Combining Seismology, Hydrogeology and Climatology for Monitoring Karstic Groundwater Reservoirs.

2021 ◽  
Author(s):  
Anthony Abi Nader ◽  
Julie Albaric ◽  
Anais Marchand ◽  
Marine Gros ◽  
Marc Steinmann ◽  
...  
Keyword(s):  
Hydrological Modeling ◽  
Seismic Velocity ◽  
Complex Structure ◽  
Rain Gauge ◽  
Velocity Variation ◽  
Catchment Basin ◽  
Rain Gauges ◽  
Fluid Saturation ◽  
Cross Correlation Analysis ◽  
Spatio Temporal

<p>Due to their heterogeneity and inaccessibility, karst aquifers are poorly understood along with their functioning, complex structure and behavior in response to flood events. Conventional methods such as piezometers or other underground equipment give only punctual observations that are not very representative of the functioning of the aquifer at the scale of the catchment basin, nor show spatio-temporal variation that occur along the karst network. The objective of this work is to image the flow of water over time from rainfall to the aquifer outlet in a target catchment basin located in the Jura Mountains near Besançon (Eastern France, Fourbanne site of the 'Jurassic Karst' observatory), which hosts a karstic aquifer monitored since 2014 (Cholet et al. 2017). The approach consists in analyzing jointly seismological, hydrogeological and atmospheric data recorded on the aquifer. The instrumentation comprises 2 permanent seismometers, 2 Conductivity Temperature and Pressure (CTD) probes and 1 rain gauge, which will be completed by 65 seismic nodes, 30 rain gauges and 1 additional CTD for an acquisition period of 4 months. We observe that underground hydrological processes occurring in the aquifer, such as water flow or sediment transport, can be precisely monitored using data from one seismometer installed inside the karst conduit. Furthermore, noise cross-correlation analysis will be carried out to detect seismic velocity variations in the medium induced by fluid saturation changes (Froment, 2011). Several studies have demonstrated that these methods can detect changes in saturation in underground aquifers (Lecocq et al. 2017; Voisin et al., 2017). Accordingly, velocity variation will be correlated with flow velocity, soil water content or even permeability, based on measurements of the volume of water entering the basin and circulating in the karstic network obtained from data collected from the CTDs and rain gauges.</p><p> </p><p><strong>References:</strong></p><p><strong>FROMENT B., 2011 –</strong> Utilisation du bruit sismique ambiant dans le suivi temporel de structures géologiques. [Grenoble]: École doctorale terre, univers, environnement.</p><p><strong>LECOCQ, T., LONGUEVERNE, L., PEDERSEN, H.A., 2017 –</strong> Monitoring ground water storage at mesoscale using seismic noise: 30 years of continuous observation and thermo-elastic and hydrological modeling. Sci Rep <strong>7, </strong>14241 (2017). https://doi.org/10.1038/s41598-017-14468-9</p><p><strong>VOISIN, C., GUZMAN, M., REFLOCH, A., TARUSELLI, M. and GARAMBOIS, S., 2017 –</strong> Groundwater Monitoring with Passive Seismic Interferometry. Journal of Water Resource and Protection, <strong>9</strong>, 1414-1427. doi: 10.4236/jwarp.2017.912091.</p><p><strong>CHOLET, C., CHARLIER, J.-B., MOUSSA, R., STEINMANN, M., DENIMAL, S., 2017 </strong>– Assessing lateral flows and solute transport during floods in a conduit-flow-dominated karst system using the inverse problem for the advection–diffusion equation. Hydrology and Earth System Sciences 21, 3635–3653. https://doi.org/10.5194/hess-21-3635-2017</p>

Satellite Data Application to Cover Lack of In-situ Observations for Mapping Precipitation and Direct Runoff in Semi-arid Basin

2020 ◽  
Author(s):  
Mahdi Akbari ◽  
Ali Torabi Haghighi
Keyword(s):  
Hydrological Modeling ◽  
Rain Gauge ◽  
Model Parameters ◽  
Lake Basin ◽  
Lake Urmia ◽  
Direct Runoff ◽  
Rain Gauges ◽  
High Elevations ◽  
Semi Arid

<div> <p>Hydrological modeling in arid basins located in developing countries often lacks sufficient hydrological data because, e.g., rain gauges are typically absent at high elevations and inflow to ungauged areas around large closed lakes such as Lake Urmia is difficult to estimate. We tried to improve precipitation and runoff estimation in Lake Urmia, Iran as an arid basin using satellite-based data. We estimated precipitation using interpolation of rain gauge data by kriging, downscaling Tropical Rainfall Measuring Mission (TRMM), and cokriging interpolation of in-situ records with Remote Sensing (RS)-based data. Using RS-based data in estimations gave more precise results, by compensating for lack of data at high elevations. Cokriging interpolation of rain gauges by TRMM and Digitized Elevation Model (DEM) gave 4–9 mm lower Root Mean Square Error (RMSE) in different years compared with kriging. Downscaling TRMM improved its accuracy by 14 mm. Using the most accurate precipitation model, we modeled annual direct runoff with Kennessey and Soil Conservation Service Curve Number (SCS-CN) models. These models use land use, permeability, slope maps and climatic parameter (Ia) to represent the annual climatic condition of modeled basin in sense of wetness or dryness. In runoff modeling, Kennessey gave higher accuracy in annual scale. It was found that classification of years to wet, dry and normal states in Kennessey by default assumptions on Ia is not accurate enough for semi-arid basins so by solving this issue and calibration Kennessey model parameters, we made this model applicable for Urmia Lake basin. Calibrating Kennessey reduced the Normalized RMSE (NRMSE) from 1 in the standard model to 0.44. Direct runoff coefficient map by 1 km spatial resolution was generated by calibrated Kennessey. Validation by the closest gauges to the lake gave a NRMSE of 0.41 which approved the accuracy of modeling.</p> </div>

scholarly journals Avaliação da precipitação baseada em dados do CHIRPS e GPCC com medições na superfície para a sub-bacia do rio Guamá no Nordeste Paraense

2020 ◽  
Vol 42 ◽  
pp. e32
Author(s):  
Ivan Carlos da Costa Barbosa ◽  
Emerson Renato Maciel da Silva ◽  
Helder José Farias da Silva ◽  
Luiz Gonzaga da Silva Costa ◽  
Maria Isabel Vitorino ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Temporal Distribution ◽  
Rain Gauge ◽  
Rainfall Data ◽  
Temporal Behavior ◽  
Large Variability ◽  
Rain Gauges ◽  
Spatio Temporal ◽  
Basin Region ◽  
Climate Studies

Rain is one of the most important variables in climate studies in Amazon because of it is large variability in time and space scales. Many basins and sub-basins in the region are deficient in regular and uniform monitoring of data observed on the surface. Today, the remote sensing products available provide satellite estimated rainfall data for a large spatio-temporal distribution and for almost every globe. Therefore, this study aims to evaluate the performance of rainfall data obtained from remote sensing for the sub-basin region of the Guamá River, Northeastern Pará, compared to data observed on terrestrial rain gauges. In addition to identifying the spatio-temporal behavior of rain in the area. The rainfall data used were: rain measured by rain gauge (Hidroweb) and rain estimated by remote sensing and made available by the high resolution precipitation database of GPCC and CHIRPS products, for the period between 1988 and 2018. The data were compared with a remarkably high correlation (r = 0.99) and a satisfactory agreement index (d = 0.98). The two estimated databases showed an approximate overestimation of the observed precipitation and a spatio-temporal distribution consistent with that expected for the region.

scholarly journals Comparing the Hydrological Responses of Conceptual and Process-Based Models with Varying Rain Gauge Density and Distribution

2018 ◽  
Vol 10 (9) ◽  
pp. 3209 ◽  
Author(s):  
Zhaokai Yin ◽  
Weihong Liao ◽  
Xiaohui Lei ◽  
Hao Wang ◽  
Ruojia Wang
Keyword(s):  
Hydrological Modeling ◽  
Eastern China ◽  
Rain Gauge ◽  
Hydrologic Model ◽  
Distributed Model ◽  
Rain Gauges ◽  
Precipitation Measurement ◽  
Average Accuracy ◽  
Reliability And Robustness ◽  
Model Structures

Precipitation provides the most crucial input for hydrological modeling. However, rain gauge networks, the most common precipitation measurement mechanisms, are sometimes sparse and inadequately distributed in practice, resulting in an imperfect representation of rainfall spatial variability. The objective of this study is to analyze the sensitivity of different model structures to the different density and distribution of rain gauges and evaluate their reliability and robustness. Based on a rain gauge network of 20 gauges in the Jinjiang River Basin, south-eastern China, this study compared the performance of two conceptual models (the hydrologic model (HYMOD) and Xinanjiang) and one process-based distributed model (the water and energy transfer between soil, plants and atmosphere model (WetSpa)) with different rain gauge distributions. The results show that the average accuracy for the three models is generally stable as the number of rain gauges decreases but is sensitive to changes in the network distribution. HYMOD has the highest calibration uncertainty, followed by Xinanjiang and WetSpa. Differing model responses are consistent with changes in network distribution, while calibration uncertainties are more related to model structures.

The Prediction of Rainfall and the Direction of Rainfall by a Narrow Area Radar Rain Gauge

1993 ◽  
Vol 28 (11-12) ◽  
pp. 79-85
Author(s):  
Shinichi Kondo
Keyword(s):  
Small Area ◽  
Arrival Time ◽  
Rain Gauge ◽  
Rain Gauges ◽  
Sewage Plant ◽  
Future Prediction ◽  
Near Future ◽  
Narrow Area

Narrow area radar rain gauges are currently used for measuring rainfall. These radar gauges can measure rainfall accurately in a small area. In sewage plants it is important to predict stormwater. To calculate predicted stormwater the results of rainfall and a prediction of the near future are necessary. Recently urbanization has made the arrival time of flooding to the sewage plant much shorter. This paper deals with system technologies for the near future prediction of radar rain gauge rainfall. The method of prediction of rainfall, calculation of results and other considerations are described.

scholarly journals Assessment of Snowfall Accumulation from Satellite and Reanalysis Products Using SNOTEL Observations in Alaska

2021 ◽  
Vol 13 (15) ◽  
pp. 2922
Author(s):  
Yang Song ◽  
Patrick D. Broxton ◽  
Mohammad Reza Ehsani ◽  
Ali Behrangi
Keyword(s):  
Snow Water Equivalent ◽  
Stage Iv ◽  
Microwave Remote Sensing ◽  
Rain Gauge ◽  
Snow Accumulation ◽  
Correction Factors ◽  
Rainfall Analysis ◽  
Rain Gauges ◽  
Snow Properties ◽  
Precipitation Estimates

The combination of snowfall, snow water equivalent (SWE), and precipitation rate measurements from 39 snow telemetry (SNOTEL) sites in Alaska were used to assess the performance of various precipitation products from satellites, reanalysis, and rain gauges. Observation of precipitation from two water years (2018–2019) of a high-resolution radar/rain gauge data (Stage IV) product was also utilized to give insights into the scaling differences between various products. The outcomes were used to assess two popular methods for rain gauge undercatch correction. It was found that SWE and precipitation measurements at SNOTELs, as well as precipitation estimates based on Stage IV data, are generally consistent and can provide a range within which other products can be assessed. The time-series of snowfall and SWE accumulation suggests that most of the products can capture snowfall events; however, differences exist in their accumulation. Reanalysis products tended to overestimate snow accumulation in the study area, while the current combined passive microwave remote sensing products (i.e., IMERG-HQ) underestimate snowfall accumulation. We found that correction factors applied to rain gauges are effective for improving their undercatch, especially for snowfall. However, no improvement in correlation is seen when correction factors are applied, and rainfall is still estimated better than snowfall. Even though IMERG-HQ has less skill for capturing snowfall than rainfall, analysis using Taylor plots showed that the combined microwave product does have skill for capturing the geographical distribution of snowfall and precipitation accumulation; therefore, bias adjustment might lead to reasonable precipitation estimates. This study demonstrates that other snow properties (e.g., SWE accumulation at the SNOTEL sites) can complement precipitation data to estimate snowfall. In the future, gridded SWE and snow depth data from GlobSnow and Sentinel-1 can be used to assess snowfall and its distribution over broader regions.

scholarly journals Spatio-Temporal Hydrological Model Structure and Parametrization Analysis

2021 ◽  
Vol 9 (5) ◽  
pp. 467
Author(s):  
Mostafa Farrag ◽  
Gerald Corzo Perez ◽  
Dimitri Solomatine
Keyword(s):  
Hydrological Modeling ◽  
Model Building ◽  
Model Performance ◽  
Model Description ◽  
Generation Model ◽  
Model Structure ◽  
Runoff Generation ◽  
Spatial Sensitivity ◽  
Spatio Temporal ◽  
Set Up

Many grid-based spatial hydrological models suffer from the complexity of setting up a coherent spatial structure to calibrate such a complex, highly parameterized system. There are essential aspects of model-building to be taken into account: spatial resolution, the routing equation limitations, and calibration of spatial parameters, and their influence on modeling results, all are decisions that are often made without adequate analysis. In this research, an experimental analysis of grid discretization level, an analysis of processes integration, and the routing concepts are analyzed. The HBV-96 model is set up for each cell, and later on, cells are integrated into an interlinked modeling system (Hapi). The Jiboa River Basin in El Salvador is used as a case study. The first concept tested is the model structure temporal responses, which are highly linked to the runoff dynamics. By changing the runoff generation model description, we explore the responses to events. Two routing models are considered: Muskingum, which routes the runoff from each cell following the river network, and Maxbas, which routes the runoff directly to the outlet. The second concept is the spatial representation, where the model is built and tested for different spatial resolutions (500 m, 1 km, 2 km, and 4 km). The results show that the spatial sensitivity of the resolution is highly linked to the routing method, and it was found that routing sensitivity influenced the model performance more than the spatial discretization, and allowing for coarser discretization makes the model simpler and computationally faster. Slight performance improvement is gained by using different parameters’ values for each cell. It was found that the 2 km cell size corresponds to the least model error values. The proposed hydrological modeling codes have been published as open-source.

scholarly journals Evaluation of IMERG Level-3 Products in Depicting the July to October Rainfall over Taiwan: Typhoon Versus Non-Typhoon

2021 ◽  
Vol 13 (4) ◽  
pp. 622
Author(s):  
Wan-Ru Huang ◽  
Pin-Yi Liu ◽  
Ya-Hui Chang ◽  
Cheng-An Lee
Keyword(s):  
Real Time ◽  
Tropical Rainfall Measuring Mission ◽  
Temporal Variations ◽  
Rain Gauge ◽  
Advantages And Disadvantages ◽  
Level 3 ◽  
Spatio Temporal ◽  
Typhoon Season ◽  
Early Late ◽  
Better Than

This study assesses the performance of satellite precipitation products (SPPs) from the latest version, V06B, Integrated Multi-satellitE Retrievals for Global Precipitation Mission (IMERG) Level-3 (including early, late, and final runs), in depicting the characteristics of typhoon season (July to October) rainfall over Taiwan within the period of 2000–2018. The early and late runs are near-real-time SPPs, while final run is post-real-time SPP adjusted by monthly rain gauge data. The latency of early, late, and final runs is approximately 4 h, 14 h, and 3.5 months, respectively, after the observation. Analyses focus on the seasonal mean, daily variation, and interannual variation of typhoon-related (TC) and non-typhoon-related (non-TC) rainfall. Using local rain-gauge observations as a reference for evaluation, our results show that all IMERG products capture the spatio-temporal variations of TC rainfall better than those of non-TC rainfall. Among SPPs, the final run performs better than the late run, which is slightly better than the early run for most of the features assessed for both TC and non-TC rainfall. Despite these differences, all IMERG products outperform the frequently used Tropical Rainfall Measuring Mission 3B42 v7 (TRMM7) for the illustration of the spatio-temporal characteristics of TC rainfall in Taiwan. In contrast, for the non-TC rainfall, the final run performs notably better relative to TRMM7, while the early and late runs showed only slight improvement. These findings highlight the advantages and disadvantages of using IMERG products for studying or monitoring typhoon season rainfall in Taiwan.

Single-station seismic monitoring of permafrost on Mt. Zugspitze (Germany) over the past 15 years

2021 ◽  
Author(s):  
Fabian Lindner ◽  
Joachim Wassermann
Keyword(s):  
Travel Time ◽  
Seismic Velocity ◽  
Temperature Record ◽  
Lapse Time ◽  
Resistivity Tomography ◽  
Single Station ◽  
Velocity Variations ◽  
Spatio Temporal ◽  
Velocity Changes ◽  
Permafrost Thawing

<p>Permafrost thawing affects mountain slope stability and can trigger hazardous rock falls. As rising temperatures promote permafrost thawing, spatio-temporal monitoring of long-term and seasonal variations in the perennially frozen rock is therefore crucial in regions with high hazard potential. With various infrastructure in the summit area and population in the close vicinity, Mt. Zugspitze in the German/Austrian Alps is such a site and permafrost has been monitored with temperature logging in boreholes and lapse-time electrical resistivity tomography. Yet, these methods are expensive and laborious, and are limited in their spatial and/or temporal resolution.</p><p>Here, we analyze continuous seismic data from a single station deployed at an altitude of 2700 m a.s.l. in a research station, which is separated by roughly 250 m from the permafrost affected ridge of Mt. Zugspitze. Data are available since 2006 (with some gaps) and reveal high-frequency (>1 Hz) anthropogenic noise likely generated by the cable car stations at the summit. We calculate single-station cross-correlations between the different sensor components and investigate temporal coda wave changes by applying the recently introduced wavelet-based cross-spectrum method. This approach provides time series of the travel time relative to the reference stack as a function of frequency and lag time in the correlation functions. In the frequency and lag range of 1-10 Hz and 0.5-5 s respectively, we find various parts in the coda that show clear annual variations and an increasing trend in travel time over the past 15 years of consideration. Converting the travel time variations to seismic velocity variations (assuming homogeneous velocity changes affecting the whole mountain) results in seasonal velocity changes of up to a few percent and on the order of 0.1% decrease per year. Yet, estimated velocity variations do not scale linearly with lag time, which indicates that the medium changes are localized rather than uniform and that the absolute numbers need to be taken with caution. The annual velocity variations are anti-correlated with the temperature record from the summit but delayed by roughly one month.</p><p>The phasing of the annual seismic velocity change (relative to the temperature record) is in agreement with a previous study employing lapse-time electrical resistivity tomography. Furthermore, the decreasing trend in seismic velocity happens concurrently with an increasing trend in temperature. The results therefore suggest that the velocity changes are related to seasonal thaw and refreeze and permafrost degradation and thus highlight the potential of seismology for permafrost monitoring. By adding additional receivers and/or a fiber-optic cable for distributed acoustic sensing, hence increasing the spatial resolution, the presented method holds promise for lapse-time imaging of permafrost bodies with high spatio-temporal resolution from passive measurements.</p>

Evaluation of Global Forecast System (GFS) Medium-Range Precipitation Forecasts in the Nile River Basin

2021 ◽  
Author(s):  
Haowen Yue ◽  
Mekonnen Gebremichael ◽  
Vahid Nourani
Keyword(s):  
Temporal Dynamics ◽  
Disease Outbreaks ◽  
Rain Gauge ◽  
Global Forecast System ◽  
Forecast System ◽  
Forecast Performance ◽  
Weather Forecasts ◽  
Rain Gauges ◽  
Basin Scale ◽  
Medium Range

Abstract Reliable weather forecasts are valuable in a number of applications, such as, agriculture, hydropower, and weather-related disease outbreaks. Global weather forecasts are widely used, but detailed evaluation over specific regions is paramount for users and operational centers to enhance the usability of forecasts and improve their accuracy. This study presents evaluation of the Global Forecast System (GFS) medium-range (1 day – 15 day) precipitation forecasts in the nine sub-basins of the Nile basin using NASA’s Integrated Multi-satellitE Retrievals (IMERG) “Final Run” satellite-gauge merged rainfall observations. The GFS products are available at a temporal resolution of 3-6 hours, spatial resolution of 0.25°, and its version-15 products are available since 12 June 2019. GFS forecasts are evaluated at a temporal scale of 1-15 days, spatial scale of 0.25° to all the way to the sub-basin scale, and for a period of one year (15 June 2019 – 15 June 2020). The results show that performance of the 1-day lead daily basin-averaged GFS forecast performance, as measured through the modified Kling-Gupta Efficiency (KGE), is poor (0 < KGE < 0.5) for most of the sub-basins. The factors contributing to the low performance are: (1) large overestimation bias in watersheds located in wet climate regimes in the northern hemispheres (Millennium watershed, Upper Atbara & Setit watershed, and Khashm El Gibra watershed), and (2) lower ability in capturing the temporal dynamics of watershed-averaged rainfall that have smaller watershed areas (Roseires at 14,110 sq. km and Sennar at 13,895 sq. km). GFS has better bias for watersheds located in the dry parts of the northern hemisphere or wet parts of the southern hemisphere, and better ability in capturing the temporal dynamics of watershed-average rainfall for large watershed areas. IMERG Early has better bias than GFS forecast for the Millennium watershed but still comparable and worse bias for the Upper Atbara & Setit, and Khashm El Gibra watersheds. The variation in the performance of the IMERG Early could be partly explained by the number of rain gauges used in the reference IMERG Final product, as 16 rain gauges were used for the Millennium watershed but only one rain gauge over each Upper Atbara & Setit, and Khashm El Gibra watershed. A simple climatological bias-correction of IMERG Early reduces in the bias in IMERG Early over most watersheds, but not all watersheds. We recommend exploring methods to increase the performance of GFS forecasts, including post-processing techniques through the use of both near-real-time and research-version satellite rainfall products.

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