Hydrologic Modeling Using SWAT and GIS, Application to Subwatershed Bab-Merzouka (Sebou, Morocco)

Water management in morocco faces significant challenges in both levels, quality and quantity, in order to address these challenges and to secure the rational and efficient management of water resources, it is necessary to try to understand the different issues involved in the water cycle works! The large number of hydrological studies and techniques used at the Sebou watershed are limited to water quality, and the use of the simpler empirical models, that, do not permit to take into consideration all the factors affecting flow and cycle of water. In the purpose of using the hydrological model SWAT to study the hydrological functioning of the Sebou watershed (Morocco) which faces many problems regarding the management of its water resources like all the other watersheds in our country, this study focuses on the creation, organization and generation of input data (soil, soil occupation, climate…) using a GIS (ARCGIS) then, the configuration and calibration of the model and after that, the evaluation of the results. The main reason behind this work is to measure the adaptability of this model to the selected area in order to generalize the model to the whole Sebou watershed. The results are satisfying;they show that this model can represent the overall hydrological regime of the pilot basin Bab-Merzouka. This model can open up new perspectives regarding the effects of climate change, and the changes caused by humans on water resources, in terms of both quantity and quality.

Hydrological Modelling of Sidi Jabeur Watershed (Morocco) Using Spatially Distributed Model ATHYS

The objective of this study is to model the hydrology in the Sidi Jabeur basin, located in Bouregreg watershed at the north-central of Morocco, using the spatially distributed model (ATHYS) in order to understand and determine the different watershed hydrological processes. The study requires the collection of a series of data as inputs models namely rainfall data, water quantity, soil occupation, digital terrain model and requires also a calibration in order to evaluate the model in validation phase. The simulation results are obtained from the validation phase aim to replicate the operation of Sidi Jabeur watershed, and present a suitable adjustment perspective of the observed hydrograph. These results show that the objective is achieved and a model distributed like ATHYS plays an effective role in improving the efficiency and presents a high advantage in anticipation of runoff volume.

Do model choice and sample ratios separately or simultaneously influence soil organic matter prediction?

This study was performed to examine the separate and simultaneous influence of predictive models’choice alongside sample ratios selection in soil organic matter(SOM).The research was carried out in northern Morocco,characterized by relatively cold weather and diverse geological conditions.The dataset herein used accounted for 1591 soil samples,which were randomly split into the following ratios:10%(∼150 sample ratio),20%(∼250 sample ratio),35%(∼450 sample ratio),50%(∼600 sample ratio)and 95%(∼1200 sample ratio).Models herein involved were ordinary kriging(OK),regression kriging(RK),multiple linear regression(MLR),random forest(RF),quantile regression forest(QRF),Gaussian process regression(GPR)and an ensemble model.The findings in the study showed that the accuracy of SOM prediction is sensitive to both predictive models and sample ratios.OK combined with 95%sample ratio performed equally to RF in conjunction with all the sample ratios,as the latter did not show much sensitivity to sample ratios.ANOVA results revealed that RF with a∼10%sample ratio could also be optimum for predicting SOM in the study area.In conclusion,the findings herein reported could be instrumental for producing cost-effective detailed and accurate spatial estimation of SOM in other sites.Furthermore,they could serve as a baseline study for future research in the region or elsewhere.Therefore,we recommend conducting series of simulation of all possible combinations between various predictive models and sample ratios as a preliminary step in soil organic matter prediction.

Mapping soil nutrients via different covariates combinations:theory and an example from Morocco

Background:Mapping of soil nutrients using different covariates was carried out in northern Morocco.This study was undertaken in response to the region’s urgent requirement for an updated soil map.It aimed to test various covariates combinations for predicting the variability in soil properties using ordinary kriging and kriging with external drift.Methods:A total of 1819 soil samples were collected at a depth of 0–40 cm using the 1-km grid sampling method.Samples were screened for their pH,soil organic matter(SOM),potassium(K_(2)O),and phosphorus(P_(2)O_(5))using standard laboratory protocols.Terrain attributes(T)computed using a 30-m resolution digital elevation model,bioclimatic data(C),and vegetation indices(V)were used as covariates in the study.Each targeted soil property was modeled using covariates separately and then combined(e.g.,pH~T,pH~C,pH~V,and pH~T+C+V).k=tenfold cross-validation was applied to examine the performance of each employed model.The statistical parameter RMSE was used to determine the accuracy of different models.Results:The pH of the area is slightly above the neutral level with a corresponding 7.82%of SOM,290.34 ppm of K_(2)O,and 100.86 ppm of P_(2)O_(5).This was used for all the selected targeted soil properties.As a result,the studied soil properties showed a linear relationship with the selected covariates.pH,SOM,and K 2O presented a moderate spatial autocorrelation,while P2O5 revealed a strong autocorrelation.The cross-validation result revealed that soil pH(RMSE=0.281)and SOM(RMSE=9.505%)were best predicted by climatic variables.P_(2)O_(5)(RMSE=106.511 ppm)produced the best maps with climate,while K_(2)O(RMSE=209.764 ppm)yielded the best map with terrain attributes.Conclusions:The findings suggest that a combination of too many environmental covariates might not provide the actual variability of a targeted soil property.This demonstrates that specific covariates with close relationships with certain soil properties might perform better than the compilation of different environmental covariates,introducing errors due to randomness.In brief,the approach of the present study is new and can be inspiring to decision-makers in the region and other world areas as well.