Spatial Runoff Estimation and Mapping of Potential Water Harvesting Sites: A GIS and Remote Sensing Perspective, Northwest Ethiopia

Freshwater resources scarcity is becoming a limiting factor for development and sustenance in most parts of Ethiopia. The Debre Mewi watershed, in northwest Ethiopia, is one of such areas where the need for supplemental water supply through rainwater harvesting is essential. Suitable water harvesting sites were identified through overlay analysis considering both social and technical parameters, such as land use/land cover, slope gradient, soil texture, flow accumulation and stakeholders’ priority. This was performed with the integration of GIS and remote sensing applications. Knowledge of runoff resulting from rainfall is most important for designing any water harvesting structure. Direct field-level measurement of runoff is always good, but it is time consuming, labour intensive and expensive. In conditions where direct measurement of runoff could not be possible, remote sensing technology and GIS combined with runoff models are proven to be effective. In this study, the remotely sensed satellite data (Quickbird2) provided spatial information on land use/land cover. Precipitation was obtained from the nearest meteorological station, and soil data were acquired form laboratory analysis. The GIS tools were used to store, manipulate and estimate runoff depth, surface storage and runoff volume, applying Soil Conservation Service (SCS) Curve Number (CN) formula. The direct runoff volume estimated using SCS-CN model is 146,697 m3 for the month of August, at Debre Mewi watershed, which covers about 508 ha. The result was compared with measured values, and closer relationship was found. This indicates that there is enough runoff water to be harvested for different uses. Remote sensing was found to be a very important tool in providing input parameters. GIS was also found to be a very important tool in mapping and integrating the different variables, in the process of runoff estimation and suitable water harvesting sites selection

Remote sensing sensors and applications in environmental resources mapping and modelling

The history of remote sensing and development of different sensors for environmental and natural resources mapping and data acquisition is reviewed and reported. Application examples in urban studies, hydrological modeling such as land- cover and floodplain mapping, fractional vegetation cover and impervious surface area mapping, surface energy flux and micro-topography correlation studies is discussed. The review also discusses the use of remotely sensed-based rainfall and potential evapotranspiration for estimating crop water requirement satisfaction index and hence provides early warning information for growers. The review is not an exhausted application of the remote sensing technique rather summary of some important applications in environmental studies and modeling

Evaluating sediment storage dams: structural off-site sediment trapping measures in northwest Ethiopia

Reservoir and lake sedimentation is a vital problem in Ethiopia. Constructing small and medium size dams at the outlets of sub-catchments within larger catchment helps to reduce the transport of sediment downstream to reservoirs or lakes. This study assessed the sediment trapping efficacy (STE) of sediment storage dams (SSDs) built at the outlets of eight small sub-catchments in northwest Ethiopia, as an off-site sediment trapping measure. Satellite imagery and topographic maps were used to assess land use/land cover and delineate the boundaries of sub-catchments. In the field, trapped sediment by SSDs was measured directly, as well as in- and outflow of suspended sediment with which the STE of each SSD was estimated. Sediment yield of each sub-catchment was calculated from the measured trapped sediment and estimated suspended sediment loss. Results show that SSDs trapped an average of 1584 t yr-1 of the inflow sediment and catchment specific sediment yield ranged from 8.6-55 t ha-1 yr-1. Two representative SSDs constructed from gabion and stone were evaluated with regard to their STE. Results showed that their efficacy was 74% and 67% for the gabion and stone SSD, respectively. In general, although SSDs might be costly for small scale farmers and have a relatively short life span depending on their size, they are promising off-site structural measures to trap significant amounts of sediment at the outlets of sub-catchments and subsequently reducing sediment movement to downstream water bodies

Suspended sediment load prediction of river systems: An artificial neural network approach

Information on suspended sediment load is crucial to water management and environmental protection. Suspended sediment loads for three major rivers (Mississippi, Missouri and Rio Grande) in USA are estimated using artificial neural network (ANN) modeling approach. A multilayer perceptron (MLP) ANN with an error back propagation algorithm, using historical daily and weekly hydroclimatological data (precipitation P(t), current discharge Q(t), antecedent discharge Q(t-1), and antecedent sediment load SL(t-1)), is used to predict the suspended sediment load SL(t) at the selected monitoring stations. Performance of ANN was evaluated using different combinations of input data sets, length of record for training, and temporal resolution (daily and weekly data). Results from ANN model were compared with results from multiple linear regressions (MLR), multiple non-linear regression (MNLR) and Autoregressive integrated moving average (ARIMA) using correlation coefficient (R), mean absolute percent error (MAPE) and model efficiency (E). Comparison of training period length was also made (4, 3 and 2 years of training and 1, 2 and 3 years of testing, respectively). The model efficiency (E) and R2 values were slightly higher for the 4 years of training and 1 year of testing (4 * 1) for Mississippi River, indifferent for Missouri and slightly lower for Rio Grande River. Daily simulations using Input 1 (P(t), Q(t), Q(t-1), SL(t-1)) and three years of training and two years of testing (3 * 2) performed better (R2 and E of 0.85 and 0.72, respectively) than the simulation with two years of training and three years of testing (2 * 3) (R2 and E of 0.64 and 0.46, respectively). ANN predicted daily values using Input 1 and 3 * 2 architecture for Missouri (R2 = 0.97) and Mississippi (R2 = 0.96) were better than those of Rio Grande (R2 = 0.65). Daily predictions were better compared to weekly predictions for all three rivers due to higher correlation within daily than weekly data. ANN predictions for most simulations were superior compared to predictions using MLR, MNLR and ARIMA. The modeling approach presented in this paper can be potentially used to reduce the frequency of costly operations for sediment measurement where hydrological data is readily available.Artificial neural network (ANN) Sediment prediction Multiple linear regressions (MLR) Multiple non-linear regression (MNLR) Autoregressive integrated moving average (ARIMA) Mississippi Missouri Rio Grande

Satellite based cloud detection and rainfall estimation in the Upper Blue Nile basin

In this study remote sensing for rainfall estimation is evaluated. For the Lake Tana basin in Ethiopia the diurnal cycle of rainfall is assessed using satellite observations at high temporal resolution and ground based observations. Also convective activity of a cloud system on the lake has been observed through satellite imagery and shows a potential to observe characteristics of a cloud that produced extreme rainfall intensity. These characteristics include the cloud area and a volume index as well as temporal evolution of distance and direction of the centroid of a cloud mass from a rain gauge at the Gurer Island in Lake Tana. In this work it is concluded that remote sensing can be very helpful in estimating rainfall, assessing the diurnal cycle and monitoring heavy rainfall producing clouds. The high potential of remote sensing observations is mainly because the observations are consistently available with spatially continuous coverage