Hydrogeologic properties of fault zones are critical to faulting processes; however, they are not well understood and difficult to measure in situ, particularly in low‐permeability fractured bedrock ...formations. Analysis of continuous water level response to Earth tides in monitoring wells provides a method to measure the in situ hydrogeologic properties. We utilize four monitoring wells within the San Andreas Fault zone near Logan Quarry to study the fault zone hydrogeologic architecture by measuring the water level tidal response. The specific storage and permeability inferred from the tidal response suggest that there is a difference in properties at different distances from the fault. The sites closer to the fault have higher specific storage and higher permeability than farther from the fault. This difference of properties might be related to the fault zone fracture distribution decreasing away from the fault. Although permeability channels near faults have been documented before, the difference in specific storage near the fault is a new observation. The inferred compliance contrast is consistent with prior estimates of elastic moduli in the near‐fault environment, but the direct measurements are new. The combination of measured permeability and storage yields a diffusivity of about 10−2 m2/s at all the sites both near and far from the fault as a result of the competing effects of permeability and specific storage. This uniform diffusivity structure suggests that the permeability contrast might not efficiently trap fluids during the interseismic period.
Key Points:
The sites near the fault have larger hydraulic storage and larger permeability
The observed fault zone has localized permeability and storage but uniform diffusivity
The diffusivity of the San Andreas Fault near Logan Quarry is about 10−2 m2/s
Groundwater depletion (GWD) compromises crop production in major global agricultural areas and has negative ecological consequences. To derive GWD at the grid cell, country, and global levels, we ...applied a new version of the global hydrological model WaterGAP that simulates not only net groundwater ions and groundwater recharge from soils but also groundwater recharge from surface water bodies in dry regions. A large number of independent estimates of GWD as well as total water storage (TWS) trends determined from GRACE satellite data by three analysis centers were compared to model results. GWD and TWS trends are simulated best assuming that farmers in GWD areas irrigate at 70% of optimal water requirement. India, United States, Iran, Saudi Arabia, and China had the highest GWD rates in the first decade of the 21st century. On the Arabian Peninsula, in Libya, Egypt, Mali, Mozambique, and Mongolia, at least 30% of the ed groundwater was taken from nonrenewable groundwater during this time period. The rate of global GWD has likely more than doubled since the period 1960–2000. Estimated GWD of 113 km3/yr during 2000–2009, corresponding to a sea level rise of 0.31 mm/yr, is much smaller than most previous estimates. About 15% of the globally ed groundwater was taken from nonrenewable groundwater during this period. To monitor recent temporal dynamics of GWD and related water ions, GRACE data are best evaluated with a hydrological model that, like WaterGAP, simulates the impact of ions on water storage, but the low spatial resolution of GRACE remains a challenge.
Key Points
Groundwater depletion is simulated by a global model
Seventy percent deficit irrigation is likely in groundwater depletion areas
About 15% of global groundwater ions are from nonrenewable sources
Diurnal fluctuations of hydrological variables (e.g., shallow groundwater level or streamflow rate) are comparatively rarely investigated in the hydrologic literature although these short-term ...fluctuations may incorporate useful information for the characterization of hydro-ecological systems. The fluctuations can be induced by several factors like (a) alternating processes of freezing and thawing; (b) early afternoon rainfall events in the tropics; (c) changes in streambed hydraulic conductivity triggered by temperature variations, and; (d) diurnal cycle of water uptake by the vegetation. In temperate climates, one of the most important diurnal fluctuation-inducing factors is the water consumption of vegetation, therefore a detailed overview is provided on the history of such research. Beside a systematic categorization of the relevant historical studies, models that calculate groundwater evapotranspiration from diurnal fluctuations of groundwater level and/or streamflow rate have been reviewed. Compared to traditional evapotranspiration estimation methods these approaches may excel in that they generally employ a small number of parameters and/or variables to measure, are typically simple to use, and yet can yield results even on a short time-scale (i.e., hours). While, e.g., temperature-based methods of evapotranspiration are simple too, they cannot be applied or become inaccurate over shorter time periods. Similarly, traditional approaches (such as eddy-correlation or Bowen-ratio based) are accurate for shorter time steps but they require a number of measurable atmospheric input variables.
► We analyzed the response of the Ebro basin to climatic conditions at different time scales. ► The response time may vary largely within the basin. ► The variability is explained by physiographic ...characteristics, snow cover and impoundment ratio.
In this study we analyzed the response of monthly runoff to precedent climatic conditions at temporal scales of 1–48months in 88 catchments of the Ebro basin (northeast Spain). The standardized precipitation evapotranspiration index (SPEI) was used to summarize the climatic conditions at different time scales, and was correlated with the standardized streamflow index (SSI) calculated at the mouth of each catchment. The Ebro basin encompasses a gradient from Atlantic to Mediterranean climates, and has remarkable complexity in topography, geology and land cover. The basin is highly regulated by dams, which were built to produce hydropower and supply water for agriculture. These characteristics explain why sub-basins of the Ebro River basin respond in differing ways to precedent climatic conditions. Three main sub-basin groups were distinguished on the basis of the correlation of their streamflow responses to different time scales of the SPEI: (1) sub-basins correlated with short SPEI time scales (2–4months), which generally corresponded to unregulated headwater areas; (2) sub-basins correlated with long SPEI time scales (10–20months), where groundwater reserves play a major hydrological role; and (3) sub-basins correlated with medium SPEI time scales (6–10months). The latter occur in the lower sectors of the Ebro basin and its tributaries, which receive river flows from the other two sub-basins, and where dam regulation has a significant influence on the hydrological characteristics. In addition to the three main sub-basin groups, other streamflow responses associated with seasonal factors were identified, particularly those related to snowpack and the various management strategies applied to reservoirs.
•Deep learning models predicted soil moisture well with limited SMAP samples.•Transfer learning improved predictions with additional samples from ERA5-land.•Transfer ConvLSTM performed the best with ...over 90% variation explained.•The predictive ability of different factors was widely investigated.•Transfer learning is advocated for datasets with limited samples like SMAP.
The skillful soil moisture (SM) for the Soil Moisture Active Passive (SMAP) L4 product can provide substantial value for many practical applications including ecosystem management and precision agriculture. Deep learning (DL) models provide powerful methods for hydrologic variables’ prediction such as SM. However, the sample size of daily SM in the SMAP product is quite small, which may lead to overfitting and further impact the accuracy of DL models. From this, we first tested whether excellent predictive performance can be achieved with limited SMAP samples by the Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Convolutional LSTM (ConvLSTM) models, which are frequent used for hydrologic prediction. Then we pre-trained the DL models in the source domain (ERA5-land) and fine-tuned them in the target domain (SMAP). The results show that the transfer ConvLSTM model had the highest R2 ranging from 0.909 to 0.916 and the lowest RMSE ranging from 0.0239 to 0.0247 for the lead time of 3, 5 and 7 days, and the regression lines between the predicted and the observed SM were closer to the ideal line (y = x) than all the other DL models. All the performances of transfer DL models were better than those of their corresponding DL models without transfer learning and some regions witnessed an increased explained variation over 20%. The predictive ability of different factors (i.e., lagged SM, soil temperature, season, and precipitation) has been widely discussed in this paper. According the results, we advocate for applying cross-source transfer learning with DL models for SM prediction in newly built datasets.
The Climate impact studies in hydrology often rely on climate change information at fine spatial resolution. However, general circulation models (GCMs), which are among the most advanced tools for ...estimating future climate change scenarios, operate on a coarse scale. Therefore the output from a GCM has to be downscaled to obtain the information relevant to hydrologic studies. In this paper, a support vector machine (SVM) approach is proposed for statistical downscaling of precipitation at monthly time scale. The effectiveness of this approach is illustrated through its application to meteorological sub-divisions (MSDs) in India. First, climate variables affecting spatio-temporal variation of precipitation at each MSD in India are identified. Following this, the data pertaining to the identified climate variables (predictors) at each MSD are classified using cluster analysis to form two groups, representing wet and dry seasons. For each MSD, SVM- based downscaling model (DM) is developed for season(s) with significant rainfall using principal components extracted from the predictors as input and the contemporaneous precipitation observed at the MSD as an output. The proposed DM is shown to be superior to conventional downscaling using multi-layer back-propagation artificial neural networks. Subsequently, the SVM-based DM is applied to future climate predictions from the second generation Coupled Global Climate Model (CGCM2) to obtain future projections of precipitation for the MSDs. The results are then analyzed to assess the impact of climate change on precipitation over India. It is shown that SVMs provide a promising alternative to conventional artificial neural networks for statistical downscaling, and are suitable for conducting climate impact studies.
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 40
years 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.
•A flexible optimal experiment design framework is developed.•The optimized network provides sufficient information with high data worth.•The monitoring locations are optimized for enhancing plume ...characterization.•The test duration is selected with information entropy and parameter uncertainty.
This study develops an integrated framework to guide the monitoring network optimization and duration selection for solute transport in heterogeneous sand tank experiments. The method is designed based on entropy and data worth analysis. Numerical models are applied to approach prior observation datasets and to support optimization analysis. Several candidate monitoring locations are synthetically assumed in numerical models. Entropy analysis considers local scale heterogeneity in experiment and identifies stable monitoring locations through extracting maximum information and minimizing optimization redundancies. Data worth analysis quantifies the potential of observation data to reduce the uncertainty of key parameters and selects the monitoring locations with higher data worth. Final monitoring network comprises of optimized monitoring locations obtained based on entropy and data worth analysis. A lab-scale tracer experiment is presented to explore the applicability of the proposed framework. Results show that the optimized monitoring network can accurately characterize the distribution of contaminant plumes in 3D domains and provides estimation of key flow and transport parameters (e.g., hydraulic conductivity and dispersivity). With the extension of experiment time, the total information of monitoring network is maximized, while the uncertainty of key parameters is minimized. The recommended experimental duration is the time by which both joint entropy and parameter variation coefficients are stabilized. Our developed methodology can be used as a flexible and powerful tool to design more complex transport experiments at different spatiotemporal scales.
The European Alps are one region of the world where climate-driven changes are already perceptible, as exemplified by the general retreat of mountain glaciers over past decades. Temperatures have ...risen by up to 2
°C since 1900 particularly at high elevations, a rate that is roughly three times the global-average 20th century warming. Regional climate models suggest that by 2100, winters in Switzerland may warm by 3–5
°C and summers by 6–7
°C according to greenhouse-gas emissions scenarios, while precipitation is projected to increase in winter and sharply decrease in summer. The impacts of these levels of climatic change will affect both the natural environment and a number of economic activities. Alpine glaciers may lose between 50% and 90% of their current volume and the average snowline will rise by 150
m for each degree of warming. Hydrological systems will respond in quantity and seasonality to changing precipitation patterns and to the timing of snow-melt in the Alps, with a greater risk of flooding during the spring and droughts in summer and fall. The direct and indirect impacts of a warming climate will affect key economic sectors such as tourism, hydropower, agriculture and the insurance industry that will be confronted to more frequent natural disasters. This paper will thus provide an overview of the current state of knowledge on climatic change and its impacts on the Alpine world.
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