A Modified SCS-CN Method Incorporating Storm Duration and Antecedent Soil Moisture Estimation for Runoff Prediction

In one of the widely used methods to estimate surface runoff - Soil Conservation Service Curve Number (SCS-CN), the antecedent moisture condition (AMC) is categorized into three AMC levels causing irrational abrupt jumps in estimated runoff. A few improved SCS-CN methods have been developed to overcome several in-built inconsistencies in the soil moisture accounting (SMA) procedure that lies behind the SCS-CN method. However, these methods still inherit the structural inconsistency in the SMA procedure. In this study, a modified SCS-CN method was proposed based on the revised SMA procedure incorporating storm duration and a physical formulation for estimating antecedent soil moisture (V ₀ ). The proposed formulation for V ₀ estimation has shown a high degree of applicability in simulating the temporal pattern of soil moisture in the experimental plot. The modified method was calibrated and validated using a dataset of 189 storm-runoff events from two experimental watersheds in the Chinese Loess Plateau. The results indicated that the proposed method, which boosted the model efficiencies to 88% in both calibration and validation cases, performed better than the original SCS-CN and the Singh et al. (2015) method, a modified SCS-CN method based on SMA. The proposed method was then applied to a third watershed using the tabulated CN value and the parameters of the minimum infiltration rate (f _c ) and coefficient (β) derived for the first two watersheds. The root mean square error between the measured and predicted runoff values was improved from 6 mm to 1 mm. Moreover, the parameter sensitivity analysis indicated that the potential maximum retention (S) parameter is the most sensitive, followed by f _c . It can be concluded that the modified SCS-CN method, may predict surface runoff more accurately in the Chinese Loess Plateau.     

Symbolic Regression Equations for Calculating Daily Reference Evapotranspiration with the Same Input to Hargreaves-Samani in Arid China

To present an alternative simple equation for reference evapotranspiration (ET _o) estimation, the symbolic regression (SR) method was applied to establish equations with the same inputs to simple Hargreaves-Samani (HS) equation in arid China. For most of the equations derived by SR method for each station, their performance increased with an increase in the equation complex index (CI). The most precise equation performed well although it was always complex and greatly varied in form. On the other hand, the simplest one was uniform in equation structure and performed slightly better than the HS equation for all the five stations, and sometimes better than the local calibrated HS equation. A trade-off equation was selected with almost the same equation form for all the five stations and low CI index. The site-specific trade-off equation performed better than the simplest one and the locally calibrated HS equation. Then parameters in the trade-off equation were unified for all the five stations, it did not perform as good as the site-specific one, but performed better than the HS equation and unified local calibrated HS equation. Thus, the SR method is suitable to determine both the site-specific and the unified equation among stations for daily ET _o calculation in arid regions.     

Estimation of Regional Sub-Daily Rainfall Ratios Using SKATER Algorithm and Logistic Regression

Developing Intensity-Duration-Frequency (IDF) curves is a paramount input in stormwater systems design. To construct these IDF curves, rainfall records at sub-daily durations, provided by continuous rainfall recorders, are required; however, these recorders are seldom available in many locations of interest. To fill this gap, available meteorological and topographical information for a study area in Saudi Arabia are investigated to get an estimate of the ratios of sub-daily rainfall depths to the 24-h depths (sub-daily ratios or SDRs), via applying the following methodology. A spatially constrained regionalization approach is implemented, using the SKATER algorithm, based on 60 gauging stations, to form regions of contiguous stations, based on the similarities of their SDRs. Four different regions are formed, where each region shows consistent SDRs; yet distinctly different from other regions. Subsequently, a multinomial logistic regression model is built and trained, with commonly available meteorological and topographical information as explanatory variables, to determine to which region a specific location belongs. The model is validated based on a hold-out validation method and assessed through confusion matrix statistics to evaluate the model performance. The model shows high performance in predicting the correct regional SDR and it is extended to produce a gridded map covering ungauged areas. Based on this procedure, one can develop the IDF curve for any location within the study area, even if there is no rainfall recorder in that location.

     

Comparing Threshold Level Methods in Development of Stream Flow Drought Severity-Duration-Frequency Curves

This study compares Severity-Duration-Frequency (SDF) curves (SDFs) of stream flow drought derived from threshold level methods. For this purpose hydrological drought of Roudzard river basin was investigated, based on run theory. Daily runoff data of Mashin hydrometery station (1970–2012) assessed using 70% (Q₇₀), 90% (Q₉₀) of mean daily and 70% of monthly average runoff (monthly) as threshold level methods. Time series of the annual maxima values of duration and volume deficit showed similar trend of increase and decreasing in different thresholds. SDFs were prepared, classifying drought durations to four intervals and fitting statistical distribution to each one. Resulted SDFs showed that, in each period, increasing of duration resulted to increased value of the volume deficit with a non-linear trend while duration and severities from the threshold levels were different. Drought deficit-volume increasing rate was also different in each class of duration-interval. For the additional analysis, the duration-frequency and deficit-frequency curves were also prepared to quantify the extent of drought duration and deficit more. SDFs developed in this study can be used to quantify water deficit for natural stream and reservoir. They could be an effective tool to identify multivariate hydrological drought using severity, duration and frequency.     

Effects of Global Warming on Precipitation Extremes: Dependence on Storm Characteristics

This study investigates the relationship between historically observed changes in extreme precipitation magnitudes and temperature (P_ex-T relationship) at multiple locations in Canada. The focus is on understanding the behavior of these relationships with regards to key storm characteristics such as its duration, season of occurrence, and location. To do so, three locations are chosen such that they have large amounts of moisture available near them whereas four locations are chosen such that they are located in the land-locked regions of Canada and subsequently have no nearby moisture source available on them. To investigate the effect of different storm durations on P_ex-T relationship, storms of durations: 5, 10, 15, 30 min, 1, 2, 6, 12, 24 h are considered. Finally, P_ex-T relationship is analyzed separately for summer and winter seasons to quantify the influence of seasons. Results indicate strong influences of storm duration, season of occurrence, and location on observed precipitation scaling rates. Drastic intensification of precipitation extremes with temperature is obtained for shorter duration precipitation events than for longer duration precipitation events, in summers than in the winters. Furthermore, in summertime, increases in the intensity of convection driven precipitation extremes is found highest at locations away from large waterbodies. On the other hand, in wintertime most drastic increases in extreme precipitation are obtained at locations near large waterbodies. These findings contribute towards increasing the current understanding of precipitation extremes in the context of rapidly increasing global temperatures.     

A Robust Method to Update Local River Inundation Maps Using Global Climate Model Output and Weather Typing Based Statistical Downscaling

Global warming is changing the magnitude and frequency of extreme precipitation events. This requires updating local rainfall intensity-duration-frequency (IDF) curves and flood hazard maps according to the future climate scenarios. This is, however, far from straightforward, given our limited ability to model the effects of climate change on the temporal and spatial variability of rainfall at small scales. In this study, we develop a robust method to update local IDF relations for sub-daily rainfall extremes using Global Climate Model (GCM) data, and we apply it to a coastal town in NW Spain. First, the relationship between large-scale atmospheric circulation, described by means of Lamb Circulation Type classification (LCT), and rainfall events with potential for flood generation is analyzed. A broad ensemble set of GCM runs is used to identify frequency changes in LCTs, and to assess the occurrence of flood generating events in the future. In a parallel way, we use this Weather Type (WT) classification and climate-flood linkages to downscale rainfall from GCMs, and to determine the IDF curves for the future climate scenarios. A hydrological-hydraulic modeling chain is then used to quantify the changes in flood maps induced by the IDF changes. The results point to a future increase in rainfall intensity for all rainfall durations, which consequently results in an increased flood hazard in the urban area. While acknowledging the uncertainty in the GCM projections, the results show the need to update IDF standards and flood hazard maps to reflect potential changes in future extreme rainfall intensities.

     

Suitability of ANN-Based Daily Streamflow Extension Models: a Case Study of Gaoping River Basin,Taiwan

It is well known that sufficiently long and continuous streamflow data are required for accurate estimations and informed decisions in water-resources planning, design, and management. Although streamflow data are measured and available at most river basins, streamflow records often suffer from insufficient length or missing data. In this work, artificial neural networks (ANNs) are applied to extend daily streamflow records at Lilin station located in Gaoping River basin, southern Taiwan. Two ANNs, including feed forward back propagation (FFBP) and radial basis function (RBF) networks, associated with various time-lagged streamflow and rainfall inputs of nearby long-record stations are employed to extend short daily streamflow records. Performances of ANNs are evaluated by root-mean-square error (RMSE), coefficient of efficiency (CE), and histogram-matching dissimilarity (HMD). Inconsistency among these evaluation measures is solved by the technique for order performance by similarity to ideal solution (TOPSIS), a widely used multi-criteria decision-making approach, to find an optimal model. The results indicate that RBF-E1 (entire-year data training with Q _t and Q _t?1 inputs) has the minimum RMSE of 104.4 m³/s, second highest CE of 0.956, and third lowest HMD of 0.0096, which outperforms other ANNs and provide the most accurate reconstruction of daily streamflow records at Lilin station.     

Development of hydrological tools using extreme rainfall events for Dhaka,Bangladesh

Annual daily maximum rainfall data for 57 years (1953 to 2009) in Dhaka, Bangladesh are analysed statistically to prepare two hydrological tools: (1) probable return periods of extreme rainfall events, and (2) intensity-duration-frequency (IDF) curves. Limited availability of short-duration rainfall data leads to difficulties in performing the task through traditional approaches; hence, the Gumbel distribution function and scaling theory have been applied in preparing the probable return periods of extreme rainfall events and IDF curves, respectively. The outcomes of this paper can be used in better designing hydraulic structures in Bangladesh.     

Development of a GIS Interface for Estimation of Runoff from Watersheds

Development of accurate surface runoff estimation techniques from ungauged watersheds is relevant in Indian condition due to the non-availability of hydrologic gauging stations in majority of watersheds. Besides this, the high budgetary requirements for installation of gauging stations are another limiting factor in India, which leads to the use of surface runoff estimation techniques for ungauged watersheds. Natural Resources Conservation Services Curve Number (NRCS-CN) method is one of the most widely used methods for quick and accurate estimation of surface runoff from ungauged watershed. Also, the coupling of NRCS-CN techniques with the advanced Geographic Information System (GIS) capabilities automates the process of runoff prediction in timely and efficient manner. Keeping view of this, a GIS interface was developed using the in-built macro programming language, Visual Basic for Applications (VBA) of ArcGIS® tool to estimate the surface runoff by adopting NRCS-CN technique and its three modifications. The developed interface named as Interface for Surface Runoff Estimation using Curve Number techniques (ISRE-CN), was validated using the recorded data for the periods from 1993 to 2001 of a gauged watershed, Banha in the Upper Damodar Valley in Jharkhand, India. The observed runoff depths for different rainfall events in this study watershed was compared with the predicted values of NRCS-CN methods and its three modifications using statistical significance tests. It was revealed that using all the rainfall data for different AMC conditions, the modified CN I performed the best [R ² (coefficient of determination)?=?0.92; E (model efficiency)?=?0.89) followed by modified CN III method (R ²?=?0.88; E?=?0.87), while the modified CN II (R ²?=?0.42; E?=?0.36) failed to predict accurately the surface runoff from Banha watershed. Moreover, under AMC based estimations, the modified CN I method also performed best ( R ²?=?0.95; E?=?0.95) for AMC II condition, while the modified CN II performed the worst in all the AMC conditions. However, the developed Interface in ArcGIS® needs to be tested in other watershed systems for wider applicability of the modified CN methods.     

A Two-stage Approach to Basin-scale Water Demand Prediction

Water demand prediction (WDP) is the basis for water allocation. However, traditional methods in WDP, such as statistical modeling, system dynamics modeling, and the water quota method have a critical disadvantage in that they do not consider any constraints, such as available water resources and ecological water demand. This study proposes a two-stage approach to basin-scale WDP under the constraints of total water use and ecological WD, aiming to flexibly respond to a dynamic environment. The prediction method was divided into two stages: (i) stage 1, which is the prediction of the constrained total WD of the whole basin (T _w ) under the constraints of available water resources and total water use quota released by the local government and (ii) stage 2, which is the allocation of T _w to its subregions by applying game theory. The WD of each subregion (T _s ) was predicted by calculating its weight based on selected indicators that cover regional socio-economic development and water use for different industries. The proposed approach was applied in the Dongjiang River (DjR) basin in South China. According to its constrained total water use quota and ecological WD, T _w data were 7.92, 7.3, and 5.96 billion m³ at the precipitation frequencies of 50%, 90%, and 95%, respectively (in stage 1). Industrial WDs in the domestic, primary, secondary, tertiary, and environment sectors are 1.08, 2.26, 2.02, 0.44, and 0.16 billion m³, respectively, in extreme dry years (in stage 2). T _w and T _s exhibit structures similar to that of observed water use, mainly in the upstream and midstream regions. A larger difference is observed between T _s and its total observed water use, owing to some uncertainties in calculating T _w . This study provides useful insights into adaptive basin-scale water allocation under climate change and the strict policy of water resource management.     

A Non-Stationary Reconnaissance Drought Index (NRDI) for Drought Monitoring in a Changing Climate

Traditionally, drought indices are calculated under stationary condition, the assumption that is not true in a changing environment. Under non-stationary conditions, it is assumed the probability distribution parameters vary linearly/non-linearly with time or other covariates. In this study, using the GAMLSS algorithm, a time-varying location parameter of lognormal distribution fitted to the initial values (α₀) of the traditional Reconnaissance Drought Index (RDI) was developed to establish a new index called the Non-Stationary RDI (NRDI), simplifying drought monitoring under non-stationarity. The fifteen meteorological stations having the longest records (1951–2014) in Iran were chose to evaluate the NRDI performances for drought monitoring. Trend analysis of the α₀ series at multiple time windows was tested by using the Mann-Kendall statistics. Although all stations detected decreasing trend in the α₀ series, eight of them were significant at the 5% probability level. The results showed that the time-dependent relationship is adequate to model the location parameter at the stations with the significant temporal trend. There were remarkable differences between the NRDI and the RDI, especially for the time windows larger than 6 months, implying monitoring droughts using the NRDI under non-stationarity. The study suggests using the NRDI where the significant time trend appears in the initial values of RDI due to changing climate.     

Influence of Trend on Short Duration Design Storms

Design storms (DS) that are determined from intensity-duration-frequency (IDF) relationships are required in many water resources engineering applications. Short duration DS are of particular importance in municipal applications. In this paper, linear trends were estimated for different combinations of durations and frequencies (return periods) of annual short-duration extreme rainfall. Numerical analysis was performed for 15 meteorological stations from the province of Ontario, Canada. The estimated magnitude (rate mm/h) and direction of trend (increasing, decreasing, or no trend) were estimated and then used to quantify the effect of trend on the frequency of design storms. Significant trends were detected for all durations. It was determined that due to the existence of trends (which might be attributed to climate change), the design storms of a given duration might occur more frequently in the future by approximately as much as 36 years depending on the duration and return period.     

A New Method for Joint Frequency Analysis of Modified Precipitation Anomaly Percentage and Streamflow Drought Index Based on the Conditional Density of Copula Functions

In this study, a new method was proposed to model the occurrence of related variables based on the conditional density of copula functions. The proposed method was adopted to investigate the dynamics of meteorological and hydrological droughts in the Zarinehroud basin, southeast of Lake Urmia, during the period 1994–2015. For this purpose, the modified precipitation anomaly percentage and streamflow drought indices were extracted. Finally, the joint frequency analysis of duration-duration and severity-severity characteristics of meteorological and hydrological droughts was analyzed. Analysis of 7 different copulas used to create the joint distribution in the Zarinehroud basin indicated that the Frank copula had the best performance in describing the relationship between the meteorological and hydrological drought severities and durations. By examining the results of the bivariate analysis of duration-duration of meteorological and hydrological droughts at different stations, the expected meteorological and hydrological drought durations were estimated in the years ahead. For example, at Chalkhmaz station, 4- to 7-month duration for the hydrological drought and 9- to 12-month duration for the meteorological drought is expected in the years ahead. The joint frequency analysis of drought characteristics allows to determine the meteorological and hydrological drought characteristics at a single station at the same time using joint probabilities. Also, the results indicated that by knowing the conditional density, the hydrological drought characteristics can be easily estimated for the given meteorological drought characteristics. This could provide users and researchers useful information about the probabilistic behavior of drought characteristics for optimal operation of surface water.

     

基于信息熵原理的东北地区参考作物蒸散量时空特征分析

根据1951—2013年东北地区116个气象站点的常规气象资料,基于信息熵理论构建了东北地区日参考作物蒸散量站点间的信息传输模型并分析了信息场的分布情况,利用聚类分析法解析了东北地区日参考作物蒸散量的区域相似性结构特征;并在此基础上选取6个代表性站点,运用重标极差法分析了参考作物蒸散量(ET_0)的时间分形特征。结果表明:空间上,东北地区日参考作物蒸散量的信息熵随纬度增加而减小,各站点信息传输指数随基站与辅站距离的增大而减小,且具有明显的各向异性;东北地区在站点群层面上的信息传输总体呈由南到北、由东到西的分布规律。时间上,东北地区多年平均ET_0总体呈下降趋势,哈尔滨、沈阳、赤峰、加格达奇、佳木斯、海拉尔6个代表性站点的ET_0在未来一定时段内的变化趋势趋于稳定且具有较强的持续性。     

近50年来西藏极端降水时空变化特征

利用1961~2010年西藏地区9个气象站点逐日降水资料,结合百分位方法定义的极端降水阈值,分析了该地区极端雨日及其平均降水强度、不同持续时间的极端降水事件、气候变化对极端降水的时空变化特征的影响。结果表明:(1)92°E以西的地区,极端雨日平均降水强度呈现出增加的趋势,而在92°E以东的地区,呈现出减小的趋势;(2)极端降水事件以持续1 d为主,其频率一般在4.3次/年以上,强度一般在20 mm/d以上,林芝站和波密站为频率和强度高值区;(3)气候变化背景下,极端降水的频率、强度表现出西移的态势。     

Regional sub-hourly extreme rainfall estimates in Sicily under a scale invariance framework

Design of urban drainage systems or flood risk assessment in small catchments often requires knowledge of very short-duration rainfall events (less than 1 h). Unfortunately, data for these events are often unavailable or too scarce for a reliable statistical inference. However, regularities in the temporal pattern exhibited by storm records, known as scaling properties of rainfall, could help in characterizing extreme storms at partially gauged sites better than the application of traditional statistical techniques. In this work, a scaling approach for estimating the distribution of sub-hourly extreme rainfall in Sicily (Italy) is presented based on data from high-resolution rain gauges with a short functioning period and from low-resolution rain gauges with longer samples. First, simple scaling assumption was tested for annual maxima rainfall (AMR) data from 10 min to 24 h duration, revealing that the simple scaling regime holds from 20 to 60 min for most of the stations. Then, scaling homogeneous regions were classified based on the values of the scaling exponent. In each region, this parameter was regionalized through power-law relationships with the median of 1 h AMR data. After that, regional Depth Duration Frequency (DDF) curves were developed by combining the scale-invariant framework with the generalized extreme value (GEV) probability distribution and used to estimate T-year sub-hourly extreme rainfalls at sites where only rainfall data for longer durations (≥ 1 h) were available. The regional GEV simple scaling model was validated against sub-hourly historical observations at ten rain gauges, generally yielding, in relation to the scaling exponent value, to similar or better sub-hourly estimates than empirical approach.

     

Potential Impact of Climate Change on Rainfall Intensity-Duration-Frequency Curves in Roorkee,India

Intensification and frequency of hydrologic events are attributed to climate change and are expected to increase in coming future. Intensity-Duration-Frequency (IDF) curves quantify the extreme precipitation and are used extensively to assess the return periods of rainfall events. It is expected that climate change will modify the occurrence of extreme rainfall events. Thus a need of updating IDF curves arises under the climate change scenario. This paper aims at updating the IDF curves for a typical Indian town using an ensemble of five General Circulation Models (GCMs) for all the Representative Concentration Pathways (RCP) scenarios. Sub-daily maximum intensities (15-, 30-, 45-, 60-, 120-, and 180 min) were obtained from the observed records. Equidistance quantile method was used to study the relationships between the historical and projected GCM data, and the historical GCM and observed sub-daily data. This relationship was used to obtain projected sub-daily intensities. The IDF curves were developed using observed and projected data. Analysis of the curves indicated increase in precipitation intensities for all the RCP scenarios. It was also found that intensities of all return periods increases with intensifying RCP scenarios. The variation in the intensities across the GCMs was attributed to the driving forces considered in a particular GCM.     

A Comparison of Nonlinear Stochastic Self-Exciting Threshold Autoregressive and Chaotic <Emphasis Type="Italic">k</Emphasis>-Nearest Neighbour Models in Daily Streamflow Forecasting

A nonlinear stochastic self-exciting threshold autoregressive (SETAR) model and a chaotic k-nearest neighbour (k-nn) model, for the first time, were compared in one and multi-step ahead daily flow forecasting for nine rivers with low, medium, and high flows in the western United States. The embedding dimension and the number of nearest neighbours of the k-nn model and the parameters of the SETAR model were identified by a trial-and-error process and a least mean square error estimation method, respectively. Employing the recursive forecasting strategy for the first time in multi-step forecasting of SETAR and k-nn, the results indicated that SETAR is superior to k-nn by means of performance indices. SETAR models were found to be more efficient in forecasting flows in one and multi-step forecasting. SETAR is less sensitive to the propagated error variances than the k-nn model, particularly for larger lead times (i.e., 5 days). The k-nn model should carefully be used in multi-step ahead forecasting where peak flow forecasting is important by considering the risk of error propagation.