The Indian Ocean Deep Meridional Overturning Circulation in Three Ocean Reanalysis Products

The time mean Indian Ocean (IO) deep meridional overturning circulation (MOC) is compared across three ocean reanalysis products (ORAS4, GECCO2, and GFDL). The MOC stream functions obtained by vertically integrating the mass flux across a latitude-depth section in three products are found to be significantly different from each other. Detailed analysis suggests that ORAS4 delivers the best depiction of IO MOC. The inferred IO deep MOC consists of two deep and strong counterclockwise cells located south of 30°S and around 10°S, respectively. The geostrophic component along with the barotropic or external mode dominates the former, and a combination of Ekman and geostrophic components dominates the latter. GECCO2 depicts a steady decline in the northward meridional transport in the bottom layer and a consequent reduction in the MOC strength. The tropical thermocline in GECCO2 responds to this MOC variability leading to rapid and monotonic warming of the tropical IO

Comparative evaluation of SCS-CN-inspired models in applications to classified datasets

One of the popular methods for estimating the depth of surface runoff for a given rainfall event is the Soil Conservation Service Curve Number (SCS-CN) method. Of late, several inconsistencies in its soil moisture accounting procedure have been pointed out by Michel et al. (2005), and a more rational procedure suggested. Recently, a modification incorporating an expression for estimation of initial soil moisture store level, a crucial parameter, was suggested by Sahu et al. (2007). The present study compares this modification with the original SCS-CN model and the other available variants on a large set of data of 76 small agricultural watersheds of the United States and finally suggests an improved model. The comprehensive comparison between these models reveals the proposed improvement to perform better than all other versions in all classified applications based on land use, soil type, combinations of land use and soil type, and precipitation regimes. A simplified version of the model is further suggested for practical applications.Rainfall-runoff modeling Curve number method Soil moisture accounting (SMA)

A GIS-integrated fuzzy rule-based inference system for land suitability evaluation in agricultural watersheds

Land suitability evaluation in water scarce agricultural watersheds consists of assessment of land potential for various crops as well as surface water potential to identify the scope for supplementary irrigation. A large amount of information related to the crop land suitability can be conveyed through linguistic terms. Capability of fuzzy sets in modeling involving uncertainty and vagueness is made use of in fuzzy rule-based systems, where various decision making criteria in linguistic terms are expressed as fuzzy rules. In the present study, a fuzzy rule-based inference system is developed in Geographic Information System (GIS) environment to assess the land suitability pertaining to the specified crop, considering both land potential and surface water potential. When large numbers of attributes are involved in decision making, representation of the attributes in a common scale, aggregation of the attributes and design of the rule-base becomes difficult tasks. In order to model the heterogeneous land suitability criteria involving large number of attributes, a new approach is proposed in this study in which the attributes are systematically classified into different groups to estimate the intermediate suitability indices. Weighted linear aggregation method and Yager's aggregation method are used for estimating the aggregated effect of the attributes in each group and the results are compared. Further, the rule-base is developed by using the intermediate land suitability indices. The model has been applied to a subwatershed of Gandheshwari area in West Bengal (India). The input attributes are prepared in raster map format in the GIS environment by using ERDAS IP ver. 9.1 and the output is generated in the form of thematic map showing the suitability of each cell (20 m x 20 m) for the selected crop. For the land suitability evaluation problem in the case study area, Yager's aggregation method has been found more appropriate than the commonly used weighted linear aggregation method. From the analysis, 23% of the existing paddy fields have been found less suitable/not suitable for paddy due to the poor surface water potential or unsuitable terrain conditions of the area. The method, integrated with GIS, is found efficient in handling large amount of attribute information, and is useful in the land suitability assessment in agricultural watersheds.Land suitability Fuzzy logic Multi-criteria evaluation Supplementary irrigation Yager's aggregation method Geographic Information System

Geospatial estimation of soil moisture in rain-fed paddy fields using SCS-CN-based model

Paddy fields are characterized by standing water and saturation condition during the entire crop growth period. However, in sub-humid and semi-arid areas, scarce rainfall and intermittent dry spells often cause soil moisture depletion resulting in unsaturated condition in the fields. These distinctive characteristics of the paddy fields have significant influence on the runoff generation and soil moisture retention characteristics of the watershed. In this study, the objective is to extend the application of the Soil Conservation Services Curve Number (SCS-CN)-based models for the geospatial and temporal simulation of soil moisture to paddy field-dominated agricultural watersheds in the water scarce areas. Different SCS-CN-based models, integrated with the soil moisture balance equation, are used to estimate the surface runoff and soil moisture content wherein, the spatial variation in the soil hydraulic characteristics is used to calculate the geospatial variation in soil moisture content. Physical significance of the terms initial abstraction (Ia) and potential maximum retention (S) in these models and their influence on the estimation of runoff and soil moisture are analysed in detail. A new SCS-CN-based model for soil moisture simulation (SCS-CN-SMS), to improve the soil moisture estimation, is proposed in this paper. The proposed model is built up on the soil moisture balance equation to account for the effect of ponding condition and soil moisture variation between the dry and saturation condition. The method is tested with 3 years observed surface runoff data and crop production statistics from a part of the Gandeshwari sub-watershed in West Bengal, India. The entire study area is divided into cells of 20 m × 20 m. Various components of the soil moisture balance equation are estimated for each cell as a function of the soil moisture content. Remote Sensing Technique and Geographic Information System (GIS) are used to extract and integrate the spatially distributed land use and soil characteristics. The Hortonion overland flow concept adopted in the SCS-CN method is used to estimate the soil hydraulic characteristics of each cell in which the curve number is used to infer the spatial variation of the land use and soil characteristics. Even though the original SCS-CN method and the existing modified versions are efficient for runoff estimation, these models are found to be inappropriate for the estimation of soil moisture distribution. On the other hand, the proposed SCS-CN-SMS model gives better results for both runoff and soil moisture simulation and is, therefore, more suitable for the hydrological modeling of paddy field-dominated agricultural watersheds.