Institute of Human Resource Advancement (IHRA)

dxfla h iudc h:d¾:h kj,s nr,a Okjdofha .% yKhg yd jiÕhg k;= lr.ekS u mßmQ ¾K lrñka ;s fí' ;jÿrg;a fYa Ij mej;s udkj iïnka O;d ^tfia mej;s fha hehs Tn úYa jdi lrka fka kï& wêfõ. fhka len,s lrKhg ,la ls Ífï m% ;s M,h ùfha mgq fm!oa .,s l;a jh úis ka h<s -h<s ;a fN!;S ia u lrk ,o yq ÿ fla j,-mq oa ., ;ks ñks iq ka f.ka ms ß iudc h:d¾:hla wm wNs uq j ks ¾udKh ù ;s îuhs ' tjeks iudchla foa Ymd,ks lj ixúOdkh ls Íu ir, ke;" myiq ke;' ula ks ido" yq ÿ fla j,-mq oa ., ;ks ñks iq ka njg iudch len,s lrKhg ,la ùu hkq iudchla ks ¾-foa Ymd,kS lrKfha .s ,S hduhs ' tfy;a " fuys § zks ¾-foa Ymd,kS lrKhZ hk jok tys mo.; wre; bla ujd hk zfoa Ymd,khla Z leáfldg f.k we;s njo y÷kd.; hq ;= h' ta wkq j ks ¾-foa Ymd,kh hkq j;a uka Okjdofha tkï" kj,s nr,a Okjdofha foa Ymd,khhs ' ks ¾-foa Ymd,kS lrKh fuh ;jÿrg;a iudc-jHjydßl wre;ls ka meyeÈ,s lrka fka kï" foa Ymd,kh idfma la I wref;ka iudc iduQ ys l;a jhla ks fhda ckh lrk úg ks ¾-foa Ymd,kh tlS iduQ ys l;a jhg mgyeks j mgq mq oa .,;a jh ks fhda ckh lrhs " foa Ymd,kh ;u iudÔh meje;a fï wkd.;h wruq Kq lrk úg ks ¾-foa Ymd,kh ;u mgq mq oa ., j;a uka fudfyd; wruq Kq lrhs ' ta fjkq fjka wkd.;h mrÿ ;nhs ' ks ¾-foa Ymd,kS lrKh ñks iq ka f.a jHjydßl Ôú;h ;= < kreujdohla f,i m% ldYhg m;a jka fka tf<is ks '

4'2 lDIs ld¾ñl wxYfha /ls hdjla f;da rd fkd.ekS fïoS n,mdk iudcS h idOl úYa f,a IKh 4'3 lDIs ld¾ñl wxYfha /ls hdjla f;da rd fkd.ekS fïoS n,mdk N+ f.da ,S h idOl úYa f,a IKh mia jk mßÉfPa oh ks .uk yd fhda ckd 5'1 ks .uk 5'2 woyia yd fhda ckd 5'2 wOHhkfha iS udjka 6 m% ia :dr igyka rEm igyk 3.1 wod< m% foa Yfha fia jd ks hq la ;s fha jhdma ;s h' rEm igyk 3.2 mq oa .,hka w;r wOHdmk uÜgfï jHdma ;s h' rEm igyk 3.3 mq oa .,hka f.a wdodhï jHdma ;s h' rEm igyk 4.1.1 fËa ;% fha /ls hdjl ks r; ùfuka ,efnk wdodhu m% udKj;a nj' rEm igyk 4.1.2 lDIs ld¾ñl fËa ;% fha /ls hd u.s ka ,efnk wdodhfï ia :djrNdjh' rEm igyk 4.1.3 lDIs ld¾ñl fËa ;% fha lghq ;= i|yd ,efnk iykdOdrj, m% udKj;a nj' rEm igyk 4.2.1 lDIs ld¾ñl fËa ;% fha /ls hdjl ks r; ùfuka ,efnk iudc ms <s .ekS u' rEm igyk 4.2.2 lDIs ld¾ñl fËa ;% fha /ls hdjla f;da rd .ekS u ms <s n|j foudms h woyi' rEm igyk 4.2.3 ;dla IK os hq Kq j;a iu. ldhs l fjfyi wju jk f,i lDIs ld¾ñl lS % hdj,s h is ÿjq jfyd;a ta fËa ;% hg fhduq õu' rEm igyk 4.2.4 hemq ï lDIs l¾udka ;h wd¾Ólh ;= < mej;s h hq ;= nj' rEm igyk 4.2.5 lDIs l¾ñl fËa ;% fha /ls hdjla f;da rd .ekS u fyda fkd.ekS u flfrys ;dla IKs l os hq Kq j n,mEu' rEm igyk 4.3.1 lDIs ld¾ñl lghq ;= i|yd fhda .H bvï mej;S u' rEm igyk 4.3.2 lDIs ld¾ñl bvïj, mfia ia jNdjh yd c, iemhq u'

m<uq mßÉfþoh yeos ka jS u 1'1 m¾fha IKh ye¢ka ùu j¾;udk Y% S ,xldfõ m% Odk úfoa Y úks uh bmehq ï ud¾.hla njg m;a ù we;a f;a úfoa Y /ls hdjhs ' 1970-80 w;r ld,h ;= < ueofmrÈ. rgj,s ka kq mq yq Kq Y% uh i|yd mej;s b,äu jeä jq w;r tys m% ;s M,hla f,i Y% S ,dxls l kq mq yq Kq Y% ñlhka g ;u Y% uh wf,ú lr .ekS fï wjia :dj Wod ùu u; Y% S ,dxls l Y% ñlhka jeä jYfhka ueofmrÈ. m% uq L fjk;a úfoa Y rgj,g /ls hdj, ks hq la ; ùfï m% jK;djhla j¾Okh úh' fï ks id ,xldjg úfoa Y úks uh úfoa Y /ls hd uÕs ka .,d ta ula is ÿùu ks id Y% S ,xld rch o fï flfrys jeä wjOdkhla fhduq l< nj fmfka ' ueofmrÈ. we;= ¿ úfoa Y rgj, Y% u fj<ofmdf,a kq mq yq Kq Y% ñlhka g we;s b,äu jeäfj;a u Y% S ,dxls lhka o ;u wd¾Ól iudÔh wfma la Idjka bgq lr .ekS fï l% uhla jYfhka jeä jeäfhka /ls hd i|yd úfoa Y .; ùula uE; ld,S kj oels h yels Wmk;s hls ' ks , ixLHd f,a Lk uÕs ka fmka kq ï lrk wdldrhg úfoa Y /ls hdj, ks hq ;= m% udKh w;s Yhs ka u nyq ;rhla ldka ;djka nj fmkS h;s ' 2002 j¾Ifha 858000la jq ixl% ñlhka w;ßka 590420la ldka ;dfjda jQ y' fuu ldka ;djka f.ka 75]la muK újdyl jk w;r újdyl ldka ;djka f.ka 90]la orejka we;s uõjreka fj;s ^chj¾Ok" 2006" ms ( 03&' fï w;ßka jvd jeä m% udKhla ks fhda ckh lrka fka .% dóh m% foa Yj,s ks ' 1977 g fmr /ls hd i|yd úfoa Y .; jq fha b;d iq ¿ fldgils ' tu fldgi wh;a jq fha ffjoH" bxðfka re jD;a ;s h jeks b;d Wiia /ls hd i|yd muKs ' tfy;a újD; wd¾Ólfha m% ;s M,hla f,i 1977 ka miq j ueofmrÈ. rgj,a a yd ihs m% ih" ud,Èjhs k jeks rgj,a j,g wmf.a ,dxls l Y% ñlhka úYd, jYfhka ixl% uKh úh' fï w;ßka úYd, ixLHdjla .Dy fia jl fia úld weÕ¨ï l¾udka ; wd § iq ¿ /ls hd i|yd tu rgj,g ixl% uKh úh' ta rgj, ixia lD;s l mßirh ;= < /ls hdjka g ldka ;d Y% ñlhs ka f.a wvq iyNd.s ;a jh o wm ráka f.ka jd .ka kd Y% uhg wvq jegq mla ,nd §ug yels neúka ,dxls l ldka ;djka g jeä jYfhka /ls hd wjia :d Wod jq Ks ' ;j ÿrg;a ,dxls l ldka ;jka fuu ixl% uKhg úfYa I we,a ula oela ùug fya ;= j jq fha tf;la mej;s rfgys w¾: l% uh hgf;a ldka ;djka g /ls hd wjia :d wvq ùu;a " hï /ls hdjl ks hq la ; jq j o ldka ;djka g fï rfÜ § ,efnk jegq mg jvd is ;a .ka kd iq ¿ by< jegq mla ,nd .; yels ùuh' fï ks id by< Ôjk ;;a ;a jhla wfma la Idfjka ráka ms g jq ldka ;djka f.a mjq ,a j, ÿ.S Ndjh yd ú/ls hdj hk m% Ya kj,g úi÷ula f,i úfoa Y /ls hdj,g ixl% uKh ùu oela úh yels h' úfoa Y fia jd ks hq la ; fuls kq mq yq Kq Y% ñlhka f.a b;s ßh rfÜ úfoa Y úks uh bmehS fï m% Odk udOHhla jq neúka fï cd;Hka ;r ixl% uKhg rdcH wkq .% yh o ,enq Ks ' úfoa Y ixl% uKh i|yd jeä jYfhka fhduq jq fha ldka ;djka h' rfÜ ck.ykfhka ñ,s hkhla muK úfoa Y fia jfha fh § is ák njg ;la fia re lr we;s w;r ths ka 70]la g wdika k ixLHdjla ldka ;djka njg wkdjrKh fõ'

Soil erosion is a widespread problem in Nillambe  catchment and it accommodates a pulation that is mainly based on agriculture. Consequently it is important to identify the
socio-economic risk in the area in order to carryout necessary mitigation measures to minimize the potential risk. This study attempts to categorize socio-economic risk levels of soil erosion.  Based on USLE (Universal Soil Loss Equation), geo-spatial input data representing rainfall, soils, land slopes and land use were used to model soil erosion hazard and a
selected set of socio-economic vulnerability and coping capacity indicator variables were spatially modeled based on AHP (Analytical Hierarchical Process) and the outcomes of
hazard, vulnerability and coping capacity measurements were further modeled using GIS
based Disaster Risk Equation to obtain the socio-economic risk index for Nillambe
catchment. 
The study revealed that approximately 25 % of the catchment is in moderate to extremely high socio-economic risk to soil erosion.  Furthermore, it indicates that the majority of
such lands are covered with moderately managed tea and a slope greater than 10 % (slope
of the catchment varies between 0 – 36 degrees) with a moderately high rainfall erosivity level between 16 - 27. 
The maps identifying the socio-economic vulnerability, coping capacity and the  risk levels  to soil erosion can be utilized to identify areas with different vulnerabilities and
coping capacities to promote necessary mitigation actions and strengthen socio-economic capacities of the community involved which would  eventually  minimize  the socio-economic risk for soil erosion in the area.

Background: Landslides area controversial issue worldwide and cause a wide range of impacts on the socioeconomic systems of the affected community. However, empirical studies of affected environments remain inadequate for prediction and decision making. This study aims to estimate the direct impact of a massive landslide that occurred around areas with Kandyan home gardens (KHGs)in Aranayake, Sri Lanka.

Landslide hazard is one of the most common global hazards. In Sri
Lanka, landslides are considered as a disaster, and thus, scientific
communities have paid attention to monitoring and prediction of
landslide hazards. Landslide hazard zonation (LHZ) is a vital factor
for preparedness and mitigation phases in a disaster management
cycle. In the Sri Lankan contest, LHZ becomes more significant
since 20% of the total lands are vulnerable. Spatial distribution of
landslides of Sri Lanka is mainly influenced by geospatial criteria,
rainfall distribution, geology, hydrology, geomorphology, land-use,
and drainage network. However, the above factors do not equally
contribute to determining the landslide susceptibility. This study
attempted to map the landslide hazard zones in one of the tropical
hilly region: Kegalle District and weighting causative factors
rationally using statistical method in GIS environment. In this
study, causative factors were weighted and modelled to define
hazardous zones by geographical information system (GIS)-
based spatial multi criteria evaluation (SMCE). The necessary
geospatial data were obtained, processed, and converted into
a grid format. The contribution level of each factor for triggering
landslides was evaluated by the Analytical Hierarchy Process
(AHP) and modelled with the SMCE. The developed SMCE
model is at an acceptable level because the acquired consistency
ratio value is 0.074 (≤ 0.1). The developed LHZ map shows a
90% level prediction accuracy compared to previous landslides.
According to landslide hazard zonation map, 13% (227 km2) of
the entire area is a very high landslide susceptible zone, while
37% (634 km2) of the total land area has a high susceptibility
to slope failure. Moderate and low susceptible zones were 32%
(542 km2) and 12% (203 km2) respectively, and only 6% (96
km2) of the entire study area belonged to the very low landslide
susceptible zone.

Presently, the urban heat island (UHI) phenomenon, and its adverse impacts, are becoming major research foci in various interrelated fields due to rapid changes in urban ecological environments. Various cities have been investigated in previous studies, and most of the findings have facilitated the introduction of proper mitigation measures to overcome the negative impact of UHI. At present, most of the mountain cities of the world have undergone rapid urban development, and this has resulted in the increasing surface UHI (SUHI) phenomenon. Hence, this study focuses on quantifying SUHI in Kandy City, the world heritage tropical mountain city of Sri Lanka, using Landsat data (1996 and 2017) based on the mean land surface temperature (LST), the difference between the fraction of impervious surfaces (IS), and the fraction of green space (GS). Additionally, we examined the relationship of LST to the green space/impervious surface fraction ratio (GS/IS fraction ratio) and the magnitude o...

Background: Landslides area controversial issue worldwide and cause a wide range of impacts on the socioeconomic systems of the affected community. However, empirical studies of affected environments remain inadequate for prediction and decision making. This study aims to estimate the direct impact of a massive landslide that occurred around areas with Kandyan home gardens (KHGs)in Aranayake, Sri Lanka. Results: Primary data were gathered by structured questionnaire from residents of the directly affected regions; the questionnaire data were combined with spatial data to acquire detailed information about the livelihoods and hazards at the household level. Satellite images were used to find affected land use and households prior to the landslide. Further, secondary data were obtained to assess the recovery cost. A multiple regression model was established to estimate the economic value of the home gardens. Field surveys and satellite images revealed that land-use practices during the past decades have caused environmental imbalance and have led to slope instability. Conclusions: The results reveal that 52% of household income is generated by the KHG and that the income level highly depends on the extent of the land (R 2 = 0.85, p < 0.05). The extent of destroyed land that was obtained from the satellite images and the age of the KHG were used to develop a multiple regression model to estimate the economic loss of the KHG. It was found that the landslide affected region had been generating approximately US$ 160,000 annually from their home gardens toward the GDP of the country. This study found that almost all houses in the area were at risk of further sliding, and all of them were partially or entirely affected by the landslide. Among the affected households, 60% (40 houses) had completelycollapsed, whereas 40% (27 houses) were partially damaged. Because of these circumstances, the government must provide US $ 40,369 to recover the fully and partially damaged households. Finally, a lack of awareness and unplanned garden cultivation were the main contributing factors that increased the severity of the damage.

A landslide inventory is a detailed register of the spatial distribution, geometry, and attributes of landslides and is essential for landslide hazard analysis, risk management, regional planning, and land use management and development, especially in landslide-prone regions. However, the development of a national landslide inventory is time-consuming and costly. Accordingly, most developing countries, including Sri Lanka, have basic landslide databases, which identify the location, date, and time of occurrence on a point map. This study, therefore, aimed to introduce a new method to report landslide information via a mobile application based on a participatory approach with information recorded in a web portal called the participatory landslide inventory (PLI). Twenty-one landslide site locations and their attributes were captured on the PLI web portal using the PLI mobile app. The system administrator then demarcated the landslide boundaries and performed geometrical calculations ...

The concept of landslide vulnerability to a given location is hard to quantify. Few studies have been carried to determine susceptibility using social and physical factors. This study is the first attempt in Sri Lanka to quantify level of vulnerability by integrating major physical and social indicators to map the spatial distribution of vulnerability. Considering the limitations of traditional weight evaluation method in calculation of the multiple indicators and ignorance of the associations among evaluating indicators, a new weight evaluation process, entropy method was introduced in this study. This improved method for determination of weight of the evaluating indicators was applied to estimate weight for the 14 selected indicators. The primary data were obtained from a comprehensive questioner survey (n = 402) of households or buildings (elements) with their coordinates based on a spatially balanced approach for ensuring spatial coverage of the entire landslide distribution. The spatial distribution of vulnerability was mapped using Kriging interpolation. According to the map, landslide vulnerabilities in the study area demonstrate notable regional specifications. Besides, the spatial distribution of vulnerability has shown a close relationship with rural and urban settlements. Results of spatial vulnerability reflect discrimination and inequalities in the development of the study area. According to landslide vulnerability analyses, 14.6% (247 km 2) of the entire area is found to be the highest vulnerable zone for a landslide and 39.8% (675 km 2) of area categorized under the lowest zone to vulnerability. Further, the study revealed a reasonable contribution by entropy method on analysis of social and physical indicators, which is useful for other vulnerability assessments.

The basic principle for the recovery of residential area from a landslide disaster is restoring the damaged area to its condition before the disaster. This study focuses on evaluates the recovery process and reinstallation of pre-disaster economic functions after the landslide occurred in the year 2016 at Aranayake, Sri Lanka. Estimated values of the collapsed infrastructure is 7,806 USD, and the affected region generates 200 000 USD for the annual country GDP. In contrast, 887 families directly or indirectly affected by the landslide. The primary data were obtained from comprehensive questioner survey of affected household (n=120), semi-structured focused group discussions, and key informant discussions. Recovery was assumed as a function of emergency recovery (ER), infrastructure resettlement (IR) and long-term recovery (LtR). Correlation analysis and multiple regression analysis were used to model the association between dependent variable Recovery and independent variables ER, IR and LtR. The findings revealed that, there is no systematic procedure used to monitor the progress of recovery programme. LtR has a profoundly positive effect on recovery with compared to IR and ER. Results suggest that ER and IR are individually insignificant but they effect on recovery jointly. Multiple regression model can be expressed as Recovery = 0.205+ 0.640ER + 0.124IR + 0.249LtR. The finding of this study is recommended to establish an institutional framework to monitor, evaluate and rectify the disaster recovery process with standardized indicators, procedures, and guidelines. Further, it is recommended to adopt a community based long-term recovery approach for sustainable landslide disaster recovery. Contribution/Originality: In many projects landslide disaster recovery limited to damaged infrastructure development in contrast, this study recommended the significant of long term recovery plan in any recovery project. Further this study proposed a recovery model which allowed monitoring recovery in a manner that is rapid, independent and reliable.

Mass movement in Sri Lanka is mainly triggered by heavy rainfall. International literature is rich of works defining rainfall intensity-duration models to identify the rainfall threshold for various types of Mass movement. However, studies have not focused to establish a relationship between intensity and duration of rainfall in Sri Lanka. Therefore, this study focused to establish rainfall intensity-duration models to identify the rainfall threshold for mass movements in Badulla district in Sri Lanka, where forty four (44) rainfall events that resulted in same number of landslides during the last three decades were considered. Results indicate the rainfall threshold relationship fits to the log linear model of the exponential function, I = α•D −β. The constructed I-D curve revealed that short duration (<2 h) and high-intensity (>54 mm/h) in rainfall events can potentially trigger the landslide. However, long-duration (>8 h) and low-intensity (<25 mm/h) in rainfall events may also trigger mass movements in Badulla. As per the results, most mass movements occur during northeast monsoons and inter-monsoons. In general, higher mean rainfall intensities trigger the debris flows, while long-duration rainfall events can trigger both landslides and debris flow. When compared to Sri Lankan mass movements triggering threshold intensities are fairly higher than the global threshold values. It confirms that within Badulla, mass movements are triggered by very high intense and/or long duration rainfalls events only. Further, time series analysis of the rainfall events shows an upward trend of extreme rainfall events, which increased landslide occurring frequency in last six (6) years.

Changes in the urban landscape resulting from rapid urbanisation and climate change have the potential to increase land surface temperature (LST) and the incidence of the urban heat island (UHI). An increase in urban heat directly affects urban livelihoods and systems. This study investigated the spatiotemporal variation of the UHI in the Kurunegala urban area (KUA) of North-Western Province, Sri Lanka. The KUA is one of the most intensively developing economic and administrative capitals in Sri Lanka with an urban system that is facing climate vulnerabilities and challenges of extreme heat conditions. We examined the UHI formation for the period 1996–2019 and its impact on the urban-systems by exploring nature-based solutions (NBS). This study used annual median temperatures based on Landsat data from 1996 to 2019 using the Google Earth Engine (GEE). Various geospatial approaches, including spectral index-based land use/cover mapping (1996, 2009 and 2019), urban-rural gradient zone...