Phase equilibria modelling and LASS monazite petrochronology: P–T–t constraints on the evolution of the Priest River core complex,northern Idaho

Phase equilibria modelling, laser‐ablation split‐stream (LASS)‐ICP‐MS petrochronology and garnet trace‐element geochemistry are integrated to constrain the P–T–t history of the footwall of the Priest River metamorphic core complex, northern Idaho. Metapelitic, migmatitic gneisses of the Hauser Lake Gneiss contain the peak assemblage garnet + sillimanite + biotite ± muscovite + plagioclase + K‐feldspar ± rutile ± ilmenite + quartz. Interpreted P–T paths predict maximum pressures and peak metamorphic temperatures of ~9.6–10.3 kbar and ~785–790 °C. Monazite and xenotime ²⁰⁸Pb/²³²Th dates from porphyroblast inclusions indicate that metamorphism occurred at c. 74–54 Ma. Dates from HREE‐depleted monazite formed during prograde growth constrain peak metamorphism at c. 64 Ma near the centre of the complex, while dates from HREE‐enriched monazite constrain the timing of garnet breakdown during near‐isothermal decompression at c. 60–57 Ma. Near‐isothermal decompression to ~5.0–4.4 kbar was followed by cooling and further decompression. The youngest, HREE‐enriched monazite records leucosome crystallization at mid‐crustal levels c. 54–44 Ma. The northernmost sample records regional metamorphism during the emplacement of the Selkirk igneous complex (c. 94–81 Ma), Cretaceous–Tertiary metamorphism and limited Eocene exhumation. Similarities between the Priest River complex and other complexes of the northern North American Cordillera suggest shared regional metamorphic and exhumation histories; however, in contrast to complexes to the north, the Priest River contains less partial melt and no evidence for diapiric exhumation. Improved constraints on metamorphism, deformation, anatexis and exhumation provide greater insight into the initiation and evolution of metamorphic core complexes in the northern Cordillera, and in similar tectonic settings elsewhere.     

Frequency analysis of nonstationary annual maximum flood series using the time‐varying two‐component mixture distributions

The most popular practice for analysing nonstationarity of flood series is to use a fixed single‐type probability distribution incorporated with the time‐varying moments. However, the type of probability distribution could be both complex because of distinct flood populations and time‐varying under changing environments. To allow the investigation of this complex nature, the time‐varying two‐component mixture distributions (TTMD) method is proposed in this study by considering the time variations of not only the moments of its component distributions but also the weighting coefficients. Having identified the existence of mixed flood populations based on circular statistics, the proposed TTMD was applied to model the annual maximum flood series of two stations in the Weihe River basin, with the model parameters calibrated by the meta‐heuristic maximum likelihood method. The performance of TTMD was evaluated by different diagnostic plots and indexes and compared with stationary single‐type distributions, stationary mixture distributions and time‐varying single‐type distributions. The results highlighted the advantages of TTMD with physically‐based covariates for both stations. Besides, the optimal TTMD models were considered to be capable of settling the issue of nonstationarity and capturing the mixed flood populations satisfactorily. Copyright © 2016 John Wiley & Sons, Ltd.     

Generation of continuous surface soil moisture dataset using combined optical and thermal infrared images

Surface soil moisture (SSM) is a critical variable for understanding water and energy flux between the atmosphere and the Earth's surface. An easy to apply algorithm for deriving SSM time series that primarily uses temporal parameters derived from simulated and in situ datasets has recently been reported. This algorithm must be assessed for different biophysical and atmospheric conditions by using actual geostationary satellite images. In this study, two currently available coarse‐scale SSM datasets (microwave and reanalysis product) and aggregated in situ SSM measurements were implemented to calibrate the time‐invariable coefficients of the SSM retrieval algorithm for conditions in which conventional observations are rare. These coefficients were subsequently used to obtain SSM time series directly from Meteosat Second Generation (MSG) images over the study area of a well‐organized soil moisture network named REMEDHUS in Spain. The results show a high degree of consistency between the estimated and actual SSM time series values when using the three SSM dataset‐calibrated time‐invariable coefficients to retrieve SSM, with coefficients of determination (R²) varying from 0.304 to 0.534 and root mean square errors ranging from 0.020 m³/m³ to 0.029 m³/m³. Further evaluation with different land use types results in acceptable debiased root mean square errors between 0.021 m³/m³ and 0.048 m³/m³ when comparing the estimated MSG pixel‐scale SSM with in situ measurements. These results indicate that the investigated method is practical for deriving time‐invariable coefficients when using publicly accessed coarse‐scale SSM datasets, which is beneficial for generating continuous SSM dataset at the MSG pixel scale.     

Climate change and future flows of Rocky Mountain rivers: converging forecasts from empirical trend projection and down‐scaled global circulation modelling

To predict future river flows, empirical trend projection (ETP) analyses and extends historic trends, while hydroclimatic modelling (HCM) incorporates regional downscaling from global circulation model (GCM) outputs. We applied both approaches to the extensively allocated Oldman River Basin that drains the North American Rocky Mountains and provides an international focus for water sharing. For ETP, we analysed monthly discharges from 1912 to 2008 with non‐parametric regression, and extrapolated changes to 2055. For modelling, we refined the physical models MTCLIM and SNOPAC to provide water inputs into RIVRQ (river discharge), a model that assesses the streamflow regime as involving dynamic peaks superimposed on stable baseflow. After parameterization with 1960–1989 data, we assessed climate forecasts from six GCMs: CGCM1‐A, HadCM3, NCAR‐CCM3, ECHAM4 and 5 and GCM2. Modelling reasonably reconstructed monthly hydrographs (R² about 0·7), and averaging over three decades closely reconstructed the monthly pattern (R² = 0·94). When applied to the GCM forecasts, the model predicted that summer flows would decline considerably, while winter and early spring flows would increase, producing a slight decline in the annual discharge (?3%, 2005–2055). The ETP predicted similarly decreased summer flows but slight change in winter flows and greater annual flow reduction (?9%). The partial convergence of the seasonal flow projections increases confidence in a composite analysis and we thus predict further declines in summer (about ? 15%) and annual flows (about ? 5%). This composite projection indicates a more modest change than had been anticipated based on earlier GCM analyses or trend projections that considered only three or four decades. For other river basins, we recommend the utilization of ETP based on the longest available streamflow records, and HCM with multiple GCMs. The degree of correspondence from these two independent approaches would provide a basis for assessing the confidence in projections for future river flows and surface water supplies. Copyright © 2010 John Wiley & Sons, Ltd.     

The data‐driven approach as an operational real‐time flood forecasting model

Accurate water level forecasts are essential for flood warning. This study adopts a data‐driven approach based on the adaptive network–based fuzzy inference system (ANFIS) to forecast the daily water levels of the Lower Mekong River at Pakse, Lao People's Democratic Republic. ANFIS is a hybrid system combining fuzzy inference system and artificial neural networks. Five ANFIS models were developed to provide water level forecasts from 1 to 5 days ahead, respectively. The results show that although ANFIS forecasts of water levels up to three lead days satisfied the benchmark, four‐ and five‐lead‐day forecasts were only slightly better in performance compared with the currently adopted operational model. This limitation is imposed by the auto‐ and cross‐correlations of the water level time series. Output updating procedures based on the autoregressive (AR) and recursive AR (RAR) models were used to enhance ANFIS model outputs. The RAR model performed better than the AR model. In addition, a partial recursive procedure that reduced the number of recursive steps when applying the AR or the RAR model for multi‐step‐ahead error prediction was superior to the fully recursive procedure. The RAR‐based partial recursive updating procedure significantly improved three‐, four‐ and five‐lead‐day forecasts. Our study further shows that for long lead times, ANFIS model errors are dominated by lag time errors. Although the ANFIS model with the RAR‐based partial recursive updating procedure provided the best results, this method was able to reduce the lag time errors significantly for the falling limbs only. Improvements for the rising limbs were modest. Copyright © 2011 John Wiley & Sons, Ltd.     

Changes in potential evapotranspiration and surface runoff in 1981–2010 and the driving factors in Upper Heihe River Basin in Northwest China

Changes in potential evapotranspiration and surface runoff can have profound implications for hydrological processes in arid and semiarid regions. In this study, we investigated the response of hydrological processes to climate change in Upper Heihe River Basin in Northwest China for the period from 1981 to 2010. We used agronomic, climatic and hydrological data to drive the Soil and Water Assessment Tool model for changes in potential evapotranspiration (ET₀) and surface runoff and the driving factors in the study area. The results showed that increasing autumn temperature increased snow melt, resulting in increased surface runoff, especially in September and October. The spatial distribution of annual runoff was different from that of seasonal runoff, with the highest runoff in Yeniugou River, followed by Babaohe River and then the tributaries in the northern of the basin. There was no evaporation paradox at annual and seasonal time scales, and annual ET₀ was driven mainly by wind speed. ET₀ was driven by relative humidity in spring, sunshine hour duration in autumn and both sunshine hour duration and relative humility in summer. Surface runoff was controlled by temperature in spring and winter and by precipitation in summer (flood season). Although surface runoff increased in autumn with increasing temperature, it depended on rainfall in September and on temperature in October and November. Copyright © 2016 John Wiley & Sons, Ltd.     

Hydrology and geomorphology of the Upper White Nile lakes and their relevance for water resources management in the Nile basin

Remote sensing data and digital elevation models were utilized to extract the catchment hydrological parameters and to delineate storage areas for the Ugandan Equatorial Lakes region. Available rainfall/discharge data are integrated with these morphometric data to construct a hydrological model that simulates the water balance of the different interconnected basins and enables the impact of potential management options to be examined. The total annual discharges of the basins are generally very low (less than 7% of the total annual rainfall). The basin of the shallow (5 m deep) Lake Kioga makes only a minor hydrological contribution compared with other Equatorial Lakes, because most of the overflow from Lake Victoria basin into Lake Kioga is lost by evaporation and evapotranspiration. The discharge from Lake Kioga could be significantly increased by draining the swamps through dredging and deepening certain channel reaches. Development of hydropower dams on the Equatorial Lakes will have an adverse impact on the annual water discharge downstream, including the occasional reduction of flow required for filling up to designed storage capacities and permanently increasing the surface areas of water that is exposed to evaporation. On the basis of modelling studies, alternative sites are proposed for hydropower development and water storage schemes. Copyright © 2012 John Wiley & Sons, Ltd.     

On the relationship between historical land‐use change and water availability: the case of the lower Tarim River region in northwestern China

Natural ecosystems in the region of the lower Tarim River in northwestern China strongly deteriorated since the 1950s due to an expanding desertification. As a result, the downstream Tarim River reaches became permanently dry land. This historical evolution in land‐use change is typically the result of the anthropogenic impact on natural ecosystems. On the basis of a spatially distributed hydrological catchment model bidirectionally linked with a fully hydrodynamic MIKE11 river model, land‐use changes characterized by historical changes in leaf area index (LAI) of vegetation, as well as the evolution of irrigated surface areas, can be causally related to changes in water resources (groundwater storage and surface water resources). An increased surface area of irrigated (agricultural) land, together with a majority of inefficient irrigation methods, did lead to a strong increase of water resources consumption of the farmlands located in the upper Tarim River area. Evidently, this evolution influenced available water resources downstream in the Tarim basin. As a result, farmland has been gradually relocated to the upstream regions. This has led to reduced flows from the upper Tarim stream, which subsequently accelerated the dropping of the groundwater level downstream in the basin. This study moreover demonstrates that land surface biomass changes (cumulative LAI) along the lower Tarim River are strongly related to the changes in groundwater storage. Copyright © 2012 John Wiley & Sons, Ltd.     

An efficient quasi-Newton approximation-based SORM to estimate the reliability of geotechnical problems

The first order reliability method (FORM) is efficient, but it has limited accuracy; the second order reliability method (SORM) provides greater accuracy, but with additional computational effort. In this study, a new method which integrates two quasi-Newton approximation algorithms is proposed to efficiently estimate the second order reliability of geotechnical problems with reasonable accuracy. In particular, the Hasofer–Lind–Rackwitz–Fiessler–Broyden–Fletcher–Goldfarb–Shanno (HLRF–BFGS) algorithm is applied to identify the design point on the limit state function (LSF), and consequently to compute the first order reliability index; whereas the Symmetric Rank-one (SR1) algorithm is nested within the HLRF–BFGS algorithm to compute good approximations, yet with a reduced computational effort, of the Hessian matrix required to compute second order reliabilities. Three typical geotechnical problems are employed to demonstrate the ability of the suggested procedure, and advantages of the proposed approach with respect to conventional alternatives are discussed. Results show that the proposed method is able to achieve the accuracy of conventional SORM, but with a reduced computational cost that is equal to the computational cost of HLRF–BFGS-based FORM.     

The Upstream “Strong Signals” of the Water Vapor Transport over the Tibetan Plateau during a Heavy Rainfall Event in the Yangtze River Basin

A heavy rainfall event that occurred over the middle and lower reaches of the Yangtze River Basin(YRB) during July11–13 2000 is explored in this study. The potential/stream function is used to analyze the upstream "strong signals" of the water vapor transport in the Tibetan Plateau(TP). The studied time period covers from 2000 LST 5 July to 2000 LST 15 July(temporal resolution: 6 hours). By analyzing the three-dimensional structure of the water vapor flux, vorticity and divergence prior to and during the heavy rainfall event, the upstream "strong signals" related to this heavy rainfall event are revealed. A strong correlation exists between the heavy rainfall event in the YRB and the convective clouds over the TP. The "convergence zone" of the water vapor transport is also identified, based on correlation analysis of the water vapor flux two days and one day prior to, and on the day of, the heavy rainfall. And this "convergence zone" coincides with the migration of the maximum rainfall over the YRB. This specific coupled structure actually plays a key role in generating heavy rainfall over the YRB. The eastward movement of the coupled system with a divergence/convergence center of the potential function at the upper/lower level resembles the spatiotemporal evolution of the heavy rainfall event over the YRB. These upstream "strong signals" are clearly traced in this study through analyzing the three-dimensional structure of the potential/stream function of upstream water vapor transport.