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Assessment of hydrological alteration from 1996 to 2017 in Brantas watershed, East Java, Indonesia

Indarto Indarto Hendra Andiananta Pradana Sri Wahyuningsih Muhammad K. Umam
Journal of Water and Land Development | 2020 | No 46 | 121-130 | DOI: 10.24425/jwld.2020.134204
Keywords Brantas watershed hydrological alteration land use rainfall
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Abstract

Climate, land use, and land cover change can propagate alteration to the watershed environment. The interaction be-tween natural and human activities probably accelerates the change, a phenomenon that will generate serious environmental problems. This study aims to evaluate the change in the hydrological regime due to natural and human-induced processes. The study was conducted in Brantas watershed, Indonesia, which is the largest watershed in East Java. This area is populat-ed by more than 8 million inhabitants and is the most urbanized area in the region. An analysis of rainfall time series use to shows the change in natural phenomena. Two land-use maps at different time intervals were used to compare the rapid de-velopment of urbanization, and the discharge from two outlets of the sub-watersheds was employed to assess hydrological changes. The indicator of hydrological alteration (IHA) method was used to perform the analysis. The daily discharge data are from 1996 to 2017. The research results show an increase in flow (monthly, 1-day, 3-day, 7-day, 30-day, and 90-day flows) in the two sub-watersheds (Ploso and Kertosono) from the pre-period (1996–2006) to the post-period (2007–2017).

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Authors and Affiliations

Indarto Indarto
ORCID: ORCID
Hendra Andiananta Pradana
Sri Wahyuningsih
Muhammad K. Umam

Comparing automatic baseflow separation filters on three watersheds in the urbanised area of East Java, Indonesia

Indarto Indarto Mujiono Hardiansyah Mohamad Wawan Sujarwo
Journal of Water and Land Development | 2021 | No 50 | 130-140 | DOI: 10.24425/jwld.2021.138168
Keywords baseflow East Java recursive digital filter (RDF) separation urbanised area water management watershed
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Abstract

Baseflow is the primary source of water for irrigation and other water needs during prolonged dry periods; accurate and rapid estimation of baseflow is therefore crucial for water resource allocation. This research aims to estimate baseflow contribution during dry periods in three small watersheds in East Java: Surabaya-Perning (114 km2), Lamong-Simoanggrok (235 km2), and Bangsal-Kedunguneng (26 km2). Six recursive digital filters (RDFs) algorithms are explored using a procedure consisting of calibration, validation, evaluation and interpretation. In this study, the period of July to September is considered as the peak of the dry season. Moreover, data for the period 1996 to 2005 is used to calibrate the algorithms. By yearly averaging, values are obtained for the parameters and then used to test performance during the validation period from 2006 to 2015. Statistical analysis, flow duration curves and hydrographs are used to evaluate and compare the performance of each algorithm. The results show that all the filters explored can be applied to estimate baseflow in the region. However, the Lyne–Hollick (with RMSE = 0.022, 0.125, 0.010 and R2 = 0.951, 0.968, 0.712) and exponentially weighted moving average or EWMA (with RMSE = 0.022, 0.124, 0.009 and R2 = 0.957, 0.968, 0.891) for the three sub-watersheds versions give the best performance.
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Authors and Affiliations

Indarto Indarto
1
ORCID: ORCID
Mujiono Hardiansyah
1
Mohamad Wawan Sujarwo
1
ORCID: ORCID
  1. University of Jember, Faculty of Agricultural Technology, Jl kalimantan No. 37 Kampus Tegalboto, 68121, Jember, Jawa Timur, Indonesia

Using Sentinel-2A to identify the change in dry marginal agricultural land occupation

Indarto Indarto Bayu T.W. Putra Marga Mandala
Journal of Water and Land Development | 2020 | No 47 | 89-95 | DOI: 10.24425/jwld.2020.135035
Keywords agricultural change land mapping marginal Sentinel-2A
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Abstract

Dry marginal agricultural land (DryMAL) potentially use as an alternative resource for crop production. DryMAL de-fined as land having low natural fertility due to its intrinsic properties and forming environmental factors. This study uses Sentinel-2A imagery to map the spatial extent, compare the result of the classification, and identify the change in DryMAL occupation. The area of study (461.9 km2) is part of Situbondo Regency and is located at the eastern part of East Java, In-donesia. Sentinel-2A image captured in dry-season of 2018 use for this study. Then, supervised image classification using a maximum likelihood algorithm use for image treatment and processing. Furthermore, 450 ground control points for train-ing areas collected during the field surveys. Five bands use in the classification process. The maps produced from the clas-sification process were then compared to the land-use map from the year 2000. The change in DryMAL occupation from 2000 to 2018 was calculated by comparing the classified and land-use map. Supervised classification yielded an overall accuracy of 95.8% and a kappa accuracy of 93.2%. The classification produced six (6) classes of land use: (1) forest, (2) pavement or built-up area, (3) irrigated paddy field, (4) non-irrigated rural area, (5) dry marginal land and (6) water body. Globally, during the last two decades, regional development led by the Regency occupied more DryMAL area for develop-ing plantation. The effort reduces the amount of non-irrigated and converting to the plantation, pavement areas, and irrigat-ed paddy-field.

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Authors and Affiliations

Indarto Indarto
ORCID: ORCID
Bayu T.W. Putra
Marga Mandala
ORCID: ORCID

Assessment of agricultural drought based on CHIRPS data and SPI method over West Papua – Indonesia

Arif Faisol Indarto Indarto Elida Novita Budiyono Budiyono
Journal of Water and Land Development | 2022 | No 52 | 44-52 | DOI: 10.24425/jwld.2021.139942
Keywords agricultural drought Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) data Standardized Precipitation Index (SPI) method West Papua – Indonesia
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Abstract

This study aims to utilise Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) data and Standardised Precipitation Index (SPI) method to assess agricultural drought in West Papua, Indonesia. The data used in this study is monthly CHIRPS data acquired from 1996 to 2019, daily precipitation data recorded from 1996 to 2019 from the five climatological stations in West Papua, Indonesia located at Sorong, Fakfak, Kaimana, Manokwari, and South Manokwari. 3-month SPI or quarterly SPI are used to assess agricultural drought, i.e., SPI January–March, SPI February–April, SPI March-May, SPI April–June, SPI May–July, SPI June–August, SPI July–September, SPI August–October, SPI September–November, and SPI October–December. The results showed that in 2019 agricultural drought in West Papua was moderately wet to severely dry. The most severely dry occurred in September– December periods. Generally, CHIRPS data and SPI methods have an acceptable accuracy in generating drought information in West Papua with an accuracy of 53% compared with climate data analysis. Besides, the SPI from CHIRPS data processing has a moderate correlation with climate data analysis with an average R2 = 0.51.
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Bibliography

BMKG 2020. The standardized precipitation index December 2019 [online]. Jakarta Pusat. Badan Meteorologi, Klimatologi, dan Geofisika [Access 01.02.2020]. Available at: https://www.bmkg.go.id/iklim/indeks-presipitasi-terstandarisasi.bmkg
BPS Provinsi Papua Barat 2019. Provinsi Papua Barat dalam angka 2019 [Papua Barat Province in figures 2019]. Manokwari. Badan Pusat Statistik Provinsi Papua Barat. ISSN 2089-1563 pp. 527.
BYUN H.R., WILHITE D.A. 1999. Objective quantification of drought severity and duration. Journal of American Meteorological Society. No. 12 p. 2747–2756. DOI 10.1175/1520-0442(1999)0122747:OQODSA>2.0.CO;2.
DAS S., CHOUDHURY M.R., GANDHI S., JOSHI V. 2016. Application of Earth observation data and Standardized Precipitation Index based approach for meteorological drought monitoring, assessment and prediction over Kutch, Gujarat, India. International Journal of Environment and Geoinformatics. No. 3(2) p. 24–37. DOI 10.30897/ijegeo.306468.
DINKU T., FUNK C., PETERSON P., MAIDMENT R., TADESSE T., GADAIN H., CECCATO P. 2018. Validation of the CHIRPS satellite rainfall estimates over Eastern Africa. Quarterly Journal of the Royal Meteorological Society. No. 144 p. 292–312. DOI 10.1002/qj.3244.
EDO 2019. Standardized Precipitation Index (SPI) [online]. Ispra. European Drought Observatory. [Access 02.02.2020]. Available at: https://edo.jrc.ec.europa.eu/documents/factsheets/factsheet_-spi.pdf
FAO 2018.The impact of disasters and crises on agriculture and food security. 1st ed. Rome. Food and Agriculture Organization. ISBN 978-92-5-130359-7 pp. 143.
FUNK C.C., PETERSON P. J., LANDSFELD M. F., PEDREROS D.H., VERDIN J.P., ROWLAND J.D., VERDIN A.P. 2014. A quasi-global precipitation time series for drought monitoring. 1st ed. Virginia. U.S. Geological Survey Data Series. No. 832. ISSN 2327-638X pp. 4. DOI 10.3133/ds832.
GEBRECHORKOS S.H., HÜLSMANN S., BERNHOFER C. 2018. Evaluation of multiple climate data sources for managing environmental resources in East Africa. Journal of Hydrology and Earth System Sciences. No. 22 p. 4547–4564. DOI 10.5194/hess-22-4547-2018.
GUTTMAN N.B. 1999. Accepting the Standardized Precipitation Index: A calculation algorithm. Journal of The American Water Resource Association. Vol. 35(2) p. 311–322. DOI 10.1111/j.1752-1688.1999.tb03592.x.
HEIM R.R. 2002. A review of twentieth-century drought indices used in the United States. Bulletin of American Meteorological Society. No. 83 p. 1149–1165. DOI 10.1175/1520-0477-83.8.1149.
KARAVITIS C.A., ALEXANDRIS S., TSESMELIS D. E., ATHANASOPOULOS G. 2011. Application of the Standardized Precipitation Index (SPI) in Greece. Journal of Water. Vol. 3 p. 787–805. DOI 10.3390/w3030787.
KUMAR M.N., MURTHY C.S., SESHA M.V. R., ROY P.S. 2009. On the use of Standardized Precipitation Index (SPI) for drought intensity assessment. Journal of Meteorological Application. Vol. 16. Iss. 3 p. 381–389. DOI 10.1002/met.136.
MAHARANI T. 2019. Pemodelan bahaya kekeringan meteorologis di Provinsi Jawa Timur menggunakan data CHIRPS [Climate Hazards Group infrared precipitation with station data] [online]. MSc Thesis. Gadjah Mada University. [Access 01.02.2020]. Available at: https://www.google.com/search?client=firefox-b- d&q=Pemodelan+bahaya+kekeringan+meteorologis+di+Provin-si+Jawa+Timur+menggunakan+data+CHIRPS+
MISHRA S.S., NAGARAJAN R. 2011. Spatio-temporal drought assessment in Tel River Basin using Standardized Precipitation Index (SPI) and GIS. Journal of Geomatics, Natural Hazards, and Risk. Vol. 2(1) p. 79–93. DOI 10.1080/19475705.2010.533703.
MISNAWATI 2018. Evaluasi performa Standardized Precipitation Index (SPI) sebagai indikator kekeringan pertanian di Jawa Tengah [An evaluation of Standardized Precipitation Index (SPI) for agricultural drought indicator in Central Java] [online]. Thesis. IPB University. [Access 01.02.2020]. Available at: http://repository. ipb.ac.id/handle/123456789/92614
NOSRATI K., ZAREIEE A.R. 2011. Assessment of meteorological drought using SPI in West Azarbaijan Province, Iran. Journal of Applied Sciences and Environmental Management. Vol. 15(4) p. 563–569.
NUGROHO P.C., PINUJI S.E., ICHWANA A.N., NUGRAHA A., WIGUNA S., SYAUQI, SETIAWAN A. 2018. Data dan informasi bencana Indonesia [Indonesian disaster risk index]. Jakarta. Badan Nasional Penanggulangan Bencana pp. 325.
PALMER W.C. 1965. Meteorological drought. Washington, DC. USDC Weather Burreau. Research Paper. No. 45 pp. 58.
SALEHNIA N., ALIZADEH A., SANAEINEJAD H., BANNAYAN M., ZARRIN A., HOOGENBOOM G. 2017. Estimation of meteorological drought indices based on AgMERRA precipitation data and station-observed precipitation data. Journal of Arid Land. No. 9 (October) p. 797–809. DOI 10.1007/s40333-017-0070-y.
TOPÇU E., SEÇKIN N. 2016. Drought analysis of the Seyhan Basin by using Standardized Precipitation Index (SPI) and L-moments. Journal of Agricultural Sciences. Vol. 22 p. 196–215. DOI 10.1501/TARIMBIL_0000001381.
NOAA 2020. Drought information [online]. National Weather Service. National Oceanic and Atmospheric Administration / National Centers for Environmental Prediction Climate Prediction Center. National Center for Weather and Climate Prediction [Access 01.02.2020]. Available at: https://www.cpc.ncep.noaa.gov/pro-ducts/Drought/
WIDODO N. 2013. Analisis dan pemetaan indeks kekeringan meteorologis menggunakan data satelit TRMM dari 36 titik stasiun BMKG di Pulau Sumatera [Meteorological drought index analysis and mapping using TRMM satellite on Sumatera Island] [online]. Thesis. IPB University. [Access 10.02.2020]. Available at: http://repository.ipb.ac.id/handle/123456789/67416.
WILHITE D.A., GLANTZ M.H. 1985. Understanding the drought phenomenon: The role of definition. Journal of Water Interna-tional. No. 10 p. 111–120. DOI 10.1080/02508068508686328.
WITONO A., CHOLIANAWATI N. 2011. Deteksi dan prediksi kekeringan meteorologis di Sumatera Selatan menggunakan satelit TRMM [Detection and prediction of meteorological drought in South Sumatera using the TRMM satellite]. Seminar nasional sains atmosfer dan antariksa 2011. ISBN 9789791458535.
WMO 2012. Standardized Precipitation Index user guide. 1st ed. (M. Svoboda, M. Hayes, D. Wood). WMO-No. 1090. Geneva. World Meteorological Organization. ISBN 978-92-63-11090-9 pp. 16.
XIA L., ZHAO F., MAO K., YUAN Z., ZUO Z., XU T. 2018. SPI-based analyses of drought changes over the past. Journal of Remote Sensing. No. 10(171) p. 1–15. DOI 10.3390/rs10020171.
ZHU Q., LUO Y., ZHOU D., XU Y., WANG G., GAO H. 2019. Drought monitoring utility using satellite-based precipitation products over the Xiang River Basin in China. Journal of Remote Sensing. Vol. 11 p. 1–17. DOI 10.3390/rs11121483.
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Authors and Affiliations

Arif Faisol
1
ORCID: ORCID
Indarto Indarto
2
ORCID: ORCID
Elida Novita
2
Budiyono Budiyono
3
  1. University of Papua, Faculty of Agricultural Technology, Jl. Gn. Salju, Manokwari, West Papua 98314, Indonesia
  2. University of Jember, Faculty of Agricultural Technology, Jember, East Java, Indonesia
  3. University of Papua, Faculty of Agriculture, Manokwari, West Papua, Indonesia

Modelling erosion and sedimentation in a small watershed, East Java, Indonesia

Mohamad Wawan Sujarwo Indarto Indarto Marga Mandala
Journal of Water and Land Development | 2021 | No 50 | 265-273 | DOI: 10.24425/jwld.2021.138182
Keywords discharge erosion modelling Sanenrejo watershed sedimentation SWAT
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Abstract

Changes in land use as a result of human activities may generate the alteration of hydrometeorological disasters. Erosion, sedimentation, floods and landslides frequently occur in the Sanenrejo watershed (±292 km2), located in East Java, Indonesia. In this paper, the soil and water assessment tool (SWAT) model is used to evaluate the hydrological processes in this small watershed. The digital elevation model (DEM) is used as the primary input for deriving the topographic and physical properties of the watershed. Other input data used for the modelling processes include soil type, land use, observed discharge data and climate variables. These data are integrated into the SWAT to calculate discharge, erosion and sedimentation processes. The existing observed discharge data used to calibrate the SWAT output at the watershed outlet. The calibration results produce Nash–Sutcliffe efficiency ( NSE) of 0.62 and determination coefficient (R2) of 0.75, then the validation result of 0.5 (NSE) and 0.63 (R2). The middle area faced the highest erosion and sedimentation that potentially contribute to hydrometeorological disasters.
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Authors and Affiliations

Mohamad Wawan Sujarwo
1
ORCID: ORCID
Indarto Indarto
1
ORCID: ORCID
Marga Mandala
1
ORCID: ORCID
  1. University of Jember, Faculty of Agricultural Technology, Jl kalimantan No. 37 Kampus Tegalboto, 68121, Jember, Jawa Timur, Indonesia

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