flows during in the final, 2010–2020 decade. The last decade of low flows on the Prut and Styr rivers was preceded by their noticeable increase in the earlier decade (2000–2010). In the case of the Sula River, a continuous decrease in monthly flows has been observed since the end of the 1990s, with a global minimum in the decade 2010–2020. Two patterns were obtained in the forecasts: a decrease in flow for the rivers Prut (6%) and the Styr (12–14%), accompanied by a decrease in precipitation and an increase in air temperature until 2030, and for the Sula River, an increase in flow (16–23%), with a slight increase in precipitation and an increase in air temperature. The predicted changes in the flows of the Prut, the Styr, and the Sula rivers correspond to forecasts in other regions of Ukraine and Europe. The performance of the models over a variety of available datasets over time was assessed and hyperparameters, which minimize the forecast error over the relevant forecast horizons, were selected. The obtained RMSE parameter values indicate high variability in hydrological and meteorological data in the catchment areas and not very good fit of retrospective data regardless of the selected horizon length. The advantages of this model, which was used in the work for forecasting monthly river flows in Ukraine, include modelling multiple time series simultaneously with a single model, the simplicity of the modelling, potentially more-robust results because of pooling data across time series, and solving the “cold start” problem when few data points were available for a given time series. The model, because of its universality, can be used in forecasting hydrological and meteorological parameters in other catchments, irrespective of their geographic location.

Integrated SWAT-MODFLOW model to study saltwater intrusion in Da Nang coastal city

This study applied an integrated surface water-groundwater (SW-GW) model, SWAT-MODFLOW to study interaction of surface and groundwater than simulate saltwater intrusion at a regional scale in Da Nang coastal city, center of Vietnam. Model components were calibrated and validated using monthly river flow data and hydraulic head data for the 2005-2020 period and showed good agreement with observed data. The results demonstrated that GW-SW exchange in the upstream areas had the most pronounced fluctuation between the wet and dry months under historical conditions. The combined potential impact is that intensive groundwater use may have more immediate effects on river flow than those of climate change, which has important implications for water resources management and supply in the future. The results showed that the total average recharge to groundwater from the rivers was varied from 12046 to 23147 m3/d relative to 50 – 74% of water resources in rain and dry season. Saltwater intrusion intrusion in 2020 is about 100 km2. In the area of Son Tra and Ngu Hanh Son districts, groundwater pumping must be reduced to zero, in some other areas, pumping rate do not exceed 250 to 1000 m3/day with distance to coastline at least 500 to 1400m depends on each area.

Surface Water and Groundwater Recharge Modeling in Sunsari District using Integrated SWAT-MODFLOW Model

This study employs an integrated groundwater-surface water (GW-SW) model, using SWAT-MODFLOW, to assess groundwater recharge and withdrawals at a regional scale in eastern Nepal. The model components were calibrated and validated using monthly river flow and hydraulic head data for the 1979-2017 period. Our results demonstrate that the average annual groundwater discharge is 2.43 m3/s. Seasonally, the average groundwater discharge is 2.73 m3/s during Summer and is 2.13 m3/s during winter. We observe that the maximum (6.9 m3/s) and minimum (0.036 m3/s) rates occurred in September-October and March-April, respectively. We also find that the average runoff fraction (i.e., runoff/ precipitation) varied from 0.34 to 0.72, and the ET component varied from 0.24 to 0.547 of the total precipitation within the SWAT model. The yearly fluctuation in TWS varied from ± 9.9% to ± 58% of the total precipitation. The Aquifer thickness varies from 16m to 57 m, and the specific yield in the Sunsari district varied from 0.12 lps to 890 lps. In the southern part, we see the water level declines from 2-4 m from July to August, where the bottom water slowly declines in September. The recharge of 1380 l/sec is observed in the Sapta Koshi area in the Bhabar zone. There is substantial underflow from hills simultaneously, resulting in the total recharge in the Sunsari district of 1.98 MCM/yr. At the same time, the total discharge, including surface, sub-surface, and spring water discharge, is nearly 29.2 MCM/yr. This study shows that the Sunsari district’s water resources have many groundwater and surface water, properly managed for future use.

Evaluating the Impact of Large-Scale Climatic Indices as Inputs for Forecasting Monthly River Flow in Mazandaran Province, Iran

Evaluating the Impact of Large-Scale Climatic Indices as Inputs for Forecasting Monthly River Flow in Mazandaran Province, Iran

Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale

Statistical analyses and descriptive characterizations are sometimes assumed to be offering information on time series forecastability. Despite the scientific interest suggested by such assumptions, the relationships between descriptive time series features (e.g., temporal dependence, entropy, seasonality, trend and linearity features) and actual time series forecastability (quantified by issuing and assessing forecasts for the past) are scarcely studied and quantified in the literature. In this work, we aim to fill in this gap by investigating such relationships, and the way that they can be exploited for understanding hydroclimatic forecastability and its patterns. To this end, we follow a systematic framework bringing together a variety of –mostly new for hydrology– concepts and methods, including 57 descriptive features and nine seasonal time series forecasting methods (i.e., one simple, five exponential smoothing, two state space and one automated autoregressive fractionally integrated moving average methods). We apply this framework to three global datasets originating from the larger Global Historical Climatology Network (GHCN) and Global Streamflow Indices and Metadata (GSIM) archives. As these datasets comprise over 13,000 monthly temperature, precipitation and river flow time series from several continents and hydroclimatic regimes, they allow us to provide trustable characterizations and interpretations of 12-month ahead hydroclimatic forecastability at the global scale. We first find that the exponential smoothing and state space methods for time series forecasting are rather equally efficient in identifying an upper limit of this forecastability in terms of Nash-Sutcliffe efficiency, while the simple method is shown to be mostly useful in identifying its lower limit. We then demonstrate that the assessed forecastability is strongly related to several descriptive features, including seasonality, entropy, (partial) autocorrelation, stability, (non)linearity, spikiness and heterogeneity features, among others. We further (i) show that, if such descriptive information is available for a monthly hydroclimatic time series, we can even foretell the quality of its future forecasts with a considerable degree of confidence, and (ii) rank the features according to their efficiency in explaining and foretelling forecastability. We believe that the obtained rankings are of key importance for understanding forecastability. Spatial forecastability patterns are also revealed through our experiments, with East Asia (Europe) being characterized by larger (smaller) monthly temperature time series forecastability and the Indian subcontinent (Australia) being characterized by larger (smaller) monthly precipitation time series forecastability, compared to other continental-scale regions, and less notable differences characterizing monthly river flow from continent to continent. A comprehensive interpretation of such patters through massive feature extraction and feature-based time series clustering is shown to be possible. Indeed, continental-scale regions characterized by different degrees of forecastability are also attributed to different clusters or mixtures of clusters (because of their essential differences in terms of descriptive features).

Estimation of Reservoir Volumes at Drafts of 40% - 90%: Drought Magnitude Method Applied on Monthly River Flows from Canadian Prairies

The draft ratios for sizing the reservoirs can vary within a wide range (40% - 90% of the mean annual flow, MAF), depending upon the demands for water by various users, and environmental and ecological considerations. The reservoir volumes based on the drought magnitude (DM) method were assessed at aforesaid draft ratios using monthly-standardized hydrological index (SHI) sequences of 10 Canadian rivers located in the Canadian prairies and northwestern Ontario. These rivers are typified by a high level of persistence lag-1 autocorrelation, ρ1m ≥ 0.50 and up to 0.94) and coefficient of variation (cvo) in the range of 0.42 to 1.48. The moving average (MA) smoothing of monthly SHI sequences formed the basis of the DM method for estimating reservoir volumes. The truncation or cutoff level in the SHI sequences was found as SHIx [=(α - 1)μo/σo], [(α - 1)μo/σmax], or [(α - 1)μo/σav], where α (=0.40 to 0.90) is the draft ratio i.e. proportion of the MAF, μo and σo are the overall mean and standard deviation of the monthly flows, σmax is the maximum value of standard deviations and σav the average of 12 monthly values. The failure probability levels (PF) were fixed at 5%, 2.5% and 0% (corresponding reliability of 95%, 97.5% and 100%). The study revealed that the coefficient of variation is the most important parameter that influences the reservoir size while the role of lag-1 autocorrelation (ρ1m) appears more pronounced at high draft ratios, α such as 0.90, 0.80 and 0.70 in increasing the reservoir size. The DM based method can be regarded as an alternative to Behavior analysis for sizing reservoirs at the desired probability of failure or reliability level.

Comparing linear and non-linear data-driven approaches in monthly river flow prediction, based on the models SARIMA, LSSVM, ANFIS, and GMDH.

River flow variations directly affect the hydro-climatological, environmental, and ecological characteristics of a region. Therefore, an accurate prediction of river flow can critically be important for water managers and planners. The present study aims to compare different data-driven models in predicting monthly flow. Two river catchments located in the Guilan province in Iran, where rivers play an essential role in agricultural productions (mainly rice), are studied. The monthly river flow dataset was provided by Guilan Regional Water Authority during 1986-2015. The models are derived from two different numerical types of stochastic and machine learning (ML) models. The stochastic model is seasonal autoregressive integrated moving average (SARIMA), and the MLs are least square support vector machine (LSSVM), adaptive neuro-fuzzy inference system (ANFIS), and group method of data handling (GMDH). The inputs were selected by autocorrelation and partial autocorrelation functions (ACF and PACF) from the flow rates of the previous months. The data was divided into 75% of training and 25% of testing phases, and then the mentioned models were implemented. Predictions were evaluated by the criteria of root mean square error (RMSE), normalized RMSE (NRMSE), and Nash Sutcliff (NS) coefficient. According to the calculated values of different criteria during the test phase, RMSE = 1.138 cms, NRMSE = 0.109, and NS = 0.826, it can be concluded that the SARIMA model was superior to its ML competitors. Among the ML models, GMDH had the best performance (by RMSE = 1.290 cms, NRMSE = 0.124, and NS = 0.777) because it has more optimization parameters and sample space for network make-up. The models were also evaluated in hydrological drought conditions of both rivers. It was resulted that the rivers' flow can be well predicted in drought conditions by using these models, especially the SARIMA stochastic model. According to the NRMSE values (ranged between 0.1 and 0.2), the accuracy of predictions is evaluated in the appropriate range, and the present study shows promising results of the current approaches. Consequently, a comparison between the performance of linear stochastic models and complex black-box MLs, reveals that linear stochastic models are more suitable for the current region's monthlyriver flow prediction.

Contributions of Human Activities and Climatic Variability to Changes in River Rwizi Flows in Uganda, East Africa

This study employed Soil and Water Assessment Tool (SWAT) to analyze the impacts of climate variability and human activities on River Rwizi flows. Changes in land use and land cover (LULC) types from 1997 to 2019 were characterized using remotely sensed images retrieved from Landsat ETM/TM satellites. SWAT was calibrated and validated over the periods 2002–2008 and 2009–2013, respectively. Correlation between rainfall and river flow was analyzed. By keeping the optimal values of model parameters fixed while varying the LULC maps, differences in the modeled flows were taken to reflect the impacts of LULC changes on rainfall–runoff generation. Impacts due to human activities included contributions from changes in LULC types and the rates of water abstracted from the river as a percentage of the observed flow. Climate variability was considered in terms of changes in climatic variables such as rainfall and evapotranspiration, among others. Variability of rainfall was analyzed with respect to changes in large-scale ocean-atmosphere conditions. From 2000 to 2014, the portion of River Rwizi catchment area covered by cropland increased from 23.0% to 51.6%, grassland reduced from 63.3% to 37.8%, and wetland decreased from 8.1% to 4.7%. Nash–Sutcliffe Efficiency values for calibration and validation were 0.60 and 0.71, respectively. Contributions of human activities to monthly river flow changes varied from 2.3% to 23.5%. Impacts of human activities on the river flow were on average found to be larger during the dry (14.7%) than wet (5.8%) season. Using rainfall, 20.9% of the total river flow variance was explained. However, climate variability contributed 73% of the river flow changes. Rainfall was positively and negatively correlated with Indian Ocean Dipole (IOD) and Niño 3, respectively. The largest percentages of the total rainfall variance explained by IOD and Niño 3 were 12.7% and 9.8%, respectively. The magnitude of the correlation between rainfall and IOD decreased with increasing lag in time. These findings are relevant for developing River Rwizi catchment management plans.

The assessment of emerging data-intelligence technologies for modeling Mg+2 and SO4−2 surface water quality

The concentration of soluble salts in surface water and rivers such as sodium, sulfate, chloride, magnesium ions, etc., plays an important role in the water salinity. Therefore, accurate determination of the distribution pattern of these ions can improve better management of drinking water resources and human health. The main goal of this research is to establish two novel wavelet-complementary intelligence paradigms so-called wavelet least square support vector machine coupled with improved simulated annealing (W-LSSVM-ISA) and the wavelet extended Kalman filter integrated with artificial neural network (W-EKF- ANN) for accurate forecasting of the monthly), magnesium (Mg+2), and sulfate (SO4−2) indices at Maroon River, in Southwest of Iran. The monthly River flow (Q), electrical conductivity (EC), Mg+2, and SO4−2 data recorded at Tange-Takab station for the period 1980–2016. Some preprocessing procedures consisting of specifying the number of lag times and decomposition of the existing original signals into multi-resolution sub-series using three mother wavelets were performed to develop predictive models. In addition, the best subset regression analysis was designed to separately assess the best selective combinations for Mg+2 and SO4−2. The statistical metrics and authoritative validation approaches showed that both complementary paradigms yielded promising accuracy compared with standalone artificial intelligence (AI) models. Furthermore, the results demonstrated that W-LSSVM-ISA-C1 (correlation coefficient (R) = 0.9521, root mean square error (RMSE) = 0.2637 mg/l, and Kling-Gupta efficiency (KGE) = 0.9361) and W-LSSVM-ISA-C4 (R = 0.9673, RMSE = 0.5534 mg/l and KGE = 0.9437), using Dmey mother that outperformed the W-EKF-ANN for predicting Mg+2 and SO4−2, respectively.

On the Investigation of Monthly River Flow Generation Complexity Using the Applicability of Machine Learning Models

Streamflow is associated with several sources on nonstationaries and hence developing machine learning (ML) models is always the motive to provide a reliable methodology to understand the actual mechanism of streamflow. The current research was devoted to generating monthly streamflows from annual streamflow. In this study, three different ML models were applied for this purpose, including Multiple Additive Regression Trees (MART), Group Methods of Data Handling (GMDH), and Gene Expression Programming (GEP). The models were developed based on annual streamflow and monthly time index of three rivers (i.e., Upper Zab, Lower Zab, and Diyala) located in the north region of Iraq. The modeling results indicated an optimistic simulation for generating the monthly streamflow time series from annual streamflow time series. The potential of the MART model was superior to the GMDH and GEP models for Upper Zab River (R2 0.84, 0.64, and 0.47), Lower Zab River (R2 0.75, 0.46, and 0.40), and Diyala River (R2 0.78, 0.42, and 0.5). The results of RMSE were 113, 169, and 208 for Upper Zab River, 95, 149, and 0.5 for Lower Zab River, and 73, 118, and 109 for Diyala River. The results have proved the possibility of changing the timescale in generating streamflow data.

Monthly river flow modeling using earlywood vessel feature changes, and tree-rings

Temporal records of river flow as essential data for water resource management plans are not available in many rivers around the world. In this study, monthly discharges of the Taskoh river in the north of Iran were simulated using dendrohydrology (the earlywood vessels and tree- rings chronologies). For this purpose, cross-dating, standardization, and time series analysis were performed on chronology records. The monthly river discharges during the growing seasons were simulated using an artificial neural network (ANN) by establishing a relationship between chronology records and the monthly river discharges. The result showed the high performance of the ANN model for simulating the monthly river flow using the mean vessels’ diameter and the tree-rings width in training (R-squared = 0.98, MSE = 0.009) and test (R-squared = 0.86, MSE = 1.2) stages. Moreover, the results showed that the vessels’ diameter is a better parameter (R coefficient = 0.87) than tree-rings width (R coefficient = 0.68) in river discharge simulation. Finally, the monthly river flow during the trees growing season was reconstructed using the tested ANN, mean vessels’ diameter, and tree-rings data for the past decades.

Uncertainty analysis of monthly river flow modeling in consecutive hydrometric stations using integrated data-driven models

Abstract The flow assessment in a river is of vital interest in hydraulic engineering for flood warning and evacuation measures. To operate water structures more efficiently, models that forecast river discharge are desired to be of high precision and certain degree of accuracy. Therefore, in this study, two artificial intelligence models, namely kernel extreme learning machine (KELM) and multivariate adaptive regression splines (MARS), were applied for the monthly river flow (MRF) modeling. For this aim, Mississippi river with three consecutive hydrometric stations was selected as case study. Using the previous MRF values during the period of 1950–2019, several models were developed and tested under two scenarios (i.e. modeling based on station's own data or previous station's data). Wavelet transform (WT) and ensemble empirical mode decomposition (EEMD) as data processing approaches were used for enhancing modeling capability. Obtained results indicated that the integrated models resulted in more accurate outcomes. Data processing enhanced the model's capability up to 25%. It was observed that the previous station's data could be applied successfully for MRF modeling when the station's own data were not available. The best-applied model dependability was assessed via uncertainty analysis, and an allowable degree of uncertainty was found in MRF modeling.

Suspended sediment load prediction using artificial intelligence techniques: comparison between four state-of-the-art artificial neural network techniques

Accurate prediction of suspended sediment (SS) concentration is a difficult task for water resource projects. In recent years, methodologies such as artificial intelligence (AI) algorithms have been applied for sediment load estimation and these models have provided efficient results. The present study investigates the abilities of four distinct AI approaches for estimating monthly SS load in Roodak station on Jajrood River, one of the longest waterways in the north of Iran, using the combinations of the present and antecedent monthly river flow data. This study aims to compare the predictive ability of artificial neural network (ANN), adaptive neuro-fuzzy inference systems (ANFIS), group method of data handling (GMDH), and least square support vector machines (LS-SVM) applied to predict the SS load. To develop the models, the monthly average river flow and the SS data for 50 years were obtained from Tehran regional water authority. Data were separated into three subsets (training, validation, and testing) and the SS concentration was predicted where the reliability of utilized approaches was assessed by statistical criterion including the correlation coefficient (R), mean absolute error (MAE), and root mean square error (RMSE). A comparison of the developed models revealed that the use of antecedent average river flow is able to enhance the prediction precision of suspended sediment concentration. The results indicate that the LS-SVM model generated superior results than the other models in terms of the mean error criteria, showing the ability of the model to reasonably predict the observed SS load values.

Global-scale massive feature extraction from monthly hydroclimatic time series: Statistical characterizations, spatial patterns and hydrological similarity

Hydroclimatic time series analysis focuses on a few feature types (e.g., autocorrelations, trends, extremes), which describe a small portion of the entire information content of the observations. Aiming to exploit a larger part of the available information and, thus, to deliver more reliable results (e.g., in hydroclimatic time series clustering contexts), here we approach hydroclimatic time series analysis differently, i.e., by performing massive feature extraction. In this respect, we develop a big data framework for hydroclimatic variable behaviour characterization. This framework relies on approximately 60 diverse features and is completely automatic (in the sense that it does not depend on the hydroclimatic process at hand). We apply the new framework to characterize mean monthly temperature, total monthly precipitation and mean monthly river flow. The applications are conducted at the global scale by exploiting 40-year-long time series originating from over 13 000 stations. We extract interpretable knowledge on seasonality, trends, autocorrelation, long-range dependence and entropy, and on feature types that are met less frequently. We further compare the examined hydroclimatic variable types in terms of this knowledge and, identify patterns related to the spatial variability of the features. For this latter purpose, we also propose and exploit a hydroclimatic time series clustering methodology. This new methodology is based on Breiman's random forests. The descriptive and exploratory insights gained by the global-scale applications prove the usefulness of the adopted feature compilation in hydroclimatic contexts. Moreover, the spatially coherent patterns characterizing the clusters delivered by the new methodology build confidence in its future exploitation. Given this spatial coherence and the scale-independent nature of the delivered feature values (which makes them particularly useful in forecasting and simulation contexts), we believe that this methodology could also be beneficial within regionalization frameworks, in which knowledge on hydrological similarity is exploited in technical and operative terms.

Applying and assessing multi-output support vector regression with rainfall as additional output for monthly river flow forecasting

Developing data-driven models for river flow forecasting has received great attention in recent years. However, no study has applied multi-output support vector regression (MSVR) for river flow forecasting. In this paper, we presented a monthly river flow forecasting model using MSVR with both river flow and rainfall data as model output. By experiments on two station data of the National River Flow Archive, we compared the proposed MSVR model with five commonly used single-output forecasting models. Besides, potential influencing factors of the proposed model related to the training samples size, the kernel function type, input variable scenario, and scenario of output combinations were tested. Results in our experiments revealed three significant findings. Firstly, MSVR outperformed the five single-output models in all test data. Secondly, the additional output-rainfall in MSVR can be conducive to forecast river flow only under the configuration of appropriate input variables and kernel type. Thirdly, with the tiny training sample size, the proposed model showed significantly superior to support vector regression. Based on these evidences, it is suggested that through the linkage of the output modeled by MSVR, more beneficial information can be learned for forecasting river flow. Therefore, the proposed method had a promising potential for river flow forecasting.

Impacts of climate change on the flow of the transboundary Koshi River, with implications for local irrigation

ABSTRACT This study assesses climate change impacts on the hydrological regime of a river basin and its implications for future irrigation water availability in the Koshi River basin using RCPs 4.5 and 8.5 over short-term (2016-2045), mid-century (2036-2065) and end-of-century (2071-2100) periods. Average flow in the Koshi River is projected to increase. Projections of average minimum monthly river flow suggest that the areas of winter wheat and monsoon paddy rice could be increased. However, the planting period of paddy rice should be delayed by one month (July to August) to capture the expected increased water availability in the river.