Abstract. A regionalized cluster-based water isotope prediction (RCWIP) approach, based on the Global Network of Isotopes in Precipitation (GNIP), was demonstrated for the purposes of predicting point- and large-scale spatio-temporal patterns of the stable isotope composition (δ2H, δ18O) of precipitation around the world. Unlike earlier global domain and fixed regressor models, RCWIP predefined 36 climatic cluster domains and tested all model combinations from an array of climatic and spatial regressor variables to obtain the best predictive approach to each cluster domain, as indicated by root-mean-squared error (RMSE) and variogram analysis. Fuzzy membership fractions were thereafter used as the weights to seamlessly amalgamate results of the optimized climatic zone prediction models into a single predictive mapping product, such as global or regional amount-weighted mean annual, mean monthly, or growing-season δ18O/δ2H in precipitation. Comparative tests revealed the RCWIP approach outperformed classical global-fixed regression–interpolation-based models more than 67% of the time, and clearly improved upon predictive accuracy and precision. All RCWIP isotope mapping products are available as gridded GeoTIFF files from the IAEA website (www.iaea.org/water) and are for use in hydrology, climatology, food authenticity, ecology, and forensics.

Abstract. We introduce a new online global database of riverine water stable isotopes (Global Network of Isotopes in Rivers, GNIR) and evaluate its longer-term data holdings. Overall, 218 GNIR river stations were clustered into three different groups based on the seasonal variation in their isotopic composition, which was closely coupled to precipitation and snowmelt water runoff regimes. Sinusoidal fit functions revealed phases within each grouping and deviations from the sinusoidal functions revealed important river alterations or hydrological processes in these watersheds. The seasonal isotopic amplitude of δ 18 O in rivers averaged 2.5 ‰, and did not increase as a function of latitude, like it does for global precipitation. Low seasonal isotopic amplitudes in rivers suggest the prevalence of mixing and storage such as occurs via lakes, reservoirs, and groundwater. The application of a catchment-constrained regionalized cluster-based water isotope prediction model (CC-RCWIP) allowed for direct comparison between the expected isotopic compositions for the upstream catchment precipitation with the measured isotopic composition of river discharge at observation stations. The catchment-constrained model revealed a strong global isotopic correlation between average rainfall and river discharge (R 2 = 0.88) and the study demonstrated that the seasonal isotopic composition and variation of river water can be predicted. Deviations in data from model-predicted values suggest there are important natural or anthropogenic catchment processes like evaporation, damming, and water storage in the upstream catchment.

A Regionalized Climatic Water Isotope Prediction (RCWIP) approach, based on the Global Network for Isotopes in Precipitation (GNIP), was demonstrated for the purposes of predicting point- and large-scale spatiotemporal patterns of the stable isotope compositions of water (δ²H, δ¹⁸O) in precipitation around the world. Unlike earlier global domain and fixed regressor models, RCWIP pre-defined thirty-six climatic cluster domains, and tested all model combinations from an array of climatic and spatial regressor variables to obtain the best predictive approach to each cluster domain, as indicated by RMSE and variogram analysis. Fuzzy membership fractions were thereafter used as the weights to seamlessly amalgamate results of the optimized climatic zone prediction models into a single predictive mapping product, such as global or regional amount-weighted mean annual, mean monthly or growing-season δ¹⁸O/δ²H in precipitation. Comparative tests revealed the RCWIP approach outperformed classical global-fixed regression-interpolation based models more than 67% of the time, and significantly improved upon predictive accuracy and precision. All RCWIP isotope mapping products are available as gridded GeoTIFF files from the IAEA website (www.iaea.org/water) and are for use in hydrology, climatology, food authenticity, ecology, and forensics

Abstract. We introduce a new online global database of riverine water stable isotopes (Global Network of Isotopes in Rivers) and evaluate its longer-term data holdings. Overall, 218 GNIR river stations were clustered into 3 different groups based on the seasonal variation in their isotopic composition, which was closely coupled to precipitation and snow-melt water run-off regimes. Sinusoidal fit functions revealed periodic phases within each grouping and deviations from the sinusoidal functions revealed important river alterations or hydrological processes in these watersheds. The seasonal isotopic amplitude of δ18O in rivers averaged 2.5 ‰, and did not increase as a function of latitude, as it does for global precipitation. Low seasonal isotopic amplitudes in rivers suggest the prevalence of mixing and storage such as occurs via lakes, reservoirs, and groundwater. The application of a catchment-constrained regionalized cluster-based water isotope prediction model (CC-RCWIP) allowed direct comparison between the expected isotopic composition for the upstream catchment precipitation with the measured isotopic composition of river discharge at observation stations. The catchment-constrained model revealed a strong global isotopic correlation between average rainfall and river discharge (R2 = 0.88) and the study demonstrated that the seasonal isotopic composition and variation of river water can be predicted. Deviations in data from model predicted values suggest there are important natural or anthropogenic catchment processes, like evaporation, damming, and water storage in the upstream catchment.

The International Atomic Energy Agency (IAEA) Water Balance Model with Isotopes (IWBMIso) is a spatially distributed monthly water balance model that considers water fluxes and storages and their associated isotopic compositions. It is composed of a lake water balance model that is tightly coupled with a catchment water balance model. Measured isotope compositions of precipitation, rivers, lakes, and groundwater provide data that can be used to make an improved estimate of the magnitude of the fluxes among the model components. The model has been developed using the Object Modelling System (OMS). A variety of open source geographic information systems and web-based tools have been combined to provide user support for (1) basin delineation, characterization, and parameterization; (2) data pre-processing; (3) model calibration and application; and (4) visualization and analysis of model results. In regions where measured data are limited, the model can use freely available global data sets of climate, isotopic composition of precipitation, and soils and vegetation characteristics to create input data files and estimate spatially distributed model parameters. The OMS model engine and support functions, and the spatial and web-based tool set are integrated using the Colorado State University Environmental Risk Assessment and Management System (eRAMS) framework. The IWBMIso can be used to assess the spatial and temporal variability of annual and monthly water balance components for input to water planning and management.