Use of Remote Sensing and GIS in Urban Hydrologic Analysis
S.Herath1, K.Musiake1, S.Wijesekara2, and L.Samarakoon3
1Institute of Industrial Science,
University of Tokyo, 7-22-1, Roppongi
Minato-ku, Tokyo 106, Japan
Tel: 03-3402-6231 Fax: 03-3402-4165
2Department of Civil Engineering,
University of Moratuwa
Moratuwa, Sri Lanka
Tel: +49-1-873148
3Nippon Koei Co. Ltd.,
203 Takasaki, Kukiezaki, Inashiki, Ibaraki Ken, Japan
Tel : 0298-71-2045 Fax: 0298-71-2022
Abstract
Urban hydrological cycle is driven by water inputs in the form of rainfall and water supply to urban area. Hydrlogy is generally analyzed by the hydrologists only considering the water cycle resulting from the rainfall, where as drainage engineers are mainly concerned with the discharge of waste water and the overland flow resulting from storms. Where urban environment is concerned, it is important to consider the total water cycle resulting from both inputs as water supplies may consist nearly half of total water input to the catchment in heavily urbanized areas. In this paper we present a method to compute water balance in urban areas. In this paper we present a method to compute water balance in urban areas using different database to estimate the water quantities that enter the catchments and their subsequent distributions. Two cases are presented, in an experimental catchment in the suburbs of Tokyo, Japan, and another in Colombo, Sir Lanka.
1. Introduction
In order to analyze the urban hydrological cycle, it is first necessary to identify the water movement for both natural and artificial water cycles. Artificial water cycle here refers to the water supply, usage and drainage by he in habitants of the catchment, whearas the natural water cycle refers to tht resulting from the rainfall, which turn into overland flow, infiltration evaportanspiration, ground water discharge and river flow. Thee are sophisticated methods for modeling each of these components individually, but only few attempts have been made to modeling each of these components individually, but only few attempts have been made to analyze the total water cycle inurbane area. Understanding of the total water cycle is important to assess and improve the urban environment, where most of the world population is going to live with the turn of the century.
Fig. 1 shows a schematic view of the main components of the urban water cycle. The quantifying of this water cycle is a major problem, because most of existing monitoring systems are grated to maintain and design different components of the system, and not to estimate the behavior of the total system. When detailed information are avaibable, it is possible to describe the complete hydrological cycle using distributed hydrologic models which can simulate the spatial and temporal variation of the different components. For example, Hearth et. al (1996) described a GIS based distributed hydrologic model and validated it with an application to hurly stream flow data and Nakamura et. al (1996) describe
the application of a sistributed model to an urban catchment which shows very good arement with observed groundwater levels and hydrographs at several streams in the catchment. However, these models requie extensive computational resource for model set up and execution. In this paper we investigate a simplified method of estimating catchment water balance using spatial datasets which would help in quantitating the components shown in Fig. 1.
Figure 1 Schematic view of the urban water cycle components
2. Estimationof the Component of the water Cycle
The water balance at the surface can e estimated by,
¶St
-----
¶t |
= Rt - Et - Inft (1) |
where S = Surface Storage, R = Rainfall, E = Evaporationand Inf= Infiltration Amount. The subscript l denote either pervious or impervous fraction of the grid. When the surface Storage, S exceeds the maximum storage capacityof the grid Smax1, surface runoff occurs. The amount of direct runoff frompervius and impervious fractions in each grid (Dr1) are then given by,
Dr1 = Smax1 - S1 (2)
To estimate Inf, th infiltration capacity of the soil has to be estimated. This is only relevant for the previous areas as the infiltration capacityof the imprevius areas is zero.For water balance estimation infiltration is approximated by the infiltration capacity of the top soil layer. The infiltration capacity is limited by the maximum infiltrating velocity which is eqivalent to the soil saturated conductivity. When rainfall data resolution is not high enough (i.e. higher than of the order of minutes) the rainfall intensity gets averaged and infiltration capacity tends to get oversged and infiltration capaicity tends to get overrstimated. Therefore an exponential distribution of rainfall is assumed and the infiltration capacity is estimated as,
Rex = Rav Exp (-K0 / Rav) (3)
Where R
ex is the rainfall excess, Rex is the average rainfall within the measuring interval and R0 is the saturated conductivity of the soil.
The infiltrating amount of water is then estimated as (1.-imf) x Rex where imf stands for the impervious fraction of the grid. This water then flows as subsurface flow or infiltrates down to the groundwater . The amount of groundwater recharge (Rchg) and subsurface flow (subft) components are estimated as,
where K (
q) is the average conductivity of the top soil layer. These equation (4) and (5) been shown to be good approximates to the more complete estimation to be derived by solution of the Richards equation (Ni et. al., 1993).
For the water supply, the transformation of this input is estimated as,
Qs(T) = I(T)xp (6)
QI(T) =Qs(T) x Lc (7)
Qd(T)=Qs(T) -QI(T) -Qu(T) (8)
Where Qs(T) is the water supply within the period T, I(T) is the per capita water consumption/demand unit, p is the demand density (i.e. population density), Ql is the loss of
water during trasmission, Qd is the drainage discharge, and Qu is the water used, or los which does not enter to the drainage network.
3. Data Source for Deriving Attrigutes for the Analysis
Table 2. shows the different information layers that can by used for the estimation of components of the water cycle.
Table 2. Information layers for the estimation of hydrologic cycle components
| Information Layer |
Attributes to be derived |
| Digital Elevation Model |
Catchment Boundaries, Surface gradients, Stream network |
| Landuse Map |
Previous and Impervious area estimation
Leaf area inde for evaporation estimation
Surface rughness for overland flow computation |
| Soil type distribution |
Soil hydraulic properties for infiltration and subsurface flow computation |
| Population distribution |
Estimation of water consumption |
| Water supply domains |
Estimation water supply and leakage |
| Drainage network |
Identification of wastewater routing |
For urban application it is desirable to have these data set in the range of 50 m gird sizes.
