SIMETAW# - a Model for Agricultural Water Demand Planning

A successful water management scheme for irrigated crops requires an integrated approach, which accounts for water, soil, and crop management. SIMETAW# is a user friendly soil water balance model that assesses crop water use, irrigation requirements, and generates hypothetical irrigation schedules for a wide range of crops experiencing full or deficit irrigation. SIMETAW# calculates reference evapotranspiration (ET_o), and it computes potential crop evapotranspiration (ET_c), and the evapotranspiration of applied water (ET_aw), which is the amount of irrigation water needed to match losses from the effective soil root zone due to ET_c that are not replaced by precipitation and other sources. Using input information on crop and soil characteristics and the distribution uniformity of infiltrated irrigation applications in full or deficit conditions, the model estimates the mean depth of infiltrated water (IW) into each quarter of the field. The impact of deficit irrigation on the actual crop evapotranspiration (ET_a) is computed separately for each of the four quarters of the cropped field. SIMETAW# simulation adjusts ET_o estimates for projected future CO₂ concentration, and hence the model can assess climate change impacts on future irrigation demand allowing the user to propose adaptation strategies that potentially lead to a more sustainable water use. This paper discusses the SIMETAW# model and evaluates its performance on estimating ET_c, ET_a, and ET_aw for three case studies.     

Development of a Hybrid Data Driven Model for Hydrological Estimation

High and low stremflow values forecasting is of great importance in field of water resources in order to mitigate the impacts of flood and drought. Most of water resources models deal with the problem of not being flexible for modeling maximum and minimum flows. To overcome that shortcoming, a combination of artificial neural network (ANN) models is developed in this study for monthly streamflow forecasting. A probabilistic neural network (PNN) is used to classify each of the input-output patterns and afterward, the classified data are forecasted using a modified multi-layer perceptron (MMLP). In addition, the performance of the MLP and generalized regression neural network (GRNN) in streamflow forecasting are investigated and compared to the proposed method. The findings indicate that the R² associated with the suggested model is 46 and 80% higher compared to MLP and GRNN models, respectively.     

湘西干旱动态分析模型的开发与应用

依托国家标准的雨水情数据库,采用新安江三层蒸散发模型和蓄满产流原理,结合旱情等级划分标准,建立了适合湘西自治州的逐日"土壤含水量"动态分析模型,对自治州的旱情发展进行分析预测,结果与实际情况较吻合.同时,系统通过调用Surfer绘图软件绘制干旱色斑图和计算各干旱等级的面积,在一定程度上实现了旱情信息的可视化、形象化和实时化,为抗旱决策提供科学依据,具有一定的技术价值和应用价值.     

Assessing Socioeconomic Drought Based on a Standardized Supply and Demand Water Index

Socioeconomic drought occurs when a water shortage is caused by an imbalance between the supply and demand of water resources in natural and human socioeconomic systems. Compared with meteorological drought, hydrological drought, and agricultural drought, socioeconomic drought has received relatively little attention. Hence, this study aims to construct a universal and relatively simple socioeconomic drought assessment index, the Standardized Supply and Demand Water Index (SSDWI). Taking the Jianjiang River Basin (JJRB) in Guangdong Province, China, as an example, we analyzed the socioeconomic drought characteristics and trends from 1985 to 2019. The return periods of different levels of drought were calculated. The relationships among socioeconomic, meteorological, and hydrological droughts and their potential drivers were discussed. Results showed that: (1) SSDWI can assess the socioeconomic drought conditions well at the basin scale. Based on the SSWDI, during the 35-year study period, 29 socioeconomic droughts occurred in the basin, with an average duration of 6.16 months and average severity of 5.82. Socioeconomic droughts mainly occurred in autumn and winter, which also had more severe droughts than other seasons. (2) In the JJRB, the joint return periods of “∪” and “∩” for moderate drought, severe drought, and extreme drought were 8.81a and 10.81a, 16.49a and 26.44a, and 41.68a and 91.13a, respectively. (3) Because of the increasing outflow from Gaozhou Reservoir, the occurrence probability of socioeconomic drought and hydrological drought in the JJRB has declined significantly since 2008. Reservoir scheduling helps alleviate hydrological and socioeconomic drought in the basin.

     

大量程步进式垂线坐标仪的研制

步进式变形监测仪器具有长期测量、测值稳定、可靠性高、测试原理简单、适用范围广等优点，在中国的大坝安全监测领域得到了广泛应用，但大量程步进式变形监测仪器的研制很难。文中介绍了一种新型步进式垂线坐标仪，该垂线坐标仪在引入独特的结构设计、先进的电子测量技术和优化的电机驱动控制策略的基础上，很好地解决了大量程工况下的高精度测量问题。     

城市需水预测模型研究

需水量预测是供水决策,水利投资数额的重要参考指数,在水资源规划与管理中起着重要的作用。针对现有需水量预测方法存在局限性等问题,结合经济社会发展状况和水资源的特点,建立了城市需水预测模型。该模型能够较好地体现出社会经济、生态、环境和水资源各个系统之间的复杂关系。     

湘中地区干旱预报模型及应用--以双峰县为例

以现有的降雨、蒸发资料为依据，假设未来的降雨、蒸发，通过模型计算，输出未来逐日土壤含水量，作为干旱预报的依据，判断现有及未来的干旱状况，作为指导农业生产和水利工程供水的依据。     

改进BP网络模型在年用水量预测中的应用

考虑城市用水量受众多因素影响,具有系统稳定性和非线性的特点,利用人工神经网络理论建立了改进BP网络预测模型,通过实例证明了该模型是一种行之有效的用水量预测模型。     

Development and Testing of Drought Indicators

Numerous definitions of droughts which are based on different climatological time series have been in use. In this paper, the development of drought indicators by using different time series is described. These drought indicators were developed for use by the Department of Natural Resources in the State of Indiana, U.S.A. The second part of the study deals with an analysis of the consistency of results obtained by using different time series, in order to select two or three of the commonly available series for drought analysis. Past drought data are used to test the performance of the drought indicators. As a result of this study, three month precipitation, monthly river flow and the Palmer Hydrologic Drought Index series are recommended for operational use.     

A Simplified Model for Predicting Drought Magnitudes: a Case of Streamflow Droughts in Canadian Prairies

The model for prediction of drought magnitudes is based on the multiplicative relationship: drought magnitude (M) = drought intensity (I) × drought duration (L), where I, L, and M are presumed to obey respectively the truncated normal probability distribution function (pdf), the geometric pdf, and the normal pdf. The multiplicative relationship is applied in the standardized domain of the streamflows, named as SHI (standardized hydrological index) sequences, which are treated equivalent to standard normal variates. The expected drought magnitude E(M _T ), i.e. the largest value of M over a sampling period of T-time units (T-year, T-month, and T-week) is predicted for hydrological droughts using streamflow data from Canadian prairies. By suitably amalgamating E(L _T ) with mean and variance of I in the extreme number theorem based relationship, the E(M _T ) is evaluated. Using Markov chain (MC), the E(L _T ) is estimated involving the geometric pdf of L. The Markov chains up to order one (MC-1) were found to be adequate in the proposed model for the annual to weekly time scales. For a given level of drought probability (q) and a sampling period T-time units; the evaluation of E(M _T ) requires only 3 parameters viz. lag-1 autocorrelation (ρ ₁ ), first order conditional probability (q _q , present instant being a drought given past instant was a drought) in SHI sequences and a parameter ø (value 0 to 1), which were estimated from historical data of streamflows. A major strength of the proposed model lies in the use of simple and widely familiar normal and geometric pdfs as its basic building blocks for the estimation of drought magnitudes.     

水利工程促进生态文明建设

按生态文明与美丽中国的科学内涵,论述了水利工程在生态文明建设中的驱动作用,并通过新安江水电站、衡水湖区域的实例,分析了水利工程驱动的区域生态文明建设模式,为类似水利工程建设提供参考。     

Development of a Non-stationary Standardized Precipitation Evapotranspiration Index (NSPEI) for Drought Monitoring in a Changing Climate

In a changing climate, drought indices as well as drought definitions need to be revisited because some statistical properties, such as the long-term mean, of climate series may change over time. This study aims to develop a Non-stationary Standardized Precipitation Evapotranspiration Index (NSPEI) for reliable and robust quantification of drought characteristics in a changing climate. The proposed indicator is based on a non-stationary log-logistic probability distribution, assuming the location parameter of the distribution is a multivariable function of time and climate indices, as covariates. The optimal non-stationary model was obtained using a forward selection method in the framework of the Generalized Additive Models in Location, Scale, and Shape (GAMLSS) algorithm. The Non-stationary and Stationary forms of SPEI (i.e., NSPEI and SSPEI) were calculated using the monthly precipitation and temperature data of 32 weather stations in Iran for the common period of 1964–2014. The results showed that almost at all the stations studied, the non-stationary log-logistic distributions outperformed the stationary ones. The AICs of the non-stationary models for 97% of the stations were lower than those of the stationary models. The non-stationary models at 90% of the stations were statistically significant at the 5% significance level. While SSPEI identified the long-term and continuous drought and wet events, NSPEI revealed the short-term and frequent drought/wet periods at almost all the stations of interest. Finally, it was revealed that NSPEI, compared to SSPEI, was a more reliable and robust indicator of drought duration and drought termination in vegetation cover during the severest drought period (the 2008 drought). Therefore, it was suggested as a suitable drought index to quantify drought impacts on vegetation cover in Iran.

     

Drought Subsidence Prediction Model for the Western San Joaquin Valley,California

Abstract

Land subsidence due to groundwater extraction in the western San Joaquin Valley has been studied and monitoredfor almost six decades. Although its subsidence rates diminished after 1970, the problem is still of great concern during droughtperiods. Knowing how big the social impact and cost of subsidence were in the past decades, the paper proposes an innovative approach to predicting its further occurrence. Extrapolation is based on analysis of historical groundwater subsidence relationship, drought pattern, and acceptance of new explanatory mechanisms for clay inelastic compaction. Average predicted magnitudes of subsidence in the western San Joaquin Valleyfor the next five decades are in the range 4 m to 8 m.     

Exploring Regional Profile of Drought History- a New Procedure to Characterize and Evaluate Multi-Scaler Drought Indices Under Spatial Poisson Log-Normal Model

Water Resources Management - Drought is recurrently occurring in many parts of the globe. In contrast to other natural hazards, drought has complex climatic characteristics. Several environmental...     

A Stochastic Model for Representing Drinking Water Demand at Residential Level

In this study, attention is initially focussed on modelling finely sampled (1 min) residential water demand time series. Subsequently, the possibility of simulating the water demand time series relevant to different time intervals and many users is analysed by using an aggregation approach. A cluster Neyman-Scott stochastic process (NSRP) is proposed to represent the residential water demand and a parameterisation procedureis implemented to respect the cyclical behaviour usually observed in any working day. A validation is performed on the basis of the one-minute datacollected on the water distribution system of Castelfranco Emilia located in the province of Modena (I). The elaborations performed show the validity both of the NSRP model and the parameterisation procedure proposedto represent the residential demand with fine time intervals (up to 5–10 min). On the other hand, when a procedure of aggregation is applied to represent the water demand of a high number of users, the results are nolonger satisfactory since only the mean is preserved while the other statistics, and in particular the variance, are underestimated.     

Model for Optimal Operation of Water Distribution Pumps with Uncertain Demand Patterns

An optimization model is presented for pump operation based upon minimizing operation costs and indirectly the maintenance costs of pumps considering uncertainty of specified demand (load) curves. The purpose of this model is to determine pump operation to meet the uncertain demands as well as to satisfy the pressure requirements in the water distribution system. In addition, constraints on the number of pump (‘on-off’) switches are included as a surrogate to indirectly minimizing the maintenance costs. This model is a mixed integer nonlinear programming (MINLP) problem using a chance constraint formulation of the uncertain demand constraint. The optimization model was solved using the LocalSolver option in A Mathematical Programming Language (AMPL). The model was first applied to the operation of an example pumping system for an urban water distribution system (WDS) illustrating a reduction in operation costs using the optimization model. The optimization model with the chance-constraint for demand was applied for a range of demand satisfaction uncertainties. A decrease in the operation costs was observed with an increased uncertainty in demand satisfaction, which shows that the model further optimizes the operations considering the relaxed constraints. Model application could be extended to operations of pumping systems during emergencies and contingencies such as droughts, component failures etc.     

枯水期径流量的中长期预估模式

为建立水文干旱预测系统，采用灰色系统理论中灰关联度分析的方法，对枯水期径流量的预估模式进行了探讨，并提出用层次分析法来考虑各影响因子对径流量的不同影响程度，这一尝试为水文干旱的预测提供了条件。     

改进的灰色灾变模型在干旱预测中的应用

为提高传统的GM(1,1)模型精度,分别从原始数据序列检验和建立残差修正模型两方面进行了改进,并将改进的灰色灾变模型与不进行原始序列检验的GM(1,1)1模型和对原始数据序列进行检验的GM(1,1)2模型进行了比较。结果表明:改进的灰色灾变模型精度最高;运用该模型预测榆林市未来可能发生干旱的年份分别为2012年、2015年和2019年。     

Development of Bio-Inspired- and Wavelet-Based Hybrid Models for Reconnaissance Drought Index Modeling

The present study aimed to model reconnaissance drought index (RDI) time series at three various time scales (i.e., RDI-6, RDI-9, RDI-12). Two weather stations located at Iran, namely Tehran and Dezful, were selected as the case study. First, support vector regression (SVR) was utilized as the standalone modeling technique. Then, hybrid models were implemented via coupling the standalone SVR with two bio-inspired-based techniques including firefly algorithm (FA) and whale optimization algorithm (WOA) as well as wavelet analysis (W). Accordingly, the hybrid SVR-FA, SVR-WOA, and W-SVR models were proposed. It is worth mentioning that six mother wavelets (i.e., Haar, Daubechies (db2, db4), Coifflet, Symlet, and Fejer-Korovkin) were employed in development of the hybrid W-SVR models. The performance of models was assessed through root mean square error (RMSE), mean absolute error (MAE), Willmott index (WI), and Nash-Sutcliffe efficiency (NSE). Generally, the implemented coupled models illustrated better results than the standalone SVR in modeling the RDI time series of studied locations. Besides, the Coifflet mother wavelet was found to be the best-performing wavelet. The most accurate results were achieved for RDI-12 modeling via the W-SVR utilizing db4(2) at Tehran station (RMSE = 0.253, MAE = 0.174, WI= 0.888, NSE = 0.934) and Coifflet(2) at Dezful station (RMSE = 0.301, MAE = 0.166, WI= 0.910, NSE = 0.936). As a result, the hybrid models developed in the current study, specifically W-SVR ones, can be proposed as suitable alternatives to the single SVR.