Mean runoff coefficient estimation for ungauged streams in the Kingdom of Saudi Arabia

Runoff coefficient estimation in ungauged watersheds has a priority for rain water harvesting and management of runoff water, for domestic and agriculture activities, in semi-arid and arid regions. To estimate mean runoff coefficient (C _Re) for ungauged streams, Pearson's coefficient of linear correlation (r) was measured. The method of linear regression (y?=?mx?+?c) was applied for 16 gauged catchments representing several regions in the Kingdom of Saudi Arabia, such as Al Qassim, Al Madinah, Riyadh, Asir, Makkah, and Jazan. The studied catchments were equally divided into two groups based on their main streams slopes; group A includes eight gauged streams having main stream slope less than 0.01, where group B includes eight gauged streams having main stream slope equal to or greater than 0.01. The result yields the constant of the linear regression for each group and the mean runoff coefficient of basin by an independent value (basin slope) for group A and an independent value (stream slope) for group B. The results indicate that the measured runoff coefficient (C _Rm) and the estimated runoff coefficient (C _Re) are almost equal to each other.     

Effect of watershed partitioning on hydrologic parameters and estimation of hydrograph of an ungauged basin: a case study in Gokirmak and Kocanaz,Turkey

The main goal of this study is to investigate the effect of the size of the subbasins of a watershed on the hydrologic parameters and their spatial variability in an estimation of the hydrologic parameters and hydrograph of a neighbouring ungauged basin. In this paper, Hydrologic Engineering Center-Hydrologic Modelling System (HEC-HMS), a semi-distributed hydrologic model, is used to calibrate and cross-validate two flood events occurred in 1998 and then validate four other flood events occurred in 1991, 1994, 2002, and 2009 in Gokirmak Basin in Western Black Sea Region, Turkey. The basin is divided into seven different subbasins to investigate the effect of watershed partitioning on calibrated hydrologic parameters of each subbasin using the peak-weighted root mean square error method as an objective function and the hydrograph at the outlet of the whole basin. It is found out that as the geometric magnitudes of the subbasins changed, the calibrated values of the hydrologic parameters of those subbasins changed as well. Then, a neighbouring basin, Kocanaz, is considered as an assumed neighbouring ungauged basin to investigate the effect of watershed partitioning of a gauged basin on the estimation of hydrograph of a neighbouring ungauged basin. Hydrologic parameters and direct runoff hydrograph of assumed ungauged neighbouring basin are estimated from the hydrologic parameters of the HEC-HMS calibration results of Gokirmak. Statistical indicators of the simulation results for each basin partitioning were graded with respect to the boundary values of the simulation outputs to find the best alternative. The grading results show that the simulation results with a single basin gave better representation among all other partitioning except two flood events.     

Analysis of low-flow characteristics for catchments in Dongjiang Basin, China

Low-flow indices have been determined from long-term daily streamflow data for 13 catchments in Dongjiang Basin in southern China. The Brutsaert-Nieber method was applied to estimate catchment-scale effective groundwater parameters; representative values were 4.5?×?10^?4 m² s^?1 for the hydraulic diffusivity; 3.19?×?10^?5 m² s^?1/2 for the hydraulic desorptivity; 2.27?×?10^?4 m s^?1 for the hydraulic conductivity; and 0.2617 for the drainable porosity. The response constants correlate well with the total stream length and catchment area. Solutions of the linearised Boussinesq equation were used to guide the development of regional multivariate regression models for estimating low-flow indices from the catchment-scale effective parameters. Results showed that these catchments exhibit similar low-flow characteristics. The 7-day lowest average streamflows with return periods of 10 and 2 years (7Q10 and 7Q2) are highly correlated with the catchment-scale response constants. The low-flow ratio Q95/Q50 (ratio of daily streamflow exceeded 95 and 50% of the time, respectively) varied between 0.3 and 0.5, indicating a high proportion of groundwater in the streamflow. The advantage of the regional regression model is its conceptual basis and use of the catchment-scale effective parameters. The method has the potential to be applied to ungauged catchments for estimating low-flow statistics from stream length and catchment area.     

缺资料流域水文模型参数区域化研究进展

缺资料流域由于缺乏历史径流资料无法进行水文模型参数率定,因此模型参数识别具有很大的难度和不确定性。目前国内外学者对缺资料流域水文模型参数识别一般采用区域化方法,即通过某种途径,利用有资料流域的模型参数推求缺资料流域的模型参数,从而对缺资料流域进行预报。文章总结分析了缺资料流域水文模型参数区域化方法中的参数移植法和回归法的研究进展,对存在的方法选择问题、尺度问题等进行了讨论,并指出在参数不确定性、尺度转换以及多种信息源利用等方面还有待于进一步研究。     

GIS-hydrological models for managing water resources in the Zarqa River basin

Jordan is part of the arid and semi-arid region of the Middle East, where water resources are known to be scarce. Thorough planning is inevitable and must be applied wisely, especially as it is known that drought conditions were evidenced for many years in Jordan.Surface water average accounts for 693 MCM/year out of which 359 MCM/year is base flow and 334 MCM/year flood flow. Therefore, management of surface water has been given great attention in Jordan because of the overexploitation of groundwater resources. Hydrological simulation models interfaced with Geographical Information Systems (GIS) were examined in this study. GIS interfaced hydrological models were considered as a major tool for surface water management at a watershed scale because they are capable of presenting the relationship between the spatial and hydrological features of the watershed in an efficient way. This study aimed at exploring the advantages of using GIS-based hydrological models as a water management tool to study the largest river basin in Jordan namely, the Zarqa River basin.In this study, the Spatial Water Budget Model (SWBM) and HEC-HMS / HEC-GeoHMS extension model were used. The models were calibrated and validated based on King Talal Reservoir inflow for a period of eight years. The calibration was performed for the most sensitive parameters. The calibration for the two models was performed for the years 1979, 1980, 1981 and 1982. Satisfactory results were obtained for both models with an R² of 0.90 and 0.85 for the HEC-HMS and SWBM model, respectively. Validation for the models was performed using the years 1993, 1994, 1995 and 1996, and the results were satisfactory with an R² of 0.75 and 0.80 for the SWBM and HEC-HMS model, respectively. Models after their calibration and verification can then be used to test scenarios related to climate change and/or land-use change at the watershed scale.     

Assessment of a conceptual hydrological model and artificial neural networks for daily outflows forecasting

Artificial neural networks (ANNs) are used by hydrologists and engineers to forecast flows at the outlet of a watershed. They are employed in particular where hydrological data are limited. Despite these developments, practitioners still prefer conventional hydrological models. This study applied the standard conceptual HEC-HMS’s soil moisture accounting (SMA) algorithm and the multi layer perceptron (MLP) for forecasting daily outflows at the outlet of Khosrow Shirin watershed in Iran. The MLP [optimized with the scaled conjugate gradient] used the logistic and tangent sigmoid activation functions resulting into 12 ANNs. The R ² and RMSE values for the best trained MPLs using the tangent and logistic sigmoid transfer function were 0.87, 1.875 m³ s^?1 and 0.81, 2.297 m³ s^?1, respectively. The results showed that MLPs optimized with the tangent sigmoid predicted peak flows and annual flood volumes more accurately than the HEC-HMS model with the SMA algorithm, with R ² and RMSE values equal to 0.87, 0.84 and 1.875 and 2.1 m³ s^?1, respectively. Also, an MLP is easier to develop due to using a simple trial and error procedure. Practitioners of hydrologic modeling and flood flow forecasting may consider this study as an example of the capability of the ANN for real world flow forecasting.     

Suspended Matter,Chl-a,CDOM, Grain Sizes,and Optical Properties in the Arctic Fjord-Type Estuary,Kangerlussuaq, West Greenland During Summer

Optical constituents as suspended particulate matter (SPM), chlorophyll (Chl-a), colored dissolved organic matter (CDOM), and grain sizes were obtained on a transect in the arctic fjord-type estuary Kangerlussuaq (66°) in August 2007 along with optical properties. These comprised diffuse attenuation coefficient of downwelling PAR (K _d(PAR)), upwelling PAR (K _u(PAR)), particle beam attenuation coefficient (c _p), and irradiance reflectance R(−0, PAR). PAR is white light between 400 and 700 nm. The estuary receives melt water from the Greenland Inland Ice and stations covered a transect from the very high turbid melt water outlet to clear marine waters. Results showed a strong spatial variation with high values as for suspended matter concentrations, CDOM, diffuse attenuation coefficient K _d(PAR), particle beam attenuation coefficients (c _p), and reflectance R(−0, PAR) at the melt water outlet. Values of optical constituents and properties decreased with distance from the melt water outlet to a more or less constant level in central and outer part of the estuary. There was a strong correlation between inorganic suspended matter (SPMI) and diffuse attenuation coefficient K _d(PAR) (r ² = 0.92) and also for particle beam attenuation coefficient (c _p; r ² = 0.93). The obtained SPMI specific attenuation—K _d^*(PAR) = 0.13 m² g⁻¹ SPMI—and the SPMI specific particle beam attenuation—c _p^* = 0.72 m² g⁻¹—coefficients were about two times higher than average literature values. Irradiance reflectance R(−0, PAR) was comparatively high (0.09−0.20) and showed a high (r ² = 0.80) correlation with K _u(PAR). Scattering dominated relative to absorption—b(PAR)/a(PAR) = 12.3. Results strongly indicated that the high values in the optical properties were related to the very fine particle sizes (mean = 2–6 μm) of the suspended sediment. Data and results are discussed and compared to similar studies from both temperate and tropical estuaries.     

Adsorption study on municipal solid waste leachate using <Emphasis Type="Italic">Moringa oleifera</Emphasis> seed

Effects of initial concentrations of Moringa oleifera seed coagulant for removing Chemical Oxygen Demand and Total Dissolved Solids from municipal solid waste leachate have been evaluated at an optimum pH of 7 and temperature of 318 K. The kinetic data obtained from the experiments were fitted to the pseudo first-order, pseudo second-order, Elovich and intraparticle diffusion models. Based on a regression coefficient (R ²), the equilibrium (kinetic) data were best fitted with the Elovich model (R ² = 0.993 for Chemical Oxygen Demand and R ² = 0.996 for Total Dissolved Solids) than that of other models. The results of the kinetic models study indicated that the adsorption capacity of M. oleifera seed as a coagulant for removing Chemical Oxygen Demand and Total Dissolved Solids in a leachate increased up to 100 mg L^?1, beyond which the adsorption capacity got reduced. Finally, the present study concluded that M. oleifera seed coagulant could be employed effectively for the removal of Chemical Oxygen Demand and Total Dissolved Solids in a municipal solid waste leachate.     

Mathematical expression of discharge capacity of compound open channels using MARS technique

In this paper, analytical methods, artificial neural network (ANN) and multivariate adaptive regression splines (MARS) techniques were utilised to estimate the discharge capacity of compound open channels (COC). To this end, related datasets were collected from literature. The results showed that the divided channel method with a coefficient of determination (R ²) value of 0.76 and root mean square error (RMSE) value of 0.162 has the best performance, among the various analytical methods tested. The performance of applied soft computing models with R ²=0.97 and RMSE = 0.03 was found to be more accurate than analytical approaches. Comparison of MARS with the ANN model, in terms of developed discrepancy ratio (DDR) index, showed that the accuracy of MARS model was better than that of MLP model. Reviewing the structure of the derived MARS model showed that the longitudinal slope of the channel (S), relative flow depth (H _r ) and relative area (A _r ) have a high impact on modelling and forecasting the discharge capacity of COCs.     

Improving a thermal conductivity model of unsaturated soils based on multivariate distribution analysis

Soil thermal conductivity (k) is a key parameter for the design of energy geo-structures, and it depends on many soil properties such as saturation degree, porosity, mineralogical composition, soil type and others. Capturing these diversified influencing factors in a soil thermal conductivity model is a challenging task for engineers due to the nonlinear dependencies. In this study, a multivariate distribution approach was utilized to improve an existing soil thermal conductivity model, Cote and Konrad model, by quantitatively considering the impacts of dry density (ρ_d), porosity (n), saturation degree (S_r), quartz content (m_q), sand content (m_s) and clay content (m_c) on thermal conductivity of unsaturated soils. A large database containing these seven soil parameters was compiled from the literature to support the multivariate analysis. Simplified bivariate and multivariate correlations for improving the Cote and Konrad model were derived analytically and numerically to consider different influencing factors. By incorporating these simplified correlations, the predicted k values were more concentrated around the measured values with the coefficient of determination (R²) increased from 0.83 to 0.95. It is concluded that the developed correlations with the information of different soil properties provide an efficient, rational and simple way to predict soil thermal conductivity more accurately. Moreover, the quartz content is a more important factor than the porosity that shall be considered in the establishment of thermal conductivity models for unsaturated soils with high quartz content.

     

Risk assessment and prediction of safety factor for circular failure slope using rock engineering systems

Circular failure is generally observed in the slope of soil, highly jointed rock mass, mine dump and weak rock. Accurate estimation of the safety factor (SF) of slopes and their performance is not an easy task. In this research, based on rock engineering systems (RES), a new approach for the estimation of the SF is presented. The introduced model involves six effective parameters on SF [unit weight (γ), pore pressure ratio (r _u), height (H), angle of internal friction (φ), cohesion (C) and slope angle (\(\beta\))], while retaining simplicity as well. In the case of SF prediction, all the datasets were divided randomly to training and testing datasets for proposing the RES model. For comparison purposes, nonlinear multiple regression models were also employed for estimating SF. The performances of the proposed predictive models were examined according to two performance indices, i.e., coefficient of determination (R ²) and mean square error. The obtained results of this study indicated that the RES is a reliable method to predict SF with a higher degree of accuracy in comparison with nonlinear multiple regression models.     

Long-duration PMP and PMF estimation with SWAT model for the sparsely gauged Upper Nujiang River Basin

For large sparsely gauged basins, it is difficult to estimate long-duration probable maximum precipitation (PMP) and probable maximum flood (PMF) due to insufficient observed data and precipitation spatial distribution uncertainty. In this paper, a framework coupling the China Grid Daily Precipitation Datasets (CGDPDs) with Soil and Water Assessment Tool (SWAT) was proposed to estimate the 15-day PMP and PMF for the sparsely gauged Upper Nujiang River Basin (with a drainage area of 73,484 km²). CGDPD was tested against the observations and further corrected considering the error distribution characteristics. Results showed that 1-, 3-, 7- and 15-day maximum areal precipitations based on the corrected CGDPD were 17, 7, 4 and 18% larger than those calculated only by six observed stations’ precipitation. Then CGDPD was used as the precipitation data to estimate PMP. For the spatial distribution of PMP, the 15-day PMP process on the sub-basin scale (PMP_sub-basin) could be obtained with the following procedure. First, the basin’s 15-day areal PMP was estimated. Among this estimation, the maximum 3-day PMP was estimated by moisture maximization, while the remaining 12-day PMP was estimated with the combined storm obtained by the similar process substitution method. Second, the model storm amplification approach based on water balance principle was used to distribute the areal PMP to each sub-basin to obtain the PMP_sub-basin at all 27 sub-basins. The designed PMF could be finally estimated through inputting PMP_sub-basin into SWAT. In comparison with PMF derived from PMP without spatial distribution, different duration PMFs could increase by 3–15% when considering PMP spatial distribution uncertainty. This study could provide a reasonable procedure to estimate long-duration PMP and PMF for similar basins.     

四川盆地理论CO_2地质利用与封存潜力评估

结合CO_2地质利用与封存技术机理,在国际权威潜力评估公式的基础上,系统地提出了适合中国地质背景的次盆地尺度CO_2封存潜力评估方法及关键参数取值。同时,以四川盆地为例,依次开展了枯竭油田地质封存与CO_2强化石油开采、枯竭气田与CO_2强化采气、不可采煤层地质封存与CO_2驱替煤层气,以及咸水层地质封存技术的CO_2地质封存潜力。结果表明,四川盆地利用深部咸水层与枯竭天然气田CO_2地质封存潜力最大,期望值分别达154.20×10~8t和53.73×10~8t。其中,枯竭天然气田因成藏条件好、勘探程度高、基础建设完善,为四川盆地及其周边利用枯竭气田CO_2地质封存技术实现低碳减排提供了早期示范机会。CO_2地质利用与封存潜力评估方法,对进一步开展全国次盆地尺度理论封存潜力评估与工程规划具有重要意义。     

Modelling of streamflow in snow dominated Budhigandaki catchment in Nepal

This study has been envisaged to model streamflow runoff-contributed mostly by snowmelt from Budhigandaki catchment in Nepal. Available hydro-meteorological data have been collected from the Department of Hydrology and Metrology, Nepal. The MODIS Aqua (MYD10A2) and Terra (MOD10A2) 8 day maximum snow cover extent product have been used to derive the snow cover area (SCA) in the basin. The modeling of streamflow has been carried out using HEC-HMS and SRM models. The performance of these models have been evaluated by using three well known evaluation criteria NSE, \({R}^{2}\) and PEV (percentage error in volume). The values of NSE, \({R}^{2}\) and PEV were found to be 0.944, 0.944 and \(-0.3\%\) for SRM and 0.778, 0.785 and \(-1.83\%\) for HEC-HMS, respectively. Similarly, in validation period the average values of NSE, \({R}^{2}\) and PEV were found to be 0.811, 0.816 and 2.01% for SRM model and 0.773, 0.777 and 3.5% for HEC-HMS, respectively. Sensitivity analysis of the models revealed that constant loss rate is very sensitive parameters for HEC-HMS model, whereas runoff coefficient is found to be most sensitive parameters for SRM model. In comparison between two models SRM model has given slightly better simulation results compared to HEC-HMS model.     

Support vector regression to predict asphalt mix performance

Material properties are essential in the design and evaluation of pavements. In this paper, the potential of support vector regression (SVR) algorithm is explored to predict the resilient modulus (M_R), which is an essential property in designing and evaluating pavement materials, particularly hot mix asphalt typically used in Oklahoma. SVR is a statistical learning algorithm that is applied to regression problems; in our study, SVR was shown to be superior to the least squares (LS). Compared with the widely used LS method, the results of this study show that SVR significantly reduces the mean‐squared error and improves the correlation coefficient. Copyright © 2008 John Wiley & Sons, Ltd.     

Satellite-based rainfall estimation and discharge measurement of Middle Indus River,Pakistan

The impacts of floods and droughts are intensified by climate change, lack of preparedness, and coordination. The average rainfall in study area is ranging from 200 to 400 mm per year. Rain gauge generally provides very accurate measurement of point rain rates and the amounts of rainfall but due to scarcity of the gauge locations provides very general information of the area on regional scale. Recognizing these practical limitations, it is essential to use remote sensing techniques for measuring the quantity of rainfall in the Middle Indus. In this research, Tropical Rainfall Measuring Mission (TRMM) estimation can be used as a proxy for the magnitude of rainfall estimates from classical methods (rain gauge), quantity, and its spatial distribution for Middle Indus river basin. In order to use TRMM satellite data for discharge measurement, its accuracy is determined by statistically comparing it with in situ gauged data on daily and monthly bases. The daily R ² value (0.42) is significantly lower than monthly R ² value (0.82), probably due to the time of summation of TRMM 3-hourly precipitation data into daily estimates. Daily TRMM data from 2003 to 2012 was used as input forcing in Soil and Water Assessment Tool (SWAT) hydrological model along with other input parameters. The calibration and validation results of SWAT model give R ² = 0.72 and 0.73 and Nash-Sutcliffe coefficient of efficiency = 0.69 and 0.65, respectively. Daily and monthly comparison graphs are generated on the basis of model discharge output and observed data.     

Identification of the best spectral indices to remotely trace the diurnal course of water use efficiency of <Emphasis Type="Italic">Tamarix ramosissima</Emphasis> in the Gurbantunggut Desert,China

Water availability is one of the most important factors limiting photosynthetic assimilation of carbon dioxide and growth of individual plants in terrestrial ecosystems. Water use efficiency (WUE) of plants has been widely assessed using ecological methods in field measurements; however, approaches for remotely sensing WUE are still lacking, particularly in arid ecosystems. In this study, a comprehensive analysis of diurnal WUE via spectral indices in arid ecosystems was assessed. Analyses were conducted on a native dominant desert shrub, Tamarix ramosissima, in its original habitat on the southern periphery of the Gurbantunggut Desert, China. Based on diurnal measurements of spectral reflectance, photosynthesis, and micrometeorological variables, simple and useful spectral indices for estimating diurnal WUE at the assimilative organ scale were explored. From six types of spectral indices, ranging from simple to sophisticated, the best wavelength domains for a given type of index were determined by screening all combinations using correlation analysis. The coefficient of determination (R ²), ranging from 0.19 to 0.60, for WUE was calculated for all indices derived from spectra taken from the assimilative organs. With only two wavelengths and a significant correlation coefficient (R ² = 0.60, P < 0.001), the simple ratio (SR) type index was the most sensitive to WUE among all of the indices. Furthermore, SR is a useful indicator to determine the dynamic and diurnal processes of photosynthesis and transpiration of T. ramosissima. Although it has a few weaknesses, SR serves as a simple and robust indicator of WUE in arid ecosystems.     

Progress in the use of the method of optimum interpolation for the redesign of the Zambian raingauge network

The Zambian Second National Development Plan calls for an increase in the number of raingauge stations from the present 500 to 1000 by 1976, to bring the national network density to above the WMO recommended minimum figure of one gauge per 900 km².The present network is examined for adequacy according to the criterion of maximum admissible error of the estimate of rainfall at any ungauged point, using the method of optimum interpolation. Areas which are inadequately gauged are delimited.Several variations of the maximum admissible error criterion are considered as a basis of network design, and it is concluded that a combination of criteria provides the most suitable basis for network design. The practical constraints in implementing the design, however, restricts the usefulness of detailed planning.     

Rainfall erosivity and erosivity density in Eastern Ghats Highland of east India

The rainfall erosivity (R-factor in USLE) is the long-term average of the sum of the product of rainfall kinetic energy and its maximum 30-min intensity. Therefore, at most 30-min time intervals pluviograph records are required to calculate R-factor. But, such high-resolution data are scarce in many parts of the world and require lengthy processing period. In this study, R-factor was correlated with daily, monthly and annual rainfall, and its spatial variability in Eastern Ghats Highland of east India was mapped. The result showed that power regression models predicted satisfactorily the daily, monthly and annual R-factor, of which annual R-factor model performed best (model efficiency 0.93). Mean monsoon season R-factor was 15.6 and 10.0 times higher than the pre- and post-monsoon season R-factor, and thus remained highly critical with respect to erosion. Annual R-factor values ranged from 3040 to 10,127 MJ mm ha^?1 h^?1 year^?1, with standard deviation of 1981 MJ mm ha^?1 h^?1 year^?1. Rainfall intensity was positively correlated with erosivity density, and numerical value of rainfall intensity was almost double of the erosivity density value. The combination of rainfall and erosivity density was used to identify flood, erosion and landslide-prone areas. The developed iso-erosivity, erosivity density and risk maps can be opted as a tool for policy makers to take suitable measures against natural hazards in Eastern Ghats Highland of east India and elsewhere with similar rainfall characteristics.

     

Integration of GIS and remote sensing for estimation of soil loss and prioritization of critical sub-catchments: a case study of Tapacurá catchment

Mapping of erosion risk areas is an important tool for the planning of natural resources management, allowing researchers to propose the modification of land use properly and implement more sustainable long-term management strategies. The objective of this study was to assess and identify critical sub-catchments for soil conservation management using the USLE, GIS, and remote sensing techniques. The Tapacurá catchment is one of the planning units for water resource management of the Recife Metropolitan Region. Maps of the erosivity (R), erodibility (K), slope (LS), cover-management (C), and support practice (P) factors were derived from the climate database, digital elevation model, and soil and land-use maps. In order to validate the simulation process, total sediment delivery ratio was estimated. The results showed a mean sediment delivery ratio (SDR) of around 11.5?% and a calculated mean sediment yield of 0.108?t?ha^?1?year^?1, which is close to the observed one, 0.169?t?ha^?1?year^?1. The obtained soil loss map could be considered as a useful tool for environmental monitoring and water resources management. The methodology applied showed acceptable precision and allowed the identification of the most susceptible areas to soil erosion by water, constituting an important predictive tool for soil and environmental management in this region, which is highly relevant for the prediction of varying development scenarios for Tapacurá catchment. This approach can be applied to other areas for simple and reliable identification of critical areas of soil erosion in catchments.