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 Assimilation of Satellite-Based Data for Hydrological Mapping of Precipitation and Direct Runoff Coefficient for the Lake Urmia Basin in Iran

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1624 ◽  
Author(s):  
Akbari ◽  
Haghighi ◽  
Aghayi ◽  
Javadian ◽  
Tajrishy ◽  
...  
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Curve Number ◽  
Rain Gauge ◽  
Runoff Coefficient ◽  
Lake Urmia ◽  
Direct Runoff ◽  
Rain Gauges ◽  
Elevation Model ◽  
High Elevations ◽  
Service Curve

Water management in arid basins often lacks sufficient hydro-climatological data because, e.g., rain gauges are typically absent at high elevations and inflow to ungauged areas around large closed lakes is difficult to estimate. We sought to improve precipitation and runoff estimation in an arid basin (Lake Urmia, Iran) using methods involving assimilation of satellite-based data. We estimated precipitation using interpolation of rain gauge data by kriging, downscaling the Tropical Rainfall Measuring Mission (TRMM), and cokriging interpolation of in-situ records with Remote Sensing (RS)-based data. Using RS-based data application 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 result, we modeled annual direct runoff with Kennessey and Soil Conservation Service Curve Number (SCS-CN) models. These models use land use, permeability, and slope data. In runoff modeling, Kennessey gave higher accuracy. 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.

Deep Learning for Precipitation Estimation from Satellite and Rain Gauges Measurements

2020 ◽  
Author(s):  
Arthur Moraux ◽  
Steven Dewitte ◽  
Bruno Cornelis ◽  
Adrian Munteanu
Keyword(s):  
Deep Learning ◽  
Rain Gauge ◽  
High Expectations ◽  
Rain Gauges ◽  
Weather And Climate ◽  
Learning Technique ◽  
Precipitation Estimation ◽  
The Many ◽  
Weather Observation

<p><span>In the coming years, Artificial Intelligence (AI), for which Deep Learning (DL) is an essential component, is expected to transform society in a way that is compared to the introduction of electricity or the introduction of the internet. The high expectations are founded on the many impressive results of recent DL studies for AI tasks (e.g. computer vision, text translation, image or text generation...). Also for weather and climate observations, a large potential for </span><span>AI</span><span> application exists. </span></p><p><span>We present the results of the recent paper [Moraux et al, 2019], which is one of the first demonstrations of the application </span><span>of </span><span>cutting edge deep learning technique</span><span>s</span><span> to a practical weather observation problem. We developed a multiscale encoder-decoder convolutional neural network using the three most relevant SEVIRI/MSG spectral images at 8.7, 10.8 and 12.0 micron and in situ rain gauge measurements as input. The network is trained to reproduce precipitation measured by rain gauges in Belgium, the Netherlands and Germany. Precipitating pixels are detected with a POD of 0.75 and a FAR of 0.3. Instantaneous precipitation rate is estimated with a RMSE of 1.6 mm/h.</span></p><p> </p><p><span>Reference:</span></p><p><span>[Moraux et al, 2019] Moraux, A.; Dewitte, S.; Cornelis, B.; Munteanu, A. Deep Learning for Precipitation Estimation from Satellite and Rain Gauges Measurements. </span><em><span>Remote Sens.</span></em> <span><strong>2019</strong></span><span>, </span><em><span>11</span></em><span>, 2463. </span></p>

From hillslope to catchment scale hydrologic prediction in a semi-arid region with in-situ observations, satellite soil moisture products, and a distributed catchment model

2020 ◽  
Author(s):  
In-Young Yeo ◽  
Ali Binesh ◽  
Garry Willgoose ◽  
Greg Hancock ◽  
Omer Yeteman
Keyword(s):  
Soil Moisture ◽  
Arid Region ◽  
Remotely Sensed ◽  
Hydrologic Model ◽  
Model Parameters ◽  
Limited Region ◽  
Semi Arid Region ◽  
Catchment Model ◽  
Semi Arid

<p>The water-limited region frequently experiences extreme climate variability.  This region, however, has relatively little hydrological information to characterize the catchment dynamics and its feedback to the climate system. This study assesses the relative benefits of using remotely sensed soil moisture, in addition to sparsely available in-situ soil moisture and stream flow observations, to improve the hydrologic understanding and prediction.  We propose a multi-variable approach to calibrate a hydrologic model, Soil and Water Assessment Tool (SWAT), a semi-distributed, continuous catchment model, with observed streamflow and in-situ soil moisture.  The satellite<span> soil moisture products (~ 5 cm top soil) from the Soil Moisture and Ocean Salinity (SMOS) and the Soil Moisture Active Passive (SMAP) are then used to evaluate the model estimates of soil moisture over the spatial scales through time.  The results show the model calibrated against streamflow only could provide misleading prediction for soil moisture.  Long term in-situ soil moisture observations, albeit limited availability, are crucial to constrain model parameters leading to improved soil moisture prediction at the given site.  </span><span>Satellite soil moisture products </span><span>provide useful information to assess simulated soil moisture results across the spatial domains, filling the gap on the soil moisture information at landscape scales.</span> <span>The preliminary results from this study suggest the potential to produce robust soil moisture and streamflow estimates across scales for a semi-arid region, using a distributed catchment model with in-situ soil network and remotely sensed observations and enhance the overall water budget estimations for multiple hydrologic variables across scales.  </span>This research is conducted on Merriwa catchment, a semi-arid region located in the Upper Hunter Region of NSW, Australia.</p>

Evaluation of the WRF/WRF-Hydro coupled system for hydrological modeling

2020 ◽  
Author(s):  
Yuchen Liu ◽  
Jia Liu ◽  
Chuanzhe Li ◽  
Fuliang Yu ◽  
Wei Wang ◽  
...  
Keyword(s):  
Hydrological Modeling ◽  
Roughness Parameter ◽  
Coupled System ◽  
Water Volume ◽  
Soil Conditions ◽  
Model Parameters ◽  
Total Water ◽  
Scaling Parameter ◽  
Sensitivity Tests ◽  
Semi Arid

<p>    WRF-Hydro is not only a stand-alone hydrological modeling architecture but also a coupling component for integrating hydrological models with atmospheric models. Sensitivity tests are carried out in this study for the most important parameters influencing the streamflow generation of the WRF/WRF-Hydro coupled system by targeting at the semi-humid and semi-arid catchments in Northern China. The main objective of the study is the parameters controlling the total water volume and the shape of the hydrograph are refined on the basis of sensitivity tests and their effects on the generation of the streamflow are addressed with the intent to apply the modeling system for streamflow forecasting. Two major aspects are considered in the calibration process for testing the sensitivity of the WRF-Hydro model parameters. On the one hand, it is to consider the parameters controlling the total water volume, which include the runoff infiltration parameter (REFKDT), and the surface retention depth (RETDEPRT) controlled by a scaling parameter named RETDEPRTFAC. One the other hand, it is to look at the parameters controlling the shape of the hydrograph, which include the channel Manning roughness parameter (MannN), and the overland flow roughness parameter (OVROUGHRT) controlled by the scaling parameter OVROUGHRTFAC. Through the sensitivity tests of the parameters affecting the runoff, it is found that REFKDT and MannN are the most sensitive parameter especially with unsaturated soil conditions. The findings of this study is to explore the variation laws of the key parameters in semi-humid and semi-arid areas, and to provide a reference for calibration and application of the WRF/WRF-Hydro coupled system.</p>

scholarly journals Multiplicative cascade models for fine spatial downscaling of rainfall: parameterization with rain gauge data

2012 ◽  
Vol 16 (3) ◽  
pp. 671-684 ◽  
Author(s):  
D. E. Rupp ◽  
P. Licznar ◽  
W. Adamowski ◽  
M. Leśniewski
Keyword(s):  
Rain Rate ◽  
Covariance Structure ◽  
Rain Gauge ◽  
Model Parameters ◽  
Spatial Covariance ◽  
Simple Method ◽  
Frequency Distributions ◽  
Rain Gauges ◽  
Cascade Generator ◽  
Radar Imagery

Abstract. Capturing the spatial distribution of high-intensity rainfall over short-time intervals is critical for accurately assessing the efficacy of urban stormwater drainage systems. In a stochastic simulation framework, one method of generating realistic rainfall fields is by multiplicative random cascade (MRC) models. Estimation of MRC model parameters has typically relied on radar imagery or, less frequently, rainfall fields interpolated from dense rain gauge networks. However, such data are not always available. Furthermore, the literature is lacking estimation procedures for spatially incomplete datasets. Therefore, we proposed a simple method of calibrating an MRC model when only data from a moderately dense network of rain gauges is available, rather than from the full rainfall field. The number of gauges needs only be sufficient to adequately estimate the variance in the ratio of the rain rate at the rain gauges to the areal average rain rate across the entire spatial domain. In our example for Warsaw, Poland, we used 25 gauges over an area of approximately 1600 km2. MRC models calibrated using the proposed method were used to downscale 15-min rainfall rates from a 20 by 20 km area to the scale of the rain gauge capture area. Frequency distributions of observed and simulated 15-min rainfall at the gauge scale were very similar. Moreover, the spatial covariance structure of rainfall rates, as characterized by the semivariogram, was reproduced after allowing the probability density function of the random cascade generator to vary with spatial scale.

scholarly journals Bias Correction of Satellite-Based Rainfall Estimates for Modeling Flash Floods in Semi-Arid regions: Application to Karpuz River, Turkey

2016 ◽  
Author(s):  
Mohamed Saber ◽  
Koray Yilmaz
Keyword(s):  
Bias Correction ◽  
Flash Flood ◽  
Flash Floods ◽  
Rainfall Rate ◽  
Assessment Model ◽  
Model Parameters ◽  
Flood Events ◽  
Rain Gauges ◽  
Semi Arid ◽  
Rainfall Estimates

Abstract. This study investigates the utility of gauge-corrected satellite-based rainfall estimates in simulating flash floods at Karpuz River - a semi-arid basin in Turkey. Global Satellite Mapping of Precipitation (GSMaP) product was evaluated with the rain gauge network at monthly and daily time-scales considering various time periods and rainfall rate thresholds. Statistical analysis indicated that GSMaP shows acceptable linear correlation coefficient with rain gauges however suffers from significant underestimation bias. A rainfall rate threshold of 1 mm/month was the best choice to improve the match between GSMaP and rain gauges implying that appropriate threshold selection is critically important for the bias correction. Multiplicative bias correction was applied to GSMaP data using the bias factors calculated between GSMaP and observed rainfall. Hydrological River Basin Environmental Assessment Model (Hydro-BEAM) was used to simulate flash floods at the hourly time scale driven by the corrected GSMaP rainfall data. The model parameters were calibrated for flash flood events during October-December 2007 and then validated for flash flood events during October-December 2009. The results show that the simulated surface runoff hydrographs reasonably coincide with the observed hydrographs.

scholarly journals Evaluation and Comparison of the GWR Merged Precipitation and Multi-Source Weighted-Ensemble Precipitation based on High-density Gauge Measurement.

2021 ◽  
Author(s):  
Jing Zhao ◽  
Yuan Qiqi ◽  
Long Yang ◽  
Hao Wu ◽  
lachun Wang
Keyword(s):  
Taihu Lake ◽  
Rain Gauge ◽  
Flood Frequency ◽  
High Density ◽  
Accurate Estimation ◽  
Lake Basin ◽  
Spatial And Temporal Variability ◽  
Taihu Lake Basin ◽  
Rain Gauges ◽  
Spatial Coverage

Accurate estimation of precipitation in both space and time is essential for hydrological research. We compared multi-source weighted ensemble precipitation (MSWEP) with multi-source fused satellite precipitation (CHIRPS) based on high-density rain gauge precipitation observations in the Taihu Lake basin. We proposed a new merge precipitation algorithm GWRMP based on the geographically weighted regression (GWR) method. GWRMP corrects the bias of MSWEP by using high-density rain gauge precipitation to address the common problem of daily precipitation underestimation in MSWEP. The large-scale spatial coverage of the water surface in this region leads to the uneven distribution of rain gauges on the lake. There are differences in the descriptive ability of the three spatial precipitation types, MSWEP, GWRMP, and IDW, for spatial and temporal precipitation information in the Taihu Lake basin. A comparison shows that GWRMP has a significant advantage in obtaining the spatial and temporal variability of precipitation in areas with complex topographic conditions. GWRMP compensates the problem of underestimation of precipitation by MSWEP (10% to 25%), and avoids the risk of the high dependence of IDW on rain gauges, and improves the accuracy of spatial and temporal precipitation in large lake areas with sparse distribution of rain gauges (Pbias limited to 10%). GWRMP improved the estimation for different rainfall intensities in the Taihu Lake basin, especially in the mid-level rainfall and above precipitation frequencies. Compared with IDW and MSWEP, GWRMP is more suitable for intense precipitation monitoring and storm flood frequency study in the basin. Therefore, GWRMP is a better choice for spatial and temporal estimation of precipitation in the Taihu Lake basin. The GWRMP algorithm can be applied to other regions with unevenly spaced high-density rain gauges.

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>

scholarly journals Development of a method of robust rain gauge network optimization based on intensity-duration-frequency results

2012 ◽  
Vol 9 (12) ◽  
pp. 14205-14230
Author(s):  
A. Chebbi ◽  
Z. K. Bargaoui ◽  
M. da Conceição Cunha
Keyword(s):  
Robust Optimization ◽  
Objective Function ◽  
Rain Gauge ◽  
Model Parameters ◽  
Optimization Approach ◽  
Rain Gauges ◽  
Hydrological Variability ◽  
Idf Curves ◽  
Intensity Duration Frequency ◽  
Rain Gauge Network

Abstract. Based on rainfall intensity-duration-frequency (IDF) curves, a robust optimization approach is proposed to identify the best locations to install new rain gauges. The advantage of robust optimization is that the resulting design solutions yield networks which behave acceptably under hydrological variability. Robust optimisation can overcome the problem of selecting representative rainfall events when building the optimization process. This paper reports an original approach based on Montana IDF model parameters. The latter are assumed to be geostatistical variables and their spatial interdependence is taken into account through the adoption of cross-variograms in the kriging process. The problem of optimally locating a fixed number of new monitoring stations based on an existing rain gauge network is addressed. The objective function is based on the mean spatial kriging variance and rainfall variogram structure using a variance-reduction method. Hydrological variability was taken into account by considering and implementing several return periods to define the robust objective function. Variance minimization is performed using a simulated annealing algorithm. In addition, knowledge of the time horizon is needed for the computation of the robust objective function. A short and a long term horizon were studied, and optimal networks are identified for each. The method developed is applied to north Tunisia (area = 21 000 km2). Data inputs for the variogram analysis were IDF curves provided by the hydrological bureau and available for 14 tipping bucket type rain gauges. The recording period was from 1962 to 2001, depending on the station. The study concerns an imaginary network augmentation based on the network configuration in 1973, which is a very significant year in Tunisia because there was an exceptional regional flood event in March 1973. This network consisted of 13 stations and did not meet World Meteorological Organization (WMO) recommendations for the minimum spatial density. So, it is proposed to virtually augment it by 25, 50, 100 and 160% which is the rate that would meet WMO requirements. Results suggest that for a given augmentation robust networks remain stable overall for the two time horizons.

scholarly journals Climate-dependent propagation of precipitation uncertainty into the water cycle

2020 ◽  
Author(s):  
Ali Fallah Maraghi ◽  
Sungmin Oh ◽  
Rene Orth
Keyword(s):  
Hydrological Modeling ◽  
Hydrological Model ◽  
Water Cycle ◽  
State Of The Art ◽  
Rain Gauge ◽  
Multiple Sources ◽  
Current State ◽  
Wide Range ◽  
The Impact

<p>Precipitation is a crucial variable for hydro-meteorological applications. Unfortunately, rain gauge measurements are sparse and unevenly distributed, which substantially hampers the use of in-situ precipitation data in many regions of the world. The increasing availability of high-resolution gridded precipitation products presents a valuable alternative, especially over gauge-sparse regions. Nevertheless, uncertainties and corresponding differences across products can limit the applicability of these data. This study examines the usefulness of current state-of-the-art precipitation datasets in hydrological modeling. For this purpose, we force a conceptual hydrological model with multiple precipitation datasets in >200 European catchments. We consider a wide range of precipitation products, which are generated via (1) interpolation of gauge measurements (E-OBS and GPCC V.2018), (2) data assimilation into reanalysis models (ERA-Interim, ERA5, and CFSR) and (3) combination of multiple sources (MSWEP V2). For each catchment, runoff and evapotranspiration simulations are obtained by forcing the model with the various precipitation products. Evaluation is done at the monthly time scale during the period of 1984-2007. We find that simulated runoff values are highly dependent on the accuracy of precipitation inputs, and thus show significant differences between the simulations. By contrast, simulated evapotranspiration is generally much less influenced. The results are further analysed with respect to different hydro-climatic regimes. We find that the impact of precipitation uncertainty on simulated runoff increases towards wetter regions, while the opposite is observed in the case of evapotranspiration. Finally, we perform an indirect performance evaluation of the precipitation datasets by comparing the runoff simulations with streamflow observations. Thereby, E-OBS yields the best agreement, while furthermore ERA5, GPCC V.2018 and MSWEP V2 show good performance. In summary, our findings highlight a climate-dependent propagation of precipitation uncertainty through the water cycle; while runoff is strongly impacted in comparatively wet regions such as Central Europe, there are increasing implications on evapotranspiration towards drier regions.</p>

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