A total of three different satellite products, CHIRPS, GPM, and PERSIANN, with different spatial resolutions, were examined for their ability to estimate rainfall data at a pixel level, using 30-year-long observations from six locations. Quantitative and qualitative accuracy indicators, as well as R2 and NSE from hydrological estimates, were used as the performance measures. The results show that all of the satellite estimates are unsatisfactory, giving the NRMSE ranging from 6 to 30% at a daily level, with CC only 0.21–0.36. Limited number of gauges, coarse spatial data resolution, and physical terrain complexity were found to be linked with low accuracy. Accuracy was slightly better in dry seasons or low rain rate classes. The errors increased exponentially with the increase in rain rates. CHIPRS and PERSIANN tend to slightly underestimate at lower rain rates, but do show a consistently better performance, with an NRMSE of 6–12%. CHRIPS and PERSIANN also exhibit better estimates of monthly flow data and water balance components, namely runoff, groundwater, and water yield. GPM has a better ability for rainfall event detections, especially during high rainfall events or extremes (>40 mm/day). The errors of the satellite products are generally linked to slope, wind, elevation, and evapotranspiration. Hydrologic simulations using SWAT modelling and the three satellite rainfall products show that CHIRPS slightly has the daily best performance, with R2 of 0.59 and 0.62, and NSE = 0.54, and the monthly aggregated improved at a monthly level. The water balance components generated at an annual level, using three satellite products, show that CHIRPS outperformed with a ration closer to one, though with a tendency to overestimate up to 3–4× times the data generated from the rainfall gauges. The findings of this study are beneficial in supporting efforts for improving satellite rainfall products and water resource implications.

PENDAHULUANSecara astronomis, Sub-DAS Brantas Hulu terletak 7º 56' 32.19"LS dan 112º 28' 18.31" -112º 35' 52.80"BT dengan luas sebesar ±20046 hektar persegi. Sub-DAS Brantas Hulu berkembang di daerah vulkanik sejak kala pleistosen. Kenampakan Gunungapi strato muda basa atau sedang mendominasi kondisi geomorfologi di wilayah ini dan tersebar di lereng atas sebelah timur dan barat Gunung Arjuno. Andosol coklat kekuningan dan andosol kelabu mendominasi jenis tanah di wilayah ini. Berdasarkan klasifikasi Schmidt dan Ferguson, kondisi iklim Sub-DAS Brantas hulu masuk dalam kategori iklim sedang dengan rata-rata curah hujan sebesar 101.25 mm/bulan.

The reliance on native MODIS-16 PET potential evapotranspiration (PET) in scarce-data-driven areas is growing in support among ecohydrological studies, yet information about its performance is limited or unknown as validation studies are mostly concentrated in developed countries. This study aimed to assess its performance at the monthly level using four ground measurements in a tropical watershed system with complex topography, applying a machine learning artificial neural network (ANN) to improve the estimates, and using the ANN-adjusted MODIS-16 PET to characterize the spatio-temporal patterns of PET in the Brantas watershed, as well as to understand the monthly patterns of water deficiency in areas under eight different vegetation covers. The results showed that the native MODIS-16 PET experienced overestimation with an RMSE of 37–66 mm/month and NRSME of up to 33%. The performance decreased in drier periods. The ANN-based adjustment using only one variable showed improved estimates with a reduction of RSME to only 14 mm and lower than 10% NRMSE. Sari-temporal patterns of PET in the Brantas watershed showed that the PET characteristics were not uniform. The southern part of the Brantas watershed has areas with relatively lower PET that are, thus, more prone to water deficiency. Complex topography and climate gradients within the watershed apparently became the multi-controllers of PET variations. The difference in vegetation cover also influenced the magnitudes of water deficiency.

At present, spatial analysis has been used on epidemiology. Spatial analysis was used to determine the environmental risk that influence to transmission of Filariasis. The aim of the study was to identify, at industrial area, the environment determinant that are associated with Filariasis cases in Pekalongan City. The geocoding method was applied on the prevalence of cases to determine the pattern of spatial. Spatial autocorrelation was used to determine the effect of the environment on filariasis transmission. The kernel method was used to determine the density of filariasis cases. Based on the spatial analysis, the statistical values associated with the correlation between the risk of filariasis transmission and environmental factors were obtained. The correlation value of the influence of the environment on the transmission of Filariasis was statistically significant, this coefficient is 0.312. The value of R indicates that the spatial pattern of filariasis cases forms a cluster pattern. The Moran’s index calculation obtained a positive spatial autocorrelation value of 0.44 with z-score is 16.05 and P-value is 0.00. Spatial autocorrelation was useful to determining the level of risk transmission of filariasis in Pekalongan City which may help to adopt effective control strategies in filariasis eradication programs in Pekalongan City.

Java’s Brantas River Basin (BRB) is an increasingly urbanized tropical watershed with significant economic and ecological importance; yet knowledge of its land-use changes dynamics and drivers as well as their importance have barely been explored. This is the case for many other tropical watersheds in Java, Indonesia and beyond. This study of the BRB (1) quantifies the land-use changes in the period 1995–2015, (2) determines the patterns of land-use changes during 1995–2015, and (3) identifies the potential drivers of land-use changes during 1995–2015. Findings show that from 1995 to 2015, major transitions from forest to shrubs (218 km2), forest to dryland agriculture (512 km2), and from agriculture to urban areas (1484 km2) were observed in the BRB. Responses from land-user questionnaires suggest that drivers include a wide range of economic, social, technological, and biophysical attributes. An agreement matrix provided insight about consistency and inconsistency in the drivers inferred from the Land Change Modeler and those inferred from questionnaires. Factors that contributed to inconsistencies include the limited representation of local land-use features in the spatial data sets and comprehensiveness of land-user questionnaires. Together the two approaches signify the heterogeneity and scale-dependence of the land-use change process.