Merging ground and satellite-based precipitation data sets for improved hydrological simulations in the Xijiang River basin of China Abstract Watershed management, disaster warning, and hydrological modeling require accurate spatiotemporal precipitation data sets. This paper presents a comprehensive assessment of a gauge-satellite-based precipitation product that merges the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) satellite precipitation product (SPP) and ground precipitation data at 134 rain gauges in the Xijiang River basin, South China. Two regression-based schemes, principal component regression (PCR) and multiple linear regression (MLR), were used to combine the gauge-based precipitation data and PERSIANN-CDR SPP and were compared at daily and annual scales. Furthermore, a hydrological model Variable Infiltration Capacity was used to calculate streamflow and to evaluate the impact of four different precipitation interpolation methods on the results of the hydrological model at the daily scale. The result shows that the PCR method performs better than MLR and can effectively eliminate the interpolation anomalies caused by terrain differences between observation points and surrounding areas. On the whole, the combined scheme consistently exhibits good performance and thus serves as a suitable tool for producing high-resolution gauge-and satellite-based precipitation datasets.

Dynamic spatio-temporal generation of large-scale synthetic gridded precipitation: with improved spatial coherence of extremes Abstract With the ongoing development of distributed hydrological models, flood risk analysis calls for synthetic, gridded precipitation data sets. The availability of large, coherent, gridded re-analysis data sets in combination with the increase in computational power, accommodates the development of new methodology to generate such synthetic data. We tracked moving precipitation fields and classified them using self-organising maps. For each class, we fitted a multivariate mixture model and generated a large set of synthetic, coherent descriptors, which we used to reconstruct moving synthetic precipitation fields. We introduced randomness in the original data set by replacing the observed precipitation fields in the original data set with the synthetic precipitation fields. The output is a continuous, gridded, hourly precipitation data set of a much longer duration, containing physically plausible and spatio-temporally coherent precipitation events. The proposed methodology implicitly provides an important improvement in the spatial coherence of precipitation extremes. We investigate the issue of unrealistic, sudden changes on the grid and demonstrate how a dynamic spatio-temporal generator can provide spatial smoothness in the probability distribution parameters and hence in the return level estimates.

Disease relative risk downscaling model to localize spatial epidemiologic indicators for mapping hand, foot, and mouth disease over China Abstract Given the limitations of current approaches for disease relative risk mapping, it is necessary to develop a comprehensive mapping method not only to simultaneously downscale various epidemiologic indicators, but also to be suitable for different disease outcomes. We proposed a three-step progressive statistical method, named disease relative risk downscaling (DRRD) model, to localize different spatial epidemiologic relative risk indicators for disease mapping, and applied it to the real world hand, foot, and mouth disease (HFMD) occurrence data over Mainland China. First, to generate a spatially complete crude risk map for disease binary variable, we employed ordinary and spatial logistic regression models under Bayesian hierarchical modeling framework to estimate county-level HFMD occurrence probabilities. Cross-validation showed that spatial logistic regression (average prediction accuracy: 80.68%) outperformed ordinary logistic regression (69.75%), indicating the effectiveness of incorporating spatial autocorrelation effect in modeling. Second, for the sake of designing a suitable spatial case–control study, we took spatial stratified heterogeneity impact expressed as Chinese seven geographical divisions into consideration. Third, for generating different types of disease relative risk maps, we proposed local-scale formulas for calculating three spatial epidemiologic indicators, i.e., spatial odds ratio, spatial risk ratio, and spatial attributable risk. The immediate achievement of this study is constructing a series of national disease relative risk maps for China’s county-level HFMD interventions. The new DRRD model provides a more convenient and easily extended way for assessing local-scale relative risks in spatial and environmental epidemiology, as well as broader risk assessment sciences.

A temporal perspective to dam management: influence of dam life and threshold fishery conditions on the energy-fish tradeoff Abstract While hydroelectric dams play a significant role in meeting the increasing energy demand worldwide, they pose a significant risk to riverine biodiversity and food security for millions of people that mainly depend upon floodplain fisheries. Dam structures could affect fish populations both directly and indirectly through loss of accessible spawning and rearing habitat, degradation of habitat quality (e.g., changes in temperature and discharge), and/or turbine injuries. However, our understandings of the impacts of dam life span and the initial fishery conditions on restoration time and hence the dynamic hydropower (energy)-fish (food) nexus remain limited. In this study, we explored the temporal energy-food tradeoffs associated with a hydroelectric dam located in the Penobscot River basin of the United States. We investigated the influence of dam life span, upstream passage rate, and downstream habitat area on the energy-food tradeoffs using a system dynamics model. Our results show that around 90% of fish biomass loss happen within 5 years of dam construction. Thereafter, fish decline slowly stabilizes and approaches the lowest value at around the 20th year after dam construction. Fish restoration period is highly sensitive even to a short period of blockage. The biomass of alewife spawners need 18 years to recover with only 1-year of blockage to the upstream critical habitats. Hydropower generation and loss of fish biomass present a two-segment linear relationship under changes in dam life span. When the dam life span is less than 5 years, generating 1 GWh energy cause around 0.04 million kg loss of fish biomass; otherwise, the loss of fish biomass is 0.02 million kg. The loss of fish biomass could be significantly decreased with minimal energy loss through increasing upstream passage rate and/or the size of downstream habitat area.

Moving correlation coefficient-based method for jump points detection in hydroclimate time series Abstract The jump points detection is critical to the understanding of hydrologic variability, especially in investigating the anthropogenic effects. Conventional methods are mainly statistical and cannot directly reflect the jump change degrees. This article proposes a moving correlation coefficient-based detection (MCCD) method for the detection of jump points (JPs) in hydroclimate data. The correlation coefficient (CC) between the potential jump component and the original data is calculated, and the CC series is realized by moving from the starting to the ending points of the original time series. Bigger CC value reflects higher jump degree; the position with the biggest absolute CC value is the JP that is the most expected. Its significance is evaluated by comparing its value with the CC threshold value at the relevant significance level. Monte-Carlo experimental results verify the MCCD method’s higher efficiency compared with four commonly used conventional methods. It is especially noteworthy that the results indicate its stable efficiency, even when encountering the influences of some unfavorable factors. By applying the MCCD method to the Lancang River Basin, the JP of runoff in 2004 is detected at the Yunjinghong station in the lower reach. It is mainly attributed to the construction and operation of some major water hydropower projects, while the stable variations of areal precipitation and actual evapotranspiration, as well as the stable land-cover conditions, contribute little to the abrupt decrease in runoff. The MCCD method can be an effective alternative for the detection of JPs in hydroclimate data.

Simulating isotropic vector-valued Gaussian random fields on the sphere through finite harmonics approximations Abstract The paper tackles the problem of simulating isotropic vector-valued Gaussian random fields defined over the unit two-dimensional sphere embedded in the three-dimensional Euclidean space. Such random fields are used in different disciplines of the natural sciences to model observations located on the Earth or in the sky, or direction-dependent subsoil properties measured along borehole core samples. The simulation is obtained through a weighted sum of finitely many spherical harmonics with random degrees and orders, which allows accurately reproducing the desired multivariate covariance structure, a construction that can actually be generalized to the simulation of isotropic vector random fields on the d-dimensional sphere. The proposed algorithm is illustrated with the simulation of bivariate random fields whose covariances belong to the \({{{\mathcal{F}}}}\) , spectral Matérn and negative binomial classes of covariance functions on the two-dimensional sphere.

Uncertainty assessment of nitrate reduction in heterogeneous aquifers under uncertain redox conditions Abstract Spatially distributed nitrate reactivity was estimated for the alluvial aquifer system within the Ruamāhanga catchment in New Zealand, according to the groundwater redox status, integrating machine learning and physically based modelling approaches. Redox classification was carried out for sampled groundwater, and linear discriminant analysis (LDA) was used to spatially discriminate between three redox classes across the catchment, using mappable physical parameters as predictive variables. The LDA model predictive uncertainty was used to quantify the spatially distributed geochemical uncertainty of the denitrification potential. Nitrate reduction was simulated as first order irreversible reaction using MT3DMS. Using a Monte Carlo approach, where random redox status realizations were aggregated to various model spatial discretization scales and each transport model realization was re-calibrated, we assessed the relationship between lower order nitrate reduction parameter statistics and aggregation scale. Our results indicate that both the average nitrate reduction parameter values and their standard deviations increase with increased spatial scale. This suggests that the parameters used to model denitrification as first-order reduction in geochemically heterogeneous environments, depend on the geochemical heterogeneity scale. This can have implications when knowledge gained at local scales needs to be applied for basin-scale assessment of effects. Similar were our findings with regards to the parameter-scale dependency on the model predictive uncertainty. Even though the average nitrate reduction across the model domain did not vary with redox scale, the standard deviation around the average almost doubled between the 250 and 5000 m scales.

Direct simulation of two-dimensional isotropic or anisotropic random field from sparse measurement using Bayesian compressive sampling Abstract Random field theory has been increasingly adopted to simulate spatially varying environmental properties and hydrogeological data in recent years. In a two-dimensional (2D) stochastic analysis, variation of the environmental properties or hydrogeological data along different directions can be similar (i.e., isotropic) or quite different (i.e., anisotropic). To model the spatially isotropic or anisotropic variability in a stochastic analysis, conventional random field generators generally require a vast amount of measurement data to identify the random field parameters (e.g., mean, variance, and correlation structure and correlation length in different directions). However, measurement data available in practice are usually sparse and limited. The random field parameters estimated from sparse measurements might be unreliable, and the subsequent random field modeling or stochastic analysis might be misleading. This underscores the significance and challenge of generating 2D isotropic or anisotropic random fields from sparse measurements. This paper develops a novel 2D random field generator, which does not require a parametric form of correlation function or estimation of correlation length and other random field parameters, and directly generates 2D isotropic or anisotropic random field samples from sparse measurements. The proposed generator is highly efficient because simulation of a 2D random field is achieved by generation of a short 1D random vector. The effectiveness and applicability of the proposed generator are illustrated using isotropic and anisotropic numerical examples.

Performance of multiple probability distributions in generating daily precipitation for the simulation of hydrological extremes Abstract Stochastic weather generators are statistical models widely used to produce climate time series with similar statistical properties to observed data. They are also used as downscaling tools to generate climate change scenarios for impact studies. Precipitation is one of the main variables simulated by weather generators and is also a key variable for impact studies, especially for hydrology. Precipitation is usually simulated by multiple precipitation models which have been proposed for simulating site-specific precipitation. However, these models’ performance in simulating watershed-averaged extreme precipitation, especially in representing hydrological extremes, has not been well-investigated. Accordingly, this study compares the performance of six probability distributions (exponential, gamma, skewed normal, mixed exponential, hybrid exponential/Pareto, and Weibull distributions) and a polynomial curve-fitting method in generating precipitation for simulating hydrological extremes over three basins using a set of extreme indices. The results show that except for the exponential distribution (EXP), all of the methods produce the distribution of observed precipitation at the daily, monthly and annual scales reasonably well for all three river basins. Although the three-parameter hybrid exponential/Pareto distribution (EXPP) overestimates precipitation extremes, other three-parameter models produce extremes accurately. The three-parameter mixed exponential (MEXP) distribution outperforms other models for simulating precipitation extremes. However, with respect to representing hydrological extremes, the MEXP distribution is not the best model. When simulating extreme streamflows with synthetic weather data, the EXP distribution shows the worst performance, while the curve fitting method (PN) performs the best. The inferiority of the EXPP distribution in generating extreme precipitation does not propagate to extreme flow simulations. Meanwhile, the performance of WEB is moderate in terms of representing hydrological extremes. Overall, finding the model that best reproduces precipitation for simulating hydrological extremes is not as clear-cut, since the performance of each model is extreme-indices dependent. Taking all of the indices into account, the MEXP and the PN appear to be superior in representing extreme precipitation and hydrological extremes.

Experimental evidence of the stochastic behavior of the conductivity in radial flow configurations Abstract We deal with the spatial distribution of the hydraulic conductivity K within heterogeneous porous formations where a radial flow (typical of pumping and slug tests) is taking place. In particular, the study provides a wide data-set which is instrumental to corroborate theoretical findings about the stochastic behavior of K in the above flow configuration. Here, K-data pertain to a series of slug tests conducted within a large caisson which was densely instrumented in order to capture the transitional behavior of the conductivity from the near field (close to the pumping well) to the far field (away from the pumping well). For the experiments at stake, it is shown that the apparent conductivity  \(K_{\mathrm{app}}\) is a very robust property. In fact, with the exception of a very tiny annulus surrounding the pumping well, \(K_{\mathrm{app}}\) can be used to solve flow (and transport) problems in close analogy to the effective theory approach adopted for a groundwater-type flow. It is hoped that the data-set exploited in the present study will be useful for other researchers who are engaged with similar studies.