Tropical Rainfall Monitoring Mission Research Articles

Geoestadística para integrar mediciones de campo con estimaciones satelitales adecuados para escala local

In countries such as Mexico, there is a lack of rain measurement stations. Additionally, in the Bajo Grijalva Basin, data of only three or fewer stations are integrated into satellite products of missions such as Tropical Rainfall Monitoring Mission (TRMM) and Global Precipitation Mission (GPM). Although Satellite missions enable obtaining rainfall at constant spacing (e.g., 11 km for GPM), this resolution is not suitable for local management. Integrating a larger quantity of gauge data with downscaled satellite values allows for obtaining local-scale precipitation data. In this work, Ordinary kriging (OK) was applied to downscale yearly aggregated precipitation satellite data (GPM-IMERG and TRMM: TMPA/3B43) and regression kriging (RK) to integrate them with the gauge measurements available in the basin of study. The resulting data were compared with the interpolation results of gauge measurements using OK and universal kriging (UK). Leave-one-out cross-validation (Lou-CV), principal components analysis, a correlation matrix, and a heat map with cluster analysis helped to evaluate the performance and to define similarity. An Inverse Distance Weighting (IDW) interpolation was included as a low-performance criterion in the comparison. OK performed well to downscale GPM satellite estimates. The RK integration of gauge data with downscaled GPM data got the best validation values compared to the interpolation of gauge measurements. Geostatistical methods are promising for downscaling satellite estimates and integrating them with all the available gauge data. The results indicate that the evaluation using performance metrics should be complemented with methods to define similarity among the values of the obtained spatial layers. This approach allows obtaining precipitation data useful for modeling and water management at the local level.

On the Use of Radar and Optical Satellite Imagery for the Monitoring of Flood Hazards on Heritage Sites in Southern Sinai, Egypt

This study focuses on the use of radar and optical satellite imagery for flood hazard mapping and monitoring around the archaeological sites of the Wadi Baba area, situated at Sinai (Egypt) and well known for its heritage treasures belonging to diverse historical periods and civilizations from the Pharaonic, Nabateans, Christian, and Islamic eras. Although this area is located in an arid to semi-arid climatic region, it is intermittently flooded due to torrential rainstorms. To assess the amount of rainfall expected and its impacts on heritage sites, satellite Sentinel-1 (C-Band) and Tropical Rainfall Monitoring Mission (TRMM) data were jointly used with measurements from meteorological stations and the Digital Elevation Model (DEM) from Shuttle Radar Topography Mission (SRTM). Envi5.1, ArcGIS 10.4.1, Snap 6.0, and the GEE platform were used to process optical and radar data, which were further analysed using the ArcHydro model. In this study, the TRMM accumulated rainfall data acquired on 17 January 2010, Sentinel-1 radar images between 2017 and 2019, and Sentinel-1 data captured from 1 to 30 March 2020 processed by GEE platform were chosen to assess the effects of flood events on the archaeological sites in the study area. The results indicated that the study area is exposed to flood risk that significantly threatens these heritage sites. Based on that, mitigation strategies were devised and recommended to mitigate the flood hazard impact around the archaeological areas.

Application of Satellite-Based and Observed Precipitation Datasets for Hydrological Simulation in the Upper Mahi River Basin of Rajasthan, India

Hydrological modeling is an important tool used for basin management and studying the impacts of extreme events in a river basin. In streamflow simulations, precipitation plays an essential role in hydrological models. Meteorological satellite precipitation measurement techniques provide highly accurate rainfall information with high spatial and temporal resolution. In this analysis, the tropical rainfall monitoring mission (TRMM) 3B42 V7 precipitation products were employed for simulating streamflow by using the soil water assessment tool (SWAT) model. With India Metrological Department and TRMM data, the SWAT model can be used to predict streamflow discharge and identify sensitive parameters for the Mahi basin. The SWAT model was calibrated for 2 years and then independently validated for 2 years by comparing observed and simulated streamflow. A strong correlation was observed between the calibration and validation results for the Paderdibadi station, with a Nash–Sutcliffe efficiency of >0.34 and coefficient of determination (R2) of >0.77. The SWAT model was used to adequately simulate the streamflow for the Upper Mahi basin with a satisfactory R2 value. The analysis indicated that TRMM 3B42 V7 is useful in SWAT applications for predicting streamflow and performance and for sensitivity analysis. In addition, satellite data may require correction before its utilization in hydrological modeling. This study is helpful for stakeholders in monitoring and managing agricultural, climatic, and environmental changes.

A Spatio-Temporal Analysis of Rainfall and Drought Monitoring in the Tharparkar Region of Pakistan

The Tharpakar desert region of Pakistan supports a population approaching two million, dependent on rain-fed agriculture as the main livelihood. The almost doubling of population in the last two decades, coupled with low and variable rainfall, makes this one of the world’s most food-insecure regions. This paper examines satellite-based rainfall estimates and biomass data as a means to supplement sparsely distributed rainfall stations and to provide timely estimates of seasonal growth indicators in farmlands. Satellite dekadal and monthly rainfall estimates gave good correlations with ground station data, ranging from R = 0.75 to R = 0.97 over a 19-year period, with tendency for overestimation from the Tropical Rainfall Monitoring Mission (TRMM) and underestimation from Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) datasets. CHIRPS was selected for further modeling, as overestimation from TRMM implies the risk of under-predicting drought. The use of satellite rainfall products from CHIRPS was also essential for derivation of spatial estimates of phenological variables and rainfall criteria for comparison with normalized difference vegetation index (NDVI)-based biomass productivity. This is because, in this arid region where drought is common and rainfall unpredictable, determination of phenological thresholds based on vegetation indices proved unreliable. Mapped rainfall distributions across Tharparkar were found to differ substantially from those of maximum biomass (NDVImax), often showing low NDVImax in zones of higher annual rainfall, and vice versa. This mismatch occurs in both wet and dry years. Maps of rainfall intensity suggest that low yields often occur in areas with intense rain causing damage to ripening crops, and that total rainfall in a season is less important than sustained water supply. Correlations between rainfall variables and NDVImax indicate the difficulty of predicting drought early in the growing season in this region of extreme climatic variability. Mapped rainfall and biomass distributions can be used to recommend settlement in areas of more consistent rainfall.

Mapping drought-induced changes in rice area in India

ABSTRACTRice is a staple food crop of India and is grown on 44 Mha (2011–12), 58.6% of which are irrigated. An inevitable phenomenon which looms over all aspects of human life and affects rice production in India is drought. Assessing drought damage using geospatial datasets available in the public domain, such as the Normalized Difference Vegetation Index (NDVI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS), can provide specific and local ecoregion information for developing drought-resistant rice varieties. Based on multi-temporal NDVI data and field observations in 2009, we developed a methodology to identify and map drought-affected areas in India. A long-term (10-year) average of NDVI during the rainy (kharif) season (June–October) was compared with NDVI from a known drought year (2002–03) to identify changes in rice area. Rainfall data from the Tropical Rainfall Monitoring Mission (TRMM) was used to support the drought analysis. Spectral matching techniques were used to categorise the drought-affected rice areas into three classes – severe, moderate, and mild based on the intensity of damage assessed through field sampling. Based on these ground survey samples, spectral signatures were generated. It was found that the rice area was about 16% less in the drought year (2002–03) than in a normal year (2000–01). A comparison of the MODIS-derived rice area affected by drought in 2002 for each state and district against the difference in the kharif season harvested rice area between 2000 and 2002 (from official statistics) revealed a substantial difference in harvested area in 2002 that was largely attributable to drought. An 84.7% correlation was found between the MODIS-derived drought-affected area in 2002 and the reduction in harvested area from 2000–01 to 2002–03. Good spatial correlation was found between the drought-affected rice areas and reduction of rice harvested areas in different rice ecologies, indicating the usefulness of such geospatial datasets in assessing abiotic stress such as drought and its consequences.

Accessing the capability of TRMM 3B42 V7 to simulate streamflow during extreme rain events: Case study for a Himalayan River Basin

The paper examines the quality of Tropical Rainfall Monitoring Mission (TRMM) 3B42 V7 precipitation product to simulate the streamflow using Soil Water Assessment Tool (SWAT) model for various rainfall intensities over the Himalayan region. The SWAT model has been set up for Gandak River Basin with 41 sub-basins and 420 HRUs. Five stream gauge locations are used to simulate the streamflow for a time span of 10 years (2000–2010). Daily streamflow for the simulation period is collected from Central Water Commission (CWC), India and Department of Hydrology and Meteorology (DHM), Nepal. The simulation results are found good in terms of Nash–Sutcliffe efficiency $$(\hbox {NSE}) {>}0.65$$ , coefficient of determination $$(R^{2}) {>}0.67$$ and Percentage Bias $$\hbox {(PBIAS)}{<}15\%$$ , at each stream gauge sites. Thereafter, we have calculated the PBIAS and RMSE-observations standard deviation ratio (RSR) statistics between TRMM simulated and observed streamflow for various rainfall intensity classes, viz., light ( $${<}7.5 \, \hbox {mm}/\hbox {d}$$ ), moderate (7.5 to 35.4 mm/d), heavy (35.5 to 124.4 mm/d) and extremely heavy ( $${>}124.4 \, \hbox {mm}/\hbox {d}$$ ). The PBIAS and RSR show that TRMM simulated streamflow is suitable for moderate to heavy rainfall intensities. However, it does not perform well for light- and extremely-heavy rainfall intensities. The finding of the present work is useful for the problems related to water resources management, irrigation planning and hazard analysis over the Himalayan regions.

Adequacy of the daily TMPA 3B42 high-resolution satellite precipitation product for monitoring hydrometeorological hazards in the Southeast Sahel of Africa

ABSTRACTRural hamlets in the Southeast Sahel, because of their pastoral setting and dependence on local agricultural production, are particularly vulnerable to flash flooding, loss of soil and the inundation of arable land caused by sudden storm events. High resolution satellite precipitation estimates (SPEs) could prove essential for detecting, monitoring, predicting and mitigating the effects of such hydrometeorological extremes. The focus of this report is on assessing – in the context of the needs of the local community − the effectiveness of what has become the gold standard for such applications in Africa and elsewhere since 1998: Tropical Rainfall Monitoring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) program’s 0.25° × 0.25°, daily 3B42 Version 7 satellite-gauge merged product. The scientific community now has almost 20 years of TMPA data that will likely play a significant role well into the future for retrospective studies in conjunction with the next generation (post-2014) Global Precipitation Mission (GPM) products. To assess the 3B42 product for the purposes of this report, we employ a pixel-to-point comparison of 3B42 daily time series with synchronous gauge data at four well-separated gauge sites. While others have found notable agreement between 3B42 and gauge observations aggregated over large areas and seasonal to annual time periods, it is a different matter downscaling to daily values at local sites, where we find a disturbing number of ‘false alarms’ and ‘missed events’. More than 90% of the total annual rainfall estimated by the 3B42 is contributed by daily events that are misaligned in time or intensity with gauge events. This largely concurs with the findings of our prior reports based on CPC-RFE2 0.1° × 0.1° daily values. Thus, users might be cautious in using the TMPA 3B42, CPC-RFE2 or, by inference, any SPE, as deterministic metrics for detecting and monitoring short term (i.e. ≤ 1 day), extreme storm events in this area of the Sahel.

Spatial Downscaling of TRMM Precipitation Data Using an Optimal Subset Regression Model with NDVI and Terrain Factors in the Yarlung Zangbo River Basin, China

High accuracy, high spatial resolution precipitation data is important for understanding basin-scale hydrology and the spatiotemporal distributions of regional precipitation. The objective of this study was to develop a reliable statistical downscaling algorithm to produce high quality, high spatial resolution precipitation products from Tropical Rainfall Monitoring Mission (TRMM) 3B43 data over the Yarlung Zangbo River Basin using an optimal subset regression (OSR) model combined with multiple topographical factors, the Normalized Difference Vegetation Index (NDVI), and observational data from rain gauge stations. After downscaling, the bias between TRMM 3B43 and rain gauge data decreased considerably from 0.397 to 0.109, the root-mean-square error decreased from 235.16 to 124.60 mm, and the r2 increased from 0.54 to 0.61, indicating significant improvement in the spatial resolution and accuracy of the TRMM 3B43 data. Moreover, the spatial patterns of both precipitation rates of change and their corresponding p value statistics were consistent between the downscaled results and the original TRMM 3B43 during the 2001–2014 period, which verifies that the downscaling method performed well in the Yarlung Zangbo River Basin. Its high performance in downscaling precipitation was also proven by comparing with other models. All of these findings indicate that the proposed approach greatly improved the quality and spatial resolution of TRMM 3B43 rainfall products in the Yarlung Zangbo River Basin, for which rain gauge data is limited. The potential of the post-real-time Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) downscaled precipitation product was also demonstrated in this study.

Monitoring Changes in Croplands Due to Water Stress in the Krishna River Basin Using Temporal Satellite Imagery

Remote sensing-based assessments of large river basins such as the Krishna, which supplies water to many states in India, are useful for operationally monitoring agriculture, especially basins that are affected by abiotic stress. Moderate-Resolution Imaging Spectroradiometer (MODIS) time series products can be used to understand cropland changes at the basin level due to abiotic stresses, especially water scarcity. Spectral matching techniques were used to identify land use/land cover (LULC) areas for two crop years: 2013–2014, which was a normal year, and 2015–2016, which was a water stress year. Water stress-affected crop areas were categorized into three classes—severe, moderate and mild—based on the normalized difference vegetation index (NDVI) and intensity of damage assessed through field sampling. Furthermore, ground survey data were used to assess the accuracy of MODIS-derived classification individual products. Water inflows into and outflows from the Krishna river basin during the study period were used as direct indicators of water scarcity/availability in the Krishna Basin. Furthermore, ground survey data were used to assess the accuracy of MODIS-derived LULC classification of individual year products. Rainfall data from the tropical rainfall monitoring mission (TRMM) was used to support the water stress analysis. The nine LULC classes derived using the MODIS temporal imagery provided overall accuracies of 82% for the cropping year 2013–2014 and 85% for the year 2015–2016. Kappa values are 0.78 for 2013–2014 and 0.82 for 2015–2016. MODIS-derived cropland areas were compared with national statistics for the cropping year 2013–2014 with a R2 value of 0.87. Results show that both rainfed and irrigated areas in 2015–2016 saw significant changes that will have significant impacts on food security. It has been also observed that the farmers in the basin tend to use lower inputs and labour per ha during drought years. Among all, access to water is the major driver determining the crop choice and extent of input-use in the basin.

Mass-balance modelling of Gangotri glacier

AbstractThe sensitivity of glacier mass balance (MB) in response to climatic perturbations has made it an important parameter of study from hydrological, climatological and glaciological point of view. To monitor the health of any glacier system, long-term MB observations are required. These observations among Himalayan glaciers are not available consistently and large glaciers are not often monitored for mass balance due to logistical challenges. One such glacier is the Gangotri, situated in the western Himalaya. In the present study an attempt is made to model the MB over the Gangotri glacier, the biggest glacier in the Ganga basin and also the point of origin of the River Ganges. The mass balance of the Gangotri glacier is estimated during the time period 1985–2014 using two different methods: ice-flow velocity; and energy balance modelling using regional model (REMO) outputs and in situ automatic weather station (AWS) data. The geodetic method is used for the nearby Dokriani glacier, where field-based MB measurements are available. MB of Gangotri glacier estimated for 2001–14 using the ice-flow velocity method is −0.92 ± 0.36 m w.e. a−1; for 2006–07, MB using AWS and Tropical Rainfall Monitoring Mission (TRMM) data with the energy balance modelling approach is −0.82 m w.e. a−1; and for 1985–2005, MB using REMO data with the energy balance modelling approach is −0.98 ± 0.23 m w.e. a−1. Using the surface velocity method, it is estimated that the glacier lost 9% of its volume during the period 2001–14. The glacier vacated an area of 0.152 km2 from the snout region, and retreated by 200 m in the last 14 years. MB values estimated for the Gangotri glacier from different methodologies are remarkably close, suggesting them to be suitable methods of MB estimation. TRMM, High Asia Refined (HAR-10) and Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of water resources (APHRODITE) data are used to estimate the precipitation over the glacier. The study suggests that the glacier-wide estimation of weather parameters needs to be improved for more accurate estimation of glacier mass balance.

Flash flood hazard zonation based on basin morphometry using remote sensing and GIS techniques: A case study of Wadi Qena basin, Eastern Desert, Egypt

Remote Sensing techniques with GIS were used to evaluate flash flood hazard in Wadi Qena. Wadi Qena represents a large, wide, dry and long valley slopes down from the south of the Galala height to the town of Qena. Its southern stretches considered as very promising land for the agriculture expansion. The surface area of Wadi Qena is about 15,588km2. Flash flood hazard in Wadi Qena is evaluating by the integration of some quantitative geo-morphometric parameters and Tropical Rainfall Monitoring Mission (TRMM) satellite data of the last flashflood in 28th January 2013. Streams were automatically extracted from ASTER DEM data. Wadi Qena classified into 151 sub-basins. The various morphometric parameters of Wadi Qena sub-basins have been computed. flash flood hazard map of the most dangerous sub-basins was prepared by combining all the spatial layers of morphometric parameters for the sub-basins included in it by GIS analysis. the majority of the sub-basins range from low to medium hazard degree Except one sub-basin located at the eastern part of the wadi is classified as high hazard degree. This technique may help in evaluating the hydrographic basin and offers a base map delineating both recharge and flooding area. Flash flood has double-edged effects one is a destructive and life terminator, the second is a beneficent, if it can be controlled and directed for the development the community. This technique helps in diminishing the flood hazards and increases recharging the shallow aquifers.

Defining Philippine Climate Zones Using Surface and High-Resolution Satellite Data

Philippine climate zones traditionally were classified from a rain-gauge network, using the Modified Coronas Classification (MCC). MCC uses average monthly rainfall totals to define four climate zones: Types I-IV. Types I and III have wet and dry seasons, whereas Types II and IV have wet seasons but no dry seasons.The present study redefines Philippine climate zones by applying cluster analysis to the average monthly rainfall amounts from surface-based rain-gauge observations, and dense, high-resolution satellite data from the Tropical Rainfall Monitoring Mission (TRMM). To determine the optimal number of climate type clusters, both single-linkage hierarchical and K-means cluster analysis algorithms were used, together with known characteristics of Philippine rainfall distributions and attributes.Employing single linkage hierarchical and K-means methods in tandem identified six different Philippine climate types, which is two climate types more than the currently accepted MCC climate classification. Due to the far greater number of TRMM observations compared with the rain gauge network, the study provides more clearly defined cluster characteristics including the spatial and temporal variability of climate divisions. This study uses known meteorological factors contributing to the identification of six distinct climate types. This paper is intended to assist agricultural stakeholders with planning and decision-making.

Estimating the flash flood quantitative parameters affecting the oil-fields infrastructures in Ras Sudr, Sinai, Egypt, during the January 2010 event

This paper aims to quantify the hydrological parameters for the flash flood event of 17th January 2010 in Sinai using multiple sets of remote sensing data and field work for the nongaged catchments (approximately 2100sqkm) of the wadis affecting Ras Sudr area, which is heavily occupied by numerous oil fields and related activities. The affected areas were visited, and several cross sections of the main active channels were surveyed to estimate the peak discharge rates. The Tropical Rainfall Monitoring Mission (TRMM) data have been used to estimate rainfall parameters for the catchments due to the absence of in situ data. The digital elevation model (DEM) of the Shuttle Radar Topography Mission (SRTM) was used to extract the hydrographic data following standard procedures and techniques of the Geographic Information Systems (GIS). Both of the surveyed and extracted parameters for the active channels were integrated into GIS to estimate the runoff parameters using the open-channel flow equation of Manning’s. The simulated hydrographs show that the total discharge exceeded 5.7million cubic meters and the peak discharge rate was 70 cubic meters per second. The mitigation of extreme flash flood is possible by altering the natural flow dispersion over the alluvial fan, and conveying the resulting flows into one adjusted channel.

Mapping Annual Precipitation across Mainland China in the Period 2001–2010 from TRMM3B43 Product Using Spatial Downscaling Approach

Spatially explicit precipitation data is often responsible for the prediction accuracy of hydrological and ecological models. Several statistical downscaling approaches have been developed to map precipitation at a high spatial resolution, which are mainly based on the valid conjugations between satellite-driven precipitation data and geospatial predictors. Performance of the existing approaches should be first evaluated before applying them to larger spatial extents with a complex terrain across different climate zones. In this paper, we investigate the statistical downscaling algorithms to derive the high spatial resolution maps of precipitation over continental China using satellite datasets, including the Normalized Distribution Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Global Digital Elevation Model (GDEM) from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and the rainfall product from the Tropical Rainfall Monitoring Mission (TRMM). We compare three statistical techniques (multiple linear regression, exponential regression, and Random Forest regression trees) for modeling precipitation to better understand how the selected model types affect the prediction accuracy. Then, those models are implemented to downscale the original TRMM product (3B43; 0.25° resolution) onto the finer grids (1 × 1 km2) of precipitation. Finally we validate the downscaled annual precipitation (a wet year 2001 and a dry year 2010) against the ground rainfall observations from 596 rain gauge stations over continental China. The result indicates that the downscaling algorithm based on the Random Forest regression outperforms, when compared to the linear regression and the exponential regression. It also shows that the addition of the residual terms does not significantly improve the accuracy of results for the RF model. The analysis of the variable importance reveals the NDVI related predictors, latitude, and longitude, elevation are key elements for statistical downscaling, and their weights vary across different climate zones. In particular, the NDVI, which is generally considered as a powerful geospatial predictor for precipitation, correlates weakly with precipitation in humid regions.

Integration of Point and Remote Sensing Data for Monitoring Surface Runoff around Madinah City, Saudi Arabia

In response to sporadic rainy storms, the fluvial catchments in the northern, eastern and southern regimes of Medina City produce surface runoff that varies in the frequency and magnitude. The determination of the most frequently flowing catchment(s) through each precipitation event and the spatial characteristics of surface runoff still represent gaps in understanding the hydrological processes associated with flash floods in arid environments. The lack of data in particular precipitation records at reliable temporal and spatial scales is considered one of the essential constraints in understanding these issues. The integration of point precipitation records and the multi-temporal satellite imagery such as LandSat TM and the Tropical Rainfall Monitoring Mission (TRMM) helped retrieve significant surface runoff events through two decades of the last century around Madinah City. The spectral signatures of surface runoff have been clearly identified from the optical remote sensing images acquired after each precipitation event. Investigating the spectral signatures of surface runoff along with its corresponding precipitation records from both Madinah climatic station and the TRMM monthly precipitation indicated that 1) there is no specific threshold of the amount of precipitation required for developing surface runoff; 2) while Madinah climatic station provided records of precipitation on the majority of surface runoff, it did not provide records on some monitored surface runoff events; 3) the spatial distribution and magnitude of the monthly precipitation of the TRMM images of 1998 have been conformed with all surface runoff events developed in W. Al-Aqeeq; and 4) W. Al-Aqeeq is considered the most frequently flowing valley around Madinah City because it developed surface runoff in response to all the precipitation events. These results emphasize the necessity of establishing a number of climatic and gauge stations in selective locations within the drainage basins around Madinah City. This proposition can facilitate studying the various rainfall and runoff characteristics for designing proper surface water harvesting strategy.

A methodological approach for spatial downscaling of TRMM precipitation data in North China

Accurate precipitation data with high spatial resolution are crucial for many applications in water and land management. Tropical Rainfall Monitoring Mission (TRMM) data, with accurate, high spatial resolution are crucial for improving our understanding of temporal and spatial variations of precipitation. However, when used in the Three-North Shelter Forest Programme of China, the spatial resolution of TRMM data is too coarse. In this study, we presented a hybrid method, i.e. a regression model with residual correction method, for downscaling annual TRMM 3B43 from 0.25° to 1 km grids from 2000 to 2009. The regression model was applied to construct the relationship among TRMM 3B43 data, continentality (CON), and the normalized difference vegetation index (NDVI) under five different scales (0.25°, 0.50°, 0.75°, 1.00°, and 1.25°). In the residual correction, three spatial interpolation techniques, i.e. inverse distance weighting (IDW), ordinary kriging, and tension spline, were employed. The 1 km monthly precipitation was disaggregated from 1 km annual precipitation by using monthly fractions. Analysis shows that (1) CON was a good variable for precipitation modelling at large-scale regions; (2) the optimum relationship between precipitation, NDVI, and CON was found at a scale of 1.25°; (3) the most feasible option for residual correction was IDW; and (4) the final annual/monthly downscaled precipitation (1 km) not only improved the spatial resolution but also agreed well with data from 220 rain gauge stations (average R2 = 0.82, slope = 1.09, RRMSE = 18.30%, and RMSE = 51.91 mm for annual downscaled precipitation; average R2 = 0.41, slope = 0.79, RRMSE = 76.88%, and RMSE = 15.09 mm for monthly downscaled precipitation).

Assessment of hydrological changes in the Nile River due to the construction of Renaissance Dam in Ethiopia

This paper assesses impact of the Renaissance Dam on Ethiopia; on the Nile discharge ultimately reaches Egypt downstream. The Landsat-8 satellite images of 2013 were obtained and interpreted to identify locations for the construction sites for the Renaissance Dam. Then the Shuttle Radar Topography Mission (SRTM) data were obtained and processed to create a digital elevation model (DEM) for the Blue Nile upstream areas that will be submerged. Different scenarios for the dams’ heights and resulting storages were simulated to estimate the resulting abstraction of the Blue Nile flows until completion of the project and the annual losses due to evaporation thereafter. The current site (506masl) for the Renaissance Dam allows the creation of a 100m deep reservoir with a total storage of 17.5km3; overflows will occur at that lake’s level (606masl) from the north western part of the developed lake into Rosaires downstream. Construction of the spillway dam to control the overflow area can allow the creation of a 180m deep lake that store up to 173km3 in a lake that will cover 3130km2. The analysis of Tropical Rainfall Monitoring Mission (TRMM) suggests that the variation of total annual rainfall could reach 20%, thus the resulting hydrological fluctuations could affect the estimated filling time, the operational functions and discharge downstream. The negative hydrological impacts of the Renaissance Dam will increase by increasing the height of its spillway dam, as increasing the storage capacity could affect the strategic storage for the reservoirs in Egypt and Sudan. It is strongly recommended that an agreement should be reached to compromise the storage capacities and water supplies for all dams on the Nile to thoroughly satisfy the necessary needs.

Estimating spatially downscaled rainfall by regression kriging using TRMM precipitation and elevation in Zhejiang Province, southeast China

Estimating regional daily rainfall accurately is of prime importance for many environmental applications, such as hydrology, meteorology, and ecology. The rainfall product from the Tropical Rainfall Monitoring Mission (TRMM) satellite is better able to estimate rainfall than rain gauge interpolation in some regions with coarse rain gauge spatial resolution. In the present article, analyses were made at 1379 rain gauge stations in Zhejiang Province, China, during January 2011 to July 2012 (536 days). A good relationship was found between the rain gauge data and the data analysis from the TRMM, especially for the precipitation that was between 2 and 10 mm day–1. However, gaps exist between TRMM products and rain gauge records, which could be considered as uncertainty. To predict rainfall more precisely, four categories of daily rainfall and three regression kriging (RK) models were selected for analysis. TRMM and elevation data were used as auxiliary variables to construct RK1. The auxiliary variable in RK2 and RK3 was TRMM and elevation data, respectively. Residuals (four rainfall categories × three RK models) of RK models showed spatial auto-correlation. Compared with RK2, which has a 0.25° resolution, RK1 and RK3 are predicted at a finer 1 km spatial resolution. However, RK1 has the best performance in rainfall prediction according to validation statistics. The root mean square error was decreased from 0.667 to 0.437 and the mean of error was improved from –0.250 to –0.007 in the prediction of mean daily rainfall. RK1 may facilitate easy downscaling of precipitation and capture the trends in daily rainfall variability.

상세화 기법을 통한 한반도 공간 강우장 분석

강우는 수문 순환에서 중요한 요소 중에 하나로 시 공간적 변동성이 크므로 정확한 공간 강우장의 파악이 요구된다. 열대강우 관측 위성(Tropical Rainfall Monitoring Mission, TRMM)에서 제공하는 3B43 월 누적 강우량 자료는 25 km의 공간 해상도를 갖고 있어 공간 강우장의 정확성을 높이기 위해 상세화 기법을 적용하여 1 km의 공간 해상도로 생성하였다. Terra 위성에 탑재된 MODIS (Moderate Resolution Imaging Spectroradiometers) 센서가 제공하는 정규식생지수(Normalized Difference Vegetation Index, NDVI) (공간 해상도 1 km)와 강우 자료의 관계성을 회귀식으로 나타냈고 상세화 기법에 적용하였다. 이에 따른 결과를 지점과 위성 강우 자료와의 차이를 통해 보정하는 방법인 GDA (Geographical Difference Analysis)와 지점과 위성 강우 자료와의 비율로 편차를 보정하는 GRA (Geographical Ratio Analysis) 상세화 기법을 사용하여 공간 강우장을 나타내었다. 우리나라의 공간 강우장 결과를 지점 자료를 기준으로 비교 검증을 실시하였다. 그 결과 GDA 상세화 기법의 경우가 2009년(Bias=4.26 mm, RMSE=172.16 mm, MAE=141.95 mm, IOA=0.64), 2011년(Bias=17.21 mm, RMSE=253.43 mm, MAE=310.56 mm, IOA=0.62)으로 가장 잘 맞는 것으로 나타났다. 이를 바탕으로 우리나라의 공간 강우장을 1 km의 공간 해상도로 파악할 수 있었으며, 더 나아가 지점의 수를 늘려 보정을 정밀하게 하거나, 강우 레이더 자료를 가지고 상세화 기법을 적용한다면 더욱 정확한 공간 강우장을 파악할 수 있을 것이다. Precipitation is one of the important factors in the hydrological cycle. It needs to understand accurate of spatial precipitation field because it has large spatio-temporal variability. Precipitation data obtained through the Tropical Rainfall Monitoring Mission (TRMM) 3B43 product is inaccurate because it has 25 km space scale. Downscaling of TRMM 3B43 product can increase the accuracy of spatial precipitation field from 25 km to 1 km scale. The relationship between precipitation and the normalized difference vegetation index(NDVI) (1 km space scale) which is obtained from the Moderate Resolution Imaging Spectroradiometers (MODIS) sensor loaded in Terra satellite is variable at different scales. Therefore regression equations were established and these equations apply to downscaling. Two renormalization strategies, Geographical Difference Analysis (GDA) and Geographical Ratio Analysis (GRA) are implemented for correcting the differences between remote sensing-derived and rain gauge data. As for considering the GDA method results, biases, the root mean-squared error (RMSE), MAE and Index of agreement (IOA) is equal to 4.26 mm, 172.16 mm, 141.95 mm, 0.64 in 2009 and 17.21 mm, 253.43 mm, 310.56 mm, 0.62 in 2011. In this study, we can see the 1km spatial precipitation field map over Korea. It will be possible to get more accurate spatial analysis of the precipitation field through using the additional rain gauges or radar data.

Ithaca worldwide flood alert system: the web framework

Information Technology for Humanitarian Assistance, Cooperation, and Action (ITHACA) is developing a worldwide early warning system for flood events (Albanese et al. 2008). The system collects rainfall data from NASA Tropical Rainfall Monitoring Mission website every 3 h, processes them, and compares them to historical data series to detect an alert level for each basin. Watershed layer of HYDRO1k, developed at the US Geological Survey’s Centre for Earth Resources Observation and Sciences, is constituted by a territorial subdivision at different levels, increasing details from levels 1 to 6; watershed at maximum detail (level-6 basin) are used for the system. A grid-computing approach is adopted to face the heavy computational load while a web-fruition framework is planned to give the widest access to the output the system produces. The user interface shows ongoing events on a map, where it is also possible to add other feeds (i.e., by Global Disaster Alert and Coordination System). The user can zoom to the alerted countries by an interactive list or have additional information on the event and possibly an estimate of the affected population shown. The framework is based on pure Free and Open Source Software (FOSS) products and common standards, according to ITHACA internal directives and following United Nations Geographic Information Working Group directives that encourage interoperability and FOSS tools adoption. The framework is a development of ITHACA infrastructure for web GIS applications; it is based on Model Controller View (MCV) architectural pattern that makes it possible to isolate the logic of the application from the interface. In this way, a change in one of the two parts does not affect the other making maintenance easier in the long term. From this point of view a controller collects, the model manipulates application data, and the view presents results to the user. Here, a Python-based MCV framework named Django is adopted; interfaces are built using JavaScript classes which some FOSS projects make available. Some OpenLayers classes are modified and combined with Ext and GeoExt libraries to increase the interaction the user can have with the interface. Data are symbolized using common Open Geospatial Consortium Styled Layer Descriptor standard. Caching mechanism for Web Mapping Service base layers is adopted.