NRG Oncology RTOG 0937 is a randomized phase II trial evaluating 1-year overall survival (OS) with prophylactic cranial irradiation (PCI) or PCI plus consolidative radiation therapy (PCI+cRT) to ...intrathoracic disease and extracranial metastases for extensive-disease SCLC.
Patients with one to four extracranial metastases were eligible after a complete response or partial response to chemotherapy. Randomization was to PCI or PCI+cRT to the thorax and metastases. Original stratification included partial response versus complete response after chemotherapy and one versus two to four metastases; age younger than 65 years versus 65 years or older was added after an observed imbalance. PCI consisted of 25 Gy in 10 fractions. cRT consisted of 45 Gy in 15 fractions. To detect an improvement in OS from 30% to 45% with a 34% hazard reduction (hazard ratio = 0.66) under a 0.1 type 1 error (one sided) and 80% power, 154 patients were required.
A total of 97 patients were randomized between March 2010 and February 2015. Eleven patients were ineligible (nine in the PCI group and two in the PCI+cRT group), leaving 42 randomized to receive PCI and 44 to receive PCI+cRT. At planned interim analysis, the study crossed the futility boundary for OS and was closed before meeting the accrual target. Median follow-up was 9 months. The 1-year OS was not different between the groups: 60.1% (95% confidence interval CI: 41.2–74.7) for PCI and 50.8% (95% CI: 34.0–65.3) for PCI+cRT (p = 0.21). The 3- and 12-month rates of progression were 53.3% and 79.6% for PCI and 14.5% and 75% for PCI+cRT, respectively. Time to progression favored PCI+cRT (hazard ratio = 0.53, 95% CI: 0.32–0.87, p = 0.01). One patient in each arm had grade 4 therapy-related toxicity and one had grade 5 therapy-related pneumonitis with PCI+cRT.
OS exceeded predictions for both arms. cRT delayed progression but did not improve 1-year OS.
Rivers and river habitats around the world are under sustained pressure
from human activities and the changing global environment. Our ability
to quantify and manage the river states in a timely ...manner is critical
for protecting the public safety and natural resources. In recent
years, vector-based river network models have enabled modeling of
large river basins at increasingly fine resolutions, but are computationally demanding. This work presents a multistage, physics-guided, graph neural network (GNN) approach for basin-scale river network learning
and streamflow forecasting. During training, we train a GNN model
to approximate outputs of a high-resolution vector-based river network
model; we then fine-tune the pretrained GNN model with streamflow
observations. We further apply a graph-based, data-fusion step to
correct prediction biases. The GNN-based framework is first demonstrated
over a snow-dominated watershed in the western United States. A series of experiments are performed to test different training and imputation strategies. Results show that the trained GNN model can effectively serve as a surrogate of the process-based model with high accuracy, with median
Kling–Gupta efficiency (KGE) greater than 0.97. Application of the
graph-based data fusion further reduces mismatch between the GNN model
and observations, with as much as 50 % KGE improvement over
some cross-validation gages. To improve scalability, a graph-coarsening
procedure is introduced and is demonstrated over a much larger basin.
Results show that graph coarsening achieves comparable prediction skills
at only a fraction of training cost, thus providing important insights
into the degree of physical realism needed for developing large-scale
GNN-based river network models.
This work presents an optimization framework for evaluating different wastewater treatment/disposal options for water management during hydraulic fracturing (HF) operations. This framework takes into ...account both cost-effectiveness and system uncertainty. HF has enabled rapid development of shale gas resources. However, wastewater management has been one of the most contentious and widely publicized issues in shale gas production. The flowback and produced water (known as FP water) generated by HF may pose a serious risk to the surrounding environment and public health because this wastewater usually contains many toxic chemicals and high levels of total dissolved solids (TDS). Various treatment/disposal options are available for FP water management, such as underground injection, hazardous wastewater treatment plants, and/or reuse. In order to cost-effectively plan FP water management practices, including allocating FP water to different options and planning treatment facility capacity expansion, an optimization model named UO-FPW is developed in this study. The UO-FPW model can handle the uncertain information expressed in the form of fuzzy membership functions and probability density functions in the modeling parameters. The UO-FPW model is applied to a representative hypothetical case study to demonstrate its applicability in practice. The modeling results reflect the tradeoffs between economic objective (i.e., minimizing total-system cost) and system reliability (i.e., risk of violating fuzzy and/or random constraints, and meeting FP water treatment/disposal requirements). Using the developed optimization model, decision makers can make and adjust appropriate FP water management strategies through refining the values of feasibility degrees for fuzzy constraints and the probability levels for random constraints if the solutions are not satisfactory. The optimization model can be easily integrated into decision support systems for shale oil/gas lifecycle management.
•We developed a wastewater management model for shale gas accounting for uncertainty.•Alternative wastewater treatment/disposal options are considered.•Optimal shale gas wastewater management strategies are generated.•Stochastic and non-stochastic uncertainties are addressed.
High-quality and high-resolution precipitation products are critically important to many hydrological applications. Advances in satellite remote sensing instruments and data retrieval algorithms ...continue to improve the quality of the operational precipitation products. However, most satellite products existing today are still too coarse to be ingested for local water management and planning purposes. Recent advances in deep learning algorithms enable the fusion of multi-source, high-dimensional data for statistical learning. In this study, we investigated the efficacy of an attention-based, deep convolutional neural network (AU-Net) for learning spatial and temporal mappings from coarse-resolution to fine-resolution precipitation products. The skills of AU-Net models, developed using combinations of static and dynamic predictors, were evaluated over a 3 × 3° study area in Central Texas, U.S., a region known for its complex precipitation patterns and low predictability. Three coarse-resolution satellite/reanalysis precipitation products, ERA5-Land (0.1°), TRMM (0.25°), and IMERG (0.1°), are used as part of the inputs, while the predictand is the 1-km PRISM data. Auxiliary predictors include elevation, vegetation index, and air temperature. The study period includes 18 years of data (2001–2018) at the monthly scale for training, validation, and testing. Results show that the trained AU-Net models achieve different degrees of success in downscaling the baseline coarse-resolution products, depending on the total precipitation, the accuracy of large-scale patterns captured by the baseline products, and the amount of information transferable from predictors. Higher precipitation rate tends to affect AU-Net model performance negatively. Use of the attention mechanism in the AU-Net models allows for infilling of multiscale features and generation of sharper images. Correction using gauge data, if there is any, can further improve the results significantly.
•Spatiotemporal patterns of extreme precipitation (P) and soil moisture (SM) are studied.•An event-based complex network theoretical framework is adopted.•High-resolution observed P data and ...simulated SM data are used.•Results reveal high spatiotemporal variability in event concurrence and coupling.•Insights gained may help to guide future flood mitigation planning efforts.
Understanding of the spatial and temporal dynamics of extreme precipitation not only improves prediction skills, but also helps to prioritize hazard mitigation efforts. This study seeks to enhance the understanding of spatiotemporal covariation patterns embedded in precipitation (P) and soil moisture (SM) by using an event-based, complex-network-theoretic approach. Events concurrences are quantified using a nonparametric event synchronization measure, and spatial patterns of hydroclimate variables are analyzed by using several network measures and a community detection algorithm. SM–P coupling is examined using a directional event coincidence analysis measure that takes the order of event occurrences into account. The complex network approach is demonstrated for Texas, US, a region possessing a rich set of hydroclimate features and is frequented by catastrophic flooding. Gridded daily observed P data and simulated SM data are used to create complex networks of P and SM extremes. The uncovered high degree centrality regions and community structures are qualitatively in agreement with the overall existing knowledge of hydroclimate extremes in the study region. Our analyses provide new visual insights on the propagation, connectivity, and synchronicity of P extremes, as well as the SM–P coupling, in this flood-prone region, and can be readily used as a basis for event-driven predictive analytics for other regions.
•Propose a drought prediction method based on the conditional distribution.•Predict hydrological drought incorporating meteorological drought conditions.•Assess the prediction performance in Texas, ...USA based on climate division data.
Prediction of drought plays an important role in drought preparedness and mitigation, especially because of large impacts of drought and increasing demand for water resources. An important aspect for improving drought prediction skills is the identification of drought predictability sources. In general, a drought originates from precipitation deficit and thus the antecedent meteorological drought may provide predictive information for other types of drought. In this study, a hydrological drought (represented by Standardized Runoff Index (SRI)) prediction method is proposed based on the meta-Gaussian model taking into account the persistence and its prior meteorological drought condition (represented by Standardized Precipitation Index (SPI)). Considering the inherent nature of standardized drought indices, the meta-Gaussian model arises as a suitable model for constructing the joint distribution of multiple drought indices. Accordingly, the conditional distribution of hydrological drought can be derived analytically, which enables the probabilistic prediction of hydrological drought in the target period and uncertainty quantifications. Based on monthly precipitation and surface runoff of climate divisions of Texas, U.S., 1-month and 2-month lead predictions of hydrological drought are illustrated and compared to the prediction from Ensemble Streamflow Prediction (ESP). Results, based on 10 climate divisions in Texas, show that the proposed meta-Gaussian model provides useful drought prediction information with performance depending on regions and seasons.
ABSTRACT
Southwest (SW) China is a drought prone region. Knowledge of the risks and coupling characteristics of drought duration and severity is essential for hazards mitigation and water management. ...Based on probability distributions, copulas, Mann–Kendall test, overlapping moving windows approach and the self‐calibrated Palmer Drought Severity Index (PDSI) at 69 stations (1959–2013), this study focused on the trends and spatial variability (SV) of (joint) probability drought duration and severity in SW China, and further investigated the potential contributors by analysing the behaviours of precipitation (P), temperature (T), relative humidity (RH), wind speed (WS), atmospheric moisture flux (MF) and divergence (MFD). In particular, the SV was represented by the standard deviation, the drought duration and severity were characterized by the number of months with PDSI < −0.5 in a year drought months (DM) and the absolute mean PDSI in drought months (DS), respectively. Results showed that, the DM, DS, DM5, DS5 (5‐year return levels for DM and DS) increased by 0.058 month year−2, 0.012 month−1 year−1, 0.039 month year−2, and 0.009 month−1 year−1, respectively, and the joint return period T(DM > 4, DS > 2) decreased by −0.20 year year−1. Furthermore, due to the decreasing SV in P, T, RH, WS, MF, and MFD, SV of DM5, DS5 and T(DM > 4, DS > 2) significantly decreased in the past decades, by −0.003 month year−2, −0.006 month−1 year−1 and −0.03 year year−1, respectively. These results indicated that drought risks and severity increased significantly in the past decades over SW China. Most importantly, the decreasing SV implied that drought risks and severity might have become more homogeneous in this region. In other words, droughts became more widely spread throughout SW China in the past decades. Therefore, water agencies and governments should be more proactive in seeking measures to improve water resources management and drought mitigation in this region.
In southwest (SW) China, the drought months (DM) number of months with Palmer Drought Severity Index (PDSI) < −0.5 in a year, drought severity (DS) (absolute mean PDSI in DM), and their 5‐year return levels (DM5 and DS5) increased significantly (α < 0.05), and the joint return period T(DM > 4, DS > 2) decreased during 1959–2013. Due to the decreasing spatial variability (SV) in the key climatic variables, SV of DM5, DS5, and T(DM > 4, DS > 2) showed significant downward trends in the past decades.
We study the convection and mixing of CO2 in a brine aquifer, where the spread of dissolved CO2 is enhanced because of geochemical reactions with the host formations (calcite and dolomite), in ...addition to the extensively studied, buoyancy-driven mixing. The nonlinear convection is investigated under the assumptions of instantaneous chemical equilibrium, and that the dissipation of carbonate rocks solely depends on flow and transport and chemical speciation depends only on the equilibrium thermodynamics of the chemical system. The extent of convection is quantified in term of the CO2 saturation volume of the storage formation. Our results suggest that the density increase of resident species causes significant enhancement in CO2 dissolution, although no significant porosity and permeability alterations are observed. Early saturation of the reservoir can have negative impact on CO2 sequestration.
► Regional groundwater models are uncertain. ► GRACE data can be used for constraining regional groundwater models. ► A multiobjective optimization approach was taken. ► The methodology was ...demonstrated for a regional aquifer in Texas. ► GRACE is promising for continuously enhancing groundwater models.
Regional groundwater models are increasingly used for short- and long-term water resources planning, in anticipation of greater climate variability and population growth. However, many of these models are subject to structural and parametric uncertainties because of the lack of field measurements. In recent years, the Gravity Recovery and Climate Experiment (GRACE) satellite mission has shown great potential for tracking total water storage changes over large regions. The pattern of groundwater storage changes inferred from GRACE may be incorporated as an additional regularization mechanism for calibrating regional groundwater models. Motivated by the demonstrated success of GRACE for monitoring groundwater storage changes, this study explores the combined use of in situ water level measurements and GRACE-derived groundwater storage changes for calibrating regional groundwater models. The resulting optimization problem is solved using an evolutionary optimization algorithm. We demonstrate the proposed calibration strategy for the hydraulically connected Edwards–Trinity Plateau and Pecos Valley aquifers (total area 115,000km2) in west Texas. Monthly GRACE data from 2002 to 2007 were used to recalibrate a regional groundwater model developed for the area. Our results indicate that (i) calibration using in situ data alone may yield multiple plausible solutions, a phenomenon well known to hydrologists; and (ii) GRACE data helped further constrain model parameters over the study period and, thus, may be continuously assimilated, among other sources of data, for enhancing existing regional groundwater models.
Assessing spatiotemporal water storage variability in the Great Lakes Watershed (GLW) is critical given its transboundary status impacting both Canada and the United States. Here, we apply a novel ...inversion strategy to global positioning system (GPS) vertical movements to achieve high spatial resolution total water storage (TWS) variations in GLW through improved processing. The steps are composed of removing load changes driven by the lake water fluctuation by forward modeling, isolating the Great Lakes grids to solve the ill‐conditioned problem in inversion, and inverting the GPS residual series to estimate TWS variations on land (TWSGPS). The results show that the regional dense continuous GPS observation network can successfully resolve TWS on land at monthly timescales with 30–45 km spatial resolution. We also could effectively capture fine‐scale TWS features than GRACE/GFO mascon products. GRACE/GFO satellites largely underestimate seasonal and long‐term TWS spatial fluctuations, but their temporal patterns coincide with those from GPS. The average annual amplitude of TWSGPS on land reaches 82.0 mm, greatly exceeding estimates from GRACE/GFO (∼48.0 mm) and composite hydrological model outputs (∼62.0 mm). The seasonal groundwater fluctuations inferred from GPS have peak‐to‐peak amplitudes of ∼40 km3 with the maximum around September. This coincides with that from GRACE/GFO. However, the magnitudes and phases of groundwater storage from GPS vary markedly among the subbasins in GLW, and the different snow and soil moisture amounts measured in each subbasin cause discrepancies among these GPS estimates. This study shows the value of GPS data in spatially downscaling GRACE/GFO data and providing high‐resolution output at spatiotemporal scales with low latency.
Key Points
A new inversion strategy for Total Water Storage (TWS) was applied to global positioning system (GPS) data by first removing lake water driven load through forward modeling
GRACE/GFO TWS underestimates spatial patterns of seasonal and long‐term TWS fluctuations but coincides with temporal TWS patterns from GPS data
GPS provides high spatiotemporal resolution in TWS relative to GRACE/GFO and improved understanding water storage dynamics in Great Lakes Watershed
PDF
