scholarly journals Optimization of Rain Gauge Networks for Arid Regions Based on Remote Sensing Data

2021 ◽  
Vol 13 (21) ◽  
pp. 4243
Author(s):  
Mona Morsy ◽  
Ruhollah Taghizadeh-Mehrjardi ◽  
Silas Michaelides ◽  
Thomas Scholten ◽  
Peter Dietrich ◽  
...  
Keyword(s):  
Arid Regions ◽  
Flash Flood ◽  
Primary Source ◽  
Remote Sensing Data ◽  
Test Site ◽  
Rain Gauge ◽  
Cumulative Distribution ◽  
Climatic Data ◽  
Empirical Cumulative Distribution Function ◽  
Rain Gauges

Water depletion is a growing problem in the world’s arid and semi-arid areas, where groundwater is the primary source of fresh water. Accurate climatic data must be obtained to protect municipal water budgets. Unfortunately, the majority of these arid regions have a sparsely distributed number of rain gauges, which reduces the reliability of the spatio-temporal fields generated. The current research proposes a series of measures to address the problem of data scarcity, in particular regarding in-situ measurements of precipitation. Once the issue of improving the network of ground precipitation measurements is settled, this may pave the way for much-needed hydrological research on topics such as the spatiotemporal distribution of precipitation, flash flood prevention, and soil erosion reduction. In this study, a k-means cluster analysis is used to determine new locations for the rain gauge network at the Eastern side of the Gulf of Suez in Sinai. The clustering procedure adopted is based on integrating a digital elevation model obtained from The Shuttle Radar Topography Mission (SRTM 90 × 90 m) and Integrated Multi-Satellite Retrievals for GPM (IMERG) for four rainy events. This procedure enabled the determination of the potential centroids for three different cluster sizes (3, 6, and 9). Subsequently, each number was tested using the Empirical Cumulative Distribution Function (ECDF) in an effort to determine the optimal one. However, all the tested centroids exhibited gaps in covering the whole range of elevations and precipitation of the test site. The nine centroids with the five existing rain gauges were used as a basis to calculate the error kriging. This procedure enabled decreasing the error by increasing the number of the proposed gauges. The resulting points were tested again by ECDF and this confirmed the optimum of thirty-one suggested additional gauges in covering the whole range of elevations and precipitation records at the study site.

The potential of remote sensing data in rain gauge network optimization in the arid regions

2021 ◽  
Author(s):  
Silas Michaelides ◽  
Mona Morsy ◽  
Ruhollah Taghizadeh-Mehrjardi ◽  
Thomas Scholten ◽  
Peter Dietrich ◽  
...  
Keyword(s):  
Arid Regions ◽  
Remote Sensing Data ◽  
Test Site ◽  
Rain Gauge ◽  
Cumulative Distribution ◽  
Optimal Size ◽  
Climatic Data ◽  
Empirical Cumulative Distribution Function ◽  
Rain Gauges ◽  
The Arid Regions

<p>Water scarcity is a growing concern in arid and semi-arid regions of the World, locations where groundwater is the main source of freshwater. In order to preserve local water budgets, it is critical that accurate climatic data be acquired. Unfortunately, the majority of these arid regions feature a very limited number of rain gauges, reducing the reliability of the data produced. The present study offers a series of steps for overcoming the issue of data scarcity. Once resolved, this could then promote greatly needed hydrological studies on topics such as the spatiotemporal distribution of rainfall, the mitigation of flash floods hazards, or the minimization of soil erosion. In the present study, the DEM file and GPM (IMERG) data were used to identify the most suitable locations for a new network of rain gauges at the Eastern side of the Gulf of Suez. These two datasets were clustered using k-means clustering to produce an elbow graph whose elbow-shaped region offered several possible options for the number of optimum clusters at the test site. The authors chose three different cluster sizes (3, 6, and 9) and calculated the possible centroids for each size. Calculations resulted in 3 centroids, 6 centroids, and 9 centroids. These centroids were tested using the empirical cumulative distribution function (ECDF), once the sum of the GPM (IMERG) scenes, the scene limits, and the elevation map limits were determined. This test revealed gaps in all centroids mentioned. Consequently, the authors established nine clusters as the optimal size. Nine centroids were therefore taken, along with the existing five gauges, as a basis for standard error kriging. This allowed the authors to gradually minimize error via looping. The newly added points were tested with an ECDF. The complete spectrum of rainfall and elevation was efficiently covered by the 31 proposed rain gauge locations, and the five existing gauges.</p>

scholarly journals Quality-Based Combination of Multi-Source Precipitation Data

2020 ◽  
Vol 12 (11) ◽  
pp. 1709 ◽  
Author(s):  
Anna Jurczyk ◽  
Jan Szturc ◽  
Irena Otop ◽  
Katarzyna Ośródka ◽  
Piotr Struzik
Keyword(s):  
Spatial Resolution ◽  
Flash Flood ◽  
Radar Data ◽  
Rain Gauge ◽  
Quality Information ◽  
Precipitation Data ◽  
Spatial Error ◽  
Rain Gauges ◽  
Precipitation Measurement ◽  
Temporal And Spatial

A quantitative precipitation estimate (QPE) provides basic information for the modelling of many kinds of hydro-meteorological processes, e.g., as input to rainfall-runoff models for flash flood forecasting. Weather radar observations are crucial in order to meet the requirements, because of their very high temporal and spatial resolution. Other sources of precipitation data, such as telemetric rain gauges and satellite observations, are also included in the QPE. All of the used data are characterized by different temporal and spatial error structures. Therefore, a combination of the data should be based on quality information quantitatively determined for each input to take advantage of a particular source of precipitation measurement. The presented work on multi-source QPE, being implemented as the RainGRS system, has been carried out in the Polish national meteorological and hydrological service for new nowcasting and hydrological platforms in Poland. For each of the three data sources, different quality algorithms have been designed: (i) rain gauge data is quality controlled and, on this basis, spatial interpolation and estimation of quality field is performed, (ii) radar data are quality controlled by RADVOL-QC software that corrects errors identified in the data and characterizes its final quality, (iii) NWC SAF (Satellite Application Facility on support to Nowcasting and Very Short Range Forecasting) products for both visible and infrared channels are combined and the relevant quality field is determined from empirical relationships that are based on analyses of the product performance. Subsequently, the quality-based QPE is generated with a 1-km spatial resolution every 10 minutes (corresponding to radar data). The basis for the combination is a conditional merging technique that is enhanced by involving detailed quality information that is assigned to individual input data. The validation of the RainGRS estimates was performed taking account of season and kind of precipitation.

scholarly journals An objective cross-validation framework for mapping rainfall hazard based on rain gauge data

2018 ◽  
Author(s):  
Juliette Blanchet ◽  
Emmanuel Paquet ◽  
Pradeebane Vaittinada Ayar ◽  
David Penot
Keyword(s):  
Goodness Of Fit ◽  
Cumulative Distribution Function ◽  
Cross Validation ◽  
Daily Rainfall ◽  
Rain Gauge ◽  
Cumulative Distribution ◽  
Rain Gauge Data ◽  
Weather Patterns ◽  
Rain Gauges ◽  
Validation Framework

Abstract. We propose an objective framework for estimating rainfall cumulative distribution function within a region when data are only available at rain gauges. Our methodology is based on the evaluation of several goodness-of-fit scores in a cross-validation framework, allowing to assess goodness-of-fit of the full distribution but with a particular focus on its tail. Cross-validation is applied both to select the most appropriate statistical distribution at station locations and to validate the mapping of these distributions. Our methodology is applied to daily rainfall in the Ardèche catchment in South of France, a 2260 km2 catchment with strong disparities in rainfall distribution. Results show preference for a mixture of Gamma distribution over seasons and weather patterns, with parameters interpolated with thin plate spline across this region. However the framework presented in this paper is general and could be likewise applied in any region, with possibly different conclusion depending on the subsequent rainfall processes.

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 Rapid Evaluation and Validation Method of Above Ground Forest Biomass Estimation Using Optical Remote Sensing in Tundi Reserved Forest Area, India

2021 ◽  
Vol 10 (1) ◽  
pp. 29
Author(s):  
Praveen Kumar ◽  
Akhouri P. Krishna ◽  
Thorkild M. Rasmussen ◽  
Mahendra K. Pal
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Remote Sensing Data ◽  
Test Site ◽  
Forest Area ◽  
Relational Model ◽  
Biomass Estimation ◽  
Optical Remote Sensing ◽  
Ground Sample ◽  
Sensing Data

Optical remote sensing data are freely available on a global scale. However, the satellite image processing and analysis for quick, accurate, and precise forest above ground biomass (AGB) evaluation are still challenging and difficult. This paper is aimed to develop a novel method for precise, accurate, and quick evaluation of the forest AGB from optical remote sensing data. Typically, the ground forest AGB was calculated using an empirical model from ground data for biophysical parameters such as tree density, height, and diameter at breast height (DBH) collected from the field at different elevation strata. The ground fraction of vegetation cover (FVC) in each ground sample location was calculated. Then, the fraction of vegetation cover (FVC) from optical remote sensing imagery was calculated. In the first stage of method implementation, the relation model between the ground FVC and ground forest AGB was developed. In the second stage, the relational model was established between image FVC and ground FVC. Finally, both models were fused to derive the relational model between image FVC and forest AGB. The validation of the developed method was demonstrated utilizing Sentinel-2 imagery as test data and the Tundi reserved forest area located in the Dhanbad district of Jharkhand state in eastern India was used as the test site. The result from the developed model was ground validated and also compared with the result from a previously developed crown projected area (CPA)-based forest AGB estimation approach. The results from the developed approach demonstrated superior capabilities in precision compared to the CPA-based method. The average forest AGB estimation of the test site obtained by this approach revealed 463 tons per hectare, which matches the previous estimate from this test site.

scholarly journals Multispectral Remote Sensing of Wetlands in Semi-Arid and Arid Areas: A Review on Applications, Challenges and Possible Future Research Directions

2020 ◽  
Vol 12 (24) ◽  
pp. 4190
Author(s):  
Siyamthanda Gxokwe ◽  
Timothy Dube ◽  
Dominic Mazvimavi
Keyword(s):  
Remote Sensing ◽  
Arid Regions ◽  
Remote Sensing Data ◽  
Sub Saharan Africa ◽  
Future Research ◽  
Multispectral Imagery ◽  
Aquatic Life ◽  
Sensing Data ◽  
Spatial Coverage ◽  
Semi Arid

Wetlands are ranked as very diverse ecosystems, covering about 4–6% of the global land surface. They occupy the transition zones between aquatic and terrestrial environments, and share characteristics of both zones. Wetlands play critical roles in the hydrological cycle, sustaining livelihoods and aquatic life, and biodiversity. Poor management of wetlands results in the loss of critical ecosystems goods and services. Globally, wetlands are degrading at a fast rate due to global environmental change and anthropogenic activities. This requires holistic monitoring, assessment, and management of wetlands to prevent further degradation and losses. Remote-sensing data offer an opportunity to assess changes in the status of wetlands including their spatial coverage. So far, a number of studies have been conducted using remotely sensed data to assess and monitor wetland status in semi-arid and arid regions. A literature search shows a significant increase in the number of papers published during the 2000–2020 period, with most of these studies being in semi-arid regions in Australia and China, and few in the sub-Saharan Africa. This paper reviews progress made in the use of remote sensing in detecting and monitoring of the semi-arid and arid wetlands, and focuses particularly on new insights in detection and monitoring of wetlands using freely available multispectral sensors. The paper firstly describes important characteristics of wetlands in semi-arid and arid regions that require monitoring in order to improve their management. Secondly, the use of freely available multispectral imagery for compiling wetland inventories is reviewed. Thirdly, the challenges of using freely available multispectral imagery in mapping and monitoring wetlands dynamics like inundation, vegetation cover and extent, are examined. Lastly, algorithms for image classification as well as challenges associated with their uses and possible future research are summarised. However, there are concerns regarding whether the spatial and temporal resolutions of some of the remote-sensing data enable accurate monitoring of wetlands of varying sizes. Furthermore, it was noted that there were challenges associated with the both spatial and spectral resolutions of data used when mapping and monitoring wetlands. However, advancements in remote-sensing and data analytics provides new opportunities for further research on wetland monitoring and assessment across various scales.

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 Evaluation of the Performance of CMIP6 Models in Reproducing Rainfall Patterns over North Africa

Atmosphere ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 475
Author(s):  
Hassen Babaousmail ◽  
Rongtao Hou ◽  
Brian Ayugi ◽  
Moses Ojara ◽  
Hamida Ngoma ◽  
...  
Keyword(s):  
North Africa ◽  
Rainfall Variability ◽  
Coupled Model ◽  
Cumulative Distribution ◽  
Climatic Research Unit ◽  
Model Ensemble ◽  
Empirical Cumulative Distribution Function ◽  
Cycle Simulation ◽  
Taylor Diagram ◽  
Statistical Metrics

This study assesses the performance of historical rainfall data from the Coupled Model Intercomparison Project phase 6 (CMIP6) in reproducing the spatial and temporal rainfall variability over North Africa. Datasets from Climatic Research Unit (CRU) and Global Precipitation Climatology Centre (GPCC) are used as proxy to observational datasets to examine the capability of 15 CMIP6 models’ and their ensemble in simulating rainfall during 1951–2014. In addition, robust statistical metrics, empirical cumulative distribution function (ECDF), Taylor diagram (TD), and Taylor skill score (TSS) are utilized to assess models’ performance in reproducing annual and seasonal and monthly rainfall over the study domain. Results show that CMIP6 models satisfactorily reproduce mean annual climatology of dry/wet months. However, some models show a slight over/under estimation across dry/wet months. The models’ overall top ranking from all the performance analyses ranging from mean cycle simulation, trend analysis, inter-annual variability, ECDFs, and statistical metrics are as follows: EC-Earth3-Veg, UKESM1-0-LL, GFDL-CM4, NorESM2-LM, IPSL-CM6A-LR, and GFDL-ESM4. The mean model ensemble outperformed the individual CMIP6 models resulting in a TSS ratio (0.79). For future impact studies over the study domain, it is advisable to employ the multi-model ensemble of the best performing models.

scholarly journals Volumetric Quantification of Flash Flood Using Microwave Data on a Watershed Scale in Arid Environments, Saudi Arabia

2021 ◽  
Vol 13 (8) ◽  
pp. 4115
Author(s):  
Jaka Budiman ◽  
Jarbou Bahrawi ◽  
Asep Hidayatulloh ◽  
Mansour Almazroui ◽  
Mohamed Elhag
Keyword(s):  
Saudi Arabia ◽  
Flash Flood ◽  
Remote Sensing Data ◽  
Surface Elevation ◽  
Flood Mapping ◽  
Depth Information ◽  
Arid Environments ◽  
Volume Calculation ◽  
Flood Depth ◽  
Flood Volume

Actual flood mapping and quantification in an area provide valuable information for the stakeholder to prevent future losses. This study presents the actual flash flood quantification in Al-Lith Watershed, Saudi Arabia. The study is divided into two steps: first is actual flood mapping using remote sensing data, and the second is the flood volume calculation. Two Sentinel-1 images are processed to map the actual flood, i.e., image from 25 May 2018 (dry condition), and 24 November 2018 (peak flood condition). SNAP software is used for the flood mapping step. During SNAP processing, selecting the backscatter data representing the actual flood in an arid region is challenging. The dB range value from 7.23–14.22 is believed to represent the flood. In GIS software, the flood map result is converted into polygon to define the flood boundary. The flood boundary that is overlaid with Digital Elevation Map (DEM) is filled with the same elevation value. The Focal Statistics neighborhood method with three iterations is used to generate the flood surface elevation inside the flood boundary. The raster contains depth information is derived by subtraction of the flood surface elevation with DEM. Several steps are carried out to minimize the overcalculation outside the flood boundary. The flood volume can be derived by the multiplication of flood depth points with each cell size area. The flash flood volume in Al-Lith Watershed on 24 November 2018 is 155,507,439 m3. Validity checks are performed by comparing it with other studies, and the result shows that the number is reliable.

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