Search

Highlights • More than 150 papers dealing with applications of GP in water resource engineering were reviewed. • Fundamentals of GP variants were provided and compared with each other. • Selection ways of GP parameters were discussed. • Future scope of studies were demonstrated. Abstract The state-of-the-art genetic programming (GP) method is an evolutionary algorithm for automatic generation of computer programs. In recent decades, GP has been frequently applied on various kind of engineering problems and undergone speedy advancements. A number of studies have demonstrated the advantage of GP to solve many practical problems associated with water resources engineering (WRE). GP has a unique feature of introducing explicit models for nonlinear processes in the WRE, which can provide new insight into the understanding of the process. Considering continuous growth of GP and its importance to both water industry and academia, this paper presents a comprehensive review on the recent progress and applications of GP in the WRE fields. Our review commences with brief explanations on the fundamentals of classic GP and its advanced variants (including multigene GP, linear GP, gene expression programming, and grammar-based GP), which have been proven to be useful and frequently used in the WRE. The representative papers having wide range of applications are clustered in three domains of hydrological, hydraulic, and hydroclimatological studies, and outlined or discussed at each domain. Finally, this paper was concluded with discussions of the optimum selection of GP parameters and likely future research directions in the WRE are suggested. [ABSTRACT FROM AUTHOR]

• New improved drought forecast model incorporating eight synoptic climate drivers. • Multivariate decomposition splits variables into intrinsic mode functions (IMF). • Simulated Annealing (SA) approach selects pertinent IMFs to inform drought pattern. • Kernel Ridge Regression (KRR) algorithm used to design new hybrid drought model. • Hybrid model provides significant hydrological and water management implications. New and improved drought models based on the World Meteorological Organization approved Standardized Precipitation Index, principally at multiple timescale horizons, are providing significant benefits to the hydrological community, by its widespread acceptance in the sub-field of water resources management, sustainable water use and precision agriculture. In this research paper, the existing challenges faced by a drought forecasting model trained at multiple time-scales are resolved where a new multi-phase, multivariate empirical mode decomposition model integrated with simulated annealing and Kernel ridge regression algorithms (i.e., MEMD-SA-KRR) is designed to attain significantly accurate drought forecasts for 3 agricultural sites (i.e., Faisalabad, Islamabad and Jhelum, located in Pakistan). Utilizing the multi-scalar Standardized Precipitation Index (SPI) time series as a target variable for characterization of drought, twelve multivariate datasets (derived from statistically significant lagged combinations of precipitation, temperature & humidity), that are enriched with eight synoptic-scale climate mode indices and periodicity, are utilized in designing a new drought model. The study constructs a hybrid MEMD-SA-KRR model, where firstly, the data are partitioned into their respective training and testing subsets after creating historically lagged SPI at timescale (t – 1). Secondly, the MEMD algorithm is conditioned to demarcate multivariate climate indices from their training and testing sets, separately, into their decomposed intrinsic mode functions (IMFs) and residues. Thirdly, the SA method is employed to decide the most suitable IMFs. Finally, the KRR algorithm is applied to the selected IMFs to forecast multi-scaler SPI, at 1-, 3-, 6- and 12-monthly forecast horizon. The results are benchmarked with Random Forest, integrated with MEMD and SA to develop the MEMD-SA-RF equivalent model. The multi-phase MEMD-SA-KRR model is tested geographically in Pakistan, revealing that the MEMD-SA-KRR hybrid model generates reliable performance in forecasting multi-scaler SPI series, relative to the comparative models based on error analysis metrics. The hybrid drought model incorporating the most pertinent synoptic-scale climate drivers, as the model inputs has significant implications for hydrological applications and water resources management including its potential use in drought policy and drought recovery plans. [ABSTRACT FROM AUTHOR]

• Scenario-neutral approaches are used to stress-test climate-sensitive systems. • It is a challenge to generate climate time series that reflect plausible futures. • This research enables the generation of perturbed time series that maintain realism. • The proposed approach is numerically more efficient than previous methods. • The proposed approach is better able to represent key modes of change. Scenario-neutral approaches are used increasingly as a means of stress-testing climate-sensitive systems to a range of plausible future climate conditions. To ensure that these stress-tests are able to explore system vulnerability, it is necessary to generate hydrometeorological time series that represent all aspects of plausible future change (e.g. averages, seasonality, extremes). A promising approach to generating these time series is by inverting the stochastic weather generation problem to obtain weather time series that capture all the relevant statistical features of plausible future change. The objective of this paper is to formalize this "inverse" approach to weather generation, by both characterizing the process of optimizing weather generator parameters and proposing a numerically efficient solution that exploits prior knowledge and accounts for the complexity of the optimization landscape. The proposed approach also provides a structured way to ensure the physical realism of the generated weather time series, by using penalty-based objective functions to focus the optimization on the climate features deemed most relevant to the system being analyzed. A case study in Adelaide, Australia, is used to demonstrate specific implementations of this approach. The use of bounds on the weather generators dramatically decreases the time taken to create time series, and the use of penalties is shown to allow for change in some statistics to be prioritized, while still ensuring the realism of the time series. [ABSTRACT FROM AUTHOR]

Highlights • Water storage trends and extreme conditions was analyzed using satellite data. • Significant connection between water storage trends and climate influence. • The climatic variability and extreme condition linked to regional climate pattern due to oceanic-atmospheric interactions. • Implication of the result in water resources planning and management is clear. Abstract Climate variability along with increase in the demand for water resources highlights the need for better understanding of the link between regional climate and terrestrial water storage. This paper examined the variation of terrestrial water storage in relation to climatic influences over the Upper Blue Nile (UBN) River Basin from GRACE Terrestrial Water Storage Anomaly (TWSA), satellite altimetry, rainfall, and Multivariate El Niño-Southern Oscillation Index (MEI) anomaly data. Although there is no statistically significant (α = 0.05) long-term trend in terrestrial water variation and rainfall in the basin (lake storage, TWSA, and rainfall, p-value = 0.45, 0.48, and 0.55, respectively), in the last decade, two visible droughts occurred between 2002 and 2004, and 2009 and 2010, which resulted in water deficit in the basin, where below average rainfall, TWSA, and lake height (storage) were observed during these periods. Extreme rainfall analysis from Standardized Precipitation Index (SPI) and strong connection between wet season rainfall, lake height, and TWSA, respectively, indicate interannual terrestrial water storage dynamics in the UBN Basin is strongly influenced by climate. Further, the El Niño-Southern Oscillation (ENSO) influences rainfall in the UBN Basin, specifically the peak rainfall season (June-September), which shows negative correlation (r = −0.62) with MEI anomaly values, which indicates that the El Niño case (positive MEI values) is linked to the dry conditions in the basin. The findings of this study, combined with studies such as socioeconomic impact of drought, will facilitate better planning and management of water resources in water stressed regions. Furthermore, this study demonstrates the application of a combination of satellite and other hydrologic data in understanding the hydro-climatic condition of a remote basin. [ABSTRACT FROM AUTHOR]

A new precipitation index-based model, which includes a snow accumulation and melt component, has been developed to simulate hydrology in high latitude catchments. The model couples a point snowmelt model with a current precipitation index ( CPI ) formulation to simulate continuous daily runoff from catchments with seasonal snow cover. A new runoff conversion factor: C T and L f , threshold flow factor Th Q and runoff transformation function Maxbas were introduced into the CPI equation, which converts and transforms the routed daily CPI into daily runoff and maintains the daily base flow in the catchment. The model was developed using twelve sub-arctic boreal catchments located above and below the Arctic Circle in northern Finland, representing a region with considerable seasonal snow cover. The results showed that the model can adequately simulate and produce the dynamics of daily runoff from catchments where the underlying physical conditions are not known. An open-access Excel-based model is provided with this paper for daily runoff simulations. The model can be used to estimate runoff in sub-arctic regions where little data is typically available but significant changes in climate are expected, with considerable shifts in the amount and timing of snowmelt and runoff. [ABSTRACT FROM AUTHOR]

Summary The multisensor Quantitative Precipitation Estimates (MQPEs) created by the US National Weather Service (NWS) are subject to a non-stationary bias. This paper quantifies the impacts of climatological adjustment of MQPEs alone, as well as the compound impacts of adjustment and model calibration, on the accuracy of simulated flood peak magnitude and that in detecting flood events. Our investigation is based on 19 watersheds in the mid-Atlantic region of US, which are grouped into small (<500 km 2 ) and large (>500 km 2 ) watersheds. NWS archival MQPEs over 1997–2013 for this region are adjusted to match concurrent gauge-based monthly precipitation accumulations. Then raw and adjusted MQPEs serve as inputs to the NWS distributed hydrologic model-threshold frequency framework (DHM-TF). Two experiments via DHM-TF are performed. The first one examines the impacts of adjustment alone through uncalibrated model simulations, whereas the second one focuses on the compound effects of adjustment and calibration on the detection of flood events. Uncalibrated model simulations show broad underestimation of flood peaks for small watersheds and overestimation those for large watersheds. Prior to calibration, adjustment alone tends to reduce the magnitude of simulated flood peaks for small and large basins alike, with 95% of all watersheds experienced decline over 2004–2013. A consequence is that a majority of small watersheds experience no improvement, or deterioration in bias (0% of basins experiencing improvement). By contrast, most (73%) of larger ones exhibit improved bias. Outcomes of the detection experiment show that the role of adjustment is not diminished by calibration for small watersheds, with only 25% of which exhibiting reduced bias after adjustment with calibrated parameters. Furthermore, it is shown that calibration is relatively effective in reducing false alarms (e.g., false alarm rate is down from 0.28 to 0.19 after calibration for small watersheds with calibrated parameters); but its impacts on detection rate are mixed. As an example, the detection rate of 2-Y events in fact declines for small watersheds after calibration is performed (from 0.4 to 0.28, and from 0.28 to 0.19 with raw and adjusted MQPE, respectively). These mixed outcomes underscore the complex interplays between errors in MQPEs, conditional bias in the reference gauge-based analysis, and structural deficiencies of the hydrologic model. [ABSTRACT FROM AUTHOR]

Summary This paper aims at exploring the anticipation phase before a flash flood, corresponding to the time between the first climatic signs and the peak-flow. We focus the analysis on people’s behaviors observing how they use this period to organize themselves for facing the event. The analysis is made through the definition of three specific scales: the timeliness scale, an analytical scale of anticipatory actions and the scale of human response network. Using a cross-scale and cross level analysis enables to define different phases in the anticipation period where different kind of environmental precursors are mobilized by the actors in order to make sense of the situation and adapt. Three main points deserve attention at the end: firstly, the concepts of timeliness, anticipatory actions and crisis network scales enable to understand differently what happens both physically and socially during an extreme event; secondly, analyzing the precursors shows that each level of crisis network uses different kinds of signs for estimating the situation, organizing and reacting; thirdly, there is a potential for improvement in observation on both social and physical processes at different scales, for verifying the theory of the anticipatory phases. [ABSTRACT FROM AUTHOR]

Summary The purpose of this paper is to contribute to the understanding of the importance of the initial soil moisture state for flash flood magnitudes. Four extreme events that occurred in different case study regions were analysed, one winter and one autumn flash flood in the Giofiros and Almirida catchments in Crete, and two summer floods in the Rastenberg catchment in Austria. The hydrological processes were simulated by the spatially distributed flash flood model Kampus. For the Crete cases Kampus model was calibrated against remotely sensed soil moisture while for the Austrian case the model was calibrated against observed runoff. Kampus model was then used to estimate the sensitivity of the stream flow peak to initial soil moisture. The largest of the events analysed (in terms of specific peak discharge) was found to have a sensitivity of less than 0.2% flood peak change per % soil moisture change while the smallest event had a sensitivity of more than 3% flood peak change per % soil moisture change. This suggests that initial soil moisture effects on the flash flood response probably depend on event magnitude rather than on the climate or region. Moreover, the Austrian catchment was found to exhibit a more nonlinear relationship between antecedent soil moisture and the peak discharge than the Cretan catchments which was explained by differences in the soil type. [ABSTRACT FROM AUTHOR]

Summary: The overall goal of the present study is to show through a simulation experiment the value of adaptive management under climatic variability and change. The Northern California water resources management system is used as an example of a multi-agency, multi-site, multi-objective water resources management system that supports a trillion dollar economy to demonstrate the utility of the methods and to exemplify the assessment studies. Simulations are performed using an adaptation of the Integrated Forecast and Reservoir Management (INFORM) system of Northern California with 6-hourly 1.4° input from the CCSM3.0 climate model for historical/control (1970–2019) and future periods (2050–2099). The present paper examines the assessment of the hydroclimatological sensitivities and a companion paper () examines the assessment of reservoir management sensitivities. The future period was based on the moderate CCSM3.0 A1B scenario. The INFORM system includes an intermediate complexity regional model for dynamic downscaling of the CCSM3.0 scenario output to produce surface precipitation and temperature fields with a 10×10km2 resolution, and snow–soil-channel modeling of all the watersheds upstream of the major reservoirs in Northern California. The flow results indicate a shift in the monthly average flow volume toward earlier times in the year and higher flow variability for the future period. Higher average temperatures in the future period and, consequently, earlier snow pack melt are mainly responsible for these flow changes. Both daytime and nighttime average monthly temperatures are higher in the simulation of the future period for the entire domain with the northern catchments experiencing higher temperature increases. Simulated monthly average precipitation for the future period is higher in the southern high Sierras and lower in the northern drainage basins than the historical period precipitation. The uncertainty in the assessments for Northern California mentioned may be estimated with additional simulations similar to those shown in this work using high spatial and temporal resolution output from different climate models. [ABSTRACT FROM AUTHOR]

Summary: This paper presents and assesses the model MedREM, in which annual-based climate and rainstorm data are used to estimate the long-term (R)Universal Soil Loss Equations and its Revisions (USLE) annual rainfall erosivity over a large region. Two known models of rainfall erosivity (named after the first author of the original paper, Torri and Yang) were also assessed and compared with the MedREM. Yang and Torri models are both based on the annual precipitation models. MedREM also takes annual maximum daily precipitation data into account, and incorporates a longitude-dependent coefficient. The test area was a large region centered on the Mediterranean basin, in which 66 weather stations were available – 43 Italian and 23 out-of-Italy sites (12 countries, about 30–50° latitude North, and 10–50° longitude East) – with multi-year data of (R)USLE annual erosivity. The three models were calibrated against (R)USLE rainfall erosivity data from 55 stations and evaluated over a validation dataset from 11 Italian stations. On the validation dataset, the MedREM estimates generally compared well with the (R)USLE data according to Nush–Sutcliffe coefficient (0.87 against 0.76 and 0.73 with Torri and Yang model, respectively). Implications for erosivity modelling were discussed in the context of climatic features concluding that accurate estimations of site-specific annual erosivity for the Mediterranean region require process-based model with spatially-explicit parameterization. [Copyright &y& Elsevier]

Summary: Time dependent observations of the gravity field are carried out to gain a better understanding about the dynamics of the Earth. Terrestrial measurements are made with gravimeters which monitor temporal changes in the vertical component of the Earth’s gravity field. At present, superconducting gravimeters (SG) are the most precise instruments for long-term stationary continuous observations. Such an instrument, as part of a worldwide gravimeter network, is located at the Geodynamic Observatory Moxa, Germany. The measurements of the SG are affected among other environmental influences by hydrological mass balance changes. The temporal and spatial variability of water distribution in the surrounding of the SG makes a quantification and reduction of such effects a challenging task. For further investigations of these influences an interdisciplinary research project which comprises scientists from hydrology, hydrogeology, geophysics and soil sciences has been launched in the surrounding of the observatory. For this project, the small (≈2km2) catchment which surrounds the observatory has been instrumented with soil moisture and groundwater probes at various locations as well as additional precipitation gauges and a climate measurement station for monitoring of climatological and hydrological parameters in high spatial and temporal resolution. Secondly, a distributed and spatially high resolution hydrological model, based on the modelling system J2000, has been set up for continuous simulation of the hydrological processes in the catchment using daily and hourly time steps. The main purpose of the project is to explore the types and magnitudes of hydrological influence on gravity monitored by the very sensitive superconducting gravimeter. Preliminary assessment showed that the gravimeter records contain noticeable influence due to variations of groundwater, soil water content and snow coverage. These influences interfere with the geodynamic signals, but the observed gravity changes carry interesting information from a hydrological point of view, in that a method for direct, integrative and non-invasive measurement of soil water contents and groundwater variations would be available if the hydrological influence on the response of the gravimeter records is extracted and utilised. This paper concentrates on the measurement and spatially distributed simulation of soil water contents and their effect on the signal of the SG. This is done by qualitative comparison of the volumetric soil water content with the observed gravity variations. Additionally, examples of how the modelled water volumes can be translated into gravity variations by means of a 3D gravimetric model is demonstrated and briefly discussed towards the end of the paper. [Copyright &y& Elsevier]

The Scripps Experimental Climate Prediction Center (ECPC) has been making routine experimental, near real-time global and regional forecasts since Sept. 27, 1997 with the National Centers for Environmental Prediction (NCEP) global spectral model (GSM) and the corresponding regional spectral model (RSM), which is based on the GSM, but which can develop higher resolution simulations and forecasts for limited regions. Images of these forecasts, at daily to seasonal time scales, are provided on the ECPC World Wide Web site (http://ecpc.ucsd.edu/) and digital forecast products are provided on the ECPC anonymous ftp site to interested collaborators. The forecast skill of the GSM for the first two years was previously described in several papers. The purpose of this paper is to further compare two additional years of GSM and the RSM precipitation forecasts over the US, with special attention to various biases and errors, as well as the significant forecast skill at weekly to seasonal time scales. While these experimental forecast results suggest that there is precipitation forecast skill, out to at least a season, to be realized from both global and regional dynamical models, it was disappointing that the RSM did not seem to provide markedly more skillful precipitation forecasts than the GSM. Excessive forecast variance is a major problem with the current RSM. [Copyright &y& Elsevier]

Summary One of the fundamental steps in regional rainfall frequency analysis is deciding the method by which rainfall stations are to be grouped together to form regions. This paper compares three methods of forming regions for use in estimating design rainfalls: a fixed region approach where all the available sites are included in a single region, a Region of Influence (ROI) approach based on geographical proximity and a hybrid approach where sites with similar topographic orientations are grouped together. The three region types were implemented in a Bayesian Generalized Least Squares Regression (BGLSR) framework which leads to regionalized regression equations that can be used to predict rainfall L-moments at ungauged sites. A leave-one-out cross validation approach was used to compare the relative accuracy, reliability and uncertainty of the derived rainfall statistics and resulting estimates of the rainfall quantiles. The study used data from two areas of Australia chosen for their highly varied topography and different climatic influences. It was found that all three methods provided good estimates of the L-moment statistics and the rainfall quantiles. The hybrid approach produced the smallest errors in the South-East Queensland region whilst for the Tasmanian region the fixed region approach was best. The results from this study show that although there is a slight benefit in using the proposed hybrid approach for BGLSR, these benefits were minor compared to maximizing the number of stations used to calibrate the BGLSR equations. This conclusion regarding the number of stations could be tested in future work by repeating the analyses in areas with sparser station density. Another test could be to simulate reduced station coverage in the current study areas by leaving stations out of the analyses. Finally it would be interesting to see if similar results are obtained by expanding the study area so that different climatological regimes are assessed. [ABSTRACT FROM AUTHOR]

Summary An increase in water demands, exacerbated by climate change and the tightening of environmental requirements, leads to a reduction in available water resources for economic uses. This situation poses challenges for water resource planning and management. Water accounting has emerged as an appropriate tool to improve transparency and control in water management. There are multiple water accounting approaches, but they generally involve a very exhaustive list of accounted concepts. According to our findings in this research, one of the best water accounting methodologies is the Australian Water Accounting Standard. However, its implementation for integrated water resource planning and management purposes calls into questioning the amount of information and level of detail necessary for the users of water accounts. In this paper, we present a different method of applying the Australian Water Accounting Standard in relation to water resource management, which improves its utility. In order to compare the original approach and that proposed here, we present and discuss an application to the Júcar Water Resource System, in eastern Spain. [ABSTRACT FROM AUTHOR]

Summary The paper presents an evaluation of real time ensemble forecasts produced during 2010–2012 by the demonstration project INFORM (Integrated Forecast and Reservoir Management) in Northern California. In addition, the innovative elements of the forecast component of the INFORM project are highlighted. The forecast component is designed to dynamically downscale operational multi-lead ensemble forecasts from the Global Ensemble Forecast System (GEFS) and the Climate Forecast system (CFS) of the National Centers of Environmental Prediction (NCEP), and to use adaptations of the operational hydrologic models of the US National Weather Service California Nevada River Forecast Center to provide ensemble reservoir inflow forecasts in real time. A full-physics 10-km resolution (10 km on the side) mesoscale model was implemented for the ensemble prediction of surface precipitation and temperature over the domain of Northern California with lead times out to 16 days with 6-hourly temporal resolution. An intermediate complexity regional model with a 10 km resolution was implemented to downscale the NCEP CFS ensemble forecasts for lead times out to 41.5 days. Methodologies for precipitation and temperature model forecast adjustment to comply with the corresponding observations were formulated and tested as regards their effectiveness for improving the ensemble predictions of these two variables and also for improving reservoir inflow forecasts. The evaluation is done using the real time databases of INFORM and concerns the snow accumulation and melt seasons. Performance is measured by metrics that range from those that use forecast means to those that use the entire forecast ensemble. The results show very good skill in forecasting precipitation and temperature over the subcatchments of the INFORM domain out to a week in advance for all basins, models and seasons. For temperature, in some cases, non-negligible skill has been obtained out to four weeks for the melt season. Reservoir inflow forecasts exhibit also good skill for the shorter lead-times out to a week or so, and provide a good quantitative basis in support of reservoir management decisions pertaining to objectives with a short term horizon (e.g., flood control and energy production). For the northernmost basin of Trinity reservoir inflow forecasts exhibit good skill for lead times longer than 3 weeks in the snow melt season. Bias correction of the ensemble precipitation and temperature forecasts with fixed bias factors over the range of lead times improves forecast performance for almost all leads for precipitation and temperature and for the shorter lead times for reservoir inflow. The results constitute a first look at the performance of operational coupled hydrometeorological ensemble forecasts in support of reservoir management. [ABSTRACT FROM AUTHOR]

[Display omitted] • The meteorological variables are correlated with altitude, latitude and longitude. • Temperature, altitude, latitude and longitude control the δ18O of precipitation. • Unrecorded rainfall in sampling points is obtained by means of correlation models. • These models are useful if the available data are scarce and discontinuous in time. • Aquifer recharge zones are identified by estimating δ18O values with these models. A knowledge of the evolution of isotopic composition of air masses humidity and precipitation in the Western Cordilleras of the Central Andes is still incomplete. This study contributes to a better understanding of the factors that control the δ18O and δ2H contents in precipitation water in the north of Chile, above 2000 m a.s.l.. This paper deals with: (1) the relevant effects and processes that control the spatial (longitude, latitude and altitude) distribution of stable isotope contents of precipitation events in northern Chile, (2) the influence of local meteorological variables: temperature, precipitation and relative humidity on the δ18O and δ2H of precipitation, and (3) the estimation of these meteorological and isotopic variables at specific sites. To achieve these objectives, the relationships between geospatial and meteorological values are identified and analysed, followed by the estimation with empirical models. These estimation models (linear and non-linear) are obtained after examining, validating and calibrating techniques to find the best fit. This results in models for temperature, relative humidity and precipitation for each month of the year. In the same way, three isotopic models are derived from the spatial and meteorological variables (summer, winter and annual). Temperature has been shown to be controlled to a greater extent by altitude and latitude, while relative humidity is by latitude and precipitation in summer is by altitude and latitude. Monthly meteorological variables have been estimated throughout the study area. Precipitation δ18O and δ2H are controlled mainly by temperature and altitude and to a lesser extent by latitude, longitude and precipitation. In the same way, three isotopic models are derived from the spatial and meteorological variables: summer, winter and annual. This opens a new perspective of precipitation and its isotopic contents, but also allows the calculation of runoff and aquifer recharge and the path for linking future precipitation and aquifer recharge through their isotopic composition. [ABSTRACT FROM AUTHOR]

Summary: Piezometric variability was investigated using monthly time series from 40 piezometers in different areas across the Upper Normandy region, over the 1968–2009 period. The piezometers were classified according to their hydrological behaviour. This paper aimed at characterising the dominant modes explaining piezometric variability and at investigating the causes of this variability. Significant components in the piezometric signals were identified: the annual cycle and an interannual mode (4–12years) with different variances for each observation borehole group. The influence of the morphostructural organisation of the Upper Normandy chalk plateau on the modalities of the water level variability in the chalk aquifer was defined. The relative contributions and standard deviations of each component found in the signal time series of the groundwater level was estimated. The highest values were recorded in the Northern study area, and clay-with-flints and aquifer thicknesses influence the transfer on an annual scale, while the loess thickness influences the transfer on a multi-year scale. [ABSTRACT FROM AUTHOR]

Summary: The main objective of this paper is to characterize the spatial and temporal variability of different types of rain events at scales that are consistent with urban hydrological applications. In this way, a total of 24 rain periods have been analyzed according to a geostatistical approach. This analysis has focused on the non-zero rainfall variogram. The studied rain periods were recorded by the weather radar of Treillières (10km north of Nantes, France) in 2009. This radar device provides rainfall radar images with a high level of spatial resolution (250m×250m) and instantaneous temporal resolution. Results indicated four different types of rainfall fields, which display very different variability scales, including double structures within the same field. These four types of rainfall fields can be characterized by their climatological variograms, as calculated for increasing time resolutions: instantaneous, 5min, 15min, and 30min, thus making it possible to estimate the decorrelation distance (i.e. range). This study highlights the benefit of radar images featuring high temporal and spatial resolution, which in turn allow studying small-scale variability. [ABSTRACT FROM AUTHOR]

Summary: Water companies in the United Kingdom have considered climate change in their water resources plans for more than a decade through studies funded by UK Water Industry Research (UKWIR). This paper presents an initial assessment of the impact of the UK Climate Projections 2009 (UKCP09) on river flows at a national scale for the 2020s under the A1B scenario and the implications for water resource planning. A daily hydrological modelling framework based on two conceptual model structures and the Generalized Likelihood Uncertainty Estimation (GLUE) methodology has been applied to 70 catchments across the UK. A Latin Hypercube Sampling approach was adopted to handle the probabilistic nature of UKCP09. Results show a decrease in mean annual flow over most of the UK, with negative median values of all monthly changes except in winter over the western and northern mountainous areas. Furthermore the results indicate a high likelihood of a significant decline in summer flows. An analysis of variance shows that the main uncertainty in river flow changes comes from the spread in climate projections. Finally results are found to be consistent with a previous UKWIR assessment based on individual projections from 6 Global Climate Models (GCMs) under the A2 scenario. The reduction in summer low-flows, critical for water resources, appears however more limited with UKCP09. Although most expected changes are within natural variability, the drier conditions overall and the greater spread of results with respect to previous assessments indicates a need for testing the robustness of water resource plans. [Copyright &y& Elsevier]

Summary: Urban irrigation is an important component of the hydrologic cycle in many areas of the arid and semiarid western United States. This paper describes a new approach that uses readily available datasets to estimate the location and rate of urban irrigation. The approach provides a repeatable methodology at 1/3km2 resolution across a large urbanized area (500km2). For this study, Landsat Thematic Mapper satellite imagery, air photos, climatic records, and a land-use map were used to: (1) identify the fraction of irrigated landscaping in urban areas, and (2) estimate the monthly rate of irrigation being applied to those areas. The area chosen for this study was the San Fernando Valley in Southern California. Identifying irrigated areas involved the use of 29 satellite images, air photos, and a land-use map. The fraction of a pixel that consists of irrigated landscaping (Firr ) was estimated using a linear-mixture model of two land-cover endmembers (selected pixels within a satellite image that represent a targeted land-cover). The two endmembers were impervious and fully-irrigated landscaping. In the San Fernando Valley, we used airport buildings, runways, and pavement to represent the impervious endmember; golf courses and parks were used to represent the fully irrigated endmember. The average Firr using all 29 satellite scenes was 44%. Firr calculated from hand-digitizing using air photos for 13 randomly selected single-family-residential neighborhoods showed similar results (42%). Estimating the rate of irrigation required identification of a third endmember: areas that consisted of urban vegetation but were not irrigated. This “nonirrigated” endmember was used to compute a Normalized Difference Vegetation Index (NDVI) surplus, defined as the difference between the NDVI signals of the irrigated and nonirrigated endmembers. The NDVI signals from irrigated areas remains relatively constant throughout the year, whereas the signal from nonirrigated areas rises and falls seasonally due to precipitation. The areas between airport runways were chosen to represent the nonirrigated endmember. Water-delivery records from 65 spatially-distributed single-family neighborhoods, consisting of nearly 1800 homes, were correlated with the NDVI surplus. The results show a strong exponential correlation (r 2 =0.94). In the absence of water-delivery records, which can be difficult to obtain, a surrogate was identified: the landscape evapotranspiration rate (ET L ). ET L was used to scale NDVI surplus (which is dimensionless) to irrigation rates using an exponential scaling function. The monthly irrigation rates calculated from satellite and climatic data compared well with irrigation rates calculated from actual water-delivery data using a paired Wilcoxan signed-rank test (p =0.0063). Identification of Firr at the pixel scale, along with identification of the irrigation rate for a fully-irrigated pixel, allows for mapping of urban irrigation over large areas. Maps showing the location and rate of monthly irrigation for the San Fernando study area were computed for January and August 1997. [ABSTRACT FROM AUTHOR]

Summary: This paper presents an analysis of seasonal precipitation extremes in an ensemble of fourteen transient regional climate model (RCM) simulations for durations varying from 1 to 30days. It is assumed that these precipitation maxima follow a generalized extreme value (GEV) distribution with time-varying parameters. In addition, spatial pooling of the maxima at various grid boxes is applied to reduce the uncertainty of the parameters of the GEV model. Relatively large positive biases in moderate quantiles of long-duration precipitation extremes have been found in autumn, winter and spring. The extreme quantiles are much better reproduced in these seasons. In summer, the ensemble mean bias in the quantiles is negative and decreases (in absolute value) with increasing duration. The ensemble mean changes in the quantiles are positive for all durations, return periods and seasons considered. Large quantiles of the distribution of summer precipitation extremes increase more than moderate quantiles and this increase is relatively large at short durations. The dependence of the changes on duration and return period is much weaker in the other seasons, in particular in winter and spring. Although the spread in the RCM ensemble is considerable, most of the RCM simulations agree on the sign of the changes. [ABSTRACT FROM AUTHOR]

Summary: This paper analyses the dew collection performance of two polyethylene (PE) foils in a semi-arid region (Southern Spain). The dew collecting devices consisted of two commercial passive radiative dew condensers (RDCs) of 1m2 tilted to 30°. They were fitted with two different high-emissivity PE foils: a white hydrophilic foil (WSF) recommended as standard for dew recovery comparisons by the International Organization for Dew Utilization (OPUR), and a low-cost black PE foil (BF) widely used for mulching in horticulture. Dew yield, foil surface temperature and meteorological variables (air temperature, relative humidity, downward long wave radiation and wind speed) were recorded hourly during a 1-year period from May-2009 to May-2010. The spectral emissivity of the foils was determined in laboratory in the range 2.5–25μm and the radiance-weighed values were calculated over different intervals, indicating that BF emitted more than WSF, especially in the range 2.5–7μm. Dew yield was well correlated with the air relative humidity and foil net radiation in both foils and was hardly detected when the relative humidity was lower than 75% or the wind speed higher than 1.5ms−1. WSF was more sensitive to dew formation due to its hydrophilic properties, registering more dewy nights (175) than BF (163) while the annual cumulative dew yield for BF was higher (20.76mm) than for WSF (17.36mm) due to the higher emissivity and emitted radiance of BF. These results suggested that increasing the surface emissivity over the whole IR spectrum could be more effective for improving RDC yield performances than increasing the surface hydrophilic properties. On a practical point of view, BF could be considered as a suitable material for large scale RDCs, as in our study it presented several advantages over the reference material, such as higher dew collection performance, longer lifespan and much lower cost. [Copyright &y& Elsevier]

Summary: This paper presents an analysis on the hydrological regimes from several rivers flowing from the western slope of the Andes Cordillera. We compiled a database of 40 unimpaired average monthly streamflow records located in central and southern Chile, spanning the region between latitudes 30°S and 40°S and the period between years 1961 and 2006. River categories encompass snowmelt, mixed and rainfall dominated watersheds. The water year hydrograph center of timing (CT) is used as an indicator of flow regime and as a proxy for streamflow distribution throughout the year. The CT for each river is correlated to local climatic variables such as seasonal temperature and precipitation, and to large-scale circulation indexes. We assess CT trends using the Mann–Kendall test for varying time windows within the study period. Overall results for the 1961–2006 period show a significant (95% confidence level) negative trend (CT date shifting towards earlier in the year) for 23 out of the 40 analyzed series. However, when studying time windows within the entire period there seems to be a break in the trend during the late 1970s, with positive or null changes for the latter half of the study period for most of the stations analyzed. We found that even though the CT is significantly correlated (r >0.5) to temperature indexes in the snowfed watersheds, there has been no significant change in CT timing for most of these watersheds despite documented warming on the region. CT for all types of watersheds present clear correlation to precipitation indexes (r >0.8), and we observed high and significant correlation between CT and ONI, SOI and MEI indexes for all the study region: higher (lower) precipitation amounts during El Niño (La Niña), rather than temperature variations, seem to be the most important factor controlling CT timing during these episodes. In order of importance, the CT showed higher correlation with annual precipitation amounts and timing, MEI, ONI and SOI, mean winter and spring temperature, and finally PDO. The detected trends are consistent only with trends in precipitation, as rivers that showed high correlation to temperature do not show any trend. These conclusions suggest that rivers on the region show a different response to temperature changes when compared to rivers of similar regime in the northern hemisphere, and that precipitation amount and timing are the source of most of the variability in streamflow timing for both snowmelt and rainfall dominated watersheds. Temperature induced variability in streamflow timing should be studied with more precise hydrological models, as the CT has a complex behavior due to multiple climatical dependencies. Studying such behavior using only historical data might be insufficient for drawing more precise conclusions. [Copyright &y& Elsevier]

Summary: This paper presents that a simple regional regression-based approach with readily available geographic and meteorological parameters as predictors could be a viable method for mapping snow water equivalent (SWE) climatology in the mountainous areas of the western United States. Such climatological information is potentially useful for several hydrologic applications, including estimation of real-time SWE grids and calibration and evaluation of SWE estimates obtained from remote sensing or through various snow models. Regional delineation for the mountainous regions of the western United States was done through cluster analysis in consideration of the characteristics of seasonal snow pack accumulation and ablation processes. Various geographic and meteorological parameters were further investigated through stepwise regression as potential predictors of monthly changes in climatological SWE in each delineated region. In-situ measurements of SWE, obtained from the Natural Resources Conservation Service’s Snow Telemetry network for the period from 1980 to 2004, were used to calibrate regional equations. The spatial and temporal resolution of the analysis was based on the resolution of available meteorological data – 4km and 1month, respectively. For a monthly time step, the reliability of the SWE estimates did not significantly increase when the number of regions was more than five. The performance of the developed regional equations was evaluated via a jack-knife technique. The regional equations developed using monthly resolution and large regions provided reliable estimates for the majority of regions from October to March, but not in April, especially in the North Pacific and Southwest regions. Sub-regionalization, a finer time step for the analysis and/or inclusion of additional SWE predictor variables may increase the reliability of SWE climatological estimates for warmer months. [ABSTRACT FROM AUTHOR]

Summary: Investigation of links between large-scale climate and river basin-scale precipitation and discharge is necessary to improve understanding of the hydroclimatological process chain. This paper aims to identify the large-scale circulation variables that yield significant statistical relationships with precipitation and river flow for ten British basins, and evaluate the dependence of the strength and location of centres of climate–hydrology correlation on basin situation. Using quasi-geostrophic theory as a framework to select appropriate explanatory variables, this study undertakes ranked correlation analysis between gridded ERA-40 atmospheric data and precipitation and river flow observations at a monthly resolution over a 26year period. Relationships between large-scale atmospheric circulation and precipitation and river flow are found to vary spatially and seasonally across Britain, with strongest associations evident in western Britain and in winter. Monthly precipitation totals and river flow demonstrate highly significant negative (positive) correlation with mean sea level pressure MSLP (Zonal Wind U), but the location of high correlation centres vary for different basins. There are weaker atmospheric links with river flow compared to precipitation, reflecting the non-linearity of the rainfall–runoff transformation and the importance of basin properties as a modifier of climate inputs. The North Atlantic Oscillation Index (NAOI) cannot capture the seasonal movement of climate–hydrology connections due to the fixed station locations used for the index calculation, and thus has weaker monthly correlations with precipitation and river flow compared to gridded ERA-40 circulation variables (and in particular the comparable MSLP). [ABSTRACT FROM AUTHOR]

Summary: Typically, flash floods are studied ex post facto in response to a major impact event. A complement to field investigations is developing a detailed database of flash flood events, including minor events and null reports (i.e., where heavy rain occurred but there was no flash flooding), based on public survey questions conducted in near-real time. The Severe hazards analysis and verification experiment (SHAVE) has been in operation at the National Severe Storms Laboratory (NSSL) in Norman, OK, USA during the summers since 2006. The experiment employs undergraduate students to analyse real-time products from weather radars, target specific regions within the conterminous US, and poll public residences and businesses regarding the occurrence and severity of hail, wind, tornadoes, and now flash floods. In addition to providing a rich learning experience for students, SHAVE has also been successful in creating high-resolution datasets of severe hazards used for algorithm and model verification. This paper describes the criteria used to initiate the flash flood survey, the specific questions asked and information entered to the database, and then provides an analysis of results for flash flood data collected during the summer of 2008. It is envisioned that specific details provided by the SHAVE flash flood observation database will complement databases collected by operational agencies (i.e., US National Weather Service Storm Data reports) and thus lead to better tools to predict the likelihood of flash floods and ultimately reduce their impacts on society. [ABSTRACT FROM AUTHOR]

Summary: Concern has been expressed regarding the impacts of climate change on freshwater aquatic ecosystems in arctic regions. Populated with lakes controlled by flooding from spring break-up ice jams, arctic deltas such as the Mackenzie Delta in northern Canada are particularly sensitive to changing ice break-up conditions and the hydroclimatic controls on break-up and ice-jam flooding. An understanding of these controls is necessary for assessing future climate change effects. This paper presents an assessment of hydroclimatic conditions controlling break-up over the period 1974–2006, with a focus on extreme flood events. Both the upstream driving force, capturing elements of the spring discharge hydrograph, and the downstream resistance force, describing the competence of the downstream ice cover, were quantified with reference to the Mackenzie River at Arctic Red River hydrometric station such that the contribution of each to the severity and timing of break-up could be explored. Results show that the severity of peak break-up stage is most influenced by upstream discharge and the balance between upstream and downstream melt, while timing is related to delta ice conditions and the rise of the spring hydrograph. The highest peak stage events require a rapid rise in discharge and high peak discharge. Minimal downstream melting degree-days and greater ice thickness are also important, although no relationship of these appears to control the level of backwater produced from broken ice and ice jamming effects. The pattern of rapid (protracted) upstream melt and lower (higher) intensity melt in the delta characterizes the highest (lowest) break-up events. For the most severe events, upstream forces are important in controlling discharge-driven events, while an altered hydrologic response occurring for ice-driven events was noted, meriting future examination. Finally, trends toward a longer prebreak-up melt interval, lower peak discharge, rate of rise in discharge, and ice thickness, and higher freeze-up stage were observed, with greater variability of these controls and break-up severity in the most recent decade. [Copyright &y& Elsevier]

Summary: In this paper, progressive methods for assessing drought severity from diverse points of view were conceived. To select a fundamental drought index, the performances of the Effective Drought Index (EDI) and 1-, 3-, 6-, 9-, 12-, and 24-month Standardized Precipitation Indices (SPIs) were compared for drought monitoring data accumulated over 200-year period from 1807 to 2006 for Seoul, Korea. The results confirmed that the EDI was more efficient than the SPIs in assessing both short and long-term droughts. We then proposed the following methods for modifying and supplementing the EDI: (1) CEDI, a corrected EDI that considers the rapid runoff of water resources after heavy rainfall; (2) AEDI, an accumulated EDI that considers the drought severity and duration of individual drought events; and (3) YAEDI, a year-accumulated negative EDI representing annual drought severity. In addition to these indices, to more accurately measure and diagnose droughts, we proposed the utilization of (4) the Available Water Resources Index (AWRI), an existing index that expresses the actual amount of available water. Using the improved methods above, we assessed and summarized important droughts that have occurred in Seoul over the 200years from 1807 to 2006. [Copyright &y& Elsevier]

Summary: A complete evaluation of the soil thermal regime can be obtained by evaluating the movement of liquid water, water vapor, and thermal energy in the subsurface. Such an evaluation requires the simultaneous solution of the system of equations for the surface water and energy balance, and subsurface heat transport and water flow. When only daily climatic data is available, one needs not only to estimate diurnal cycles of climatic data, but to calculate the continuous values of various components in the energy balance equation, using different parameterization methods. The objective of this study is to quantify the impact of the choice of different estimation and parameterization methods, referred together to as meteorological models in this paper, on soil temperature predictions in bare soils. A variety of widely accepted meteorological models were tested on the dataset collected at a proposed low-level radioactive-waste disposal site in the Chihuahua Desert in West Texas. As the soil surface was kept bare during the study, no vegetation effects were evaluated. A coupled liquid water, water vapor, and heat transport model, implemented in the HYDRUS-1D program, was used to simulate diurnal and seasonal soil temperature changes in the engineered cover installed at the site. The modified version of HYDRUS provides a flexible means for using various types of information and different models to evaluate surface mass and energy balance. Different meteorological models were compared in terms of their prediction errors for soil temperatures at seven observation depths. The results obtained indicate that although many available meteorological models can be used to solve the energy balance equation at the soil–atmosphere interface in coupled water, vapor, and heat transport models, their impact on overall simulation results varies. For example, using daily average climatic data led to greater prediction errors, while relatively simple meteorological models may significantly improve soil temperature predictions. On the other hand, while models for the albedo and soil emissivity had little impact on soil temperature predictions, the choice of the atmospheric emissivity models had a greater impact. A comparison of all the different models indicates that the error introduced at the soil atmosphere interface propagates to deeper layers. Therefore, attention needs to be paid not only to the precise determination of the soil hydraulic and thermal properties, but also to the selection of proper meteorological models for the components involved in the surface energy balance calculations. [Copyright &y& Elsevier]

Summary: In this paper we presented the analysis of two long time series of daily river flow data, 32 years recorded in the Seine river (France), and 25 years recorded in the Wimereux river (Wimereux, France). We applied a scale based decomposition method, namely Empirical Mode Decomposition (EMD), on these time series. The data were decomposed into several Intrinsic Mode Functions (IMF). The mean frequency of each IMF mode indicated that the EMD method acts as a filter bank. Furthermore, the cross-correlation between these IMF modes from the Seine river and Wimereux river demonstrated correlation among the large scale IMF modes, which indicates that both rivers are likely to be influenced by the same maritime climate event of Northern France. As a confirmation we found that the large scale parts have the same evolution trend. We finally applied arbitrary order Hilbert spectral analysis, a new technique coming from turbulence studies and time series analysis, on the flow discharge of the Seine river. This new method provides an amplitude–frequency representation of the original time series, giving a joint pdf . When marginal moments of the amplitude are computed, one obtains an intermittency study in the frequency space. Applied to river flow discharge data from the Seine river, this shows the scaling range and characterizes the intermittent fluctuations over the range of scales from 4.5 to 60 days, between synoptic and intraseasonal scales. [Copyright &y& Elsevier]

Summary: This paper presents an operational approach to integrate a storm classification method into a real-time radar rainfall estimation. A minor modification of a texturing classification algorithm proposed by Steiner et al. [Steiner, M., Houze Jr., R.A., Yuter, S.E., 1995. Climatological characterisation of three-dimensional storm structure from operational radar and rain gauge data. J. Appl. Meteorol. 34, 1978–2007] that can classify each pixel in the radar image as stratiform or convective is used to classify the instantaneous reflectivity field into convective and stratiform components. A method to derive the climatological Z–R relations for convective and stratiform rainfall is proposed. Vertical profiles of reflectivity (VPRs) are used to verify the accuracy of the storm classification. An alternative method for verification of a storm classification scheme based on differences between probability distribution functions of rain gauge rainfall of the two rainfall categories is also presented. A 6-month record of radar and rain gauge rainfall for Sydney, Australia for November 2000–April 2001 is used for training and rainfall events during February 2007–March 2007 are used to evaluate the efficiency and applicability of the proposed methods. The results show that the proposed operational approach for classifying storms in real-time radar rainfall estimates reduces RMSE between radar and rain gauge rainfall from 4.63 to 4.30mmh−1 and from 5.31 to 5.01mmh−1 for the training and verification periods compared to the case where the rainfall is assumed to have a single Z–R relationship. [Copyright &y& Elsevier]

Summary: The purpose of the paper is to show that the groundwater impacts of climatic cycles vary spatially across the aquifer depending on local hydrogeologic characteristics. Spectral analysis was applied 53 piezometers to study cycles patterns of variability in time and space in the Vega de Granada aquifer. Other variables like rainfall data, flows of rivers, and temperature were also analysed in order to determine the influence of climatological cycles. A number of long-term cycles were detected in the data series, with a time evolution that was well reflected in most piezometers. Many of the piezometers have been registering the position of the hydraulic head on a monthly basis for the past 30 years. The climatological time series represent nearly 50 years of monthly recordings, while the time series of river flow covers more than 30 years. Using spectral methods, we distinguished four different cycles in the time evolution of the aquifer hydraulic head: a decadal cycle (peaks between 8 and 11 years), a 3.2 year cycle, an annual one, and a semi-annual period. We also detected a 22 year cycle in a rainfall time series, though with less clarity. Whereas the annual cycle is ubiquitous, reflecting the annual hydrological cycle, the decadal and 3.2 year periods show a much more interesting spatial distribution. The decadal cycle can certainly be traced to hydrological variations involving a climatological regimen, probably induced by North Atlantic Oscillation (NAO). Groundwater impacts of climatic cycles vary spatially across the aquifer depending on local hydrogeologic characteristics. Specifically, the piezometers located close to streamflows reaching the aquifer, or along the main river in those areas where it recharges the aquifer, present a statistically significant decadal cycle. NAO wet periods imply more rainfall than average over Spain and thus more recharge into the aquifer increasing the water table. The opposite occurs during the dry periods. The presence and spatial distribution of the 3-year cycle would have an explanation similar to that of the decadal cycle, though it is identified at fewer points of piezometric control. [Copyright &y& Elsevier]

Summary: Although weather generators have been used for a long time, recent years have seen a surge in interest in them as potential downscaling tools for climate change impacts studies. Weather generators can only generate weather data at a single point, or independently at several points, whereas many climate change impact studies require information at the basin scale. Such studies would require the coherent generation of weather data at several locations over a basin. The literature on multi-site weather data generation is very thin, with most of the work based on the approach described by Wilks (Wilks, D.S., 1998. Multi-site generalization of a daily stochastic precipitation generation model. J. Hydrol. 210, 178–191). This approach demands significant work to set up, and may suffer from ill-defined correlation matrices due to noise in the observed data. In addition, multi-site generation must address the complex problem of spatial intermittence, in which precipitation amounts depend on neighbouring stations being wet or dry. This likely explains why multi-site generation, despite its obvious advantages, has not been widely used. This paper presents an algorithm for the efficient stochastic generation of multi-site precipitation data following the Wilks approach. The effect of noise on the performance of the algorithm is examined, and the results indicate that it performs very well, even with excessive noise added to the data. The algorithm is fast, simple, easy to implement, and significantly simplifies the generation of multi-site precipitation data for impact studies of climate scenarios. The spatial intermittence problem is dealt with by linking average precipitation to an index that describes the distribution of precipitation occurrence at the basin scale. [Copyright &y& Elsevier]

Summary: Understanding the process of groundwater recharge is fundamental to the management of groundwater resources. Quantifying the future evolution of recharge over time requires not only the reliable forecasting of changes in key climatic variables, but also modelling their impact on the spatially varying recharge process. This paper presents a physically based methodology that can be used to characterize both the temporal and spatial effect of climate change on groundwater recharge. The method, based on the hydrologic model HELP3, can be used to estimate potential groundwater recharge at the regional scale with high spatial and temporal resolution. In this study, the method is used to simulate the past conditions, with 40years of actual weather data, and future changes in the hydrologic cycle of the Grand River watershed. The impact of climate change is modelled by perturbing the model input parameters using predicted changes in the regions climate. The results of the study indicate that the overall rate of groundwater recharge is predicted to increase as a result of climate change. The higher intensity and frequency of precipitation will also contribute significantly to surface runoff, while global warming may result in increased evapotranspiration rates. Warmer winter temperatures will reduce the extent of ground frost and shift the spring melt from spring toward winter, allowing more water to infiltrate into the ground. While many previous climate change impact studies have focused on the temporal changes in groundwater recharge, our results suggest that the impacts can also have high spatial variability. [Copyright &y& Elsevier]

Summary: The knowledge of soil moisture spatial variability is an important issue for hydrological and climatic studies. This paper investigates the soil moisture spatial pattern obtained from spot measurements carried out in different experimental plots located in small basins of Central Italy. The near-surface volumetric soil moisture content was measured using a portable time domain reflectometer (TDR) over regular grids whose areas ranging in extension from 9 to 8800m2. The sampling in each site was periodically repeated in order to investigate the moisture pattern as function of wetness conditions. The statistical properties of the measurements and the relation of the statistical parameters with the field-mean soil moisture content were analyzed. The results states that the probability distribution can be assumed normal for soil moisture in flat areas and for normalized soil moisture in gentle slope areas. A general decreasing trend of variance with increasing mean moisture content was observed. Moreover, considering the relationship between the field-mean water content and the coefficient of variation an optimal measurements number ranging from 15 to 35, in the worse case, was found for the study areas. The higher number was required for the site with more significant relief. These results were also confirmed through the geostatistical analysis and the correlation with the topographical attributes. Specifically, the correlation lengths were found increasing with the mean areal slope and the mean drainage direction and ranging between 10 and 15m. This is corroborated through geostatistical analysis showing that the natural logarithm of the specific upslope area was the best spatial predictor for near-surface soil moisture when the water volume in the soil is higher. On this basis, the surface and subsurface lateral redistribution of water can be surmised fundamental in spatial organization of soil moisture. [Copyright &y& Elsevier]

Summary: This paper explores the potential impacts of climate change upon flood frequency for the gauged, Lossie catchment in the northeast of Scotland, UK. This catchment has significant flooding problems, but only limited data availability (particularly with respect to rainfall). A continuous simulation methodology, which uses a stochastic rainfall model to drive the rainfall-runoff model TOPMODEL, is utilised. Behavioural parameter sets for TOPMODEL are identified prior to the climate change runs using the Generalised Likelihood Uncertainty Estimation (GLUE) methodology. The “Low Emissions”, “Medium-Low Emissions”, “Medium-High Emissions” and “High Emissions” UKCIP02 climate change scenarios, obtained from the HadCM3 global climate model (GCM) and HadRM3 regional climate model (RCM) simulations, are used at the catchment scale. Two further scenarios (“H-Dry” and “H-Wet”), based upon the model uncertainty margins available for the UKCIP02 “High Emissions” scenario, are also developed in order to explore the possible range of changes to daily rainfall and temperature estimated from GCMs other than HadCM3. It is demonstrated that, while flood magnitude changes under all six of the climate change scenarios considered, the magnitude and direction of that change is dependent upon the choice of scenario. An overlap between the likelihood weighted uncertainty bounds estimated under the conditions of the current climate and those estimated under the four UKCIP02 scenarios and the “H-Dry” scenario is also observed. These findings highlight the need to consider multiple climate change scenarios and account for model uncertainties when estimating the possible effects of climate change upon flood frequency. [Copyright &y& Elsevier]

Abstract: The perception of a changing climate, which impacts also hydrological processes, is now generally admitted. However, the way of handling the changing nature of climate in hydrologic practice and especially in hydrological statistics has not become clear so far. The most common modelling approach is to assume that long-term trends, which have been found to be omnipresent in long hydrological time series, are ‘deterministic’ components of the time series and the processes represented by the time series are nonstationary. In this paper, it is maintained that this approach is contradictory in its rationale and even in the terminology it uses. As a result, it may imply misleading perception of phenomena and estimate of uncertainty. Besides, it is maintained that a stochastic approach hypothesizing stationarity and simultaneously admitting a scaling behaviour reproduces climatic trends (considering them as large-scale fluctuations) in a manner that is logically consistent, easy to apply and free of paradoxical results about uncertainty. [Copyright &y& Elsevier]

Abstract: This paper deals with the effects of land levelling on soil hydrological properties and spatial variability of soil moisture at different depths in bare vineyard fields under different rainfall distributions over the year. The research was carried out in a levelled vineyard in the Penedès region (NE Spain), which is representative of the land management practices in that area. Twelve locations were instrumented in the selected plot to control soil moisture periodically at different depths (0–20, 20–40, 40–60cm, using a TDR tube probe), and runoff (using Gerlach collectors located at the same positions) during the period April 2002–March 2004. Rainfalls were recorded at one-minute intervals in the experimental field using a pluviometer linked to a data-logger. The vine yield and its relationship with the rainfall distribution over the year was evaluated in this levelled vineyard vs. a less transformed vineyard. The land levelling works carried out in the plot before the establishment of the vineyard led to major differences in soil depth (ranging from 50 to 110cm) and in soil layer characteristics along and across the plot. This helps to generate different soil moisture conditions at the same depth at different positions of the plot. The areas located at the top of the plot showed soil moisture conditions in the surface layer that were up to 5% lower than down slope, although with high variability across the plot. Soil moisture in the whole profile depends on the intensity of rainfall more than total rainfall. At high intensities, soil moisture increases only occur in the surface layer, but no significant increases are observed in deeper layers, where vines have most of their roots. On the other hand, under low intensity rainfalls the soil water content increases in the whole profile. Levelled plots showed a yield reduction in comparison with those undergoing less transformation, which was exacerbated by a very irregular rainfall distribution: for a similar total rainfall, but with dry periods during the stages in which the water requirement for vines is higher, grape production was up to 50% lower in areas with high land transformation, where Chardonnay was planted, and up to 16% lower at the bottom areas, where Macabeo was planted. [Copyright &y& Elsevier]

Abstract: Floods in mesoscale catchments (with a size of a few hundreds to a few thousand square kilometers) are often related to typical large scale weather phenomena which affect a large area in continental scale. The purpose of this paper is to develop a methodology for the identification of flood producing daily atmospheric circulation patterns (CPs) from large-scale pressure maps. The CPs are described by fuzzy rules which indicate the locations of the pressure centers—lows and highs. The flood producing circulation patterns are identified using the positive increments of the observed discharge series. The rules are assessed using a discrete optimization procedure based on simulated annealing. The goal of the optimization is to find CPs corresponding to high discharge increments. The methodology is applied in two catchments: the Ardèche in France and the Llobregat in Spain. In both cases typical flood producing CPs could be identified. The occurrence of these CPs does not necessarily lead to floods, but most important floods were related to them. [Copyright &y& Elsevier]

The potential impacts of deforestation on hydrological response are of significant importance in highland regions of northern Thailand and other parts of southern Asia. In these regions, where climate exhibits strong seasonality, the availability of water in the dry season determines the feasibility of multiple crop rotations. Based on the IHACRES rainfall-runoff model, a simple scaling procedure and landcover data, this paper presents a simple hydrologic approach to predict hydrologic response to land use changes, and to predict streamflow in ungauged catchments. The latter is a major hurdle in water resource analyses in regions like northern Thailand where there is a lack of stream gauge instrumentation, or where assessment of water availability is required at locations between gauging sites as input to agricultural production models. A simple regionalisation of streamflow response was tested for three gauged subcatchments of the Mae Chaem catchment in northern Thailand. Overall performance was superior when calibrated parameters from the largest catchment were used to infer parameters for the regionalisation. Most importantly, the procedure was able to predict the relative pattern of annual and seasonal flows, whatever the reference catchment on which the regionalisation was based. Investigations of the impacts of forest cover changes on modelled hydrological response are presented and the key model parameters to which model outputs are most sensitive are discussed. [Copyright &y& Elsevier]

Temperature index or degree-day models rest upon a claimed relationship between snow or ice melt and air temperature usually expressed in the form of positive temperatures. Since air temperature generally is the most readily available data, such models have been the most widely used method of ice and snow melt computations for many purposes, such as hydrological modelling, ice dynamic modelling or climate sensitivity studies. Despite their simplicity, temperature-index models have proven to be powerful tools for melt modelling, often on a catchment scale outperforming energy balance models. However, two shortcomings are evident: (1) although working well over long time periods their accuracy decreases with increasing temporal resolution; (2) spatial variability cannot be modelled accurately as melt rates may vary substantially due to topographic effects such as shading, slope and aspect angles. These effects are particularly crucial in mountain areas. This paper provides an overview of temperature-index methods, including glacier environments, and discusses recent advances on distributed approaches attempting to account for topographic effects in complex terrain, while retaining scarcity of data input. In the light of an increasing demand for melt estimates with high spatial and temporal resolution, such approaches need further refinement and development. [Copyright &y& Elsevier]

• CIRFs are useful for exploring risks of system failure under climate change. • Synthetic streamflow series are generated using SA and linked to climate conditions. • Logistic regression relates system responses to climate drivers. • System failure defined according to performance (reliability) and threshold. • LR model detects climate conditions which would require adaptation. Climate Impact Response Functions (CIRFs) can be useful for exploring potential risks of system failure under climate change. The performance of a water resource system can be synthesized through a CIRF that relates climate conditions to system behavior in terms of a specified threshold of deliveries to demands or environmental flow requirements. However, in highly regulated water resource systems this relationship may be quite complex, depending on storage capacity and system operation. In this paper we define a CIRF for these types of systems through a multivariable logistic regression (LR) model where a binary variable (system response) is explained by two continuous variables or predictors (precipitation and temperature). The approach involves generating multivariate synthetic inflow time series and relating them to specific climate conditions. Next, these inflows are used as inputs in a water management model, and the outcome is coded as a binary variable (failure or its absence) depending on selected vulnerability criteria. To identify the time span before the failure event in which climate variables are relevant, we characterized drought development stages through relative standardized indices. Mean values of precipitation and temperature for the selected time span are computed and used as explanatory variables through a LR model, which is validated using data from several climate models and scenarios. Results show that the predictive capacity of LR models is acceptable, so that they could be used as screening tools to detect challenging climate conditions for the system which would require adaption actions. [ABSTRACT FROM AUTHOR]

• Time trend- and precipitation-informed models are tested. • 31 gauging stations with 55 years of data were used. • Precipitation-informed models outperform time trend-informed models. • Tested models yielded significantly different flood quantiles. • The best-fitting model is to some extent related to the flow regime. Estimation of reliable design discharges under variable climate is a key challenge for today's engineers. Therefore, researchers are intensively exploring different alternative approaches in order to improve standard methods for design discharge estimation. Paper investigates the performance of time-invariant, time trend- and precipitation-informed models based on generalized extreme value (GEV) distribution for 31 Slovenian discharge gauging stations with data availability from 1961 until 2015. Different rainfall durations are used as covariates in the case of precipitation-informed models. The selected catchments are located in different climate regions and characterized by five flow regimes. The results indicate that in most cases precipitation-informed models gave better fit to the measured data comparing to time-invariant and time trend-informed models. Relative differences in the design discharge estimations associated with 10- and 100-year return periods using time trend- and precipitation-informed models compared to time-invariant model were up to 60%. Additionally, the results indicate that identified best-fitting model of individual gauging station can to some extent be related to its flow regime. [ABSTRACT FROM AUTHOR]

• Two geographically multiple regression models inspect the TWS " spatial" drivers. • The OLS retains the TWS basin-wide drivers, and GWR explains the local drivers. • The GWR highlights the importance of the spatial domain in the TWS investigation. GRACE (Gravity Recovery and Climate Experiment) long-term terrestrial water storage anomaly (TWSA) is attributed to the complex interaction of climatic, physical and anthropogenic drivers. This paper, therefore, explores how different hydroclimatic and anthropogenic processes interact and combine over " space " to produce the mass variations that GRACE-TWSA detects. Using the Nile River Basin (NRB) as a case study, it explicitly analyzes nine hydroclimatic and anthropogenic processes, as well as their relationship to the TWSA in different climatic zones. The analytic method employed the long-term trends derived for both the dependent (TWSA) and independent (explanatory) variables via applying two geographically multiple regression (GMR) approaches: (i) an ordinary least square regression (OLS) model in which the contributions of all variables to TWSA variability are deemed equal at all locations; and (ii) a geographically weighted regression (GWR) which assigns a weight to each variable at different locations based on clustering occurrences. The models' efficacy was investigated using standard goodness of fit diagnostics. The OLS explains that the basin at large TWSA spatial variability significantly attributed to five variables, i.e., precipitation, runoff, surface water storage, soil moisture storage, and population density, (p < 0.0001). The OLS model, however, produced an R2 value of 0.14 with skewed standardized residuals. In contrast, the GWR model retained varying explanatory variables by different climate zone. For instance, the results showed that all nine variables contribute significantly, with varying ranking, to the trend in TWSA in the tropical zone. The evapotranspiration (ET) and population density are the only significant variables in the semiarid zone; population density contributes significantly to TWSA variability in all zones. The GWR model yielded R2 values with a median of 0.71 and normally distributed standard residuals. To evaluate the robustness of the GWR approach, the basin-wide TWSA pattern was simulated using the GWR model outputs. Herein, the GWR highlights the importance of the spatial locations to attribute the spatial variability in GRACE TSWA long-term trends. This spatial information, therefore, is critical for developing robust statistical models for reconstructing time series of proxy GRACE anomalies that predate the launch of the GRACE and for gap-filling between GRACE and GRACE Follow-On (GRACE-FO) mission. [ABSTRACT FROM AUTHOR]

• 8 regression and machine learning methods for long-term rainfall prediction are evaluated. • Atmospheric synoptic patterns are used to inform rainfall prediction methods. • Methods consider rainfall occurrence and intensities at different temporal aggregations. • Neural networks perform best to predict the average rainfall regime. • The results imply that these methods are useful for water resources studies. In this paper, we evaluate the performance of 8 statistical and machine learning methods, driven by atmospheric synoptic patterns, for long-term daily rainfall prediction in a semi-arid climate (Tenerife, Spain). Cross-validation is used to reconstruct 36 years of daily rainfall data at 17 gauges. Prediction is independent for each gauge. The reconstructed series are compared with the observed records in order to select the optimal hyperparameters within each family of models. The predictive performance of the models is evaluated using several metrics and statistics related with rainfall intensity and occurrence at daily, monthly and annual aggregation scales. Multivariate and univariate analysis of variance are used to evaluate the differences among models. The results of our work demonstrate that the performance of most machine learning models is very sensitive to the selected hyperparameters. Neural networks are found to perform best to predict rainfall occurrence and intensity. All methods underestimate the variance of the observed series at daily time scales. Generalized linear models using gamma-distributed errors perform best for predicting rainfall extremes, however, their performance limits its practical applications. Results improve significatively at larger temporal aggregations (monthly or annual) making statistical and machine learning methods more valuable for water resources studies. [ABSTRACT FROM AUTHOR]