Methods for estimating Curie temperatures of titanomaghemites from experimentalJs-T data

Methods for determining the Curie temperature (T_c) of titanomaghemites from experimental saturation magnetization-temperature (J_s-T) data are reviewed.J_s-T curves for many submarine basalts and synthetic titanomaghemites are irreversible and determining Curie temperatures from these curves is not a straightforward procedure. Subsequently, differences of sometimes over 100°C in the values ofT_c may result just from the method of calculation. Two methods for determiningT_c will be discussed: (1) the graphical method, and (2) the extrapolation method. The graphical method is the most common method employed for determining Curie temperatures of submarine basalts and synthetic titanomaghemites. The extrapolation method based on the quantum mechanical and thermodynamic aspects of the temperature variation of saturation magnetization nearT_c, although not new to solid state physics, has not been used for estimating Curie temperatures of submarine basalts. The extrapolation method is more objective than the graphical method and uses the actual magnetization data in estimatingT_c.     

Short‐term stream water temperature observations permit rapid assessment of potential climate change impacts

Assessment of potential climate change impacts on stream water temperature (T_s) across large scales remains challenging for resource managers because energy exchange processes between the atmosphere and the stream environment are complex and uncertain, and few long‐term datasets are available to evaluate changes over time. In this study, we demonstrate how simple monthly linear regression models based on short‐term historical T_s observations and readily available interpolated air temperature (T_a) estimates can be used for rapid assessment of historical and future changes in T_s. Models were developed for 61 sites in the southeastern USA using ≥18 months of observations and were validated at sites with longer periods of record. The T_s models were then used to estimate temporal changes in T_s at each site using both historical estimates and future T_a projections. Results suggested that the linear regression models adequately explained the variability in T_s across sites, and the relationships between T_s and T_a remained consistent over 37 years. We estimated that most sites had increases in historical annual mean T_s between 1961 and 2010 (mean of +0.11 °C decade^?1). All 61 sites were projected to experience increases in T_s from 2011 to 2060 under the three climate projections evaluated (mean of +0.41 °C decade^?1). Several of the sites with the largest historical and future T_s changes were located in ecoregions home to temperature‐sensitive fish species. This methodology can be used by resource managers for rapid assessment of potential climate change impacts on stream water temperature. Copyright © 2014 John Wiley & Sons, Ltd.     

IAHS Decade on Predictions in Ungauged Basins (PUB), 2003–2012: Shaping an exciting future for the hydrological sciences

Abstract

Drainage basins in many parts of the world are ungauged or poorly gauged, and in some cases existing measurement networks are declining. The problem is compounded by the impacts of human-induced changes to the land surface and climate, occurring at the local, regional and global scales. Predictions of ungauged or poorly gauged basins under these conditions are highly uncertain. The IAHS Decade on Predictions in Ungauged Basins, or PUB, is a new initiative launched by the International Association of Hydrological Sciences (IAHS), aimed at formulating and implementing appropriate science programmes to engage and energize the scientific community, in a coordinated manner, towards achieving major advances in the capacity to make predictions in ungauged basins. The PUB scientific programme focuses on the estimation of predictive uncertainty, and its subsequent reduction, as its central theme. A general hydrological prediction system contains three components: (a) a model that describes the key processes of interest, (b) a set of parameters that represent those landscape properties that govern critical processes, and (c) appropriate meteorological inputs (where needed) that drive the basin response. Each of these three components of the prediction system, is either not known at all, or at best known imperfectly, due to the inherent multi-scale space—time heterogeneity of the hydrological system, especially in ungauged basins. PUB will therefore include a set of targeted scientific programmes that attempt to make inferences about climatic inputs, parameters and model structures from available but inadequate data and process knowledge, at the basin of interest and/or from other similar basins, with robust measures of the uncertainties involved, and their impacts on predictive uncertainty. Through generation of improved understanding, and methods for the efficient quantification of the underlying multi-scale heterogeneity of the basin and its response, PUB will inexorably lead to new, innovative methods for hydrological predictions in ungauged basins in different parts of the world, combined with significant reductions of predictive uncertainty. In this way, PUB will demonstrate the value of data, as well as provide the information needed to make predictions in ungauged basins, and assist in capacity building in the use of new technologies. This paper presents a summary of the science and implementation plan of PUB, with a call to the hydrological community to participate actively in the realization of these goals.     

Measurement of the adiabats of quartz,forsterite, and magnesium oxide at high pressures and high temperatures and adiabatic gradient in the mantle

The geophysically-important adiabat (?T/?P)_s has been measured at pressures up to 50 kbar and temperatures up to 1000°C. A simple power law describes the relationship between (?T/?P)_s and the compression of the material. The power is independent of the material and of the temperature within the uncertainty. This consistency in the power allows the extrapolation of the adiabat to pressure and temperature conditions of the mantle of the earth. The adiabatic gradient is shown to be significantly smaller than the melting gradient.     

Evaluation of the change in dominant periods in the lake-bed zone of Mexico City produced by ground subsidence through the use of site amplification factors

We compute site amplification functions for several sites in Mexico City using actual accelerograms recorded from 1985 to 2010 and we present field evidence of the change in the dominant period of a given site (T_s) as a consequence of ground subsidence produced by groundwater withdrawal. The changes in T_s are larger in the lake-bed zone where thicker clay deposits exist, although there are sites in the southwest part of the lake-bed zone where T_s has remained constant. With the information obtained from the site amplification functions and available geotechnical soundings we develop an empirical model to estimate the future value of T_s for several sites in Mexico City. Because the practical application of the model requires extrapolation we also present a method to compute the uncertainty of the model when it is used to forecast a future value of T_s at a given site. Our results suggest that significant changes in the dominant period at several sites in Mexico City can be expected in the future.     

Spatial variability of the surface energy balance of Lake Kasumigaura and implications for flux measurements

ABSTRACT

The spatial variability of the lake surface energy balance and its causes are not well-understood. Energy balance maps (90 m resolution) of Lake Kasumigaura (172 km²), Japan, obtained by interpolating station data and bulk equations, allowed an investigation of these issues. Due to lake-scale variations in meteorological variables and small-scale fluctuations of surface temperature, T_s, surface heat fluxes differed horizontally at two distinct scales, while radiative fluxes were more uniform. As the key variable to surface flux T_s was only homogeneous for directions with a longer fetch or under calm wind conditions. Using these findings, the suitability of two flux station locations, one at the centre of the lake and another within a cove, was considered. Although both locations satisfied the fetch requirements, T_s was not always found to be homogeneous in the cove, making this location less suitable for flux measurements, an issue that, to date, has been overlooked.     

Bayesian autoregressive spatiotemporal model of PM<Subscript>10</Subscript> concentrations across Peninsular Malaysia

Rapid industrialization and haze episodes in Malaysia ensure pollution remains a public health challenge. Atmospheric pollutants such as PM₁₀ are typically variable in space and time. The increased vigilance of policy makers in monitoring pollutant levels has led to vast amounts of spatiotemporal data available for modelling and inference. The aim of this study is to model and predict the spatiotemporal daily PM₁₀ levels across Peninsular Malaysia. A hierarchical autoregressive spatiotemporal model is applied to daily PM₁₀ concentration levels from thirty-four monitoring stations in Peninsular Malaysia during January to December 2011. The model set in a three stage Bayesian hierarchical structure comprises data, process and parameter levels. The posterior estimates suggest moderate spatial correlation with effective range 157 km and a short term persistence of PM₁₀ in atmosphere with temporal correlation parameter 0.78. Spatial predictions and temporal forecasts of the PM₁₀ concentrations follow from the posterior and predictive distributions of the model parameters. Spatial predictions at the hold-out sites and one-step ahead PM₁₀ forecasts are obtained. The predictions and forecasts are validated by computing the RMSE, MAE, R² and MASE. For the spatial predictions and temporal forecasting, our results indicate a reasonable RMSE of 10.71 and 7.56, respectively for the spatiotemporal model compared to RMSE of 15.18 and 12.96, respectively from a simple linear regression model. Furthermore, the coverage probability of the 95% forecast intervals is 92.4% implying reasonable forecast results. We also present prediction maps of the one-step ahead forecasts for selected day at fine spatial scale.     

Water quality variations in different climates of Iran: toward modeling total dissolved solid using soft computing techniques

In present paper, wavelet analysis of total dissolved solid that monitored at Nazlu Chay (northwest of Iran), Tajan (north of Iran), Zayandeh Rud (central of Iran) and Helleh (south of Iran) basins with various climatic conditions, have been studied. Daubechies wavelet at suitable level (db4) has been calculated for TDS of each selected basins. The performance of artificial neural networks (ANN), two different adaptive-neurofuzzy inference system (ANFIS) including ANFIS with grid partition (ANFIS-GP) and ANFIS with subtractive clustering (ANFIS-SC), gene expression programming (GEP), wavelet-ANN, wavelet-ANFIS and wavelet-GEP in predicting TDS of mentioned basins were assessed over a period of 20 years at twelve different hydrometric stations. EC (μmhos/cm), Na (meq L^?1) and Cl (meq L^?1) parameters were selected (based on Pearson correlation) as input variables to forecast amount of TDS in four studied basins. To develop hybrid wavelet-AI models, the original observed data series was decomposed into sub-time series using Daubechies wavelets at suitable level for each basin. Based on the statistical criteria of correlation coefficient (R), root mean square error (RMSE) and mean absolute error (MAE), the hybrid wavelet-AI models performance were better than single AI models in all basins. A comparison was made between these artificial intelligence approaches which emphasized the superiority of wavelet-GEP over the other intelligent models with amount of RMSE 18.978, 6.774, 9.639 and 318.363 mg/l, in Nazlu Chay, Tajan, Zayandeh Rud and Helleh basins, respectively.     

Regional recharge assessment in the crystalline bedrock aquifer of the Kenogami Uplands,Canada

Abstract

Estimates of trends of climatic changes at basin and state scales are required for developing adaptation strategies related to planning, development and management of water resources. In the present study, seasonal and annual trends of changes in maximum temperature (T _max), minimum temperature (T _min), mean temperature (T _mean), temperature range (T _range), highest maximum temperature (H _max) and lowest minimum temperature (L _min) have been examined at the basin scale. The longest available records over the last century, for 43 stations covering nine river basins in northwest and central India, were used in the analysis. Of the nine river basins studied, seven showed a warming trend, whereas two showed a cooling trend. The Narmada and Sabarmati river basins experienced the maximum warming and cooling, respectively. The majority of basins in the study area show increasing trend in T _range, H _max and L _min. Seasonal analysis of different variables shows that the greatest changes in T _max and T _mean were observed in the post-monsoon season, while T _min experienced the greatest change in the monsoon season. This analysis provides scenarios of temperature changes which may be used for sensitivity analysis of water availability for different basins, and accordingly in planning and implementation of adaptation strategies.     

Deep learning long short-term memory combined with discrete element method for porosity prediction in gravel-bed rivers

The porosity of gravel riverbed material often is an essential parameter to estimate the sediment transport rate, groundwater-river flow interaction, river ecosystem, and fluvial geomorphology. Current methods of porosity estimation are time-consuming in simulation. To evaluate the relation between porosity and grain size distribution (GSD), this study proposed a hybrid model of deep learning Long Short-Term Memory (LSTM) combined with the Discrete Element Method (DEM). The DEM is applied to model the packing pattern of gravel-bed structure and fine sediment infiltration processes in three-dimensional (3D) space. The combined approaches for porosity calculation enable the porosity to be determined through real time images, fast labeling to be applied, and validation to be done. DEM outputs based on the porosity dataset were utilized to develop the deep learning LSTM model for predicting bed porosity based on the GSD. The simulation results validated with the experimental data then segregated into 800 cross sections along the vertical direction of gravel pack. Two DEM packing cases, i.e., clogging and penetration are tested to predict the porosity. The LSTM model performance measures for porosity estimation along the z-direction are the coefficient of determination (R²), root mean squared error (RMSE), and mean absolute error (MAE) with values of 0.99, 0.01, and 0.01 respectively, which is better than the values obtained for the Clogging case which are 0.71, 0.14, and 0.03, respectively. The use of the LSTM in combination with the DEM model yields satisfactory results in a less complex gravel pack DEM setup, suggesting that it could be a viable alternative to minimize the simulation time and provide a robust tool for gravel riverbed porosity prediction. The simulated results showed that the hybrid model of the LSTM combined with the DEM is reliable and accurate in porosity prediction in gravel-bed river test samples.     

Modelling snowpack surface temperature in the Canadian Prairies using simplified heat flow models

Three practical schemes for computing the snow surface temperature T_s, i.e. the force–restore method (FRM), the surface conductance method (SCM), and the Kondo and Yamazaki method (KYM), were assessed with respect to T_s retrieved from cloud‐free, NOAA‐AVHRR satellite data for three land‐cover types of the Paddle River basin of central Alberta. In terms of R², the mean T_s, the t‐test and F‐test, the FRM generally simulated more accurate T_s than the SCM and KYM. The bias in simulated T_s is usually within several degrees Celsius of the NOAA‐AVHRR T_s for both the calibration and validation periods, but larger errors are encountered occasionally, especially when T_s is substantially above 0 °C. Results show that the simulated T_s of the FRM is more consistent than that of the SCM, which in turn was more consistent than that of the KYM. This is partly because the FRM considers two aspects of heat conduction into snow, a stationary‐mean diurnal (sinusoidal) temperature variation at the surface coupled to a near steady‐state ground heat flux, whereas the SCM assumes a near steady‐state, simple heat conduction, and other simplifying assumptions, and the KYM does not balance the snowpack heat fluxes by assuming the snowpack having a vertical temperature profile that is linear. Copyright © 2005 John Wiley & Sons, Ltd.     

基于长短时记忆神经网络的鄱阳湖水位预测

湖泊水位是维持其生态系统结构、功能和完整性的基础.鄱阳湖受流域"五河"和长江来水双重影响,水位变化复杂.为了准确预测鄱阳湖水位变化,采用长短时记忆神经网络方法(LSTM)构建了鄱阳湖水位预测模型.该模型以赣江、抚河、信江、饶河和修水"五河"入湖流量和长江干流流量作为输入条件,预测鄱阳湖湖区不同代表站(湖口、星子、都昌、吴城和康山)的水位过程.研究以1956—1980年的水文时间序列数据作为训练集,1981—2000年作为验证集,探讨了LSTM模型输入时间窗、隐藏神经元数目、初始学习率等模型参数对预测精度的影响,并确定了鄱阳湖水位预测模型的最优参数.结果表明,采用LSTM神经网络方法可基于流域"五河"和长江来水量历时数据合理预测鄱阳湖不同湖区的水位过程,五站水位预测的均方根误差为0.41～0.50 m,纳什效率系数和决定系数达0.96～0.98.为考察模型训练数据集对鄱阳湖水位预测结果的影响,进一步选取了随机5年(1956—1960年)的资料和5个典型水文年(1954年、1973年、1974年、1977年和1978年)的日均流量资料来训练模型.结果显示随机5年资料作为训练数据的预测精度要差于典型年水文资料训练得到的模型,尤其是洪、枯水位的预测;由于典型水文年数据量仍远低于20年的资料,故其总体预测精度要略低于采用20年资料训练的模型.建议应用这类基于数据驱动的模型时,应该尽可能多选取具有代表性的资料来训练.     

Analysing the contribution of snow water equivalent to the terrestrial water storage over Canada

In this study, the spatial and temporal variabilities of terrestrial water storage anomaly (TWSA) and snow water equivalent anomaly (SWEA) information obtained from the Gravity Recovery and Climate Experiment (GRACE) twin satellites data were analysed in conjunction with multisource snow products over several basins in the Canadian landmass. Snow water equivalent (SWE) data were extracted from three different sources: Global Snow Monitoring for Climate Research version 2 (GlobSnow2), Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), and Canadian Meteorological Centre (CMC). The objective of the study was to understand whether SWE variations have a significant contribution to terrestrial water storage anomalies in the Canadian landmass. The period was considered from December 2002 to March 2011. Significant relationships were observed between TWSA and SWEA for most of the 15 basins considered (53% to 80% of the basins, depending on the SWE products considered). The best results were obtained with the CMC SWE products compared with satellite-based SWE data. Stronger relationships were found in snow-dominated basins (R_s > = 0.7), such as the Liard [root mean square error (RMSE) = 21.4 mm] and Peace Basins (RMSE = 26.76 mm). However, despite high snow accumulation in the north of Quebec, GRACE showed weak or insignificant correlations with SWEA, regardless of the data sources. The same behaviour was observed in the Western Hudson Bay basin. In both regions, it was found that the contribution of non-SWE compartments including wetland, surface water, as well as soil water storages has a significant impact on the variations of total storage. These components were estimated using the Water-Global Assessment and Prognosis Global Hydrology Model (WGHM) simulations and then subtracted from GRACE observations. The GRACE-derived SWEA correlation results showed improved relationships with three SWEA products. The improvement is particularly important in the sub-basins of the Hudson Bay, where very weak and insignificant results were previously found with GRACE TWSA data. GRACE-derived SWEA showed a significant relationship with CMC data in 93% of the basins (13% more than GRACE TWSA). Overall, the results indicated the important role of SWE on terrestrial water storage variations.     

Performance evaluation of satellite-based approaches for the estimation of daily air temperature and reference evapotranspiration

Different satellite-based radiation (Makkink) and temperature (Hargreaves-Samani, Penman-Monteith temperature, PMT) reference evapotranspiration (ET_o) models were compared with the FAO56-PM method over the Cauvery basin, India. Maximum air temperature (T_max) required in the ET_o models was estimated using the temperature–vegetation index (TVX) and an advanced statistical approach (ASA), and evaluated with observed T_max obtained from automatic weather stations. Minimum air temperature (T_min) was estimated using ASA. Land surface temperature was employed in the ET_o models in place of air temperature (T_a) to check the potency of its applicability. The results suggest that the PMT model with T_a as input performed better than the other ET_o models, with correlation coefficient (r), averaged root mean square error (RMSE) and mean bias error (MBE) of 0.77, 0.80 mm d^?1 and ?0.69 for all land cover classes. The ASA yielded better T_max and T_min values (r and RMSE of 0.87 and 2.17°C, and 0.87 and 2.27°C, respectively).     

Factors controlling specific sediment yield in the upper Indus River basin,northern Pakistan

Estimates of sediment yield are essential in water resources analysis, modelling and engineering, in investigations of continental denudation rates, and in studies of drainage basin response to changes in climate and land use. The availability of high resolution, global environmental datasets offers an opportunity to examine the relationships between specific sediment yield (SY_sp) and drainage basin attributes in a geographical information system (GIS) environment. This study examines SY_sp at 14 long‐term gauging stations within the upper Indus River basin. Twenty‐nine environmental variables were derived from global datasets, the majority with a 1 × 1 km resolution. The SY_sp ranges from 194 to 1302 t km^?2 yr^?1 for sub‐basins ranging from 567 to 212 447 km². The high degree of scatter in SY_sp is greatly reduced when the stations are divided into three groups: upper, glacierized sub‐basins; lower, monsoon sub‐basins; and the main Indus River. Percentage snow/ice cover (LC_s) emerges as the single major land cover control for SY_sp in the high mountainous upper Indus River basin. A regression model with percentage snow/ice cover (LC_s) as the single independent variable explains 73·4% of the variance in SY_sp for the whole Indus basin. A combination of percentage snow/ice cover (LC_s), relief and climate variables explains 98·5% of the variance for the upper, glacierized sub‐basins. For the lower monsoon region, a regression model with only mean annual precipitation (P) explains 99·4% of the variance. Along the main Indus River, a regression model including just basin relief (R) explains 92·4% of the variance in SY_sp. Based on the R²_adj and P‐value statistics, the variables used are capable of explaining the majority of variance in the upper Indus River basin. Copyright © 2007 John Wiley & Sons, Ltd.     

考虑记忆时间的LSTM模型在赣江流域径流预报中的应用

在气候变化条件下,准确的径流预测对水资源的规划与管理十分重要。本文基于长短时记忆神经网络(LSTM)模型,采用赣江流域外洲、峡江以及栋背水文站的逐日流量以及CN05.1日降水数据构建3个不同面积流域的径流预测模型,并通过设置不同情景分析:模型的有效预见期与不同流域平均产汇流时间之间的关系,有效预见期内LSTM径流预测模型精度与记忆时间之间的关系,不同长度的预见期与模型最佳记忆时间之间的关系,同时探讨LSTM径流预测所需的记忆时间与流域面积的关系。结果表明:(1)综合考虑降水和前期径流情景下的径流预测效果最好,当预见期为1 d时,外洲、峡江、栋背站的纳什效率系数(NSE)分别可达0.98、0.96以及0.90;且其有效预见期与仅考虑降水信息的有效预见期相同,均与流域平均产汇流时间相近。(2)随着预见期的延长,不同情景下的预测精度均有不同程度的下降,其中仅考虑前期径流情景的下降率最大,说明降水信息较前期径流对径流预测效果的提升更重要。同时,随着流域面积的增加,相同预见期内径流预测精度均有所提升。(3)当预见期相同时,随记忆时间的延长,不同径流预测模型的预测精度均先上升至最高,接着具有下降趋势,最后逐渐趋于稳定。且在有效预见期内,随着预见期的延长,最佳记忆时间均有增大趋势,当达到最长的有效预见期时,对应的最佳记忆时间均为14 d。此外,在赣江流域的模拟结果表明,随着流域面积的增大,LSTM的最佳记忆时间减小。研究结果可为赣江流域的径流预报提供参考,同时有助于推求其他流域采用机器学习进行径流预测所需的最佳记忆时间。     

Seasonal climate patterns and their influence on calibration of the Hargreaves-Samani equation

Abstract

Annual patterns in climate parameters were studied to evaluate how these influence the quality of reference evapotranspiration (ETo) estimates obtained from the Hargreaves-Samani (HS) equation, since the method only uses the measured temperature directly. The work evaluates how these patterns can be used to improve the HS ETo estimates. Ten-year moving averages from a set of California Irrigation Management Information System (CIMIS) stations were used to evaluate the relationships between solar radiation (R_s), temperature (T) and ETo. The results indicate that T treads behind solar radiation and its value peaks some 25 days later. Thus, the main irrigation season in the Mediterranean climate (1 May–30 September) can be divided into three phases: increasing R_s and T; decreasing R_s with increasing T; and decreasing R_s and T. Non-univocal annual cycles were observed between R_s and T, ETo and R_s, and ETo and T. These annual patterns result in important seasonal changes in the ratio between the HS and Penman-Monteith (FAO PM) ETo estimates. The changes are particularly important during the irrigation season, where the FAO PM initially calculates greater ETo values than the HS methodology, and from the end of May to early September, where the HS equation overestimates the ETo values (by 17 mm, or 3%). These patterns obtained from 2000–2009 data were used to calibrate and improve HS ETo estimates at new sites for the 2010–2011 period. Calibration based on the proposed seasonal region-wide FAO PM/HS ETo ratios improved both the bias (decreased from 0.40 to 0.36 mm d^-1) and r² (increased from 0.67 to 0.87) of the ETo estimates for the irrigation season. The proposed methodology can be easily applied to other regions, even when the existing weather stations are sparse.

Editor Z.W. Kundzewicz     

可解释性长短期记忆模型用于预测湖泊总磷浓度变化

对湖泊总磷的变化预测和来源识别对水资源调度和流域生态治理有着重要的意义,然而复杂的生化反应和水动力条件导致的非平稳性给湖泊总磷浓度的准确预测带来极大的困难。为克服这一挑战,本文引入了基于加权回归的季节趋势分解(seasonal and trend decomposition using Loess,STL)技术和夏普利加法(SHapley additive exPlanations,SHAP)结合长短期记忆网络(long short-term memory neural network,LSTM)和门控循环单元(gated recurrent unit,GRU)构建了一个可解释的预测框架,以增强对湖泊总磷浓度演变的预测并提高其可解释性。研究表明:(1)在骆马湖总磷浓度的预测中,该框架拥有较好的预报精度(R²=0.878),优于LSTM和卷积长短期记忆模型(convolutional neural networks and long short term memory network,CNN-LSTM)。当预测时间步长增加到8 h时,该框架有效提高了总磷浓度的预测精度,平均相对误差和均方根误差分别降低了47.1%和33.3%。从预测趋势来看,骆马湖在汛期的总磷平均浓度为0.158 mg/L,相较于非汛期的平均浓度,增加了202.1%。(2)运河来水是骆马湖总磷浓度最重要的影响因素,贡献权重为60.0%,并且不同断面(三湾、三场)的污染源受水动力、气象等因素的影响存在显著的时空差异。本文凸显了神经网络模型在预警水体污染方面的可实施性,并且为提高传统神经网络的学习能力和可解释性的开发与验证提供了重要方向。     

Estimation of monthly reference evapotranspiration using novel hybrid machine learning approaches

ABSTRACT

In this research, five hybrid novel machine learning approaches, artificial neural network (ANN)-embedded grey wolf optimizer (ANN-GWO), multi-verse optimizer (ANN-MVO), particle swarm optimizer (ANN-PSO), whale optimization algorithm (ANN-WOA) and ant lion optimizer (ANN-ALO), were applied for modelling monthly reference evapotranspiration (ET_o) at Ranichauri (India) and Dar El Beida (Algeria) stations. The estimates yielded by hybrid machine learning models were compared against three models, Valiantzas-1, 2 and 3 based on root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), Pearson correlation coefficient (PCC) and Willmott index (WI). The results of comparison show that the ANN-GWO-1 model with five input variables (T_min, T_max, RH, U_s, R_s) provides better estimates at both study stations (RMSE = 0.0592/0.0808, NSE = 0.9972/0.9956, PCC = 0.9986/0.9978, and WI = 0.9993/0.9989). Also, the adopted modelling strategy can build a truthful expert intelligent system for estimating the monthly ET_o at study stations.     

Drainage area and the variation of channel geometry downstream

Recent geomorphological studies tend to deal with small basins. The understanding of small basin dynamics provides important information for the understanding of large basin dynamics assuming that the extrapolation of small basin data to larger basins is valid. This work tests the validity of this extrapolation of data with reference to channel geometry. An analysis of the variation of channel width downstream reveals that the value b =0.5 (W = aQ^b) is a ‘good’ average. However, the use of a one-line model consisting of a simple power function incurs a loss of a considerable amount of relevant information concerning the channel form and hence the channel processes. It has been shown that the –b– value for small basins and very big basins is lower than the one for the intermediate basins.