Shakir Ali

Rainfall–runoff simulation using a normalized antecedent precipitation index

Abstract The normalized antecedent precipitation index (NAPI) model by Heggen for the prediction of runoff yield is analytically derived from the water balance equation. Heggens model has been simplified further to a rational form and its performance verified with the Soil Conservation Service Curve Number (SCS-CN) model. The simplified model has three coefficients specific to a watershed, and requires two inputs: rainfall and the derived parameter, NAPI. The characteristic behaviour of the NAPI has resonance with the curve number (CN) of the SCS model. The proposed NAPI model was applied to three watersheds in the semi-arid region of India to simulate runoff yield. The model showed improved correlation between the observed and predicted runoff data compared to the SCS-CN model. The F test and paired t test also confirmed the reliability of the model with significance levels of 0.01 and 0.001%, respectively. The proposed model could be used successfully for rainfall–runoff modelling in a watershed. Citation Ali, S., Ghosh, N. C. & Singh, R. (2010) Rainfall–runoff simulation using a normalized antecedent precipitation index. Hydrol. Sci. J. 55(2), 266–274.

Simulation of Water Temperature in a Small Pond Using Parametric Statistical Models: Implications of Climate Warming

AbstractChanges in temperature and precipitation patterns due to global warming are likely to affect the quantity and quality of water in different water bodies. Water temperature modeling techniques are usually employed to study the effects of global climate change on stream and river ecosystems. This study aims to identify a suitable air–water temperature relationship for a small aquatic pond in a semiarid region of India and examine the effects of increased water temperature on the small pond’s attributes. The performance of two parametric statistical models—simple linear regression (SLR) and four-parameter nonlinear logistic regression (NLR) models—was evaluated. The developed models were field tested for mean, minimum, and maximum air–water temperatures on daily, weekly, and monthly timescales. The model parameters were estimated from the measured air–water temperature time-series data using the least-squares optimization method. Model performance was evaluated using three statistical indicators—the ...

Ravines: Prospective Zone for Carbon Sequestration

Ravines are the extreme form of land degradation owing to water erosion and along with gullies of various sizes occupy about 10.37 million ha area in the country which could be utilized for sequestering carbon through improving vegetation densities/plantations. In fact, ravines are the most fragile ecosystems that have very low soil carbon content due to their light texture and poor aggregate stability. Thus, there is an urgent need to manage and restore these lands with suitable cultural and management practices. In fact, utilization of medium and deep ravine lands for regular cultivation always remains challenging; however, these ravines can be alternatively utilized for energy plantation, augmentation of fuel and fodder demands for local populace and production of hardy underutilized fruits and oil seed-bearing tree/shrub species. Ravine lands, which are economically unsuitable for agriculture, can be successfully stabilized by planting fast-growing species like bamboo on the gully beds and buffel grass/dhaman grass (Cenchrus ciliaris) on the side slopes and the interspaces of gully bed for economic utilization of gullied land. It was also evident that Acacia nilotica, Bambusa species and Aegle marmelos are highly suitable for ravine area and provide a substantial role in carbon sequestration under ravine landforms of Chambal and Yamuna river. Similarly, Prosopis juliflora and Azadirachta indica have greater ability for sustaining site productivity due to their greater leaf litterfall and fine root production under Yamuna ravines. In this chapter, an attempt has been made to address some of the issue of ravines vis-a-vis potential zone for carbon sequestration.

Solution to Green–Ampt infiltration model using a two-step curve-fitting approach

Infiltration is an important hydrological process affecting the runoff, groundwater recharge and solute transport process. The Green–Ampt (GA) model, describing one-dimensional infiltration process, yields an implicit equation for estimation of cumulative infiltration with time. In this paper, an explicit approximation to the GA model is derived using two-step curve-fitting technique. The Marquardt’s algorithm is employed for least-squares estimation of nonlinear parameters. Performance of the proposed model is compared with the implicit GA model using numerical and published field experimental data. The quantitative statistical indicators namely, percent relative error (RE), maximum absolute percent relative error (MARE), percent bias (PB), and Nash–Sutcliffe modeling efficiency (E), are used to assess the performance of the proposed model. The solution of the proposed model matched very well with that of the implicit GA model with MARE ≤ 0.146%, PB ≤ 0.070%, and E value approximately equal to 1 for both the dimensionless numerical and dimensional field experimental infiltration estimates. Simulation results of the proposed model demonstrated the capability of the derived model in estimating infiltration rate and cumulative infiltration accurately and can be applied to solve variety of real-life hydrological problems.

Methodology for the Estimation of Wetting Front Length and Potential Recharge under Variable Depth of Ponding

AbstractA methodology for determining the length of advancement of wetting front and potential groundwater recharge under variable depth of ponding is presented. The methodology provides no restrictions to infiltration time period, depth, and nature of ponding and soil types. Performance of the proposed methodology has been compared with other models using the published laboratory and field experimental data. The quantitative statistics, namely, coefficient of determination (R2), index of agreement (d), and percent bias (PB), are utilized to assess the performance of the proposed methodology. Results show that the proposed methodology worked with the same potential as the numerically rigorous solution of the other models. The quantitative statistics R2 and d between the models’ estimates approached unity. Analyzed results shows that the proposed methodology for estimation of advancement of wetting front, cumulative potential recharge, and 5rate of potential recharge has a maximum PB of −12.98, −14.33, and...