Due to its important spatiotemporal variability, accurate rainfall monitoring is one of the most difficult issues in semi-arid mountainous environments. Moreover, due to the inconsistent distribution of gauge measurement, the availability of precipitation data is not always secured and totally reliable at the instantaneous time step. As a result, earth observation of precipitation estimations could be an alternative for overcoming this restriction. The current study presents a framework for either the hydro-statistical evaluation and bias correction of the Global Precipitation Measurement (GPM) Integrated Multi-SatellitE Retrievals version 06 Early (IMERG-E), Late (IMERG-L), and Final (IMERG-F) products. On a sub-daily duration, from the Taferiat rain gauge-based station, which was used as a benchmark from 1 September 2014 to 31 August 2018. Statistical analysis was performed to examine each precipitation product’s performance. The results showed that all Post_Real_Time and Real_Time IMERG had a high level of awareness accuracy. The IMERG-L results were statistically similar to the gauge data, succeeded by the IMERG-F and IMERG-E. The Cumulative Distribution Function (CDF) has been employed to adjust the precipitation values of the three IMERG products in order to decrease bias estimation. The three products were then integrated into the “HEC-HMS” hydrological model to assess their dependability in flow modeling. Six flood occurrences were calibrated and validated for each product at 30-minute time steps. With a mean Nash-Sutcliffe coefficient of NSE 0.82, the calibration findings demonstrate that IMERG-F provides satisfactory hydrological performance. With an NSE = 0.80, IMERG-L displayed good hydrological utility, slightly better than IMERG-E with an NSE = 0.77. However, when the flood events were validated using the initial soil conditions, IMERG F and IMERG E overestimated the discharge by 13% and 10%, respectively. While IMERG L passed the validation phase with an average score of NSE = 0.69.
Water management has become one of the major interests in arid and semi-arid regions. Scientists have suggested different criteria and methodologies for the identification of suitable dam sites. According to our literature review, we have used two major methodologies for the selection of suitable dam site location: geographic information system and remote sensing (GIS/RS) and multicriteria analysis (MCA) integrated with GIS/RS. The most common criteria used for the selection of suitable dam sites were slope, rainfall, land use land cover, soil type, lithology, lineament density, and hydrographic typology. All the factors were superimposed to prepare the synthesis map of water-harvesting structures, each thematic layer’s weight was determined, and storage water potential indices were calculated using water accumulation conditions. According to the water-harvesting location map, where the spatial distribution of the excellent (5%), very good (9%), and good (17%) aptitude classes is established in the northeast and central parts of the westward zone, the average located in the center of the zone. Study and weak are located south of the map; the area of moderate (25%) to poor (44%) suitability is situated in the south and southwest zone. The MCA was validated using an existing dam across the study area, where the MCA provides for the dam located in the good and moderate zones. The approach adopted in this study can be applied for any other location globally to identify potential dam-construction sites. From the point of view of the literature of multicriteria analyses of water recovery, areas unsuitable for surface water harvesting and dam projects are suitable for groundwater recharge.
<p>Accurate measurement of precipitation is very important for flood forecasting, hydrological modeling, and estimation of the water balance of any basin. The lack of a weather monitoring network is an obstacle to the accurate measurement of precipitation.</p><p>In most of the Moroccan High Atlas Mountains regions, ground observation stations are still unreliable and difficult to access due to several parameters, such as a large spatial and temporal variation of rainfall and ruggedness of topography, which lead to irregularity and scarcity of measuring stations. This area is characterized by arid and semi-arid climates where generally occurred a few rainy days but have experienced significant flash floods.</p><p>Consequently, floods are causing extended damages to the population and infrastructures every year. However, research on hydrological processes is limited due to the irregularity of the gauge station network and the large number of gaps frequently observed in the rainfall and runoff data acquired from the gauge stations. Remote sensing precipitation data with high spatial and temporal resolution are a potential alternative to gauged precipitation data.</p><p>This study evaluates the performance of the two satellite products: the Tropical Rainfall Measuring Mission (TRMM 3B43V7) Multi-satellite Precipitation Analysis (TMPA) and the Integrated Multi-satellite Retrievals for GPM (IMERG V06) (SPPs) to observed rainfall, at different time scales (daily, monthly, and annual) from 1 September 2000 to 31 August 2017 over the Ghdat watershed, with different statistical indices and hydrological assessment, to evaluate the reliability of these (SPPs) data to reproduce rainfall events by implementing them in a hydrological model, to determine their ability to detect all types of rainfall events.</p><p>Daily, monthly, and annual rainfall measurements were validated using widely used statistical measures (CC, RMSE, MAE, Bias, Nash, POD, FAR, FBI and ETS).</p><p>The results showed that: (1) The correlation between satellite precipitation data and rainfall precipitation demonstrated a high correlation on all daily, monthly, and annual scales. (2) The product (TRMM 3B42V7) exhibits better quality in terms of correlation on the monthly and annual scale, while the (GPM IMERG V06) product shows a high correlation on the daily scale compared to the measurements of the gauges. (3) The (GPM IMERG V06) product has better performance regarding the precipitation detection capability, compared to the (TRMM 3B42V7) product which could detect only tiny precipitation events, but not able to capture moderate or strong precipitation events. (4) Flood events can be simulated with the hydrological model using both observed precipitation data and satellite data with the Nash &#8211; Sutcliffe model efficiency coefficient (NSE) ranging from 0.65 to 0.90.</p><p>According to the results of this study, we concluded that (TRMM 3B42V7) and (GPM IMERG V06) satellite precipitation products can be used for flood modeling and water resource management, particularly in the semi-arid and Mediterranean region.</p>
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