Use of Different Statistic Approaches on Variability in Hydrologic Variables

The unnatural change in the globe under influence of devastating global warming has been quashing the overall functioning of ecosystem since industrial revolution. Thus, the human-induced disaster caused by proportional increase of greenhouse gases in the atmosphere has affected the normal functioning of hydrologic cycle. Under the undesirable condition, the amount of hydrologic variables began to diverge over time. Hydrologic variable should be homogeneous for the reliability of hydraulic structure while predicting necessary design criteria for its construction. Therefore, the test of whether this requirement is true should be performed in the context of any given hydrologic data’s homogeneity before being passed to the implementation of statistical approaches to the data. The study carried out in Yesilirmak basin was realized on homogeneity of seasonal maximum streamflow data from eight gauging stations operated by The General Directorate of State Hydraulic Works (DSI). Yesilirmak River basin area is approximately 5% of surface area of Turkey. Yesilirmak River is one of the major rivers of Turkey and its long is 519 kilometers. There are three main tributaries of the Yesilirmak River, named as Kelkit, Cekerek and Tersakan. Its water is mostly used for purposes as irrigation, drinking, fisheries and wildlife. The parametric and non-parametric procedures, called as standard normal homogeneity, Pettitt, Buishand range and von Neuman ratio were used for this reason. Statistically significant inhomogeneity with respect to the all of the statistic tests taken into account in the study was detected in the considered streamflow data sequences presented.

Long-Term Drought Trends in Ethiopia with Implications for Dryland Agriculture

Intraseason and seasonal drought trends in Ethiopia were studied using a suite of drought indicators—standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI), Palmer drought severity index (PDSI) and Z-index for Meher (long-rainy), Bega (dry), and Belg (short-rainy) seasons—to identify drought-causing mechanisms. Trend analysis indicated shifts in late-season Meher precipitation into Bega in the southwest and southcentral portions of Ethiopia. Droughts during Bega (October–January) are largely temperature controlled. Short-term temperature-controlled hydrologic processes exacerbate rainfall deficits during Belg (February–May) and highlight the importance of temperature- and hydrology-induced soil dryness on production of short-season crops such as tef. Droughts during Meher (June–September) are largely driven by precipitation declines arising from the narrowing of the intertropical convergence zone (ITCZ). Increased dryness during Meher has severe consequences on the production of corn and sorghum. PDSI is an aggressive indicator of seasonal droughts suggesting the low natural resilience to combat the effects of slow-acting, moisture-depleting hydrologic processes. The lack of irrigation systems in the nation limits the ability to combat droughts and improve agricultural resilience. There is an urgent need to monitor soil moisture (a key agro-hydrologic variable) to better quantify the impacts of meteorological droughts on agricultural systems in Ethiopia.

Runoff Estimation of Watershed in Hemavathy Basin using GIS based SCS-CN Method

Estimation and quantification of catchment surface runoff an important hydrologic variable used in most of the water resources applications watershed development and management problems. In this study, rainfall-runoff relationship of Hemavathy river basin is determined using Soil Conservation Services-Curve Number (SCS-CN) method for runoff estimation for ungauged watersheds. The important parameters considered include land use/land cover, soil, vegetation, drainage, precipitation, contour, slope, daily rainfall data. From the 11year daily rainfall data daily runoff was estimated using SCS CN equation considering antecedent moisture conditions. Daily runoff depth in the watershed was then computed using SCS-CN equation was later converted to runoff volume. It was observed in runoff potential of the watershed about 41% of area having high CN value interprets in more runoff. The runoff thus calculated was compared with gauged flow at dam site observed that regression coefficient is almost same for both estimated and observed data and an increase of about 15% in inflow data as per project authorities in the catchment which may be due to regenerated water from irrigation and presences of perennial streams in the catchment even during non-monsoon months there is inflow observed.

An improved ARIMA model for precipitation simulations

Auto regressive integrated moving average (ARIMA) models have been widely used to calculate monthly time series data formed by interannual variations of monthly data or inter-monthly variation. However, the influence brought about by inter-monthly variations within each year is often ignored. An improved ARIMA model is developed in this study accounting for both the interannual and inter-monthly variation. In the present approach, clustering analysis is performed first to hydrologic variable time series. The characteristics of each class are then extracted and the correlation between the hydrologic variable quantity to be predicted and characteristic quantities constructed by linear regression analysis. ARIMA models are built for predicting these characteristics of each class and the hydrologic variable monthly values of year of interest are finally predicted using the modeled values of corresponding characteristics from ARIMA model and the linear regression model. A case study is conducted to predict the monthly precipitation at the Lanzhou precipitation station in Lanzhou, China, using the model, and the results show that the accuracy of the improved model is significantly higher than the seasonal model, with the mean residual achieving 9.41 mm and the forecast accuracy increasing by 21%.

Regionalization of landscape characteristics to map hydrologic variables

By integrating groundwater, surface water and vadose zone systems, the terrestrial hydrologic system can be used to spatially map water balance characteristics spanning local to global scales, even when long-term stream gauge data are unavailable. The Watershed Characteristics Approach (WCA) is a hydrologic estimation model developed using a system-based approach focused on the regionalization of landscape characteristics to define unique hierarchical hydrogeological units (HHUs) and establish their link to hydrologic characteristics. Although the WCA can be used to map any hydrologic variable, its validity is demonstrated by summarizing results generated by applying the methodology to quantify the renewable groundwater flux at a spatial scale lacking long-term stream gauge monitoring data. Landscape components for 97 East-Central Minnesota (ECM) watersheds were summarized and used to identify which unique combinations of characteristics statistically influenced mean annual minimum groundwater recharge. These resulting combinations of landscape characteristics defined each HHU; as additional characteristics were applied, units were refined to create a hierarchical organization. Results were mapped to spatially represent the renewable groundwater flux for ECM, demonstrating how hydrologic regionalization can address knowledge gaps in multi-scale processes and aid in quantifying water balance components, an essential key to sustainable water resources management.

Rainfall and runoff forecasting with SSA–SVM approach

Real time operation studies such as reservoir operation, flood forecasting, etc., necessitates good forecasts of the associated hydrologic variable(s). A significant improvement in such forecasting can be obtained by suitable pre-processing. In this study, a simple and efficient prediction technique based on Singular Spectrum Analysis (SSA) coupled with Support Vector Machine (SVM) is proposed. While SSA decomposes original time series into a set of high and low frequency components, SVM helps in efficiently dealing with the computational and generalization performance in a high-dimensional input space. The proposed technique is applied to predict the Tryggevælde catchment runoff data (Denmark) and the Singapore rainfall data as case studies. The results are compared with that of the non-linear prediction (NLP) method. The comparisons show that the proposed technique yields a significantly higher accuracy in the prediction than that of NLP.

FRACTALITY OF RAINFALL: IDENTIFICATION OF TEMPORAL SCALING LAW

This paper introduces the concept of point-fractal to analyze the time-scale variability of rainfall data on the northern and southern regions of Taiwan. It is evident that scale invariance exists in time and clustering will decrease in accord with the increase of threshold. Under the variation of threshold, it can be verified that the maximum values of the homogenous scale-invariant interval are just the same. In addition, taking the probability-scale law on different levels of threshold, the relation can be established between the saturation scale (return period) and the threshold (design hydrologic variable). The methodology is different from the traditional one, i.e. frequency analysis method. When both methods are compared, it is found that the former does not require either the probability density function or the calculation of parameters. We conclude that this study provides a new alternative method in computation.