Forecast model of water consumption for Naples

The data refer to the monthly water consumption in the Neapolitan area over more than a 30 year period. The model proposed makes it possible to separate the trend in the water consumption time series from the seasonal fluctuation characterized by monthly peak coefficients with residual component. An ARMA (1,1) model has been used to fit the residual component process. Furthermore, the availability of daily water consumption data for a three-year period allows the calculation of the daily peak coefficients for each month, and makes it possible to determine future water demand on the day of peak water consumption.Notation j numerical order of the month in the year - i numerical order of the year in the time series - t numerical order of the month in the time series - h numerical order of the month in the sequence of measured and predicted consumption values after the final stage t of the observation period - Z _ji effective monthly water consumption in the month j in the year i (expressed as m³/day) - T _ji predicted monthly water consumption in the month j in the year i minus the seasonal and stochastic component (expressed as m³/day) - C _ji monthly peak coefficient - E _ji stochastic component of the monthly water consumption in the month of j in the year i - Z _i water consumption in the year i (expressed as m³/year) - Z _j (t) water consumption in the month j during the observation period (expressed as m³/day) - evaluation of the correlation coefficient - Z _j (t) water consumption in the month j during the observation period minus the trend - Y _t transformed stochastic component from E _t : Y _t =ln Et - Y _t+h measured value of stochastic component for t+h period after the final stage t of the observation period - Y _t (h) predicted value of stochastic component for t+h period after the final stage t of the observation period - _j transformation coefficients from the ARMA process (m, n) to the MA () process     

A water policy intercomparison model for a region with three seasons

A water policy model is proposed as a solution to the problem of obtaining maximum net benefit from providing irrigation and urban water in regions where the major source of supply is groundwater. In essence, the model introduces an innovative scheme based on two types of penalties. These intervene when either watertable elevation falls below a critical value during the operation of a system of wells or a remote source is used to partially cover the needed amount of water expected from the basic groundwater source. Another specific idea of the model is the consideration, for southern regions, of a three-season division in the climatic character of a year. The algorithm is illustrated by a numerical example in which five possible alternatives are compared. The conclusion of the study (although a function of regional economics, natural conditions, as well as specific zonal water policy constraints) reveals a compromise between limiting the amount provided from remote sources and confining the aquifer operation to critical values of the water-table elevation.Notation B benefits from water use, 10³ US$ - C sum of total costs, 10³ US$ - CMR total maintenance-repair cost, 10³ US$ - CO total cost of operation, 10³ US$ - C _HO unit cost of operation per hour, US$ hr^–1 - C _y unit average cost of a repaired pump, US$/(year × well) - d screen diameter for each well, m - H _G average groundwater elevation, m - H _{W cr} critical water elevation value in wells, m - H _{W dj} hydraulic head deficit in wells (belowH _{W cr} ), m - H _{W j} seasonal water elevation in each well, m - j current season - K average hydraulic conductivity of the aquifer, m s^–1 - NB net benefit, 10³ US$ - Ns maximum number of seasons - N _HO number of hours of operation per well and month - N _{W j} number of wells in operation over a seasonj - n _j number of months over each seasonj - P sum of total penalties, 10³ US$ - PH total penalty for pumping whenH _{W dj} >0, 10³ US$ - PQ total penalty for remote source use, 10³ US$ - P _RSj unit penalty for remote source use, 10³ US$ month^–1 - P _{W dj} unit penalty for pumping whenH _{W dj} >0, 10³ US$ month^–1 - pRM percentage of repaired and maintained pumps yearly, % - Q _iRj discharge needed for irrigation use, m³ s^–1 - Q _Nj total discharge needed by users, m³ s^–1 - Q _Pj total seasonal yield capacity of the battery, m³ s^–1 - Q _RSj discharge covered from remote sources, m³ s^–1 - Q _UWj discharge needed for urban water use, m³ s^–1 - Q _j seasonal operated pumping rate in each well, m³ s^–1 - Q _waj weighted average of pumping rate at timet _j , m³ s^–1 - S _Y average specific yield of the aquifer - S _cr critical drawdown value in wells, m - S _j seasonal drawdown in each well - T average transmissivity of the aquifer, m² s^–1 - t _OPj current duration of system operation - t _j –t _j–1 duration of each seasonj     

An Empirical Assessment of On-Farm Water Productivity using Groundwater in a Semi-Arid Indian Watershed

Realistic estimation of irrigation volume applied to any crop at farm level generally requires information on event based discharge rates and corresponding periods of irrigation application. Use of mean seasonal discharge rates leads to erroneous estimation of volume due to unaccounted seasonal fluctuations in the water table, upon which the discharge rate of tube well is dependent. In the absence of such information, an alternative approach of estimating farm level water application based upon water table fluctuation data has been adopted in this study. The total actual water extracted during each irrigation event from the watershed was distributed among the farms irrigating crops in proportion to the product of irrigation time and the pump capacity (h_p). Volume of water withdrawal concurrent to an irrigation event was computed based on the water level fluctuations in the wells in conjunction with potential recharge contribution from the surface storage structures to the groundwater aquifer. A production function approach was used to estimate the marginal productivity of water for selected crops at various stages of plant growth. Water, as an input in the production function, encompassed either in-situ soil moisture storage from rainfall or irrigation from groundwater or both. The inter-season as well as intra-season groundwater use, and the consequent groundwater withdrawals were analyzed based on the marginal value and output elasticity of water at different crop growth stages during the season. The cotton crop realized marginal value product of water, ranging from Rs. 1.03/m³ to Rs. 10.43/m³ at different crop growth stages in cotton. Castor crop had the marginal value product ranging from Rs. 2.89/m³ to Rs. 6.81/m³. The availability and use of water, including soil moisture, in the two seasons, coupled with the local harvest prices received, yielded the differential marginal values of water.     

Optimization-simulation models for the design of an Irrigation project

Optimization-simulation models were used for the systems analysis of a water resources system. The Karjan Irrigation reservoir project in India was taken as the system. Two types of optimization models, i.e., linear programming, and dynamic programming (continuous and discontinuous) were used for preliminary design purposes. The simulation technique was used for further screening. The linear programming model is most suitable for finding reservoir capacity. Dynamic programming (continuous and discontinuous models) may be used for further refining the output targets and finding the possible reservoir carry-over storages, respectively. The simulation should then be used to obtain the near optimum values of the design variables.Notations a ₁ Unit irrigation benefit [Rs.10⁵ L^–3] - B ₁ Gross annual irrigation benefit [Rs.10⁵] - B _1,t Gross irrigation benefit in periodt [Rs.10⁵] - C ₁ Annual capital cost of irrigation [Rs.10⁵] - C ₁ Annual capital cost function for irrigation [Rs.10⁵ L^–3] - C _1,t Fraction of annual capital cost for irrigation in periodt [Rs.10⁵] - C ₂ Annual capital cost of reservoir [Rs.10⁵] - C ₂ Annual capital cost function for reservoir [Rs.10⁵ L^–3] - C _2,t Fraction of annual capital cost for reservoir in periodt [Rs.10⁵] - El _t Reservoir evaporation in timet [L³] - f _t Optimal return from staget [Rs.10⁵] - g _t The return function for periodt [Rs.10⁵] - I _t Catchment inflow into the reservoir in periodt [L³] - I _t Water that joins the main stem just above the irrigation diversion canal in timet [L³] - _t Local inflow to the reservoir from the surrounding area in timet [L³] - Ir Annual irrigation target [L³] - K _t Proportion of annual irrigation targetIr to be diverted for irrigation in timet - K _t Amount by whichK _t exceeds unity is the fraction of the end storage which is assigned to reservoir evaporation losses - L Loss in irrigation benefits per unit deficit in the supply [Rs.10⁵ L^–3] - L ₁ Lower bound on annual irrigation target,Ir [L³] - L ₂ Lower bound on reservoir capacity,Y [L³] - N Number of time periods in the planning horizon - O _t Total water release from the reservoir in periodt [L³] - O _t ^* The optimal total water release from the reservoir in timet [L³] - _t Secondary water release from the reservoir in timet [L³] - O _t Reservoir release to the natural channel in timet [L³] - Od _t Irrigation demand in timet [L³] - Om ₁ Annual OM cost of irrigation [Rs.10⁵] - Om ₁ Annual OM cost function for irrigation [Rs.10⁵ L^–3] - Om _1,t Fraction of annual OM cost for irrigation in periodt [Rs.10⁵] - Om ₂ Annual OM cost of reservoir [Rs.10⁵] - Om ₂ Annual OM cost function for reservoir [Rs.10⁵ L^–3] - Om _2,t Fraction of annual OM cost for reservoir in periodt [L³] - Omin_t Lower bound onO _t in timet [L³] - Omax_t Upper bound onO _t in timet [L³] - P _t Precipitation directly upon reservoir in timet [L³] - S _t Gross/live reservoir storage at the end of timet (gross storage in the linear program and live storage in the dynamic program) [L³] - S _t–1 Gross/live reservoir storage at the beginning of timet [L³] - t Any time period - Tr_t Transformation function - U ₁ Upper bound onIr [L³] - U ₂ Upper bound onY [L³] - Y Total capacity of reservoir at maximum pool level [L³] - Ya Fixed active (live) capacity of the reservoir (Y-Yd) [L³] - Ya _t Active (live) capacity (Ymax_t –Ymin_t) of the reservoir in timet [L³] - Yd Dead storage of the reservoir [L³] - Ymax_t Capacity up to the normal pool level of the reservoir in timet [L³] - Ymax_t Live capacity up to the normal pool level of the reservoir in timet [L³] - Ymin_t Capacity up to the minimum pool level of the reservoir in timet [L³] - Ymin_t Live capacity up to the minimum pool level of the reservoir in timet [L³]     

The artificial recharge of groundwater: A solution by successive variations of steady states

The artificial recharge of groundwater aims at the modification of water quality, an increase of groundwater resources, and the optimization of the exploitation and recovery of contaminated aquifers. The purpose of this work is to develop a new mathematical model for the problem of an artificial recharge well, using the method of successive variations of steady states. Applying this method, one arrives at an expression of time as a double integral. This integral contains the time-dependent radius of the recharge boundary and the piezometric head of the well, calculated with the finite-element method. The new model is simple and useful, and can be applied to many practical problems, using the designed dimensionless graphs.Notations A area of the finite element (m²) - c the Euler constant (0.5772156649...) - e index of the finite element - E _i the exponential integral function - F _j nodal values of the functionF - h piezometric head, (m) - h ₀ piezometric head at timet=0 (m) - h _w piezometric head on the well contour (m) - i, j, k nodal indices of the finite element - K hydraulic contactivity (ms^–1) - N _i interpolation function - Q discharge (m³ s^–1) - r cylindrical coordinate (m) - r ₀ the action radius of the well (m) - r _w the radius of the well (m) - S the effective porosity - t the time (s) - T the transmissivity of the aquifer (m²s^–1) - V the stored water volume (m³) - x, y, dummy variables     

Optimal flood control in multireservoir cascade systems with deterministic inflow forecasts

This article presents the formal analysis of a problem of the optimal flood control in systems of serially connected multiple water reservoirs. It is assumed, that the basic goal is minimization of the peak flow measured at a point (cross-section) located downstream from all reservoirs and that inflows to the system are deterministic. A theorem expressing sufficient conditions of optimality for combinations of releases from the reservoirs is presented together with the relevant proof. The main features of the optimal combinations of controls are thoroughly explained. Afterwards, two methods of determining the optimal releases are presented. Finally, the results of the application of the proposed methodology to a small, four reservoir system are presented.Notations c _i contribution of theith,i=1, ...,m, reservoir to the total storage capacity of the multireservoir system - d _i (t) one of the uncontrolled inflows to the cascade at timet (fori=1 main inflow to the cascade, fori=2, ...,m, side inflow to theith reservoir, fori=m+1 side inflow at pointP) - total inflow to theith reservoir,i=2, ...,m, at timet (i.e., inflowd _i augmented with properly delayed releaser _i–1 from the previous reservoir) (used only in figures) - d(t),d _S (t) (the first term is used in text, the second one in figures) aggregated inflow to the cascade (natural flow at pointP) at timet - time derivative of the aggregated inflow at timet - i reservoir index - m number of reservoirs in cascade - P control point, flood damage center - minimal peak of the flow at pointP (cutting level) - Q _p (t) flow measured at pointP at timet - flow measured at pointP at timet, corresponding to the optimal control of the cascade - r _i (t) release from theith reservoir at timet, i=1, ...,m - optimal release from theith reservoir at timet, i=1, ...,m - r ₁ ^* (t) a certain release from theith reservoir at timet, different than ,i=1, ...,m, (used only in the proof of Theorem 1) - a piece of the optimal release from themth reservoir outside period at timet - assumed storage of theith reservoir at time (used only in the proof of Theorem 1) - s _i (t) storage of theith reservoir at timet, i=1, ...,m - time derivative of the storage of theith reservoir at timet, i=1, ...,m - storage capacity of theith reservoir,i=1, ...,m - (the first term is used in text, the second one in figures) total storage capacity of the cascade of reservoirs - S* sum of storages, caused by implementingr _i ^* ,i=1, ...,m, of all reservoirs measured at (used only in the proof of Theorem 1) - t time variable (continuous) - t ₀ initial time of the control horizon - t _a initial time of the period of constant flow equal at pointP - initial time of the period of the essential filling of theith reservoir,i=1, ...,m (used only in the proof of Theorem 1) - t _b final time of the period of constant flow equal at pointP - final time of the period of the essential filling of theith reservoir,i=1, ...,m (used only in the proof of Theorem 1) - time of filling up of theith reservoir while applying method with switching of the active reservoir - t _f final time of the control horizon - fori=1, ...,m–1, time lag betweenith andi+1th reservoir; fori=m time lag between the lowest reservoir of the cascade and the control pointP     

Optimal allocation of water resources in large river basins: I. Theory

The major purpose of this paper is to present the useful techniques in the optimal allocation of water resources (OAWR) and to demonstrate using water resources applications how these methods can be conveniently employed in practice for systematically studying both simple and complex water resources problems. Formal modelling techniques in multiobjective decision-making provide many benefits to professionals working in water resources and elsewhere. A new Large-system Hierarchical Dynamic Programming (LHDP) method to solve the model can be carried out to ascertain the consequences of meaningful parameter changes upon the optimal or compromise solution.As a case study, the techniques and methods are applied to the OAWR of the Yellow River Basin (YRB) of China. The next paper shares with the reader recent research results on the OAWRYRB.Notation L _i inflows from the trunk stream in the subregioni. - S _i run-off volume of the river sectioni. - Q _i net inflows of intervals in the subregioni. - W _i volumes of water drawn the trunk stream ofi into subdistricti. - H _i volumes of water returning to the trunk stream in the subdistricti. - B _i(W _i) the maximum net benefits (in hundred million yuan) from the annual-water consumption ofW _iin subregioni. - W _ik the annual-water consumption (in hundred million m³) of sectorK in subregioni, k = 1, 2, 3, 4. - B _ik(W _ik the maximum net benefits (in hundred million yuan) from the annual consumptionW _ikof sectork in subregioni. - BS _i(S _i) the maximum net benefits (in hundred million yuan) obtained from the optimal allocation of the run-off volumeS _iof river trunki among different sectors within the months of a year.     

LARGE EDDY SIMULATION OF THE INTERACTION BETWEEN WALL JET AND OFFSET JET

The interaction between a plane wall jet and a parallel offset jet is studied through the Large Eddy Simulation (LES).In order to compare with the related experimental data,the offset ratio is set to be 1.0 and the Reynolds number Re is 1.0×10 4 with respect to the jet height L and the exit velocity 0 U.The Finite Volume Method (FVM) with orthogonal-mesh (6.17×10 6 nodes) is used to discretize governing equations.The large eddies are obtained directly,while the small eddies are simulated by using the Dynamic Smagorinsky-Lily Model (DSLM) and the Dynamic Kinetic energy Subgrid-scale Model (DKSM).Comparisons between computational results and experimental data show that the DKSM is especially effective in predicting the mean stream-wise velocity,the half-width of the velocity and the decay of the maximum velocity.The variations of the mean stream-wise velocity and the turbulent intensity at several positions are also obtained,and their distributions agree well with the measurements.The further analysis of dilute characteristics focuses on the tracer concentration,such as the distributions of the concentration (i.e.,0 C /C or /m C C),the boundary layer thickness δ c and the half-width of the concentration c b,the decay of the maximum concentration (0 /m C C) along the downstream direction.The turbulence mechanism is also analyzed in some aspects,such as the coherent structure,the correlation function and the Probability Density Function (PDF) of the fluctuating velocity.The results show that the interaction between the two jets is strong near the jet exit and they are fully merged after a certain distance.     

Water Quality Monitoring and Hydraulic Evaluation of a Household Roof Runoff Harvesting System in France

The quality of harvested rainwater used for toilet flushing in a private house in the south-west of France was assessed over a one-year period. Twenty-one physicochemical parameters were screened using standard analytical techniques. The microbiological quality of stored roof runoff was also investigated and total flora at 22°C and 36°C, total coliforms, Escherichia Coli, enteroccocci, Cryptospridium oocysts, Giardia cysts, Legionella species, Legionella pneumophila, Aeromonas, and Pseudomonas aeruginosa were analysed. Chemical and microbiological parameters fluctuated during the course of the study, with the highest levels of microbiological contamination observed in roof runoffs collected during the summer. Overall, the collected rainwater had a relatively good physicochemical quality but variable, and, did not meet the requirements for drinking water and a microbiological contamination of the water was observed. The water balance of a 4-people standard family rainwater harvesting system was also calculated in this case study. The following parameters were calculated: rainfall, toilets flushing demand, mains water, rainwater used and water saving efficiency. The experimental water saving efficiency was calculated as 87%. The collection of rainwater from roofs, its storage and subsequent use for toilet flushing can save 42 m³ of potable water per year for the studied system.     

Nitrogen processing by biofilms along a lowland river continuum

While numerous studies have examined N dynamics along a river continuum, few have specifically examined the role of biofilms. Nitrogen dynamics and microbial community structure were determined on biofilms at six sites along a 120 km stretch of the lowland Ovens River, South Eastern Australia using artificial substrates. Terminal restriction fragment length polymorphism (T‐RFLP), chlorophyll a and protein analyses were used to assess biofilm microbial community composition. N dynamics was determined on the biofilms using the acetylene (C₂H₂) block technique and assessing changes in NH, NO_x and N₂O. Unlike microbial community structure, N dynamics were spatially heterogeneous. Nitrification, determined from the difference in accumulation of NH before and after addition of C₂H₂, occurred mostly in the upper sites with rates up to 1.4 × 10^?5 mol m^?2 h^?1. The highest rates of denitrification occurred in the mid‐reaches of the river (with rates up to 1 × 10^?5 mol m^?2 h^?1) but denitrification was not detected in the lower reaches. At the very most, only 50% of the observed uptake of NO_x by the biofilms following addition of C₂H₂ could be accounted for by denitrification. Copyright © 2005 John Wiley & Sons, Ltd.     

Quantitative Estimation Models and Their Application of Ecological Water Use at a Basin Scale

Ecological water use (EWU) is urgent in need in the lower reaches of Tarim River in China. Estimation of water amount for EWU is depending on some parameters and modeling. EWU is mainly consists of two parts in no runoff area in the basin, i.e. total water amount for restoration groundwater table and total stand water amount of the all river courses. The former is including water amount for restoration of groundwater table, lateral discharge and evaporation of water surface. The estimated values are 8.18 × 10⁸ m³, 0.68 × 10⁸ m³/a and 0.132 × 10⁸ m³/a respectively. Based on the groundwater depth rising 4.0 meters requiring 5 years, the total water amount for restoration groundwater table is 2.448 × 10⁸ m³/a. The latter, i.e., total stand water amount is 1.992 × 10⁸ m³/a. However, the development of water management measures could alleviate the issue and lead to sustainable EWU in the lower reaches of Tarim River.     

乌裕尔河1951-2015年径流量变化对扎龙盐沼演替的影响

使用Mann-Kendall和滑动t检验方法探讨了乌裕尔河流域径流量的变化对扎龙湿地演替的影响。结果表明：扎龙湿地迅速退化与乌裕尔河流域径流量的变化密切相关。乌裕尔河中下游的径流量自20世纪80年代初显著性减少。上游来水进入湿地的径流量从20世纪60年代的8×10⁸ m³迅速减少至21世纪的不足3×10⁸ m³,最低年份不足1×10⁸ m³。径流减少导致扎龙湿地持续干旱缺水,并使湿地水质迅速恶化和大面积沼泽退化消失。1979年扎龙湿地盐渍土面积为150 km²,到2014盐渍土面积增加到245 km²以上。扎龙湿地大部分水体属于富营养化水体,部分区域达到极富营养化程度,并且整体上呈现恶化的趋势。长期干旱的胁迫将削弱扎龙湿地的生态功能,甚至影响整个松嫩平原西部的生态安全。     

A Water Model for Water and Environmental Management at Mae Moh Mine Area in Thailand

It is revealed that the water quality in Mae Moh Reservoir, Thailand, has been deteriorated by lignite mine drainage and power station effluent. This study aims to manipulate water quantity and quality to reduce environmental impacts in Mae Moh area through a model for water management. The model was constructed on the basis of materials balance to predict water flow, which includes concentrations of TDS and SO ^2– ₄. Data collected during 1996–2000 were used. Model validation showed that the mean of predicted and actual values of TDS and SO ^2– ₄ load were significantly similar at 95% confidence limit. The test result is acceptable and the water model can be used as a tool for water system management in the area. In 2006, Mae Moh mine excess water will be discharged at 10.76 Mm³, with a pH of 7.3, TDS and SO ^2– ₄ concentrations of 2,547 and 1,803 mg/l, respectively. Mae Moh power station effluent will be 14.59 Mm³, with pH of 7.1, TDS and SO ^2– ₄ concentrations of 610 and 358 mg/l, respectively. Predicted results showed that the outflow of Mae Moh Reservoir will be 83.67 Mm³ and the concentrations of TDS and SO ^2– ₄ will be as high as 1,501 and 822 mg/l, respectively. Mine excess water management measures are recommended according to the following strategy. All mine excess water should be stored during dry season. During wet season, 50% of the excess water should be stored and the remaining treated at 90% of TDS removal before being discharged. The end result would be a significant improvement in water quality in the Mae Moh Reservoir over the 4-year period to 2010. Pollutants in terms of TDS would be reduced by 35% from 1,501 mg/l in the beginning of 2006 to 975 mg/l at the end of 2009.     

Trophic evolution of Lake Lugano related to external load reduction: Changes in phosphorus and nitrogen as well as oxygen balance and biological parameters

Lake Lugano is located at the border between Italy and Switzerland and is divided into three basins by two narrowings. The geomorphologic characteristics of these basins are very different. The catchment area is characterized by calcareous rock, gneiss and porphyry; the population amounts to approximately 290 000 equivalent inhabitants. The external nutrient load derives from anthropogenic (85%), industrial (10%) and agricultural (5%) sources. The limnological studies carried out by Baldi et al. (1949) and EAWAG (1964) revealed early signs of eutrophication, with a phosphorous concentration of about 30–40 mg m^–3 and an oxygen concentration of less than 4 g m^–3 in the deepest hypolimnion. Subsequently Vollenweider et al. (1964) confirmed these data and was the first to point out the presence of a meromictic layer in the hypolimnion of the northern basin. From the 1960s, as a result of an increase in the population and internal migration, the lake became strongly eutrophic with the P concentration reaching 140 mg m^–3 and the oxygen in the hypolimnion reduced to zero. Fifty‐five per cent of the P was from metabolic sources and 45% from detergents and cleaning products. In 1976, a partial diversion of waste water from the northern to the southern basin was begun, and gradually eight waste water treatment plants came into operation using mechanical, chemical and biological treatments. In 1986, Italy and Switzerland began to eliminate the P in detergents and cleaning products. Since 1995, the main sewage treatment plants have improved their efficiency by introducing P post‐precipitation, denitrification and filtration treatments. The recovery of the lake is due to be completed by the year 2005. Altogether, during the last 20 years recovery measures have reduced the external P load from about 250 to 70–80 tonnes year^–1; the goal to be reached is 40 tonnes year^–1. In‐lake phosphorous concentrations have decreased from 140 to 50–60 mg m^–3, with the target at 30 mg m^–3. Dissolved oxygen concentration is satisfactory only between the depths of 0 and 50 m, falling rapidly to zero in the deepest layers. Below a depth of 90 m, high CH₄, HS^–, NH₄⁺, Fe²⁺ and Mn²⁺ concentrations exist. Primary production has decreased from 420 to 310 g C_ass m^–2 year^–1, notwithstanding an increase in the thickness of the trophogenic layer. Structure and dynamic biomass show marked changes: phytoplankton dry weight has decreased from 16 to 7 g m^–2, while zooplankton dry weight has increased from 3 to 4.5 g m^–2. Chlorophyll concentration has fallen from 14 to 9 mg m^–3 and Secchi disk transparency has increased from 3.5 to 5.5 m. The current sources of the external load are uncollected small urban conglomerations, storm‐water overflows from outfall sewers, and the residual load from sewage treatment plants, particularly those without P post‐precipitation.     

Rainwater Harvesting: An Alternative to Safe Water Supply in Nigerian Rural Communities

Rainwater harvesting (RWH) is an economical small-scale technology that has the potential to augment safe water supply with least disturbance to the environment, especially in the drier regions. In Nigeria, less than half of the population has reasonable access to reliable water supply. This study in northeastern Nigeria determined the rate of water consumption and current water sources before estimating the amount of rainwater that can potentially be harvested. A survey on 200 households in four villages namely, Gayama, Akate, Sidi and Sabongari established that more than half of them rely on sources that are susceptible to drought, i.e. shallow hand-dug wells and natural water bodies, while only 3% harvest rainwater. Taraba and Gombe states where the villages are located have a mean annual rainfall of 1,064 mm and 915 mm respectively. Annual RWH potential per household was estimated to be 63.35 m³ for Taraba state and 54.47 m³ for Gombe state. The amount could meet the water demand for the village of Gayama although the other three villages would have to supplement their rainwater with other sources. There is therefore sufficient rainwater to supplement the need of the rural communities if the existing mechanism and low involvement of the villagers in RWH activities could be improved.     

Analytical solutions of unsteady drainage problems for soils subjected to variable recharge from a semi-confined aquifer

In drainage of agricultural lands, the upward vertical recharge from a semi-confined aquifer depends on the difference of the piezometric heads on the two sides of the semi-impermeable layer through which this recharge takes place. This means that the recharge through the semi-impermeable base depends on the unknown height of the unsteady water table. In the nonhomogeneous Boussinesq equation, which describes the drainage problems, the downward uniform rate of the recharge from rain or irrigation and the recharge from the semiconfined aquifer are expressed by two terms. By solving the Boussinesq equation expressions for the nondimensional height of the water table and the nondimensional discharge of the drains per unit drained area are obtained for three different initial conditions. Some known solutions are shown as special cases of the present solutions. Variation of nondimensional water table heights at half distance of the drain spacing and the nondimensional discharge of the drains with nondimensional time have been graphically illustrated with the help of synthetic examples.Notation B _s thickness of the semi-impervious layer [L] - c hydraulic resistance of the semi-impervious layer [T] - D depth of the drains from the base [L] - d _e equivalent depth [L] - h=h(x, t) height of the water table [L] - h ₀ initial height of the water table [L] - h _t water table height at mid-distance of drains att [L] - h _j ,h _k water table height at mid-distance of drains at timej andfk, respectively [L] - H ₀ piezometric head in the semi-confined aquifer [L] - K hydraulic conductivity of the soil [LT^–1] - K _s hydraulic conductivity of the semi-impervious layer [LT^–1] - k ₀,k ₁,k ₂ nondimensional constants - L distance between the drains [L] - q ₀ upward recharge per unit surface area through the semi-impervious layer [LT^–1] - q _t discharge per unit drainable area of drains at timet [LT^–1] - R,R ₀ recharge per unit surface area from rain or irrigation during the unsteady and steady-state, respectively, [LT^–1] - S specific yield of the soil - t time of observation [T] - x distance measured from the drain [L] - leakage factor [L] - nondimensional distance - nondimensional time     

面向总量闭合的水资源配置模型与应用

水资源总量控制是水资源管理的主要手段,针对区域来水、取用水分开统计,导致区域来水、取用耗排水、断面出境水量不闭合问题,本文从水量控制核算的角度出发,构建基于水循环转化的水资源配置模型,通过模拟区域水资源取用耗排过程下的水循环过程,以区域来水和出境断面水量双向控制方法核算区域水量取用情况。以天津市为例,在全境多年平均来水42.1亿m~3(其中外调水14.7亿m~3)情景下,2020年水平年天津市取用水、耗水和排水等总量闭合控制阈值分别为38.25、22.0和16.25亿m~3,出境水量为22.1亿m~3,根据水资源实际管理需求,模型给出了各区县不同水源和行业相应的控制阈值,研究成果可为水资源消耗双控行动、最严格水资源管理和节水型社会建设提供科学支撑。     

若尔盖高原径流量变化与储水量计算

若尔盖高原的降水量微弱减少与蒸发量持续上升,使若尔盖高原径流量与储水量逐年降低,直接减少了若尔盖高原的湿地面积和对黄河上游径流量的补给。基于红原、若尔盖和玛曲站的气象数据和7个水文站的径流量数据(1981-2011年),并对数据序列进行插补与计算,获得若尔盖高原的径流量变化与气候因子的响应关系,进而计算储水量变化。计算结果表明:若尔盖高原向黄河年均补水(67. 08±14. 90)×108m3,并以0. 48×108m3/a速率持续减少。降水量每减少1 mm将导致黑河与白河的年径流量分别减少0. 02×108和0. 05×108m3。蒸发量每增加1 mm将导致黑河与白河的年径流量分别减少0. 12×108和0. 27×108m3。1981-2011年若尔盖高原的年均储水量为(59. 30±18. 69)×108m3,其年均递减速率为0. 49×108m3/a。本研究有助于认识若尔盖高原对于黄河上游水资源保障的重要性。     

Calcium Carbonate in Postglacial Lake Erie Sediment

Geotechnical, geochemical, electron-microscopic and biostratigraphic investigation of a 16.8–m long core of a postglacial lacustrine mud collected from the central Lake Erie basin revealed zones containing up to 30% calcite. These zones were associated with a very fine sediment, 80% of which was finer than 4 y.m. Shear strength values of the sediment ranged from 2 kN/m² near the lake bottom to about 9 kN/m² close to the Pleistocene boundary. The concentration of CaO was positively correlated with the concentration of inorganic C and Sr. The concentration of SiO₂, A1₂O₃, Fe₂O₃, K₂O, and Rb gradually decreased with depth. The major crystalline phases were illite (44 wt %), chlorite (10 -13 wt %), calcite (6 - 30 wt %), quartz (6 - 20 wt %), albite (6 - 9 wt %), K-feldspar (2 wt %), and dolomite (2 wt %). The shells of mollusc species present in the lower portion of the postglacial sedimentary column indicated that the carbonate-high sediments were deposited in warmer water than the carbonate-low sediments above and between them.     

Peak coefficients of household potable water supply

An experimental data-gathering project was carried out on two boulding aggregations of limited size in the urban area of Naples. Flow-rate measurements were continuously taken during a period of 24 h while the delivered volumes were recorded for approximately three years. The pattern of water demand in the day of maximum consumption is presented in a histogram of the hourly distribution which allows the hourly and istantaneous peak coefficients to be determined. The determination of the average daily consumption per year and the daily peak coefficient is made possible by the availability of quarterly invoicings.Notations F _g Average daily supply - F _g Maximum daily supply - F _h Maximum hourly supply - F _i Instantaneous supply - C _g Daily peak coefficient - C _h Hourly peak coefficient - C _i Instantaneous peak coefficient - F _c Load factor - K Simultaneity coefficient - n Number of taps