https://vital.seals.ac.za/vital/access/manager/Index ${session.getAttribute("locale")} 5 https://vital.seals.ac.za/vital/access/manager/Repository/vital:42922 Wed 19 May 2021 10:55:23 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:54939 Wed 17 Aug 2022 14:39:15 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:41050 Wed 12 May 2021 15:05:06 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:41155 Wed 12 May 2021 15:03:14 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:41148 Wed 12 May 2021 14:54:29 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:41019 Wed 12 May 2021 14:53:50 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:42482 Wed 12 May 2021 14:47:22 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:42934 0.5). The model also performed generally well compared to established models that had been previously applied in some of the study catchments. The highest sediment yields recorded per country were 153 t km-2 year-1 (Tsitsa River; South Africa), 90 t km-2 year-1 (Odzi River; Zimbabwe) and 340 t km-2 year-1 (Rio Tanama; Puerto Rico). The results also displayed consistent underestimations of peak sediment yield events, partly attributed to sediment emanating from gullies that are not explicitly accounted for in the WQSED model structure. Furthermore, the calibration process revealed that the WQSED storage model is generally challenging to calibrate. An alternative simpler version of the storage model was easier to calibrate, but the model may still be challenging to apply to catchments where calibration data are not available. The additional evaluation of the WQSED simulated sediment yield rates against observed reservoir sediment rates showed a broad range of differences between the simulated and observed sediment yield rates. Differences between WQSED simulated sediment and observed reservoir sediment ranges from a low of 30% to a high of > 40 times. The large differences were partly attributed to WQSED being limited to simulating suspended sediment from sheet and rill processes, whereas reservoir sediment is generated from more sources that include bedload, channel and gully processes. Nevertheless, the model simulations replicated some of the regional sediment yield patterns and are assumed to represent sheet and rill contributions to reservoir sediment in selected catchments. The outcome of this study is an improved WQSED model that has successfully undergone preliminary testing and evaluation. Therefore, the model is sufficiently complete to be used by independent researchers and water resources managers to simulate erosion and sediment transport. However, the model is best applicable to areas where some observed data or regional information are available to calibrate the storage components and constrain model outputs. The report on potential MUSLE scale dependencies is relevant globally to all studies applying the MUSLE model and, therefore, can improve MUSLE application in future studies. The WQSED model offers a relatively simple, effective and applicable tool that is set to provide information to enhance catchment, land and water resources management in catchments of Africa.]]> Wed 12 May 2021 14:47:01 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:42950 Wed 12 May 2021 14:46:17 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:41139 Wed 12 May 2021 14:43:20 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:42952 Wed 12 May 2021 14:42:52 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:42485 Wed 12 May 2021 14:40:35 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:41032 Wed 12 May 2021 14:40:25 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:42810 Wed 12 May 2021 14:39:44 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:41134 Wed 12 May 2021 14:36:19 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:41051 Wed 12 May 2021 14:34:28 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:42847 Wed 12 May 2021 14:24:55 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:41142 Wed 12 May 2021 14:21:50 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:41029 Wed 12 May 2021 14:19:55 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:42782 Wed 12 May 2021 14:11:52 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:41151 Wed 12 May 2021 14:11:42 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:41125 Wed 12 May 2021 14:07:40 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:41156 Wed 12 May 2021 14:04:32 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:41010 Wed 12 May 2021 14:00:32 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:41002 Wed 12 May 2021 13:59:29 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:41108 Wed 12 May 2021 13:56:41 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:41147 Wed 12 May 2021 13:55:16 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:42942 Wed 12 May 2021 13:51:34 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:42897 Wed 12 May 2021 13:50:47 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:42807 Wed 12 May 2021 13:48:55 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:42939 Wed 12 Jul 2023 08:28:50 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:42790 Tue 25 May 2021 09:30:39 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:42791 Tue 25 May 2021 08:48:47 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:42769 Tue 23 May 2023 11:45:11 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:42798 Tue 18 May 2021 15:55:51 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:42938 Tue 16 Nov 2021 09:37:21 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:42891 Tue 14 Feb 2023 07:55:10 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:33249 Thu 13 May 2021 07:39:35 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:27449 Thu 13 May 2021 06:54:36 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:38593 Thu 13 May 2021 05:49:52 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:28307 Thu 11 Apr 2024 19:25:38 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:57236 Sun 09 Oct 2022 18:49:45 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:57234 Sun 09 Oct 2022 18:07:39 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:42937 4. The modeling methods used in the study were artificial neural network (ANN), linear regression (LR) and polynomial functions. The approach taken was to first test the modeling techniques on a single station before expanding the study to cover the regional aspect. The single station modeling was developed based on ionosonde data over Grahamstown. The inputs for the model which related to seasonal variation, diurnal variation, geomagnetic activity and solar activity were considered. For the geomagnetic activity, three indices namely; the symmetric disturbance in the horizontal component of the Earth’s magnetic field (SYM − H), the Auroral Electrojet (AE) index and local geomagnetic index A, were included as inputs. The performance of a single station model revealed that, of the three geomagnetic indices, SYM − H index has the largest contribution of 41% and 54% based on ANN and LR techniques respectively. The average correlation coefficients (R) for both ANN and LR models was 0.8, when validated during the selected storms falling within the period of model development. When validated using storms that fall outside the period of model development, the model gave R values of 0.6 and 0.5 for ANN and LR respectively. In addition, the GPS total electron content (TEC) derived measurements were used to estimate foF2 data. This is because there are more GPS receivers than ionosonde locations and the utilisation of this data increases the spatial coverage of the regional model. The estimation of foF2 from GPS TEC was done at GPS-ionosonde co-locations using polynomial functions. The average R values of 0.69 and 0.65 were obtained between actual and derived _foF2 over the co-locations and other GPS stations respectively. Validation of GPS TEC derived foF2 with RO data over regions out of ionospheric pierce points coverage with respect to ionosonde locations gave R greater than 0.9 for the selected storm period of 4-8 August 2011. The regional storm-time model was then developed based on the ANN technique using the four South African ionosonde stations. The maximum and minimum R values of 0.6 and 0.5 were obtained over ionosonde and GPS locations respectively. This model forms the basis towards the regional ionospheric storm-time index.]]> Mon 31 May 2021 15:12:57 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:41020 Mon 18 Dec 2023 18:00:37 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:42936 Mon 17 May 2021 15:55:32 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:42802 Mon 17 May 2021 12:32:33 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:57256 Mon 10 Oct 2022 09:43:43 SAST ]]> https://vital.seals.ac.za/vital/access/manager/Repository/vital:42896 Fri 28 May 2021 11:35:51 SAST ]]>