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Existing nonlinear time series methods are mostly used to capture the conditional variance or volatility of hydrometeorological factors. Insufficient attention has been paid to the time-varying variance-covariance and dynamic conditional correlation among multivariate hydrometeorological time series. This paper introduces a type of multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) model, named the dynamic conditional correlation (DCC) model, to characterize the time-varying relationship between rainfall (P), temperature (T), and streamflow (Q). For this purpose, the Yellow River basin is used as an example, and three association models are established, including the bivariate relationships of P-Q, T-Q, and the three-variable relationship of P&T-Q (rainfall and temperature jointly on streamflow). The results of the univariate generalized autoregressive conditional heteroscedasticity (GARCH) model show that the volatility of the temperature series is weaker than that of rainfall and streamflow. The conditional variance of the streamflow and temperature series has long-term persistence, while that of rainfall has short-term persistence, but the temperature has the lowest degree of variance memory. The bivariate DCC model shows that the streamflow is more sensitive to precipitation changes than temperature, and both rainfall-streamflow (P-Q) and temperature-streamflow (T-Q) associations have stronger long-term persistence and covariance memory. The P&T-Q correlation shows that the covariance of rainfall and temperature has stronger long-term persistence than streamflow fluctuations. In addition, the effectiveness of the P&T-Q relationship model is also stronger than that of the P-Q and T-Q relationship models. DCC, considering the combined effects of rainfall and temperature, can better represent the impact of environmental changes on streamflow than a single variable. [ABSTRACT FROM AUTHOR]

This paper presents an evaluation of next generation weather radar (NEXRAD) Stage III and MPE precipitation estimates for the Missouri River Basin River Forecast Center (MBRFC) using daily gauge data from the National Weather Service cooperative network. This gauge network is independent of data used to develop the NEXRAD operational products, and, thus, provides an independent assessment of NEXRAD error characteristics. NEXRAD bias, correlation, and percent detection are mapped across the MBRFC to demonstrate the spatial distribution of NEXRAD errors. In general terms, the NEXRAD estimates perform much better in the southeastern portion of the basin than they do in the central and mountainous areas. Although the overall NEXRAD-gauge statistics appear to be worse than those reported in other published studies, the results of this study show marked improvement in NEXRAD estimates of warm season precipitation over the eight-year study period. [ABSTRACT FROM AUTHOR]

General circulation models (GCMs) are routinely used to simulate future climatic conditions. However, rainfall outputs from GCMs are highly uncertain in preserving temporal correlations, frequencies, and intensity distributions, which limits their direct application for downscaling and hydrological modeling studies. To address these limitations, raw outputs of GCMs or regional climate models are often bias corrected using past observations. In this paper, a methodology is presented for using a nested bias-correction approach to predict the frequencies and occurrences of severe droughts and wet conditions across India for a 48-year period (2050-2099) centered at 2075. Specifically, monthly time series of rainfall from 17 GCMs are used to draw conclusions for extreme events. An increasing trend in the frequencies of droughts and wet events is observed. The northern part of India and coastal regions show maximum increase in the frequency of wet events. Drought events are expected to increase in the west central, peninsular, and central northeast regions of India. [ABSTRACT FROM AUTHOR]

This paper describes techniques used operationally by the National Weather Service (NWS) to prepare gridded multisensor (gauge, radar, and satellite) quantitative precipitation estimates (QPEs) for input into hydrologic forecast models and decision-making systems for river forecasting, flood and flash flood warning, and other hydrologic monitoring purposes. Advanced hydrologic prediction techniques require a spatially continuous representation of the precipitation field, and remote sensor input is critical to achieving this continuity. Although detailed descriptions of individual remote sensor estimation algorithms have been published, this review provides a summary of how the estimates from these various sources are merged into finished products. Emphasis is placed on the Weather Surveillance Radar-1988 Doppler (WSR-88D) Precipitation Processing System (PPS) and the Advanced Weather Interactive Processing System (AWIPS) Multisensor Precipitation Estimator (MPE) algorithms that utilize a combination of in situ rain gauges and remotely sensed measurements to provide a real-time suite of gridded radar and multisensor precipitation products. These two algorithm suites work in series to combine both computer-automated and human-interactive techniques, and they are used routinely at NWS field offices [river forecast centers (RFCs) and weather forecast offices (WFOs)] to support NWS's broader hydrologic missions. The resulting precipitation products are also available to scientists and engineers outside the NWS; a summary of characteristics and sources of these products is presented. [ABSTRACT FROM AUTHOR]

This paper presents a new forecasting methodology that uses self-learning cellular automata (SLCA) for including variables that consider the spatial dynamics of the mass of precipitation in a radar forecast model. Because the meteorological conditions involve nonlinear dynamic behavior, an automatic learning model is used to aid the cellular automata rules (SLCA). The new methodology is applied to the western part of England (Brue river basin) using NIMROD data. The radar information from 1 month of hourly radar measurements is used. Two models, a regression model tree (MT) and an artificial neural network (ANN) model, are used to learn the dynamics of the spatially local effects within the cellular automation (CA) neighboring areas. A spatial correlation (tracking pattern) reference model is built for comparing the first hour of precipitation forecast. Model results show that the SLCA is more accurate than conventional tracking. Furthermore, it appears that this technique can be extended to include other important atmospheric variables in forecasting processes. [ABSTRACT FROM AUTHOR]

This paper presents a general expression that may be used to determine the minimum number of locations in a river basin at which cross-correlated point precipitation time series must be stochastically generated to adequately capture the temporal variance of precipitation over large areas, based on the spatial correlation structure of the precipitation field. Using fewer precipitation time series than necessary leads to an overestimate of the variance of the areal precipitation. Application of the derived expression is demonstrated by using the Anseba River basin in Eritrea, East Africa, as an example. This test case is validated using a series of stochastic precipitation fields that are generated at different spatial scales. The expression was derived by expanding the method of moments equation for the variance at the field scale. [ABSTRACT FROM AUTHOR]

The determination of the annual exceedence probability (AEP) of extreme water levels in complex estuarine systems is an important and challenging issue in flood management. Extreme estuarine levels are caused by the combined effects of river flows, local winds, and coastal ocean levels. This paper describes a stochastic, event-based approach for generating concurrent hourly mean sea level pressure (MSLP) and wind speed, which are needed in a larger project to derive stochastic hourly river flows, winds, and coastal ocean levels to drive a hydrodynamic model for estuarine flood level simulation. The minimum MSLP versus rainfall and maximum wind speed versus rainfall relationships for the extreme flood events at the daily time scale are first used to generate minimum daily MSLP and maximum daily wind speed using a nonparametric resampling method. The generated minimum daily MSLP and maximum daily wind speed are then used to scale concurrent hourly MSLP and wind speed sequences resampled from the historical record. This approach is novel because it is simple, generic, and computationally efficient for generating stochastic cross-correlated forcing time series for any estuarine hydrodynamic and flood risk study. The approach is tested using 50 years of forcing data from the Gippsland Lakes system in southeast Australia. The results indicate that the approach produces realistic stochastic concurrent hourly sequences of MSLP and wind speed that preserve and distinguish the MSLP-rainfall and wind speed-rainfall relationships for different synoptic weather conditions and times of the year. The approach also produces hourly and daily minimum MSLP and maximum wind speed with similar AEP characteristics as the historical data. [ABSTRACT FROM AUTHOR]

This paper summarizes methods and results for a recent precipitation frequency study of the Kansas City metropolitan area. The study was motivated by prevalent concern that frequency estimates used for hydrologic design and analysis are based on studies published decades ago. Data currently used for design were extracted from National Weather Service (NWS) rainfall atlases TP-40 and HYDRO-35, published in 1961 and 1977, respectively. Results of this study are compared to those two atlases and a third regional study published by Huff and Angel in 1992. In general, the rainfall depths predicted by this study are higher than TP-40 and HYDRO-35 for return periods longer than 10 years, yet for short durations and low return periods, the converse is true. For the most part, rainfall depths from this study are within a few percent of NWS estimates. [ABSTRACT FROM AUTHOR]

This paper presents a stochastic model for analyzing the effects of variable precipitation on the Nordic power system. The model is formulated as a multistage stochastic linear program and solved by the ReSa sampling based Benders decomposition method. The stochastic parameter representing the inflow to the hydro reservoirs has been split into two in the model to describe separately the inflow from rain and snowmelt. This captures the fact that information about the snow reservoirs reduces the decision maker’s level of uncertainty about future inflow levels. Computational analysis of the Nordic power market undertaken using the model confirms the importance of modeling multiple inflow sources, with the addition of the snow reservoirs reducing the span between the highest and lowest expected future spot price. [ABSTRACT FROM AUTHOR]

The response to flood disasters is of great importance to protect people's lives. Proper recognition of the factors affecting floods will lead to the prevention of negative consequences. In this study, three types of Archimedean copulas, including Clayton, Gumbel, and Frank, have been applied to the depth and duration variables of maximum annual precipitation in four rain gauges' data sets (eight variables) throughout the primarily urban eastern catchment of Tehran, Iran. The results indicated that the Gumbel copula is the most suitable function of the Archimedean copulas. The average depth of rainfalls produced by the selected copula increased up to 24% compared with different varieties of single-station scenarios. Also, the average duration of produced rainfalls differed up to 14% difference compared with the single-station scenarios. Finally, the average volume of surface flooding varied between +54% and −154% with respect to the single-station scenarios. As a result, taking into account spatial distribution in rainfall will have a significant impact on the generation of runoff. The overall distribution pattern of runoff is significantly influenced by several factors. Firstly, the simultaneous impact of four rain gauge stations on synthetic runoffs plays a crucial role. The second factor is the spatial distribution of precipitation within the catchment, which is influenced by the distribution of rain gauge stations and the interpolation technique used. Additionally, the routing of synthetic surface runoff through the channel system also contributes to the overall distribution pattern of runoff. [ABSTRACT FROM AUTHOR]

Precipitation estimates from Doppler weather radar (DWR) provide much better spatial resolution as compared to rain gauges and are therefore becoming more popular in hydrological applications. However, traditional estimates of precipitation from radar-measured reflectivity (e.g., Z = aRb) are deterministic and thus do not offer any information about the uncertainty associated with the estimate. However, the radar scans may contain significant errors that propagate to the rainfall estimates. This gives rise to the need for the probabilistic estimates of rainfall. This paper proposes a copula-based approach to obtain the joint cumulative distribution function (CDF) of reflectivity (Z) and precipitation (R) from which the conditional CDF of precipitation is determined. Three copulas are implemented, and the temporal and spatial transferability of each model is evaluated using different measures of performance. It is established that the precipitation estimates are better than those obtained from the four different existing methods, including the traditional approach. In addition, uncertainty estimates are also available from the proposed approach. Though the mean (50th quantile) of the probabilistic estimates does not correspond well to the gauge rainfall data, availability of the estimates at various confidence levels makes the proposed copula-based approach suitable for different applications. [ABSTRACT FROM AUTHOR]

Future projection of air temperature and precipitation due to climate change plays an important role in shaping the water resources management plans in arid and semi-arid countries such as the United Arab Emirates (UAE). The effect of local topography, land use, and sealand contrasts are generally dwarfed in the coarse global circulation models [ocean atmosphere global climate models (OAGCMs)] results. Statistical downscaling (SD) is a statistical tool relating OAGCM outputs (predictors) to finer local observed weather data (predictands) to improve future predictions. SD is used in this study to downscale the Hadley Model (HadCM3) predictors using local observations at two stations representing the dominating bioclimatic zones in the UAE. The paper proposes a rigorous and systematic methodology to guide the selection of dominant predictors in the projection scenarios. The proposed methodology is applied for the UAE but can be easily adopted for any other area. The calibrated model, used to project future scenarios in the region, revealed a range of increase of the annual mean maximum temperature of 2.79-3.80°C and a range of reduction of annual precipitation of 16.80-37.00% by 2080 at the considered stations. Impacts of climate change on the UAE's water resources management are discussed in light of the downscaled estimates for temperature and precipitation. Because a major portion of the water resources is devoted to agriculture, temperature impacts are quite significant. However, the impacts of precipitation are hardly detectable at the national scale. Instead, the impacts of reduced precipitation will be of more localized significance in mountainous regions that are critically dependent on precipitation for water resources. [ABSTRACT FROM AUTHOR]

Road networks play an essential role in economic activities and are required to have high level of reliability. Heavy precipitation could lead to disruptions to road networks, causing service failure and associated economic and social impacts. In this study, we proposed a framework for assessing the reliability of road networks to precipitation based on historical recorded disruption data. We first developed a vulnerability disruption model, which describes the relationship between precipitation intensity and the probability of road disruption, as well as the amount of disruptions, based on recorded data. The vulnerability of roads shows a nonlinear increasing trend with increasing precipitation. Based on the vulnerability model and Monte Carlo approach, the travel time reliability of road network of Fujian province, China was assessed. Two precipitation events in Fujian road network were selected to validate the proposed framework. The travel time reliability of 11.12% city pairs in Fujian Province decreased to below 0.85 with an average precipitation of 73.32 mm. The proposed vulnerability model and the framework for assessing reliability can provide guidance to highway departments so that they can prepare for possible adverse impacts caused by precipitation on roads and plan for road network service. [ABSTRACT FROM AUTHOR]

Intensity-duration-frequency (IDF) curves are commonly used in engineering planning and design. Considering the possible effects of climate change on extreme precipitation, it is crucial to analyze potential variations in IDF curves. This paper presents a quantile-based downscaling framework to update IDF curves using the projections of future precipitation obtained from general circulation models (GCMs). Genetic programming is applied to extract duration-variant and duration-invariant mathematical equations to map from daily extreme rainfall quantiles at the GCM scale to corresponding daily and subdaily extreme rainfall quantiles at the local scale. The proposed approach is applied to extract downscaling relationships and to investigate possible changes in the IDF curves for the City of Saskatoon, Canada. The results show that genetic programming is a promising tool for extracting mathematical mappings between extreme rainfall quantiles at the GCM and local scales. The duration-variant mappings were found to be more accurate than the duration-invariant relationships. Using the extracted relationships, future changes in IDF curves in the City of Saskatoon are estimated using projections obtained from the CGCM3.1 based on A1B, A2, and B1 emission scenarios. The results show that future IDF curves in the City of Saskatoon are subject to change, but the sign, magnitude, and uncertainty in the estimates of possible changes depend on the emission scenario, storm duration, return period, and mapping equations. Regardless of the emission scenario and/or the mapping relationships, the results of this study show increases in short-duration extreme rainfall with short return periods in Saskatoon. This study shows that the downscaling of extreme precipitation quantiles directly from the corresponding large-scale estimates can be an efficient approach when estimating the design precipitation values under climate change are sought. [ABSTRACT FROM AUTHOR]

Bacterially induced precipitation is a general phenomenon in nature. It has been proposed as an environmentally friendly strategy for the protection of cementitious materials. This paper investigates the biochemistry of precipitation induced by non-ureolytic bacteria of the genus Bacillus. Different calcium sources are compared for their effectiveness in bacterial mediation of precipitation. Surface treatment using this biodeposition technique is evaluated by parameters affecting the durability of cementitious materials. Outcomes from this study reveal that the type of calcium source has a profound impact on the biochemical process and the crystal form, size, and morphology of bacterially mediated mineralization of . An organic calcium source, particularly calcium glutamate, is beneficial for efficient precipitation. Bacterial surface treatment on specimens results in a decrease of more than 50% in capillary water absorption and an increase of nearly 50% in resistance to carbonation; this is mainly attributed to pore blocking effects. This novel biological surface treatment shows particularly promising prospects for increasing the durability aspects of concrete constructions. [ABSTRACT FROM AUTHOR]

Taking Suxichang and Hangjiahu urban agglomerations in Taihu Lake Basin as study areas, the authors explore differences in annual precipitation, flood season precipitation, annual maximum daily precipitation, and rainfall days of different types between cities and suburbs from 1961 to 2006. The authors also investigate the trends of precipitation change by means of Mann-Kendall test and rescaled range analysis. The results show that the amount of precipitation increased with the level of urbanization at spatial and temporal scales. The maximum daily precipitation and the number of rainstorms in urban areas also increased. With further development of urbanization, the increase would continue in the future. The two urban agglomerations with similar level of urbanization and climatic conditions are consistent in terms of all research indicators applied in this paper. The results are basically consistent with many previous studies, while the study area has its own characteristics in that the maximum rainfall generally happened after the plum rains, and that rainstorms often occurred later than they did in other regions. The results above suggest that urbanization has an effect on precipitation, and this is a matter of concern for the cities located in the East Asian Monsoon Region with its heavy rainfall. [ABSTRACT FROM AUTHOR]

Highly uncertain precipitation input greatly reduces the overall reliability of rainfall-runoff modeling for estimating stream flows, which in turn forms the basis for decisions leading towards sustainable management of water resources. Probabilistic weather forecasting using multiple numerical weather prediction (NWP) models known as ensemble prediction system (EPS), has gained popularity amongst meteorologists in recent years. However, keeping in mind the computational burden and complexities of hydrologic modeling, scientists are constrained to use a limited number of ensemble members to drive the operational river flow forecasting systems. In this paper, 12 weather ensembles of different size are evaluated for their performance in forecasting occurrence of precipitation over a wet month in a large New Zealand catchment by comparing them against point observations of the corresponding events at rain gauge stations. The authors investigate various configurations of 12-h accumulated precipitation forecasts including members from the grand global ensemble consisting of 179 member forecasts issued at eight data centers connected through the observing research and predictability experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) network. The ensembles are developed using four different techniques including a one-dimensional (1D) clustering approach to group the members into clusters of different sizes in order to study the impact of ensemble size on precipitation forecasting with lead time of 10 days. This approach groups the members into different clusters by minimizing the sum of square distances between the members and the cluster centre. The results indicate that there is generally little improvement in the forecast skill by increasing the ensemble size, and some smaller ensembles can reasonably replace the grand global ensemble without significantly compromising the overall quality of the probabilistic forecast for precipitation occurrence. The results further infer that the grand control ensemble (GCE) combining the control forecasts from all the centers is as good a candidate as the grand global ensemble despite being much smaller in size. [ABSTRACT FROM AUTHOR]

Retrieving precipitation data from a rain gauge network is a classical and common practice in hydrology and climatology. These data represent the key input in hydrological modeling to reproduce, for example, the characteristics of a flood phenomenon. The accuracy of the model results is strongly dependent on the consistency of the monitoring network in terms of spatial scale, i.e., network density and location of the rain gauges and time resolution. In this context, several studies have been carried out to analyze how the rainfall sampling influences the estimation of total runoff volume. The aim of this paper is to use a physically based and distributed-parameter hydrologic model to investigate how the number and the spatial distribution of a rain gauge network influence the estimation of the hydrograph and its characteristics in conjunction with different spatial and temporal characteristics of rainfall forcing and different soil-type characteristics. The TIN-based real-time integrated basin simulator (tRIBS) hydrologic model was used to simulate hydrologic response at Baron Fork Basin, Oklahoma. Downscaled next-generation radar (NEXRAD) measurements were assumed to represent the true precipitation distribution over the basin. Additional precipitation fields have been derived from the interpolation of eight fictitious rain gauges randomly placed in the area. The hydrological response from tRIBS that is driven by these precipitation fields has been compared with the response of the model forced with NEXRAD precipitation. The analysis has been carried out assuming first simplified spatial distributions of soil characteristics and then the real soil-type distribution. Results have shown the dependence of the best rain gauges configuration for the estimation of runoff on the spatiotemporal characteristics of storm events and the soil-type distribution. [ABSTRACT FROM AUTHOR]

Synthetic weather generation models that depend on statistics of past weather observations are often limited in their applications to issues that depend on historical weather characteristics. Enhancing these models to take advantage of increasingly available and skillful seasonal climate outlook products would broaden applications to include proactive soil and water resources management, better prediction of achieving production targets, and weather-related risk assessment. In this paper, an analytical method was developed that enables generation of daily precipitation time series for seasonal forecasts up to 12 months ahead. The method uses historical weather observations to establish reference precipitation statistics (monthly precipitation amount, number of rainy days per month, and wet-wet and dry-wet day transition probabilities) and subsequently adjusts these statistics to reflect the forecast departures from long-term average monthly precipitation. This reference and forecast departure approach ensures that generated precipitation is consistent and compatible with the forecast and the local climate characteristics as well. The method was tested with precipitation data from the USDA Agricultural Research Service (ARS) weather station at Temple, Texas, and the National Weather Service Cooperative Observer Program (NWS-COOP) data at Tallahassee, Florida, for a hypothetical seasonal precipitation forecast. Several 100-year time series of generated daily precipitation reproduced average monthly precipitation within of expected forecast values and mean absolute error (MAE) of less than 3%, wet-dry day transition probabilities within 5% and MAE of less than 2%, and average number of rainy days per calendar month within and MAE of less than 1%. The successful testing of the method validated the approach, analytical solution, and implementation of the method in an experimental climate generator. This forward-looking capability of synthetic weather generation will benefit water resource managers, farm loan officers, agricultural consultants, risk management agencies, and anyone relying on seasonal climate forecast information for decision making. [ABSTRACT FROM AUTHOR]

This paper presents a weather generator that produces new values of precipitation to generate realistic weather sequences. The model has been applied to a network of 14 meteorological stations around the Upper Thames River Basin (UTRB), Ontario, Canada. We developed a simple model that employs the k-nearest neighbor resampling approach with gamma kernel perturbation. This gamma kernel perturbation enables the production of new values rather than merely reshuffling the historical data to generate realistic weather sequences. Daily precipitation was simulated at all the locations in and around the considered basin. The comparison of simulated data to the observed data led to the conclusion that the proposed perturbation algorithm performs quite well at preserving the monthly and annual historical statistics. The improved model was shown to produce precipitation amounts different from those observed in the past record. [ABSTRACT FROM AUTHOR]

This work provides an analysis of the Weisseritz 2002 flood event in Eastern Germany, which was characterized by intense upslope storm movement and associated orographic enhancement of precipitation. Examination of the event is based on the availability of weather radar observations and post-event surveys to document the peak runoff rates. The analysis is based on the use of a set of statistics that clarify the dependence existing between spatial rainfall distribution, basin morphology, and runoff response. The statistics provide a description of overall spatial rainfall organization at the catchment scale in terms of concentration and dispersion statistics and storm motion, as a function of the flow distance coordinates, i.e., distance to the outlet measured along the flow path. The statistics are based on the observation that runoff routing through branched channel networks imposes an effective averaging of spatial rainfall at equal flow distance, despite the inherent spatial variability. A spatially distributed rainfall-runoff model is used to evaluate the effects of the rainfall spatial variability and storm velocity on flood modeling at various river sections. It is shown that storm velocity exhibits rather moderate values, despite the strong kinematic characteristics of individual storm elements. Consistently with this observation, hydrologic simulations show that orographically controlled rainfall spatial variability and storm motion have an almost negligible effect on flood response modeling. The drainage network structure filters out the effects of spatial rainfall variability for storm event hydrologic response, resulting in Weisseritz exhibiting striking uniformity of response to a storm with strongly varying precipitation. Overall, this shows the value of using the rainfall statistics introduced in this paper to isolate and describe the features of rainfall space-time variability that have significant impacts on runoff simulation. [ABSTRACT FROM AUTHOR]

This paper proposes deriving regional intensity-duration-frequency (IDF) curves for Edmonton, Canada, based on the scaling property of precipitation data using ensemble empirical mode decomposition (EEMD). Selected sets of annual maximum precipitation data were decomposed by the EEMD to intrinsic mode functions (IMFs), and the scaling property was investigated. Next, representative scale exponents were extracted. The results show that quantiles derived from general extreme value (GEV) probability distribution (PD) with parameters derived by the probability-weighted moment (PWM) are more accurate than those derived from the extreme value type I (EVI) PD with parameters derived by the method of moment (MOM), whose underestimation of rainfall intensity becomes obvious for high return period (greater than 25 years) and short duration (less than 1 h). The results also show that for Edmonton, generally three of four IMFs of the precipitation data showed a simple scaling property, and regional IDF curves derived from the scaling IDF and EEMD approach predict accurate storm intensities for rain-gauging sites at both the calibration and validation stages, but there could be errors associated with predicted storms of high return periods (100 year). [ABSTRACT FROM AUTHOR]

In this paper, a bivariate-copula-based methodology is presented to assess the risk associated with hydroclimatic variability on groundwater levels in an unconfined aquifer at the Manjara basin in India. Rank correlation analysis is used to identify the association between the El Niño-Southern Oscillation (ENSO) index, precipitation, and groundwater levels. It is found that the dependencies among the hydroclimatic variable pairs are statistically significant and the dependence structure can be modeled by using bivariate Archimedean copulas. The groundwater level or depth-to-groundwater table (DGWT) in the study region is found to be responsive toward interannual precipitation variations that are influenced by the ENSO phenomenon. For probabilistic representation of hydroclimate variables, various probability distributions are evaluated and it is found that the precipitation and DGWT are best fitted using lognormal and Weibull distributions, respectively, whereas the ENSO index is best fitted using nonparametric-based normal kernel density function. For modeling joint dependence structure of hydroclimatic variable pairs (precipitation-DGWT, ENSO index-precipitation, and ENSO index-DGWT), appropriate Archimedean copulas (viz, Ali-Mikhail-Haq, Clayton, Gumbel-Hougaard, and Frank families) are evaluated. On performing standard statistical tests, it is found that the Frank copula is best representing the joint dependence structure for all three variable pairs. Then the Frank copula-based joint distributions are used to derive conditional distributions and to perform risk analysis of groundwater levels. The study suggest that the copula-based methodology can be used effectively for modeling dependence structure of hydroclimatic variables and for risk assessment of groundwater levels under changes in hydroclimatic conditions. [ABSTRACT FROM AUTHOR]

The climate impact studies in hydrology often rely on climate change information at fine spatial resolution. Because general circulation models (GCMs) operate on a coarse scale, the output from a GCM has to be downscaled to obtain the information relevant to hydrologic studies. In this paper, downscaling models are developed using the state-of-the-art nonparametric method K-Nearest Neighbor (K-NN) approach, with an emphasis on optimal choice in selection of nearest neighbors for obtaining simultaneous projections of mean monthly maximum and minimum temperatures (Tmax and Tmin) as well as monthly precipitation and pan evaporation to lake-basin scale in a semiarid region that is considered to be a climatically sensitive region in India. The performance of the K-NN approach was evaluated based on several statistical performance indicators. A comparison of K-NN has been made with a linear multiple regression (LMR)-based downscaling model. Also, the prevailing view in the literature regarding optimal choice of selection of nearest neighbors is checked with different perturbations. A simple multiplicative shift was used for correcting predictand values. The K-NN-based models are found to be superior to LMR-based models, and, subsequently, the K-NN-based model is applied to obtain future climate projections of the predictands. An increasing trend is observed for Tmaxand Tmin for A1B, A2, and B1 scenarios, and the precipitation is projected to increase in the future for A2 and A1B scenarios, whereas no trend has been observed for pan evaporation in future. [ABSTRACT FROM AUTHOR]

A methodology for maximum precipitation (MP) estimation that uses a physically based numerical atmospheric model is proposed in this paper. As a case study, the model-based 72-h MP was estimated for the American River watershed (ARW) in California for the December 1996-January 1997 flood event. First, a regional atmospheric model, MM5, was calibrated and validated for the December 1996-January 1997 historical major storm event for the ARW, on the basis of the U.S. National Center for Atmospheric Research (NCAR) reanalysis data to demonstrate the model capability during the historical period. Then, the model-simulated historical storm event was maximized by modifying its boundary conditions. The model-simulated precipitation field in the ARW was successfully validated at nine individual rain gauge stations in the watershed. The computed basin-averaged precipitation was somewhat higher than observations obtained by the spatial interpolation of the rain gauge observations. This result suggests a limitation of the spatial interpolation of ground rain gauge observations because they are mostly located in valleys, and the distribution of precipitation is highly heterogeneous over the mountainous terrain of the ARW. Next, to maximize precipitation over the watershed, the initial and boundary conditions in the outer nesting domain of the atmospheric model were modified. In this demonstrative study, the boundary conditions were modified by three methods: (1) maximizing the atmospheric moisture by setting the relative humidity at 100%; (2) maintaining the atmospheric boundary conditions corresponding to the state of the heaviest precipitation (maintaining equilibrium conditions); and (3) spatially shifting the atmospheric conditions to render the atmospheric moisture flux to hit the watershed. Because these modifications significantly increased the precipitation over the ARW, they clearly show the importance of wind and moisture conditions at the boundary of the atmospheric modeling domain. These different maximization methods produced similar 72-h precipitation depths, which were 549 mm by the combination of 100% relative humidity and equilibrium high precipitation conditions at the outer boundary of the model domain, and 541 mm from shifting the historical atmospheric conditions to the south by 5.0°. Accordingly, the 72-h maximum precipitation over the ARW was estimated to be approximately 550 mm. Although this study presents only a demonstrative maximization work, it shows that the presented modeling approach can be a potential alternative to standard probable maximum precipitation (PMP) estimation without depending on the linear relationships required in the standard PMP method. Also, because the proposed modeling approach is based on the initial and boundary atmospheric conditions from a synoptic scale that may be obtained from the NCAR/National Centers for Environmental Prediction reanalysis data for the historical period and from the general circulation model (GCM) climate projections, it can account for any nonstationarity that may be present in the hydro-climate system. [ABSTRACT FROM AUTHOR]

The South Florida Water Management District (SFWMD) relies on a network of nearly 300 rain gauges in order to provide rainfall data for use in operations, modeling, water supply planning, and environmental projects. However, the prevalence of convective and tropical disturbances in South Florida during the wet season presents a challenge in that the current rain gauge network may not fully capture rain events that demonstrate high spatial variability. Next Generation Radar (NEXRAD) technology offers the advantage of providing a spatial account of rainfall, although the quality of radar-rainfall measurements remains largely unknown. The comparison of rainfall estimates from a gauge-adjusted, NEXRAD-based product developed by the OneRain Company with precipitation measurements from SFWMD rain gauges was performed for the Upper and Lower Kissimmee River Basins over a four-year period from 2002 to 2005. Overall, NEXRAD was found to underestimate rainfall with respect to the rain gauges for the study period, demonstrating a radar to gauge ratio of 0.95. Further investigation of bias revealed the tendency for NEXRAD to overestimate small rainfall amounts and underestimate large rainfall amounts relative to the gauge network. The nature of bias present in the data led to the development of a radar-rain gauge relationship to predict radar precipitation estimates as a function of rain gauge measurements. The intent of this paper is to demonstrate the importance of identifying systematic offsets which may be present in radar-rainfall data before application in hydrologic analysis. [ABSTRACT FROM AUTHOR]

Regulations for mitigating nonpoint source pollution from urban areas often include a requirement for treatment of a first-flush depth of stormwater. When the stormwater treatment technology requires specification of a design flow rate, the first-flush depth must be related to a first-flush flow rate. This paper describes a methodology to determine the relation between accumulated storm depth and corresponding flow to a treatment device for small, urban watersheds. The approach uses the rational method under the assumption that the time-of-concentration is small. Intensity–frequency relations, expressed in the form of return periods and associated intensities, were determined using a partial duration frequency analysis of regionalized precipitation data. The results of the method can be used to determine the flow rate to a treatment device that will meet a specified return period for a first-flush depth. The method is demonstrated using 13 years of 15-min data from seven Massachusetts precipitation stations. [ABSTRACT FROM AUTHOR]

Precipitation forecasts play a key role in decision making for the water resource sector. The use of an ensemble numerical weather prediction model known as the ensemble prediction system to account for the uncertainties in weather forecasting has become common practice at various meteorological centers around the world. The current ensemble prediction systems operational at different forecasting centers vary in their parameters, including but not limited to spatial and temporal resolution, ensemble size, model physics, and initial perturbation strategy. This paper presents a comparison of six ensemble systems of varying characteristics, evaluating the quantitative precipitation forecasts issued for the Waikato River catchment in New Zealand. Three derivative products of the ensemble forecasts were used, most likely precipitation event, probability of above average rain, and ensemble mean precipitation, to test the potential application of the ensembles for resource allocation, hazard management, and catchment modeling. The forecasts of three-day cumulative precipitation, issued seven days in advance at different forecasting centers and connected through the Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) network, were compared with the corresponding areal average precipitation observed in the selected catchment over a one-year period. The research investigated the effect of ensemble size on its performance and provides users an insight into the performance of various operational EPSs of different sizes in different situations of interest. The research presented in this paper also provides a preliminary framework of the evaluation of ensembles based on their intended use by different sectors. The results of the study indicate that an increased ensemble size is not always related to better performance. It was further inferred from the results that a slight gain in accuracy may be achieved by employing larger ensembles for their intended use in hazard management than the other two applications. Additionally, the grand ensemble, constructed by combining forecasts from all other ensembles tested in the study, adds no more value to an ensemble precipitation forecast if used in the context of the three scenarios adopted in this study. [ABSTRACT FROM AUTHOR]

In this paper, a hydrometeorological approach to the probabilistic simulation of soil moisture is proposed. The time series of in situ soil moisture and meteorological variables at a monthly scale from different monitoring stations across India are utilized. Preliminary investigation with both precipitation and near-surface air temperature as meteorological variables reveals that the strength of association between soil moisture and precipitation is more significant compared to that between soil moisture and temperature. Precipitation-based probabilistic estimation of soil moisture using the proposed hydrometeorological approach is tested with in situ observed soil moisture data and with soil moisture data of the Climate Change Initiative project, funded by the European Space Agency. The results are found to be promising. In addition, the proposed hydrometeorological approach is able to provide the information on uncertainty associated with the estimation. It is also shown that the parameter of the developed model is linked to the predominant soil textural class. Higher values of the model parameter [dependence parameter () for the selected copula] correspond to clay loam, medium values correspond to loamy sand or loam, and low values correspond to sandy loam to sandy soil. Three stations outside India, which are widely separated across the globe with known soil textural class, are also considered, and the performance of the proposed model is tested by estimation of the model parameter depending on the soil textural class. The results are found to be reasonably good, indicating the spatial transferability of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper presents a laboratory study of the ability of two bacteria to improve the geomechanical properties of a sandy soil by calcium carbonate precipitation. The performance of two bacteria ( Sporosarcina pasteurii and Idiomarina insulisalsae) is compared, based on unconfined compressive strength and splitting tensile strength tests. The effects of curing time and the concentration of I. insulisalsae on the strengthening process are also analyzed. Although the optimum environmental conditions for the bacterial growth were not adopted, the potential to improve the geomechanical properties of natural sandy soils is shown by the results. The bacteria I. insuliasalsae is shown to be more efficient than S. pasteurii in strengthening the soil, and increases in the concentration of I. isuliasalsae induce an increase in the geomechanical properties. However, this effect is not proportional to the amount of bacteria, probably due to a lack of nutrient. [ABSTRACT FROM AUTHOR]

Large changes in runoff in the north-central United States have occurred during the past century, with larger floods and increases in runoff tending to occur from the 1970s to the present. The attribution of these changes is a subject of much interest. Long-term precipitation, temperature, and streamflow records were used to compare changes in precipitation and potential evapotranspiration (PET) to changes in runoff within 25 stream basins. The basins studied were organized into four groups, each one representing basins similar in topography, climate, and historic patterns of runoff. Precipitation, PET, and runoff data were adjusted for near-decadal scale variability to examine longer-term changes. A nonlinear water-balance analysis shows that changes in precipitation and PET explain the majority of multidecadal spatial/temporal variability of runoff and flood magnitudes, with precipitation being the dominant driver. Historical changes in climate and runoff in the region appear to be more consistent with complex transient shifts in seasonal climatic conditions than with gradual climate change. A portion of the unexplained variability likely stems from land-use change. [ABSTRACT FROM AUTHOR]

A 43-year database of reconstructed atmospheric data at 3-km spatial resolution and hourly temporal resolution was created using a nonhydrostatic regional atmospheric model and archived global, coarse-resolution atmospheric data. The archived atmospheric data at approximately 200-km spatial resolution and 6-h temporal resolution were used as initial and boundary conditions in the regional model to generate the reconstructed data. Data for seven variables were extracted from model results for the database: precipitation, atmospheric temperature, relative humidity, wind speed, incoming solar radiation, downward longwave radiation, and latent heat flux. In addition, dew point temperature was computed from simulation results and stored in the database. Model results for temperature and precipitation were compared to observed data collected at three sites in the basin over a 6-year period. Model-generated results match the seasonal, annual, and interannual fluctuations in the observed data quite well. Results indicate that the method for reconstructing atmospheric data for use in distributed parameter hydrologic and environmental models provides spatial detail unmatched by existing observed data networks and is a viable alternative to statistical manipulation of limited observed data. [ABSTRACT FROM AUTHOR]

The recovery of ethylenediaminetetraacetic acid (EDTA) from washing effluents is essential to reduce the cost of EDTA-enhanced soil washing and the production of wastewater. This study evaluated a recovery method, in which Pb or Zn was first dissociated from Pb– or Zn–EDTA complex through the replacement reaction by adding FeCl3 and then removed as phosphate precipitates through adding Na2HPO4. Finally, Fe(III) was removed as Fe(OH)3 precipitates through adding Ca(OH)2. As a result, EDTA was recovered as Ca–EDTA for further use in soil washing process. The optimal conditions for EDTA recovery, including the molar ratios of FeCl3 and Na2HPO4 to EDTA as well as the pH after adding Na2HPO4 and adding Ca(OH)2, were well investigated. Under the optimal conditions, 96% of Pb or 83% of Zn was removed from the Pb– or Zn–EDTA, respectively. The four-cycle recovery and reuse of EDTA experiments indicated the recovered EDTA from soil washing effluents did not lose much chelating capacity for Pb removal. However, there is a loss of 15% of its chelating capacity in the first cycle reuse for Zn-contaminated soil washing due to substantial Zn residual in the recovered EDTA solution. [ABSTRACT FROM AUTHOR]

Determining the contributions of climate change and human activities to runoff variation is important for water resource management. An improved double mass curve method (IDMC), which can distinguish the individual impact of each climatic factor (precipitation and potential evapotranspiration), is proposed in this study to attribute runoff variation to climate change and human activities. The runoff variation characteristics of four subregions of the Jinsha River Basin (JRB) were detected using statistical methods, and the contributions of climate change and human activities to runoff variation were comprehensively assessed using IDMC, Budyko-based elasticity method, and slope change ratio of cumulative quantity analysis. Results show that annual runoff had an increasing trend in the upstream, midstream, and downstream of JRB and a decreasing trend in Yalong River Basin (YRB) of JRB during 1966–2016. Changed points in 2004, 1986, 1996, and 2005 were detected for the upstream, midstream, and downstream of JRB and YRB, respectively; the study period was, therefore, partitioned into the base period and impacted period for each subregion. Suggested attribution methods gave consistent conclusions that climate change dominated the runoff variation in the upstream of JRB with an average contribution rate of 86.5%, whereas human activities were the major force for the runoff variation in the midstream and downstream of JRB and YRB with average contribution rates of 73.0%, 113.6% and 75.8%, respectively. Furthermore, precipitation caused the increase of runoff in the upstream and midstream of JRB and the reduction of runoff in the downstream of JRB and YRB. Potential evapotranspiration caused the reduction of runoff in the subregions of JRB except for the midstream of JRB. The findings of this study are meaningful for water resources management in the JRB. [ABSTRACT FROM AUTHOR]

This study aims to construct the multiple time-scale joint distributions of drought duration and severity using two-dimensional copulas and compare the drought characteristics in India's two contrasting climate regions: the arid Rajasthan and humid, tropical Kerala. The drought occurrences were defined by the standardized precipitation index (SPI) with a threshold below −0.8 at time scales of 3, 6, 12, and 24 months for 1900–2016. Significant correlations were noted between the drought severity and drought duration in both regions. The Clayton copula gave a better fit than other copulas for modeling the dependence among the observed drought duration and severity. The results indicate that the probability of short-term droughts (SPI-3 and SPI-6) is more significant than those of long-term droughts (SPI-12 and SPI-24) for an identical drought event in both regions. Also, the probability of severe drought events with greater duration and severity for long-term droughts (SPI-12 and SPI-24) is higher in Kerala than that in western Rajasthan. For all the time-scale SPIs, the conditional probability of drought severity for a given duration exceeding a threshold showed an increasing trend in both regions. Furthermore, the conditional probability of the drought duration given the severity for short-term droughts is greater than that of the long-term droughts for the same drought event. For short-term droughts, the conditional return period of an identical drought event is lower in Kerala than in western Rajasthan. In contrast, the conditional return period of long-term droughts is lower in western Rajasthan. Additionally, copula-based nonexceedance conditional distributions for the major crops were established based on rainfall. [ABSTRACT FROM AUTHOR]

Floods are the leading cause of fatalities connected with natural disasters in Texas. A combination of physiography and precipitation often result in extreme hydrologic conditions that cause floods in the state. This paper reviews flood-related fatalities in Texas between 1959 and 2008. Information on flood-fatality victims and the flood-causing events was obtained from the National Climatic Data Center. The data collected included the date, time, location, and weather conditions and the gender and age of the flood victims. Comparison with other states reveals that the size of the population of Texas is a major factor in the increased number of fatalities. The data also suggest that driving or walking into floodwaters may be responsible for more than 93% of flood fatalities in Texas. Although most high-fatality counties are located in the Texas 'Flash Flood Alley' that includes major urban centers, normalization of fatality data shows that the flood-fatality risk is actually higher in other areas of the state. The analysis also indicates that the annual flood-fatality rates are decreasing significantly. A combination of improved hydrometeorological forecasting, educational programs aimed at enhancing public awareness of flood risk and the seriousness of flood warnings, and timely and appropriate action by local emergency and safety authorities will help further reduce flood fatalities in Texas. [ABSTRACT FROM AUTHOR]

Rainfall frequency analysis has been routinely adopted for the estimation of design rainfall for a specific return period. Annual maximum rainfall data are generally used for frequency analysis in practice, but the parameters of the probability distribution are estimated from the limited data that are often available back to the 1970s in many regions, including South Korea. As an alternative, this study aims to utilize century-long the ECMWF twentieth century reanalysis (ERA-20C) daily precipitation data, provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). To reduce the systematic errors in the reanalysis data, a quantile delta mapping method using a composite gamma-Pareto distribution (QDM-GP) is introduced that can better represent temporal trends and extreme events compared with stationary quantile mapping (SQM). In addition, the degree of uncertainty reduction in the estimation of design rainfall is evaluated using bias-corrected ERA-20C within a Bayesian modeling framework. Finally, the bias-corrected data are applied to explore the spatiotemporal change in design rainfall in South Korea in the 20th century. To investigate changes in design rainfall under the nonstationary assumption, this study estimates the design rainfall using data from three different periods (1900–1936, 1937–1973, and 1974–2010). It is found that QDM can substantially reduce the bias in annual maximum rainfall (AMR). The uncertainty ranges of the design rainfall using the bias-corrected ERA-20C reanalysis data are generally within the design rainfalls in the observed, suggesting that the use of bias-corrected reanalysis data can reduce uncertainties in design rainfall by increasing the sample size. Furthermore, this study explores the role of bias-corrected rainfall for uncertainty reduction in design rainfall via three different experiments in the context of prior information within a Bayesian framework. In the experimental study, it is concluded that the uncertainty reduction in design rainfall can be mainly attributed to the use of a prior distribution for the shape parameter, informed by long-term reanalysis data. Moreover, a significant spatiotemporal change in design rainfall is observed over all of South Korea. The significant change in design rainfall is mainly attributed to the recent increase in rainfall intensity, leading to a potential increase in flood risk in most areas. [ABSTRACT FROM AUTHOR]

This paper deals with the physicochemical treatment of petrochemical wastewater emanating from a purified terephthalic acid (PTA) manufacturing unit. The PTA wastewater has a very high chemical-oxygen demand () and contains various acids like terephthalic acid (TA), benzoic acid (BA), acetic acid, and -toluic acid (), in addition to aldehydes. The treatment of this wastewater with 1 N sulfuric acid (pH 2.0) at 25°C resulted in the removal of 53.2% of the COD, of TA, and of BA in the form of solid precipitate. Subsequent treatment of the clear supernatant with granular activated carbon (GAC) resulted in an overall removal of of TA and BA in addition to 82.7% of the COD. The GAC is basic in nature with its pH at the point of zero charge being 8.3. It was also predominantly microporous with a BET surface area of . Fourier-transform infrared spectroscopy of the GAC, before and after the adsorption of PTA wastewater components, was carried out to understand the COD and acid-removal mechanism. The optimum conditions for the treatment were pH 4.0, GAC dosage of , and ambient temperature. The COD removal mechanism was adsorption and precipitation of undissociated acid molecules within the pores. The adsorption was exothermic in nature and the Freundlich isotherm equation represented the experimental adsorption data well. Thermal regeneration of the spent GAC showed an improvement in the adsorption characteristics with the formation of mesopores, and resulted in the enhanced COD removal during five consecutive adsorption-desorption cycles. The spent carbon can be used as a cofuel for firing in the boiler furnaces for extracting its energy value. [ABSTRACT FROM AUTHOR]

In the quest for a best approach to estimate the spatial distribution of monthly precipitation for the state of Pernambuco, Brazil, this work tests different versions (choices of parameters and functions) of seven interpolation methods, totaling 26 distinct interpolation schemes. The performance of each method in estimating monthly precipitation was evaluated through a set of seven measures of performance evaluation through a cross-validation procedure. It was found that the trend surface analysis yielded the best overall interpolation results, followed by natural neighbor method, inverse distance weighting, and kriging. Performance ranking of all 26 methods is provided. Moreover, in order to assess the regional performance quality of the best identified method (trend surface analysis) in estimating monthly precipitation, a spatially explicit error analysis was also performed for each of the evaluation measures. Finally, temporal evolution of the error evaluation measures of the best performing methods is presented in order to demonstrate the seasonal impact of interpolation. [ABSTRACT FROM AUTHOR]

The regional climate models (RCMs) used in the analysis of the impact of climate variables on the hydrology of river basins needs appropriate preprocessing (bias correction) to represent and reproduce future climate with a fair degree of accuracy. The performance of bias corrections methods was assessed in this investigation on the basis of their ability to minimize error on climate variables and streamflow. This work compares the performance of five bias correction methods applied for precipitation and four methods for temperature in modeling the hydrology of the river catchments of the Western Ghats of India. The Western Ghats are a mountainous forest range along the entire west coast of India that plays a major role in the distribution of Indian monsoon rains. Simulations were used to evaluate the performance of the bias correction methods. Using raw RCM, bias corrected precipitation and temperature time series, streamflows were estimated by the soil and water assessment tool (SWAT) hydrological model. The results indicated that the raw RCM-simulated precipitation was biased by 42% and the temperature was biased by 12% across the catchments investigated. Subsequently, a bias of 65% was found in the streamflow. The performance of the delta change correction method was consistently better for precipitation (with Nash-Sutcliffe efficiency, NSE > 0.75 for 5 catchments) and temperature (NSE = 1) compared with other methods. Good performance was observed between the observed and bias corrected streamflow (daily time scale) for the catchments Purna (NSE = 0.97), Ulhas (NSE = 0.64), Aghanashini (NSE = 0.82), Netravathi (NSE = 0.89), and Chaliyar (NSE = 0.90); low performance with an NSE of 0.3 was observed for the catchments Kajvi and Vamanapuram. The methods failed for Malaprabha and Tunga catchments. The results indicate that the delta change correction method performed best in analyzing the hydrological impact of climate variables on the windward side of Western Ghats of India. [ABSTRACT FROM AUTHOR]