Watershed morphometric analysis of Wadi Baish Dam catchment area using integrated GIS-based approach

The integration of Remote Sensing (RS) and Geographic Information Systems (GIS) constitutes a powerful tool for the evaluation of watershed morphometric parameters. The benefits of this integration include saving time and effort as well as improving the accuracy of the analysis. Moreover, this technique is appropriate for describing the watershed and its streams. In this study, a detailed morphometric analysis of the Wadi Baish catchment area has been performed using the Shuttle Radar Topography Mission (SRTM). The performed morphometric analysis includes linear, areal, and relief aspects. The results of the morphometric analysis reveal that the catchment can be described as of eighth stream order and consists of an area of 4741.07 km². Additionally, the basin is characterized by a relatively high mean value of bifurcation (4.012), indicative of the scarcity of permeable rocks with high slope in the area. This value of bifurcation ratio is consistent with the high drainage density value of 2.064 km/km² and confirms the impermeability of the subsurface material and mountainous relief. The hypsometric integral of the catchment is 47.4%, and the erosion integral of the catchment is 52.6%, both were indications of a mature catchment area.     

Adaptation of climate variability/extreme in arid environment of the Arabian peninsula by rainwater harvesting and management

Water management in Saudi Arabia is facing major challenges due to the limited water resources and increasing uncertainties caused by climate change. The rainfall and temperature records of the Saudi meteorological data for more than three decades were analyzed for policy suggestions in water sectors based on the changing rainfall patterns. The trends in the annual aridity and rain indices were also examined to define the changing climate conditions and for determining the dry months in different cities of the Kingdom. An increased annual and maximum rainfall was observed for six cities while a decreasing trend in both annual and maximum rainfall was observed for the same number of cities highlighting the variability of rainfall in the whole region. An increasing maximum rainfall with decreasing annual rainfall was observed for the rest of the cities signifying the more extreme rainfall evens and resulting floods of short durations. The changing rainfall trends were also observed for different months during 31 years of the recorded period in addition to the varying climate pattern for different cities within the same district. Finally, these preliminary assessments of any systematic changes in view of the increased rain intensities and extreme climate events are viewed to demonstrate the value rainwater harvesting and management as a local adaptation to the climate variability and extreme in the Kingdom.     

Land cover change dynamics mapping and predictions using EO data and a GIS-cellular automata model: the case of Al-Baha region,Kingdom of Saudi Arabia

The present study designed to monitor and predict land cover change (LCC) in addition to characterizing LCC and its dynamics over Al-Baha region, Kingdom of Saudi Arabia, by utilizing remote sensing and GIS-cellular automata model (Markov-CA). Moreover, to determine the effect of rainwater storage reservoirs as a driver to the expansion of irrigated cropland. Eight Landsat 5/7 TM/ETM images from 1975 to 2010 were analyzed and ultimately utilized in categorizing LC. The LC maps classified into four main classes: bare soil, sparsely vegetated, forest and shrub land, and irrigated cropland. The quantification of LCC for the analyzed categories showed that bare soil and sparsely vegetated was the largest classes throughout the study period, followed by forest, shrubland, and irrigated cropland. The processes of LCC in the study area were not constant, and varied from one class to another. There were two stages in bare soil change, an increase stage (1975–1995) and decline stage (1995–2010), and the construction of 25 rainwater-harvesting dams in the region was the turning point in bare soil change. The greatest increase was observed in irrigated cropland after 1995 in the expense of the other three categories as an effect of extensive rainwater harvesting practices. Losses were evident in forest and shrubland and sparsely vegetated land during the first stage (1975–1995) with 5.4 and 25.6 % of total area in 1995, while in 1975, they covered more than 13.8 and 32.7 % of total area. During the second stage (1995–2010), forest and shrubland witnessed a significant increase from 1569.17 km² in 1975 to 1840.87 km² in 2010. Irrigated cropland underwent the greatest growth (from 422.766 km² in 1975 to 1819.931 km² in 2010) during the entire study period, and this agriculture expansion reached its zenith in the 2000s. Markov-CA simulation in 2050 predicts a continuing upward trend in irrigated cropland and forest and shrubland areas, as well as a downward trend in bare soil and sparsely vegetated areas; the spatial distribution prediction indicates that irrigated cropland will expand around reservoirs and the mountain areas. The validation result showed that the model successfully identified the state of land cover in 2010 with 97 % agreement between the actual and projected cover. The output of this study would be useful for decision makers and LC/land use planners in Saudi Arabia and similar arid regions.     

Simple statistical equivalents of Penman–Monteith formula’s parameters in the absence of non-basic climatic factors

The Penman?CMonteith (PM) method is the most recommended method for estimating reference evapotranspiration (ET_o). The PM equation requires several parameters to be available, either measured or computed. Some of these parameters are conventionally calculated by some slightly sophisticated formulas, especially for handy calculations. This paper aimed to derive some simpler statistical equivalents to these formulas. Simplifications were performed to the formulas of the saturation vapor pressure e _o[T], slope of vapor pressure (?), atmospheric pressure (P), the psychrometric constant (??), wind speed correction, the long-wave radiation, R _nl; the sunset hour angle, ?? _s; and the extraterrestrial radiation, R _a. For the first five parameters, the parameter-independent factor was analyzed for its extremes, then fitted by interpolation to a simpler equivalent formula. The last three parameters were fitted to simpler form through data from the FAO-CLIMWAT database. Each of the simplified formulas was compared to the conventional one; some correlation indices were applied to validate the new formulas. The ET_o was calculated for all stations in the CLIMWAT database by both simplified and conventional formulas. All the correlation results were excellent, with a minimum correlation coefficient of 0.9966. The simplified formulas were proven to be equivalent in performance, with almost no loss in accuracy but simpler in form and faster in execution in the online database applications.     

Estimating potential direct runoff for ungauged urban watersheds based on RST and GIS

Estimating the potential direct runoff for urban watersheds is essential for flood risk mitigation and rainwater harvesting. Thus, this study aims to estimate the potential runoff depth based on the natural resources conservation service (NRCS) method and delineation of the watersheds in Riyadh, Saudi Arabia. To accomplish this objective, the geographic information systems (GIS) and remote sensing technique (RST) data were integrated to save time and improve analysis accuracy. The employed data include the digital elevation model (DEM), soil map, geology map, satellite images, and daily precipitation records. Accordingly, the hydrologic soil groups (HSG), the land use/land cover (LULC), and curve number (CN) were determined for each watershed in the study area. The results of this analysis show that the study area can be delineated into 40 watersheds with a total area of 8500 km². Furthermore, the dominant HSG is group D, which represents about 71% of the total area. The LULC maps indicate four major land types in the entire study area: urban, barren land, agricultural land, and roads. The CN of the study area ranges from 64 to 98, while the weighted CN is 92 for the city. The rainfall-runoff analysis shows that the area has a high and very high daily runoff (35–50 and >?50 mm, respectively). Therefore, in this case, the runoff leads to flooding, especially in the urban area and agricultural lands.     

Determination and prediction of standardized precipitation index (SPI) using TRMM data in arid ecosystems

Drought over a period threatens the water resources, agriculture, and socioeconomic activities. Therefore, it is crucial for decision makers to have a realistic anticipation of drought events to mitigate its impacts. Hence, this research aims at using the standardized precipitation index (SPI) to predict drought through time series analysis techniques. These adopted techniques are autoregressive integrating moving average (ARIMA) and feed-forward backpropagation neural network (FBNN) with different activation functions (sigmoid, bipolar sigmoid, and hyperbolic tangent). After that, the adequacy of these two techniques in predicting the drought conditions has been examined under arid ecosystems. The monthly precipitation data used in calculating the SPI time series (SPI 3, 6, 12, and 24 timescales) have been obtained from the tropical rainfall measuring mission (TRMM). The prediction of SPI was carried out and compared over six lead times from 1 to 6 using the model performance statistics (coefficient of correlation (R), mean absolute error (MAE), and root mean square error (RMSE)). The overall results prove an excellent performance of both predicting models for anticipating the drought conditions concerning model accuracy measures. Despite this, the FBNN models remain somewhat better than ARIMA models with R?≥?0.7865, MAE?≤?1.0637, and RMSE?≤?1.2466. Additionally, the FBNN based on hyperbolic tangent activation function demonstrated the best similarity between actual and predicted for SPI 24 by 98.44%. Eventually, all the activation function of FBNN models has good results respecting the SPI prediction with a small degree of variation among timescales. Therefore, any of these activation functions can be used equally even if the sigmoid and bipolar sigmoid functions are manifesting less adjusted R² and higher errors (MAE and RMSE). In conclusion, the FBNN can be considered a promising technique for predicting the SPI as a drought monitoring index under arid ecosystems.