Application of ANN-Based Streamflow Forecasting Model for Agricultural Water Management in the Awash River Basin,Ethiopia

This paper presents the application of a long-term streamflow forecasting model developed using artificial neural networks at a stream gauging station in the Awash River Basin, Ethiopia. The gauging station is located above the headworks of a large irrigation scheme called the Middle Awash Agricultural Development Enterprise (MAADE). Based on the forecasted streamflow time series and water requirements for irrigation and environmental purposes, appropriate agricultural water management strategies have been proposed for the irrigation scheme (MAADE). The water management strategies which were evaluated in this study are based on different scenarios of abstraction demands. These demands were formulated based on a range of options for agricultural development and change in MAADE. The scenarios evaluated were based on such factors as the existing planting patterns, changing planting dates, changing crop varieties and reducing the area under cultivation. An appropriate scenario of agricultural development was decided on the basis of the modified flows in the river vis-à-vis the trigger/threshold value established at the Melka Sedi stream gauging station. Considering all the scenarios, it is suggested that a 1–24% reduction in the area currently irrigated in the scheme will ensure a reliable supply of water to the scheme throughout the growing season and will provide sustainable environmental flow in the river.     

Evaluating Methods to Establish Habitat Suitability Criteria: A Case Study in the Upper Delaware River Basin,USA

Defining habitat suitability criteria (HSC) of aquatic biota can be a key component to environmental flow science. HSC can be developed through numerous methods; however, few studies have evaluated the consistency of HSC developed by different methodologies. We directly compared HSC for depth and velocity developed by the Delphi method (expert opinion) and by two primary literature meta‐analyses (literature‐derived range and interquartile range) to assess whether these independent methods produce analogous criteria for multiple species (rainbow trout, brown trout, American shad, and shallow fast guild) and life stages. We further evaluated how these two independently developed HSC affect calculations of habitat availability under three alternative reservoir management scenarios in the upper Delaware River at a mesohabitat (main channel, stream margins, and flood plain), reach, and basin scale. In general, literature‐derived HSC fell within the range of the Delphi HSC, with highest congruence for velocity habitat. Habitat area predicted using the Delphi HSC fell between the habitat area predicted using two literature‐derived HSC, both at the basin and the site scale. Predicted habitat increased in shallow regions (stream margins and flood plain) using literature‐derived HSC while Delphi‐derived HSC predicted increased channel habitat. HSC generally favoured the same reservoir management scenario; however, no favoured reservoir management scenario was the most common outcome when applying the literature range HSC. The differences found in this study lend insight into how different methodologies can shape HSC and their consequences for predicted habitat and water management decisions. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.     

Detection of Streamflow Change in the Susquehanna River Basin

Streamflow statistics are commonly used for purposes of planning and managing water resources in the Susquehanna River Basin. For accurately estimating streamflow statistics, it is important to identify whether there are increasing or decreasing changes during the period of records and whether the change is gradual or abrupt. This study employs repeated monotonic trend tests with varying beginning and ending time for detecting changes in streamflow in tributaries within the Susquehanna River Basin. The method is employed to analyze long-term streamflow trends and detect change for annual minimum, median, and maximum daily streamflow for eight unregulated watersheds within the basin. Monthly baseflow and storm runoff are investigated. The results show a considerable increase in annual minimum flow for most of the examined watersheds and a noticeable increase in annual median flow for about half of the examined watersheds. Both these streamflow increases were abrupt, with only a few years of transition centered around 1970. The abrupt change in annual minimum and median flows appears to occur in the summer and fall seasons. The abrupt change in annual minimum and median flows is due to increased flows in the summer and fall seasons. The results also indicate there is no long-term significant increasing or decreasing change in annual maximum flow in the examined watersheds.     

Streamflow Response to Climate Change in the Brahmani River Basin,India

Climate change can significantly affect the water resources availability by resulting changes in hydrological cycle. Hydrologic models are usually used to predict the impacts of landuse and climate changes and to evaluate the management strategies. In this study, impacts of climate change on streamflow of the Brahmani River basin were assessed using Precipitation Runoff Modeling System (PRMS) run under the platform of Modular Modeling System (MMS). The plausible hypothetical scenarios of rainfall and temperature changes were used to assess the sensitivity of streamflow to changed climatic condition. The PRMS model was calibrated and validated for the study area. Model performance was evaluated by using joint plots of daily and monthly observed and simulated runoff hydrographs and different statistical indicators. Daily observed and simulated hydrographs showed a reasonable agreement for calibration as well as validation periods. The modeling efficiency (E) varied in the range of 0.69 to 0.93 and 0.85 to 0.95 for the calibration and validation periods, respectively. Simulation studies with temperature rise of 2 and 4°C indicated 6 and 11% decrease in annual streamflow, respectively. However, there is about 62% increase in annual streamflow under the combined effect of 4°C temperature rise and 30% rainfall increase (T4P30). The results of the scenario analysis showed that the basin is more sensitive to changes in rainfall as compared to changes in temperature.     

Long-term Streamflow Forecasting Based on Ensemble Streamflow Prediction Technique: A Case Study in New Zealand

Streamflow forecasts are essential for optimal management of water resources for various demands, including irrigation, fisheries management, hydropower production and flood warning. Despite operational application of Ensemble Streamflow Prediction (ESP) for long-range streamflow forecasts in United States of America by the National Weather Service River Forecast System, no such approach has been explored in New Zealand. The objective of the present paper is to explore ESP-based forecasts in New Zealand catchments, highlighting its capability for seasonal flow forecasting. In this paper, a probabilistic forecast framework based on ESP technique is presented, with the basic assumption that future weather patterns will reflect those experienced historically. Hence, past forcing data (input to hydrological model) can be used with the current initial condition of a catchment to generate an ensemble of flow predictions. In the present study employs the ESP-based approach using the TopNet hydrological model with a range of past forcing data and current initial conditions. An ensemble stream flow predictions which provide probabilistic hydrological forecasts, reflecting the intrinsic uncertainty in climate, with lead time up to three months is presented for the Rangitata, Ahuriri, and Hooker and Jollie catchments in South Island, New Zealand. Verification of the forecast over the period 2000-2010 indicates a Ranked Probability Skill Score of 23 to 69 % (over climatology) across the four catchments. In general, improvement in ESP forecasting skill over climatology is greatest in summer for all catchments studied. The ESP based forecast exhibited higher skill for a greater percentage of the forecasting period than climatology. As a result, the ESP forecast can provide better over all information for integrated water resources management purpose. ESP-based forecasts using the TopNet hydrological model have potential as tools for water resource management in New Zealand catchments.     

A Comparative Study of Linear Stochastic with Nonlinear Daily River Discharge Forecast Models

Accurate forecast of the magnitude and timing of the flood peak river discharge and the extent of inundated areas during major storm events are a vital component of early warning systems around the world that are responsible for saving countless lives every year. This study assesses the forecast accuracy of two different linear and non-linear approaches to predict the daily river discharge. A new linear stochastic method is produced by evaluating a detailed comparison between three pre-processing approaches, differencing, standardization, spectral analysis, and trend removal. Daily river discharge values of the Bow River with strong seasonal and non-seasonal correlations located in Alberta, Canada were utilized in this study. The stochastic term for this daily flow time series is calculated with an auto-regressive integrated moving average. We found that seasonal differencing is the best stationarization method for periodic effect elimination. Moreover, the proposed non-linear Group Method of Data Handling (GMDH) model could overcome the known accuracy limitations of the classical GMDH models that use only two inputs in each neuron from the adjacent layer. The proposed new non-linear GMDH-based method (named GS-GMDH) can improve the structure of the classical linear GMDH. The GS-GMDH model produced the most accurate forecasts in the Bow River case study with statistical indices such as the coefficient of determination and Nash-Sutcliffe for the daily discharge time series higher than 97% and relative error less than 6%. Finally, an explicit equation for estimation of the daily discharge of the Bow River is developed using the proposed GS-GMDH model to showcase the practical application of the new method in flood forecasting and management.

     

Integrated Index for Assessment of Vulnerability to Drought,Case Study: Zayandehrood River Basin,Iran

Drought is an extended period of deficient precipitation that causes damage to crops and reducing their performance, causes temporary scarcity of water for human/livestock consumption. Over the years, various indices have been proposed to identify onset, characterize and quantify the attributes of meteorological, hydrological and agricultural drought by various researchers. Because of the spatial and temporal variability and multiple impacts of drought, it is necessary to develop an integrated index for assessment of vulnerability of this natural phenomenon. The aim of this paper is presenting an integrated index for assessment of vulnerability to drought using multiple factors which includes hydrological, meteorological, land use and other factors. Spatial information of various factors was categorized in to various sub-classes and maps were prepared in spatial domain using Geographic Information System (GIS). This study has been carried out in the Zayandehrood River basin located in west-central Iran with semi-arid region. Due to continue droughts at recent decade, this area has been chosen as a case study. The long-term climate data (1991–2011) used for assessment. The results show that Zayandehrood River basin has experienced 11 dry years, 4 normal years, and 6 wet years in the 21 years. The results have been validated with intensive field surveys.     

Integrated River Basin Management in the Multi-jurisdictional River Basins: The Case of the Mekong River Basin

Achieving integrated river basin management in large multi-jurisdictional river basins is a difficult task. In the Mekong River basin some of the countries have begun to implement a cooperative framework, which indicates a desire to achieve a form of integrated management. Significant progress has been made but results still fall short of the ideal. The primary reasons for this includes the lack of institutional capacity of the multi-jurisdictional cooperative authority and its counterpart organizations in each of the participating countries, together with a lack of political drive to develop integrated management as a priority.     

Identification of Streamflow Response to Climate Change and Human Activities in the Wei River Basin,China

In this study, the calibration and validation period with stable underlying surface conditions was determined by using a statistically significant change point of the annual streamflow in several catchments of the Wei River basin (WRB). The effects of climate changes and human activities on streamflow were estimated by using the sensitivity-based method and the dynamic water balance model, respectively. The contributions of climate effects and human activities effects on streamflow were also investigated. The results showed that almost all the catchments exhibited significant decreasing trend of streamflow in the early 1990s. The streamflow was more sensitive to changes in precipitation than changes in potential evapotranspiration (PET). Effects of climate due to changes in precipitation and PET are weak in Linjiacun, Weijiabao and Xianyang catchments, while it is strong in the catchments controlled by other hydrological stations, accounting for more than 40 % of streamflow reduction. Effects of human activities on streamflow in Linjiacun, Weijiabao, Xianyang and Zhangjiashan catchments accounted for more than 50 % of the streamflow reduction. The study provides scientific foundation to understand the causes of water resources scarcity and useful information for the planning and management of water resources in the ecological fragile arid area.     

Drought Governance in Transition: a Case Study of the Meuse River Basin in the Netherlands

Water Resources Management - The Netherlands is internationally renowned for its flood risk management, but three consecutive dry years between 2018–2020 fuelled the debate on how to deal...     

Influence of Scale on SWAT Model Calibration for Streamflow in a River Basin in the Humid Tropics

The Soil Water Assessment Tool (SWAT) was applied to the 2,530 km² Chaliyar river basin in Kerala, India to investigate the influence of scale on the model parameters. The study was carried out in this river basin at two scales. Parameters such as land use, soil type, topography and management practices are similar at these scales. The model was initially calibrated for streamflow and then validated. Critical parameters were the curve number (CN2), soil evaporation compensation factor (ESCO), available water holding capacity (SOL_AWC), average slope length (SLSUBBSN), and base flow alpha factor (ALPHA_BF). Using the optimized value of various parameters, stream flow was estimated from parts of the basin at two different scales—an area of 2,361.58 km² and an area of 1,013.15 km². The streamflow estimates at both these scales were statistically analysed by computing the coefficient of determination (R ²) and the Nash–Sutcliffe efficiency (E_NS). Results indicate that the SWAT model could simulate streamflow at both scales reasonably well with very little difference between the observed and computed values. However, the results also indicate that there may be greater uncertainty in SWAT streamflow estimates as the size of the watershed increases.     

Real-time Neural-network-based Ensemble Typhoon Flood Forecasting Model with Self-organizing Map Cluster Analysis: A Case Study on the Wu River Basin in Taiwan

Accurate hourly real-time flood forecasting is necessary for early flood warning systems, especially during typhoon periods. Artificial intelligence methods have been increasingly used for real-time flood forecasting. This study developed a real-time flood forecasting model by using back-propagation networks (BPNs) with a self-organizing map (SOM) to create ensemble forecasts. Random weights and biases were set for the BPNs to learn the characteristics of a catchment system. An unsupervised SOM network with a classification function was then used to cluster representative BPN weights and biases; clusters of BPNs with high accuracy were selected to act as experts for the ensemble models to forecast flow rates. The model was applied to flood events in the Wu River Basin of Taiwan. Most observed values were within the forecasting intervals of the BPN clusters in the calibration and validation phases, indicating that the models had acceptable accuracy. For the large flood events of typhoons Saola in the calibration phase and Soulik in the validation phase, the mean average error of the ensemble mean model for the cluster A was 143.1 and 327.4 m³/s, respectively; these values were lower than those for the best individual model within the cluster (194.3 and 917.9 m³/s). The ensemble model thus outperformed the individual models and can accurately forecast flood values and intervals. Therefore, the model can be used to accurately forecast floods.

     

河流生态健康评价研究——以潮白河为例

在综述分析前人研究成果的基础上,简要介绍了生态系统健康评价的基础理论,分析了河流生态系统健康的概念内涵和河流生态系统健康评价的基本方法.并以潮白河为研究案例,按照"病态、不健康、亚健康、健康、很健康"5级评价标准,对其进行了生态系统健康评价.结果表明,潮白河(1.473)处于不健康状态.     

Relating Trends in Streamflow to Anthropogenic Influences: A Case Study of Himayat Sagar Catchment,India

Catchment development has been identified as a potentially major cause of streamflow change in many river basins in India. This research aims to understand changes in the Himayat Sagar catchment (HSC), India, where significant reductions in streamflow have been observed. Rainfall and streamflow trend analysis for 1980–2004 shows a decline in streamflow without significant changes in rainfall. A regression model was used to quantify changes in the rainfall-runoff relationship over the study period. We relate these streamflow trends to anthropogenic changes in land use, groundwater abstraction and watershed development that lead to increased ET (Evapotranspiration) in the catchment. Streamflow has declined at a rate of 3.6 mm/y. Various estimates of changes in evapotranspiration/irrigation water use were made. Well inventories suggested an increase of 7.2 mm/y in groundwater extractions whereas typical irrigation practices suggests applied water increased by 9.0 mm/y, while estimates of evapotranspiration using remote sensing data showed an increasing rate of 4.1 mm/y. Surface water storage capacity of various small watershed development structures increased by 2 mm over 7 years. It is concluded that the dominant hydrological process responsible for streamflow reduction is the increase in evapotranspiration associated with irrigation development, however, most of the anthropogenic changes examined are interrelated and occurred simultaneously, making separating out individual impacts very difficult.     

Streamflow Forecasting Using Four Wavelet Transformation Combinations Approaches with Data-Driven Models: A Comparative Study

This study investigates the use of wavelet transformation (WT) as preprocessing tool in data-driven models (DDMs) for forecasting streamflow 7 days ahead. WT used are Continuous wavelet transformation (CWT), discrete wavelet transformation (DWT), and a new proposed combination of CWT and DWT, namely discrete continuous wavelet transformation (DCWT). In addition to these three different WTs, the single DDMs were used also to create four different schematic layouts. The DDMs applied were artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machines (SVM). The lagged rainfall, temperature, and streamflow were incorporated as inputs into the WT-DDMs. It was found that CWT improved the forecasting accuracy of models which only included the rainfall and temperature but not the streamflow. Moreover, DWT improved the performance dramatically for the models with streamflow. Notably, DWT layout outperformed CWT layout in general while CWT layouts resulted in higher improvement to the models with rainfall and temperature only. The proposed DCWT in which CWT applied on the rainfall and temperature variables and DWT applied on the streamflow improved the forecasting ability in several models combinations when ANN was applied. Nevertheless, improvement in the forecasting accuracy was deteriorated in those with SVM while no improvement was observed with ANFIS. ANN outperformed both ANFIS and SVM while ANFIS performed better than SVM.     

Streamflow Modeling in a Highly Managed Mountainous Glacier Watershed Using SWAT: The Upper Rhone River Watershed Case in Switzerland

Streamflow simulation is often challenging in mountainous watersheds because of irregular topography and complex hydrological processes. Rates of change in precipitation and temperature with respect to elevation often limit the ability to reproduce stream runoff by hydrological models. Anthropogenic influence, such as water transfers in high altitude hydropower reservoirs increases the difficulty in modeling since the natural flow regime is altered by long term storage of water in the reservoirs. The Soil and Water Assessment Tool (SWAT) was used for simulating streamflow in the upper Rhone watershed located in the south western part of Switzerland. The catchment area covers 5220 km², where most of the land cover is dominated by forest and 14 % is glacier. Streamflow calibration was done at daily time steps for the period of 2001–2005, and validated for 2006–2010. Two different approaches were used for simulating snow and glacier melt process, namely the temperature index approach with and without elevation bands. The hydropower network was implemented based on the intake points that form part of the inter-reservoir network. Subbasins were grouped into two major categories with glaciers and without glaciers for simulating snow and glacier melt processes. Model performance was evaluated both visually and statistically where a good relation between observed and simulated discharge was found. Our study suggests that a proper configuration of the network leads to better model performance despite the complexity that arises for water transaction. Implementing elevation bands generates better results than without elevation bands. Results show that considering all the complexity arising from natural variability and anthropogenic influences, SWAT performs well in simulating runoff in the upper Rhone watershed. Findings from this study can be applicable for high elevation snow and glacier dominated catchments with similar hydro-physiographic constraints.     

Turbidity Prediction in a River Basin by Using Artificial Neural Networks: A Case Study in Northern Spain

Chemical and physical-chemical parameters define water quality and are involved in water body type and habitat determination. They support a biological community of a certain ecological status. Water quality controls involve a large number of measurements of variables and observations according to the European Water Framework Directive (Directive 2000/60/EC). In some cases, such as areas with especially critical uses or points in which potential pollution episodes are expected, the automatic monitoring is recommended. However, the chemical and physical-chemical measurements are costly and time consuming. Turbidity is shown as a key variable for the water quality control and it is also an integrative parameter. For this reason, the aim of this work is focused on this main parameter through the study of the influence of several water quality parameters on it. The artificial neural networks (ANNs) have been used in a wide range of biological problems with promising results. Bearing this in mind, turbidity values have been predicted here by using artificial neural networks (ANNs) from the remaining measured water quality parameters with success taking into account the synergistic interactions between the input variables in the Nalón river basin (Northern Spain). Finally, the main conclusions of this study are exposed.     

Employing Machine Learning Algorithms for Streamflow Prediction: A Case Study of Four River Basins with Different Climatic Zones in the United States

Water Resources Management - Streamflow estimation plays a significant role in water resources management, especially for flood mitigation, drought warning, and reservoir operation. Hence, the...     

基于熵权法的流域水资源脆弱性评价——以淮河流域为例

流域水资源脆弱性评价可以量化表现出水资源的脆弱性程度,客观地反映研究区的水安全状况。根据水资源脆弱性的表现形式及成因,设置了水量、水质、洪涝灾害、旱灾4个一级指标,自然因素、人为因素、承载因素3个二级指标,分级构建了水资源脆弱性评价指标体系,在线性加权法的基础上采用熵权法构建了水资源脆弱性评价模型。以淮河流域为例进行实证分析,从时间、空间2个维度上对2003—2012年淮河流域整体及流域内的4个省份进行水资源脆弱性评价。结果表明时间上,10 a间淮河流域整体水资源脆弱度数值逐年减小,水资源脆弱性呈现好转趋势,流域内河南、安徽、江苏、山东4省的水资源脆弱性在10 a间均有所缓解;空间上,淮河流域水资源自西向东越来越脆弱。研究结果表明目前淮河流域水资源脆弱性状况呈现好转趋势,但仍然存在一些问题,有待进一步管理和调控。     

Analysis of Impacts of Climate Change and Human Activities on Hydrological Drought: a Case Study in the Wei River Basin,China

Climate change and human activity are the two major drivers that can alter hydrological cycle processes and influence the characteristics of hydrological drought in river basins. The present study selects the Wei River Basin (WRB) as a case study region in which to assess the impacts of climate change and human activity on hydrological drought based on the Standardized Runoff Index (SRI) on different time scales. The Generalized Additive Models in Location, Scale and Shape (GAMLSS) are used to construct a time-dependent SRI (SRI_var) considering the non-stationarity of runoff series under changing environmental conditions. The results indicate that the SRI_var is more robust and reliable than the traditional SRI. We also determine that different driving factors can influence the hydrological drought evolution on different time scales. On shorter time scales, the effects of human activity on hydrological drought are stronger than those of climate change; on longer time scales, climate change is considered to be the dominant factor. The results presented in this study are beneficial for providing a reference for hydrological drought analysis by considering non-stationarity as well as investigating how hydrological drought responds to climate change and human activity on various time scales, thereby providing scientific information for drought forecasting and water resources management over different time scales under non-stationary conditions.